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Doctoral Thesis

The genetic dissection of key factors involved in the droughttolerance of tropical maize (Zea mays L.)

Author(s): Messmer, Rainer Ernst

Publication Date: 2006

Permanent Link: https://doi.org/10.3929/ethz-a-005273099

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DISS. ETH NO. 16695

THE GENETIC DISSECTION OF KEY FACTORS

INVOLVED IN THE DROUGHT TOLERANCE

OF TROPICAL MAIZE (ZEA MAYS L.)

A dissertation submitted to the

SWISS FEDERAL INSTITUTE OF TECHNOLOGY ZURICH

for the degree of

Doctor of Sciences

presented by

RAINER ERNST MESSMER

Dipl. Ing.-Agr. ETH

born 21.05.1976

citizen of

Thal SG

accepted on the recommendation of

Prof. Dr. Peter Stamp, examiner

PD. Dr. Christof Sautter, co-examiner

Dr. Jean-Marcel Ribaut, co-examiner

Dr. Yvan Fracheboud, co-examiner

2006

3

TABLE OF CONTENTS

ABBREVIATIONS ......................................................................................... 7

ABSTRACTS...............................................................................................11

Summary............................................................................................................. 11

Zusammenfassung..............................................................................................15

GENERAL INTRODUCTION .......................................................................... 19

The drought environments.................................................................................19

What is drought? ................................................................................................19

Secondary traits ..................................................................................................21

Detection and application of QTLs.................................................................... 22

Working hypothesis ........................................................................................... 24

Goal and objectives............................................................................................ 25

GENERAL MATERIAL AND METHODS............................................................ 27

Plant material .................................................................................................... 27

Field evaluations................................................................................................28

The experimental site in Mexico ............................................................28

The drought-stress treatment in Mexico ...............................................30

The non-stress treatment in Mexico .......................................................31

The drought-stress treatment in Zimbabwe ..........................................31

The non-stress treatment in Zimbabwe .................................................31

Meteorological data ............................................................................... 32

Experimental evaluation ....................................................................... 32

Data analysis ...................................................................................................... 33

Heritability ............................................................................................. 33

Spatial analysis ...................................................................................... 33

Phenotypic correlations ......................................................................... 34

QTL identification .................................................................................. 34

CONSTRUCTION OF THE GENETIC LINKAGE MAP FOR A TROPICAL MAIZE

POPULATION............................................................................................ 37

Introduction....................................................................................................... 37

Material and Methods ....................................................................................... 38

DNA extraction....................................................................................... 38

4

RFLP analysis......................................................................................... 38

SSR analysis ........................................................................................... 39

Construction of the genetic linkage map............................................... 39

Results.................................................................................................................41

Discussion.......................................................................................................... 43

QTL-BY-ENVIRONMENT INTERACTIONS FOR FLOWERING TRAITS, PLANT HEIGHT

AND GRAIN YIELD IN A TROPICAL MAIZE POPULATION ................................... 45

Introduction....................................................................................................... 45


Plant material and field experiments.................................................... 47

Phenotypic data...................................................................................... 47

Data analysis and QTL mapping ..........................................................48

Results................................................................................................................ 49

Environments ......................................................................................... 49

Phenotypic results and correlations...................................................... 50

QTL results ............................................................................................. 55

Discussion...........................................................................................................61

Genetic control of flowering time ...........................................................61

Genetic control of ASI and grain yield .................................................. 63

The genetic basis of improved drought tolerance................................. 63

Autonomous genetic control of grain filling ......................................... 66

Conclusions........................................................................................................ 67

THE GENETIC CONTROL OF STAY-GREEN CHARACTERISTICS AND ROOT

CAPACITANCE IN A TROPICAL MAIZE POPULATION ......................................... 69

Introduction....................................................................................................... 69



Phenotypic data...................................................................................... 72

Data analysis and QTL mapping .......................................................... 73

Results................................................................................................................ 74


QTLs for stay-green characteristics.......................................................77

QTLs for root capacitance ..................................................................... 79

Discussion..........................................................................................................84

5

Chlorophyll content of the leaves and stay-green ................................84

Leaf senescence.......................................................................................84

The major QTL on chromosome 2 ......................................................... 85

The major QTL on chromosome 10 ....................................................... 87

The QTL on chromosome 1.....................................................................88

Other QTLs for chlorophyll content.......................................................88

Root capacitance ....................................................................................88

Conclusions........................................................................................................90

QTL ANALYSIS OF TASSEL SIZE AND EAR GROWTH AT FLOWERING IN A TROPICAL

MAIZE POPULATION.................................................................................. 93

Introduction....................................................................................................... 93



Phenotypic data...................................................................................... 95

Data analysis and QTL mapping .......................................................... 96

Results................................................................................................................ 97


QTL results ............................................................................................. 99

Discussion........................................................................................................ 104

Conclusions...................................................................................................... 106

GENERAL CONCLUSIONS AND OUTLOOK ......................................................107

The problem......................................................................................................107

The achievements .............................................................................................107

Genetic control of target traits .............................................................107

Target loci for yield .............................................................................. 108

Correlative responses among traits .................................................... 109

Target loci affecting time of flowering and size of inflorescences ......110

Target loci for stay-green characteristics............................................ 111

QTL application in marker-assisted selection ..................................... 112

REFERENCES .......................................................................................... 115

ACKNOWLEDGEMENTS ............................................................................ 129

AGRADECIMIENTOS.................................................................................. 131

CURRICULUM VITAE .................................................................................133

7

ABBREVIATIONS

asg Some RFLP probes (Asgrow Seed Company)

ASI Anthesis-silking interval: The interval [days] between pollen release

and silk emergence

bin Segment of a chromosome located between two Core Markers. The

segments are given the chromosome number followed by a two-place

decimal (e.g., 1.00, 1.01, 1.02, etc.). A bin is the interval that includes all

loci from the far left or top Core Marker to the next Core Marker

(MGDB 2005, http://www.maizegdb.org).

BLUP Best linear unbiased predictor: The BLUPs result from the spatial

analysis of the plot-wise phenotypic raw data in the field experiments.

bnl Some RFLP probes (Brookhaven National Laboratory)

CIMMYT International Maize and Wheat Improvement Center, Mexico

cM CentiMorgan: A unit for measuring genetic distance. One cM

corresponds to approximately 1 % of recombination if double and high

levels of crossovers are ignored. 100 cM = 1 Morgan.

CSPD Disodium-3-(4-methoxyspiro{1,2-dioxetane-3,2'(5'-chloro)-tricyclo

[3.3.1.1.3,7]decan}-4-yl) phenyl phosphate

csu Some RFLP probes (California State University)

CTAB Mixed alkyltrimethyl-ammonium bromide

cx my Marker y on chromosome x (e.g., c5m3 = marker 3 on chromosome 5).

Defines the marker closest to the peak in the LOD score of a QTL.

DM Drought-stressed conditions in Mexico. When related to QTLs, DM

refers to the joint QTL analysis of data from the experiments DM1 and

DM2.

DM* Drought-stressed conditions in Mexico. When related to QTLs, DM*

refers to the joint QTL analysis of data from the experiments DM3 and

DM4.

DNA Deoxyribonucleic acid: Carrier of genetic information in the cell

dNTP Deoxynucleoside 5'-triphosphate

dupssr Some SSR markers (Dupont Company)

8

dUTP deoxyuridine 5'-triphosphate

DZ Drought-stressed conditions in Zimbabwe. When related to QTLs, DZ

refers to the joint QTL analysis of data from the experiments DZ1 and

DZ2.

DZ* Drought-stressed conditions in Zimbabwe. When related to QTLs, DZ*

refers to the joint QTL analysis of data from the experiments DZ3 and

DZ4.

EcoRI Restriction endo-nuclease from an Escherichia coli strain (RY13) that

carries the cloned EcoR I gene. This enzyme recognizes DNA sites, six

base-pairs long and cleaves the phosphate backbone.

EDTA Ethylene-diamine-tetra-acetate

ELC Chlorophyll content of the ear leaf (SPAD value). In the experiments, in

which the leaf chlorophyll content was measured twice, ELC1 and ELC2

refer to the first and second measurement, respectively.

EtOH Ethanol

EW0 Dry weight of the ears at anthesis

EW7 Dry weight of the ears one week after anthesis

FFL Time of female flowering: The number of days from sowing to silk

emergence of the husk leaves

GEI Genotype-by-environment interactions: Non-parallelism of phenotypic

responses of a set of genotypes in different environments as a

consequence of differential expression of genes in different

environments

GYA Grain yield per area [g m-2]

HindIII Restriction endo-nuclease from an Escherichia coli strain that carries

the Hind III gene from Haemophilis influenza

HKW Hundred kernel weight [g]

KNA Kernel number per area [m-2]

LOD Base 10 logarithm of the likelihood ratio (LR) between the null

hypothesis and the alternative hypothesis (LOD = ½·LR·log10(e))

MAS Marker-assisted selection

MFL Time of male flowering: The number of days from sowing to anthesis

(i.e., the day, on which pollen is released for the first time)

MgCl2 Magnesium chloride

9

MGDB Maize Genetics and Genomics Database (http://www.maizegdb.org)

mM miliMolar (1 mM = 10-3 mol/l)

mmc Some SSR markers (Maize Microsatellite Consortium)

NaOAc Sodium acetate

nF Faraday (F): A unit of electric charge quantity

(109 nF = 1 F = 1.04 · 10-5 C = 1.04 · 10-5 A·s)

NH4OAc Ammonium acetate

npi Some RFLP probes (Native Plants Incorporated)

PCR Polymerase chain reaction

pH Potential of hydrogen: A measure of the acidity or alkalinity of a

solution

phi Some SSR markers (Pioneer Hi-Bred International)

PHT Plant height: The distance between the soil surface and the first tassel

branch

PL1 Parental line 1: The CIMMYT maize line 444 (CML444). The drought-

tolerant parent of the RIL population.

PL2 Parental line 2: The African maize line SC-Malawi. The drought-

susceptible parent of the RIL population.

QEI QTL-by-environment interaction: Non-parallelism of QTL detection in

a set of genotypes in different environments as a consequence of

genotype-by-environment interactions (GEI)

QTL Quantitative trait locus: A genetic region that explains phenotypic

differences in a quantitative trait between genotypes of a segregating

population derived from a bi-parental cross.

R2 Percentage of phenotypic variance explained by one or several QTLs

RCT Root capacitance [nF] measured with a BK Precision 810A meter

(Maxtec Inc, Chicago, IL)

RFLP Restriction fragment length polymorphism: Intraspecies variation in

the length of DNA fragments generated by the action of restriction

enzymes

RIL Recombinant inbred line

SEN Leaf senescence: A visual score on a scale from 1 (plants completely

green) to 9 (plants completely dry)

10

SNP Single nucleotide polymorphism: A change in which a single base in the

DNA differs from the usual base at that position

SPAD Soil and Plant Analyze Developments: The numerical SPAD value

calculated by the portable SPAD-502 meter (Konica Minolta Sensing,

Inc.) is proportional to the amount of chlorophyll in a leaf

SSR Simple sequence repeat: A tandem repeat of one or more short simple

sequences of two to six nucleotides, also referred to as microsatellite

SW0 Dry weight of the silks at anthesis

SW7 Dry weight of the silks seven days after anthesis

Taq Taq polymerase: DNA polymerase of the bacterium Thermophilus

aquaticus. The tolerance to high temperatures of this polymerase

enables its use for DNA amplification during PCR.

TBW Dry weight of tassel branches

TE Tris-EDTA (buffer)

TLC Treatment-location combination: The experiments performed under

the same water-management system (drought-stressed or rain-fed) at

the same location (Mexico or Zimbabwe) were grouped into six TLCs

(DM, DM*, DZ, DZ*, WM, WZ) for joint QTL mapping of two traits.

ug; ul Microgram (1 ug = 10-6 g); microliter (1 ul = 10-6 l)

umc Some SSR markers (University of Missouri, Columbia)

WM Rain-fed conditions in Mexico: When related to QTLs, WM refers to the

joint QTL analysis of data from the experiments WM1 and WM2.

WZ Rain-fed conditions in Zimbabwe (experiment WZ1)

YLC Chlorophyll content of the second leaf from the tassel (SPAD value). In

the experiments, in which the leaf chlorophyll content was measured

twice, YLC1 and YLC2 refer to the first and second measurement,

respectively.

11

ABSTRACTS

Summary

Maize is an important source of human nutrition, especially in the tropics, where

most of the maize is grown under rain-fed conditions and where drought is a major

constraint to agricultural production. Sub-Saharan Africa is currently one of the most

severely affected regions and the occurrence of drought spells is predicted to increase

in the future. The development of more drought-tolerant genotypes can contribute to

ensure food security in this area and worldwide. However, selection for drought

tolerance is difficult because of the unpredictability of stress under natural

conditions, because of the occurrence of strong interactions between genotypes and

the environment and because of limited knowledge about the role and regulation of

tolerance mechanisms. Classical approaches to breeding have identified secondary

traits for grain yield under drought stress. Most of these traits are polygenic, but

grain yield probably remains the most polygenic and complex trait. The genetic

control of polygenic and complex traits is mainly quantitative. The mapping of

quantitative trait loci (QTLs) is, therefore, a promising tool for detecting genomic

regions controlling polygenic trait expression as well as for studying changes in the

expression of these loci across varying environmental conditions.

The goal of this project was to provide an understanding of the genetic basis of

morphological and physiological traits involved in response to water-limited

conditions at flowering of two tropical maize lines with different levels of drought

tolerance. The target regions identified by QTL mapping will contribute to

complementing the evaluation and selection of improved germplasm, especially in

sub-Saharan Africa. To achieve this goal, a genetic linkage map was constructed for a

population of recombinant inbred lines (RILs). These lines were developed by

crossing the drought-tolerant tropical maize line CML444 (PL1) with the drought-

susceptible tropical maize line SC-Malawi (PL2) and five generations of inbreeding.

The genetic linkage map consisted of the allelic segregation of 236 RILs at 160

molecular marker loci. The map was 2105 cM long and had an average distance

between two consecutive markers of 13.2 cM.

12

The RIL population and the parental lines were evaluated in a total of 11 field

experiments in Mexico and Zimbabwe. The experiments were performed in 2003 and

2004 under managed drought stress at flowering (D) or under rain-fed conditions

(W) with sufficient water supply. The plants were phenotyped for flowering time,

plant height, chlorophyll content of two leaves, leaf senescence and root capacitance

as well as for the dry weight of the tassels, ears and silks during the critical stress

period; grain yield parameters were measured at maturity. The QTLs were identified

by composite interval mapping.

Drought stress caused large reductions in total grain yield and, correlatively, in kernel

number per area compared to the highest-yielding experiment under rain-fed

conditions in Zimbabwe (WZ1). Grain yield (GYA) was controlled by only a few QTLs,

whose effects on trait expression were larger and more significant under rain-fed

conditions than under drought. The QTLs for GYA showed strong interactions with

the environment (QEI) and changed their positions on the genome across

environments. The strong genetic control of hundred kernel weight (HKW), in

contrast, was stable and unrelated to that of grain yield.

The anthesis-silking interval (ASI), a common secondary trait for grain yield under

drought, was negatively correlated with GYA both within and across experiments.

However, QTLs with direct effects on both traits were not observed under drought

stress. The QTL on chromosome 1 close to marker 15 (c1m15) was the only locus that

controlled ASI in more than one environment. It was detected between the QTL

c1m11, with a large positive additive effect on grain yield in the high-yielding and

rain-fed experiment in Zimbabwe and with negative effects on plant height (PHT) in

the other environments, and the QTL c1m17 controlling male flowering time (MFL)

and ear dry weight at anthesis (EW0). Contrary to expectations, the allele of the

drought-tolerant parent (PL1) was associated with an increase in ASI, which is

unfavorable under drought stress. The effect of these QTLs on chromosome 1 on trait

expression demonstrated that GYA was more closely associated genetically with PHT

and that ASI was more closely related with MFL than was GYA with ASI in this

tropical maize population. The PL1 realized its high yield potential only under rain-

fed conditions and showed larger drought-induced yield reductions than PL2.

Nevertheless, PL1 produced more grains than PL2 in the drought-stress experiments,

but the differences between the two lines were smaller than in the rain-fed

experiments and not always significant. Drought stress concomitantly reduced the

13

PHT of PL1 to a greater extent than the PHT of PL2; MFL of PL1 was delayed.

Apparently, PL2 escaped drought stress through early maturity when exposed to

water-limited conditions at flowering. Considering these distinct phenotypic

responses of the two parental lines to water shortage, the QTLs for GYA, PHT, ASI,

MFL and EW0, detected on chromosomes 1 and 3, but also the QTLs for MFL and

leaf chlorophyll content on chromosome 2, revealed the important genetic basis of

the morpho-physiological differences between the two parental lines.

Each of the morpho-physiological traits evaluated in this study, including root

capacitance (RCT) and tassel dry weight (TBW), was controlled by at least one QTL

detected in more than one environment, which suggested that all the traits were

controlled by some intrinsic genes. However, not all of these genes were

constitutively expressed in all experiments.

MFL, in particular, was under strong genetic control. The four most important QTLs

for this trait corresponded to universal QTLs for flowering time in maize. TBW did

not influence MFL, but there was a weak negative correlation between TBW and ear

dry weight at anthesis (EW0). This correlation suggested that selection for small

tassels could increase the flux of assimilates to the ear before and during the critical

period at flowering. However, such an effect would be small, because the phenotypic

correlation was weak and the dry weight of the male and the female inflorescences

were controlled by distinct QTLs.

The phenotypic response of the parental lines to drought stress in terms of EW0

showed notable similarities to GYA. The phenotypic differences between the two lines

were largest under rain-fed conditions and PL1 showed larger drought-stress-induced

reductions in EW0 than PL2. Nevertheless, the QTL c3m7, important for the dry

weight of the ears and silks at flowering under drought-stressed conditions, was not

detected for grain yield.

Co-locating QTLs for grain yield and for physiological secondary traits, such as the

anthesis-silking interval, dry weight of the ears and silks at anthesis, leaf chlorophyll

content and leaf senescence, but also for plant height were observed on chromosomes

8, 9 and 10. The pattern of QTL expression across experiments, together with the

additive effects of the PL1 allele, suggested the presence of stress-adaptive genes.

Their effect on drought-tolerance mechanisms contrasted with the effect of the rather

structural and constitutive QTLs located on chromosomes 1, 2 and 3. Therefore, the

middle sections of chromosomes 8, 9 and, in particular, the middle section of

14

chromosome 10 were identified as potential target regions for marker-assisted

selection (MAS) for improving drought tolerance. The QTL regions on the middle

sections of chromosomes 1, 2 and 3 should also be used for MAS, since the effects of

the respective QTLs on vegetative growth, organ development and other plant

characteristics such as leaf chlorophyll content suggested that functional gene

clustering can be expected in maize. This would be an important prerequisite for the

development and the successful application of novel MAS techniques without the

common drawbacks of MAS, namely cross specificity and the narrow range of target

environments.

15

Zusammenfassung

Mais ist ein wichtiges Nahrungsmittel in den Tropen, wo er meist ohne künstliche

Bewässerung angebaut wird. Trockenstress stellt deshalb oftmals ein grosses Problem

dar. Viele Regionen des afrikanischen Subkontinents sind ausgesprochen

trockenheitsgefährdet, in Zukunft voraussichtlich noch stärker als bisher. Die

Entwicklung und der Anbau von trockenheitstoleranten Maisgenotypen könnten die

Nahrungsmittelproduktion im südlichen Afrika und weltweit verbessern. Die

Züchtung von Mais auf Trockenheitstoleranz wird jedoch durch das unberechenbare

Auftreten meteorologischer Dürre erschwert. Genotyp-Umwelt-Interaktionen und

Unklarheiten bezüglich Funktion und Regulation von physiologischen

Toleranzmechanismen verlangsamen den Fortschritt und stellen grosse

Herausforderungen and Pflanzenphysiologen und -züchter. Die konventionelle

Züchtung zeigte den Nutzen sekundärer Merkmalen auf. Sie stehen mit dem

Kornertrag in Zusammenhang, doch wird ihre phänotypische Ausprägung durch

Trockenheit weniger stark beeinträchtigt als der Kornertrag selber. Die meisten

sekundären Merkmale werden durch das Zusammenspiel mehrerer Gene beeinflusst,

deren Gesamtheit schliesslich den Kornertrag kontrollieren. Dieses Zusammenspiel

der Gene ist meist quantitativer Art, weshalb das Kartieren von Genorten für

quantitative Merkmale (QTLs) eine Erfolg versprechende Methode ist um

genomische Regionen zu charakterisieren welche die Merkmalsausprägung

massgeblich beeinflussen.

Ziel dieses Projektes war, die Auswirkung der genetischen Unterschiede zwischen

zwei tropischen Maisinzuchtlinien auf deren Phänotyp zu untersuchen, um die

physiologischen und morphologischen Reaktionen auf Wasserknappheit während der

Blütezeit besser zu verstehen. Die so gewonnene Information trägt dazu bei, die

Selektion von trockenheitstoleranten tropischen Maisgenotypen effizienter zu

gestalten.

Dazu wurde eine genetische Karte für eine Population von 236 rekombinanten

Maisinzuchtlinien (RILs) erstellt. Die RILs wurden durch fünf Generationen

Selbstbestäubung entwickelt, ausgehend von der Kreuzung zwischen der

trockenheitstoleranten tropischen Maisinzuchtlinie CML444 (PL1) und der

trockenheitsanfälligen tropischen Maisinzuchtline SC-Malawi (PL2). Die genetische

Karte war insgesamt 2105 cM lang und wies eine mittlere Markerdistanz von 13.2 cM

auf.

16

Die RIL Population und die Elternlinien wurden in den Jahren 2003 und 2004 in 11

Feldversuchen in Mexiko und Zimbabwe angebaut. Die Pflanzen wurden entweder

unter Trockenstress während der Blütezeit oder unter normaler Bewässerung

(meteorologischer Niederschlag) ausgewertet, indem verschiedene Parameter wie

Blütezeit, Pflanzenhöhe, Chlorophyllgehalt der Blätter, Blattseneszenz und

Wurzelleitfähigkeit, aber auch das Trockengewicht der Fahnen, Ährchen und Seiden

während der Blüte sowie der Kornertrag bei der physiologischen Reife gemessen

wurden.

Trockenstress während der Blütezeit führte zu grossen Ertragseinbussen durch

reduzierte Kornzahlen, verglichen mit dem Ertrag unter normaler Bewässerung in

Zimbabwe. Der Kornertrag pro Fläche (GYA) wurde insgesamt von nur wenigen

QTLs beeinflusst, deren Effekt unter normaler Bewässerung grösser war als unter

Trockenstress und deren Ausprägung stark von den Umweltbedingungen abhing. Im

Gegensatz zu GYA war das Hundertkorngewicht (HKW) von umweltunabhängig

wirkenden Genen kontrolliert.

Der zeitliche Verzug der weiblichen Blüte (erstes Erscheinen der Seiden) zur

männlichen Blüte (erste Pollenfreisetzung), das so genannte „anthesis-silking

interval“ (ASI), ist ein wichtiges sekundäres Merkmal für den Ertrag von Mais. ASI

korrelierte negativ mit GYA in allen Experimenten in welchen beide Parameter

gemessen wurden. Eine nicht lineare negative Beziehung zwischen ASI und GYA

bestund auch über alle Experimente hinweg. Trotzdem gab es keine QTLs mit

gleichzeitigem Effekt auf beide Merkmale in derselben Umwelt. Der QTL bei Marker

15 auf Chromosom 1 (c1m15) war der einzige Genort mit Einfluss auf ASI in mehreren

Umwelten. Interessanterweise lag er zwischen dem QTL c1m11 mit positiv additivem

Effekt auf GYA im Experiment mit normaler Bewässerung in Zimbabwe und negativ

additivem Effekt auf die Wuchshöhe (PHT) in allen anderen Umwelten und dem QTL

c1m17 mit Effekt auf den männlichen Blühzeitpunkt (MFL) und auf das

Ährentrockengewicht zur Blüte (EW0). Das Allel des trockenheitstoleranten Elters

(PL1) war jedoch mit einem längeren ASI verbunden, obwohl sich ein längeres ASI in

der Regel negativ auf den Ertrag unter Trockenstress auswirkt. Die Ausprägung der

erwähnten QTLs auf Chromosom 1 zeigten, dass GYA und PHT, sowie ASI und MFL

genetisch enger verbunden waren in dieser Maispopulation als ASI und GYA. Der

tolerante Elter PL1 konnte sein hohes Ertragspotential nur unter normaler

Bewässerung realisieren und erlitt grössere relative Ertragsreduktionen unter

17

Trockenstress als PL2. Nichtsdestotrotz war sein Kornertrag höher als derjenige von

PL2 in den Stressumwelten, doch die Unterschiede fielen geringer aus als unter

normaler Bewässerung und waren nicht immer signifikant. Die Pflanzen von PL1 in

den Stressexperimenten wuchsen deutlich weniger hoch und ihr männlicher

Blühzeitpunkt war verzögert, verglichen mit den Experimenten unter normaler

Bewässerung. PL2 versuchte hingegen dem Stress durch eine vorgezogene Blüte zu

entgehen. Damit verbunden war jedoch ein niedriges Ertragspotential und eine

geringe morphologischer Plastizität. In diesem Zusammenhang repräsentierten die

QTLs für GYA, PHT, ASI, MFL und EW0 auf den Chromosomen 1 und 3, aber auch

die QTLs für MFL und Chlorophyllgehalt der Blätter auf Chromosom 2 wichtige

Genorte für die konstitutiven morphologischen und physiologischen Unterschiede

zwischen den zwei Elternlinien.

Jedes der ausgewerteten morpho-physiologischen Merkmale wurde durch

mindestens einen in mehr als einer Umwelt signifikanten QTL beeinflusst. Somit

schien jedes Merkmal von mindestens einem intrinsischen, jedoch meist nicht

gänzlich konstitutiv wirkenden Gen kontrolliert zu sein. Die genetische Kontrolle von

MFL war besonders stabil. Die Position der vier wichtigsten QTLs für MFL in dieser

Studie stimmte mit der Position universeller QTLs für den Blühzeitpunkt in Mais

überein. MFL wurde nicht durch die Trockenmasse der Fahnen (TBW) beeinflusst.

Eine schwache negative Korrelation konnte hingegen zwischen TBW und EW0

festgestellt werden. Anscheinend könnte die Selektion von tropischem Mais auf

kleinen Fahnen das Ährenwachstum zur Blüte, und folglich die frühe

Kornentwicklung, positiv beeinflussen. Ein solcher positiver Effekt wäre

wahrscheinlich aber nur gering, da die zwei Merkmale nur schwach korrelierten und

auch nicht von gemeinsamen QTLs abhingen.

Die Messwerte für EW0 der beiden Elternlinien zeigten eine auffallende

Übereinstimmung mit denjenigen von GYA. Die Unterschiede in EW0 zwischen PL1

und PL2 waren unter normaler Bewässerung am grössten. Zudem reduzierte

Trockenstress das EW0 von PL1 stärker als das EW0 von PL2. Dennoch hatte der für

das Ähren- und Seidentrockengewicht zur Blüte wichtige QTL auf Chromosom 3

(c3m7) keinen signifikanten Effekt auf den Kornertrag.

Genorte auf den Chromosome 8 und 10, in etwas geringerem Masse auch solche auf

Chromosom 9, beeinflussten den Kornertrag, das ASI, den Chlorophyllgehalt der

Blätter, die Blattseneszenz und die Wuchshöhe der Pflanzen in verschiedenen

18

Umwelten. Ihre stressinduzierbare Ausprägung unterschied sich deutlich von der

Ausprägung der QTLs auf Chromosom 1, 2 und 3. Aus diesem Grund sollten die

mittleren Regionen der Chromosome 8, 9 und 10 in markergestützten

Selektionsexperimenten berücksichtigt werden. Gleichzeitig dürfen aber die

beschriebenen Genorte auf den Chromosomen 1, 2 und 3 nicht vernachlässigt

werden, da sich dort offensichtlich wichtige Gene für die Kontrolle des vegetativen

Pflanzenwachstums, die Frühentwicklung der Ähren und andere physiologische

Merkmale wie den Chlorophyllgehalt der Blätter befinden. Die Akkumulation

wichtiger QTLs in bestimmten Regionen des Genoms deutet auf funktionale

Gruppierung von Genen hin. Dies ist eine wichtige Voraussetzung für Entwicklung

und erfolgreiches Anwenden neuer Techniken für die markergestütze Selektion.

19

GENERAL INTRODUCTION

The drought environments

Maize is the third most important cereal worldwide after wheat and rice. It is grown

in both temperate zones and in the tropics. Maize is an important source of human

nutrition in many maize growing areas, especially in the tropical and sub-tropical

zones of Africa and the Americas. The successful and continuous production of maize

is a key to global food security (Edmeades et al. 2000). The gap between achievable

and actual yields in tropical farming systems is quite large because of various biotic

and abiotic stresses, even when improved germplasm is available. Most of the tropical

maize is grown under rain-fed conditions. Drought stress and soil infertility were the

two major abiotic constraints to agricultural production in the past (Beck et al. 1996)

and will have large negative effects on agricultural production in coming decades,

particularly in Asia and Africa (Rijsberman 2006). This effect is strongly influenced

by the continuing changes in the global climate (Hillel and Rosenzweig 2002). The

increases in temperature will be accompanied by an increasing number of stronger

storms as well as by more severe drought events in certain areas of the world, while in

others, flooding will occur more frequently as a consequence of increased

precipitation (CGIAR 2000, Ribaut et al. 2004). Sub-Saharan Africa will suffer from

a decrease in precipitation and the negative impact of water deficit will be aggravated

by higher temperatures. The FAO estimated that sub-Saharan Africa is the most

severely affected region where almost half of the land surface is exposed to a high risk

of meteorological drought (Ribaut et al. 2004). Over the last three decades the gap

between food demand and food supply has widened because the population has

grown faster than the agricultural production (IPCC 2001). These facts demonstrate

impressively that there is an urgent need to develop germplasm with improved

tolerance to water-limited conditions.

What is drought?

Drought is a water deficit in the plant’s environment that has the potential to reduce

crop yield (Cooper et al. 2006). When the deficit occurs before the crop is fully

developed, it can also reduce vegetative growth. The negative impact of drought

20

depends mainly on the timing, duration and intensity of the stress. However, the

occurrence of natural drought is largely unpredictable, making it difficult or almost

impossible to distinguish between water-limited and non-limited agricultural systems

(Cooper et al. 2006). The unpredictability of drought events implies that improved

genotypes should perform well not only under water-limited conditions but also

when rainfall is adequate. The use of genetics to improve drought tolerance is

important for stabilizing global maize production, although genetic improvements

are unlikely to close more than 30 % of the gap between potential and realized yield

under water stress (Edmeades et al. 2004).

The fundamental problem of selecting for drought tolerance is twofold. On the one

hand, the complexity of the drought-stress phenomenon itself makes it difficult to

define the ideal drought-tolerant genotype (Ribaut et al. 2004). On the other hand,

the plant’s complex responses to low water potential are complicated by their

dependence on the developmental stage and on the type of the stress. Until now, it

has been impossible to determine the key processes of tolerance (Bartels and Sunkar

2005). It was also impossible to develop quantitative gene-to-phenotype models,

which suggest a better approach to the breeding process (Cooper et al. 2006).

Although the distinction between stress and non-stress environments is artificial, a

clear description of the major drought scenario is required for the efficient screening

of drought tolerance in the target genetic background. The screening tools must offer

the possibility to control the timing, duration and intensity of the stress.

A description of major drought scenarios does not solve all the problems. There is

still limited knowledge about drought perception and signal transduction as well as

about adaptation mechanisms on a genetic, biochemical and physiological level.

Drought stress leads to cellular dehydration and to the production of reactive oxygen

species, which negatively affect the cellular structures and the cell metabolism

(Bartels and Sunkar 2005). The network of adaptation mechanisms of the cell

involves the activation or the increased expression of stress-induced genes, transient

increases in the concentration of phytohormones (i.e., abscisic acid), the

accumulation of compatible solutes and protective proteins, increased levels of

antioxidants and the suppression of energy-consuming pathways. These adaptation

mechanisms in the cell cause morpho-physiological alterations and, eventually,

drought-stress symptoms of an organ and the entire plant. Drought stress negatively

affects maize production at all stages of development, but maximum damage is

21

inflicted when it occurs shortly before and during flowering (Saini and Westgate

2000, Salter and Goode 1967). A farmer may respond to drought early during the

vegetative growth phase by replanting the crop. When drought occurs late during

grain filling, some yield may still be salvaged. Drought stress at flowering, however,

can only be mitigated by irrigation (Boyer and Westgate 2004).

Secondary traits

Classical approaches to breeding compared the effect of the differential expression of

drought-stress symptoms with genotypic differences in grain yield under drought.

Secondary traits for grain yield under water-limited conditions were identified. The

role, regulation and importance of these secondary traits in maize have been subject

of several studies (Bänziger et al. 2002, Bolanos and Edmeades 1996, Chapman and

Edmeades 1999). Secondary traits should help to overcome the drawbacks of

breeding for high grain yield under water-limited conditions, namely the low

heritability of yield, which is due to the small genetic variance and the occurrence of

poorly understood genotype-by-environment interactions (GEI). Not only should an

ideal secondary trait be highly heritable and genetically associated with yield under

drought without causing a decrease in yield under favorable conditions, but it should

also be easy and inexpensive to measure (Campos et al. 2004, Chapman and

Edmeades 1999). An ideal secondary trait should be observable before flowering or at

flowering to avoid undesirable cross-pollinations (Edmeades et al. 1998).

When drought stress occurs during the critical period of flowering, the achievable

grain yield depends largely on proper pollination, kernel set and adequate early

development of the kernels. Bad pollination at low water potential in maize was

reported not to be due to pollen sterility (Schoper et al. 1987) but due to delayed silk

extrusion, which results in the characteristic widening of the anthesis-silking interval

(ASI) (Edmeades et al. 2000). A large ASI reduces the number of kernels per ear. The

number of kernels is highly correlated with total grain yield under water-limited

conditions at flowering; it is more important in determining grain yield than the

weight of the kernels (Bolanos and Edmeades 1996). Successful pollination alone,

however, cannot avoid reductions in kernel number. A continuous flux of assimilates

to the developing ears is essential for early development of the kernels. Drought-

stress-induced reductions in current photosynthesis and correlative reductions in the

flux of assimilates to the ear provoke the abortion of already developing kernels

22

(Saini and Westgate 2000, Zinselmeier et al. 1995b). Photosynthesis can be reduced

by the susceptibility of the photosynthetic apparatus to a low cell water potential, by

reactive oxygen species or by the remobilization of nitrogen from the leaves in

response to a decreased N-uptake by the roots. The nitrogen that is remobilized in the

leaves and transported to the developing ears originates to a large extent from the

chloroplasts. The disintegration of the chloroplasts, carrier of the photosynthetic

apparatus, results in the characteristic yellowing of the leaves, a symptom which is

commonly referred to as senescence. Rajcan and Tollenaar (1999a) showed that the

two main driving forces for senescence in maize are nitrogen remobilization and a

disturbed source-sink balance either through an overly high or too low ratio between

the size of the source and the sink. The tolerance to premature senescence is referred

to as stay-green. Plants that stay green retain green leaves for a longer period of time

and produce grain normally (Borrell et al. 2000b, Thomas 1992, for sorghum). The

stay-green trait was not found to be associated with a decrease in yield under normal

water availability.

In brief, key secondary traits for drought tolerance in maize are: minimal flowering

asynchrony between male and female flowering structures (i.e., a short ASI), reduced

barrenness, stay-green characteristics and, to a lesser extent, epinasty or leaf rolling.

Conventional breeding showed that primary and secondary stress-tolerance traits are

mainly quantitative loci (Bartels and Sunkar 2005), which makes the selection of

traits difficult.

Detection and application of QTLs

Secondary traits can largely contribute to dissecting and understanding the

physiological basis of drought-stress responses and drought tolerance. Their

quantitative nature makes them particularly well-suited for genetic analyses by

mapping quantitative trait loci (QTLs). Both the molecular-marker techniques and

the statistical methods for QTL mapping evolved fast during the last 20 years.

Independent of the type of molecular marker, QTL mapping can be performed using

regression methods, likelihood methods or composite interval mapping, which is a

combination of both (Tuberosa et al. 2003, for review). The objective when using

these methods is to test the association between the phenotype and the

corresponding marker genotype. A large amount of QTL data was produced for many

plant species, including maize. The idea of pyramiding favorable alleles in a targeted

23

genetic background through marker-assisted selection (MAS) was the logical follow-

up step in order to make use of the QTL data. Molecular markers can help to predict

the genotypic value of the respective individuals (Johnson 2004) and they contribute

to reducing time, effort and costs of the conventional selection approach. Marker-

assisted selection can be accomplished without conducting an evaluation of the

phenotypes, thus, reducing the risk of losing a selection cycle because of unfavorable

and unpredictable environmental conditions.

Marker-assisted selection was successful for simple traits controlled by only few

major genes. For complex agronomic traits such as yield under drought stress,

however, marker-assisted selection strategies have contributed less to improving

germplasm than initially thought (Ribaut et al. 2004). Some limiting factors for MAS

of complex traits were the specificity of QTLs to either stress or non-stress conditions,

the low percentage of phenotypic variance explained by the individual QTLs, the

cross-specificity of the QTLs and their sensitivity to changing environmental

conditions (Campos et al. 2004).

QTL-by-environment interactions (QEI) can be ignored or explored. There were

many attempts to exploit the QEI arising from non-genetic variation in multi-site or

multi-environment field trials. Some of these methods were described in brief by

Verbyla et al. (2003). They include multiplicative models, which take into account the

genetic correlations between the target environments (Piepho 1998, Smith et al.

2001), and multi-trait models based on different statistical algorithms (Jiang and

Zeng 1995, McLachlan and Krishnan 1996). More recently, several mixed model

approaches (one-stage or two-stage) were proposed (Piepho 2000, Verbyla et al.

2003, respectively). Malosetti et al. (2004) included environmental covariables in

their studies of QTL-by-environment interactions. Although many of the statistical

methods for exploiting the QEI seemed to be very promising, they were still

unsuitable for large-scale routine QTL analysis because of the need for complex and

difficult parameter estimations. It is important to consider here that even the most

sophisticated tool for data analysis cannot compensate for unsound phenotyping.

Precise phenotyping remains the critical and the most important step in practical

breeding as well as in studies combining physiology and genetics with the aim of

dissecting the plant’s responses to stress conditions, especially under field conditions.

24

Working hypothesis

For more than 30 years, the Consultative Group on International Agriculture

Research (CGIAR) and the International Maize and Wheat Improvement Center

(CIMMYT) have put a great deal of effort to improve drought tolerance in cereals.

During this time considerable progress has been achieved through conventional

selection (Banziger et al. 2000, Heisey and Edmeades 1999). The tropical CIMMYT

maize line 444 (PL1) was the product of this selection process. SC-Malawi (PL2), in

contrast, the other maize line used in the present study, was developed in southern

Rhodesia (Zimbabwe) in the 1960s. PL1 was considered to have greater tolerance to

drought because its yield was greater than that of PL2 when both genotypes were

exposed to the same environmental conditions. Even so, the achieved improvements

of drought tolerance can not belie that the genetic progress by conventional breeding

remains slow because selection is hampered by only one drought cycle per year in the

tropics and by the difficulty of applying the amount of water necessary to obtain the

desired stress level. A selection cycle can also be lost when unexpected rain occurs.

We are still far from a complete understanding of the plant’s mechanisms involved in

drought perception, signal transduction and the regulation of physiological pathways.

A combined approach using conventional breeding, physiology and biotechnology

would allow us to genetically characterize the plant material more precisely and to

enhance our knowledge of the plant's responses to water-limited conditions.

The construction of a genetic linkage map for a population of recombinant inbred

lines (RILs) segregating for drought tolerance would allow for the detection of

quantitative trait loci (QTLs) involved in the expression of yield components,

secondary morpho-physiological traits and structural plant characteristics. The

evaluation of the plants at different locations (in Mexico and Zimbabwe) under

different levels of water stress and in different seasons might help to detect

interactions between QTL expression and the environment. Studying the changes in

trait-trait interactions across environments can be particularly informative because

the relative contribution of a set of target traits to grain yield depends on the

environment. The treatment (water-management system), the location and the

growing cycle (summer or winter) were the factors that defined the “environment” in

the broad sense. In the narrow sense, however, each experiment was considered as

one specific environment, as there was always some variation in abiotic factors

(meteorological conditions, soil characteristics, etc.) among experiments, even when

25

they were performed under the same management at the same location and during

the same growing cycle.

The usefulness of the QTL data of one segregating population is limited, even when

this population has been evaluated in different environments. It might be impossible

to draw conclusions for other maize lines with a different genetic background

(Tuberosa et al. 2003). This major limitation of marker-assisted selection (MAS) for

complex traits calls for novel tools to make use of the QTL data for improving the

drought tolerance of maize. One of these novel tools is the consensus map of drought

tolerance in which QTL and gene expression data of a number of segregating

populations is compiled (Ribaut et al. 2004). This approach rests upon the hypothesis

that genes involved in the drought response are probably located at the same position

in the maize genome, independent of the performance of the germplasm and that

phenotypic differences across germplasm are created by the nature of the alleles at

those genes. The consensus map is being developed at CIMMYT and will evolve over

time. The results of the present study will contribute notably to its construction, since

the RIL population can be well characterized phenotypically in several experiments

differing in the water-management system and in other environmental

characteristics.

Goal and objectives

The overall goal of this project was to provide a good understanding of the

physiological and genetic mechanisms of drought tolerance in a tropical maize

population grown in different stress environments by QTL analysis of morphological

and physiological traits in order to complement the evaluation and selection of

improved germplasm. A linkage map was constructed using restriction fragment

length polymorphisms and simple sequence repeats. The density of the markers on

the linkage map and their distribution across the genome allowed to map QTLs for

yield components, flowering parameters and other morpho-physiological traits.

Although not all the traits were measured in all the trials, the large number of

experiments allowed to estimate QTL-by-environment interactions for most of the

traits trait as well as interactions among traits in order to elucidate the plant’s

responses to varying environmental conditions and to different levels of water supply

at flowering.

27

GENERAL MATERIAL AND METHODS

Plant material

The CIMMYT maize line 444 (CML444) is a white dent maize inbred that matures

late and is adapted to the subtropical African mid-altitudes. It was developed from

CIMMYT’s Population 43 by nine cycles of recurrent selection during the 1990s. This

line represented the most drought-tolerant germplasm available at CIMMYT and was

also tolerant to low nitrogen conditions. CML444 has a compact phenotype with

strong, erectophile, dark green leaves. SC-Malawi is a subtropical white dent line with

intermediate to late maturity and moderate tolerance to water-limited conditions.

This inbred line was developed in southern Rhodesia (today Zimbabwe) in the 1960s.

It was widely used in crosses for developing public and private hybrids. The

phenotype of SC-Malawi is characterized by long, horizontal leaves, light green in

color, and by short internodes at higher positions on the stem. Here, the terms

“drought-tolerant parent” and “drought-susceptible parent” refer to CML444 and SC-

Malawi, respectively. For simplicity, the abbreviations PL1 (for CML444) and PL2

(for SC-Malawi) are used.

A segregating population of 300 F3 plants from the cross PL1 x PL2 was developed at

the CIMMYT experimental station in Tlaltizapán, Mexico in 1999 to 2000. This

population was grown in the field and evaluated under drought stress at flowering as

well as under normal irrigation. Based on these evaluations, it was decided to develop

a population of recombinant inbred lines (RILs). Five generations of inbreeding

through single-seed decent resulted in 250 F7(F2)-lines (S6) at the end of the first

growing cycle in 2002. Fifty progenies were still at the F5(F2)-level (S4) because they

had been excluded from two cycles of self pollination. During the summer season

2002, all the 300 lines were grown in the field to increase seed. The 250 S6 plants

were kept at the same inbreeding level through plant-to-plant pollination within each

line. The fifty S4 lines were self-pollinated to obtain S5 seeds.

A set of 236 RILs was selected from the 300 S6 and S5 lines for constructing the

genetic linkage map. Later, thirty-nine of the lines were replaced by other lines from

the pool, either because they had a high degree of heterozygosity in the molecular

28

marker data (18 RILs, cf. “Linkage Map”), or because they reached anthesis very early

or very late (21 RILs, data not shown).

Field evaluations

The RILs were grown and phenotyped in a total of eleven field experiments in Mexico

(M) and Zimbabwe (Z), either under drought stress at flowering (D) or under

adequate water supply in the rain-fed experiments (W). Table 1 gives the most

important characteristics of the experiments.

The experimental site in Mexico

Six experiments were conducted at the CIMMYT experimental station in Tlaltizapán,

Mexico (18.41 °N, 99.08 °W, 940 masl). According to the CIMMYT classification of

Mega-Environments (Hartkamp et al. 2000), this site belongs to the non-equatorial

tropical to subtropical lowland Mega-Environment 4, which is the major

environment in Central and South America, sub-Saharan Africa, West Africa and

Asia. The climate in Tlaltizapán is hot sub-humid with summer rainfall. The average

annual temperature is 23 °C and the average total annual rainfall is 850 mm. The

coldest months are December and January with an average daily temperature of

approximately 18 °C. The temperature typically reaches its maximum of 28 °C in

May. These climatic conditions allow for two cropping cycles per year, an irrigated

cycle during the winter dry season (November to April) and a rain-fed cycle during

the summer rainy season (May to October). The winter dry season is well suited for

drought-stress experiments. The soil at the Tlaltizapán station is a Vertisol (USDA

taxonomy) developed from calcareous subsoil with more than 40 % clay. Soil depth is

about 1 m and the pH is 7.8. The concentration (in ppm) of the following elements in

the soil was measured in October 2004: Ca (5800), N (1600), Mg (631), K (332), Na

(65), Mn (13), Fe (11), Cu (1.6), Zn (1.2). All the experiments performed in Tlaltizapán

were managed according to the standard procedures for field operations at the

CIMMYT experimental station. A basal fertilization of 75 kg/ha N (ammonium

sulphate with 20.5 % N) and 50 kg/ha P2O5 (triple super phosphate with 46 % P2O5)

was applied before sowing.

Table 1: Key characteristics of 11 field experiments with respect to design, trait evaluation and climate. (+) and (-) indicate which traits were measured or not.

Trait abbreviations are defined in the text (c.f. “Experimental evaluation”). PL1 is the drought-tolerant and PL2 the drought-susceptible parental line.

Temperatures correspond to the average daily minimum, mean and maximum temperature from sowing to flowering (before flw) and to the average

temperature of 10 days during the flowering period (at flw).

Param Details DM1 DM2 DM3 DM4 DZ1 DZ2 DZ3 DZ4 WM1 WM2 WZ1 Dates Sowing [yymmdd] 021128 031206 021128 031206 030531 030531 040520 040512 030603 040701 031208 Harvest [yymmdd] 030508 040520 na na 031107 031107 na 041013 030923 041011 040328 Design plot area [m2] 1.875 1.875 1.875 1.875 2.25 2.25 3 3 1.875 1.875 3 # plots/rep PL1 9 2 9 2 4 4 4 4 1 2 4 # plots/rep PL2 1 2 1 2 4 4 3 3 1 2 4 # RILs evaluated 197 236 197 236 228 228 233 233 236 236 229 Traits ASI GYA + + + + - - - - + + + + - - + + + + + + + + CHL SEN RCT + + + + + + + + + + + + - - - - - - + - - + - - + + + + + + - - - EW0 SW0 EW7 SW7 - - - - - - - - + + + + + + + + - - - - - - - - + - + - - - - - - - - - + + - - - - - - TBW + + - - - - - - - + - Temp. Min before flw [°C] 10.3 10.2 10.3 10.2 10.3 10.3 na na 19.0 18.1 15.2

Mean before flw [°C] 20.4 20.3 20.4 20.3 17.8 17.8 na na 25.2 24.5 21.8 Max before flw [°C] 30.4 30.5 30.4 30.5 25.4 25.4 na na 31.5 30.9 28.3 Min at flw [°C] 13.9 16.4 13.9 16.4 14.1 14.1 na na 18.0 18.9 15.8 Mean at flw [°C] 24.5 25.3 24.5 25.3 24.5 24.5 na na 25.0 24.9 21.2 Max at flw [°C] 35.1 34.0 35.1 34.0 34.3 34.3 na na 31.9 30.9 26.5

30

The distance between the rows was 0.75 m and between the plants in a row 0.2 m.

The experiments were hand-sown with two seeds per hole. The canopy was later

thinned to one plant per hole, which corresponded to an expected plant density of

approximately 6.4 m-2. The seeds were treated with a mixture of an insecticide

(thiodicarb), two fungicides (fludioxonyl and metalaxyl), a polymer and water. An

herbicide (atrazine 2.24 kg/ha and metolachlor 1.74 kg/ha) was applied to the soil

directly after sowing before the soil was watered through sprinkler irrigation.

Approximately three weeks after sowing, 18 kg/ha of permethrin were applied as

granules to the whorl to combat the armyworm (Spodoptera frugiperda), which is a

common pest of maize throughout America. A second N-fertilization of 75 kg/ha N

(ammonium sulphate with 20.5 % N) was applied during the vegetative development

of the plants.

The drought-stress treatment in Mexico

The four drought-stress experiments in Mexico were performed during the winter dry

season (November to April) in 2003 and 2004. The fields were watered twice by

sprinklers, at sowing and 8 days after sowing, and then by seven furrow irrigations

every 10 days. The last irrigation during the vegetative development of the plants was

applied to every second row in the field not later than three weeks before the expected

date of flowering. The onset of flowering was predicted by observing the border

plants, which were sown around the experimental field. The well-characterized

CIMMYT maize line in the borders was known to reach flowering approximately two

weeks before the plants of PL1 under the given growing conditions. The plants were

not irrigated for about five weeks, i.e., until the end of the flowering period. Once this

target stress period was terminated, the plants were watered again with two furrow

irrigations during grain filling to ensure adequate kernel development.

All four drought-stress experiments were performed in the same field. The previous

crop of the two experiments in 2003 (DM1 and DM3) was maize. The legume

Mucuna deeringiana, cultivated as green manure on this field during the summer

rainy season in 2003, was the previous crop in the drought-stress experiments in

2004 (DM2 and DM4).

31

The non-stress treatment in Mexico

The two non-stress experiments under rain-fed conditions were conducted during the

summer rainy seasons of 2003 and 2004. The field was irrigated once, right after

sowing; sufficient rainfall rendered redundant additional irrigations. The second

rain-fed experiment (WM2) was conducted on the same field as the drought-stress

experiments. The first rain-fed experiment (WM1), however, was conducted on a

neighboring field. The previous crop of both experiments was maize.

The drought-stress treatment in Zimbabwe

The four drought-stress experiments in Zimbabwe were conducted in Chiredzi during

the winter dry season (May to October); two in 2003 (DZ1 and DZ2) and two in 2004

(DZ3 and DZ4). The Chiredzi site (21.03 °S, 31.57 °E, 392 masl) belongs to the Mega-

Environment 4, as does Tlaltizapán, the experimental site in Mexico. The average

daily mean temperature from May to November 2003 was 19 °C. The soil at Chiredzi

is a Alfisol (USDA taxonomy).

The distance between the rows was 0.75 m and between the plants in a row 0.25 m.

The rows were 3 m long in DZ1 and DZ2 and 4 m long in DZ3 and DZ4. The

experiments were hand-sown with two seeds per hole, later thinned to one plant, and

had an expected plant density of approximately 5.4 m-2. The plants in the drought-

stress experiments were sprinkler-irrigated once a week. Irrigation was stopped 60 or

50 days before anthesis (in DZ2 and DZ4 or DZ1 and DZ3, respectively) and the

growing cycle was completed without further irrigation. A basal NPK-fertilization was

applied prior to sowing, two more N-fertilizations during the vegetative growth. The

plants were checked weekly for insect damage and treated with insecticide if

necessary.

The non-stress treatment in Zimbabwe

One non-stress experiment (WZ1) was conducted at Art-Farm in Harare under rain-

fed conditions during the summer rainy season of 2004 (December 2003 to April

2004). Harare (17.80 °S, 31.05 °E, 1468 masl) is classified as a non-equatorial

tropical to subtropical mid-altitude site. According to Hartkamp et al. (2000), Harare

belongs to Mega-Environment 5, which is the major environment of the highlands in

sub-Saharan Africa and Mexico and has large variations in rainfall. The total average

annual rainfall in Harare is 750 mm and the average daily mean temperature was

32

21 °C during experiment WZ1. This rain-fed experiment was irrigated only once, right

after sowing. Additional irrigations were redundant. The soil at Harare is an Alfisol

(USDA taxonomy). The rows were 4 m long and 0.75 m apart. The plants in each row

were 0.25 m apart. The experiment was over-sown with two seeds and later thinned

to one plant per hole. The expected plant density was 5.4 m-2. A basal NPK-

fertilization was applied prior to sowing and two more N-fertilizations during

vegetative growth. The plants were treated with insecticide if necessary.

Meteorological data

The meteorological data at the experimental site in Mexico were collected with a

CR10X control module (Campbell Scientific, Inc.) equipped with sensors for

temperature and humidity (Viasala HMP45C), solar radiation (LI200X, Li-Cor, Inc.)

and rainfall (TR525M, Texas Electronics, Inc.). The maximum and minimum air

temperature and the rainfall were recorded daily.

Experimental evaluation

All the field experiments were designed as incomplete alpha (0, 1) lattices with two

replications and one-row plots. Each recombinant inbred line (RIL) was grown in one

plot per replication. The number of plots for growing the parental lines varied across

experiments. Plot size was constant in Mexico (1.875 m2) but varied in Zimbabwe

(2.25 or 3.0 m2, Table 1). Theoretically, the whole mapping RIL population, together

with the parental lines, should have been evaluated in each experiment, but the first

two field experiments (DM1 and DM3) were planted before changing the genotypic

composition of the mapping population. Therefore, only 197 of the 236 RILs of the

final mapping population were phenotyped in these two experiments. The

inconsistent number of RILs evaluated in the experiments in Zimbabwe was due to a

shortage of seeds.

Most of the traits were measured during or at the end of the flowering period. Only

grain yield parameters were evaluated at the physiological maturity of the plants. Two

different sampling procedures were used: (1) the non-destructive measurement of the

anthesis-silking interval (ASI) and grain yield (GYA) and (2) the destructive

measurements of the dry weight of the ears and silks anthesis (EW0, SW0) and one

week after anthesis (EW7, SW7). Both procedures were not combined in the drought-

stress experiments, but the experiments with destructive sampling were always

33

accompanied by an independent, complementary, non-destructive experiment on ASI

and grain yield. Only in WM2, the second experiment under rain-fed conditions in

Mexico, both procedures were combined: The dry weight of the ears and silks at

anthesis were measured on five of the ten plants per plot, the remaining five plants

were left intact to measure ASI and grain yield.

The standard traits, days to anthesis (MFL) and plant height (PHT), were measured

in all eleven experiments. The chlorophyll content of the ear leaf (ELC) and the

second leaf from the tassel (YLC) was measured in all the experiments in Mexico and

in two drought-stress experiments in Zimbabwe (DZ3, DZ4). Whole-plant senescence

(SEN) and root capacitance (RCT) were measured in all six experiments in Mexico,

tassel dry weight (TBW) only in DM1, DM2 and WM2. The first and last plant in the

plots were not used for measurements. Table 1 shows which of the non-standard

traits were measured in which experiments. Detailed information on sampling

procedures is given in “Material and Methods” of the respective chapters.

Data analysis

Heritability

Trait heritability (h2) was calculated as the ratio between the random genetic variance

and the sum of the genetic variance and the variance of the residuals:

( )2222eggh σσσ +⋅= . The variance components were estimated for the standardized

(0, 1) phenotypic values of the traits per plot in a linear mixed model (ProcMixed) in

SAS (The SAS Institute 2001). The three experimental factors Replication,

Incomplete Block and Genotype were set as random.

Spatial analysis

The plot-wise raw data of each trait in each experiment was adjusted for local and

global variation with the software ASREML (Gilmour et al. 2002). The factor

Replication was fixed; Incomplete Block, Genotype and Residual were random

factors. The alpha lattice means were calculated first, and then a two-dimensional

spatial analysis was performed to adjust the mean values of neighboring plots. The

resulting best linear unbiased predictors (BLUPs) of the phenotypic value of each

genotype were used to calculate the minimum, average and maximum phenotypic

values and the phenotypic correlations among traits as well as for QTL identification.

34

Phenotypic correlations

The phenotypic correlations among traits were calculated as simple Pearson’s

correlation coefficients using the “cor” command with the option

“pairwise.complete.obs” in R (The R Development Core Team 2004).

QTL identification

The quantitative trait loci (QTLs) were identified by composite interval mapping

(Zeng 1994) using the software QTLMMAP (CIMMYT). Missing phenotypic values

remained unchanged. Three successive models were used for QTL mapping. First, a

simple interval mapping across the whole genome tested for the existence of a QTL

with a potential effect on each cross at each locus (Goffinet and Gerber 2000). The

genetic window size was 400 cM, larger than the longest linkage group (Figure 1).

The peaks in the resulting LOD-score profile exceeding the threshold value (cf. below)

were considered as putative QTLs. The closest marker of each was selected as a

cofactor in order to block the effects on QTL expression in the second model, where

the window size remained unchanged. The closest markers at positions where new

putative QTLs appeared were also selected as cofactors. The analysis was re-run until

no new putative QTLs were detected. The latest set of cofactors of model 2 remained

active in model 3, but the size of the genetic window was reduced to 30 cM in order to

block the effects of possible linked QTLs outside the interval of interest.

The output of model 3 determined the LOD score and the position of the QTL peaks,

the marker closest to the peak and the additive genetic effect. The expression of a

QTL was considered significant when the peak in the LOD score exceeded the

threshold value (cf. below). Positive or negative signs of additivity indicated that the

allele of PL1 (CML444) or PL2 (SC-Malawi) contributed to higher phenotypic values

of the traits. The percentage of phenotypic variance explained by each QTL for a given

trait as well as the total percentage of phenotypic variance explained by all the QTLs

together was calculated separately by multiple regression (Zeng 1994). The positions

on the chromosome where the LOD score at the QTL peak decreased by half defined

the QTL confidence interval.

The software QTLMMAP offered the possibility of calculating combined or joint QTLs

for more than one trait simultaneously. The rationale behind this approach was that

combined QTL mapping might increase the statistical power through a possible

improvement in estimating parameters (Jiang and Zeng 1995). Especially when

35

putative QTLs have pleiotropic effects on both traits, the joint mapping may be better

than QTL mapping of single traits. The above-mentioned procedure for QTL mapping

applied to both single and joint QTL mappings; only the significance threshold for

QTL detection had to be adapted to the number of traits included in the analysis. The

significance threshold for QTL detection was LOD = 2.5 for single QTL analyses and

LOD = 3.0 for joint analyses on two traits. In both cases the theoretical probability of

a Type-I error was below 5 % (P < 0.05) (M. Vargas, personal communication 2004).

In the case of the joint analyses on two traits, the program calculated the LOD-score

profile for each individual trait (E1, E2) as well as for the combined effect of both

traits (Joint), the latter being decisive for the selection of cofactors and for testing the

significance of putative QTLs. An LOD score for the interaction (QEI) among traits

was also calculated. The interaction was considered significant (at P < 0.05) when the

LOD(QEI) > 1.3 in case of the joint QTL analysis on two traits.

37

CONSTRUCTION OF THE GENETIC LINKAGE MAP FOR A

TROPICAL MAIZE POPULATION

Introduction

DNA markers were extensively used in genetic research during the last 20 years. The

first genetic linkage maps were constructed with restriction fragment length

polymorphisms (RFLPs). Such linkage maps were used for marker-assisted selection,

for detecting quantitative trait loci (QTLs) and for positional cloning in many species

(Sibov et al. 2003a). With the development of the polymerase chain reaction (PCR)

(Mullis and Fallona 1987) a new class of PCR-based markers became available. They

include random amplified polymorphic DNA markers (RAPDs) (Williams et al. 1990),

amplified fragment length polymorphisms (AFLPs) (Vos et al. 1995), simple sequence

repeats (SSRs) (Powell et al. 1996) and, more recently, different types of single

nucleotide polymorphisms (SNPs) (Gilles et al. 1999). PCR-based markers were

better suited for the analysis of large populations than RFLPs, because their

application was less expensive and time-consuming and could be automated. The

SSR markers became particularly important for plant breeding and genetic

applications such as QTL mapping because they are commonly found in eukaryotic

genomes and because they are stable and evenly distributed throughout the genomes

(Holland et al. 2001). Most of the SSRs are co-dominant and allow for the

differentiation between homozygous and heterozygous individuals.

Regardless of the type of markers used for constructing genetic linkage maps, the

basic idea of QTL mapping is to analyze genotype-phenotype associations in order to

detect the genomic basis of the expression of complex traits.

Our objective was to construct a genetic linkage map, which could be used as a tool to

dissect the genetic basis of drought tolerance in a population of tropical maize inbred

lines derived from two parents with contrasting responses to water-limited

conditions. The map should be comparable, by means of common anchor markers, to

linkage maps of other populations of tropical maize developed at CIMMYT so that the

QTL data of this study could be included without difficulty in the drought consensus

map (Ribaut et al. 2004).

38

Material and Methods

DNA extraction

The leaf samples for DNA extraction were harvested from plants grown for seed

production in Tlaltizapán, Mexico in summer of 2002. Each sample consisted of leaf

sections cut from ten plants per plot. The samples were frozen in liquid nitrogen,

lyophilized, ground and stored at -20 °C. DNA was extracted from approximately 300

mg ground, lyophilized leaf tissue according to the protocol of Hoisington et al.

(1994), a CTAB extraction based on the method of Saghaimaroof et al. (1984). The

total volume of the extract was 15 ml per sample. The DNA was washed, first with a

solution of 76 % EtOH and 0.2 M NaOAc and then with a solution of 76 % EtOH and

10 mM NH4OAc (Option C in the protocol). The DNA was dissolved in TE-8 buffer

(Tris-EDTA, pH 8.0). The concentration of each DNA sample was measured with a

Beckman DU-65 spectrophotometer and adjusted to 0.3 ug/ul by adding the amount

of TE-buffer calculated by the spectrometer. The samples were stored at 4 °C.

RFLP analysis

The DNA of 236 RILs and the two parental lines were digested with two restriction

enzymes (EcoRI and HindIII), loaded on 12 double thick 0.7 % agarose gels and

separated by gel electrophoresis at a constant current (15 mA) overnight. Two gels

were needed to separate the DNA fragments of the whole RIL population and the

parental lines, since one gel could accommodate a total of 119 DNA samples and a

molecular-weight marker. The DNA fragments were transferred to uncharged nylon

membranes (MSI Magnagraph, 0.45 um pore size) by Southern Blotting, according to

the protocols of Hoisington et al. (1994). A total of 24 membranes were produced –

six sets with two EcoRI-membranes and six sets with two HindIII-membranes.

Ninety-six RFLP probes were selected from the genetic linkage map of the

F3 population of the same cross. The available information on the approximate

mapping position and the sizes of the polymorphic fragments enabled us to combine

two suitable RFLP probes on one set of membranes. The probes were labeled with

digoxigenin-dUTP and hybridized to the membranes in siliconized glass bottles

(Robbins Scientific Corp.). The polymorphisms were visualized with the

antidigoxigenin-alkaline phosphatase-CSPD chemiluminescent reaction exposing the

membranes to XAR-5 X-ray films overnight (Hoisington et al. 1994). The membranes

39

were reused five to seven times. Ninety-one RFLP probes from different laboratories

(ASG, BNL, NPI, UMC, PHP) were finally analyzed.

SSR analysis

Both parental lines were screened for polymorphisms with more than 500 SSR

primers. Sixty-three primers with clear polymorphisms between the parental lines

and with variable mapping positions (bins) were directly analyzed with the DNAs of

all the 236 RILs. Almost 100 more polymorphic SSRs were analyzed on a subset of 46

RILs in order to estimate their mapping position on a preliminary map. Twenty-

seven with the desired mapping positions were analyzed with the DNAs of all the 236

RILs.

The polymerase chain reaction (PCR) to amplify the DNA segments was carried out in

96-well PCR plates containing a mixture of 4 ul genomic DNA (at 10 ng/ul), 6.6 ul

double-distilled water, 2 ul of Taq buffer (10 x), 1.2 ul of a commercial nucleotide mix

(dNTP, 2.5 mM), 1 ul MgCl2, 6 ul of a solution containing the forward and backward

reverse primers and 0.4 ul of Taq polymerase to give a total reaction volume of

17.2 ul. The samples were protected from evaporation by adding 25 ul of mineral oil.

The PCR was processed in a PTC-225 Peltier Thermal Cycler (MJ Research, Waltham,

MA, USA). After the initial denaturation of the DNA at 94 °C for two minutes, 30

cycles of denaturation (94 °C for 30 s), primer hybridization (56 °C for 60 s) and

synthesis (72 °C for 60 s) were repeated. Once the last cycle was completed,

temperature was kept at 72 °C for 5 minutes, before the samples were cooled to 10 °C.

The PCR products were separated by electrophoresis in 4 % agarose gels with 50 %

MetaPhor Agarose and 50 % SeaKem LE Agarose (Cambrex Bio Science Rockland

Inc) at a constant voltage (130 V). The gels were stained in an ethidium bromide

water bath (100 ul/l) for 10 minutes, rinsed for 10 minutes and photographed under

UV light. The gels were reused two to three times.

Construction of the genetic linkage map

The molecular marker data were gathered manually by two persons using the

HyperMapData software (CIMMYT). The two readings were compared and

inconsistencies were corrected. The linkage map was constructed in MAPMAKER

applying the Haldane’s mapping function (Haldane 1919) to transform the

recombination frequencies to centiMorgans (cM).

40

In a first step, major linkage groups were identified with the two-point analytic

“Group” function. Two markers were considered to be linked when the LOD score

exceeded 3.0 and the maximum recombination fraction was below 0.4, which

corresponds to a maximum distance between two loci to form groups of 40 cM. The

markers within each group were then ordered by making a “First Order”. The

outcome of this multi-point analytic function was confirmed or corrected by

comparing the LOD scores and the recombination fractions between marker pairs

obtained from multiple LOD tables. Linkage groups, which belonged to the same

chromosome (according to publicly available information at the MGDB) but which

were separated by more than 40 cM were merged manually and the gaps were filled

with additional markers. The position of unlinked markers was tested with the “Try”

command, a multi-point analysis function, which tests for the best position of a

particular marker in a group of linked markers. Multiple LOD tables again confirmed

the indicated positions. Some of the markers were not included in the map; either

they could not be integrated into a linkage group because they interfered with the

flanking markers and caused large increases in the respective marker interval or they

were located less than 1 cM away from the next marker. Finally, the “Ripple”

command verified the map order through permutations in the order of neighboring

markers and comparisons of the likelihoods of the resulting maps.

41

Results

The allelic information of the first 63 SSRs, which were directly analyzed on the

whole population, showed that 18 RILs had a high degree of heterozygozity (> 20 %).

These RILs and 21 more (with too early or too late flowering dates) were replaced by

other RILs initially not considered for genotyping. The DNA of the newly selected

RILs was analyzed with the same markers as for the other RILs.

The final genetic linkage map consisted of the allelic information of 236 RILs at 160

molecular marker loci (81 SSRs and 79 RFLPs). The map was 2105.6 cM long and had

an average marker distance of 13.2 cM (Figure 1). The longest interval (58.9 cM) was

located on chromosome 3 between the markers umc1307 (c3m10) and bnl10.24

(c3m11). Most of the markers were co-dominant, only 14 were dominant. The 26

markers (16.3 %) with significant (P < 0.01) distortion from the chi-squared

distribution were located on chromosomes 2, 3, 4, 5, 6 and 10. Four (c2m12, c3m14,

c5m15, c6m1) were flanked by non-distorted markers; the remaining distorted

markers were located in seven groups of two to seven markers.

The allelic information was missing at 1.4 % of all genetic data points. The PL1 allele

was present at 48.5 % of the informative data points, the PL2 allele at 46.4 % and

3.7 % were heterozygous. These results corresponded well to the expected ratio of

48.4 % : 48.4 % : 3.2 % between the amount of homozygous bands for the two

parental alleles and the heterozygous bands, respectively, after five generations of

inbreeding.

42

Figure 1: Genetic linkage map for the cross PL1 x PL2 (CML444 x SC-Malawi) based on the allelic

segregation of 236 recombinant inbred lines at 160 molecular marker loci. The loci are numbered

continuously within each linkage group. A scale with the absolute distances in centiMorgan (cM) is

displayed along chromosome 1.

01-

ph

i056

02

-bn

l5.6

20

3-u

mc1

041

04-

um

c157

a0

5-bn

lg11

780

6-b

nlg

1429

07-

bnlg

162

70

8-u

mc1

1a0

9-b

nlg

439

10-b

nlg

223

8

11-b

nlg

208

612

-um

c177

a13

-csu

61b

14-b

nlg

1057

15-u

mc1

122

16-u

mc1

128

17-u

mc1

2818

-um

c16

6b

19-d

up

ssr1

2

20

-ph

i011

21-

bn

lg17

202

2-u

mc1

06

a2

3-u

mc1

47b

24-

bnlg

233

1

25-

bnlg

2123

26

-bn

l6.3

2

c1

01-

ph

i40

28

93

02-

bnlg

129

7

03-

bnlg

20

42

04-

um

c44

b0

5-cs

u40

06-

um

c135

07-

um

c8g

08

-csu

54a

09

-um

c55a

10-u

mc1

5211

-um

c14b

12-c

su15

4a

13-d

up

ssr2

514

-um

c150

b15

-um

c155

116

-csu

109

a17

-um

c36

a

c2

01-

um

c32a

02

-ph

i10

4127

03-

bn

lg13

25

04

-bn

lg14

470

5-u

mc1

540

6-u

mc9

2a0

7-bn

lg10

19a

08

-ph

i053

09

-bn

lg42

010

-um

c130

7

11-b

nl1

0.2

4a

12-u

mc7

13-u

mc3

b

14-u

mc1

6a

15-u

mc6

3a16

-bn

lg11

82

17-c

su36

c18

-bn

lg17

54

c3

01-

um

c10

170

2-u

mc1

29

40

3-p

hi0

21

04-

um

c155

00

5-u

mc1

652

06

-bn

lg4

90

07-

csu

100

08

-um

c156

a0

9-b

nlg

229

110

-um

c19

11-m

mc0

341

12-u

mc1

33a

13-u

mc1

5a14

-csu

11b

15-n

pi5

93a

16-b

nlg

589

17-b

nlg

1337

18-p

hi0

1919

-ph

i00

6

c4

01-

bn

l8.3

30

2-n

pi4

09

03-

um

c147

a0

4-u

mc9

00

5-u

mc1

07b

06

-bn

lg10

46

07-

um

c166

a0

8-b

nl6

.22

09

-csu

36b

10-b

nl5

.71a

11-u

mc4

8b

12-n

pi2

3713

-um

c54

14-b

nlg

1346

15-b

nlg

118

16-u

mc1

225

17-u

mc1

04b

18-b

nlg

188

5

c5

01-

um

c85a

02-

bn

lg42

60

3-u

mc3

6c

04-

bn

lg21

510

5-u

mc1

88

70

6-u

mc6

5a0

7-u

mc1

014

08

-bn

lg19

22

09

-mm

c024

110

-bn

lg17

32

11-u

mc3

612

-um

c39

13-b

nlg

174

0

14-u

mc2

059

c6

01-

csu

130

2-u

mc1

06

60

3-b

nlg

109

40

4-u

mc1

393

05-

bnlg

180

80

6-b

nl1

5.21

07-

bnlg

339

08

-bn

lg15

50

9-b

nlg

180

510

-bn

l14

.07

11-u

mc1

125

12-p

hi0

82

13-u

mc1

799

c7

01-

np

i114

a0

2-u

mc1

327

03-

np

i110

a

04-

um

c10

3a0

5-bn

lg6

69

06-

um

c18

58

07-

um

c2c

08

-um

c48

a0

9-a

sg52

a10

-um

c150

a11

-um

c138

412

-um

c713

-bn

lg10

5614

-um

c39

b

c8

01-

bnlg

1272

02

-um

c10

90

3-u

mc1

13a

04

-um

c10

5a

05-

um

c81

06

-bn

l8.1

70

7-u

mc1

231

08

-bn

lg15

88

09

-um

c173

3

c9

01-

ph

i118

02-

np

i28

5a

03-

um

c130

04-

bnlg

1079

05-

um

c111

50

6-n

pi2

32a

07-

um

c44

a0

8-u

mc1

82

09

-bn

lg2

3610

-bn

l7.4

9a

11-b

nlg

1450

12-u

mc1

038

c10

0 10 20 30 40 50 60

70 80

90

100

110

120

130

140

150

160

170

180

190

200

210

220

230

240

250

260

270

280

290

300

310

320

330

340

350

360

370

43

Discussion

Seven marker intervals were longer than 30 cM but only two exceeded 40 cM. The

average marker density was relatively high, in spite of these gaps and the fact that

accumulations of two or more markers over a very short distance (< 1 cM) were

avoided. The marker density exceeded one marker per 15 to 20 cM, which Darvasi et

al. (1993) considered to be the threshold density at which an increase in the number

of progenies contributes more to enhancing the accuracy of QTL mapping than

increasing the marker density (Tuberosa et al. 2003). The number of progenies

considered here was intermediate to that of other studies, in which between 100

(Agrama and Moussa 1996, Lebreton et al. 1995, Tuberosa et al. 1998) and 400

genotypes (Melchinger et al. 1998, Openshaw and Frascaroli 1997) were analyzed.

Considerable effort was put into finding suitable markers to fill the large gap between

markers 11 and 12 on chromosome 3, but this was impossible. The proximity to the

centromere might explain the difficulty in mapping markers to this chromosome

segment because of the usually high rates of crossing-over close to the centromere.

All seven markers with a significantly distorted segregation ratio on chromosome 3

were located around the supposed position of the centromere. The PL2 allele was

more frequent than the PL1 allele at all of these markers. According to Zamir and

Tadmor (1986), this might indicate that the distortion was caused by a particular

gene, which segregated in the population. Segregation distortion has often been

observed during the construction of genetic linkage maps (Wendel et al. 1987, for

maize). Lu et al. (2002) speculated that, in most cases, only one gametophyic factor

was present in a region with distorted segregation and that the segregation distortion

caused by a single gametophytic factor did not affect the estimation of recombination

frequency. The congruent distortion towards the same parental allele on

chromosome 3 suggested the presence of only one underlying gene at each of the

distorted loci. The same situation was observed in all other distorted regions.

Therefore, segregation distortion probably did not perturb the recombination

frequencies between markers.

45

QTL-BY-ENVIRONMENT INTERACTIONS FOR FLOWERING

TRAITS, PLANT HEIGHT AND GRAIN YIELD IN A TROPICAL

MAIZE POPULATION

Introduction

There is evidence that the global mean temperatures are increasing and the climate is

becoming more erratic, with drought and more and stronger storms (IPCC 2001).

Sub-Saharan Africa in particular will suffer from the combined effects of higher

temperature and reduced rainfall (CGIAR 2000, Ribaut et al. 2004). Given such

environmental conditions, the development of more drought-tolerant crops is

important to maintain and increase agricultural production. The most sensitive stage

of reproductive development in cereals is around flowering when pollination,

fertilization and grain initiation occurs (Saini and Westgate 2000, Salter and Goode

1967). Maize is particularly susceptible to water-limited conditions at flowering

(Claassen and Shaw 1970, Edmeades et al. 1999, Grant et al. 1989, Westgate and

Boyer 1985). Extensive research in the area of tolerance of maize to drought stress

during this period identified key secondary traits for grain yield. They include

reduced barrenness, the anthesis-silking interval, stay-green and epinasty or leaf

rolling (Banziger et al. 2000, Bruce et al. 2002). The main cause of barrenness is a

reduced flush of assimilates to the developing ear (Schussler and Westgate 1995,

Zinselmeier et al. 1995b). The reduced availability of assimilates in the ear slows

down ear and silk growth and delays silk emergence. Tassel growth is less affected

than ear growth. The resulting characteristic widening of the anthesis-silking interval

negatively affects pollination and kernel set because of the time lag between pollen

release and silk emergence and because of a decline in silk receptivity as a

consequence of dehydration (Saini and Westgate 2000).

Although conventional selection for grain yield and secondary traits improved

considerably the tolerance of maize to water-limited conditions (Heisey and

Edmeades 1999 for review, Banziger et al. 2000) this process remains slow and time-

consuming (Ribaut et al. 2004). A multidisciplinary approach combining agronomy,

46

physiology and biotechnology could enhance the understanding of the genetic basis of

trait expression by identifying quantitative trait loci (QTLs) for key traits.

Many QTL studies of maize focused on the dissection of yield in general or under

stress conditions (Beavis et al. 1994, Bertin and Gallais 2001, Kraja and Dudley 2000,

Melchinger et al. 1998, Ragot et al. 1995, Stuber et al. 1992, Veldboom and Lee

1996a), but only a few studies aimed at the genetic dissection of yield parameters and

the anthesis-silking interval (ASI) of tropical maize under drought stress (Agrama

and Moussa 1996, Li et al. 2003, Ribaut et al. 1996, Ribaut et al. 1997, Xiao et al.

2004). The complex trait yield is particularly suitable for QTL mapping. Many genes,

if not all, contribute to yield formation. Therefore, this trait is probably affected by a

large number of QTLs (Moreau et al. 2004). It is known that grain yield is often

associated with early flowering and that QTLs for grain yield are subjected to

considerable interactions with the environment, despite the fact that some QTLs for

grain yield and ASI have been identified at the same loci under contrasting growth

conditions (c.f. Campos et al. 2004, Moreau et al. 2004). The negative association

between grain yield and flowering time becomes particularly relevant when water is

limited at flowering. Drought stress, on the one hand, intensifies over time and

penalizes late-flowering genotypes. On the other hand, drought stress favors the

occurrence of genotype and QTL-by-environment interactions (GEI and QEI) as a

consequence of the reduced heritability of yield (Tuberosa et al. 2002).

This study focused on changes in QTL expression in response to different levels of

water stress in a segregating population of tropical maize. QTLs were identified by

joint mapping on data of two experiments of the same treatment at the same location

in order to improve the precision of parameter estimation.

Our objectives were (1) to measure flowering traits, plant height and yield parameters

in a tropical maize population of recombinant inbred lines (RILs) in order to

characterize the phenotypic differences among the parental lines and to define the

genetic basis for these differences by QTL mapping, (2) to investigate changes in QTL

expression for a given trait across different treatment-location combinations (TLCs)

and (3) to assess possible cross-dependences among traits in different environments

in order to elucidate causal relationships between morphological and physiological

secondary traits and grain yield under changing environmental conditions.

47


Plant material and field experiments

The population of recombinant inbred lines (RILs) of the cross PL1 x PL2 was grown

together with the parental lines in seven field experiments. Four were conducted in

Mexico, either under drought stress at flowering (DM1 and DM2) or under rain-fed

conditions (WM1 and WM2), two were conducted under drought stress at flowering

in Zimbabwe (DZ1 and DZ2) and one under rain-fed conditions in Zimbabwe (WZ1).

Drought stress was induced by stopping irrigation approximately seven weeks (in DZ)

or three weeks (in DM) before the expected average date of anthesis. The drought-

stress experiments in Zimbabwe were completed without further irrigation, those in

Mexico were irrigated again once flowering was finished. All experiments were

designed as alpha (0, 1) lattices with one-row plots and two replications. Detailed

information about plant material, experimental sites and experimental designs is

given in “General Material and Methods”.

Phenotypic data

The time of male flowering (MFL), the anthesis-silking interval (ASI), plant height

(PHT), grain yield per area (GYA), kernel number per area (KNA) and hundred

kernel weight (HKW) were recorded in all seven experiments. MFL [d] was measured

as the number of days from sowing to pollen release (anthesis). In Mexico, the MFL

of 10 plants per plot was recorded and the average value was the MFL of the

respective plot. In Zimbabwe, the MFL of each plot was estimated as the number of

days from sowing to the day, on which 50 % of the plants per plot had the first

anthers extruded. The time of female flowering (FFL), defined as the number of days

from sowing to silk extrusion, was recorded in an analogous manner. FFL was used to

calculate the ASI but is not presented here. The ASI [d] was calculated as the plot-

wise difference between MFL and FFL in the experiments in Zimbabwe. In Mexico,

however, the ASI was calculated individually for each plant except for those with

missing MFL. For plants with a known MFL but without FFL, the ASI was set at the

average ASI of the remaining plants in this particular plot plus twice the standard

deviation. The average value and the standard deviation for ASI were calculated for

plots having at least three plants with known dates of male and female flowering. If

there were fewer than three recorded values for ASI in a plot, the average value for all

48

the other plots in this replication plus two days was set at the ASI for that plot. PHT

was recorded as the average value of the distance [cm] from the soil surface to the

first tassel branch measured on five plants per plot. The mature ears were hand-

harvested, bagged, air-dried and shelled using an electric shelling device. The total

grain yield of each plot was weighed on electronic scales. The total weight of the

grains per plot was divided by the plot surface to calculate GYA [g m-2]. HKW [g]

corresponded to the weight of one hundred kernels, which were counted by hand and

weighed separately. GYA was divided by HKW and multiplied by 100 to obtain KNA

[m-2].

Data analysis and QTL mapping

The methods to calculate the heritability of traits, the adjusted means for each

genotype and the phenotypic correlations among traits were described in “General

Material and Methods”. The spatial analysis for ASI measured in the first drought-

stress experiment in Mexico (DM1) differed somewhat from the spatial analysis of

other traits since the corresponding MFL data was included as a covariate in the

statistical model in order to reduce the strong co-segregation of these two traits

(r > 0.6).

The seven experiments were grouped into four treatment-location combinations

(TLCs): DM (DM1, DM2), DZ (DZ1, DZ2), WM (WM1, WM2) and WZ (WZ1). Since

under rain-fed conditions in Zimbabwe data from only one experiment were

available, the QTLs affecting trait expression in WZ were identified by single trait

QTL mapping (critical LOD = 2.5). In the remaining three TLCs, the QTLs were

identified by joint QTL mapping (critical LOD = 3.0) combining, for each trait, the

phenotypic data of both experiments forming a TLC. Detailed information about the

QTL analysis is given in “General Material and Methods”.

49

Results

Environments

Drought stress was not alleviated by unexpected rainfall in any of the experiments

under water-limited conditions (not shown). When the plants were grown in the dry

winter, they were first exposed to lower minimum temperatures before flowering (i.e.,

the average from sowing to anthesis) and then to higher maximum temperatures at

flowering compared to the plants grown in the wet summer (Figure 2). Higher

minimum temperatures (5 to 9 °C) before flowering and lower maximum

temperatures at flowering in the three rain-fed experiments led to small

thermoperiods quite close to optimum growth requirements, both during the

vegetative phase and at flowering. The mean temperatures were slightly lower in the

rain-fed experiment in Zimbabwe (WZ1) than the rain-fed experiments in Mexico

(WM1 and WM2).

10

15

20

25

30

35

ExperimentDM1 DM2 DZ1 DZ2 WM1 WM2 WZ1

Tem

pera

ture

[°C

]

Figure 2: Average daily minimum, mean and

maximum temperatures [°C] before flowering

(point down) and at flowering (point up) for

the experiments performed under drought-

stressed (D) or under rain-fed conditions (W)

in Mexico (M) and Zimbabwe (Z).

0 2 4 6 8 10ASI [d]

0

100

200

300

GY

A [g

/m2]

Figure 3: Relationship between the anthesis-

silking interval (ASI) [d] and grain yield

(GYA) [g m-2]. Each point corresponds to the

average value of one experiment. Horizontal

and vertical bars indicate twice the standard

deviation. The formula of the fitted regression

is GYA = exp(5.499 – 0.206 * ASI) with a

residual standard error of 62.97 on 1533

degrees of freedom.

50

Phenotypic results and correlations

The phenotypic data of days to anthesis (MFL) and the anthesis-silking interval (ASI)

are presented in Table 2. In the two rain-fed experiments in Mexico (WM1 and

WM2), the plants reached anthesis (MFL) on average 64 days after sowing, i.e., 37

days earlier than in the drought-stress experiments at the same location. In

Zimbabwe, a similar difference (41 days) was observed between the rain-fed

experiments (WZ1) and the drought-stress experiments (DZ1 and DZ2). The plants

reached anthesis 10 to 19 later in Zimbabwe than in the comparable experiments in

Mexico under both water-management conditions. The parental lines PL1 and PL2

responded differently to varying environmental conditions. Under rain-fed

conditions in Zimbabwe (WZ1), they reached anthesis simultaneously. Under drought

stress in Zimbabwe (DZ1 and DZ2), however, PL1 reached anthesis seven days later

than PL2; the average value of the RIL population was between these two dates. In

the experiments in Mexico, such a difference between the parental lines was also

present under drought stress, although it was smaller than under drought stress in

Zimbabwe and less significant.

PL1 had a shorter ASI than PL2 in all experiments except in the rain-fed experiment

in Zimbabwe (WZ1), where the ASI of both parental lines corresponded to the

population mean of one day. The average value for ASI was small to moderate in the

rain-fed experiments in Mexico (WM1 and WM2) and in the drought-stress

experiments in Zimbabwe (DZ1 and DZ2; Table 2). The maximum average value for

ASI was observed under drought stress in Mexico (8.9 days in DM1). ASI and MFL

were correlated in three of the four experiments in Mexico (not WM2), although ASI

depended more on the date of silking than on the date of anthesis (data not shown).

MFL was a highly heritable trait (h2 > 0.6) in most of the experiments except WZ1.

The heritability of ASI was also relatively high in all the experiments in Mexico

(0.39 < h2 < 0.57) but considerably lower in Zimbabwe, especially in the rain-fed

experiment WZ1.

The average plant height (PHT) of the RIL population ranged from 133 cm in DZ1 to

175 cm in WM2 (Table 2). The plants grew higher in the rain-fed experiments than in

the drought-stressed experiments. While the parental lines did not differ in PHT

under rain-fed conditions, the plants of PL2 were significantly taller than those of PL1

under drought-stressed conditions. PHT was a highly heritable trait in Mexico

51

(h2 ≥ 0.7) but not in Zimbabwe, where the lower heritability was accompanied by a

lower phenotypic variance of the trait.

PHT correlated positively with grain yield per area (GYA) (0.3 < r < 0.44) in the three

experiments under rain-fed conditions (Table 3). ASI and PHT were not correlated.

The average grain yield was highest in the experiment under rain-fed conditions in

Zimbabwe (WZ1), in relation to which drought stress reduced grain yield by 35 to

53 % in Zimbabwe and by 67 to 83 % in Mexico. Under rain-fed conditions in Mexico

(WM1 and WM2), the yield was 8 and 50 % lower than in WZ1. PL2 failed to produce

grains in two experiments: in DM1 because of the strong stress and in WM1 because

of a thunderstorm that bent all the plants of PL2 shortly after flowering. The GYA of

PL1 was significantly higher than the GYA of PL2 under drought stress in Zimbabwe

(DZ1, DZ2). In the second drought-stress experiment in Mexico (DM2), the GYA of

PL1 was also higher than that of PL2, but the difference was not significant. Under

rain-fed conditions (WM2 and WZ1) the differences in GYA between PL1 and PL2

were larger and more significant than under drought-stressed conditions and the

variability of GYA in the RIL population was larger. The heritability of GYA tended to

be lower under drought-stressed (0.13 < h2 < 0.6) than under rain-fed conditions

(0.46 < h2 < 0.61), but the large variation across the drought-stress experiments did

not allow for a clear interpretation.

Figure 3 shows the negative association across experiments between GYA and ASI,

which is an important secondary trait for yield under stress. Both traits were also

negatively correlated within each experiment. The correlation was weak in WZ1 and

moderate in all the other experiments (0.3 < |r| < 0.5). Late anthesis was associated

with low yields under drought-stressed and, to some extent, also under rain-fed

conditions in Mexico (WM) but not in Zimbabwe (WZ; Table 3).

Kernel number per area (KNA) was highly correlated with GYA (Table 3). The

differences between the parental lines were less significant for KNA than for GYA

(Table 2). The correlation between KNA and other traits was comparable to the

correlation between these traits and GYA, with hundred kernel weight (HKW) being

the exception as it was not correlated with KNA in any of the experiments.

The changes in the average value of HKW per experiment (19 to 26 g) did not depend

on the treatment since HKW in DM1 and WM1 was considerably lower than in the

other experiments. HKW of PL2 could not be determined in these two experiments

because PL2 failed to produce grains. HKW of the two parental lines differed

52

significantly under rain-fed conditions in Zimbabwe (WZ1) only. HKW was not

correlated with flowering traits, but a weak to moderate positive correlation was

observed with PHT and GYA in most of the experiments (Table 3).

53

Table 2: Average, minimum and maximum values for the parental lines and the RILs and trait

heritability (h2) for the following traits: days to anthesis (MFL), anthesis-silking interval (ASI), plant

height (PHT), grain yield (GYA), kernel number (KNA) and hundred kernel weight (HKW). The

experiments were performed under drought-stressed (D) or under rain-fed conditions (W) in Mexico

(M) and Zimbabwe (Z). Differences between parental lines were significant at P < 0.1 ('), 0.05 (*), 0.01

(**), 0.001 (***), not significant (ns), or the test could not performed (na) due to the lack of replicates.

Parental lines RILs

Trait Exp PL1 PL2 Mean Min Max h2

MFL [d] DM1 101.1 99.0 na 98.0 92.6 103.0 0.70 DM2 107.3 103.3 ' 104.2 100.4 109.3 0.77 DZ1 120.9 113.1 ** 117.0 110.9 124.4 0.62 DZ2 121.4 115.1 ** 117.6 109.5 125.3 0.57 WM1 64.7 64.2 na 63.4 59.1 70.4 0.77 WM2 66.0 64.8 * 64.7 60.2 71.2 0.78 WZ1 75.6 74.9 ns 75.5 73.2 79.8 0.24 ASI [d] DM1 6.6 10.7 na 8.9 3.4 13.6 0.50 DM2 3.7 9.0 * 6.2 1.1 10.3 0.39 DZ1 1.9 3.3 * 2.4 0.5 5.8 0.26 DZ2 2.6 4.2 * 3.3 1.0 6.7 0.22 WM1 -0.4 6.5 na 2.3 -1.0 7.3 0.52 WM2 -0.5 3.0 * 1.1 -1.5 7.8 0.57 WZ1 0.9 1.1 ns 1.0 0.4 1.6 0.09 PHT [cm] DM1 122 145 na 142 105 174 0.70 DM2 135 172 ' 157 110 212 0.79 DZ1 124 134 * 133 117 157 0.28 DZ2 130 137 ** 135 118 154 0.28 WM1 162 162 na 161 118 203 0.72 WM2 173 184 ns 175 116 225 0.84 WZ1 162 166 ns 165 132 208 0.50 GYA [g/m2] DM1 36.6 na na 40.9 29.9 76.8 0.14 DM2 72.7 29.0 ns 78.2 19.5 302.7 0.59 DZ1 180.4 130.4 * 154.8 40.5 421.7 0.49 DZ2 124.0 78.7 * 112.3 41.3 238.6 0.36 WM1 201.1 na na 119.4 44.7 317.4 0.47 WM2 326.1 108.6 ** 219.5 81.5 465.1 0.53 WZ1 385.3 185.6 *** 239.8 84.0 541.6 0.61 KNA [m-2] DM1 178 na na 200 148 334 0.14 DM2 268 134 ns 309 46 1144 0.64 DZ1 673 508 * 593 179 1382 0.44 DZ2 515 336 ns 440 186 804 0.29 WM1 1161 na na 650 229 2000 0.53 WM2 1286 516 * 865 329 1717 0.51 WZ1 1643 871 ** 1042 412 1986 0.52 HKW [g] DM1 19.7 na na 19.7 17.1 23.5 0.32 DM2 25.5 22.0 ns 24.6 21.0 29.0 0.28 DZ1 26.8 25.7 ns 26.3 18.4 34.4 0.57 DZ2 24.0 22.7 ns 25.9 18.1 35.1 0.49 WM1 18.4 na na 18.7 12.9 26.0 0.51 WM2 25.4 21.1 ns 25.4 17.4 32.3 0.54 WZ1 23.5 21.3 ** 23.0 16.9 32.2 0.55

54

Table 3: Linear phenotypic correlations (Pearson’s) among traits measured in the RIL population.

Correlations were significant at P < 0.1 ('), 0.05 (*), 0.01 (**) and 0.001 (***) or not significant (ns).

See Table 2 for explanation of abbreviations.

Trait Exp ASI PHT GYA KNA HKW

MFL DM1 0.24 *** -0.17 * -0.40 *** -0.43 *** 0.06 ns DM2 0.22 *** 0.05 ns -0.50 *** -0.51 *** -0.04 ns DZ1 0.15 * -0.04 ns -0.42 *** -0.45 *** 0.06 ns DZ2 0.08 ns 0.00 ns -0.24 *** -0.29 *** 0.17 * WM1 0.31 *** 0.17 * -0.20 ** -0.16 * 0.05 ns WM2 0.12 ' 0.25 *** -0.29 *** -0.32 *** 0.05 ns WZ1 0.00 ns 0.04 ns -0.12 ' -0.11 ' -0.04 ns ASI DM1 0.00 ns -0.43 *** -0.44 *** -0.03 ns DM2 -0.02 ns -0.32 *** -0.35 *** 0.04 ns DZ1 0.07 ns -0.38 *** -0.37 *** -0.07 ns DZ2 0.05 ns -0.30 *** -0.32 *** 0.06 ns WM1 -0.21 ** -0.50 *** -0.49 *** -0.09 ns WM2 -0.12 ' -0.42 *** -0.44 *** -0.08 ns WZ1 -0.01 ns -0.19 ** -0.19 ** -0.04 ns PHT DM1 0.04 ns 0.03 ns 0.08 ns DM2 0.03 ns -0.02 ns 0.21 ** DZ1 0.28 *** 0.19 ** 0.30 *** DZ2 0.17 * 0.09 ns 0.26 *** WM1 0.31 *** 0.24 *** 0.36 *** WM2 0.42 *** 0.29 *** 0.44 *** WZ1 0.40 *** 0.35 *** 0.24 *** GYA DM1 0.97 *** 0.31 ** DM2 0.97 *** 0.23 ** DZ1 0.93 *** 0.32 *** DZ2 0.90 *** 0.32 *** WM1 0.93 *** 0.28 *** WM2 0.92 *** 0.36 *** WZ1 0.94 *** 0.37 *** KNA DM1 0.09 ns DM2 0.07 ns DZ1 -0.03 ns DZ2 -0.10 ns WM1 -0.01 ns WM2 -0.02 ns WZ1 0.04 ns

55

QTL results

Figure 4 displays the location on the genome and the confidence intervals of all the

QTLs for the target traits and tables 4 to 6 give their genetic characteristics. Only the

most informative QTLs are mentioned and discussed in the text.

There was no QTL involved in the expression of MFL in WZ (Table 4). The loci on

chromosome 2, near marker 8 (c2m8), and on chromosome 3, near marker 7 (c3m7),

however, were involved in the expression of this trait in the other three TLCs. The

presence of the PL1 allele was associated with an earlier anthesis date at locus c2m8

and with a delayed anthesis date at locus c3m7. Both QTLs explained up to 10 % of

the phenotypic variance in the trait (R2). Two additional QTLs with positive additive

effects of the PL1 allele were detected at c1m17 and c4m14 for MFL in both

treatments in Mexico but not in Zimbabwe.

Only two QTLs for ASI were detected in Zimbabwe, one in each treatment (Table 4).

In Mexico, three QTLs were expressed for ASI under drought-stressed (DM) and

seven QTLs under rain-fed conditions (WM), explaining together up to 30 % of the

phenotypic variance in the trait. The QTL c1m15, where PL1 carried the unfavorable

allele associated with a larger ASI, was expressed in three TLCs (not WZ). At three

other loci (c4m10 for ASI in DM, c8m8 for ASI in WM and c10m7 for ASI in DM) in

contrast, the PL1 allele caused reductions in ASI, which is favorable for stress

tolerance at flowering.

The number of QTLs detected for PHT was considerably higher in Mexico than in

Zimbabwe (Table 5). The seven QTLs detected in DM explained together up to 46 %

of the phenotypic variance in the trait, the same as the nine QTLs in WM. Only two

and three QTLs were detected in DZ and WZ, accounting for a maximum of 16 % of

the phenotypic variance. The most important QTL for PHT was located on

chromosome 1 close to marker 11 (c1m11). Its LOD score exceeded 9.0 in three TLCs,

and the negative additive effect of the PL1 allele explained between 16 and 24 % of the

phenotypic variance in the traits in DM and WM and between 7 and 13 % in DZ. This

QTL was not expressed for PHT in WZ. Three more loci were involved in the

expression of PHT and other traits. They were located at c8m8, c9m4 and c10m5

(Figure 4 and Table 5). Although they were less important in terms of the LOD score

and R2 they explained some of the plant’s response to water-limited conditions.

56

A total of twelve QTLs for GYA were detected on eight chromosomes (Table 6),

between two and four QTLs per TLC. Most of them had a low LOD score and

explained less than 10 % of the phenotypic variance. The QTL c1m11 was highly

significant (LOD > 10) with an R2 of almost 16 % in WZ, the environment in which the

PL1 allele had a positive additive effect on the expression of the trait. The additivity

was also positive at most of the other QTLs for GYA except for two QTLs detected

under drought stress in Mexico (DM) where the PL1 allele was associated with lower

yields.

Four of the 10 QTLs for KNA (c1m11, c8m8, c9m4 and c10m7) were detected at

exactly the same positions on the genome and in the same TLCs as the QTLs for GYA

(Figure 4). They underlined the strong phenotypic correlation between these two

traits. The locus c9m4 was involved in the expression of KNA and GYA in two TLCs.

The additive genetic effects of the PL1 allele were in repulsion, positive in WZ and

negative in DM.

The number of significant QTLs for HKW varied considerably across the four TLCs

(Table 6). Nevertheless, the QTL c7m3 was significantly and stably expressed

(LOD(QEI) << 1.3) with positive additivity in all four TLCs. The inconsistent sign of

additivity at the eight additional loci involved in the expression of HKW indicated

that both parental lines carried alleles, which contributed to higher kernel weight, a

possible explanation for the lack of phenotypic differences between PL1 and PL2.

Most of the 91 QTLs identified for the six traits in the four TLCs were detected under

rain-fed conditions in Mexico (42 QTLs) followed by the drought-stress treatment in

Mexico (24 QTLs). Only 14 and 11 QTLs were detected under drought-stressed and

rain-fed conditions in Zimbabwe, respectively. At 60 of the 80 QTLs identified in the

three TLCs (DM, DZ and WM) where a joint QTL could be performed for each of the

traits, the LOD(QEI) was below the significance threshold of 1.3, indicating that these

QTLs were stably expressed across both experiments included in the respective

analyses.

57

Table 4: Genetic characteristics of the QTLs involved in the expression of days to anthesis (MFL) and

the anthesis-silking interval (ASI) with a joint LOD score above 3.0 or a single LOD score above 2.5

(only in WZ). The environments (Env) were drought-stressed (D) or rain-fed (W) in Mexico (M) and

Zimbabwe (Z). Chr: chromosome number, Mark: number of the nearest marker on the respective

chromosome, Peak: position of the peak in LOD score in centiMorgan, Joint: LOD score of the joint

analysis of experiments 1 (E1) and 2 (E2), QEI: LOD score of the QTL-by-environment interaction,

Add: additive genetic effect of the PL1 allele on trait expression, R2: percentage of phenotypic variance

explained by the QTL.

Distance [cM] LOD score Add R2 [%]

Trait Env Chr Mark Peak Interval E1 E2 Joint QEI Joint E1 E2 MFL DM 1 13 163 144 - 179 0.3 2.3 3.3 2.9 -0.17 0.3 2.1 17 218 193 - 230 4.8 3.0 6.1 0.4 0.55 9.7 2.7 2 8 120 105 - 127 2.3 3.7 4.6 0.0 -0.52 1.6 3.7 3 7 72 49 - 83 4.0 2.7 5.2 0.3 0.57 8.9 7.7 4 14 162 144 - 172 1.1 3.9 4.1 0.5 0.43 1.4 9.1 6 1 3 0 - 18 0.9 3.0 3.1 0.4 -0.37 3.9 7.3 7 10 101 93 - 118 1.0 3.1 3.3 0.3 0.39 1.1 1.4 8 10 142 122 - 156 3.3 0.5 3.3 1.2 0.34 4.7 0.8 10 2 12 2 - 36 0.5 2.7 4.1 3.6 0.19 2.6 1.1 Total 33.7 36.0 DZ 1 2 12 0 - 22 3.1 1.4 3.1 0.5 0.54 4.8 2.7 2 8 120 115 - 125 2.5 4.4 4.5 0.4 -0.70 1.6 4.4 3 5 54 43 - 64 4.0 4.4 5.1 0.0 0.77 10.8 9.1 6 5 52 19 - 56 1.9 0.0 3.7 3.3 0.20 0.8 0.7 Total 18.4 18.1 WM 1 17 217 186 - 222 4.4 1.5 4.5 0.3 0.50 5.8 2.9 19 246 230 - 265 2.4 2.7 3.4 0.2 0.52 6.0 7.8 2 8 120 112 - 125 2.6 2.4 3.4 0.1 -0.46 2.7 1.8 3 8 81 69 - 89 3.6 2.0 3.9 0.0 0.50 6.3 4.7 4 14 161 151 - 173 5.1 2.4 5.3 0.1 0.56 9.6 4.9 6 13 168 153 - 183 3.0 0.5 3.0 0.6 0.35 1.4 0.1 8 5 62 53 - 81 3.8 0.8 3.8 0.5 -0.49 8.8 3.6 Total 33.8 20.0 WZ - - - - - - na na na - - na ASI DM 1 15 192 169 - 212 2.1 5.0 5.5 0.1 0.83 1.8 3.5 4 10 109 100 - 122 3.0 3.6 4.7 0.1 -0.81 5.0 5.0 10 7 99 86 - 129 2.0 2.4 3.0 0.0 -0.65 7.9 4.7 Total 14.7 13.6 DZ 1 15 186 168 - 213 3.8 1.5 4.0 0.3 0.21 4.9 2.2 WM 1 3 34 21 - 48 2.9 0.0 3.1 2.4 -0.19 5.6 0.1 7 66 55 - 82 3.7 0.6 3.7 1.4 -0.28 5.2 0.1 16 200 167 - 215 2.4 4.0 4.4 0.2 0.40 1.8 4.1 25 362 348 - 372 2.9 1.9 3.5 0.2 -0.31 3.7 3.2 2 8 120 115 - 126 3.4 0.7 3.5 1.1 -0.30 2.7 1.1 8 4 51 40 - 62 5.6 1.8 5.8 1.3 -0.39 10.3 3.9 8 131 120 - 140 1.1 4.7 4.7 0.9 -0.36 2.8 7.2 Total 30.8 21.2 WZ 4 17 200 183 - 208 3.4 na na na 0.06 6.2 na

58

Table 5: Genetic characteristics of the QTLs involved in the expression of plant height (PHT) with a

joint LOD score above 3.0 or a single LOD score above 2.5 (only in WZ). See Table 4 for details.


Trait Env Chr Mark Peak Interval E1 E2 Joint QEI Joint E1 E2 PHT DM 1 11 143 117 - 158 12.0 13.2 15.6 1.0 -6.97 23.7 24.1 2 8 119 115 - 122 1.8 2.9 3.1 0.4 -2.83 1.3 1.3 8 2 11 0 - 25 5.6 0.7 6.2 2.3 -3.87 13.8 2.9 5 65 58 - 75 2.5 3.6 4.0 0.5 -3.42 7.4 5.2 8 130 121 - 137 0.2 2.9 3.4 2.6 1.42 0.2 4.5 9 7 118 96 - 129 3.9 1.0 3.9 0.7 -3.09 2.3 0.6 10 5 79 62 - 90 2.9 0.4 3.2 1.2 -2.67 4.2 2.1 Total 46.5 37.9 DZ 1 11 136 117 - 155 4.3 7.6 9.1 0.4 -2.58 7.5 12.7 4 3 31 21 - 35 1.5 2.5 3.0 0.1 -1.42 3.2 3.5 Total 11.3 17.0 WM 1 11 135 114 - 153 12.9 12.0 15.5 0.2 -8.19 18.3 16.2 2 8 119 115 - 127 0.9 4.1 4.2 2.0 -2.96 0.2 3.0 4 3 31 23 - 35 3.8 0.4 4.2 1.5 -3.26 5.3 1.2 6 58 56 - 71 1.3 3.0 3.1 0.9 -3.20 7.5 6.1 9 103 97 - 126 2.1 3.6 3.7 0.6 3.83 1.7 3.7 6 4 31 18 - 46 4.0 0.6 4.2 1.3 -3.18 3.4 1.2 8 8 131 121 - 144 3.4 3.1 4.1 0.0 3.86 5.5 5.9 9 4 61 38 - 74 3.9 2.0 4.1 0.1 4.48 8.0 4.0 10 5 87 69 - 93 1.8 0.6 5.1 4.8 -1.05 3.8 0.1 Total 44.6 37.9 WZ 1 4 37 21 - 48 2.6 na na na 3.03 2.9 na 4 6 58 50 - 68 4.3 na na na -3.6 5.8 na 9 4 63 31 - 74 2.6 na na na 3.14 6.3 na Total 14.8 na

59

Table 6: Genetic characteristics of the QTLs involved in the expression of grain yield (GYA), kernel

number (KNA) and hundred kernel weight (HKW) with a joint LOD score above 3.0 or a single LOD

score above 2.5 (only in WZ). See Table 4 for details.


Trait Env Chr Mark Peak Interval E1 E2 Joint QEI Joint E1 E2 GYA DM 4 8 86 76 - 108 1.3 3.1 3.7 2.8 1.65 6.1 2.1 7 10 100 90 - 110 1.1 3.8 4.3 3.6 -0.89 1.6 4.3 9 4 57 44 - 73 2.1 2.3 3.4 1.7 -2.17 7.7 3.2 Total 14.8 10.1 DZ 5 1 3 0 - 20 3.2 1.0 3.3 2.0 5.58 5.6 1.2 6 11 134 116 - 146 2.7 1.6 3.1 1.2 7.07 6.8 3.4 Total 11.9 4.4 WM 1 1 5 0 - 16 3.0 0.3 3.1 0.5 11.85 6.0 0.5 7 6 56 45 - 66 2.0 2.4 3.5 0.2 11.71 3.4 4.0 8 8 130 119 - 133 0.3 3.5 3.6 1.8 8.46 2.1 7.8 10 6 98 86 - 120 2.0 4.1 4.8 0.7 15.19 1.7 9.2 Total 14.3 21.5 WZ 1 11 138 117 - 151 10.2 na na na 36.62 15.9 na 5 7 90 78 - 105 3.6 na na na 24.04 6.3 na 9 4 43 27 - 69 2.8 na na na 22.31 4.7 na Total 27.8 na KNA DM 9 4 54 43 - 70 2.6 3.3 4.4 2.8 -9.80 8.4 3.6 DZ 2 2 14 6 - 36 3.3 0.9 3.4 2.0 -19.94 7.5 2.9 WM 1 25 364 353 - 371 2.1 2.1 3.4 1.7 7.73 4.4 2.6 5 2 10 0 - 24 3.1 1.8 4.0 1.4 9.97 3.3 1.3 10 135 122 - 146 3.4 0.0 3.7 0.2 -11.28 1.7 0.7 7 9 82 74 - 99 3.9 0.8 4.0 0.4 11.03 5.0 0.8 8 8 130 121 - 133 0.5 3.6 3.6 3.4 2.80 1.3 6.5 10 7 99 86 - 118 1.7 4.6 5.2 4.1 6.94 1.7 8.1 Total 19.7 18.9 WZ 1 11 135 114 - 150 10.9 na na na 156.5 18.7 na 9 4 41 26 - 68 3.5 na na na 98.21 4.9 na Total 25.3 na HKW DM 7 4 25 1 - 34 2.2 5.3 5.6 0.6 0.62 11.0 8.0 DZ 2 9 128 121 - 145 2.3 2.3 3.3 0.1 -0.65 5.3 5.8 6 11 138 122 - 186 2.0 3.5 4.0 0.0 0.82 4.0 8.8 14 179 122 - 186 0.0 3.1 3.9 2.6 0.47 0.5 7.3 7 3 22 2 - 34 2.7 3.4 4.3 0.0 0.82 9.5 10.0 Total 17.2 23.5 WM 1 2 14 1 - 22 4.9 0.7 4.9 1.0 0.58 2.2 0.1 8 74 64 - 139 2.2 4.5 5.3 0.8 -0.63 1.9 4.4 10 121 57 - 139 3.9 1.2 4.3 0.2 -0.61 6.0 2.6 15 193 185 - 214 3.1 3.4 4.9 0.1 0.62 2.2 2.1 2 11 139 121 - 152 4.9 3.0 6.4 0.0 -0.77 6.5 5.6 5 15 219 204 - 231 1.1 2.6 3.0 0.5 0.43 2.6 4.0 7 3 23 2 - 34 3.1 4.0 5.7 0.3 0.68 5.9 6.3 9 84 74 - 95 2.8 0.9 3.0 0.2 -0.48 6.3 4.2 10 9 126 110 - 141 2.5 0.6 3.7 3.3 -0.22 2.2 1.2 Total 34.5 29.9 WZ 1 8 85 81 - 93 2.8 na na na -0.82 1.1 na 7 3 15 1 - 34 3.5 na na na 0.77 9.6 na Total 10.6 na

60

Figure 4: Position on the genome

of the QTLs involved in the

expression of days to male

flowering (MFL), anthesis-silking

interval (ASI), plant height (PHT),

grain yield (GYA), kernel number

(KNA) and hundred kernel weight

(HKW) measured under drought-

stressed (D) or under rain-fed

conditions (W) in Mexico (M) and

Zimbabwe (Z). Black areas

represent the confidence intervals

of the QTLs where the LOD score

decreased by half. See Tables 4, 5

and 6 for details.

61

Discussion

Genetic control of flowering time

The large variation in the average values for the number of days from sowing to

anthesis across experiments (Table 2) demonstrated impressively that flowering time

was important for the adaptation of maize to various environmental conditions

(Chardon et al. 2004). The differences in MFL between the drought-stressed and the

rain-fed experiments at both locations were much smaller when converted to the sum

of the growing-degree days for the daily mean temperatures (approx. 5 % difference)

assuming a minimal threshold for growth of 10 °C (Hunt et al. 2001, Stewart et al.

1998), than when the number of days only was considered (approx. 35 % difference,

data not shown). Although the adaptation process seemed to be strongly influenced

by temperature (Baron et al. 1975, Stewart et al. 1998) some of the differences in

flowering time among the experiments cannot be explained by the temperature

suggesting that this trait was influenced by other environmental factors as well.

The drought-stress experiments were characterized by large diurnal fluctuations in

temperature and by high maximum temperatures during the flowering period; they

reflected the environmental conditions, which will pose a challenge to tropical maize

production in the future, especially in certain arid and semi-arid areas in Africa,

where temperature will continue to increase and rainfall will decrease (Sivakumar et

al. 2005).

The two parental lines responded differentially to changing environmental

conditions. In the experiment under rain-fed conditions in Zimbabwe (WZ), where

the thermoperiod during the vegetative growth phase was smallest (Figure 2), they

reached anthesis simultaneously. The lack of QTLs for MFL in this experiment

(Table 4) was not surprising, considering the small phenotypic segregation of the RIL

population for MFL and the low trait heritability. In all other experiments, in

contrast, the high heritability and the QTLs c1m17, c2m8, c3m7 and c4m14 (Table 4

and Figure 4) suggested that MFL was under strong genetic control. This was in

agreement with reports in the literature showing that QTLs for flowering date were

relatively stable (Chardon et al. 2004, Reymond et al. 2003, Yin et al. 1999). Chardon

et al. (2004) performed a meta-analysis on data from 22 QTL studies of maize by

calculating an overview statistic for the probability of identifying QTLs for flowering

time at each position on a reference map. We aligned the chromosomes of our genetic

62

linkage map (Figure 1) to the chromosomes of their reference map by means of

common genetic markers. Chromosomes without at least two genetic markers in

common were aligned indirectly using the IBM2 2004 Neighbors Map available at

the Maize Genetics and Genomics Database (http://www.maizegdb.org). The four

important QTLs for MFL in our population coincided with consensus loci identified

by Chardon et al. (2004), corresponding to their prediction that new, independent

QTL mapping experiments for maize flowering time would be more likely to confirm

loci already known to affect the trait than to identify new regions.

The positive additive effects at three of the four QTLs for MFL (negative at c2m8)

were in agreement with the higher phenotypic values of PL1 in the drought-stress

experiments. The delayed anthesis of PL1 under stress forced this line to cope with

more intense drought stress at anthesis than PL2. Nevertheless, PL1 had a shorter

anthesis-silking interval (ASI) than PL2 in all the experiments under water-limited

conditions (Table 2), which demonstrated the achievements of breeding for drought

tolerance at flowering through selection for a short ASI and low levels of barrenness.

The positive correlation between MFL and ASI in DM (DM1 and DM2) was not

provoked by drought stress, since such a correlation was not observed in DZ. The fact

that MFL and ASI were positively correlated in WM as well indicated some location-

specific causes of correlation, probably in response to environmental factors, which

also provoked the larger phenotypic variance and the larger heritability of ASI in the

experiments in Mexico. The expression of genetic differences among the parental

lines and their progenies was favored at this location and led to the larger number of

QTLs detected for ASI, particularly under rain-fed conditions (Table 4). ASI was

controlled by one QTL of general importance (c1m15), although this QTL did not

affect ASI in WZ1 where the very small phenotypic segregation of non-genetic origin

(h2 = 0) suggested that the plants were not stressed at all. The unfavorable effect of

the PL1 allele on ASI in the other TLCs was in contradiction to the phenotypic data

for the parental lines and to the usual association of drought tolerance with a short

ASI (Edmeades et al. 2000). However, the close genetic linkage between this QTL for

ASI (c1m15) and the QTL for MFL (c1m17) suggested that ASI was causally related to

MFL, especially under the growing conditions in Mexico.

63

Genetic control of ASI and grain yield

From a breeder’s point of view, it would be more interesting if a QTL for ASI was

closely linked to a QTL for grain yield. Such a genetic region would be a predestinated

target region for marker-assisted selection (MAS). Our results confirmed that ASI is a

secondary trait for grain yield. The importance of the highly significant negative

correlation between these two traits in all the experiments (Table 3), considerably

lower in WZ1, as expected, was emphasized by the non-linear correlation across all

the experiments (Figure 3), which corresponded well to previous findings (Bolanos

and Edmeades 1996, Chapman and Edmeades 1999). The important QTL for ASI on

chromosome 1 (c1m15), however, did not co-locate with a QTL for grain yield: the

peak in the LOD score at the highly significant QTL for GYA at c1m11 (Table 6) was

located 50 to 60 cM away (Table 4); the respective confidence intervals did not

overlap. Marker-assisted selection solely based on ASI was again found to be

inefficient in improving grain yield under drought (Ribaut et al. 1996). Independent

of the genetic distance between the two loci c1m11 and c1m15, it was conspicuous that

the unfavorable additive effect of the PL1 allele at c1m15 on ASI was not detected in

WZ1, the only experiment, where the PL1 allele at c1m11 was associated with a large

increase in grain yield. This pattern of QTL expression was in agreement with the

favorable effect of a short ASI on kernel set and grain yield.

The genetic basis of improved drought tolerance

The expression of the highly significant QTL for PHT at c1m11 (Table 5) in three TLCs

was closely related to the expression of the QTL for GYA. The presence of the PL1

allele at this locus resulted in shorter plants in three TLCs, but not in WZ1, where a

strong QTL effect (R2 ≥ 16 %) on GYA and kernel number (KNA) was detected. The

QTL c1m11 not only revealed the important genetic basis for the high yield potential

of PL1 under optimal growing conditions, it suggested a causal relationship between

GYA and PHT in the RIL population. The precise co-location of the QTLs and the

short confidence intervals suggested that this relationship was caused by one or a few

major gene(s) affecting overall assimilation and the partitioning of assimilates within

the plant through pleiotropic effects on both traits. The QTL effect at c1m15 on ASI,

which was expressed in the same TLCs as the QTL c1m11 for PHT, in contrast,

seemed to have been caused by different gene(s), which were probably involved in the

control of the differences in phenology among the parental lines. Flowering time is an

64

important component of phenology (Chardon et al. 2004, Irish and Nelson 1991) and

the QTL c1m15 for ASI was located close to the QTL c1m17 for MFL. The positive

additive genetic effects at these loci on MFL and ASI were in agreement with the

positive phenotypic correlation between these traits in several experiments.

Several authors (Beavis et al. 1991, Khairallah et al. 1998, Koester et al. 1993,

Melchinger et al. 1998, Sibov et al. 2003b, Veldboom and Lee 1996b) reported QTLs

for plant height on chromosome 1, suggesting the presence of gene clusters that

control development (Khavkin and Coe 1997, Sibov et al. 2003b). The results of the

meta-analysis by Chardon et al. (2004) gave further evidence for the arrangement of

QTLs in clusters along the genome. The QTL c1m11 in our study corresponded to a

consensus region associated with the variation in days to anthesis, days to silking,

plant height and leaf number as well as to a hot-spot region of the overview statistic

for the time of male flowering identified by these authors.

Conventional studies without molecular markers also disclosed strong interactions

between the anthesis-silking interval, plant height and grain yield of tropical maize

grown under water-limited conditions. Chapman and Edmeades (1999) reported that

selection for high rates of ear growth at flowering and short ASI produced correlated

reductions in plant height. Competition for assimilates among ears and stems was

important in determining ear fertility under stress. Selection for drought tolerance

was also important in redistributing biomass from the stem to the ear rather than

increasing the overall production of biomass (Hay and Gilbert 2001); the inverse

relationship between the harvest index and stem biomass increased the rates of ear

growth by diverting assimilates to the developing ear. The combined effects of

drought stress and other environmental factors in our experiments resulted in

shorter plants of PL1 than of PL2. A 10 cm decrease in PHT was associated with a

decrease in grain yield of 49 g m-2 (r = 0.81) for PL1. The grain yield of PL2, in

contrast, was not correlated with plant height across experiments (data not shown).

We hypothesize, based on the reports by Andrade et al. (1999 and 2002), that PL2

was unable to take advantage of higher plant growth rates under favorable conditions

in order to realize a high yield potential, whereas the stress-induced reductions in

plant height did not cause lower yield. The differential phenotypic interactions

between ASI, PHT and GYA of PL1 and PL2 clearly proved that the two parental lines

represent different stages in the global process of selection for drought tolerance. The

higher absolute yield of PL1 compared to PL2 under the water-limited conditions

65

proved that PL1 was more drought-tolerant than PL2 (c.f. Cooper et al. 2006) despite

the fact that PL1 suffered from larger stress-induced yield reductions relative to its

yield potential under non-stress conditions (WZ). It is likely that PL1 would have

outperformed PL2 to an even greater extent in terms of grain yield if the two lines

had reached anthesis simultaneously in the drought-stress experiments. In such a

case, PL2 would not have embarked on a drought-escape strategy through early

maturity. Drought escape can help early flowering genotypes to reduce the negative

impact of evolving drought stress on pollination and kernel set, thus minimizing yield

losses (Chapman and Edmeades 1999). However, drought escape is often associated

with a low yield potential under optimal water supply. This was clearly the case for

PL2. However, the four main QTLs for MFL (c1m17, c2m8, c3m7 and c4m14) did not

co-locate with QTLs for GYA. Therefore, the genetic basis of the differences in MFL

between the parents seemed not to be linked with the genetic basis of grain yield,

despite the highly significant negative correlation between the two traits in most of

the experiments (Table 3).

The negative additive effects of the PL1 allele on ASI and the positive effects on GYA

or KNA at the four loci c1m25, c4m8/10, c8m8 and c10m5/7 were in agreement with

the negative correlations between ASI and both of the two strongly correlated yield

parameters. Nevertheless, the QTLs did not uncover the important genetic basis of

drought-tolerance mechanisms through a short ASI. Only at c4m8/10 the favorable

effect of the PL1 allele on both traits was detected in the same TLC (DM), but the QTL

effect on GYA was unstable across the two experiments in the analysis

(LOD(QEI) > 1.3, Table 6). At the other three loci, the quantitative effect on yield was

detected only in the rain-fed experiments in Mexico (WM) but not in the drought-

stress experiments. These QTL effects emphasized the superiority of the PL1 allele

towards a high yield potential, but they also showed that drought stress reduced the

genetic variance in yield, not allowing for the detection of the same QTLs with

positive additive effects as under rain-fed conditions. The locus at c9m4 (Table 6)

demonstrated particularly well the consequences of the environmental interactions

which the QTLs for GYA and KNA were subjected to, since the positive additive effect

of the PL1 allele on GYA in the high-yielding rain-fed experiments WZ1 (R2 ≈ 5 %)

became a negative additive effect under drought stress in Mexico (DM) (R2 ≈ 8 %).

This QTL also confirmed the close genetic association between PHT and GYA (and

KNA) which was already observed at c1m11. The effects on GYA of the QTLs detected

66

for ASI in the environments in Mexico cannot be considered apart from their effects

on PHT at most of these loci (except c1m25) because of the strong interrelation

among these traits. Although a long ASI resulted in poor pollination, poor kernel set

and low grain yield in situations of drought stress at flowering, GYA was genetically

more closely related to PHT than to ASI.

Autonomous genetic control of grain filling

The largest part of the yield reductions in the different experiments, compared to the

high-yielding non-stress experiment WZ1, were due to reductions in kernel number

not in kernel weight (Bolanos and Edmeades 1993a, 1996), although the average

value of hundred kernel weight (HKW) was lower in two experiments (DM1 and

WM1) than in the other experiments. GYA and HKW were not controlled by common

QTLs, despite a significant, moderate, positive correlation between them in all the

experiments. The QTL c7m3 (R2 ≥ 11 %) suggested that HKW was under particularly

strong, environmentally insensitive genetic control; it was the only trait for which a

stable QTL was detected consistently and with the same additive effect in all four

TLCs. The expression of this QTL was not altered by the water-management system,

which showed that the stress treatments at flowering did not negatively affect kernel

development later during the grain filling period. The higher number of QTLs

detected for HKW in WM corresponded to the tendency of increased number of QTLs

for all traits when measured in the experiments in Mexico, especially under rain-fed

conditions, which probably resulted from the larger phenotypic and for most of the

traits also genetic variances in the RIL population. The two lines PL1 and PL2 were

apparently not well adapted to that environment. Effects of adaptation can, on the

one hand, result in unexpected phenotypic responses. On the other hand, the

evaluation of plants in environments to which they were not well adapted seemed to

improve the accuracy of and the information provided by QTL mapping. In this study

the experiments performed in Mexico gave rise to QTLs affecting several traits

simultaneously (c8m8 and c10m5-c10m7), which suggested the activation of

drought-tolerance mechanisms. Such a functional concurrence of QTLs was less

obvious in the experiments in Zimbabwe.

67

Conclusions

Grain yield, the target trait for improving germplasm, was subject of large

interactions with the environment with regard to genotypes and QTLs. The data of

seven field experiments conducted under changing environmental conditions did not

enable us to detect QTLs involved in the expression of grain yield in more than one

treatment-location combination (TLC). The effects of most QTLs for GYA were small,

which might reflect in parts the effect of inbreeding. Drought stress, in combination

with other changing environmental factors, clearly reduced the phenotypic and the

genetic variance of yield in the RIL population. The variance and the power of QTL

detection for morphological traits (e.g., for MFL and PHT) were unaffected by the

stress treatment or even increased. The most important QTLs for GYA, PHT and ASI

were located on chromosome 1 close to a major QTL for MFL. The negative additivity

of the QTL c1m15 for ASI indicated that PL1 still carried unfavorable alleles for this

trait, despite the attempt to breed for improved tolerance to water-limited conditions

at flowering. The effect of these negative alleles might have been aggravated by the

delayed anthesis of PL1 under drought stress compared to PL2, although the main

QTLs for MFL suggested that the expression of the genes controlling floral transition

did not depend on the water-management system.

Co-locating QTLs for ASI and GYA were detected at three positions on the genome

(4m8/10, c8m8, c10m5/7) in the TLCs in Mexico. Two of these loci were also

involved in the expression of PHT (not c4m8). Together with the QTLs detected on

the middle section of chromosome 1, they revealed that the genetic interaction

between GYA and PHT as well as between ASI and MFL were stronger than between

ASI and GYA. The additive allelic effects at these QTLs corresponded well to the

phenotypic results of the parental lines and characterized PL1 as a maize line with a

high yield potential under normal irrigation. Its superior drought tolerance was

clearly attributed to the morphological plasticity, which improved the allocation of

assimilates to the developing ears when overall assimilation was limited. The negative

impacts of this strategy, however, were the large stress-induced yield reductions

relative to the yield potential under unstressed conditions. The drought escape

strategy of PL2, in contrast, resulted in low stress-induced yield reductions. At the

same time, the yield potential of PL2 under optimal conditions was low.

The genes at the QTLs on chromosome 1 were crucial for the plasticity of vegetative

growth, the control of floral transition and silk emergence. However, these genes

68

contributed to high grain yield only under rain-fed conditions in Zimbabwe, where

the narrow thermoperiod, amongst other climatic conditions and soil characteristics

of the African mid-altitudes, ensured an homogeneous development of the plants.

69

THE GENETIC CONTROL OF STAY-GREEN CHARACTERISTICS

AND ROOT CAPACITANCE IN A TROPICAL MAIZE POPULATION

Introduction

Drought stress is a major abiotic constraint of tropical maize production. The

negative impact of drought stress depends largely on the timing, intensity and

duration of the stress (Bruce et al. 2002). It is well known, that the maximum damage

to grain yield of maize is inflicted when drought stress occurs shortly before and at

flowering (Claassen and Shaw 1970, Westgate and Boyer 1985). At these stages,

stress-induced yield reductions are mainly caused by reductions in kernel number

due to poor pollination and early kernel abortion. While grain yield can only be

quantified at maturity, other morphological or physiological traits can be measured at

earlier developmental stages. Some of these traits, like the flowering date and the

maximum plant height are fix. The expression of other traits, like the chlorophyll

content and senescence of the leaves, change over time under both stress and non-

stress conditions. Mopho-physiological traits reflect the biochemical and

physiological processes, which finally contribute to yield.

Senescence of the leaf or of the whole plant is the result of naturally occurring aging

processes (Thomas and Howarth 2000). During senescence, the plants remobilize

structural and functional components of the leaf. Proteins are degraded to amino

acids, which are transported to growing organs. The characteristic loss of greenness

of the leaves is caused by the loss of chlorophyll resulting from the degradation of the

chloroplasts, the carrier of the main photosynthetically active pigments (He et al.

2005, Smart 1994, Smart et al. 1995). Senescence in maize grown under non-stress

conditions follows the genetically determined transition from the vegetative state to

maturity (Masclaux et al. 2001, Thomas and Smart 1993). Senescence depends

largely on the source-sink relationship. It can be triggered by an increased demand

for nitrogen in the grains (Borrell et al. 2000a), but it can also be induced or

accelerated by drought stress. Senescence is generally considered to be a major

determinant of yield in many crops (He et al. 2005, Thomas 1992).

Sorghum genotypes with different levels of tolerance to post-flowering drought stress

exhibited different levels of senescence. Genotypes that stayed green longer filled

70

their grains normally under drought (Rosenow and Clark 1981). They also possessed

increased resistance to charcoal rot (Rosenow 1984) and lodging (Henzell et al. 1984)

compared to early-senescent genotypes (Sanchez et al. 2002). The stay-green trait is

related to grain yield, mainly through the balance between N demand by the grains

and N supply during grain filling. There is some evidence that the proportion of N

derived from the soil is higher in sorghum genotypes that stay green longer than in

early-senescent genotypes, which depend more on N remobilized from vegetative

tissues for grain filling (Borrell et al. 2001). Stay-green genotypes also maintain their

photosynthetic capacity longer. The relationship between the stay-green trait and

grain yield, however, is not always positive (Borrell et al. 2001).

It seems to be easy to measure the stay-green trait by measuring the relative leaf

chlorophyll content with a portable chlorophyll meter (SPAD meter, Konica Minolta

Inc). The SPAD values provide an indication of the relative amount of total

chlorophyll [mg/cm2] in the leaves (Xu et al. 2000). Leaf chlorophyll content was

reported to correlate positively with leaf nitrogen concentration in maize at silking,

which, in turn, was found to be highly correlated with grain yield (Blackmer and

Schepers 1995, Xu et al. 2000). Nevertheless, as stated by Borrell et al. (2001),

understanding the stay-green phenomenon is like piecing together a jig-saw puzzle of

considerable complexity. Five classes of stay-green have been identified (Thomas and

Howarth 2000, Thomas and Smart 1993). The three functional classes are caused by

a delayed initiation of senescence (Type A), by a slower rate of senescence (Type B)

and by enhanced leaf greenness because of the higher absolute pigment content of the

leaf (Type E). In practice, the phenotypic stay-green characteristics are often

combinations of two or more functional types (Thomas and Howarth 2000).

A large part of the work on stay-green has been done with sorghum, but the

expression of stay-green characteristics has also been reported for maize (Rajcan and

Tollenaar 1999a, 1999b, Tollenaar and Daynard 1978). It is likely that the interactions

between structural plant characteristics and physiology leading to functional stay-

green are similar in both crops.

The stay-green trait is known to be controlled by the dominant action of major genes.

Around 50 genes associated with leaf senescence have been cloned from several

species and have been assigned possible functions in senescence on the basis of

sequence homology (Buchanan-Wollaston 1997, Thomas and Howarth 2000).

Although leaf senescence seems to be tightly regulated by specific genes, QTL

71

mapping may be very helpful in detecting the underlying genetic basis because of the

involvement of multiple, interrelated but distinct signaling pathways (He et al. 2005).

As the expression of stay-green can be induced or accelerated by abiotic stress such as

drought, the estimation of the QTL-by-environment interactions (QEI) can further

help to dissect the genetic basis of stay-green characteristics and, if possible, to detect

genomic segments containing clusters of genes important for the response to water-

limited conditions.

Root characteristics play an important role in the tolerance of maize to drought stress

(Lebreton et al. 1995). The definition of an ideal root system depends largely on the

drought environment. The timing, duration and intensity of the drought stress are

particularly important (Ribaut et al. 2004). A large root system does not necessarily

guarantee good drought tolerance (Bolanos et al. 1993b, Campos et al. 2004,

Tuberosa et al. 2003). It is practically impossible to measure the root mass of large

samples in field experiments, not to mention root architecture. The non-destructive

measurement of root capacitance with a portable meter offers a feasible way of

approximating relative differences in the extension of the root system, since the

capacitance readings correlate positively to the mass of the fresh roots (van Beem et

al. 1998). A major limitation of this method is that the root capacitance does not

provide an absolute value and the sampling procedure is very vulnerable to changes

in edaphic factors affecting electrical capacitance, with soil water content being the

most important among them (Dalton 1995, van Beem et al. 1998).

The morpho-physiologically oriented objectives of this study were: (1) to identify

QTLs controlling the relative chlorophyll content (estimated by SPAD readings) of the

ear leaf and the second leaf from the tassel in a population of recombinant inbred

lines segregating for these traits; (2) to determine whether putative QTLs were

related to higher initial chlorophyll contents or whether QTL expression changed

over time pointing at adaptive tolerance mechanisms; (3) to relate the phenotypic

data and the QTLs of the relative leaf chlorophyll content to a visual score of whole-

plant senescence for the purpose of linking the chlorophyll content of the leaf to stay-

green characteristics of the whole plant; (4) to determine possible effects of the

extension of the root system on leaf chlorophyll content and senescence by

calculating QTLs for the root capacitance trait.

72




together with the parental lines in eight field experiments; six were conducted in

Mexico, either under drought stress at flowering (DM1-DM4) or under rain-fed

conditions (WM1 and WM2) and two were conducted under drought stress at

flowering in Zimbabwe (DZ3, DZ4). Drought stress was induced by stopping

irrigation approximately seven (in DZ) or three (in DM) weeks before the expected

average date of anthesis. Water was withheld at least until the target stress period at

flowering was completed. All the experiments were designed as alpha (0, 1) lattices

with one-row plots and two replications. Detailed information about plant material,

experimental sites and experimental designs is given in “General Material and

Methods”.

Phenotypic data

Data for the chlorophyll content of the ear leaf (ELC) and the second leaf from the

tassel (“young leaf”, YLC) were recorded in all the experiments. Data for senescence

(SEN) and root capacitance (RCT) was measured only in the experiments conducted

in Mexico. Relative leaf chlorophyll content, quantified with a portable Minolta

Chlorophyll Meter SPAD-502 (Konica Minolta 2003), was recorded as the average of

five measurements done on the middle leaf sections of five plants per plot. Under

rain-fed conditions in Mexico (WM), ELC and YLC were measured once, when

approximately 50 % of all the plants had reached anthesis. In the drought-stress

experiments in Mexico (DM), ELC and YLC were measured twice, at the beginning of

the flowering period (ELC1, YLC1) and approximately 10 days later, towards the end

of the flowering period (ELC2, YLC2). In the two drought-stress experiments in

Zimbabwe (DZ), the chlorophyll content was also recorded twice, but the first

measurement was performed at an earlier developmental stage than in the drought-

stress experiments in Mexico. The rationale behind this was to sample the plants

under early stress conditions at both locations. Considering the fact that irrigation

had to be stopped earlier in Zimbabwe than in Mexico, the first sampling date was

advanced proportionally.

73

SEN was recorded as a visual score on a scale from 1 (all leaves green) to 9 (all leaves

dry). The visual senescence ratings were conducted at most two days later than the

last measurement of leaf chlorophyll content in the respective experiment.

Root capacitance [200 nF] was quantified with a BK Precision 810A Meter (Maxtec

Inc, Chicago, IL), by connecting the negative electrode to the stem above the first

node and the positive electrode to a rod inserted into the soil in the middle section of

the furrow next to the plot under consideration. The instrument was set at 200 nF

and always calibrated before measuring five plants per plot, the average value of

which was recorded. RCT was measured as soon as the flowering period was

completed. In the drought-stress experiments, the measurement was done on the day

following the first irrigation after flowering.


The methods for calculating the heritability of traits, the adjusted means for each

genotype and the phenotypic correlations among traits are described in “General

Material and Methods”.

The QTLs involved in the expression of each trait were identified by joint QTL

mapping (critical LOD = 3.0) on data from two experiments. The four groups of

experiments for which the joint QTLs were calculated were DM (i.e., the experiments

DM1 and DM2), DM* (i.e., DM3 and DM4), DZ* (i.e., DZ3 and DZ4) and WM (i.e.,

WM1 and WM2). Detailed information about the QTL analysis is given in “General

Material and Methods”.

74

Results


The phenotypic results of the traits are given in Table 7. The average values of the

relative amount of total leaf chlorophyll of the population of 236 recombinant inbred

lines (RILs) were always higher for the ear leaf (ELC) than for the second leaf from

the tassel (YLC). The average chlorophyll content of both leaves decreased

substantially over time in the drought-stress experiments in Mexico (DM), indicating

some nitrogen remobilization. In Zimbabwe, this reduction was lower or did not

occur at all (YLC in DZ3). The stress-induced chlorophyll degradation was apparently

lower in Zimbabwe than in Mexico. The trait heritability remained mostly unaffected

by the decrease in chlorophyll content over time but it was clearly lower in DM3 than

in the other experiments. This was probably due to the particularly severe stress in

this experiment, which also caused the lowest average phenotypic values and the

lowest phenotypic variance for chlorophyll content. The drought-tolerant parent

(PL1) had higher relative chlorophyll contents than the drought-susceptible parent

(PL2), irrespective of the treatment (drought-stressed or rain-fed), the location

(Mexico or Zimbabwe) or the time of measurement (early or late stress). The

experiment DM3 was the only exception where the YLC1 and YLC2 of both lines were

almost identical. The quality of the significance test for the differences was low

because of the low number of replicates of the parental lines.

The average values of whole-plant senescence (SEN) as well as the trait heritability

and the ranking of the parental lines were relatively stable across experiments. The

first rain-fed experiment (WM1) was the only exception where trait heritability

remained below 0.4. The lower senescence value of PL1 compared to PL2 on a scale

from 1 (no symptoms of senescence) to 9 (complete loss of greenness) corresponded

to the higher relative chlorophyll content of the leaves. The spatial analysis, which

was performed to reduce the effects of local and global variation in the field, reduced

the phenotypic variance and eliminated the extreme phenotypic values of SEN (i.e., 1

or 9), although they had been assigned to certain plots in the field.

The average values of root capacitance (RCT) were much lower in DM2 and DM4

than in DM1 and DM3. The relative nature of this trait and its sensitivity to changes

in the soil water content made it difficult to compare the experiments. The ranking of

75

the parental lines was not consistent across experiments. The heritability of RCT was

generally low and varied across experiments.

The maintenance of a high leaf chlorophyll content under drought stress was favored

by a high initial chlorophyll content (Table 8). However, the variable coefficient of the

positive correlation between the first and the second measurement of leaf chlorophyll

content across experiments indicated the presence of genotype-by-environment

interactions, especially in DM1 and DM3. SEN was negatively correlated with

chlorophyll content to a varying extent, but the strength of the correlation never

exceeded r = 0.5 (data not shown). As an exception, the chlorophyll content of the

young leaf (YLC) was not correlated with SEN in the experiments under rain-fed

conditions (WM1 and WM2). Based on the phenotypic correlations, the relationship

between SEN and the chlorophyll content of both types of leaves was similar. RCT did

not correlate with chlorophyll content (data not shown). RCT and SEN were

correlated only in the first experiment under drought stress in Mexico (DM1,

r = -0.27, data not shown).

76

Table 7: Average, minimum and maximum values of the parental lines (PL1, PL2) and the RIL

population and trait heritability (h2) for chlorophyll content of the second leaf from the tassel (YLC),

chlorophyll content of the ear leaf (ELC), senescence (SEN) and root capacitance (RCT). Chlorophyll

contents were measured twice (1, 2) under drought-stressed conditions in Mexico (DM) and Zimbabwe

(DZ) and once under rain-fed conditions in Mexico (WM). Differences between parental lines were

significant at P < 0.1 ('), 0.05 (*), 0.01 (**) and 0.001 (***), not significant (ns), or the test could not

performed (na) due to the lack or replicates.

Parental lines RILs

Trait Exp PL1 PL2 Mean Min Max h2

YLC1 [SPAD] DM1 33.5 25.7 na 31.3 25.0 37.2 0.41 DM2 29.4 24.8 ns 28.4 22.0 37.5 0.56 DM3 27.6 25.7 na 26.5 22.9 29.5 0.25 DM4 27.3 25.1 ns 26.9 21.2 32.9 0.35 DZ3 32.5 30.6 ns 31.4 27.0 36.5 0.40 DZ4 37.1 33.5 ** 34.8 27.9 41.2 0.50 YLC2 [SPAD] DM1 26.3 20.1 na 23.0 17.9 29.8 0.42 DM2 26.7 20.6 ns 24.0 17.1 34.0 0.52 DM3 20.5 19.0 na 20.1 14.5 23.3 0.26 DM4 22.8 16.9 * 21.4 16.0 28.9 0.45 DZ3 33.4 30.1 * 31.7 25.5 37.5 0.44 DZ4 36.7 30.7 ** 33.6 24.7 42.3 0.56 YLC [SPAD] WM1 26.7 23.5 na 27.3 19.2 35.8 0.60 WM2 29.0 26.8 ns 28.6 22.3 35.2 0.43 ELC1 [SPAD] DM1 39.3 32.9 na 38.9 32.0 43.8 0.43 DM2 43.7 39.1 ' 41.0 34.1 48.0 0.39 DM3 36.0 32.9 na 35.8 29.7 42.2 0.38 DM4 41.2 33.9 * 39.2 31.5 45.9 0.45 DZ3 40.2 36.7 ns 37.8 30.3 42.7 0.34 DZ4 43.6 39.5 * 40.9 32.6 46.1 0.44 ELC2 [SPAD] DM1 29.5 24.5 na 27.9 22.4 33.3 0.39 DM2 37.4 28.7 * 32.8 25.9 40.0 0.37 DM3 27.1 23.8 na 26.2 22.9 32.1 0.29 DM4 33.7 23.6 * 29.4 22.0 36.4 0.46 DZ3 39.9 32.4 * 35.7 26.9 43.8 0.46 DZ4 40.3 34.1 ** 37.2 26.4 43.3 0.49 ELC [SPAD] WM1 43.9 36.0 na 40.9 31.9 47.8 0.45 WM2 45.0 40.7 ns 43.7 34.3 49.5 0.46 SEN DM1 4.3 6.4 na 5.3 4.1 6.5 0.44 DM2 3.9 6.1 * 5.0 3.5 6.5 0.48 DM3 4.7 6.4 na 5.9 4.3 7.1 0.42 DM4 4.0 6.2 ' 5.0 3.3 6.5 0.47 WM1 2.7 7.2 na 4.7 2.5 8.0 0.26 WM2 3.9 6.6 * 4.9 2.5 8.0 0.50 RCT [200 nF] DM1 8.9 8.5 na 9.0 7.3 10.9 0.30 DM2 2.9 3.4 ns 3.3 2.5 5.6 0.26 DM3 10.2 9.6 na 10.3 9.2 11.8 0.14 DM4 3.9 4.4 ns 4.0 2.8 6.1 0.35 WM1 7.5 8.9 na 7.9 6.5 9.7 0.26 WM2 7.8 7.9 ns 7.7 6.3 9.1 0.19

77

Table 8: Linear phenotypic correlations (Pearson’s) among traits measured in the RIL population. All

correlations were significant at P < 0.001. See Table 7 for explanation of abbreviations.

Trait Exp YLC2 ELC1 ELC2

YLC1 DM1 0.54 0.71 0.54

DM2 0.86 0.57 0.53 DM3 0.63 0.61 0.32 DM4 0.80 0.48 0.50 DZ3 0.68 0.60 0.50 DZ4 0.74 0.63 0.54 YLC2 DM1 0.53 0.75 DM2 0.60 0.61 DM3 0.55 0.60 DM4 0.51 0.63 DZ3 0.64 0.77 DZ4 0.67 0.76 ELC1 DM1 0.57 DM2 0.78 DM3 0.49 DM4 0.68 DZ3 0.69 DZ4 0.76

QTLs for stay-green characteristics

The fourteen QTL analyses done with data of the SPAD readings of both leaves

resulted in a total of 61 QTLs, the characteristics of which are listed in Tables 9 and

10. At 45 of these QTLs, the drought-tolerant parent (PL1) carried the allele favorable

for high chlorophyll content. Three genomic regions were identified as being of major

importance; they were located in the middle sections of chromosomes 1, 2 and 10.

The QTL on chromosome 1, close to marker 11 (c1m11), was expressed for chlorophyll

content of the ear leaf under rain-fed conditions in Mexico (ELC in WM), for the first

measurement of the chlorophyll content of the ear leaf under drought stress in

Mexico (ELC1 in DM and DM*) as well as for the first measurement of the

chlorophyll content of the young leaf (YLC1) in DM. In all these cases, the favorable

allele was carried by the drought-tolerant parent PL1. The same allele enhanced

senescence in WM. This unfavorable effect was not stable (LOD(QEI) > 1.3) across

the two experiments included in the analysis, which suggested that the QTL effect on

SEN was less important than the QTL effect on chlorophyll content.

A significant, positive, additive effect of the PL1 allele on leaf chlorophyll content was

detected on chromosome 2, between markers 4 and 8 (c2m4-c2m8), for 12 of the 14

QTL analyses. The peaks in LOD score in WM and DZ* were separated by up to

78

35 cM from those in DM and DM*. The QTL effects were stable under drought-

stressed but not under rain-fed conditions. The QTLs explained a maximum of 12 %

of the phenotypic variance of the traits (R2).

Another major QTL for leaf greenness was identified on chromosome 10. The PL1

allele also contributed to higher SPAD values and the peaks in the LOD score did not

match precisely among treatments: The peak in LOD score resulting from the QTL

analyses for the drought-stress experiments in Mexico (except ELC1 in DM) was

located at c10m5 (Tables 9 and 10), while the peak was located at c10m7 for ELC

under rain-fed conditions in Mexico (WM, Table 10). The middle section of

chromosome 10 was also involved in the expression of the chlorophyll content of both

leaves under late drought stress in Zimbabwe (YLC2 and ELC2 in DZ*, Table 9 and

10). Compared to the expression of the QTL under drought stress in Mexico (DM and

DM*), however, the position of the peak in the LOD score shifted by approximately

25 cM towards the short-arm end of the chromosome. The percentage of phenotypic

variance explained by these QTLs was low in Zimbabwe, compared to Mexico where

R2 went up to 16 %. The presence of the PL1 allele between markers 4 and 5 on

chromosome 10 was also associated with lower senescence (SEN) under drought

stress in Mexico. The negative additivity indicated a favorable effect of the PL1 allele,

since low values of SEN stand for better stay-green properties of the plants.

A negative additive effect of the PL1 allele on the chlorophyll content of both leaves

(not ELC1) under drought stress in Zimbabwe (DZ*) was detected on the short arm of

chromosome 10. The QTL effects were stable but with a low R2 (Tables 9 and 10).

Other QTLs with negative additive effects on the chlorophyll content of the leaves

were detected on chromosomes 3, 4, 5 and 9 (Figure 5). Most of these QTLs

interacted significantly with the environment. They were not described in detail,

because they did not contribute to a better understanding of stay-green mechanisms

under drought stress.

Two other QTLs, at c7m8 and c8m8, contributed to a higher chlorophyll content of

the ear leaf in WM of genotypes carrying the PL1 allele at these loci. The QTL c8m8

was also detected in DZ for both measurements (ELC1 and ELC2; Table 10 and

Figure 5).

79

QTLs for root capacitance

A total of seven loci controlled root capacitance (RCT), a trait which was measured

only in the experiments in Mexico. The QTL c2m12 was detected for RCT in DM,

DM* and WM, while the QTL c7m5 was expressed only for RCT under drought stress

(DM and DM*). The allele of the drought-tolerant parent (PL1) was associated with

lower values of root capacitance at both loci (Table 11). Each QTL accounted for up to

12 % of the phenotypic variance in the trait, but the values varied considerably across

experiments. The QTL c2m12 was located near the major QTL region for leaf

chlorophyll content, but the QTLs for RCT and chlorophyll content or SEN did not

co-locate. Both traits were apparently under the control of distinct genes.

80

Table 9: Genetic characteristics of the QTLs involved in the expression of chlorophyll content of the

second leaf from the tassel measured once (YLC) under rain-fed conditions in Mexico (WM) and twice

(YLC1, YLC2) under drought-stressed conditions in Mexico (DM, DM*) and Zimbabwe (DZ*). Chr:

chromosome number, Mark: number of the nearest marker on the respective chromosome, Peak:

position of the LOD-score peak in centiMorgan, Joint: LOD score in the joint analysis of experiments 1

(E1) and 2 (E2), QEI: LOD score of the QTL-by-environment interaction, Add: additive genetic effect

of the PL1 allele on trait expression, R2: percentage of phenotypic variance explained by the QTL.


Trait Env Chr Mark Peak Interval E1 E2 Joint QEI Joint E1 E2

YLC1 DM 1 12 151 112 - 165 3.3 0.1 3.4 1.1 0.52 10.2 1.3 2 5 84 70 - 113 3.3 1.6 3.9 0.0 0.76 11.8 2.5 6 8 77 60 - 96 0.2 3.4 4.1 3.9 0.16 0.3 4.6 10 5 74 61 - 87 3.0 2.2 4.3 0.1 0.81 6.0 3.7 Total 24.9 12.2 DM* 10 5 78 61 - 94 6.4 6.6 10.7 1.2 0.64 16.1 5.5 DZ* 2 9 125 105 - 139 2.8 4.7 5.2 0.5 0.64 5.6 6.1 3 15 215 177 - 234 0.2 2.9 3.2 1.9 -0.31 0.8 5.2 5 2 11 0 - 28 0.4 6.1 6.4 3.9 -0.42 0.9 11.3 10 1 5 0 - 18 2.9 2.1 3.5 0.0 -0.49 3.0 0.7 Total 11.5 24.7 YLC2 DM 2 5 83 69 - 100 3.9 1.1 4.1 0.2 0.67 9.5 3.5 6 2 8 0 - 18 3.0 2.0 3.8 0.0 0.55 6.4 6.1 10 4 71 53 - 106 1.4 3.0 3.5 0.9 0.55 2.5 4.6 Total 17.5 13.4 DM* 1 14 176 164 - 186 0.0 2.9 3.1 2.9 0.08 0.2 2.9 2 4 76 52 - 96 3.9 3.6 6.0 1.1 0.45 8.0 6.8 6 2 13 0 - 26 0.0 3.5 3.6 3.2 0.11 0.0 6.5 9 3 26 9 - 37 1.4 1.3 3.5 2.9 -0.12 0.6 2.5 10 5 78 62 - 100 4.5 5.0 7.9 2.0 0.49 10.3 7.0 Total 20.9 24.1 DZ* 1 6 59 49 - 63 2.5 4.7 5.5 0.9 0.61 1.2 6.1 2 4 75 57 - 87 2.4 1.6 3.1 0.0 0.57 2.8 7.3 8 123 90 - 133 1.3 5.7 5.7 2.4 0.56 6.0 8.5 5 2 6 0 - 22 0.0 3.1 3.7 3.2 -0.17 0.1 7.0 6 8 90 66 - 107 3.5 1.4 4.0 0.1 0.61 4.3 1.8 10 1 2 0 - 17 4.9 3.8 6.3 0.0 -0.68 3.3 1.0 3 50 46 - 56 3.2 2.2 3.9 0.0 0.59 3.3 1.5 Total 24.6 32.5 YLC WM 2 7 114 97 - 131 5.6 0.3 5.7 3.6 0.48 12.4 2.8 8 7 99 84 - 115 0.2 3.8 3.9 1.1 0.63 0.9 5.5 9 4 47 29 - 67 3.6 0.8 3.6 1.5 -0.53 4.2 0.4 Total 20.1 8.5

81

Table 10: Genetic characteristics of the QTLs involved in the expression of chlorophyll content of the

ear leaf measured once (ELC) under rain-fed conditions in Mexico (WM) and twice (ELC1, ELC2)

under drought-stressed conditions in Mexico (DM, DM*) and Zimbabwe (DZ). See Table 9 for details.



ELC1 DM 1 12 158 140 - 166 2.6 1.4 3.1 0.1 0.50 8.0 4.2 2 6 106 69 - 120 2.7 1.5 3.3 0.0 0.62 7.0 4.2 8 6 85 77 - 109 1.6 2.7 3.3 0.3 0.55 5.7 4.6 Total 18.3 11.1 DM* 1 12 156 138 - 167 4.5 4.7 6.7 0.2 0.69 11.2 8.2 2 5 78 41 - 95 4.1 0.7 4.3 0.5 0.62 8.5 4.0 3 14 194 181 - 217 1.3 3.9 4.3 1.1 -0.53 1.8 4.2 10 5 78 63 - 94 7.0 3.7 8.3 0.1 0.92 10.4 3.2 Total 29.3 18.4 DZ* 4 10 108 99 - 118 2.7 1.3 3.1 0.2 -0.48 2.1 1.3 8 8 125 105 - 137 4.2 0.1 4.3 2.3 0.44 7.5 0.6 Total 10.1 1.9 ELC2 DM 1 8 75 64 - 93 0.2 4.1 5.0 4.6 0.19 0.1 7.0 2 5 84 82 - 101 2.5 1.7 3.4 0.0 0.59 8.8 4.7 7 8 75 69 - 82 2.7 0.9 3.0 0.1 0.44 5.9 1.5 10 5 76 61 - 94 2.4 3.4 4.7 0.4 0.67 1.4 8.2 Total 17.1 21.5 DM* 1 15 185 173 - 197 1.7 3.5 4.1 1.5 0.35 0.5 2.6 2 4 74 49 - 98 3.0 2.6 4.4 0.5 0.48 4.9 8.4 7 114 104 - 119 3.5 0.0 3.6 1.0 0.34 7.5 0.2 3 14 193 180 - 207 0.5 3.6 3.8 2.5 -0.27 1.2 1.7 4 6 65 50 - 89 0.8 1.9 3.5 3.3 -0.09 0.0 5.4 5 17 238 230 - 244 2.4 0.9 4.3 3.2 -0.21 3.0 0.6 6 9 101 82 - 117 3.8 0.1 3.8 0.6 0.45 3.0 0.0 9 3 16 0 - 41 4.0 1.3 6.7 4.9 -0.33 4.7 3.4 10 5 83 67 - 94 6.5 8.5 11.6 3.2 0.69 7.6 9.1 Total 35.0 28.5 DZ* 1 6 58 45 - 63 2.4 2.6 3.7 0.0 0.65 2.3 5.5 2 5 77 66 - 101 2.3 1.6 3.0 0.1 0.63 3.0 5.2 8 8 123 87 - 137 4.5 2.1 5.1 0.4 0.78 9.1 5.5 10 1 9 0 - 18 5.5 1.4 5.6 1.3 -0.73 3.8 0.3 3 51 50 - 79 5.4 2.0 5.9 0.8 0.79 4.6 1.5 Total 28.5 19.2 ELC WM 1 12 148 118 - 160 1.6 3.2 3.7 0.4 0.62 0.6 2.6 2 7 108 99 - 119 3.4 0.1 4.1 3.2 0.34 5.8 0.0 6 2 10 0 - 28 4.1 0.2 4.1 1.8 -0.47 6.3 0.2 7 9 80 72 - 95 3.6 0.4 3.7 1.1 0.51 4.1 0.1 8 8 128 117 - 137 2.0 2.2 3.1 0.0 0.59 4.7 4.3 14 180 170 - 181 2.7 4.3 5.3 0.3 -0.71 2.2 4.9 10 7 106 93 - 118 0.9 3.8 4.0 0.9 0.55 0.4 6.3 Total 26.7 20.3

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Table 11: Genetic characteristics of the QTLs involved in the expression of senescence (SEN) and root

capacitance (RCT) measured under drought-stressed (DM, DM*) or under rain-fed conditions (WM)

in Mexico. See Table 9 for details.



SEN DM 3 7 65 57 - 81 4.2 0.3 4.3 1.0 -0.13 6.0 0.0 10 4 71 56 - 87 2.4 4.2 4.8 0.9 -0.16 5.2 13.7 Total 12.7 13.7 DM* 10 5 74 61 - 94 0.8 6.8 6.9 3.0 -0.17 8.6 20.3 10 134 120 - 139 4.1 1.1 4.3 0.5 -0.14 11.8 4.6 Total 16.4 21.5 WM 1 11 130 108 - 150 3.2 0.1 3.6 2.0 0.11 4.4 0.1 2 13 164 157 - 168 0.2 2.7 3.4 3.2 0.04 0.2 4.4 5 10 124 106 - 142 0.0 4.0 4.2 3.6 -0.08 0.1 9.7 Total 4.5 14.0 RCT DM 1 19 241 219 - 258 1.7 2.0 3.0 0.2 0.12 2.7 3.9 2 13 164 135 - 185 1.5 3.3 3.8 0.0 -0.13 7.0 10.9 6 8 92 82 - 109 0.0 3.6 3.9 1.5 0.12 0.1 3.9 7 5 43 29 - 55 3.2 2.2 4.1 0.8 -0.12 8.2 3.9 Total 15.7 21.3 DM* 2 12 146 135 - 168 0.2 4.2 4.2 1.8 -0.1 1.6 7.5 5 14 189 166 - 203 4.5 0.2 4.5 1.6 0.12 8.3 0.8 7 5 48 23 - 61 0.1 4.2 4.2 2.0 -0.09 1.2 6.2 9 5 88 64 - 111 0.0 3.0 3.1 1.9 -0.08 0.7 7.3 Total 11.6 21.3 WM 1 7 64 55 - 76 2.3 2.4 3.6 0.0 -0.15 5.2 5.1 2 12 155 139 - 162 6.0 0.4 6.0 3.3 -0.12 12.0 1.5 Total 19.0 7.1

83



expression of chlorophyll content

of the second leaf from the tassel

(YLC) and the ear leaf (ELC)

measured once under rain-fed

conditions in Mexico (WM) and

twice (YLC1, YLC2) under

drought-stressed conditions in

Mexico (DM, DM*) and Zimbabwe

(DZ*) as well as for senescence

(SEN) and root capacitance

(RCT). Black areas represent the

confidence intervals of the QTLs

where the LOD score decreased by

half. See Tables 9, 10 and 11 for

details.

84

Discussion

Chlorophyll content of the leaves and stay-green

Senescence is a naturally occurring process, during which chlorophyll is degraded

(Thomas and Howarth 2000). It can be induced or accelerated by abiotic stress such

as drought. Delayed onset and a lower rate of senescence under drought stress are

referred to as “stay-green”. The stay-green trait of sorghum (Rosenow and Clark

1981) and maize (Tollenaar and Daynard 1978) is a potential secondary physiological

trait for grain yield under drought stress (Ribaut et al. 2004); genotypes that stay

green can maintain active photosynthesis longer than plants that senesce early.

However, the relationship between the stay-green trait and grain yield is not always

positive (Borrell et al. 2001).

We used the portable SPAD-502 meter (Konica Minolta 2003) to screen our RIL

population for relative chlorophyll content of two target leaves. The SPAD values

were measured on the ear leaf, because the ear leaf was reported to be particularly

important for the assimilation and translocation of assimilates to the developing ear

in the period bracketing flowering (He et al. 2005). When drought stress occurs

during this developmental stage, the reduced photosynthetic capacity of the ear leaf

as a consequence of chlorophyll degradation can negatively affect kernel set and early

kernel development (Kamara et al. 2003, Schussler and Westgate 1995, Zinselmeier

et al. 1995a). The other target leaf was the second leaf from the tassel (“young leaf”).

We wanted to detect possible alterations in the genetic control of the chlorophyll

content of these leaves, since naturally occurring senescence as well as drought-

stress-induced degradation of chlorophyll usually becomes visible first on lower

leaves and then on the upper parts of the maize plants (Apariciotejo and Boyer 1983).

Leaf senescence

Xu et al. (2000) demonstrated that the visual stay-green ratings provide a reliable

indication of leaf senescence in sorghum and should be useful in evaluating large

numbers of progenies segregating for post-flowering drought tolerance because of the

observed high correlation (r = 0.9) between the SPAD values and the visual rating.

The coefficient of this correlation was much lower in our population, irrespective of

the leaf, the sampling date or the water-management system (|r| < 0.5, data not

shown) and suggested that the visual stay-green rating cannot replace the SPAD

85

readings. The phenotypic correlations did not allow for the determination of the leaf,

the chlorophyll content of which provided the best estimation of the stay-green trait.

Instead, the senescence ratings provided a tool to determine whether the differences

in the leaf chlorophyll content among the RILs and/or over time were accompanied

by visual differences in the senescence of the whole plant or whether the differences

in the chlorophyll content were due mainly to constitutive differences in the initial

chlorophyll contents between the parental lines. The phenotypic variability and the

heritability of SEN were quite consistent across the four experiments under water-

limited conditions (Table 7), but the average SEN values under rain-fed conditions

were surprisingly high. This seemed to be a problem of the relative nature of the trait

(Rosenow 1994). It was impossible to directly compare the plants grown under

drought-stressed conditions with those grown under rain-fed conditions, because the

two types of experiments were conducted in different growing seasons. The

phenotypic differences in SEN between the two parental lines, which seemed to be

constitutive, were not caused by genes constitutively involved in the control of SEN,

since the QTL positions as well as the respective additive allelic effects changed

drastically across water-management systems (Table 11, Figure 5).

The major QTL on chromosome 2

Most of the 61 QTLs for leaf chlorophyll content were characterized by positive

additive effects of the PL1 allele, which was in agreement with the phenotypic results

of the parental lines. The PL1 allele was associated with a low chlorophyll content at

16 QTLs; this suggested that PL1 still carried unfavorable alleles at the respective loci,

despite its good drought tolerance, or it might indicate epistatic interactions.

The observed accumulation of QTLs in some of the genetic regions confirmed that

leaf chlorophyll content was controlled by relatively few genes (He et al. 2005).

Twelve of the 14 QTL analyses revealed a significant effect of the segment between

markers 4 and 8 on chromosome 2 (c2m4–c2m8, approx. 50 cM) on trait expression,

irrespective of the treatment, the location or the measuring date under drought-

stressed conditions (Tables 9 and 10, Figure 5). The almost permanent expression of

this QTL with positive additivity suggested that the underlying gene(s) were

constitutively involved in the expression of leaf chlorophyll content. The shift in the

QTL position by approximately 30 cM towards the long-arm end of the chromosome,

as observed in WM and DZ*, indicated the presence of clusters of interrelated genes

86

on chromosome 2, whose transcription products were involved in chlorophyll

metabolism and in different signaling pathways (He et al. 2005). The shift in position

was not caused by different sets of co-factors in the QTL analysis (data not shown).

There is evidence that the middle section of chromosome 2 is of general importance

for the control of the leaf chlorophyll content. Fracheboud et al. (2004) detected a

QTL for relative chlorophyll content, carbon exchange rate and chlorophyll

fluorescence parameters at the same position on chromosome 2 in a population of

temperate F2:3 maize lines evaluated under controlled conditions. The importance of

this genetic region was emphasized by Jompuk et al. (2005), who reported a co-

locating QTL for relative leaf chlorophyll content in the same population of temperate

F2:3 lines when grown in the field for studying the effects of cold stress. Moreover,

QTLs for leaf chlorophyll content were detected in the middle section of

chromosome 2 in other mapping populations of tropical maize lines, including the

F2:3 lines from the cross PL1 x PL2, which were evaluated at CIMMYT under different

levels of drought stress (unpublished results). Such a co-location of QTLs for relative

leaf chlorophyll content across different genetic backgrounds strongly indicates that

universal genes are involved in the accumulation of chlorophyll, as suggested by

Fracheboud et al. (2004). These genes seemed to constitutively control the

chlorophyll content of the leaves in our RIL population, because they did not affect

the senescence of the plants (SEN). They seem to have led to the expression of Type E

stay-green (Thomas and Howarth 2000), which is the result of a higher initial

pigment content but not the result of delayed onset or a lower rate of senescence.

Thus, the genes on chromosome 2 controlling leaf chlorophyll content did not

enhance the photosynthetic capacity through prolonged leaf area duration under

drought stress as mentioned by Rosenow (1994) and they did not control functional

stay-green mechanisms under water-limited conditions at flowering.

Jompuk et al. (2005) proposed the gene hcf106 as a possible positional candidate

gene for the QTL on chromosome 2. This gene codes for the high chlorophyll

fluorescence protein 106 and nuclear mutations result in pale green, non-

photosynthetic seedlings (Martienssen and Baron 1994). We did not observe

seedlings with this phenotype in our population, but, interestingly, the gene hcf106c,

a homologue of hcf106, is located close to the position on chromosome 10 where

Jompuk et al. (2005) identified a second QTL for leaf greenness in a temperate maize

population grown in the field. Since our analyses also revealed important QTLs for

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leaf chlorophyll content on chromosome 10, we tested for the hypothetical position of

the hcf106c-gene on our linkage map by aligning it with the IBM 2004 neighbors 2

map (Maize Genetics and Genomics Database, http://www.maizegdb.org) through

common molecular markers. The gene hcf106c would be located in the interval

between markers 5 and 7 on our linkage map, a genomic segment, which was also

important for the expression of chlorophyll content of the leaves in our RIL

population.

The major QTL on chromosome 10

Corresponding to the situation on chromosome 2, a positive additive effect of the PL1

allele in the middle section of chromosome 10 was observed for the chlorophyll

content of both leaves under drought stress in Mexico (DM). Under rain-fed

conditions in Mexico (WM), the position of the QTL shifted by approximately 30 cM

towards the long-arm end of the chromosome and the QTL controlled the chlorophyll

content of the ear leaf only. Therefore, this genomic region was particularly

important for trait expression under stress conditions. The data from Zimbabwe

clearly supported this interpretation, even though the peak in the LOD score was

shifted slightly (c10m3) compared to its position in the drought-stress experiments in

Mexico (c10m5). Moreover, the effects of the PL1 allele on high chlorophyll content

and low senescence were combined under drought stress in Mexico (DM and DM*)

but not in WM; this suggests that the effect of the underlying genes resulted in

functional stay-green mechanisms (Rajcan and Tollenaar 1999a, 1999b).

The overall confidence interval of the stay-green QTL on chromosome 10 added up to

40 cM (Tables 9, 10 and 11). This distance was too large to precisely determine

positional candidate gene(s). Nevertheless, there was striking congruence between

our QTL results on chromosomes 2 and 10, those reported by Jompuk et al. (2005)

and the position of the candidate genes hcf106 and hcf106c (MGDB). Our QTL results

were also conform to the conclusions of many independent studies of maize, which

suggested that QTL clustering for drought related traits takes place in some

chromosomal regions, especially on chromosomes 1, 2 and 10 (Campos et al. 2004).

88

The QTL on chromosome 1

While the QTL on chromosome 2 was constitutive and the QTL on chromosome 10

revealed the genetic basis of functional stay-green mechanisms under drought stress,

the QTL c1m12 was related mainly to leaf chlorophyll content under early stress in

Mexico. The positive effect of the PL1 allele on the chlorophyll content of the ear leaf

under rain-fed conditions (WM) and under early drought stress in Mexico (ELC1 in

DM and DM*), but not under late stress (ELC2), reflected the genetic basis of a

delayed onset of chlorophyll degradation of the ear leaf (Type A stay-green, Thomas

and Smart 1993), but not a reduced rate of senescence (Type B stay-green). The fact

that this QTL was also expressed for the difference between the two measurements

(ELC2-ELC1, data not shown) confirmed this hypothesis. The lack of a QTL for the

difference YLC2-YLC1 (data not shown) was probably due to the fact that senescence

first affects the lower leaves of the plants (Apariciotejo and Boyer 1983). The

senescence-enhancing effect of the PL1 allele at the nearby locus c1m11 in WM was in

contrast to its positive additive effect on ELC in WM and to the lower SEN value of

PL1 in general. Under drought-stressed conditions, however, such an unfavorable

effect on SEN was not observed.

Other QTLs for chlorophyll content

Two more loci were associated with a high chlorophyll content of the ear leaf of the

lines carrying the PL1 allele at the respective position. The locus c7m8 was irrelevant

under drought stress. The expression of the QTL c8m8, in contrast, did not depend

on the treatment; the positive effect of the PL1 allele was also present under drought

stress in Zimbabwe (DZ*) but not in Mexico (DM and DM*).

The remaining QTLs for the relative leaf chlorophyll content were not discussed in

detail (cf. Tables 9 and 10, Figure 5). They were unstable across the experiments

included in the respective analyses and contributed little to a better understanding of

the mechanisms of drought tolerance in our RIL population. They suggested at least

that the QTLs for chlorophyll content interacted with the environment to some

extent, although the expression of the three major QTLs was relatively stable.

Root capacitance

The importance of the root system for nutrient and water uptake is well-known

(Ludlow and Muchow 1990, Tuberosa et al. 2003). Both the extension and the

89

capacity of the root system become particularly important under water-limited

conditions. It was shown that selection for improved drought tolerance in maize

caused reductions in the extension of the root system in the top 50 cm of the soil

(Bolanos et al. 1993b, Bruce et al. 2002). Based on the report by van Beem et al.

(1998) on the use of a portable capacitance meter, we applied this technique to assess

the fresh mass of the roots in the upper soil layer. Changes in the soil water status

were probably responsible for the large changes in the average values of root

capacitance (RCT) across the drought-stress experiments (Dalton 1995, van Beem et

al. 1998). The fact that the experiments DM1 and DM3 (with high average values of

RCT) were performed together at the same location as were DM2 and DM4 (with low

average values of RCT) one year later, supported this explanation.

Despite the changes in the ranking of the phenotypic values of the two parental lines

and despite the low trait heritability of RCT in all the experiments (Table 7), some

highly significant QTLs were detected for RCT (Table 11). Similar observations were

made by Ribaut et al. (2004) in two other mapping populations of tropical maize

lines evaluated at the same experimental site in Mexico.

The negative additive effect of the PL1 allele on RCT at both loci c2m12/13 and c7m5

(Table 11) were in agreement with the reductions in the extension of the root system

in the topsoil reported in the literature. The QTL c2m12/13 suggested the presence of

intrinsic genes controlling the extension of the root system because it was detected

under drought-stressed as well as under rain-fed conditions. However, the significant

LOD score of the QTL-by-environment interaction (QEI) in DM* and WM showed

that these genes were not constitutively expressed. The QTL c7m5, in contrast, was

expressed under drought stress only, which indicated some drought-responsive

alterations in the root system. A few more QTLs were detected under drought stress,

but they were considered unimportant for a general response to drought stress at

flowering, since none of them was detected in both groups of drought-stress

experiments (DM and DM*). Nevertheless, they showed that the plants were able to

make medium-term adjustments (i.e., within weeks) in the structure of their root

systems compared to the situation under rain-fed conditions. These additional QTLs

for RCT had positive additivity, demonstrating that the drought-tolerant parent did

not carry all the alleles associated with a decrease in RCT. It is questionable whether

the presence of RCT-reducing alleles at all the loci would additionally enhance the

tolerance of PL1 to drought stress at flowering. There might also be a minimum for

90

the extension of the root system in the topsoil, below which the uptake of water and

nutrients or the stability of the plants would be negatively affected. As the

correlations between RCT, SEN and chlorophyll content of the leaves were very low

(data not shown) and co-locating QTLs lacked, the structural differences in the root

system did not seem to be directly linked to important stay-green characteristics

during drought stress at flowering in this RIL population.

Conclusions

The QTLs for leaf chlorophyll content and leaf senescence identified four genomic

segments with major effects on these two traits under diverse environmental

conditions. They demonstrated impressively that there were relatively few genes

controlling the stay-green mechanisms of PL1 and PL2. The additivity at these loci

evidenced the genetic progress achieved by conventional selection for better drought

tolerance, since the drought-tolerant line PL1 carried the favorable alleles for a high

leaf chlorophyll content at all four loci on chromosomes 1, 2, 8 and 10. Negative

additive effects of the PL1 allele were also observed at certain QTLs for chlorophyll

content, but the effect of these loci on trait expression was relatively small and

depended largely on the environment. The most important stress-adaptive stay-green

QTL was located in the middle section of chromosome 10. Chromosome 2, in

contrast, carried genes that constitutively controlled leaf chlorophyll content. The

constitutive effect on leaf chlorophyll content of these genes was endorsed by the QTL

affecting root capacitance under both water-management systems. Although the QTL

peaks for both traits were separated by 40 to 80 cM, depending on the environment,

they suggested that the chlorophyll content of the leaves was genetically linked to the

extension and capacitance of the root system in the topsoil, which could be important

for drought tolerance of maize in general. The results showed that both constitutive

and stress-adaptive genes controlled the stay-green mechanisms in this population of

tropical maize inbred lines.

The physiological roles of stay-green mechanisms are very complex, despite the

apparently low number of genes controlling them. The effects of the enhanced initial

chlorophyll content, delayed onset and low rate of chlorophyll degradation, on the

formation of grain yield are not fully understood. Our results suggested that leaf

chlorophyll content is a promising trait, which should be addressed in breeding

programs for improving drought tolerance. As leaf chlorophyll content can be

91

measured easily in the field it can be evaluated in conventional breeding programs as

well as in marker-assisted selection programs. In the latter, molecular markers can

help to distinguish between constitutive and stress-adaptive causes of morphological

differences in chlorophyll content among genotypes. By these means, the efficiency of

selection for functional stay-green could be considerably increased.

93

QTL ANALYSIS OF TASSEL SIZE AND EAR GROWTH AT

FLOWERING IN A TROPICAL MAIZE POPULATION

Introduction

Maize is a tall, annual grass of subtropical origin. While most grasses produce

bisexual flowers, the peculiarity of maize is the separation of the male and female

flowering structures on the plant. The male inflorescence (the tassel) is at the stem

apex, whereas the female inflorescences (the ears) are at the apex of the lateral

branches protruding from leaf axils (adapted from Salvador 1997).

The ear is a relatively weak sink at flowering. Ear growth depends largely on a

continuous supply of assimilates above a certain threshold in order to ensure grain

formation (Andrade et al. 2002, 1999). Drought stress reduces assimilation and this

reduction is the main cause of barrenness of maize exposed to drought stress

(Kamara et al. 2003, Schussler and Westgate 1995). Drought stress affects ear growth

to a greater extent than tassel growth, resulting in the characteristic widening of the

anthesis-silking interval (ASI). The ASI is defined as the asynchrony between pollen

release and silk emergence and can easily be observed due to the separation of the

male and female flowering structures.

Ear growth at flowering depends largely on a continuous, exogenous carbohydrate

supply. Even so, a high ear biomass at anthesis can favor ear development and kernel

set under water-limited conditions through short-term reserves stored in the young

ovules (Andrade et al. 2002, Zinselmeier et al. 1995b). Edmeades et al. (1993)

observed that drought tolerance was associated with increased ear biomass in

subsequent cycles of selection in the tropical maize population “Tuxpeño Sequía”.

The results from “Tuxpeño Sequía” also showed that selection for higher ear growth

rates through reduced ASI resulted in correlated reductions in tassel size. Therefore,

Edmeades et al. (1999) proposed reduced tassel size as a putative drought-adaptive

trait because of reduced shading of the photosynthetically active leaves and because

of weaker intraplant competition for assimilates.

The potential size of the ears of maize is under strong genetic control. Nevertheless, it

can be modified by environmental conditions (Carcova et al. 2003, for review), which

influence the development of the ears and the silks at early developmental stages. The

94

analysis of quantitative trait loci (QTLs) for the dry weight of the ears, silks and

tassels at flowering is a promising tool for dissecting differences in the genetic control

of these traits of two inbred maize lines with different responses to drought stress at

flowering.

The objectives of this study were (1) to identify QTLs controlling the dry weight of the

ears, silks and tassels to determine whether these traits were controlled by a common

genetic basis and (2) whether QTL expression was altered by drought stress at

flowering compared to rain-fed conditions. For these purposes, the dry weight of the

ears and silks at anthesis and one week after anthesis and the dry weight of the

tassels when they were fully developed were measured in a population of

recombinant inbred lines derived from the cross PL1 x PL2.

95




together with the parental lines in five field experiments in Mexico. Four were

conducted under drought stress at flowering (DM1, DM2, DM3 and DM4) and one

was conducted under rain-fed conditions (WM2). Drought stress was induced by

stopping irrigation approximately three weeks before the expected average date of

anthesis. Water was withheld from the experiments until the target stress period at

flowering was completed. All the experiments were designed as alpha (0, 1) lattices

with one-row plots and two replications. Detailed information about plant material,

experimental sites and experimental design is given in “General Material and

Methods”.

Phenotypic data

The dry weight of the tassels (TBW) was recorded in the experiments DM1, DM2 and

WM2. The dry weight of the ears and silk at anthesis (EW0, SW0) was measured in

DM3, DM4 and WM2; the dry weight of the ears and silks seven days after anthesis

(EW7, SW7) was recorded in DM3 and DM4, but not in WM2.

The ear of the first plant per plot to extrude anthers was cut off on that day. The husk

leaves were carefully removed and the silks and the ear were bagged and labeled “day

0”. The ear of the second plant to extrude anthers in this particular plot– which

happened on the same day or one or several days later – was harvested seven days

after anthesis. The husk leaves were also removed and the ear and the silks were put

into separate bags labeled “day 7”. These two steps were repeated alternatively until

the seventh day after anthesis of the last plant per plot. This procedure resulted

ideally for each plot in five ears and their silks collected right at the anthesis of the

respective plants and five ears and their silks collected seven days after the anthesis

of the respective plants. The bags were then transported to the laboratory and oven-

dried at 65 °C for 72 hours. All the ears labeled “day 0” were weighed together as were

all the silks labeled “day 0”. These two values were then divided by the number of

ears collected on “day 0” to give the average dry weight of the ears and silks [g] at

96

anthesis (EW0, SW0). The dry weight of the ears and silks seven days after anthesis

(EW7, SW7) was quantified similarly.

The tassels of five plants per plot were cut off when the flowering period was

completed; they were put into bags, labeled and transported to the laboratory. The

tassels were oven-dried for 72 h at 65 °C before they were weighed on a precision

balance. The total dry weight of the tassels per plot was divided by the number of

collected tassels to obtain the average dry weight of the tassels (TBW) [g].


The methods for calculating the heritability of the traits, the adjusted means and the

phenotypic correlations among traits are described in “General Material and

Methods”.

The QTLs were identified by single QTL mapping (critical LOD = 2.5) for each trait.

Detailed information about the QTL analysis is given in “General Material and

Methods”.

97

Results


Table 12 lists the phenotypic differences between the parental lines as well as the

variation and the heritability of the traits in the RIL population. The tassels of the two

parental lines were equal in terms of dry weight under rain-fed but not under

drought-stressed conditions. The tassels of PL1 were heavier in DM1 and lighter in

DM2 than those of PL2. PL1 tended to have a higher dry weight of the ears and silks

than PL2 at anthesis (EW0, SW0) and one week later (EW7, SW7). The differences

were largest for EW0 and SW0 under rain-fed conditions (WM2). The significance

test for the differences between the parental lines was hampered by the low number

of replicates: in DM2, DM4 and WM2 only 2 observations per line were available,

while in DM1 and DM3 the test could not be calculated because only one observation

was available for PL2.

The heritability of TBW was very high (h2 > 0.7) and stable across experiments. The

heritability of the dry weight of the ears and silks was notably lower but still moderate

to high (0.4 ≤ h2 ≤ 0.6). The heritability of all the traits was unaffected by the water

management and the time of harvest.

Silk dry weight correlated strongly with ear dry weight on both sampling dates (EW0

and SW0, EW7 and SW7, Table 13). The correlations between the first and the second

measurement of the dry weight of the ears and silks (EW0 and EW7, SW0 and SW7)

were slightly weaker. There were weak negative correlations between TBW and the

dry weight of the ears and silks under drought stress. The extent of these correlations

decreased during the first post-anthesis week, being lower for EW7 and SW7 than for

EW0 and SW0. Under rain-fed conditions, TBW was not correlated with EW0 and

SW0.

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Table 12: Average, minimum and maximum values of the parental lines (PL1, PL2) and the RIL

population and trait heritability (h2). The traits were dry weight of the tassels (TBW), dry weight of the

ears and silks at anthesis (EW0, SW0) and one week after anthesis (EW7, SW7). The traits were

measured under drought-stressed (DM) or under rain-fed conditions (WM) in Mexico. Significance

levels of the differences between parental lines were not calculated because of a lack of replicates.

Parental lines RILs Trait Exp PL1 PL2 Mean Min Max h2

TBW [g] DM1 5.96 4.76 5.93 3.10 12.83 0.75 DM2 4.10 5.55 4.74 2.40 12.30 0.71 WM2 4.14 3.95 4.33 2.32 9.77 0.78 EW0 [g] DM3 0.68 0.45 0.73 0.26 2.35 0.47 DM4 0.86 0.60 0.86 0.41 1.84 0.48 WM2 0.93 0.35 0.70 0.29 2.04 0.59 EW7 [g] DM3 0.96 0.62 1.14 0.38 3.71 0.40 DM4 1.44 1.09 2.05 0.72 5.32 0.50 SW0 [g] DM3 0.24 0.10 0.23 0.06 0.58 0.60 DM4 0.34 0.18 0.31 0.09 0.70 0.58 WM2 0.51 0.17 0.36 0.17 0.63 0.46 SW7 [g] DM3 0.31 0.23 0.29 0.08 0.69 0.46 DM4 0.48 0.24 0.46 0.17 0.97 0.55

Table 13: Linear phenotypic correlations (Pearson’s) among traits measured in the RIL population.

Correlations were calculated for traits measured in the same experiments (a) or for traits measured in

two independent experiments performed simultaneously at the same locations (b). Correlations were

significant at P < 0.05 (*), 0.01 (**) and 0.001 (***) or not significant (ns). See Table 12 for

explanation of abbreviations.

Trait Exp EW0 EW7 SW0 SW7

TBW DM1 / DM3 -0.19 b,*** -0.10 b,ns -0.16 b,* -0.15 b,*

DM2 / DM4 -0.23 b,*** -0.19 b,** -0.22 b,*** -0.20 b,**

WM2 -0.08 a,ns . -0.02 a,ns .

EW0 DM3 0.67 a,*** 0.83 a,*** 0.60 a,***

DM4 0.64 a,*** 0.76 a,*** 0.44 a,***

WM2 . 0.65 a,*** .

EW7 DM3 0.67 a,*** 0.84 a,***

DM4 0.57 a,*** 0.81 a,***

WM2 . .

SW0 DM3 0.79 a,***

DM4 0.61 a,***

WM2 .

99

QTL results

The main characteristics of the QTLs for the dry weight of the ears and silks at

anthesis (EW0, SW0) and seven days later (EW7, SW7) as well as for the dry weight

of the tassels (TBW) are given in Tables 14, 15 and 16. Figure 6 shows their location

on the genome and the corresponding confidence intervals.

The TBW measured in DM1, DM2 and WM2 was controlled by a total of six genomic

regions (Figure 6). Those located on chromosome 2 near marker 1 (c2m1), with

negative additivity, and on chromosome 5 near marker 4 (c5m4), with positive

additivity, were involved in the expression of TBW in three experiments under both

water-management regimes. They explained together up to 29 % of the phenotypic

variance of the trait (Table 14). The genetic control of TBW was largely independent

of that of the dry weight of the ears or the silks. A matching QTL for TBW, EW0 and

SW0 was detected only at c8m4 (Figure 6).

The large number (48) of QTLs involved in the expression of the dry weight of ears

and silks at anthesis (EW0, SW0) and one week after anthesis (EW7, SW7) revealed

eight genetic regions of particular interest. All the QTLs detected for the dry weight of

the ears and/or the silks within each of these regions had consistent signs of

additivity. Only one QTL (c4m10) was detected for both the dry weight of the ears

and the silks at anthesis under rain-fed conditions (Tables 15 and 16). This QTL with

positive additive effect of the PL1 allele explained 11 and 6 % of the respective

phenotypic variance.

The QTL c10m7 was mainly associated with SW0 and SW7 under drought stress

(Table 16, Figure 6), but it also contributed to the greater EW0 of the plants carrying

the PL1 allele in DM4 and WM2. The respective peaks in LOD score, however, were

separated by approximately 20 cM (Figure 6).

The other six loci (c1m18, c3m7, c4m14, c7m4, c7m8, c7m12) were associated with

the dry weight of the ears and/or silks under drought stress only. The additivity at

most of these QTLs, except that at c7m8, was negative, which indicated that the RILs

carrying the allele of the drought-tolerant parent PL1 at the respective loci had a

lower dry weight of the ears and silks than their counterparts with the PL2 allele. The

QTL c3m7 seemed to be particularly important for the growth of the ears and silks at

flowering. It was expressed for the dry weight of the ears and silks at both sampling

dates in both drought-stress experiments and, therefore, suggested a very stable,

stress-induced expression of the underlying genes.

100

Table 14: Genetic characteristics of the QTLs involved in the expression of the dry weight of the

tassels (TBW) measured under drought-stressed (DM1, DM2) or under rain-fed conditions (WM2) in

Mexico. Chr: chromosome number, Mark: number of the nearest marker on the respective

chromosome, Peak: position of the LOD-score peak in centiMorgan, LOD: LOD score in the single trait

analysis, Add: additive genetic effect of the PL1 allele on trait expression, R2: percentage of phenotypic

variance explained by the QTL.

Distance [cM] Trait Env Chr Mark Peak Interval LOD Add R2 [%] TBW DM1 2 1 6 0 - 17 5.6 -0.58 8.2 5 4 40 31 - 58 4.0 0.47 6.3 Total 15.2 DM2 2 2 14 0 - 24 3.3 -0.36 7.5 3 14 192 178 - 200 4.1 -0.45 5.4 5 4 40 30 - 64 5.8 0.48 12.5 8 4 51 40 - 53 2.9 -0.34 6.8 Total 29.2 WM2 2 1 3 0 - 14 3.1 -0.26 4.6 16 189 172 - 199 4.2 -0.33 5.2 3 11 155 123 - 170 2.5 -0.23 5.0 5 4 42 34 - 60 3.4 0.29 9.1 Total 21.5

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Table 15: Genetic characteristics of the QTLs involved in the expression of the dry weight of the ears

at anthesis (EW0) and one week after anthesis (EW7) measured under drought-stressed (DM3, DM4)

or under rain-fed conditions (WM2) in Mexico. See Table 14 for details.

Distance [cM] Trait Env Chr Mark Peak Interval LOD Add R2 [%] EW0 DM3 1 17 219 202 - 227 3.5 -0.07 6.8 3 7 65 55 - 87 3.9 -0.08 9.3 4 1 8 0 - 22 2.7 0.07 3.9 7 12 129 115 - 134 4.8 -0.08 7.6 9 6 110 89 - 125 2.5 -0.06 3.6 Total 29.5 DM4 1 18 222 197 - 249 4.5 -0.06 5.8 2 12 156 141 - 162 5.5 0.07 8.6 3 7 72 58 - 83 4.5 -0.07 7.9 7 4 31 17 - 39 2.7 -0.05 3.3 12 131 119 - 134 4.1 -0.07 3.5 8 4 51 43 - 52 3.9 0.06 8.1 9 2 6 1 - 19 2.8 0.05 7.0 10 7 105 97 - 111 2.5 0.05 3.9 Total 43.3 WM2 4 10 114 100 - 137 4.2 0.08 10.8 10 9 122 105 - 137 2.6 0.05 3.7 Total 15.6 EW7 DM3 3 7 70 58 - 84 2.7 -0.15 8.5 4 14 161 150 - 170 2.6 -0.15 2.5 Total 11.1 DM4 3 7 74 48 - 84 4.5 -0.27 8.9 6 3 18 9 - 38 4.3 0.21 7.4 10 1 0 0 - 11 3.4 -0.19 4.5 Total 20.8

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Table 16: Genetic characteristics of the QTLs involved in the expression of the dry weight of the silks

dry at anthesis (SW0) and one week after anthesis (SW7) measured under drought-stressed (DM3,

DM4) or under rain-fed conditions (WM2) in Mexico. See Table 14 for details.

Distance [cM] Trait Env Chr Mark Peak Interval LOD Add R2 [%] SW0 DM3 1 18 220 198 - 229 2.6 -0.03 6.4 3 7 73 58 - 84 3.7 -0.03 8.2 4 15 167 154 - 173 3.3 -0.03 3.1 7 4 34 24 - 41 2.5 -0.02 2.1 12 129 118 - 134 2.5 -0.02 5.1 10 7 108 87 - 120 3.3 0.03 7.7 Total 32.9 DM4 3 7 74 58 - 84 4.7 -0.04 7.5 5 2 5 0 - 16 4.2 0.03 5.9 7 4 34 26 - 40 3.7 -0.03 3.4 8 75 65 - 81 2.6 0.02 1.6 8 4 50 36 - 57 3.8 0.03 6.2 10 7 102 93 - 113 3.3 0.03 5.2 Total 30.0 WM2 4 10 106 95 - 131 2.5 0.02 5.8 5 2 5 0 - 18 2.5 0.02 5.2 Total 10.4 SW7 DM3 1 18 222 218 - 229 3.4 -0.04 6.7 3 7 72 58 - 84 3.6 -0.04 7.3 4 14 161 150 - 172 3.9 -0.04 4.2 7 8 74 64 - 85 2.3 0.03 3.3 12 129 115 - 134 2.3 -0.03 3.5 10 8 111 105 - 118 2.9 0.03 7.4 Total 36.6 DM4 1 22 296 289 - 303 3.1 -0.04 7.8 25 357 339 - 372 2.6 0.04 4.6 3 7 74 47 - 84 3.6 -0.05 6.0 4 14 158 140 - 173 3.0 -0.04 5.7 5 1 1 0 - 13 4.1 0.04 5.9 6 3 18 10 - 26 2.5 0.03 3.9 7 8 70 57 - 81 3.1 0.04 3.9 10 6 95 88 - 104 2.9 0.04 6.9 Total 37.4

103



expression of the dry weight of the

tassels (TBW), the ears and the

silks at anthesis (EW0, SW0) and

one week after anthesis (EW7,

SW7) measured under drought-

stressed (DM) or rain-fed

conditions (WM) in Mexico. Black

areas represent the confidence

intervals of the QTLs where the

LOD score decreased by half. See

Tables 14, 15 and 16 for details.

104

Discussion

Previous experiments showed that the two parental lines differed remarkably in their

number of tassel branches and that the F2:3 families from the cross PL1 x PL2

segregated well for this trait (Ribaut et al. 2004). The number of tassel branches

correlated positively with the anthesis-silking interval and negatively with grain yield.

Although these correlations were low, they suggested some competition for the

allocation of resources between the tassels and the ears during flowering. The large

differences between the parental lines in the number of tassel branches did not result

in large, consistent differences in the dry weight of the tassel branches (TBW) in this

study (Table 12). Apparently, the dry weight of the tassels depended more on their

size than on the number of tassel branches. Despite the lack of clear phenotypic

differences between the parental lines, TBW was a highly heritable trait, suggesting

that it was under a strong genetic control. TBW was mainly controlled by two QTLs

(c2m1 and c5m4) detected in all three experiments and explaining up to one third of

the phenotypic variance of the trait. The insensitivity of these QTLs to the water-

management system suggested that the underlying genes determined TBW before the

onset of flowering, i.e., before the effects of drought stress emerged. Neither of both

QTLs influenced the dry weight of the ears or the silks. We can infer that the growth

of the tassels and the ears were mainly controlled by distinct genes. Nevertheless, co-

locating QTLs for the dry weight of both the male and the female flowering structures

were observed at one position on the genome (c8m4), but only in the drought-stress

experiments in 2004 (DM2 and DM4). The opposed signs of additivity for TBW (in

DM2) and EW0/SW0 (in DM4) were in agreement with the weak negative correlation

between these traits under drought stress (Table 13). Small tassels favored pre-

anthesis growth of the ears and the silks under water-limited conditions, probably

because of low intra-plant competition for assimilates.

It is obvious that the tassels compete for assimilates with the ears under both rain-fed

and drought-stressed conditions (Edmeades et al. 2000, Fischer et al. 1989,

Monneveux et al. 2006). The competition among tassels and ears, however, does not

negatively affect ear growth when water supply is adequate since overall assimilation

is not reduced and concurrent photosynthesis produces enough assimilates to ensure

the development of both the male and female flowering structures. It is, therefore,

not surprising that TBW was not correlated with EW0 and SW0 under rain-fed

105

conditions (Table 13). The lower correlation between TBW and the dry weights of the

ears or the silks one week after anthesis (EW7, SW7) than at anthesis (EW0, SW0)

under water-limited conditions proved that the final TBW was determined to a large

extent before anthesis. Once the tassels were fully developed, the competition for

assimilates between the tassels and the ears ceased and ear growth was no longer

negatively affected after anthesis.

The growth rates of the ears and the silks during the first week after anthesis

(calculated as (EW7-EW0)/EW0 and (SW7-SW0)/SW0, data not shown) were

considered to be important indicators of the tolerance to drought stress at flowering

(Andrade et al. 1999, Otegui and Bonhomme 1998). Contrary to expectations, the

heritability of the growth rate of the ears and the silk was very low; these traits were

not correlated with the dry weight of the ears, the silks or the tassels and no QTLs

were detected for them (data not shown). These results were in contrast to our

working hypothesis that the drought-tolerant genotype (PL1) had higher rates of ear

and silk growth during the first week after anthesis than the drought-susceptible

genotype (PL2). In case the growth rates were the same, the tolerant genotype was

considered to have at least higher ear dry weights at anthesis than the susceptible

line. This hypothesis was based on reports that drought tolerance was associated with

increased ear biomass at anthesis (Edmeades et al. 1993). Therefore, the negative

additive effect of the PL1 allele at the loci c1m18, c3m7, c4m14 and c7m12 on the dry

weights of the ears and the silks was surprising. The additivity at these QTLs not only

seemed to disprove the greater drought tolerance of PL1 over PL2 through increased

partitioning of assimilates to the ears but it was also in disagreement with the

phenotypic results: the dry weight of the ears and the silks of PL1 were higher than

those of PL2 on both sampling dates in all the experiments. However, the negative

additive effects on the dry weight of the ears and silks were detected under drought

stress only. It seems that the QTL effects were due to alleles that induced greater

reductions in the dry weights of the ears and silks for PL1 than for PL2 under drought

stress. As a consequence, the differences in EW0 and SW0 between the two parental

lines were smaller under drought-stressed than under rain-fed conditions. Despite

this unfavorable effect of the PL1 alleles, the dry weight of the ears and the silks at

anthesis and one week after anthesis of PL1 was superior to that of PL2.

106

Conclusions

The results of this study suggested that the dry weight of the tassels, which is an

estimation of the size of the tassels, was under the strong genetic control of probably

two major genes located on chromosomes 2 and 5 and of a few minor genes. The

opposite additive allelic effect on TBW at the two major loci was responsible for the

lack of phenotypic differences between PL1 and PL2. There seems to have been only

limited effort to reduce tassel size in the process of selection for improved tolerance

to drought stress at flowering and there is still some potential for genetic

improvement. Small tassels are expected to favor the growth, fertility and

productivity of the ears through weaker intra-plant competition for assimilates,

although the growth of the male and female inflorescences was controlled by different

QTLs.

The better tolerance of PL1 to drought stress at flowering was due mainly to genetic

improvement in the partitioning of assimilates to the ears before and during

flowering. This effect was particularly strong under rain-fed conditions where the

water supply was not limited. The results for the dry weight of the ears and the silks

at flowering suggested that the differences in grain yield between the two parental

lines should be largest under rain-fed conditions and that spillover effects of the high

yield potential of PL1 could lead to a relatively high grain yield of PL1 under drought-

stressed conditions.

107

GENERAL CONCLUSIONS AND OUTLOOK

The problem

Successful and continuous maize production is the key to ensuring global food

security (Edmeades et al. 2000). The development of maize lines with improved

tolerance to water-limited conditions can greatly contribute to the success of tropical

maize production and thereby to improved food security in these regions. However,

conventional breeding for drought tolerance in maize is slow and selection based

solely on grain yield is inefficient. Only a multidisciplinary approach combining

conventional breeding, physiology and biotechnology can reveal the genetic basis of

the complex physiological and morphological responses of maize to water-limited

conditions.

The achievements

Genetic control of target traits

We were successful in defining the genetic basis of the phenotypic differences

between a modern tropical maize inbred line with good drought tolerance, originating

from CIMMYT's maize breeding program, and an inbred line with only moderate

drought tolerance, originating from Zimbabwe. The two lines had been crossed to

develop a segregating population of recombinant inbred lines (RILs) and the

corresponding genetic linkage map was constructed to map quantitative trait loci

(QTLs). The RILs were evaluated phenotypically in eleven field experiments at three

locations in Zimbabwe and Mexico in four subsequent growing seasons and under

different levels of water supply.

Each of the measured traits, except for grain yield and kernel number per area, was

controlled by at least one QTL detected in more than one environment. The genetic

control of total grain weight and the number of kernels per area, two tightly

correlated yield components, was subjected to the largest interactions with the

environment. These interactions did not enable us to detect stable yield QTLs across

treatments (drought-stressed or rain-fed) or across locations (Mexico or Zimbabwe).

Several other studies showed also that grain yield was a polygenic trait whose

108

expression depended largely on the environment. Nevertheless, the position of some

yield QTLs was reported to be relatively stable on the genome (Cockerham and Zeng

1996, Melchinger et al. 1998, Ragot et al. 1995, Stuber et al. 1992). In our study, in

contrast, the yield QTLs were clearly unstable across the different environments.

The two QTLs, which explained the largest percentage of the phenotypic variance in

grain yield, were detected on chromosomes 1 and 10, approximately at the same

positions where Ribaut et al. (1997) reported stable QTLs for yield and yield

components under well-watered and under severely drought-stressed conditions in a

population of F2:3 with different genetic background. Vargas et al. (2006) re-analyzed

the data published by Ribaut et al. (1997) with new methods for QTL mapping and

confirmed these two loci to be the most stable yield QTLs detected for that data set.

These results correspond well to those of many other QTL studies, which suggest that

the chromosomes 1 and 10 of maize carry clusters of drought-related traits (Campos

et al. 2004).

Target loci for yield

QTL studies aim at understanding the complex genetic regulation of target traits and

the identification of suitable genomic regions that can be included in marker-assisted

selection (MAS) programs (Johnson 2004, Vargas et al. 2006). Based on our results,

the middle sections of chromosomes 1 and 10 were the most promising target regions

for improving drought tolerance through marker-assisted selection. The middle

section of chromosome 1, close to marker 11, carried genes that affected growth and

development by controlling the distribution of assimilates within the plants. The

presence of important developmental genes on the middle section of chromosome 1

was confirmed by QTLs detected for almost all the target traits, except for the dry

weight of the tassels, within a genetic distance of approximately 80 cM (between

markers 11 and 17). It was not surprising that the QTL expression for most of the

traits changed to some extent across the environments.

Despite the fact that the QTL on chromosome 10, close to marker 6 (c10m6), was

detected for grain yield under rain-fed conditions only – similar to the QTL for grain

yield on chromosome 1 (c1m11) – this locus was the most important locus for the

control of drought-tolerance mechanisms. The combination of high leaf chlorophyll

content and low senescence under drought stress of the lines carrying the allele of the

drought-tolerant parent PL1 suggested the presence of genes that contribute to the

109

expression of functional stay-green mechanisms. Moreover, the PL1 allele was

associated with high dry weight of the silks at anthesis. This might be advantageous

for a short anthesis-silking interval under stress conditions, which in turn is

important for proper pollination and adequate kernel set (Edmeades et al. 2000).

Indeed, the PL1 allele was also associated with a short ASI under drought stress in

Mexico, but simultaneous QTL effects on kernel number or total grain yield under

drought stress were not detected.

The third region, which might serve as a potential region for yield improvement

through marker-assisted selection strategies, was located on chromosome 8 close to

marker 8 (c8m8). This QTL affected the expression of traits in a similar way as the

locus on chromosome 10. The two most obvious differences between these two loci,

however, were that the PL1 allele at c8m8 did not increase silk dry weight at

flowering but delayed the date of anthesis. Therefore, the QTL c8m8 was considered

to be involved in both drought-tolerance and drought-escape mechanisms.

Correlative responses among traits

The fundamental component of the three most important QTLs with respect to grain

yield (c1m11, c8m8, c10m6) was the tight genetic association between plant height

and grain yield. This association was caused either by genetic linkage or by

pleiotropic effects of genes controlling vegetative and reproductive growth. Assuming

that the position on the genome and the effect of the underlying genes are stable

across different genetic backgrounds, these QTLs explained in parts why selection for

drought tolerance in tropical maize has been important in redistributing assimilates

within the plants rather than increasing overall assimilation (Hay and Gilbert 2001).

The phenotypic correlations between the anthesis-silking interval (ASI) and grain

yield within and across experiments in the present study were in agreement with the

general value of ASI as a secondary trait for grain yield under drought stress at

flowering (Edmeades et al. 2000, Edmeades et al. 1993). The QTL results, however,

suggested that the genetic association between grain yield and plant height was

stronger than between grain yield and ASI. ASI, in turn, was closely related with the

date of anthesis. The QTLs detected on chromosome 1 between markers 11 and 17

clearly demonstrated these interactions.

110

Target loci affecting time of flowering and size of inflorescences

The number of days from sowing to anthesis (MFL) was under strong genetic control

in our RIL population. The four major QTLs controlling MFL (on chromosomes 1, 2,

3 and 4) corresponded to universal QTLs for flowering time in maize (Chardon et al.

2004). The tassels of most lowland tropical inbreds are still relatively large (Ribaut et

al. 2004). We observed strong genetic control of the dry weight of the tassels in our

population and therefore, we conclude that there is still a potential for improving the

drought tolerance of tropical maize by selecting for small tassels, even in modern

germplasm. The high heritability of tassel dry weight and the detection of a couple of

loci constitutively controlling the trait demonstrated that such a selection step could

be effectively carried out using molecular markers. The relative contribution to

improved drought tolerance would result from the weaker competition for assimilates

among tassels and ears and, according to Edmeades et al. (1999), from the reduced

shading of the leaves. However, the progress is expected to be small, because the

QTLs controlling tassel size did not have pleiotropic effect on other traits and the

phenotypic correlations between tassel size and ear size at flowering were low

(Table 13).

The dry weight of the tassels and the ears were controlled by independent QTLs.

However, the QTL on chromosome 3 close to marker 7 (c3m7) controlling MFL was

also important for dry weight of the ears and silks on both sampling dates under

drought stress. The precise co-location of the QTLs for these traits and the additive

effect of the PL1 allele towards delayed anthesis and reduced dry weight of the ears

and silks suggested the presence of genes with a direct and constitutive effect on the

plants’ phenology; the detection of the QTL for MFL at c3m7 did not depend on the

water management, although the LOD score and the percentage of phenotypic

variance were higher under drought-stressed than under rain-fed conditions

(Table 4). Apical morphogenesis in cereals is quite sensitive to water deficit during

vegetative and floral development. Water stress at these stages slows down the rate of

inflorescence development (Saini and Westgate 2000). The ear, which is a weak sink

for assimilates at flowering, suffers more from poor environmental conditions than

other parts of the plants. An important reason might be that the ears are subordinate

to apical dominance because of their axial position on the maize plants (Andrade et

al. 1999). Combined effects of apical dominance and delayed anthesis of the RILs

carrying the PL1 allele at locus c3m7 were probably responsible for the negative

111

additive effects on the dry weight of the ears and silks at flowering under drought

stress. Co-locating QTLs for anthesis date and the dry weight of the ears and/or silks

during the flowering time were also observed on chromosomes 1 and 4 and the

additive genetic effect on trait expression confirmed the causal negative association

among these traits.

None of these three QTLs on chromosomes 1, 3 and 4 were detected for grain yield,

disproving the hypothesis of a close genetic linkage or pleiotropic effects of heavier

ears and silks at flowering on grain yield or kernel number at maturity. Nevertheless,

the phenotypic results for these traits revealed a striking correspondence. The

phenotypic differences between the two parental lines in terms of grain yield and dry

weight of the ears at anthesis were largest under rain-fed conditions. Drought stress

reduced the dry weight of the ears of PL1 to a greater extent than of PL2, resulting in

smaller phenotypic differences between the two parents under drought-stressed than

under rain-fed conditions.

Considering the significant positive phenotypic correlation between the dry weight of

the ears or silks and grain yield, a comparable response of the two parental lines for

these traits across experiments and the highly significant and stable QTL effect at

c3m7 on ear and silk dry weights suggested that this locus was the fourth target

region for marker-assisted selection. Since marker-assisted selection for increased

ear and silk growth before and at flowering at this locus alone would favor drought

escape rather than drought tolerance, it should only be used to complement MAS

strategies aimed at other loci associated with drought-tolerance mechanisms.

Target loci for stay-green characteristics

The three potential loci for MAS on chromosomes 1, 8 and 10 also influenced the

chlorophyll content of the leaves. The locus in the middle section of chromosome 2

was constitutively involved in the expression of chlorophyll content of the ear leaf and

the second leaf from the top of the plants. Comparisons with reports in the literature

showed that the respective genes on chromosome 2 were present in different genetic

backgrounds. Our results fully supported the hypothesis by Fracheboud et al. (2004)

who suggested the involvement of universal genes on chromosome 2 in the

accumulation of leaf chlorophyll. The alleles at these genes that were present in our

RIL population did, however, not contribute to functional stay-green mechanisms

under water-limited conditions. They were constitutively associated with a high

112

initial chlorophyll content, which Thomas and Howarth (2000) defined as Type E

stay-green. Although this locus was not involved in the control of grain yield and/or

the dry weight of the ears and silks at flowering – which might have indicated positive

effects of high leaf chlorophyll content on photosynthesis, assimilation and assimilate

supply to the developing ears during flowering– we strongly recommend considering

the middle section of chromosome 2 in MAS experiments. The negative additivity of

the PL1 allele at this locus on the date of anthesis and plant height is not fully

understood, but it clearly showed that this QTL also controlled other developmental

and structural characteristics of the plants, which, in different genetic backgrounds,

might have important effects on grain yield.

QTL application in marker-assisted selection

Marker-assisted selection strategies have been considered promising tools for

improving complex traits in field crops. Successful applications of the large number

of QTL data generated during the last two decades, however, are scarce (Mohan et al.

1997, Ribaut et al. 2004). The weak associations between markers and target QTLs,

the high costs of MAS (Salvi et al. 2001, Tuberosa et al. 2003), the interactions of

QTLs with the environment (Beavis and Keim 1996, Chapman et al. 2003, Wang et al.

1999), the lack of stable QTLs for grain yield (Moreau et al. 2004), the sensitivity of

the QTLs to the genetic background (Campos et al. 2004) and the genetic complexity

of the trait as well as the interactions among genes (Ribaut et al. 2004) are the main

causes of the lack of success stories about MAS.

If it were possible to reduce the cross- and environment-specificity of QTLs, the

efficiency of MAS for manipulating QTLs for polygenic traits could be largely

increased. The novel approach for MAS based on the drought consensus map intends

to achieve this objective by compiling QTL data for morphological and physiological

traits in order to predict the genotypic value, with respect to drought tolerance, of

new germplasm without the need for QTL mapping. Several populations of tropical

maize lines segregating for their response to water-limited conditions at flowering

were phenotypically and genetically evaluated at CIMMYT. The corresponding

genetic linkage maps were combined by means of anchor markers present on all the

maps. The resulting consensus map is a dynamic system, which evolves over time as

more QTL data are included. The basic assumption is that genes involved in the

drought response are probably located at the same position in the maize genome and

113

that phenotypic differences across germplasm are created by the nature/quality of the

alleles at those genes, independent of the performance of the germplasm. A

comprehensive description of the potential of MAS based on the drought consensus

map, its construction and the rationale behind this method is given by Ribaut et al.

(2004). Marker-assisted selection without mapping QTL for a target cross will be

feasible only if gene clustering based on function occurs. Both QTL results (Khavkin

and Coe 1997) and information on gene location (Langridge et al. 2002) give evidence

of functional gene clustering in maize; the final proof, however, has yet to be found.

The accumulation of QTLs in some genetic regions, as observed in this study,

corresponded to the results of other published studies on drought tolerance in maize

(Campos et al. 2004, for review) as well as to other results produced at CIMMYT (c.f.

Ribaut et al. 2004). Five QTL regions, located on chromosomes 1, 2, 3, 8 and 10, were

predestined for marker-assisted selection. They were either constitutive or associated

with trait expression under drought stress, pointing at the genetic basis of adaptation

mechanisms. In any case, the value of the QTL data identified in this study for MAS

of universal drought QTLs will become evident as soon as the data are included in the

drought consensus map.

115

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ACKNOWLEDGEMENTS

A PhD thesis is often considered to be the result of the doctoral candidate’s

emotional, theoretical and practical commitment to the area under investigation. My

personal commitment alone, however, would not have lead to a successful PhD. The

whole project depended on the contribution of many people. I would like to say a

sincere “Thank you!” to all of them. Although it is not possible to put on record all of

them, I would like to mention a few persons in particular.

My professor Peter Stamp and my supervisors Jean-Marcel Ribaut and Yvan

Fracheboud were working on the proposal of this project and made important

preparations already before I decided to undertake the PhD. Later, during the PhD,

they guided me with professionalism and they supported me patiently and with a

friendly spirit. Although there would have been good reasons for them to loose

patience they always kept faith with me.

Marianne Bänziger’s dedication to maize improvement under poor environmental

conditions was of major importance: the drought-tolerant parental line CML444 is

“her baby”. She did not only provide the plant material for this study but also a

valuable set of data from several field experiments conducted in Zimbabwe under her

supervision.

A considerable number of field workers prepared the plant material and made

possible the large-scale screening of the plants on two continents at three locations in

four cropping periods. They have done a great job, just as did the technicians who

taught me the various techniques in the laboratory and assisted me in applying them.

I would like to mention Simón Pastrana and Eva Huerta as two representatives for

many more field workers and lab technicians.

Mateo Vargas, José Crossa and Juan Burgueño helped me considerably to sort out

statistical difficulties during data analysis.

130

Once my PhD thesis was basically finished Christof Sautter was willing to read it and

to deliver his expert opinion in a short time.

I am deeply grateful to all these persons for their commitment to the project and for

the continuous support they gave me.

***

The first year of this doctoral project was funded by the Swiss Center of International

Agriculture (ZIL). The second and third years were funded by the Swiss Agency for

Development and Cooperation (SDC). My sincere thanks go to both institutions.

***

The most part of my life in Mexico was wonderful. The least part of it was almost

unbearable. By now, I do not want to miss either part of it. I hope that those of my

friends and colleagues, whose sensibilities I offended, accept my sincere apologies. I

would like to say – to those friends who helped me to overcome the almost

unbearable part – thank you for your unconditional love.

REM

131

AGRADECIMIENTOS

Una tesis de doctorado generalmente se considera como el resultado de la dedicación

teórica, práctica y emocional del candidato en el área de investigación. Sin embargo,

solamente mi dedicación no hubiera sido lo suficiente para realizar este proyecto que

dependió de la contribución de muchas personas. Quisiera dedicar a todas aquellas

personas – evitando a propósito una lista larga e incompleta – un sincero “¡muchas

gracias!”. Aun así me gustaría mencionar algunas personas en particular.

Ya antes de que yo me decidiera al favor de este doctorado mi profesor Peter Stamp y

mis asesores Jean-Marcel Ribaut e Yvan Fracheboud preparaban el proyecto. Luego,

durante el doctorado, me guiaron profesionalmente, me ayudaron y me respaldaron

con amistad y con paciencia aunque a veces hubieran tenido buenas razones para

perderla.

La dedicación de Marianne Bänziger al mejoramiento del maíz fue muy importante.

No solamente desarrolló el genotipo de maíz CML444, su “niño”, también puso a

disposición un importante juego de datos de Zimbabwe.

Muchos trabajadores de campo prepararon las semillas, cuidaron los experimentos

facilitando la evaluación de un gran numero de plantas en los experimentos que se

hicieron en dos continentes en tres sitios y en el transcurso de cuatro estaciones.

Ellos, junto con los técnicos de laboratorio quienes me enseñaron varios métodos de

análisis molecular y quienes me ayudaron aplicándolas, hicieron un trabajo excelente.

Quisiera mencionar únicamente a dos personas representativas: Simón Pastrana y

Eva Huerta.

Mateo Vargas, José Crossa y Juan Burgueño me ayudaron bastante a solucionar

tareas estadísticas.

Después de haber básicamente terminado la tesis, Christof Sautter fue dispuesto a

leerla en relativamente poco tiempo y a juzgarla como experto independiente.

132

Quisiera expresar a estas personas importantes mis agradecimientos profundos por

haber contribuido al la realización del proyecto y por haberme respaldado

constantemente.

***

El primer año de este proyecto de doctorado fue financiado por el Centro Suizo de

Agricultura Internacional (ZIL). La Agencia Suiza para el Desarrollo y la Cooperación

(COSUDE) financió el segundo y el tercer año. Les doy a las dos instituciones mis

gracias expresivas.

***

La mayor parte de mi estancia en México fue maravillosa. La menor parte fue casi

insoportable. Hoy en día, no quiero carecer de las dos experiencias. Espero que los

amigos y compañeros que ofendí o lastimé acepten mis disculpas. A otros de mis

amigos – los que me ayudaron a sobrellevar la parte casi insoportable – quisiera

decirles muchísimas gracias por su amor incondicional.

REM

133

CURRICULUM VITAE

Rainer E. Messmer

Dipl. Ing.-Agr. ETH

Born 21.05.1976 in Zurich

Citizen of Thal SG

2002 - 2006 PhD candidate at ETH Zurich, based at the International Maize and

Wheat Improvement Center (CIMMYT) in Mexico

2002 “Willi-Studer-Preis“ 2002

Award for the best degree in the Department of Agronomy and Food

Sciences at the ETH in 2001

2001 Diploma thesis: „Genetic analysis of drought tolerance in maize

seedlings“, ETH Zurich, Institute of Plant Sciences, Group of

Agronomy and Plant Breeding

Prof. Dr. Peter Stamp, examiner

Dr. Monika Messmer, co-examiner

Dr. Yvan Fracheboud, supervisor

Dr. Jean-Marcel Ribaut, supervisor

1996 - 2001 Studies in Plant Production in Agronomy

ETH Zurich, Department of Agricultural and Food Sciences

1996 General qualification for university entrance („Matura” Type D) with

the modern languages English, French and Spanish

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