K. Sadaiah - KSIConnectksiconnect.icrisat.org/wp-content/uploads/2015/10/30102015.pdfPJ Telangana...

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K. Sadaiah Ph.D. Scholar PJ Telangana State Agricultural University, Hyderabad

Groundnut (Arachis hypogaea L.)

• Groundnut is an important food, fodder and oilseed crop of

the world.

• India -world’s second largest producer of groundnut with a

total production of 9.7 million tons (FAOSTAT, 2014).

• Being abundant in nutrients, groundnut can attribute to fight

against micronutrient malnutrition prevalent in the world.

• Malnutrition statistics: International Food Policy Research

Institute (IFPRI) and hunger notes web page for 2015 World

Hunger and Poverty Facts and Statistics

• Bio-fortified groundnut can be used as Ready-to-Use

Therapeutic Food (RUTFs) like Plumpy’nut

(https://www.globalgiving.org) and project peanut butter

(http://www.projectpeanutbutter.org).

2

https://www.globalgiving.org/







http://www.projectpeanutbutter.org/







3

OBJECTIVES

• Development of RIL mapping populations using contrast parents for levels of kernel iron and zinc concentration

• Identification of molecular markers linked to the putative genomic regions (QTLs) controlling kernel iron and zinc concentration using F2:3 mapping population.

• Validation of putative candidate gene markers in an alternate mapping population

• Studying the gene action governing kernel iron and zinc concentrations.

STATISTICAL ANALYSIS

The data collected was subjected to,

• Analysis of Variance (ANOVA)

• Genetic parameters viz., PCV, GCV, Heritability and GA.

• Single Marker Analysis.

• Generation mean analysis (Hayman,1958 and Jinks and Jones, 1958)

• Correlation analysis (Karl Pearson, 1920)

Desi Kabuli

4

I. QTL (Single Marker) analysis

5

A. Genotyping: • Parental lines viz., ICGV 06099

(higher parent) ICGV 93468 (lower parent) contrasting for kernel iron and zinc concentrations were taken for the study.

• F2 mapping population- 184 lines from ICGV 06099 × ICGV 93468

• DNA was extracted from the plants using CTAB method.

ICGV 06099 ICGV 93468

F2:3 mapping population

Polymorphism between the parents

6

•Initial screening of parental lines viz., ICGV 06099 and ICGV 93468 was done using SSR markers to identify polymorphic markers •300 SSR markers were taken for the study. •From this screening, 33 SSR markers (16.5 %) detected showing scorable polymorphism. •These polymorphic markers were used for genotyping of the F2 mapping population

7

Marker Sequence Linkage Group Position

(cM) Size (bp) Reference

Forward Reverse

GM1954 GAGGAGTGTGAGGTTCTGACG TGGTTCATTGCATTTGCATAC A03 114.43 115

Nagy et al. (2010) GM1991 GAAAATGATGCCGAGAAATGT GGGGAGAGATGCAGAAAGAGA B06 92.89 122

GM1742 GCCTTGTTGCAATCATCACA ACCTCCAACAGGAACATTGC B10 38.69 270

GM2536 AGCCTCCACCTTCTCCTATTG GATGCAGTGGAGGGATAACAA A06 115.72 336

GM1577 GCGGTGTTGAAGTTGAAGAAG TAACGCATTAACCACACACCA A05 53.75 278

GM2032 GCCGATGATGTACGTTTCTTC GAGACGGCATGTCAAAAGAAT B10 24.30 149

TC3B05 GGAGAAAACGCATTGGAACT TTTGTCCCGTTGGGAATAGT A08 23.09 248-270 Moretzsohn et al. (2005)

GM2053 ACAAGGAAAACCCATCCAATC ACGTGATGGATTCTTGTGGAG B03 74.42 405 Guo et al. (2012)

GM2301 GTAACCACAGCTGGCATGAAC TCTTCAAGAACCCACCAACAC B03 113.75 137

GM2120 TCCACTGCCACCTCTATCATC TCCACCCACATAGACAGAAGC B09 90.39 139 Nagy et al. (2010)

TC9F10 ATCACAATCACAGCTCCAACAA GGCAAGTCTAATCTCCTTTCCA A08 73.94 286-320 Moretzsohn et al. (2005)

GM2638 ATGCTCTCAGTTCTTGCCTGA CAGACATAACAGTCAGTTTCACC A04 86.55 107 Nagy et al. (2010)

IPAHM245 CCCAAGGACCTAGTGACCAA GGACCCTTAGCACATTCCAA A06 55.16 290 Cuc et al. (2008)

GM2746 TCAACCTCAAGGGTGATTGTC ACACAAACCCGCTCACTCTAA B08 60.30 120 Nagy et al. (2010)

IPAHM103 GCATTCACCACCATAGTCCA TCCTCTGACTTTCCTCCATCA A03 133.84 160 Cuc et al. (2008)

IPAHM524 GCCATGGATAAGAACCTGAAA CAGTAAGCTGAGCTGGCAGA B02 46.11 300

PM36 ACTCGCCATAGCCAACAAAC CATTCCCACAACTCCCACAT A05 54.89 190-240 He et al. (2003)

SEQ19B01 TTGGTGATGGTGTTGGAGAA TTAAACCAGGCCAAAAGTGG A09 54.44 198 Ferguson et al. (2004)

TC7E04 GAAGGACCCCATCTATTCAAA TCCGATTTCTCTCTCTCTCTCTC A03 127.20 300 Moretzsohn et al. (2005)

S109 AAGGGAGCACAATCATA GAGCACGAGTTCATACAC A04 55.62 370-430 Wang et al. (2007)

SEQ2B09 GCAACATGCTCTGAATTTTGAC TGTGCAACCCAATTCAATAACTT B09 82.55 259

Ferguson et al. (2004) SEQ5D01 TGGCCAAAACAACTGATTGA TCCCAACTTTTCCGTTCTTG A01 65.76 264

SEQ17E03 TTTCCTTTCAACCCTTCGTG AATGAGACCAGCCAAAATGC A09 85.93 193

SEQ19G07 ATTCAATTCCTCTCTCCCCC TCAATCAATCAATCGCAGGA A03 106.08 149

SEQ1B09 CGTTCTTTGCCGTTGATTCT AGCACGCTCGTTCTCTCATT A02 38.49 282

SEQ3A08 ATACGTGACTTGGGCCAGAC AGTGAAAAATACACCCAACGAA A08 53.56 152

SEQ9G05 CAAATTGTGCAGCCAAGAGA CATATGCCCAGGAAGAGGAA B05 32.05 273

GM2079 GGCCAAGGAGAAGAAGAAAGA GAAGGAGTAGTGGTGCTGCTG B03 115.71 418 Guo et al. (2012)

TC1B02 AACATGCATGCAAATGGAAA GCCAAAGTCACTTGTTTGCTT B02 55.56 220-270

Moretzsohn et al. (2005) TC4G02 GATCCAACTGTGAATTGGGC CACACCAGCAACAAGGAATC B03 88.70 130-166

TC4F12 GATCTTTCCGCCATTTTCTC GGTGAATGACAGATGCTCCA A02 34.51 230

IPAHM689 GATGACAATAGCGACGAGCA GTAAGCCTGCAGCAACAACA A06 52.22 240 Cuc et al. (2008)

TC1E05 GAAGGATAAGCAATCGTCCA GGATGGGATTGAACATTTGG A08 60.27 215-260 Moretzsohn et al. (2005)

Data Processing

8

•GeneMapper® version 4.0 software (Applied Biosystems, USA) was used to size the peak pattern in relation to the internal size standard, GeneScan 500™ LIZ®. •The peaks were displayed with base pair values and height (amplitude) in a chromatogram. •The banding pattern of each of amplified PCR products of various marker systems were scored accordingly.

Desi Kabuli

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B. Phenotyping

•The experiment consisting of parental lines and the corresponding F2:3 mapping population were sown in Alfisols fields at ICRISAT. •Alpha lattice design •Spacing: 60 × 10cm. •Soil analysis to estimate the iron and zinc status of the experimental block was conducted. •In all the locations, the iron and zinc concentrations of the soil were above critical limits (iron 2·0 mg kg-1 and zinc 0·75 mg kg-1, Olsen’s method) •ICP-OES method was used to estimate the iron and zinc status.

Desi Kabuli

Details of de-fatted kernel iron zinc concentrations among parents and population

10

Characters

Parents F2:3 Population

(n = 184) ICGV 06099

(n = 4) ICGV 93468

(n = 4) Mean SD (±) Mean SD (±) Mean Range SD

Kernel iron concentration (mg kg-1)

52.5

5.93

37.3

3.30

45.2

31.8 - 61.4

6.10

Kernel zinc concentration (mg kg-1)

79.5 4.67 65.0 4.14

76.7 59.6 - 90.4 5.60

11

Histogram representing frequency distribution of the population for kernel iron and zinc concentrations

Normal distribution was observed for kernel iron and zinc concentrations in phenotyping population

P2 P1 P2 P1

Desi Kabuli

Analysis of variance for several characters using alpha lattice design in phenotyping population of cross ICGV 06099 × ICGV 93468

12

Sources of variation Replication Block/Replication Genotypes Error

df 1 50 259 209

100-seed weight (g) 1.45 21.54* 20.40* 14.66

Pod yield (g plot-1) 443.75* 1042.38** 1673.52** 78.53

Oil content (%) 12.90* 4.55* 4.38* 3.27

Protein content (%) 0.4 1.99 0.93 1.03

Kernel iron concentration

(mg kg-1) 11.4 50.37** 44.81** 20.39

Kernel zinc concentration

(mg kg-1) 0.79 42.11** 37.42** 17.84

Oleic acid (%) 8.42 14.35 14.29* 10.9

Linoleic acid (%) 7.3 10.26 11.24** 7.84

Descriptive statistics of F2:3 phenotyping population of cross ICGV 06099 × ICGV 93468

Trait Mean Range CV (%) SEm Skewness

100-seed weight 31.65 19.20-43.0. 13.57 0.197 -0.062

Pod yield per plot (g plot-1) 104.2 25.60-

244.00

21.44 1.793 0.614

Oil content (%) 48.53 43.49-59.61 4.13 0.092 0.714

Protein Content (%) 27.22 24.89-29.75 3.84 0.047 -0.221

Kernel iron (defatted) concentration

(mg kg-1)

45.29 31.77-61.41 13.49 0.284 0.516

Kernel zinc (defatted) concentration

(mg kg-1)

76.74 59.64-90.40 7.30 0.261 -0.115

Oleic acid (%) 41.31 31.66-53.93 8.75 0.166 0.377

Linoleic acid (%) 34.08 24.04-42.17 9.24 0.672 -0.495

Estimates of genetic parameters for different agronomic traits in F2:3 population of cross ICGV 06099 × ICGV 93468 of groundnut

Trait PCV % GCV % h2b GA GAM

100-kernel weight (g) 13.22 5.35 37.00 1.41 4.46

Pod yield (g plot-1) 28.40 27.10 91.00 55.50 53.26

Oil content (%) 10.72 4.09 44.59 0.58 3.22

Protein content (%) 2.30 1.93 70.40 0.91 3.34

Kernel iron concentration (mg kg-1) 12.60 7.71 64.24 4.40 9.72

Kernel zinc concentration (mg kg-1) 6.84 4.07 62.21 3.83 4.99

Oleic acid (%) 8.59 3.15 31.81 0.98 2.38

Linoleic acid (%) 9.06 3.82 39.42 1.13 3.32

PRINCIPAL COMPONENT ANALYSIS

15

CORRELATION STUDIES

TRAITS CORRELATION

HKW-Fe -0.028ns

HKW-Zn 0.008ns

PY-Fe -0.060ns

PY-Zn -0.045ns

OC-Fe 0.082ns

OC-Zn -0.006ns

PC-Fe -0.08ns

PC-Zn 0.074ns

Fe-Zn 0.491**

OAC-Fe -0.022ns

OAC-Zn -0.049ns

LAC-Fe 0.043ns

LAC-Zn 0.041ns

17

r =0.49

0

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70

80

90

100

0 10 20 30 40 50 60 70

Ker

nel

zin

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mg

kg-1

)

Kernel iron concentration (mg kg-1)

r = 0.06

0.0

10.0

20.0

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70.0

0.0 50.0 100.0 150.0 200.0 250.0Ker

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(mg

kg-1

)

Pod Yield (g plot-1)

r = 0.045

0.0

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-1)

Pod Yield (g plot-1)

Single Marker Analysis

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•It is performed using both genotypic and phenotypic of all the individuals of the population. •To identify the linked markers associated with the trait. •33 SSR markers were used for single marker analysis. •Results revealed that out of twenty eight, three markers viz., IPAHM245 (2.19 %), SEQ1B09 (0.23 %) and SEQ9G05 (6.24 %) showed significant association for kernel iron concentration. •Three markers viz., GM2638 (1.75 %), IPAHM245 (2.25 %) and SEQ9G05 (6.01 %) showed significant association with kernel zinc concentration.

Results of Single Marker Analysis (SMA) for kernel iron and zinc concentration using three significant markers each

19

For kernel iron concentration

Marker Name probability R2 (%)

IPAHM245 0.009 ** 2.19

SEQ1B09 0.049 * 0.23

SEQ9G05 0.002 ** 6.34

For kernel zinc concentration

Marker Name probability R2 (%)

GM2638 0.038 * 1.75

IPAHM245 0.012 * 2.25

SEQ9G05 0.001 ** 6.01

20

•Validation of putative markers is used to confirm the reproducibility of usefulness in marker aided breeding program. •The markers which were found significant on F2:3 population of cross ICGV 06099 × ICGV 93468 were validated on another population of cross ICGV 06040 × ICGV 87141 by doing biochemical analysis of entries. •Out of 34 entries, 19 recorded higher iron (fatted) concentration (21.12 to 27.22 mg kg-1) whereas 22 entries recorded higher zinc (fatted) concentration (40.13 to 45.9 mg kg-1).

Validation in the cross ICGV 06040 × ICGV 87141

ICGV 06040 ICGV 87141

F2:3 mapping population

21

GENERATION MEAN ANALYSIS

Generation of breeding material

22

•Generation mean analysis is often used to estimate components of mean, additive and dominance effects and interactions of individual traits. •The material comprised of six basic generations viz., P1, P2, F1(P1 × P2), F2 (selfing of F1), B1 (F1 × P1 )and B2 (F1 × P2) generated during rainy, 2013 at ICRISAT. •These generations were derived from two crosses (ICGV 06040 × ICGV 87141 and ICGV 06099 × ICGV 93468) involving four genotypes with contrasting levels of kernel iron and zinc concentration.

Pedigree and characteristics of the genotypes used in the experiment

Parental line Pedigree Characteristics

ICGV 06040 [{(ICGS 35 x NC Ac 1705) x CS 16-B2-B2} x {(NC Ac 343 x (Dh. 3-20 x Robut 33-1)} x {(NC Ac 343 x (Dh. 3-20 x

Robut 33-1)}]

Confectionary types, Virginia bunch, medium duration,

smooth pod, tan colour and medium size seed. Rich in

defatted iron (56.1 mg kg-1) and zinc (80.1 mg kg-1).

ICGV 87141 (TMV 10 x Chico)


smooth pod, tan colour and medium size seed. Low in


ICGV 06099 [{(ICGS 35 x NC Ac 1705) x CS 16-B2-B2} x {(NC Ac 343 x (Dh. 3-20 x Robut 33-1)} x {(NC Ac 343 x (Dh. 3-20 x

Robut 33-1)}]


smooth pod, tan colour and medium size seed. Rich in


ICGV 93468 [(ICGS 44 x TG 2E) x {ICGS 30 x (TMV 10 x Chico)}]

Confectionary types, Spanish bunch, medium duration,

smooth pod, purple colour and medium size seed. Low

in defatted iron (45.2 mg kg-1) and zinc (60.7 mg kg-1).

Evaluation of breeding material

24

•The six generations were evaluated during post-rainy 2013-14 to understand the gene action involved in inheritance of trait of interest. •Evaluated in a compact family block design with three replications. •Each block comprised of one row each of P1, P2 and F1, two rows each of B1 and B2 and eight rows of F2. Each row is of 2 m length with a spacing of 30 cm between rows and 10 cm between the plants.

sources of

variation df

100-kernel

weight (g)

Pod yield per

plant (g)

Kernel iron

concentration

(mg kg-1)

Kernel zinc

concentration

(mg kg-1)

Analysis of variance between crosses

Rep 2 0.53 0.40 0.07 1.45

Cross 1 3.3856* 2.783336* 6.881878* 14.65614*

Error 2 0.18 0.06 0.12 0.54

Analysis of variance between generations within crosses

ICGV 06040 × ICGV 87141

Rep 2 7.99 -27.24 6.23 8.95

Gen 5 57.56** 93.47* 21.17** 42.76**

Error 10 9.99 20.69 2.38 8.66

ICGV 06099 × ICGV 93468

Rep 2 11.04 -23.42 0.39 18.61

Gen 5 42.10* 76.35** 16.80* 36.09*

Error 10 10.05 9.80 4.22 10.41

25

Analysis of Variance (ANOVA)

Comparison of mean performance of different generations of cross ICGV 06040 × ICGV 87141

26

Trait HKW PY(g) Fe (Fatted) Zn (Fatted)

P1 Mean 44.53 24.82 33.32 50.91

Range 31.4-52.3 15.2-45.7 29.2-38.6 45.9-57.4

P2 Mean 34.82 31.09 25.54 36.05

Range 30.2-51.5 16.5-53.8 21.7-27.7 27.7-41.9

F1 Mean 43.94 31.67 28.49 40.27

Range 31.0-54.5 14.5-53.0 23.7-32.2 28.2-55.2

F2 Mean 43 31.4 28.38 39.8 Range 22.7-73.9 7.6-86.3 20.1-39.1 22.7-57.9

B1 Mean 44.54 39.99 31.49 42.46

Range 22.2-68.8 15.4-100.3 20.4-41.4 27.6-54.2

B2 Mean 36.49 34.12 29.42 41.98

Range 23.1-57.1 11.0-105.7 21.0-42.2 27.1-60.3

S.Em. ± 1.82 2.63 0.89 1.7 C.D. 5.75 8.28 2.81 5.35

Comparison of mean performance of different generations of cross ICGV 06099 × ICGV 93468

Trait HKW PY(g) Fe (Fatted) Zn (Fatted)

P1 Mean 46.32 30.71 25.49 36.58

Range 26.0-60.5 20.1-66.9 22.6-31.1 35.0-54.6

P2 Mean 44.78 26.77 20.83 30.39

Range 39.5-54.5 16.1-46.7 16.8-21.5 24.5-33.8

F1 Mean 46.37 37.51 21.95 32.01

Range 30.0-67.4 18.5-68.6 18.7-31.1 21.9-41.2

F2 Mean 36.43 30.82 25.19 35.08

Range 26.0-77.7 4.7-91.3 17.3-48.9 23.4-60.8

B1 Mean 45.03 33.5 26.25 37.27

Range 28.6-58.7 9.4-66.7 16.2-41.2 23.7-50.6

B2 Mean 44.33 35.9 24.07 32.91

Range 23.2-63.6 9.1-71.7 17.6-35.2 25.0-42.1

S.Em. ± 1.83 1.81 1.19 1.86

C.D. 5.77 5.7 3.74 5.87

Trait PCV % GCV % h2b GA GAM h2

n

ICGV 06040 × ICGV 87141

100- kernel weight (g) 12.33 9.66 61.36 6.43 15.59 0.45

Pod yield per plant (g) 20.7 15.21 53.96 7.45 23.01 0.56


(mg kg-1)

10.01 8.52 72.43 4.39 14.93 0.66


(mg kg-1)

13.11 11.06 71.15 8.03 19.21 0.51

ICGV 06099 × ICGV 93468

100- kernel weight (g) 10.39 7.42 51.52 4.83 10.91 0.60

Pod yield per plant (g) 16.7 13.9 69.36 8.08 23.85 0.32


(mg kg-1)

12.01 8.48 49.85 2.98 12.34 0.23


(mg kg-1)

12.75 8.57 45.12 4.05 11.86 0.19

Estimation of genetic parameters

Results of scaling tests and genetic components for traits in the cross ICGV 06040 × ICGV 87141

29

Scaling

test

100-Kernel

weight (g)

Pod yield per plant

(g)

Kernel iron

concentration

(mg kg-1)

Kernel zinc

concentration

(mg kg-1)

A 4.336±3.90 23.489±6.96** -2.976±1.57 -6.271±2.82*

B -15.835±3.17** 5.478±7.79 8.949±1.80** 7.636±3.25*

C -0.792±4.42 6.346±8.62 -2.335±1.71 -8.305±4.09*

Genetic

component

100-Kernel weight (g) Pod yield per plant

(g)

Kernel iron

concentration

(mg kg-1)

Kernel zinc

concentration

(mg kg-1)

m 53.599±4.96** 5.336±9.63 21.129±2.47** 33.815±3.84**

d 0.508±1.23 -3.135±1.93 3.891±0.42** 7.431±0.73**

h -30.219±13.71* 77.922±27.64** 21.647±6.97** 17.496±10.88

i -10.706±4.81* 22.621±9.43* 8.308±2.44** 9.671±3.77**

j 10.085±2.35 9.006±4.682 -5.963±1.15 -6.953±1.80

l 22.205±9.15* -51.588±19.11** -14.281±4.61** -11.036±7.74

Interaction Duplicate Duplicate Duplicate Duplicate

Results of scaling tests and genetic components for traits in the cross ICGV 06099 × ICGV 93468

30

Scaling test

100-Kernel

weight (g)

Pod yield per

plant (g)

Kernel iron

concentration

(mg kg-1)

Kernel zinc

concentration

(mg kg-1)

A -0.795±3.75 -7.855±5.16 5.051±1.67** 5.943±2.46*

B -3.40±3.39 5.664±5.53 5.371±1.77** 3.421±2.04

C 11.891±2.25* -19.70±7.32* 10.550±2.30** 12.197±3.16**

Genetic

component

100-Kernel

weight (g)

Pod yield per

plant (g)

Kernel iron

concentration

(mg kg-1)

Kernel zinc

concentration

(mg kg-1)

m 59.984±4.67** 13.210±7.60 23.294±0.36** 36.324±2.93**

d -1.193±1.20 1.972±1.90 2.334±0.703** 3.096±0.88**

h -32.293±12.61* 46.154±21.03* 8.957±6.56 2.220±8.03

i -16.085±4.51** 15.537±7.36* -0.128±2.32 -2.834±2.79

j 1.303±2.11 -4.371±3.58 -0.160±1.15 1.261±1.43

l 20.280±8.83* -21.851±14.66 -10.295±4.32* -6.529±5.49*

Interaction

Duplicate Duplicate Duplicate Duplicate

Correlation studies among traits in crosses ICGV 06040 × ICGV 87141 and ICGV 06099 × ICGV 93468

TRAITS CORRELATION (ICGV 06040 × ICGV 87141)

CORRELATION (ICGV 06099 × ICGV 93468)

HKW-PY 0.936** -0.018

HKW-Fe -0.025ns -0.018ns

HKW-Zn 0.134* 0.175**

PY-Fe -0.082ns -0.103ns

PY-Zn -0.077ns -0.002ns

Fe-Zn 0.590** 0.549**

32

S.No. Entry K part Pr. Wt. Fe (ppm) Fe

(mg/100 g) PW total Fe % Fe

1

ICGV 06099

Seed coat 3.6 93.69 9.37 33.73 14.01

Cotyledons 91.8 17.77 1.78 163.14 67.78 Embryo 4.6 95.25 9.52 43.81 18.2

2

ICGV 93468

Seed coat 3.3 73.94 7.39 24.4 13.64


3

ICGV 91114

Seed coat 3.6 133.49 13.35 48.06 21.2 Cotyledons 92.1 16.54 1.65 152.31 67.18

Embryo 4.3 61.3 6.13 26.36 11.63

4

ICGV 06040

Seed coat 4 73.42 7.34 29.37 12.66 Cotyledons 90.4 16.81 1.68 151.98 65.5

Embryo 5.6 90.5 9.05 50.68 21.84

5

ICGV 87141

Seed coat 4 52 5.2 20.8 13.24


Pr. Wt. Kernel part wise weight per 100 g

PW total Fe Part wise total iron

% Fe Percentage iron contribution

LOCALIZATION OF IRON IN KERNEL TISSUES

33

LOCALIZATION OF IRON IN KERNEL TISSUES

0

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80

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120

140

SC C E SC C E SC C E SC C E SC C E

Ke

rne

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pm

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Kernel tissue

Proportional contribution of each kernel tissue to the total iron concentration in the kernel

SC- Seed Coat C- Cotyledons E- Embryo

0

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Percentage contribution of each kernel tissue to the total iron concentration in the kernel

ICGV 06099 ICGV 93468 ICGV 91114 ICGV 06040 ICGV 87141


34

S.No. Entry K part Pr. Wt. Zn (ppm) Zn (mg/100 g) PW Zn % Zn

1

ICGV 06099

Seed coat 3.6 39.17 3.92 14.1 3.33

Cotyledons 91.8 37.7 3.77 346.08 81.61

Embryo 4.6 138.92 13.89 63.9 15.07

2

ICGV 93468

Seed coat 3.3 16.57 1.66 5.47 1.54

Cotyledons 92.4 33.81 3.38 312.43 87.92

Embryo 4.3 87.11 8.71 37.46 10.54

3

ICGV 91114

Seed coat 3.6 11.19 1.12 4.03 1.32

Cotyledons 92.1 28.84 2.88 265.66 86.75

Embryo 4.3 84.97 8.5 36.54 11.93

4

ICGV 06040

Seed coat 4 37.8 3.78 15.12 3.58

Cotyledons 90.4 37.1 3.71 335.35 79.4

Embryo 5.6 128.38 12.84 71.89 17.02

5

ICGV 87141

Seed coat 4 25.57 2.56 10.23 3.48

Cotyledons 91.7 27.24 2.72 249.78 84.99

Embryo 4.3 78.82 7.88 33.89 11.53

Pr. Wt. Kernel part wise weight per 100 g

PW total Zn Part wise total zinc

% Zn Percentage zinc contribution

LOCALIZATION OF ZINC IN KERNEL TISSUES

35

LOCALIZATION OF ZINC IN KERNEL TISSUES

0

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160


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Proportional contribution of each kernel tissue to the total zinc concentration in the kernel


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Ke

rne

l zi

nc

con

cen

trat

ion

(%

)

Kernel tissue

Percentage contribution of each kernel tissue to the total iron concentration in the kernel


SC- Seed Coat C- Cotyledons E- Embryo

• Additive effect was found influencing kernel iron and zinc concentrations along with additive × additive interaction in first cross. Thus selections in segregating generations would be effective in trait improvement.

• The gene interaction was considered to be of duplicate type for all the characters understudy, since the estimates of dominance (h) and dominance x dominance (l) had opposite signs.

• Kernel iron concentration had shown positive significant association with kernel zinc concentration, suggesting simultaneous improvement of Fe and Zn.

• Kernel iron and zinc concentrations have not shown any significant association with pod yield. Thus achieving high yielding varieties with higher kernel Fe and Zn is possible.

• Three markers viz., SEQ1B09 (0.23), IPAHM245 (2.19) and SEQ9G05 (6.34) showed significant association with the kernel iron concentration.

• Three markers viz., GM2638 (1.75), IPAHM245 (2.25) and SEQ9G05 (6.01) showed significant association with kernel zinc concentration.

• These markers were validated in another F2:3 population derived from cross ICGV 06040 × ICGV 87141 also showed the strong association with the trait of interest.

Summary and conclusions

• Genotyping with DArT is in progress which allows to construct linkage map and identification of linked markers for QTLs- deployment in breeding.

• Conclusions drawn from the gene effects are based on digenic interaction model and in two crosses. possibilities of trigenic or higher order interaction and or linkages among the interacting genes cannot be ruled out. Need for further study with more number of generations using crosses involving parents contrasting for the respective traits.

Future strategy

Acknowledgements

Groundnut Breeding Unit, ICRISAT Dr. P. Janila(Co-Chairperson) Dr. T V Murali Surendra Singh Manohar

Advisory committee Dr. K Radhika (Chairperson) Dr. VLN Reddy Dr. V Padma

Centre of Excellence in Genomics, ICRISAT Dr. Rajeev Varshney, Director (RPGL) Dr. Manish K Pandey Dr Manish K Viswakarma and Groundnut genomics team

Department of Genetics & Plant Breeding, CA, PJTSAU, R’Nagar CGIAR Research Programme (CRP), Grain Legumes, CRAL and BMGF Learning System Unit, ICRISAT Library and information services, ICRISAT Housing and food services, ICRISAT

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