A Tail of Two Flies: the Genetic Basis of Abdominal Pigmentation Differences Between Two Species of...

8/14/2019 A Tail of Two Flies: the Genetic Basis of Abdominal Pigmentation Differences Between Two Species of Drosophila

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THE EVOLUTION OF MORPHOLOGIC LDIVERSITY IN CLOSELY REL TED SPECIES OF

DROSOPHIL

Maria Margarita Womack

DISSERT TION PRESENTED TO THEF CULTY OF PRINCETON UNIVERISTY IN

C NDID CY FOR THE DEGREE OFDOCTOR OF PHILOSOPHY

RECOMMENDED FOR ACCEPTANCE BY THE DEPARTMENT OF ECOLOGY ANDEVOLUTIONARY BIOLOGY

Advisers: David L. Stern and Peter R. Grant

June 2009


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Copyright by Maria Margarita Womack, 2009. All rights reserved.


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There is a grandeur in this view of life, with its several powers, having been breathed

into a few forms or into one; and that, whilst this planet has gone circling on according tothe law of gravity, from so simple a beginning endless forms most beautiful and most

wonderful have been, and are being evolved

Charles Darwin, 1859, On the origin of species by means of natural selection, or, The preservation offavoured races in the struggle for life, J. Murray, London.

I am not trying to prove anything, by the way. Im a scientist, and I know what

constitutes a proof. But the reason I call myself by my childhood name is to remindmyself that a scientist must also be absolutely like a child. If he sees a thing, he mustsay that he sees it, whether he thought he was going to see it or not. See first, think

later, then test. But always see first. Otherwise you will only see what you were

expecting. Most scientists forget that.

Douglas Adams, 1996, So long and thanks for all the fish, in The Ultimate Hitchhikers Guide, Wings Books,NY.


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DISSERT TION BSTR CT

One of the central goals of evolutionary biology is to understand the processes

that underlie the generation and diversification of phenotypes. Identifying the genetic

changes underlying phenotypic differences between species is an essential component

in understanding how diversity is generated. Species generally vary in form, and such

variation often plays an essential role in adaptation to ecological conditions, sexual

selection, and many other relevant evolutionary processes.

Little information exists about the molecular genetic basis of complex

morphological traits as multiple genes, environmental conditions and interactions

between these two factors typically influence their expression making their study

particularly challenging. Yet to answer important standing theoretical questions about the

genetic basis of such traits, it is essential to streamline current methods to study

quantitative variation and expand the number of empirical studies identifying genes

underlying their variation.

I studied the genetic basis of complex morphological differences between closely

related species of Drosophilawithin the melanogaster species subgroup. I focused on

variation in eye size/shape between D. simulans and D. mauritiana, and abdominal

pigmentation between D. yakuba and D. santomea. In the case of eye size/shape I test

several methods to accurately quantify the variation between species and generate a

rough QTL map of the X-chromosome. In the case of abdominal pigmentation I

generated for the first time a high-resolution map of most of the genes involved in the

generation of differences in a quantitative trait between species. I show how through

repeated backcrossing coupled with selection it is possible to isolate each of the four

different QTLs affecting abdominal pigmentation in a common background. Three of

these QTLs produce discrete, traceable phenotypes in isolation thus making the study of


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individual genes considerably simpler. I narrowed all four QTLs to a fraction of their

original size, in one case to an interval 2 orders of magnitude smaller. Finally, I show

how this method lead to identification of a gene of previously unknown function that

affects abdominal pigmentation variation in Drosophila and is likely to be involved in the

evolution of abdominal pigmentation differences between D. yakuba and D. santomea.


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CKNOWLEDGMENTS

I owe many for successfully completing my PhD. First of all, to my families: both

my biological and academic families. The unconditional support and continuous

encouragement of my biological family, in particular my mother Sylvia Vegalara, has

been a decisive factor in fulfilling my dreams. I am also deeply grateful to my academic

family. In earlier stages of my career, Dr. Duncan Irschick and Dr. Terry Christenson

nurtured my passion for science and taught me the very basics of research. At

Princeton University for my PhD, I had great academic parents: Dr. David Stern and

Dr. Peter Grant (and by extension Dr. Rosemary Grant) have been generous guides

through my academic growth as a graduate student. The others members of my

committee, Dr. Leonid Kruglyak and during most of my time at Princeton Dr. Martin

Wikelski, were always accessible and offered great insights into my research. Also, as

an unofficial member of my committee, Dr. Enrico Coen support was instrumental to

my research. The various members of the Stern lab, my academic siblings, played a

significant role in my training. I am particularly indebted to Alistair McGregor, Virginie

Orgogozo, Tony Frankino, and Dayalan Srinivasan and Nicolas Frankel. In my

academic home while at Princeton, the Ecology and Evolutionary Biology department, I

enjoyed a large extended academic family to share my passion for scientific research.

The support of the administrative staff in EEB was also invaluable to sort out all the little

things of academic life. Furthermore, I am grateful to the larger Princeton University

community, where I was able to find everything I could need during these years: curing

strange malaises brought back from field trips, enjoying activities outside my field,

finding solace from the sometimes scabrous academic path. Finally, I am eternally

grateful to my husband Andy Womack, yet another gift from my time at Princeton, for his

continuous understanding, love and friendship.


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To all the mysteries of the world that stir our curiosity and allow us to rejoice in science.


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T BLE OF CONTENTS

Abst rac t

Acknow ledgements

Table of con tents

IntroductionThe study of morphological evolutionThe Genetic Basis of Evolutionary Change in ComplexTraitsDrosophilaas a Model OrganismDissertation OverviewReferences

Section I: Evolution Through The eye of a FlyChapter 1: A primer to the study of the genetic of eye sizeand shape variation in Drosophila

AbstractIntroductionMethodsResultsDiscussionReferences

Section II: A Tail of Two FliesChapter 2: Rapid, efficient di ssection of an interspecificquantitative trait into its underlying Mendelian factors in

DrosophilaAbstractIntroductionMethodsResultsDiscussionReferencesAppendix

Chapter 3:From QTL to gene: characterization andevolution of Truffle, a gene likely to be involved in theevolution of pigmentation di fferences in Drosophila

AbstractIntroductionMethodsResultsDiscussionReferences

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Overall Discussion, Conclus ions, and Future Research

Overall Discussion and Conclusions

Future Research

References

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INTRODU TION

An important challenge in evolutionary genetics is to map and examine the

genetic polymorphisms that lead to phenotypic diversity. Understanding how variation at

the genetic level affects variation at the phenotypic level is essential to elucidate general

patterns of evolution such as the genetic architecture of phenotypic change, the number

and types of changes at the nucleotide level that are necessary for generating variation

in a trait, or whether some genes or parts of genes are more likely than others to

generate phenotypic diversity. Few studies, however, have been able to point to a

specific gene, and even less often to specific nucleotide differences responsible for

variation in a phenotypic trait. This is particularly true in the case of complex traits,where only a handful of genes have been found (Glazier et al., 2002). Yet identifying and

determining the properties of the individual genes underlying variation in complex traits

is imperative to properly determine the molecular genetic basis of evolution (Mackay,

2001).

THE STUDY OF MORPHOLOGICAL EVOLUTION

Modifications in development, the link between genotype and phenotype,

generate phenotypic variation upon which natural selection can act (Brakefield et al.,

2003). The study of these processes falls within the realm of evolutionary development

or evo-devo, a field that strives to understand the mechanisms and laws underlying

morphological evolution (Gilbert & Burian, 2003; Stern, 2003). This is a comprehensive

field drawing from disciplines that had been largely independent until recently, such as

embryology, evolution, genetics, and phylogenetics (Gilbert & Burian, 2003; Carroll et

al., 2005). Most research in evo-devo has focused so far on trends and differences at a

macro scale (Stern, 2000b; Simpson, 2002) by comparing a small number of widely


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disparate species both from a phylogenetic and morphological point of view. While this

approach has generated a number of important concepts (Johnson & Porter, 2001;

Burke & Brown, 2003; Gilbert, 2003; Gilbert & Burian, 2003; Laubichler, 2003; Love,

2003; Vergara-Silva, 2003; Raff & Love, 2004), the generation of morphological

differences in natural populations is still poorly understood. To properly examine how

evolutionarily relevant genetic variants first arise it is necessary to focus on a small

evolutionary time-scale and study small phenotypic differences between closely related

species (Stern, 1998; Stern, 2000a; Stern, 2000b; Simpson, 2002). In this way, it is

possible to examine how small evolutionary changes might add up to give rise to larger

differences in gene function and activity.

In recent years, our general understanding of how morphology evolves has

progressed substantially, providing the first few insights on its general patterns and

slowly enabling evaluation of some of the standing theoretical hypotheses on the

principles of morphological evolution. One of the oldest debates concerns whether

evolution proceeds through the accumulation of many changes of small effect at multiple

loci or through a few large effect mutations (Stern, 2000b; Orr, 2005a; Orr, 2005b).

Though so far most studies suggest quantitative traits evolve through the accumulation

of a few mutations of large or moderate effect and several mutations of small effect (e.g.

Tanksley, 1993; Doebley et al., 1997; True et al., 1997; Zeng et al., 2000; Fishman et al.,

2002; Kerje et al., 2003), a recent study (McGregor et al., 2007) suggest other

intermediate mechanisms are possible, such as multiple mutations of small effect at a

single locus of overall large effect. A second debate concerns whether morphological

evolution occurs more often through changes in coding or cis-regulatory sequences

(Carroll, 2000; Hoekstra & Coyne, 2007). Empirical data suggests it might depend in part

on the term of divergence, so that between species (long-term evolution) the trend

favors cis-regulatory mutations, while within species (short-term evolution) coding


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mutations appear more frequent (Stern & Orgogozo, 2008; Stern & Orgogozo, 2009).

Many other factors that are likely to influence the distribution of evolutionarily relevant

mutations need to also be considered, such as pleoitropy, epistasis, population history,

plasticity and strength of selection. Therefore, further resolution of this problem will

require a fusion of molecular biology, development and population genetics (Stern &

Orgogozo, 2008; Stern & Orgogozo, 2009). There is also no consensus on the

importance of pleiotropy in evolution, particularly in the case of complex morphological

structures (Fisher, 1930; Turelli, 1985; Wagner & Altenberg, 1996; Welch & Waxman,

2003; Wagner et al., 2008). One view posits that there is a cost of complexity (Orr,

2000), so that modularity can increase evolvability, and various mathematical models

support this argument (Welch & Waxman, 2003; Otto, 2004). However, the sparse

empirical data suggests that evolution is not hindered by such costs, as naturally

occurring mutations affect a small number of traits and the magnitude of their effect does

not scale with pleiotropy (Wagner et al., 2008). Finally, it is possible that certain genes

are more likely than others to evolve between species to generate morphological

variation, because their position in developmental networks allows for reduced

pleiotropic and/or epistatic effects, thus making evolution predictable to a certain

degree (Rockman & Stern, 2008; Stern & Orgogozo, 2008; Stern & Orgogozo, 2009).

Various studies showing examples of parallel evolution of the same trait in different

populations and species support this hypothesis (ffrench-Constant et al., 1998; Sucena

et al., 2003; Colosimo et al., 2005; Hoekstra, 2006; Protas et al., 2006). Further studies

on the genetic basis of morphological traits will gradually generate the necessary

empirical data to further resolve these issues and thus define general laws of

morphological evolution.


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THE GENETIC BASIS OF EVOLUTIONARY CHANGES IN COMPLEX TRAITS

Morphological variation is most often quantitative rather than qualitative,

presenting a continuous distribution rather than falling into discrete categories (Falconer

& Mackay, 1996; Liu, 1998; Griffiths et al., 2005). Medelian traits are usually the result of

a single mutation of large but discrete effect at a single locus (Hartl, 2004). At the other

end of the spectrum, quantitative traits are affected by multiple genes, often of small

effect, that can interact in complex ways (Falconer & Mackay, 1996; Christians &

Keightley, 2002; Erickson et al., 2004; Erickson, 2005). In addition, genes determining

complex traits often interact with the environment so that a given genotype does not

present a single phenotype but rather a norm of reaction: a pattern of expression

according to an environmental variable (Falconer & Mackay, 1996; Griffiths et al., 2005).

Non-additive effects and genotype-environment interactions add an extra level of

complexity to the relationship between genotype and phenotype making the dissection of

the genetics of quantitative traits a formidable task (Lynch, 1998; Mackay, 2001).

Various aspects of the genetics of quantitative traits have been subject to

extensive theoretical modeling (e.g. Barton & Turelli, 1987; Zeng et al., 1999; Otto &

Jones, 2000; Barton & Keightley, 2002; Barton & Turelli, 2004; Blows & Hoffmann, 2005;

Johnson & Barton, 2005). Quantitative trait locus (QTL) analysis is a method often used

to assess the genetic basis of quantitative traits. QTLs, regions of the genome

contributing to complex traits, can be identified through a combination of linkage

mapping and quantitative genetic analysis (Lander & Botstein, 1989; Lynch, 1998). A

QTL is identified based on the association of the trait value with visible or molecular

polymorphic markers of known location on the genome (Falconer & Mackay, 1996; Liu,

1998; Doerge, 2002; Griffiths et al., 2005). Using this technique it is possible to infer the

location of QTLs and the magnitude of their effect. QTL mapping has been used in an


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extensive number of studies seeking to understand the genetic basis of a variety of

phenomena, including adaptation, human disease, and crop productivity (Falconer &

Mackay, 1996). However, QTL analysis usually does not provide enough resolution to

identify the actual genes involved. Yet to successfully determine the molecular genetic

basis of morphological evolution, it is necessary to identify and determine the properties

of the individual genes underlying variation in complex traits.

Few examples exist of genes and their relevant sequence variants that are

partially responsible for variation in a complex trait (Glazier et al., 2002). In addition, the

best-studied examples involve variation within species, such a bristle pattern variation in

Drosophila (Mackay, 1996; Bourouis et al., 1997; Gurganus et al., 1999; Dilda &

Mackay, 2002; Robin et al., 2002; Westerbergh & Doebley, 2002; Macdonald & Long,

2004; Gibert et al., 2005; Mackay & Lyman, 2005), characteristics of domesticated plant

varieties (Paterson et al., 1995; Doebley et al., 1997; Grandillo et al., 1999; Frary et al.,

2000; Fridman et al., 2002; Tanksley, 2004), or complex variation in mice (Flint & Mott,

2001; Nadeau, 2001; Hoekstra & Nachman, 2003; Christians et al., 2004; Darvasi, 2005;

Arbilly et al., 2006; Darvasi, 2006). Understanding open questions such as the

relationship between intra- and interspecies genetic variation, the role of evolutionary

time scale, or the importance of the strength of selection will also require dissecting the

genetic basis of complex morphological differences between species.

DROSOPHILA AS A MODEL ORGANISM

For nearly a century, research on Drosophilahas generated some of the most

important insights into genetics and other branches of biology. Other than the reasons

that make Drosophila an amenable study organism, such as ease of culture, short life

cycle and low cost, the wealth of molecular, genomic and technological tools make

Drosophila a powerful model for the study of evolutionary genetics (Letsou & Bohmann,


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2005). In addition, recent findings of evolutionary development suggest that broad

generalizations can be made from conclusions drawn from studies on model organisms

such as Drosophila (Carroll, 1995; Burke & Brown, 2003; Carroll et al., 2005; Mackay &

Anholt, 2006). Thus, findings from this study might be relevant for understanding the

evolution of complex traits in other taxa, such as humans (Mackay, 2001; Burke &

Brown, 2003; Carroll, 2003; Mackay & Anholt, 2006).

Species closely related to D. melanogaster

are a suitable system for the study of morphological

diversification. The melanogasterspecies

subgroup is composed of 9 species of Afrotropical

origin (Lachaise et al., 1988) (Figure 1). Many

parallels exist between the pairs composed by D.

simulans and D. mauritiana, and D. santomea and

D. yakuba. A member of each pair is widely

distributed while the second one is restricted to an

island. D. simulansand D. yakuba exist throughout

most of Africa, with D. simulans having also expanded to many other parts of the world

(Lachaise et al., 1988; Lachaise & Silvain, 2004). D. mauritianaand D. santomea are

restricted to islands off the coast of Africa, the first to the island of Mauritius (Indian

Ocean, East Africa) (David et al., 1974; Lachaise & Silvain, 2004) and the latter to the

island of Sao Tome (West Africa) (Lachaise et al., 2000). Bothdiverged only about 400

thousand years ago (Kliman et al., 2000; Cariou et al., 2001) and differ in various

aspects of their morphology, physiology, and behavior. Each species within the pair is

the closest known relative of the other. Although the resolution of the node between D.

simulans, D. sechelliaand D. mauritiana is controversial because nuclear and mtDNA

yield different relationships (Tsakas & Tsacas, 1984; Solignac & Monnerot, 1986; Hey &


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Kliman, 1993; Harr et al., 1998; Kliman et al., 2000), it is generally accepted that the split

between D. simulans and D. mauritiana is more recent (Harr et al., 1998). In the case of

D. yakuba and D. santomea, all phylogenetic evidence consistently groups them as

sister species (Lachaise et al., 2000; Cariou et al., 2001). As the species in both pairs

are so closely related, they are very similar at the genetic level, which facilitates the

identification of genetic variation that influences phenotypic differences between them.

Crosses between the species in each pair produce fertile females and sterile males

(Lachaise et al., 1988; Lachaise et al., 2000; Cariou et al., 2001), so that genetic

changes responsible for a particular morphological difference can be mapped by

backcross designs (Stern, 1998; Stern, 2000a; Stern, 2000b; Simpson, 2002). Finally,

the close relationship of all four species to D. melanogaster enables access to a large

number of tools for genetic and functional analysis, particularly useful in the dissection of

the genetic basis of morphological diversity.

DISSERTATION OVERVIEW

In the next three chapters, I characterize morphological differences between

closely related species of Drosophilaand examine the genetic changes responsible for

their evolution. In chapter one, I examine eye size/shape differences betweenD.

simulans and D. mauritiana. D. mauritiana has larger, differently shaped eyes than D.

simulans (Figure 2A).However, a qualitative description is not informative enough to

study variation, as it is necessary to assign each individual a numerical value. As with

many quantitative traits, transitioning to a quantitative description for eye size/shape

cannot be done with standard methods. I quantified eye size/shape variation between

species using both simple linear methods and multivariate analysis. For the latter, I

used software designed by the research group of Dr. Enrico Coen (John Innes Centre,

UK) and collaboratively customized it for the quantification of Drosophila morphology. I


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many tools for functional and genetic analysis in D. melanogaster, I showed through

quantitative methods that only one of these loci appears to be involved in abdominal

pigmentation development. This gene of previously unknown function that I will name

truffle (CG6353) is likely to partly explain the loss of abdominal pigmentation in D.

santomea. I also carried out an evolutionary analysis of this gene to show how it is highly

conserved across cellular organisms, thus suggesting it might play an essential role

across many taxa. Also, I discussed how there are no coding differences at this locus

between D. yakuba and D. santomea, so that any differences in the role of trufflewould

be most likely the result of cis-regulatory differences between these species.


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SECTION I

EVOLUTION THROUGH THE EYE OF A FLY


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CHAPTER

A PRIMER TO THE STUDY OF THE GENETICS OF EYE S IZE ANDSHAPE VARIATION IN DROSOPHIL


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ABSTRACT

To generate a comprehensive understanding of the mechanisms underlying

morphological evolution we need to characterize and identify genetic changes

responsible for phenotypic variation. The eye is a complex morphological structure that

has diversified into a large array of types to fit the lifestyle of its bearer. Within the

melanogasterspecies subgroup, Drosophila mauritianahas larger eyes (about 30%

more ommatidia) than its sibling species Drosophilasimulans. Here I present two

different approaches to quantifying variation in eye size and shape and rough

quantitative trait locus (QTL) maps for the X chromosome. First, a preliminary analysis

using simple linear measures revealed that the eyes of D. mauritiana are longer and

wider than the eyes of D. simulans and that this difference is particularly pronounced in

the males. In a small backcross population eye width mapped to the same location as in

a previous publication mapping ommatidia number differences between these species.

Second, using a MatLab based software I quantified eye size and shape variation

applying principal component analysis in two large backcross populations. Five different

models were built using these data and the resulting PC values were used as

phenotypes in QTL mapping. In all models a large percent of the variation is due to

photography artifacts or digitizing error. Most of the remaining PCs within the 95%

variation affect eye size and shape and map to the X chromosome. While both

phenotyping methods yield a significant association with markers on the X chromosome,

both have caveats that should be considered in further mapping. Identifying the genetic

basis of morphological differences between closely related taxa might help us to better

understand patterns of morphological evolution. Such studies are likely to pinpoint

important mechanisms generating variation in morphological characters from a

conserved set of genes.


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INTRODUCTION

A large fraction of morphological diversity both within and between species

involves variation in size and shape. Despite their prevalence, surprisingly little is known

about the genetic basis of variation in these characters (Liu et al., 1996; Laurie et al.,

1997; Zeng et al., 2000; Zimmerman et al., 2000; Klingenberg & Leamy, 2001;

Klingenberg, 2002; Albertson et al., 2003; Tanksley, 2004; Albertson & Kocher, 2006).

This can be explained in part by the difficulty of devising and adequate yet economical

method to quantify the phenotype, particularly in the case of shape (Liu et al., 1996;

Coen et al., 2004). Yet elucidating the number, nature and effect of the genes

underlying such diversity will lead to important insights into the mechanisms of

evolutionary processes driving the generation and diversification of complex phenotypes.

In this chapter I characterize differences in eye size

and shape between the sister species Drosophila simulans

and Drosophila mauritiana and generate a rough QTL map

of the X chromosome. The mapping of genetic factors

responsible for morphological evolution is particularly

successful in closely related species that can still be

hybridized (Sucena & Stern, 2000; Zeng et al., 2000) such

as D. simulansand D. mauritiana. D. mauritianahas

approximately 30% more ommatidia than Drosophila

simulansand D. melanogaster (994 vs 726 ommatidia on

average) (Hammerle & Ferrus, 2003) thus subtly modifying

its overall size and shape.Also, in Drosophila eye development has been extensively

studied so that entire pathways and their mechanisms are known facilitating the study of

how such processes might be modified to generate variation in size and shape. In

addition, eye characteristics are clearly adaptive features: eye morphology and


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physiology generally correlate with specific niche demands (Land & Fernald, 1992;

Jonson et al., 1998; Land et al., 1999; Land, 2002). So the differences in eye size and

shapebetween Drosophila species are likely to have evolved through natural selection.

Table 1:Summary of morphological measurements for the species studied and their hybrids. A

total of 211 individuals were included.

Size and shape variation between species is generally polygenic and as such it is

usually studied through quantitative trait locus (QTL) analysis (e.g. Liu et al., 1996; True

et al., 1997; Bradshaw et al., 1998; Grandillo et al., 1999; Zeng et al., 2000; Klingenberg

& Leamy, 2001; Albertson et al., 2005; Long et al., 2006; Bergland et al., 2008). The

generation of a QTL map is frequently followed by higher resolution mapping

approaches [for example,

marker-assisted meiotic

recombination mapping (e.g.

Orgogozo et al., 2006), nearly

isogenic lines (NILs) (e.g.

Frary et al., 2000), deletion

mapping (Presgraves, 2003), germline transformation (e.g. Jeong et al., 2008)] to

identify individual genes and nucleotide changes within them responsible for phenotypic

change.

n Wing Size SE Average EyeLength SE

Average EyeWidth SE

Distance betweenEyes SE

D. mauritianafemales 15 676.70 7.03 278.79 5.89 135.88 2.54 180.80 4.23

D. mauritianamales 29 600.33 3.75 276.71 2.60 138.97 2.9 161.91 2.34

D. simulansfemales 15 720.93 6.27 266.67 4.41 112.81 1.50 200.90 6.23

D. simulansmales 30 610.89 5.51 215.63 7.36 112.62 2.91 174.22 2.43

Backcross to D. mauritiana 75 698.00 3.42 270.46 2.15 135.55 1.41 178.12 1.65

Backcross to D. simulans 41 705.30 4.33 272.25 2.15 133.76 1.27 179.32 2.21


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An important first consideration for the genetic mapping of size and shape variation

is how to quantify the trait. Proper measurement is essential for both the accuracy and

resolution of the QTL map and later for detecting individual genes in high-resolution

mapping. One obvious option in a regular lattice such as the eye is to count ommatidia

and assess their distribution, but doing so in the large sample size needed for QTL

mapping makes it unpractical. A second option is

to use simple measurements such as width or

length (e.g. Tanksley, 2004), and though easily

captured, it is likely some of the intricate variation

in a complex character such as eye size/shape

might be missed. A third method, used

successfully to quantify and map the genetic

effects of shape variation in other complex

morphological structures involves geometric

morphometrics (Zimmerman et al., 2000;

Klingenberg et al., 2001) or elliptic Fourrier

analysis (Rohlf & Archie, 1984; Liu et al., 1996; Zeng et al., 2000). Though such

analyses generate richer data sets and more detailed quantifications of shape variation,

the large volume of data and correlation between different factors does not identify the

best variable(s) for mapping. However, a combination of such shape analyses and

principal component analysis (PCA) facilitates the process by reducing a large set of

correlated variables into independent axes of variation while identifying the major

sources of variation.

Here I use both simple linear measurements and PCA of coordinates outlining

the eye to quantify variation in eye size and shape in D. simulans, D. mauritiana, and

their hybrids to determine what is the best method to use in QTL analysis. Eye width


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appears to be sufficient to generate a significant association with visible markers on the

X, but photography artifacts generate a large error that could hinder high-resolution

mapping. PCA reveals that the variation between the species variation in eye

size/shape is much richer than simple linear measurements captured and can easily

correct for photography artifacts. Most PCs describing eye size/shape variation map to

one of markers on the X. However, it is not clear what principal component should be

used for further mapping as no PC discriminates fully between D. simulansand D.

mauritiana and the basic map for any PC is not consistent in the reciprocal backcrosses.

Table 2:Scaling relationships for both males and females of the three head/eye variables

(based on wing length as the independent variable).

MATERIALS AND METHODS

DROSOPHILA STRAINS

Initial basic analysis

For the scaling analyses, I used stock number 14021-0251.146 for D. simulans (y, v, f

bb, Drosophila Species Stock Center), and stock 14021-0241.01 for D. mauritiana

(DrosophilaSpecies Stock Center).

Formal multidimensional analysis

I used stock number 14021-0251.147 for D. simulans (y, v, f bb, DrosophilaSpecies

Stock Center), and stock G105 D. mauritiana obtained from C. I. Wu. To look at

variability in eye size and shape within the melanogasterspecies subgroup, I also used

Species Dependent slope SE r P

D. mauritiana Distance Between Eyes 0.25 0.05 0.42


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stock number 14021-0261.00(Drosophila Species Stock Center) forD. yakuba, stock

number 14021-0271.00 (Drosophila Species Stock Center) forD. santomea, stock

number 14021-0248.07 for D. sechellia (Drosophila Species Stock Center), and a wild

isolate for D. melanogaster collected in Beltsville, MD part of the Stern lab collection. All

flies were maintained on standard media enriched with live yeast in an incubator at

25C.

CROSSES

To generate a backcross population for QTL analysis, virgin D. simulans femaleswere

crossed in groups of 6-10 to twice as many D. mauritianamales. F1 females were then

crossed in groups of 6-10 to either twice as many D. simulans males or D. mauritiana

males.

PHENOTYPING

Basic preliminary analysis

Linear measurements of general morphology: I examined variation in wing length as a

proxy for body size, eye width, length and distance between the eyes in D. simulans,D.

mauritiana and their hybrids (Figure 1). The analysis was performed on wing and rostral


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pictures of the severed head taken with a digital camera (Photometrics Coolsnap cf)

connected to a Nikon E 1000 microscope at 40x. Linear measurements were obtained

using the software tpsDIG version XX (F. J. Rohlf, available online at

http://life.bio.sunysb.edu/morph/soft-dataacq.html).

Statistical analyses: Allometric relationships between body size, head and eye size were

examined using linear regressions (linear least-squares) after log10transforming all data

as it is standard in scaling studies. To control for body size and thus compare variation

in head and eye shape between species I used the residuals of these linear regressions.

All statistical analyses were conducted on SYSTAT (version 10, SPSS 2000).

Formal multidimensional analysis

Measurements: To be able to capture the overall variation in eye size and shape in D.

simulans and D. mauritianaI implemented a MatLab based software, the AAM

Toollbox package, designed to build and visualize statistical models of shape and

appearance using principal component analysis (available at

http://www2.cmp.uea.ac.uk/~aih/). This software allows distinguishing biological relevant

variation from non-biological variation resulting from positional or photographical effects

common when working with live animals as in this case. Up to five individual flies at a


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time were positioned on their side on small depressions carved on 4% 5mm x 3mm x

30mm apple juice/agar strips while anesthesized with CO2. I then captured an image of

rostral view of the head with a digital camera (Photometrics Coolsnap cf) connected to a

Nikon E 1000 microscope at 40x. The final image is the result of the combination (3D

extended focus) of snapshots taken every 5 microns by programming the Z-stage with

the help of IPlab software (version 3.9.4 r2), so that the whole head appears in focus.

Light conditions were kept constant with a ring light attached to the microscopes

objective. To quantify the variation in eye size and shape, each image was digitized

using the MatLab based software described above. I first created a point model template

(Figure 2) using the Point Model Editor in the AAM ToolBox package. The 42 points

capture the overall size and shape variation in the head and eyes. On the basis of these

templates, corresponding points were placed on the images of individual flies of interest.

Statistical analyses: I generated statistical models of size and shape using principal

components analysis (Stats Model Generator in the AAM Toolbox). These models are

based on the variation after alignment of the multi-dimensional coordinates of the points

from the point model. The resulting principal components (PCs) can be visualized as


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videos showing the variation across their axis (2 standard deviations) (Figure 3). I

conducted six different analyses:

1) Backcross to D. simulans: in this model I included three groups, male D.

simulans (n=50), male D. mauritiana (n=50), and male hybrids resulting from the

backcross of F1 females to D. simulans (n=430). The statistical model was

based solely on the hybrids, while the parentals were simply projected onto this

multi-dimensional space.

2) Scaled backcross to D. simulans: the model includes the same groups as

analysis (1), but this time a procrustes correction for size was included thus

eliminating the effect of body size.

3) Backcross to D. mauritiana: in this model I included three groups, male D.

simulans (n=50), male D. mauritiana (n=50), and male hybrids resulting from the

backcross of F1 females to D. mauritiana (n=430). The statistical model was

based solely on the hybrids, while the parentals were simply projected onto this

multi-dimensional space.


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4) Scaled backcross to D. mauritiana the model includes the same groups as

analysis (3), but this time a procrustes correction for size was included thus

eliminating the effect of body size.

5) A combination of both backcross populations: in this model I included four

groups, male D. simulans (n=50), male D. mauritiana (n=50), male hybrids

resulting from the backcross of F1 females to D. simulans (n=430), and male

hybrids resulting from the backcross of F1 females to D. mauritiana (n=430). The

statistical model was based solely on the hybrids, while the parentals were

simply projected onto this multi-dimensional space.

6) Variation of eye size and shape in the melanogasterspecies subgroup: in this

model I analyzed variation across males of 6 different species, D. melanogaster

(n=5), D. simulans (n=20), D. mauritiana (n=20), D. sechellia (n=20), D. yakuba

(n=20), and D. santomea (n=20). All 6 groups were used to generate the

statistical model.

All figures for these analyses were made with MatLab 7.0.2. (MathWorks).

GENOTYPING

Only the four visible markers (y, v, f for the basic analysis and y, v, f, bb for the formal

analysis) on the X chromosome from the D. simulans parental strains were used for

genotyping. However, phenotyped backcross individuals from the formal

multidimensional analysis were kept at -20C for further genotyping in the future.

QTLANALYSIS

Basic preliminary analysis: using QTL cartographer (version 1.17), I mapped eye width

variation based on the segregation on 3 visible markers on the X chromosome (y, v, f).

The analysis was performed on small backcross populations (backcross to D. simulans

n=42, backcross to D. mauritianan=45).


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Table 3:Scaling relationships between head and eye variables (corrected for body size) in

males of D. simulans and D. mauritiana.

Formal

multidimensional analysis: composite interval mapping was performed using R/qtl

(Broman et al., 2003) and was based on the segregation on 4 visible markers on the X

chromosome (y, v, f, bb). The most relevant principal components were used as

phenotypes. Statistical significance was calculated using permutation analysis (Churchill

& Doerge, 1994). I carried out a separate QTL mapping for each of the statistical

models 1 through 5 described above. For analysis 5, though the PCs were generated by

combining both backcross populations, the QTL map was performed on each population

separately.

Species Independent Dependent Slope SE r PD. mauritiana Eye Length Eye Width 0.54 0.18 0.34


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RESULTS

BASIC PRELIMINARY ANALYSIS OF EYE SIZE AND SHAPE VARIATION BETWEEN D. SIMULANS

AND D. MAURITIANA

I found differences in body size associated with species and sex: D. simulans is

in general larger than D. mauritiana; also, as is the

general case in Drosophila, the strains studied show

reverse sexual size dimorphism. All variables, but

eye width, follow theses trends suggesting a strong

association with body size (Table 1). Scaling

analyses show indeed a significant association of

body size with eye length and distance between the

eyes but not eye width in both species (Table 2).

The relationship in both cases is hypoallometric

(Table 2). Correcting for body size, D. mauritiana

has the longest and widest eyes in both sexes. D.

mauritianafemales have eyes approximately 12% longer and wider than D. simulans

females, while the difference between the males is approximately 50%. Also, eye size

is a dimorphic trait in D. mauritiana: the eyes of males are 12% longer and 15% wider

than those of females, while in D. simulans the eyes of the two sexes are not

significantly different. There are no strong relationships between the three variables for

head and eye shape consistent between the two species (Table 3). Yet in D. mauritiana,

eye length and eye width are correlated, while in D. simulans eye length and distance

between the eyes are correlated (Table 3). A QTL analysis on eye width yielded a

significant result for the marker forked on the backcross to D. mauritiana but not D.

simulans (Table 4).


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Table 4:results from a basic QTL analysis of eye width on the backcross to D. mauritiana

(males only, n=45).

Marker b0 b1 2ln(L0/L1) F(1,n-2) P

y 131.401 -4.096 1.277 1.238 0.272v 131.102 -6.603 3.042 3.007 0.09

f 128.843 -13.444 21.575 26.452 >0.001

MULTI-DIMENSIONAL ANALYSIS OF EYE SIZE AND SHAPE VARIATION BETWEEN D. SIMULANS

AND D. MAURITIANA

Individual models:

1) Backcross to D. simulans

PCs 1-9 represent 95% of the total variation in the model. Approximately 66.8% of the

variation, primarily represented by 3 PCs (1,2 and 4), was considered the result of

photography artifacts or digitizing error (Table 5). The 6 remaining PCs clearly affect eye

size and shape and map to one of the four markers on the X chromosome used in the

QTL analysis (Table 5). Three of these PCs (3, 5 and 6) explain 79.1% of the biological

variation in the model (Figure 4). All three show significant overlap between the parental

species even though the means for each are in some cases more than 2 standard

deviations (SD) apart (PC3: 1.5 SD, PC5: 3SD, PC6: 0.7 SD), and even though these

PCs explain most of the difference in terms of Eucledian distance between D. simulans

and D. mauritiana (Table 5). Only by combining the three PCs (Figure 4D) it is possible

to obtain a good, but not complete, resolution between the species. In PC3 and 5, the

backcross population presents transgressive variation; i.e. the range of the data is larger

than in either parental species (Figure 4). Except for PC3, the mean of the backcross

population is closer to the mean of D. simulans than to the mean of D. mauritiana as

expected (Figure 4). PC3 is correlated with body size (using wing length as a proxy,

Figure 1) (n = 25, r2= 0.2, P


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2) Backcross to D. simulans (corrected for body size)

PCs 1-10 represent 95% of the total variation in the model. Approximately 86% of the

variation, primarily represented by 3 PCs (1,2 and 3), was considered the result of

photography artifacts or digitizing error (Table 6). All of the remaining PCs clearly affect

eye size and shape (Table 6). Within these relevant PCs, 6 (86%) map to one of the

four markers on the X chromosome used in the QTL analysis (Table 6). Three of these

PCs (4, 5 and 6) explain 53.6% of the biological variation in the model (Figure 5). All

three show almost complete overlap between the parental species even though the

means for each are in some cases more than 2SD apart (PC4: 2 SD, PC5: 1.2SD, PC6:

3 SD), and even though these PCs explain most of the difference in terms of Eucledian

distance between D. simulans and D. mauritiana (Table 6). Only by combining the three

PCs (Figure 5D) it is possible to obtain a good, but not complete, resolution between the

species. In PC4 and 6, the backcross population presents transgressive variation.

Except for PC4, the mean of the backcross population is closer to the mean of D.

simulans than to the mean of D. mauritiana as expected.


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Table 5:Summary of PCA results and QTL analysis for the backcross to D. simulans. PC 1-9 explain 95% of the total variation. Together, the

PCs presenting significant LOD valuesexplain 84.6% of the biological variation, and 91% of the Eucledian distance between the mean D.

mauritiana and D. simulansin multidimensional space. Note however that most of the biological variance is due to PC3, and most of the

Eucledian distance between the parentals is accounted by PC 3 and 5.

LOD3scores

PC EffectTot. Variance

explained (%)

Biological variance explained

(%)1

Eucledian distance between parental species

explained (%)2 y v f bb

1 rotation back-front 38.7 0 10

2 rotation left-right 24 0 3

3 overall eye size/bulginess 22.2 66.5 35 4.27

4 asymmetry 4.1 0 0

5 eye top-tier shape 2.5 7.5 30 1.08 4 2.43

6 eye shape/width 1.7 5.1 4 3.42

7 eye shape 0.9 2.7 7 1.56

8 eye shape 0.6 1.8 8 2.3 2.3

9 eye shape 0.3 1 0 1.13 4.42 3.33

other various 5 5.4 3

1After eliminating PCs that are clearly an artifact of the photography; i.e. indicating variation in the positioning of the head or rotation or digitizing error; i.e. pints

moving in discordance generating asymmetry or unrealistic deformities.

2The distance moved on each PC to move from the mean of D. simulans to the mean D. mauritiana in multidimensional space.

3Significance threshold = 1 (based on permutation analysis, Churchill and Doerge, 1994)


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3) Backcross to D. mauritiana:


variation, primarily represented by 5 PCs (1, 3 and 5-7), was considered the result of

photography artifacts or digitizing error (Table 7). The three remaining PCs (2, 4 and 8)

clearly affect eye size and shape and map to one of the four markers on the X

chromosome used in the QTL analysis (Table 7). These PCs explain 88.4% of the

biological variation in the model (Figure 6). All three complete overlap between the

parental species (Figure 6), even though these 3 PCs account for 75% of the Eucledian

distance between D. simulans and D. mauritiana (Table 7). In all three cases, the mean

of the parental species is very close to the mean of the backcross population (0 in all

cases as the PCA is based on this population). Even combining the three PCs (Figure

6D) it is not possible to obtain a good resolution between the species. In all three PCs,

the backcross population presents some degree of transgressive variation (Figure 6).

PC2 is correlated with body size (using wing length as a proxy, Figure 1) (n = 25, r2=

0.6, P


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Table 6:Summary of PCA results corrected for body size and QTL analysis for the backcross to D. simulans. PC 1-10 explain 95% of the total

variation. Together, the PCs presenting significant LOD values explain 82% of the biological variation, and 66.4% of the Eucledian distance

between the mean D. mauritiana and D. simulansin multidimensional space. Note however that most of the biological variance is due to PC4 and

5, and most of the Eucledian distance between the parentals is accounted by PC 4 and 6.

LOD3scores

PC EffectTot. Variance

explained (%)

Biological variance

explained (%)1

Eucledian distance between parental

species explained (%)2 y v f bb


2 rotation left-right 31.4 0 5.8

3 asymmetry 5.5 0 0

4 eye top-tier shape/forehead width 4.2 30 42.2 3.36 1.94

5 eye width 2.1 15 6 3.61

6 eye roundness 1.2 8.6 12.5 1.87

7 eye shape 0.8 5.7 4.8 2.00 2.14

8 eye shape 0.5 3.6 0.2 2.05 1.49

9 eye shape 0.2 1.4 0.3 2.79

10 eye shape


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(Figure 7D) the two parental species fall in distinct, barely overlapping clouds, while the

backcross to D. mauritiana encapsulates most of the range of D. mauritiana. In all three

PCs however, the backcross population presents transgressive variation. These three

PCs explain together only 37.4 % of the difference in terms of Eucledian distance

between D. simulans and D. mauritiana, while the 3 PCs (1, 2 and 4), considered the

result of photography artifacts or digitizing error explain 57.5% of the Eucledian distance

between the parental species in this model (Table 8).

5) A combination of both backcross populations:


variation, primarily represented by 4 PCs (1, 2, 4 and 9), was considered the result of

photography artifacts or digitizing error (Table 9). All of the remaining PCs clearly affect

eye size and shape. Within these relevant PCs, 4 (80%) map to one of the four markers

on the X chromosome used in the QTL analysis (Table 9). One of these PCs map in

one backcross but not the other or just marginally (3 and 8), and in the remaining there


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Table 7:Summary of PCA results and rough QTL analysis for the backcross to D. mauritiana.PC 1-8 explain 95% of the total variation. Together,

the PCs presenting significant LOD values explain 98.2% of the biological variation, and 77.3% of the Eucledian distance between the mean D.

mauritiana and D. simulansin multidimensional space. Note however that most of the biological variance is due to PC2, and most of the


LOD3scores

PC EffectTot. varianceexplained (%)

Biological varianceexplained (%)

1

Euclidean distance between parentalspecies explained (%)

2 y v f bb

1 rotation back-front 38 0 4

2 overall head and eye size/eye to-tier shape 33 77.8 38 3.94

3 rotation left-right 15.1 0 5 1.42

4 eye top-tier width/forehead width 3.6 8.5 12 1.27

5 asymmetry 2.1 0 2

6 asymmetry 1.4 0 3

7 asymmetry 1 0 5 1.17

8 eye roundness 0.9 2.1 25 1.29 2.04

other various 5 11.6 6

1After eliminating PCs that are clearly an artifact of the photography; i.e. indicating variation in the positioning of the head or rotation or digitizing error ; i.e. pints moving in discordance

generating asymmetry or unrealistic deformities.


3Significance threshold = 1(based on permutation analysis, Churchill and Doerge, 1994).


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is often a disagreement in the markers showing a significant LOD (Table 9). Three of

the PCs mapping in both populations (3, 5 and 6) explain most of the biological variation

and Eucledian distance between the parental species in the model (Figure 8). All three

present a significant overlap between the parental species despite the means for each

being in some cases more than 2SD apart (PC 3: 1.7 SD, PC5: 3.2 SD, PC 6: 1.5 SD),

and despite of these PCs explaining most of the difference in terms of Eucledian

distance between D. simulans and D. mauritiana (Table 9). Combining the three PCs

(Figure 8D) it is possible to obtain a complete resolution between the species. The

average shape for D. simulans and D. mauritiana when considering only the combination

of these three PCs is a good representation of the main size and shape differences

between these two species (Figure 9).The backcross populations present transgressive

variation in most instances (except for the backcross to D. simulans in PC5), though in

general the backcross to D. mauritiana presents higher variation than any of the other

groups (Figure 8). In all three cases, the mean of the each backcross population is

closer to the mean of the respective parental species as expected. For comparison,

plots of the three PCs explaining most non-biological variation show both parental

species and the two backcross populations with practically indistinct mean and ranges

(Figure 10) so that a in 3D plot the clouds overlap completely. All four groups show

similar cloud shapes, while the backcross to D. mauritiana has the highest variation and

thus encapsulates the other three groups (Figure 10D).

5) Variation of eye size and shape in the melanogasterspecies subgroup

PCs 1-10 represent 95% of the total variation in the model (Table 10). Approximately

71.7% of the variation (PCs 1, 2,5 and 10) was considered the result of photography

artifacts or digitizing error (Table 10). All of the remaining PCs affect head and eye size

and shape (Table 10). Three of these PCs (3, 4 and 6) explain 65.1% of the biological

variation in the model and are sufficient to discriminate between the species (Figure 11).


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Each species mean shape shows that each has subtle differences in eye size and shape

(Figure 12).

DISCUSSION

The eye in Drosophila is a complex three-dimensional morphological structure

whose shape and size variation are not easy to capture in an efficient yet detailed

manner for large sample sizes. A preliminary analysis using simple linear

measurements revealed several differences in the size and shape of the eyes in D.

simulansand D. mauritiana, especially between the males. Variation in how the different

head and eye shape variable correlate in the two species suggest the overall shape and

scaling is different between the two species. This analysis also revealed some common

trends in both species. Length and width are hypoallometric (regression slope < 1) in

both D. simulansand D. mauritiana, consistent with other studies on eye morphology in

other fly species (Stevenson et al., 1995; Blanckenhorn & Llaurens, 2005). However, it

is important to consider that the expected isometric slope for eye width is not necessarily

1. The simple measurement in this analysis ignores the 3D nature of the structure and

the possible changes in curvature.

Through the formal multi-dimensional analyses it is possible to further describe

and quantify how the eyes of D. simulansand D. mauritiana differ in shape while

correcting for certain types of error. All 5 models on D. simulans, D. mauritiana and their

hybrids suggest variation in the shape of the dorsal, top-tier part of the eye and

maximum eye width are major axis of variation in eye shape between these species.

Consistently, more than 60% of the variation in the various multidimensional analyses

was the result of photography artifacts or digitizing error. Elimination of such PCs

mathematically corrects for this type of error, as PCs are orthogonal by definition. In

general, the remaining PCs (within 95% of the total variation in the model) clearly define


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Table 8:Summary of PCA results corrected for body size and QTL mapping for the backcross to D. mauritianaPC 1-9 explain 95% of the total

variation. Together, the PCs presenting significant LOD values explain 93.1% of the biological variation, and 40.4% of the Eucledian distance

between the mean D. mauritiana and D. simulansin multidimensional space. Note however that most of the biological variance is due to PC3,

and most of the Eucledian distance between the parentals is accounted by PC3, 6 and 7.

LOD3scores

PC EffectTotal varianceexplained (%)

Biological varianceexplained (%)

1

Euclidean distance between parentalspecies explained (%)

2 y v f bb


2 rotation left-right 22.8 0 18.5

3 eye top-tier size and shape/forehead width 6.1 36.7 14.5 1.15

4 asymmetry 3.4 0 0

5 eye shape? 1.5 9 2.2

6 eye roundness 1.4 8.4 11.5 2.4

7 eye width 1.2 7.2 11.4 1.55 2.17

8 eye shape 0.8 4.8 0.2

9 eye shape 0.6 3.6 0.1 2.06

other various 5 30.3 2.6

1

After eliminating PCs that are clearly an artifact of the photography; i.e. indicating variation in the positioning of the head or rotation or digitizing error ; i.e. pints moving in discordance

generating asymmetry or unrealistic deformities.


3Significance threshold =1 (based on permutation analysis, Churchill and Doerge, 1994).


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eye size and shape variation. Using the three PCS explaining most of this variation is

sufficient in some of the models to obtain a good resolution between the 2 species. No

single PC on its own is however enough to discriminate between D. simulansand D.

mauritiana.

Both backcrosses were analyzed separately with and without Procrustes

correction for size. This correction made only an obvious difference in the analysis in

the case of the backcross to D. mauritiana: without the Procrustes correction in all three

main PCs the backcross population and both parental species are indistinguishable. This

might be associated with the fact that body size was a larger factor in the backcross to

D. mauritiana than in the backcross to D. simulans.As some aspects of eye morphology

are highly correlated with body size in the strains studied in the preliminary analysis, and

body size is likely to introduce noise in the QTL analysis, adding the Procrustes

correction for scaling might be appropriate.

While PCA captured in detail the variation in eye size and shape, interpreting the

different PCs and comparing them across analyses is difficult. Thus, while visually some

of the PCs in the separate analyses for each backcross yielded visually similar PCs,

these are unlikely to be identical mathematically. For the QTL analysis this is an issue,

as if the phenotypes mapped for each backcross population are not the same the two

resulting maps are not comparable. The combined analysis might be a proper solution

for this problem, as the multidimensional space is defined on the variation in both

populations so that each PC is exactly the same for each population in QTL analysis.

This combined space is more likely to also encompass more variation in eye size and

shape for two main reasons: First, simply because of the larger sample size (430 vs

860); second, each population has a different genetic background so they are likely to

show different phenotypes. This larger phenotypic space might explain why only in this


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model there is no overlap between the two parental species this larger space might

provide a better fit for their variation.

Most PCs in all analyses affecting eye size or shape map to one of the four

markers on the X chromosome. However, all of the LOD scores are particularly low, and

for the preferred combined analysis there is considerable mismatch in the resulting

rough map between the two populations. This might be explained by the presence of

many factors on the X chromosome and a strong genetic background effect.

Nevertheless, all PCs resulting from photography artifacts or digitizing error (within 95%

of the total variation) do not show a significant LOD thus suggesting this quantification

technique might be a good approach.

All 5 models on D. simulans, D. mauritiana and their hybrids suggest variation in

the shape of the dorsal, top-tier part of the eye is the largest difference between the

species. In D. mauritiana males the eyes bulge sideways, in particular at the dorsum

(Figure 9). Therefore, it is likely that this area possesses derived physiological

characteristics generating most of the observed difference in size and shape between

the species. A model including other four species within the melanogaster species

subgroup also suggests this axis is important in eye shape variation across the group as

a whole. Functional studies in Drosophilaand other fly species show this part of the eye

can have specialized roles in vision and suggest some possible explanations for the

enlarged eye in D. mauritiana. The eye of D. melanogaster is composed of at least 4

subtypes of ommatidia (Pichaud et al., 1999; Wernet et al., 2003; Mazzoni et al., 2008).

The most common two kinds, called pale (short wave discrimination) and yellow (long

wave discrimination) are randomly interspersed in a 30 to 70% ratio throughout the eye.

The third kind of ommatidia are located in the first few lanes of the dorsal rim area (DRA)

of the eye and are specialized detect polarized light (Wernet et al., 2003). Finally, a

newly described fourth class of specialized ommatidia called dorsal y are located in the


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Table 9:Summary of PCA results corrected for body size and QTL mapping for the backcross to D. simulans and D. mauritiana. The PCA

analysis in this case was based on both backcross populations PC 1- 8 explain 95% of the total variation. Together, the PCs presenting significant

LOD values in both backcross populations (3, 5 and 6)explain 61.2% of the biological variation, and 62.2% of the Eucledian distance between the

mean D. mauritiana and D. simulansin multidimensional space. Note however that most of the biological variance is due to PC, and most of the


LOD3scores, backcross to D.

simulans

LOD3scores, backcross

D. mauritianaPC EffectTotal varianceexplained (%)

Biological variance1

explained (%)Euclidean distance

2between

parental species explained (%)y v f bb y v f

1 rotation back-front 53.5 0 12.5

2 rotation left-right 26.1 0 21

3 eye top-tier size and shape/forehead width 5.3 34.9 15 2.58 1.58

4 asymmetry 3.8 0


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dorsal eye (Mazzoni et al., 2008). These ommatidia have unique molecular and

physiological properties and it is speculated that they might have a specialized role for

navigation (Mazzoni et al., 2008). In other fly species unique eye characteristics likely to

be associated with shape modifications have been described. In domestic and simuliid

flies there is a forward or upward pointing area in the eye of particularly high acuity (i.e.

higher ommatidia density, higher facet surface area, and anatomical differences at the

receptor level (Land, 2002). This acute zone is frequently only present in the male and

is related to mate detection or pursuit during flight (Land, 1992; Land & Fernald, 1992;

Land, 2002). In Musca domesticathis dimorphism involves just a local increase in the

acuity of the forward flight acute zone (Wehrhahn, 1979; Wehrhahn & Hausen, 1980;

Land, 2002; Burton & Laughlin, 2003) commonly referred as the love spot.

Table 10:PCA of eye size and shape discriminates 6 species from the melanogaster species

subgroup. PC 1-10 explain 95% of the total variation in the model (results corrected for body

size). After eliminating PC 1,2

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