a tail of two flies: the genetic basis of abdominal pigmentation differences between two species of...
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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:
PCs 1-8 represent 95% of the total variation in the model. Approximately 57.6% of the
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
1 rotation back-front 49.1 0 20
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:
PCs 1-9 represent 95% of the total variation in the model. Approximately 84.8% of the
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
Eucledian distance between the parentals is accounted by PC 2 and 4.
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.
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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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
1 rotation back-front 57.2 0 39
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.
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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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
Eucledian distance between the parentals is accounted by PC 3 and 5.
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