Copyright ? 2008 by the Genetics Society of America
Microarray Analysis of Replicate Populations Selected Against a
Wing-Shape Correlation in Drosophila melanogaster
Kenneth E. Weber,*,1Ralph J. Greenspan,†David R. Chicoine,* Katia Fiorentino,*
Mary H. Thomas* and Theresa L. Knight*
*Department of Biological Sciences, University of Southern Maine, Portland, Maine 04104-9300 and†The Neurosciences Institute,
San Diego, California 92121
Manuscript received October 18, 2007
Accepted for publication November 26, 2007
We selected bidirectionally to change the phenotypic correlation between two wing dimensions in
Drosophila melanogaster and measured gene expression differences in late third instar wing disks, using
microarrays. We tested an array of 12 selected lines, including 10 from a Massachusetts population (5
divergently selected pairs) and 2 from a California population (1 divergently selected pair). In the
Massachusetts replicates, 29 loci showed consistent, significant expression differences in all 5 line-pair
comparisons. However, the significant loci in the California lines were almost completely different from
these. The disparity between responding genes in different gene pools confirms recent evidence that
surprisingly large numbers of loci can affect wing shape. Our results also show that with well-replicated
selection lines, of large effective size, the numbers of candidate genes in microarray-based searches can be
reduced to realistic levels.
select against phenotypic correlations within the wing,
using the metric of ‘‘angular offsets’’ (Weber 1990).
Angular offsets reduce shape to its simplest quantifiable
aspect—the allometric relation between two interland-
mark distances—by measuring each individual’s deviation
from the mean line of allometry of the base population
(see materials and methods). This converts variation
that is orthogonal to the correlation into a univariate
scale that is independent of size. Thus, angular offsets
focus directly on the evolutionary constraints posed by
correlations (Lande 1979) and quantify the breakage
of these constraints (cf. Beldade et al. 2002).
Many genes affect wing shape. Quantitative trait locus
(QTL) mapping found at least 20 genes affecting one
wing-shape trait in divergently selected lines from a wild
sample (Weber et al. 1999, 2001). Experimental selec-
tion can change wing shape in many directions (Weber
1990; Mezey and Houle 2005) and can affect small
isolated parts (Weber 1992). A screen of 50 random P-
element insertions found 11 insertions with rigorously
validated wing-shape effects (Weber et al. 2005). In
these cases, gene effects were in the range of 0.1–1
phenotypic standard deviations of the base population.
These results show that the wing has a high short- and
HE Drosophila wing is a convenient model system
for shape genetics. Our approach has been to
existing alleles with small effects and many more loci
that could mutate to produce such alleles. This suggests
that replicate lines under identical selection might use
Identical selection regimes often produce different
1953) selected for heavier mice and created a large-
boned line, while Macarthur (1949) selected mice in
the same way from a different stock and obtained a line
that was not large boned, but obese. In another case
(Swallow et al. 1998; Houle-Leroy et al. 2003), mice
from a single stock were selected for wheel running in
four replicate lines. All lines responded with compara-
ble performance increases, but two distinct suites of
physiological and morphological traits emerged. Cohan
and Hoffmann (1986) selected for ethanol tolerance in
Drosophila melanogaster from five locations along the
North American West Coast and found that response
occurredin geneticallydifferent ways. In theseand other
cases (e.g., Gromko 1995; Bult and Lynch 1996),
replicate selection lines from different strains, or even
from the same strain, produced the same selected
phenotype by different genetic means.
parallel genetic outcomes, even when starting with
different strains or species. For example, in cereals like
sorghum, rice, and maize, orthologous loci have been
involved in parallel changes during domestication for
traits such as large seeds and seasonal independence of
flowering (Paterson et al. 1995; Devos 2005). Experi-
ments with bacteriophages adapting to high tempera-
1Corresponding author: Department of Biological Sciences, University of
Southern Maine, 96 Falmouth St., Portland, ME 04104-9300.
Genetics 178: 1093–1108 (February 2008)
ture (Bull et al. 1997; Wichman et al. 1999) or toxic
selection in different lines can lead to parallel and
sometimes almost identical changes in the same genes.
Wood et al. (2005) review other cases of parallel genetic
change in strains and species subjected to the same
In adaptive radiations, parallel evolution often in-
volves the same few major loci. Parallel morphological
and genetic differences were found in independent
cases of marine sticklebacks adapted to fresh water
(Schluter et al. 2004; Shapiro et al. 2004), in species of
Drosophila that independently evolved similar pigmen-
tation of wings (Prud’homme et al. 2006) or abdomens
(Gompel and Carroll 2003), and in beak length in
Darwin’s finches (Abzhanov et al. 2006). Schluter
et al. (2004) provide other examples both supporting
and contradicting this principle.
In the most striking cases of genetic convergence,
identical amino acid substitutions have occurred in the
same proteins in unrelated groups (Patthy 1999;
Carroll 2006). Yet when selection responses can be
more fully dissected, genetic differences emerge. In
cereal domestication, genetic parallelism is high in
some traits but low in others (Gale and Devos 1998;
Morrell and Clegg 2007). In the bacteriophage
studies (Wichman et al. 1999), loci with parallel changes
were not those with the largest effects. In Drosophila,
the same gene caused wing spots in independent
lineages, but the regulatory modules were different
(Prud’homme et al. 2006).
Genetic divergence or convergence during selection
depends partly on population size and selection inten-
sity and the nature of the selected trait. In the wheel-
running study cited above, the alternative outcomes
were attributed to drift in small populations, because
they depended on the presence of a single allele with
low frequency in the base population (Houle-Leroy
et al. 2003). Selection intensity may influence the rel-
ative recruitment of major or minor genes (Lande
1983), as studies of the evolution of insecticide and
herbicide resistance have emphasized (McKenzie et al.
1992; Gardner et al. 1998; Neve and Powles 2005).
The primary determinant of genetic divergence or
convergence may be the complexity of the trait. Fea-
tures that utilize much information in development,
and can be molded easily in many ways by selection,
should permit alternative paths to similar phenotypes,
and their responses in parallel selection lines should
vary more. Wing shape seems to be such a trait.
In this study, we used microarrays to measure gene
expression in the whole genome in a large panel of
selection lines. The lines were created in different
experiments, originated from separate populations,
and included multiple replicates of one population,
shape trait. Here we evaluate the data with two aims: (1)
to identify candidate wing-shape genes and (2) to assess
variation in the outcome of identical selection regi-
mes—between replicate lines from a single source and
between lines from two geographically remote, local
gene pools in Massachusetts and California.
MATERIALS AND METHODS
The trait and the selected lines: D1andD2arewidthsatthe
wing is thepolar angle, in radians, between the point (D1, D2)
and the point with equal radius (r) on the line of the polar
equation u ¼ 0.4048r?0.043(males) or u ¼ 0.4148r?0.134
(females). The polar equation is a curve approximating the
mean allometric relation between D1 and D2, derived by
regression of log u on log r in wild-type flies (Weber 1990).
Clockwise and counterclockwise deviations from this baseline
are called positive and negative, respectively. Angular offsets
are independent of body size.
Lines H and L (Weber 1990) were created by divergent
a sample of a laboratory population established in 1981 from
350 isofemale lines captured in Lincoln, Massachusetts. The
lines were selected for 20 generations, and all three major
chromosomes were isogenized using balancer chromosomes.
Lines H and L were then used to map QTL on chromosomes
three (Weberet al. 1999) and two (Weberet al. 2001), using in
situ-labeled insertion sites of the transposon roo as markers.
Figure 2 outlines the creation of eight more selection lines.
In September 1999, lines H and L were crossed in four
separate crosses of 500 males and 500 virgin females to create
Figure 1.—Definition of the trait. The dashed line shows
the mean allometric relationship of D1 and D2 in wild-type
males. The phenotype of each wing is the angular offset of
its point (D1, D2) from this baseline in radians of rotation
about the origin. Selection on this angle produces antagonis-
tic changes in D1 and D2. The figure shows samples of male
wings from a wild-type population (open circles), with a mean
angular offset of approximately zero, and from two diver-
gently selected populations (solid circles) with mean offsets
of ?0.0883 and 10.0819 radians (K. E. Weber, unpublished
results). Environmental variables like temperature and cul-
ture density have large effects on body size (i.e., the mean
and variance of r), but little effect on angular offset. Offsets
are normally distributed in control and selected flies.
1094K. E. Weber et al.
both H and L were still isogenic for all the same roo markers
that were used in QTL mapping on all three main chromo-
somes. Thus we can be certain that all hybrid populations
began with identical fixed backgrounds and segregating
alleles. Founder males and females were H and L, respectively,
in A and B; C and D were the reciprocal cross. ½About 10% of
the phenotypic difference between H and L arises from the X
chromosome (Weber et al. 1999, 2001).? For 34 generations,
each hybrid line was maintained in 30 vials with three female
and five male parents per vial. In every generation, virgin
offspring were collected for 9 days to include all eclosing flies,
kept at 12? to prevent mating, and then randomly mated. Our
aim was to mingle the H and L genomes in large panmictic
populations with minimal selection.
After 34 generations of genetic mingling in these four
hybrid lines, selection was reinitiated to derive a new pair of
divergently selected lines from each hybrid line. The eight
lines were designated A1, A2, B1, B2, and so on and were
selected in the positive (1) or negative (2) direction. The
parents were the most extreme 20% of 315 measured flies of
each sex.In several generationsfewer flieswere measured, but
the number of parents was always at least 63 of each sex per
line.After 25generationsofselection, sublinesweresib-mated
for 10 generations to create inbred lines.
The California selected lines came from a base population
that was derived from ?40 D. melanogaster isofemale lines,
captured in Davis, California, and kindly supplied by Michael
Turelli (University of California, Davis, CA). In each genera-
selected as parents. After 21 generations of selection, the lines
mating for 10 more generations, prior to these experiments.
Staging and dissection of larvae: Culture vials were set up
with potato-flake medium plus 0.05% bromphenol blue to
obtain semisynchronous larvae. The blue medium was re-
tained by late third instar larvae but gradually excreted,
permitting visual staging (Maroni and Stamey 1983; Andres
pupariation of 6.2 hr (data not shown). Larvae were dissected
wing disks were cleaned with tungsten needles. Each pair of
disks was transferred to a tiny droplet of fresh PBS retained on
the tip of a needle after dipping it in boiling deionized water
and then in ice-cold sterile PBS. Disks were transferred from
this needle tip to the bottom of a 0.5-ml microcentrifuge tube
in a covered gel-filled cooler (embedded in dry ice), where
the droplet with disks was instantly frozen to the bottom. No
damaged disks were used. Male and female larvae were not
separated, so samples included both sexes about equally.
RNA was extracted (Dierick and Greenspan 2006) by
homogenization of 100 disks per sample, representing at least
each sample, 90 culture vials were set up with blue medium
and one mated female apiece. Only a few larvae came from
each vial so any differences between cultures were averaged
over many vials. Samples were analyzed using Affymetrix tech-
Statistical analysis of microarray data: The datasetincludes
the scans of 36 Affymetrix Drosophila 2.0 microarray chips,
representing six pairs of divergently selected lines and three
samples per line. Results were analyzed using the software
programs dChip (http:/ /www.dchip.org; version of June 27,
2005), CyberT (Institute for Genomics and Bioinformatics,
University of California, Irvine, CA), SAS and JMP5 (SAS
Institute, Cary, NC), and Excel (Microsoft). Normalization of
or absent by dChip was carried out anew within each subset of
the data used in different tests. When line pairs were tested
separately, the data for the six chips of each line pair were
independently normalized using dChip and tested using a
Bayesian analysis in CyberTafter converting normalized inten-
sities to natural logs. We used Bayesian methods for t-tests of
individual line pairs because the sample sizes were small, with
three chips for each selected line. For Bayesian statistical
comparisons, variances were conditioned on the closest 50
probe sets above and below each probe set in order of their
rank according to normalized intensity. Probabilities were
corrected for the number of tests within each line pair by
setting the significance threshold at 0.05/N, where N was the
number of tested probe sets for that pair. Probe sets were also
tested using the combined data for all five Massachusetts line
pairs in nested ANOVAs, with chips nested within lines and
lines within treatments, using natural log-transformed in-
tensities in SAS to provide overall P-values for each probe
set, similarly corrected for the total number of tests.
The raw array data files are available at http:/ /www.ncbi.
nlm.nih.gov/projects/geo/ under accession no. GSE9107.
Plan of data analysis: Our analysis began with two pre-
liminary steps. First, we examined the entire data set to assess
the consistency and quality of the normalized intensity data
and the performance of the analysis software. We then
screened the data for genes with zero expression (null alleles)
either in one direction of selection or in one gene pool. These
preliminary analyses indicated that the data were of consistent
quality and that the main analysis could be based solely on
comparisons of expression levels among transcribed genes
(see results for details).
In the main analysis, our first aim was to identify a small
number of high-quality candidate genes. We assumed that the
Massachusetts lines had the same contributory genes since
they all came from the same original sample, but the con-
tributory genes in Massachusetts and California lines might be
different. Therefore, we derived our candidate gene list solely
from the Massachusetts lines, since we had five pairs of them
but only one pair of California lines.
We had two criteria for candidate genes: our list includes
only genes that were (1) consistently significant in all five
Massachusetts line pairs, when each line pair was tested
independently, and that were also (2) significant in the nested
Figure 2.—Derivation of lines. A wild Massachusetts popu-
lation sample (M) was selected divergently for 20 generations
to produce lines H and L. H and L were isogenized and
crossed to make populations A–D. After 34 generations of re-
combination in large populations (‘‘Mingle’’), hybrid lines
were divergently selected for 25 generations. (Meanwhile,
H and L were maintained with inbreeding, and a pair of di-
vergent lines was selected from a California population.)
Wing-Shape Genes in Flies1095
ANOVA of the combined Massachusetts data set. In the end,
the candidate genes were completely decided by the first
criterion, which was more stringent. This plan of analysis
allowed us to use the same results to evaluate the incremental
value of additional replicates, to investigate sources of varia-
tion in the outcome of selection among lines, and to compare
outcomes between the Massachusetts and California lines.
Estimation of centimorgan values: We created a look-up
table to estimate locations of genes on the genetic map by
entering the experimental centimorgan values listed by
Ashburner in Lindsley and Zimm (1992, pp. 1117–1133)
and interpolating values for intervening genes. The centimor-
address for genes as given in the Affymetrix gene annotation
list. Curves were fit to the data using the spline function in
JMP5. The spline parameters of the best-fit curve for each
major chromosome arm were:
l ¼ 5:00E116r2¼ 0:9995
l ¼ 3:00E116r2¼ 0:9982
l ¼ 3:00E117r2¼ 0:9986
l ¼ 4:00E116r2¼ 0:9987
l ¼ 2:00E116r2¼ 0:9997:
Calculations of heritabilities and effective factors: Herit-
abilities and effective gene numbers were calculated for the
four hybrid Massachusetts populations (A, B, C, and D) using
the selection data (Falconer and Mackay 1996). We calcu-
lated realized heritabilities for each of the eight derived
selection lines by the method of Hill (1972) on the basis of
the first six generations of selection. We used the mean
heritability of each pair of high and low lines to estimate the
heritability of each hybrid population and the means of the
two phenotypic standard deviations in the first generation of
selection to estimate the standard deviation for each hybrid
population. Total response was the difference between high
and low lines after 25 generations of selection and 10
generations of inbreeding.
The phenotypic variances of isogenic lines H and L
and of their four F1hybrids were all nearly equal (Table
1). In the F2generation, hybrid variance increased by a
factor of ?6, as large blocks of high and low selected
alleles began to segregate. During 34 subsequent gen-
erations of random mating in populations of an effec-
tive size of ?180, mean hybrid variance declined almost
to the same value as base population variance, indicat-
ing approximate linkage equilibrium for alleles affect-
ing the trait.
In generation F35, we began selecting divergently on
paired sublines from each hybrid line. After 25 gener-
ations of selection, these new lines diverged about as
much as the parental H and L lines and more in some
cases(Table2).Thus nosignificantgeneticvariation for
the trait was lost during the 34 generations of hybrid
for 10 generations to reduce genetic variation within
lines. The two California lines were selected for 21
generations and then also inbred by sib-mating. Most of
the phenotypic divergence between high and low lines
remained after inbreeding. Tables 2 and 3 summarize
the phenotypes of all 12 selected lines before and after
inbreeding or, in the case of the H and L lines, before
and after isogenization with balancers. The measure-
ments in Table 3 were made just before microarray
assays were performed on all lines.
Preliminary analysis of microarray data quality: On
the Affymetrix chip, each probe is present in two ver-
sions on contiguous spots: a 25-base version (PM) that is
a perfect genomic match and another 25-base version
(MM) with a single mismatch at the 13th base. A probe
set includes 14 different paired PM/MM probes from
one transcript, and the chip includes probe sets repre-
senting most D. melanogaster transcripts. The dChip soft-
ware normalizes hybridization intensities across chips
and classifies probe sets as present, absent, or marginal.
zero intensities when evaluated as the sum of PM–MM
differences. We eliminated these as well as Affymetrix
control sequences. We then looked at several funda-
mental aspects of the data after normalization by dChip
across all 36 chips.
Figure 3A shows distributions of hybridization R-
values, where R ¼ (PM ? MM)/(PM 1 MM) for all
PM/MM probe pairs on all 36 chips (?9.5 3 106probe
pairs) after separation into present, absent, and mar-
ginal probe sets. In absent probe sets, R is distributed
symmetrically around zero, as expected for completely
random hybridization. Inthepresent probe sets,R hasa
primary mode on the positive side and a secondary
mode showing a minor population of randomly hybrid-
izing probe pairs within present probe sets. Figure 3B
shows the distributions of probe sets on all 36 chips
Means and variances of parental and hybrid
Parental line H
Parental line L
Mean of F31–F34
Means in radians of angular offset as defined in materials
and methods. For lines H and L, N ¼ 100. For generations
F1–F34, values are grand means of the means and variances
of four H 3 L hybrid lines, with N ¼ 100 for each line/gen-
eration. For wild-type base, values are means of the mean and
variance of two lines of the unselected Massachusetts base
population (CN1 and CN2) with N ¼ 150 in each sample.
Data are from males only.
1096 K. E. Weber et al.
(?6.8 3 105probe sets), according to the number of
probe pairs (0–14) in each probe set showing the
anomalous condition MM . PM for present, absent,
are well differentiated, and the distribution for absent
probe sets is that expected for random hybridization.
Figure 3 shows that present and absent calls by dChip
efficiently separated our probe sets into two distinct
groups with appropriate distributions. Probe sets classi-
fied as marginal were much more similar to absent
probe sets than to present ones. We therefore did not
include the marginal probe sets in our analysis.
We also asked how consistently each transcript was
called present across all 36 chips. Figure 4 shows the
distribution of all ?18,800 probe sets on the Affymetrix
chip, according to the number of times each one was
called present in 36 chips. Almost 7000 probe sets were
called present on every chip. At the other extreme,
?5000 probe sets were not called present even once.
The remaining ?36% of probe sets were present on
some but not all chips. Thus the majority were either
always present or never present.
The dChip notes recommend special attention to any
chip where .5% of probe sets are classified as outliers.
The average percentage of outliers per chip, among all
36 chips, was only 0.49% according to dChip. A single
atypical chip (H-3) had 4.08% outliers.
Absent genes associated with lines or treatments:
Absent probe sets could be null expression alleles,
which could be associated with trait variation. We first
searched all 30 Massachusetts chips for probe sets that
were called absent in one direction of selection but
present in the other. We found only seven. Visual
inspection of the PM/MM profiles in dChip showed
that, in all cases, both the present and absent probe sets
showed nearly identical profiles and essentially zero
expression. These cases clearly represent random error
in the wing.
We next compared present and absent calls between
Massachusetts and California to check for genes ex-
pressed in one population and not the other. There are
10 possible comparisons between Massachusetts line
pairs, with a mean of 7187 shared present probe sets
(Table 4). There are five possible comparisons between
theCalifornialinepairand theMassachusettsline pairs,
within-population comparisons must represent a single
group of probe sets and the between-population com-
parisons show almost the same numbers, most present/
absent differences are probably due to random error.
We conclude that null expression alleles may not be
important in the genetic variance of this trait. By
eliminating absent genes from further comparisons
Mean phenotypes of all selected lines before inbreeding or isogenization
Up lines Phenotypes
Down lines Phenotypes
0.0537 6 0.0011
0.0706 6 0.0006
0.0688 6 0.0007
0.0506 6 0.0006
0.0609 6 0.0007
0.0516 6 0.0006
?0.0819 6 0.0007
?0.0672 6 0.0007
?0.0755 6 0.0007
?0.0716 6 0.0009
?0.0777 6 0.0007
?0.0771 6 0.0008
Means and SE in radians of angular offset. Data are from selection generation 20 in H and L; 22 in Ca1 and
Ca2, and 24 in line pairs A–D. N is the sample size. All data are from males.
Mean phenotypes of all selected lines after inbreeding or isogenization
Up lines Phenotypes
0.0575 6 0.0007
0.0711 6 0.0010
0.0664 6 0.0009
0.0534 6 0.0016
0.0686 6 0.0010
0.0449 6 0.0010
?0.0751 6 0.0007
?0.0665 6 0.0013
?0.0545 6 0.0011
?0.0636 6 0.0014
?0.0650 6 0.0012
?0.0657 6 0.0012
Means and SE in radians of angular offset. H and L were isogenized after selection and then maintained by
sib-mating. The other lines were inbred by sib-mating after selection. These measurements were made just be-
fore the microarray assays. All data are from males.
Wing-Shape Genes in Flies1097
between populations or treatments, we simplified the
rest of the analysis, which was focused on differences in
expression intensity among genes called present on
chips in both directions of selection or in both gene
Transcripts associated with the trait in individual line
pairs: We began our main analysis by testing each line
pair separately. For each pair we tested probe sets for
significant expression differences between high and low
lines by t-test as explained in materials and methods.
Table5 showsthenumberof testableprobesetsforeach
line pair and the number with significant expression
differences, treating each line pair as an independent
experiment and correcting for the number of tests in
each pair. Testable probe sets included all for which at
least two of the three samples were present in both the
high and low line. This number was consistent with a
range of 8346–8435. By contrast, the number of signif-
icant probe sets was highly variable. We tested the
numbers of nonsignificant vs. significant probe sets in
proportion of significant probe sets was independent
of line pair. The G-value was ?268, with d.f. ¼ 5 and
P , 10?55. The low P-value points to some factor or fac-
tors with a large effect on the numbers of significant
The effect of population size in selection: Figure 5
shows that the number of significant probe sets in line
pairs is not correlated with phenotypic divergence, but
may be influenced by the effective population sizes of
lines during selection. Larger lines have fewer signifi-
cant transcripts (r2¼ 0.872; P ¼ 0.0067, assuming equal
variance at all population sizes). Perhaps alleles with no
effect on the trait are more likely to be fixed in opposite
directions by drift in smaller lines, but are controlled
line in Figure 5B does not estimate the isolated effect of
population size, because different base populations are
the derived lines even at the same population size. In
any case, the four derived Massachusetts line pairs show
a large, concerted decrease in significant probe sets
compared to their parental lines, H and L. Yet they have
approximately the same phenotypic divergence (Table
3). We conclude that many allelic differences that do
not affect the trait were fixed oppositely in the parental
lines and that in the larger, derived lines these differ-
ences were more likely to be fixed for the same allele or
to remain unfixed.
Consistency of significant probe sets in Massachusetts
lines: We next compared the five Massachusetts line
pairs to see how often the same probe sets were
significant in different line pairs. Table 6 shows that 35
probe sets were significant in all five cases. Many more
most numerous in the H/L lines, which were the par-
ents of the other four pairs. For example, of 449 probe
sets that were significant only once, almost half (218)
were significant in the H/L comparison. Again, the
greater abundance of significant transcripts in the H/L
line pair is likely to be due to alleles that do not affect
the trait. These alleles might have been separated
from selected alleles by recombination in the hybrid
Figure 3.—Distributions of ?9.5 3 106probe
pairs and ?6.8 3 105probe sets from 36 chips.
(A) Values of R for probe pairs in probe sets
called present, absent, and marginal, where R ¼
(PM ? MM)/(PM 1 MM). (B) Values of X for
probe sets called present, absent, and marginal,
1098K. E. Weber et al.
populations with their larger size. This recombination
could have occurred either during the 34 generations
of mingling in the hybrid lines or during the 25 sub-
sequent generations of selection.
Some probe sets that were significantly different in
multiple line pairs showed expression differences that
were inopposite directionsinindividual line pairs. That
is, their expression was significantly higher in the high
line and significantly higher in the low line in different
line pairs. The final column in Table 6 shows the num-
ber of such cases. For example, of 164 probe sets that
were significant in just two line pairs, 21 were significant
in opposite directions. Overall, ?10% (39/375) of all
probe sets that were significant more than once were
expressed in opposite directions in different line pairs.
They might be genes that are expressed oppositely in
different epistatic complexes to produce equivalent
phenotypic effects. A simpler explanation would be that
they represent randomly fixed expression polymor-
phisms with no relation to the trait.
The value of additional replicates: We asked how the
numberof consistently significantprobe sets declines as
more replicates are added to the analysis. For this
purpose we treated all five Massachusetts line pairs as
replicates, representing approximately the same set of
alleles and selection history. The average number of
significant probe sets for individual Massachusetts line
pairs in Table 5 is 312. Figure 6 shows that the addition
of a second replicate line pair eliminated about two-
Figure 4.—Distribution of probe sets according to the
number of chips on which each probe set was called present.
Includes all Drosophila probe sets and Affymetrix control
probe sets. Most probe sets were either always present or al-
ways absent, indicating accurate detection.
Consistently present probe sets within and between
pairs of lines
Cal1/Ca2 H/L A1/A2 B1/B2 C1/C2 D1/D2
Table includes probe sets called present in all 12 chips in
each possible comparison of two line pairs.
Testable and significant probe sets for each line pair
Line pair: H/L A1/A2 B1/B2 C1/C2 D1/D2 Ca1/Ca2
835084358367 8376 84058346
534335 253 198 238 450
Probabilities were computed with log-transformed intensi-
ties using Cyber-T with Bayesian methods and compared to
P ¼ 0.05/(testable probe sets in each line pair).
Figure 5.—The proportion of significant probe sets within
each line pair (Table 5) is not correlated with phenotypic di-
vergence, but with population size. Each point represents one
line pair. (A) Proportion of significant probe sets as a function
of the phenotypic divergence between high and low lines
from Table 3; r2¼ 0.003. (B) Proportion of significant probe
sets as a function of the number of parents per generation
during selection; r2¼ 0.872. Lines H and L had 40 parents,
lines Ca1 and Ca2 had 80, and the derived Massachusetts lines
Wing-Shape Genes in Flies 1099
thirds of significant probe sets, on average, by the
criterion of consistent significance. The addition of a
third replicate eliminated about half of the remaining
probe sets. With the addition of more and more rep-
licates, probe sets that remain consistently significant
eventually be eliminated by the occurrence of occa-
sional false negatives. According to Figure 6, in retro-
fourth and fifth replicates was still possibly worth the
By extrapolation, a sixth replicate would be of marginal
quent among the significant probe sets in Table 6, espe-
cially among those with self-contradictory expression
differences, described above as ‘‘flip-flops.’’ The total of
39 flip-flop probe sets in the final column of Table 6
includes at least 11 transposons. The two flip-flops that
were significant in all five line pairs are both trans-
posons. The large number of transposon flip-flops
probably arises in part because transposons are present
in numerous copies in the genome and most active
insertion sites are not fixed. This allows their copy num-
ber to diverge between selection lines. The divergence
may be random or could be affected by hitchhiking due
to associated genes for the trait. But not only flip-flops
are transposons: among the 35 consistently significant
probe sets in Table 6, 4 are transposons that show sig-
nificant expression differences in the same direction of
expression in all five line pairs. These four transposons,
with their lengths and approximate copy numbers per
genome according to FlyBase (Crosby et al. 2007), are
hopper (1435 bp; ?15), Rt1b (?5100 bp; ?40), 3S18
(6126 bp; ?20), and S (1736 bp; ?40). One interpreta-
tion of such anassociation might be that the transposon
itself affects the trait. It is also possible that these are
cases where actively transcribed transposons are in-
serted in genes that affect the trait in such a way that
they create selectable alleles. This would mean that the
insertion is a marker of a trait allele, but one that is dif-
ficult to utilize in gene hunting because of the existence
of many other copies of the same transposon. We re-
moved all six transposons (two flip-flop, four non-flip-
flop) from the list of 35 consistently significant probe
sets, leaving 29 candidate genes for the trait. These are
listed in Table 7 with some annotation according to
A test of the combined data: We regarded indepen-
dent significance in all five of five Massachusetts line
pairs as a pragmatic first criterion for choosing candi-
be easier to validate. For a second, overall perspective,
Repeated significance of the same probe sets in Massachusetts replicates
Times significant Total H/LA1/A2B1/B2 C1/C2 D1/D2Opposite
Total probe sets tested: 9365
The first column gives the number of times a probe set was significant in the five comparisons. The final
column gives the numbers of probe sets, included in the totals, that were significant multiple times but not
always in the same direction (‘‘flip-flops’’).
Figure 6.—As the number of replicate line pairs increases,
the mean number of consistently significant probe sets falls
rapidly and then levels off. The first ordinate value (311.6)
is the mean of significant probe sets in individual Massachu-
setts lines from Table 5. The final value (33) is the number of
probe sets that were consistently significant in all five Massa-
chusetts line pairs (Table 6), minus the two cases with contra-
dictory signs (‘‘flip-flops’’). The values for two, three, and four
line pairs were calculated as the mean numbers of consistently
significant probe sets in all possible two-way, three-way, and
four-way combinations, minus cases with contradictory signs.
1100K. E. Weber et al.
we combined the data from all five line pairs for all 7542
probe sets that were called present on at least two of
three chips for every line and performed a nested
ANOVA on the natural log-transformed intensities.
Using a probability threshold corrected conservatively
for the number of tests (P ¼ 0.05/7542), there were 792
probe sets with significant expression differences, or
disk. Figure 7A shows a ‘‘volcano plot’’ of probability vs.
fold difference for the pooled Massachusetts data. The
solid circles represent the 29 candidate genes from
Table 7. Figure 7B shows the same probe sets and prob-
The candidate genes have reasonably strong intensi-
ties and fold differences, with absolute fold differences
of 1.5–9.1 and a mean of 2.8. In the ANOVA-based
Candidate genes from the Massachusetts lines
GeneArm BandcM Fold California?Function
Unknown (Jensen et al.
binding protein 2
Long-chain fatty acid
RNA polymerase II
CG40293 (Stlk) 2R 41C1-6 55.0
CG31522 3R82B2-3 47.2
CG1163 (Rpll18) 3R83A1 47.5
Band locations and gene functions are according to FlyBase (Crosby et al. 2007). Centimorgans (cM) are
estimated as described in materials and methods. Fold differences from Massachusetts line means with sign
indicating greater expression in high lines (1) or low lines (?). Entries under the column heading‘‘Califor-
nia?’’ indicate whether the gene was not significant (0) in California lines, was significant in the same direction
of expression (1), or was significant in the opposite direction (?). FlyBase reports GH15538 only as a cDNA
with an exact match on 2L coding for a putative protein. BLASTsearches with the 14 Affymetrix probes for this
detected transcript all give unique hits at this site. Jensen et al. (2006) report differential expression of GH15538
after insecticide treatment.
Wing-Shape Genes in Flies1101
probability ranking of Figure 7, the 29 candidate genes
are all highly significant, but some other probe sets look
interesting as well. However, our immediate aim was to
define a small preliminary set of the highest quality can-
didates that would be the most likely to show detectable
effects in a variety of tests. Our 29 candidate genes
represent the intersection of two ranking methods. The
first method emphasizes consistent association with the
trait, while the second (Figure 7) allows for genes that
may be less consistent but show significant overall asso-
should be better than the other sometimes. Where both
agree, the inference seems especially reliable.
Clustering among candidate genes: Table 7 indicates
the probable importance of hitchhiking. Five genes fall
in an interval of 0.3 cM on the left end of chromosome
three in division 61. Table 7 also includes two triplets
of linked, coexpressed genes have been found in
Drosophila and other organisms (reviewed in Hurst
et al. 2004). However, every cluster in Table 7 includes
genes that are expressed oppositely—some more in
high lines and some more in low lines. Only two genes
in one cluster are side by side (CG9186 and CG2211),
but even these two are expressed oppositely with re-
spect to the trait. The others are not adjacent, and
most occur in regions of reduced recombination. For
example, the 0.3-cM interval with five candidate genes
covers 792 kbp containing ?90 protein-coding genes.
Despite their highly significant associations with the
likely to represent hitchhiking alleles that do not affect
We asked whether we would find larger clusters of
genes associated with the trait if we relaxed our criteria
to include more loci. We looked at the 200 most-
significant probe sets in the list of 792 from the ANOVA
of the Massachusetts lines, after eliminating all trans-
posons. In addition to the 29 candidate genes, this
larger group probably includes all expression differ-
ences that contribute to the phenotypic divergence of
the Massachusetts lines, and undoubtedly many that do
not. Figure 8 shows how these 200 genes are distributed
on the five main chromosome arms in bins of width 0.5
of all D. melanogaster genes. In regions of low recombi-
(Bartolome ´ et al. 2002), but higher on the genetic
map. The 200 candidate genes are not strongly clus-
isolated genes. To some extent, their clustering reflects
the overall distribution of gene densities.
Comparison of Massachusetts candidate genes to
significant California genes: We next compared the 29
450 probe sets that were significant in the single pair of
only eight genes in common, and five of these were flip-
flops—significant in both the California and the Mas-
sachusetts lines, but in opposite directions (column 6 in
confirmed that the California–Massachusetts flip-flop
transcripts allshow largeexpression differencesthatare
associated consistently with the trait in all chips, but
in opposite directions in the two populations. Again,
various interpretations are possible for these flip-flop
genes. They may affect the trait in a context-dependent
way that is opposite in the two genetic backgrounds.
They may also be cosmopolitan expression polymor-
phisms that do not affect the trait in either population.
Figure 7.—Probability vs. fold difference and intensity in
combined Massachusetts data. Solid circles show the 29 non-
transposon probe sets that were significant in the same direc-
tion in all five line pairs (Table 7). (A) Volcano plot for data
from all testable probe sets in all five Massachusetts line pairs.
y-Axis is the ?log (base 10) of P. P-values were calculated by
nested ANOVA of log-transformed intensities of high and
low lines. x-Axis is the 6log (base 2) of average fold differen-
ces. Fold differences were calculated by comparing high-line
mean intensities (h) to low-line mean intensities (l). Fold dif-
ferences with l . h were quantified as ?log (base 2) (l/h);
those with h . l were quantified as 1log (base 2) (h/l). P-val-
ues were not corrected for the number of tests. (B) Plot of P
vs. mean intensity; same probe sets with same P-values. Inten-
sities were calculated as log (base 2) of mean intensity of all
present samples for each probe set.
1102 K. E. Weber et al.
suggesting fixation by hitchhiking in the Massachusetts
lines. Overall, the California data give virtually no sup-
port to the candidate genes that we would choose by the
criteria that we applied to the Massachusetts data. The
simplest explanation is that the wing-shape genes seg-
regating in these populations were very different.
Visualizing the differences between gene pools: The
Massachusetts and California data sets showed large
differences in loci associated with the trait. To study
these differences further, we pooled and ranked both
data sets separately by t-test probabilities, using the
?7000 probe sets called present on every one of the 36
chips, and then compared the 1000 probe sets with the
Figure 8.—Genetic map
locations of the 200 most-
ANOVA using combined
Massachusetts data. The
downward histograms show
the distribution of the 200
genes in 0.5-cM bins. The
the total numbers of genes
in the same bins. Map loca-
tions were derived as ex-
plained in materials and
Wing-Shape Genes in Flies 1103
lowest P-values for each population. Of these, only 208
were the same for both populations. (In random draws
of 1000 from 7000, 143 would be the same.) We
combined these probe sets from both populations for
a total of 2000 ? 208 ¼ 1792. The chips from both
populations were renormalized as a group in dChip, so
that we could perform a principal components analysis
objects and the 36 chips as variables, to visualize the
associationsbetween chips.The firsttwocomponentsof
variation explained 49.8% of the variance. In Figure 9,
each line is represented by three replicate chips that
form a tight cluster. The 10 Massachusetts lines form
distinct clusters of high lines and low lines, differenti-
ated along the first principal component, and the high
lines are more dispersed than the low lines. The two
California lines are differentiated along the second
principal component. California and Massachusetts
genes clearly show different associations with the trait.
The derived Massachusetts line pairs came from repli-
cate crosses of two isogenic lines (H and L), thus creat-
ing hybrid lines with identical segregating alleles and
identical fixed genetic backgrounds. We asked how
much genetic differentiation occurred between them in
every chip. For each probe set, we tested the variance in
expression among lines over the variance among chips
within lines, within each direction of selection, by
ANOVA. We found significant variances (i.e., P # 0.05,
without correcting for the total number of ANOVAs)
among high-line means for 34.4% of all probe sets and
among low-line means for 21.9% of all probe sets. This
result agrees with the PCA analysis (Figure 9), showing
chips highly clustered within differentiated lines and
greater differentiation among high lines than low lines.
random fixation of many segregating effects on gene
expression that do not affect the trait. Alleles that do not
affect a selected trait will often be fixed inconsistently
among lines selected in the same direction.
Effective number of contributory loci: We calculated
realized heritabilities and effective gene numbers for
the four hybrid Massachusetts lines from the selection
data. The formula assumes that all allele frequencies
were 0.5 when selection began—a reasonable approxi-
mation since the lines were hybrids of two isogenic lines
and were maintained at large size. The formula also
assumes that alleles were fixed in the high and low
selectedlines, so weused the phenotypesof the selected
lines after inbreeding. The estimates of gene number
were fairly consistent. The closeness between the mean
estimated gene number of Table 8 (26.5) and the
number of candidate genes in Table 7 (29) owes much
to chance. But the result may indicate that our previous
estimate of ?20 detectable genes for this trait, from
QTL mapping of the Massachusetts H and L lines, was
not too far off (Weber et al. 1999, 2001).
We identified candidate genes for a wing-shape trait
by microarray expression analysis of divergent selection
Figure 9.—Principal components analysis showing cluster-
ing of all 36 chips along the first two components of variance,
according to the 1792 probe sets with lowest the P-values.
Solid symbols, high lines; open symbols, low lines. For each
line, three replicate samples (individual chips) are tightly
clustered. The five Massachusetts low lines are clustered more
closely than the five high lines. Massachusetts and California
lines diverge orthogonally for the probe sets associated most
closely with the trait by their expression differences.
Calculation of effective gene numbers for the derived Massachusetts lines
h26 SEResponse (radians)
0.59 6 0.05
0.38 6 0.03
0.45 6 0.10
0.49 6 0.05
Realized heritability (h2) was calculated as explained in materials and methods. ‘‘Response’’ is the pheno-
typic difference from Table 3. Spis the square root of mean phenotypic variance in the first generation of se-
lection. Rpis the response in standard deviations. neis the effective number of loci.
1104 K. E. Weber et al.
lines. The lines were replicated within one population
and also between two distant locations. The genetic
outcomes of selection were variable both within and
between gene pools. The results confirm the highly
polygenic and degenerate basis of wing shape and shed
light on the use of microarrays with selection lines to
find quantitative trait genes.
Our data suggest that large population size usefully
reduces the number of genes identified as candidates in
selected lines by reducing the fixation of alleles with no
effect on the trait. The parental lines (H and L) showed
534 significant loci (Table 5), but the hybrid lines
derived from them had far fewer (198–335), despite
having almost the same phenotypic divergence (Table
3). We attribute this difference to reduced fixation of
noncontributory alleles due to recombination in the
hybrid lines. This was probably enhanced bytheir larger
size: lines H and L had 40 selected parents per line
during selection, whereas the hybrid lines had 126.
Selection is more deterministic in larger populations,
and more efficient in sorting additive effects. This
should reduce variation among outcomes and the
number of significant genes.
Resolution was also improved by the replication of
selection lines (Figure 6). When tested separately, the
individual line pairs all showed unrealistically high
numbers of significant probe sets (Table 5). But many
probe sets were significant in only one line pair or in
several (Table 6). Genes that were significant in some
comparisons, but not in all, in some cases would be
genes of lesser effect that were not consistently fixed,
but many of them must simply be randomly fixed genes
with no effect on the trait. Random fixation and
hitchhiking are inevitable in finite selection lines. As
manyasfourorfivepairs ofreplicate selection linesmay
be useful in eliminating these false positives and genes
with marginal or inconsistent effects.
the derived Massachusetts replicates represented too
few recombinant haplotypes, because of the way in
which they were constructed from crosses of isogenic
lines. This is almost certainly the reason that we found
some tightly linked clusters of genes showing the same
repeated association with the trait. Even after 34
generations of hybrid mingling, and even after the trait
variance declined to nearly base population levels, the
originally isogenic H and L genomes would not dissolve
into global linkage equilibrium. Many tight linkages
persisted in the same phase in all lines. The same
problem will occur whenever replicates come from a
restricted source. Residual linkage disequilibrium will
preserve numerous blocks of alleles in the same phase
that have no effect on the trait, and this will inflate the
number of significant genes, even in the combined
analysis. Thus replicates for gene hunting should arise
from wide sampling within a gene pool. Yet our results
also suggest a potential dilemma: if sampling comes
from locations that are too far apart, results may be
inconsistent because different contributory alleles are
Nevertheless, selection lines from distant gene pools
may also play a role when looking for genes in highly
polygenic traits. We found five genes that were signifi-
cant in opposite directions in the Massachusetts and
California lines. These may be ubiquitous expression
polymorphisms, unrelated to the trait. Even after com-
an allele might be associated with the trait only because
it is closely linked to some other allele (not necessarily
an expression polymorphism) and always in the same
phase. The Californiaresults flag the flip-flop candidate
genes as potential false positives, although worthy of
More interesting than these flip-flops is the finding
that virtually no genes with significant expression differ-
ences were associated with the trait in the same direction
This result is credible because the replicated Massachu-
setts lines do agree with each other, showing a substan-
tial core group of consistently significant genes. This
surements. Equally convincing is the close agreement
between chips within lines, since each chip represents
an independent sample of disks. By the same token, the
striking disparity between the Massachusetts and Cal-
ifornia lines—all tested simultaneously—must be a real
phenomenon. This result argues that most expression
differences with important effects on this trait are dif-
ferent in the two gene pools. Several recent studies sup-
port this interpretation.
In a screen of 50 random homozygous-viable P-
element insertions, 11 (22%) had significant and re-
peatable effects on wing shape (Weber et al. 2005). All
11 were in sites that would be likely to cause expression
differences in associated genes. The sample is small, but
if it is even approximately representative, then the total
number of genes in the genome that can affect wing
shape by changes in expression is vastly greater than the
number that could be segregating in any local popula-
tion sample. This implies that, in population samples
from widely separated locations, most genes that con-
tribute to selection response for wing-shape traits might
be different, as we report here.
QTL analyses of genetic variation for wing shape are
also consistent with this conclusion. Wing-shape QTL
have now been mapped in three different labs using
different genetic stocks and trait definitions (Weber
et al. 1999, 2001; Zimmerman et al. 2000; Mezey et al.
although each one detected many wing-shape genes,
the loci in each study were probably mostly different
(Mezey et al. 2005).
On the other hand, it is hard to say whether any pres-
ent candidategenes confirmprevious QTL assignments.
Wing-Shape Genes in Flies 1105
Our QTL map for lines H and L (Weber et al. 1999,
peaks, with QTL assigned at high points. Some points
fall within 0.5 cM of the candidate genes in Table 7, but
locations are only approximate, and, in regions of aver-
age gene density, 0.5 cM can include 25 genes. More-
over, genes that could affect wing shape are not obvious
from their known features (Weber et al. 2005).
The candidate genes that we identified in the
Massachusetts lines show a notable lack of coincidence
neural aspects of wing development (Brody 2005),
including genes implicated in regulating wing shape
through cell proliferation such as vestigial (Kim et al.
1996; Baena-Lopez and Garcia-Bellido 2006), Notch,
or Delta (Baonza and Garcia-Bellido 2000). They in-
clude none of the 43 genes in the transforming growth
tested by Dworkin and Gibson (2006), the majority of
which have small effects on wing shape. Of the 11 genes
affecting wing shape in our P-element screen (Weber
et al. 2005), one (hephaestus) coincided with a known
effects in the survey of Dworkin and Gibson (2006).
There could be many reasons for the differences be-
Perhaps genes previously identified as major mutants
were involved in our experiments but their expression
differences were too small to be significant. Or perhaps
some classical developmental genes affect the timing or
location of transcription without changing mean ex-
pression levels in disks in the stage at which we assayed
them. It is clear that some morphological traits are
influenced by many genes with small effects (Norga
fact alone may account for the lack of extensive overlap
of wing-shape genes identified by different approaches.
Several of the genes in Table 7 affect functions that
could be relevant to wing development, including the
well-studied cell death gene reaper (White et al. 1996),
the CG17090 gene with homology to serine/threonine
protein kinases associated with apoptosis, and the Ste20
kinase-like gene Stlk associated with cytokinesis in yeast
(Cvrckova et al. 1995). Three other genes are associ-
ated with the regulation of gene expression: the RNA
polymerase subunit Rpll18, the histone gene His1, and a
putative RNA-binding protein component of the spli-
ceosome CG4291. Others with possible developmental
two genes associated with lipid biosynthesis (CG31522
There is one striking correspondence between the
present results and the 11 genes with shape effects
caused by P-element insertions in Weber et al. (2005).
10 of them were among the 6843 transcripts that were
called present on all 36 chips. This result is extremely
nonrandom. It makes sense because Weberet al. (2005)
was a loss-of-function screen, where insertions affecting
wing shape should occur in genes that are normally
expressed in wing disks. Shape changes may also arise
by ectopic expression of genes not normally active in
wings. But from this coincidence of results, and other
results reported here, it appears that selection has
ample scope to act simply by modulating the expression
of active genes. These genes are expressed at different
intensities in localized regions of wing disks (Butler
etal.2003),but mostareexpressed notonly inwingsbut
also in all imaginal disks (Klebes et al. 2002).
In our view, selection penetrates an ever-expanding
field of potential variation, with incremental improve-
transfer of selection to modifier genes (Weber 1996).
This is enhanced by the degenerate and network nature
2001, 2004; Dworkin and Gibson 2006). In some traits,
alleles may affect a trait inconsistently in different ge-
netic backgrounds (Van Swinderen and Greenspan
2005). In such cases, microarray analysis of replicated
selection lines might be used to seek Wrightian peaks in
the genetics of quantitative traits. However, the amount
of replication and validation that may be required to
achieve definitive results is still daunting. On the other
effects in different genetic backgrounds, we will have a
case worth further study.
Microarray analysis of divergently selected lines is an
established method of gene hunting (Tomaet al. 2002;
Mackay et al. 2005; Dierick and Greenspan 2006;
Edwards et al. 2006). The use of more replicate lines
candidates caused by random fixation and hitchhiking.
Replicates should have the same genetic base, but also
the likelihood of high recombinational diversity among
replicates. Larger populations may also help improve
resolution. The consistency of recent results shows that
microarray expression measurements are reliable. With
further development of methods, microarray testing of
replicated selection lines should become increasingly
efficient in the search for quantitative trait genes.
Thanks go to L. Harshman and two anonymous reviewers for
comments on the manuscript. This work was supported by the
National Science Foundation under grant no. DEB-0344003 to
K.E.W. and grant no. 0432063 to R.J.G. R.J.G. is the Dorothy and
Lewis B. Cullman Fellow at the Neurosciences Institute, which is
supported by the Neurosciences Research Foundation.
Abzhanov, A., W. P. Kuo, C. Hartmann, B. R. Grant, P. R. Grant
et al., 2006The calmodulin pathway and evolution of elongated
beak morphology in Darwin’s finches. Nature 442: 563–567.
Andres, A. J., and C. S. Thummel, 1994
analysis of transcription in larvae and prepupae, pp. 565–573
in Drosophila melanogaster:Practical Uses in Cell and Molecular Biology
Methods for quantitative
1106K. E. Weber et al.
(Methods in Cell Biology, Vol. 44), edited by L. Goldstein and
E. Fyrberg. Academic Press, San Diego.
Baena-Lopez, L. A., and A. Garcia-Bellido, 2006
growth and positional information by the graded vestigial expres-
sion pattern in the wing of Drosophila melanogaster. Proc. Natl.
Acad. Sci. USA 103: 13734–13739.
Baonza, A., and A. Garcia-Bellido, 2000
controls cell proliferation in the Drosophila wing disc. Proc. Natl.
Acad. Sci. USA 97: 2609–2614.
Bartolome ´, C., X. Maside and B. Charlesworth, 2002
abundance and distribution of transposable elements in the ge-
nome of Drosophila melanogaster. Mol. Biol. Evol. 19: 926–937.
Beldade, P., K. Koops and P. M. Brakefield, 2002
constraints versus flexibility in morphological evolution. Nature
Brody, T., 2005
The Interactive Fly. http:/ /rail.bio.indiana.edu/
Bull, J. J., M. R. Badgett, H. A. Wichman, J. P. Huelsenbeck, D. M.
Hillis et al., 1997 Exceptional convergent evolution in a virus.
Genetics 147: 1497–1507.
Bult, A., and C. B. Lynch, 1996
house mice bidirectionally selected for thermoregulatory nest-
building behavior: crosses of replicate lines. Behav. Genet. 26:
Butler, M. J., T. L. Jacobsen, D. M. Cain, M. G. Jarman, M. Hubank
et al., 2003Discovery of genes with highly restricted expression
patterns in the Drosophila wing disc using DNA oligonucleotide
microarrays. Development 130: 659–670.
Cohan, F. M., and A. A. Hoffmann, 1986
uniform selection. II. Different responses to selection for knock-
down resistance to ethanol among Drosophila melanogaster popu-
lations and their replicate lines. Genetics 114: 145–163.
Crosby, M. A., J. L. Goodman, V. B. Strelets, P. Zhang, W. M.
Gelbart et al., 2007 FlyBase: genomes by the dozen. Nucleic
Acids Res. 35: D486–D491.
Cunningham, C. W., K. Jeng, J. Husti, M. Badgett, I. J. Molineux
et al., 1997 Parallel molecular evolution of deletions and non-
sensemutationsinbacteriophageT7. Mol. Biol. Evol. 14: 113–116.
Cvrckova, F., C. De Vergilio, E. Manser, J. R. Pringle and K.
Nasmyth, 1995 Ste20-like protein kinases are required for
normal localization of cell growth and for cytokinesis in budding
yeast. Genes Dev. 9: 1817–1830.
Devos, K. M., 2005 Updating the ‘‘crop circle.’’ Curr. Opin. Plant
Biol. 8: 155–162.
Dierick, H. A., and R. J. Greenspan, 2006
selected for aggressive behavior. Nat. Genet. 38: 1023–1031.
Dworkin, I., and G. Gibson, 2006
tor and transforming growth factor-b signaling contributes to var-
iation for wing shape in Drosophila melanogaster. Genetics 173:
Edwards, A. C., S. M. Rollmann, T. J. Morgan and T. F. C. Mackay,
2006Quantitative genomics of aggressive behavior in Drosoph-
ila melanogaster. PLoS Genet. 2(9): e154.
Falconer, D. S., and T. F. C. Mackay, 1996
tive Genetics. Longman, Harlow, UK.
Gale, M. D., and K. M. Devos, 1998
ter 10 years. Science 282: 656–659.
Gardner, S. N., J. Gressel and M. Mangel, 1998
strategy to delay the evolutionof both quantitative vs. major mon-
ogene resistances to pesticides and drugs. Int. J. Pest Manage. 44:
Gompel, N., and S. B. Carroll, 2003
straints governing the evolution of correlated traits in drosophil-
id flies. Nature 424: 931–935.
Goodale, H. D., 1941Progress report on possibilities in progeny-
test breeding. Science 94: 442–443.
Goodale, H. D., 1953Appendix: third progress report on breeding
larger mice, in A study of selection limits in the mouse, D. S.
Falconer and J. W. B. King. J. Genet. 51: 561–581.
Greenspan, R. J., 2001The flexible genome. Nat. Rev. Genet. 2:
Greenspan, R. J., 2004E pluribus unum, ex uno plura: quantitative-
and single-gene perspectives on the study of behavior. Annu. Rev.
Neurosci. 27: 79–105.
Notch signaling directly
Multiple selection responses in
Genetic divergence under
Molecular analysis of flies
Epidermal growth factor recep-
Introduction to Quantita-
Plant comparative genetics af-
A revolving dose
Genetic mechanisms and con-
Gromko, M. H., 1995
lection: pleiotropy and selection interact. Evolution 49: 685–693.
Hill, W. G., 1972Estimation of realized heritabilities from selection
experiments. II. Selection in one direction. Biometrics 28: 767–
Houle-Leroy, P., H. Guderley, J. G. Swallow and T. Garland, Jr.,
2003Artificial selection for high activity favors mighty mini-
muscles in house mice. Am. J. Physiol. Regul. Integr. Comp. Phys-
iol. 284: 433–443.
Hurst, L. D., C. Pa ´l and M. J. Lercher, 2004
namics of eukaryotic gene order. Nat. Rev. Genet. 5: 299–310.
Jensen, H.R.,I.M.Scott,S.R.Sims,V.L.TrudeauandJ.T. Arnason,
2006 The effect of a synergistic concentration of a Piper nigrum
in Drosophila melanogaster. Insect Mol. Biol. 15: 329–339.
Kim, J., A. Sebring, J. J. Esch, M. E. Kraus, K. Vorwerk et al.,
1996Integration of positional signals and regulation of wing
formation and identity by Drosophila vestigial gene. Nature 382:
Klebes, A., B. Biehs, F. Cifuentes, and T. B. Kornberg, 2002
pression profiling of Drosophila imaginal discs. Genome Biol.
Lande, R., 1979Quantitative genetic analysis of multivariate evolu-
tion, applied to brain:body size allometry. Evolution 33: 402–416.
Lande, R., 1983The response to selection on major and minor mu-
tations affecting a metrical trait. Heredity 50: 47–65.
Lindsley, D. L., and G. G. Zimm, 1992
anogaster. Academic Press, San Diego.
Macarthur, J. W., 1949Selection for small and large body size in
the house mouse. Genetics 34: 194–209.
Mackay, T. F. C., S. L. Heinsohn, R. F. Lyman, A. J. Moehring, T. J.
Morgan et al., 2005 Genetics and genomics of Drosophila mat-
ing behavior. Proc. Natl. Acad. Sci. USA 102: 6622–6629.
Maroni, G., and S. C. Stamey, 1983
chronous, late third-instar larvae. Dros. Inf. Serv. 59: 142–143.
McKenzie, J. A., A. G. Parker and J. L. Yen, 1992
gle gene responses to selection for resistance to diazinon in Lu-
cilia cuprina. Genetics 130: 613–620.
Mezey, J. G., and D. Houle, 2005
iation for wing shape in Drosophila melanogaster. Evolution 59:
Mezey, J. G., D. Houle and S. V. Nuzhdin, 2005
ing quantitative trait loci affecting wing shape of Drosophila mela-
nogaster. Genetics 169: 2101–2113.
Morrell, P. L., and M. T. Clegg, 2007
ond domestication of barley (Hordeum vulgare) east of the Fertile
Crescent. Proc. Natl. Acad. Sci. USA 104: 3289–3294.
Neve, P., and S. Powles, 2005Recurrent selection with reduced her-
bicide rates results in the rapid evolution of herbicide resistance
in Lolium rigidum. Theor. Appl. Genet. 110: 1154–1166.
Norga, K. K., M. C. Gurganus, C. L. Dilda, A. Yamamoto, R. F.
Lyman et al., 2003Quantitative analysis of bristle number in
Drosophila mutants identifies genes involved in neural develop-
ment. Curr. Biol. 13: 1388–1396.
Oleksiak, M. F., G. A. Churchill and D. L. Crawford, 2002
ation in gene expression within and among natural populations.
Nat. Genet. 32: 261–266.
Paterson, A. H., Y.-R. Lin, Z. Li, K. F. Schertz, J. F. Doebley et al.,
1995Convergent domestication of cereal crops by independent
mutations at corresponding genetic loci. Science 269: 1714–1718.
Patthy, L., 1999
Protein Evolution. Blackwell Science, Oxford.
Prud’homme, B., N. Gompel, A. Rokas, V. A. Kassner, T. M.
Williams et al., 2006Repeated morphological evolution through
cis-regulatory changes in a pleiotropic gene. Nature 440: 1050–
Schluter, D., E. A. Clifford, M. Nemethy and J. S. McKinnon,
2004 Parallel evolution and inheritance of quantitative traits.
Am. Nat. 163: 809–822.
Shapiro, M. D., M. E. Marks, C. L. Peichel, B. K. Blackman, K. S.
Nereng et al., 2004Genetic and developmental basis of evolu-
tionary pelvic reduction in threespine sticklebacks. Nature 428:
Swallow, J. G., P. A. Carter and T. Garland, Jr., 1998
selection for increased wheel-running behavior in house mice.
Behav. Genet. 28: 227–237.
Unpredictability of correlated responses to se-
The evolutionary dy-
The Genome of Drosophila mel-
Use of blue food to select syn-
Polygenic and sin-
The dimensionality of genetic var-
Genetic evidence for a sec-
Wing-Shape Genes in Flies 1107
Toma, D. P., K. P. White, J. Hirsch and R. J. Greenspan,
2002Identification of genes involved in Drosophila melanogaster
geotaxis, a complex behavioral trait. Nat. Genet. 31: 349–353.
Van Swinderen, B., and R. J. Greenspan, 2005
network affecting a simple behavior in Drosophila melanogaster. Ge-
netics 169: 2151–2163.
Weber, K. E., 1990 Selection on wing allometry in Drosophila mela-
nogaster. Genetics 126: 975–989.
Weber, K. E., 1992How small are the smallest selectable domains of
form? Genetics 130: 345–353.
Weber, K. E., 1996 Largegenetic changeat smallfitnesscost in large
populations of Drosophila melanogaster selected for wind-tunnel
flight: rethinking fitness surfaces. Genetics 144: 205–213.
Weber, K., R. Eisman, L. Morey, A. Patty, J. Sparks et al., 1999
analysis of polygenes affecting wing shape on chromosome 3 in
Drosophila melanogaster. Genetics 153: 773–786.
Weber, K., R. Eisman, S. Higgins, L. Morey, A. Patty et al.,
2001 An analysis of polygenes affecting wing shape on chro-
Flexibility in a gene
mosome 2 in Drosophila melanogaster. Genetics 159: 1045–
Weber, K., N. Johnson, D. Champlin and A. Patty, 2005
element insertions affect wing shape in Drosophila melanogaster.
Genetics 169: 1461–1475.
White, K., E. Tahaoglu and H. Steller, 1996
Drosophila gene reaper. Science 271: 805–807.
Wichman, H. A., M. R. Badgett, L. A. Scott, C. M. Boulianne and J.
J. Bull, 1999 Different trajectories of parallel evolution during
viral adaptation. Science 285: 422–424.
Wood, T. E., J. M. Burke and L. H. Rieseberg, 2005
typic adaptation: when evolution repeats itself. Genetica 123:
Zimmerman, E., A. Palsson and G. Gibson, 2000
loci affecting components of wing shape in Drosophila melanogaster.
Genetics 155: 671–683.
Cell killing by the
Communicating editor: L. Harshman
1108 K. E. Weber et al.