Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Tandem Duplications and the Limits of Natural Selection in Drosophila yakuba and Drosophila simulans

  • Rebekah L. Rogers ,

    bekah@berkeley.edu

    Affiliation Ecology and Evolutionary Biology, University of California, Berkeley, California, United States of America

  • Julie M. Cridland,

    Affiliation Ecology and Evolutionary Biology, University of California, Davis, Davis, California, United States of America

  • Ling Shao,

    Affiliation Ecology and Evolutionary Biology, University of California, Irvine, Irvine, California, United States of America

  • Tina T. Hu,

    Affiliation Ecology and Evolutionary Biology and the Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America

  • Peter Andolfatto,

    Affiliation Ecology and Evolutionary Biology and the Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America

  • Kevin R. Thornton

    Affiliation Ecology and Evolutionary Biology, University of California, Irvine, Irvine, California, United States of America

Tandem Duplications and the Limits of Natural Selection in Drosophila yakuba and Drosophila simulans

  • Rebekah L. Rogers, 
  • Julie M. Cridland, 
  • Ling Shao, 
  • Tina T. Hu, 
  • Peter Andolfatto, 
  • Kevin R. Thornton
PLOS
x

Abstract

Tandem duplications are an essential source of genetic novelty, and their variation in natural populations is expected to influence adaptive walks. Here, we describe evolutionary impacts of recently-derived, segregating tandem duplications in Drosophila yakuba and Drosophila simulans. We observe an excess of duplicated genes involved in defense against pathogens, insecticide resistance, chorion development, cuticular peptides, and lipases or endopeptidases associated with the accessory glands across both species. The observed agreement is greater than expectations on chance alone, suggesting large amounts of convergence across functional categories. We document evidence of widespread selection on the D. simulans X, suggesting adaptation through duplication is common on the X. Despite the evidence for positive selection, duplicates display an excess of low frequency variants consistent with largely detrimental impacts, limiting the variation that can effectively facilitate adaptation. Standing variation for tandem duplications spans less than 25% of the genome in D. yakuba and D. simulans, indicating that evolution will be strictly limited by mutation, even in organisms with large population sizes. Effective whole gene duplication rates are low at 1.17 × 10−9 per gene per generation in D. yakuba and 6.03 × 10−10 per gene per generation in D. simulans, suggesting long wait times for new mutations on the order of thousands of years for the establishment of sweeps. Hence, in cases where adaptation depends on individual tandem duplications, evolution will be severely limited by mutation. We observe low levels of parallel recruitment of the same duplicated gene in different species, suggesting that the span of standing variation will define evolutionary outcomes in spite of convergence across gene ontologies consistent with rapidly evolving phenotypes.

Introduction

Tandem duplications are an essential source of genetic novelty that is useful for the development of novel traits [13] and their prevalence in populations is therefore expected to influence the arc of evolutionary trajectories. The observed landscape of tandem duplications in Drosophila spans only a few percent of the genome [47], and it is unclear to what extent duplications among new mutations or standing variation can provide a sufficient source of adaptive genetic variation. Tandem duplications produce a variety of novel gene structures including chimeric genes, recruited non-coding sequence, dual promoter genes, and whole gene duplications [4, 8, 9]. Surveys based on single sequenced reference genomes have suggested that whole gene duplications may form at low rates in comparison with SNPs, with even lower mutation rates for complex variants such as chimeric genes [4, 8, 10, 11]. Yet, these alternative genetic structures are known forces of evolutionary innovation [1216]. Whole gene duplications often develop novel functions or specialize in ancestral functions [1], and chimeric genes are more likely still to produce novel molecular effects and play a role in adaptive evolution [12]. Although these variants contribute substantially to the evolution of genome content [8, 10, 17], their lower rates of formation may render evolution of tandem duplications more likely to be limited by mutation.

If population-level mutation rates are sufficiently large, new mutations will accumulate quickly and adaptation is expected to proceed rapidly [18]. However, if population-level mutation rates are low, then there will be long waiting times until the next new mutation and evolutionary trajectories are likely to stall at suboptimal solutions during the mutational lag [1820]. Drosophila have large population sizes in comparison to other multicellular eukaryotes with Ne ≈ 105−106 [2123] and absolute numbers of individuals sufficient to provide large numbers of SNPs at many sites every generation [24]. However, the ability of SNPs to traverse adaptive landscapes is often limited [25] and, the prevalence of other types of mutations beyond SNPs has not been systematically surveyed. Alternative genetic constructs such as chimeric genes can readily traverse mutational landscapes to obtain structures that cannot be readily reached via point mutations [26, 27] whereas whole gene duplications often free sequences from functional constraints to allow for the development of new gene functions [1, 2]. If the supply of tandem duplications is limited by mutation, we expect to see suboptimal outcomes in adaptive walks, limited ability to adapt to changing environments, and low rates of evolution through parallel recruitment of the same genetic solutions in different species.

The Drosophila offer an excellent model system for population genomics, allowing for a whole genome survey of the genetic landscape of standing variation across species in natural populations and determination of genetic convergence across taxa. There are multiple sequenced reference genomes for Drosophila, and genomes are small and compact, making whole genome population surveys using next generation sequencing readily tractable. Here, we focus on D. yakuba and D. simulans, which are separated by 12 MY of divergence [28], offering distantly related groups which are not expected to share polymorphic variation due to ancestry. Thus, we can measure the limits of standing variation and the incidence of parallel duplication across species, which should be broadly applicable to multicellular eukaryotic evolution.

Convergent evolution is regarded as the ultimate signal of natural selection: if the same solution is favored for a given environment then selection should result in similar phenotypes [29]. There are many known cases of convergent phenotypic evolution, but the understanding of convergence at the genetic level is limited to a small number of case studies across diverse clades [30]. These case studies have revealed convergent evolution through different genetic solutions in vertebrates [3133], and arthropods [3437]. Parallel evolution through similar genetic solutions, however, appears to be more common at mutational hotspots where high mutation rates at targeted sites produce mutations at a steady rate [3840]. Beyond these results from natural populations, convergence has often been observed in experimental evolution and is considered a signal of selection favoring alleles [38, 4043]. However, most studies of laboratory evolution take advantage of microbes or viruses with large population sizes roughly 109−1010 such that every mutation is likely to be sampled every generation [38, 40] or from small populations that share a common pool of standing variation [41, 44] and may therefore be qualitatively different outcomes in comparison to natural evolution in multicellular eukaryotes. Indeed, known examples of evolution through parallel recruitment of the same genetic solutions in natural populations often occur through a common ancestral genetic pool [45] or through introgression [46]. In D. melanogaster, parallel selection on standing variation results in high levels of convergence at the genetic level due to shared pools of ancestral variation [47]. These results suggest that given the same mutational spectrum with which to work, convergent evolution will be common. However, whether similar genetic solutions can arise independently and result in sweeps on similar variants without shared ancestry is largely unknown.

Identifying factors that influence convergent evolution across distantly related taxa that do not share population level variation due to ancestry is essential to understanding the ways mutation limits evolution, the role of standing variation in evolutionary trajectories, and the genetic architecture of adaptation. Here, we survey standing variation for tandem duplications in Drosophila yakuba and Drosophila simulans and the role that this standing variation plays in adaptive evolution in natural populations. We identify signals of reduced diversity surrounding tandem duplications, and an overabundance of high frequency variants on the D. simulans X chromosome, pointing to a role for adaptation through gene duplication. We observe high levels of convergence at the level of gene ontology but limited shared variation across species at specific genes, pointing to limited rates of convergent evolution at the level of single genes. We show that the span of tandem duplications in populations is limited to a small fraction of the genome and that low mutation rates will lead to long waiting times for sweeps on new mutations. These results imply that evolution by tandem duplication will be limited by mutation and that parallel recruitment of gene duplicates across species is likely to be exceedingly rare even in the face of strong selection on similar phenotypes in different species.

Results

We previously identified hundreds to thousands of segregating duplications in natural populations of D. yakuba and D. simulans, including large numbers of gene duplications [4]. We assess the numbers and types of gene duplications, differences in duplication rates across species and explore the limits of the landscape of standing variation for tandem duplications present in each species to determine the extent to which these variants can serve as a source of genetic novelty. Recently derived, segregating tandem duplications were previously detected using paired-end read mapping and coverage changes in D. yakuba and D. simulans in samples of 20 isofemale lines derived from natural populations of each species [4]. Using divergently oriented paired-end reads, we identified 1415 segregating tandem duplications in D. yakuba, in comparison to 975 in D. simulans. Strains have been sequenced to high coverage of 50–150X and duplicate identification methods have a 96% validation rate using PacBio long molecule sequencing, and a low false negative rate less than 1% based on comparisons across strains [4]. Thus, this dataset represents a high quality portrait of variation for population genomics. Here, we describe signals of selection acting on these tandem duplications, the limits of standing variation for tandem duplicates, and their role in adaptive evolution.

Widespread selection on the D. simulans X chromosome

If tandem duplications are common targets for adaptation and selective sweeps, we should observe a shift in the site frequency spectrum (SFS) toward high frequency variants relative to neutral markers [48]. We compare the SFS for duplications with the SFS for SNPs from 8–30 bp of short introns used as a putatively neutral proxy to determine whether duplicates are subject to selection (Figure A in S1 File). The SFS for duplications is significantly different from that of intronic SNPs on the D. simulans autosomes using a Wilcoxon rank sum test (W = 268, P = 2.981 × 10−6) and D. yakuba autosomes (W = 212, P = 3.507 × 10−6). In D. yakuba the SFS for duplicates on the X is significantly different from that of SNPs (W = 211, P = 4.781 × 10−4). Duplicates show an excess of singleton variants on the autosomes in both species (Figure A in S1 File), suggesting deleterious impacts on average. We find a significant difference between the SFS of duplicates on the X chromosome and the autosomes in D. yakuba (W = 172, P = 0.0128) but not in D. simulans (W = 183.5, P = 0.1848) (Fig 1, Tables A-B in S1 File).

thumbnail
Fig 1. SFS for tandem duplications in D. yakuba and D. simulans, corrected for ascertainment bias.

A. Site frequency spectra on the autosomes (black) and on the X (grey) in D. yakuba. B. SFS on the autosomes (black) and on the X (grey) in D. simulans. C. SFS for X-linked intronic SNPs (black) and duplicates (white) in D. simulans. The excess of high frequency variants on the X in D. simulans suggests widespread selection for tandem duplicates on the D. simulans X.

https://doi.org/10.1371/journal.pone.0132184.g001

We have calculated average heterozygosity per site (θπ) [49], Wattersons’s θ ( per site) [50], and Tajima’s D [51] for the four major autosomal arms and the X chromosome in D. yakuba and D. simulans using 5 kb windows with a 500 bp slide correcting for the number of sites with coverage sufficient to confidently identify SNPs (Figures B-K in S1 File). We compare θπ in windows immediately surrounding tandem duplications and for windows surrounding putatively neutral SNPs from 8–30 bp of short introns to search for signals of reduced diversity consistent with selection acting on tandem duplications. These tandem duplications are polymorphic and represent putative sweeps in progress and such comparisons to within-genome controls of neutral SNPs offer greater power than alternative tests of selection [52]. A significant excess of diversity surrounding duplicates is seen on chromosome 2 in D. yakuba (W = 31594, P = 2.517 × 10−4), a putative product of alternative evolutionary dynamics driven by segregating inversions on 2L [53, 54]. We find a reduction in θπ per site surrounding newly arisen tandem duplications on D. yakuba chromosome 3, which is not known to contain inversions (single tailed Wilcoxon rank sum test, W = 170168 P = 0.00665, see Table C in S1 File, Fig 2).

thumbnail
Fig 2. Diversity (θπ) as a function of distance from new mutations in D. yakuba for putatively neutral intronic SNPs (black) and for tandem duplications (red) by chromosome with lowess smoothing.

Duplicates show a reduction in diversity approaching duplications on chromosome 3L, whereas neutral SNPs show no reduction in diversity. Plots exclude centromeric regions and the 4th chromosome which have atypical nucleotide diversity. D. yakuba chromosome 2 displays an atypical pattern of increased diversity and was handled separately from chromosome 3 due to segregating inversions in populations.

https://doi.org/10.1371/journal.pone.0132184.g002

In D. simulans autosomes we observe a significant reduction in diversity for 5 kb windows immediately surrounding tandem duplications (single tailed Wilcoxon rank sum test, W = 627683.5 P = 2.267 × 10−7). Chromosome 3L in D. simulans contains a region encompassing multiple duplications with signals of a broad selective sweep encompassing multiple loci located at roughly 8.5 Mb that is excluded from these tests of selection (Figure I in S1 File) but results remain significant. The D. simulans X shows signals of reduced diversity surrounding tandem duplications (single tailed Wilcoxon rank sum test, W = 13450.5, P = 0.01819); see Table C in S1 File) (Fig 3). We observe an excess of tandem duplications at a sample frequency of 20 out of 20 sample strains (with no indication of duplication or misassembly in the resequenced reference) on the X chromosome of D. simulans in comparison to neutral SNPs (P < 10−6). We observe no duplicates at a sample frequency of 20 out of 20 in D. yakuba on the X or autosomes. Furthermore comparisons of the SFS for neutral SNPs and duplications segregating in populations show an excess of highest frequency duplicates ≥ 16 out of 17 on the D. simulans X (χ2 = 21.8334, df = 1, P = 2.974 × 10−6). The excess of high frequency duplicates on the D. simulans X chromosome is indicative of selection favoring large numbers of tandem duplicates. These results imply that adaptation through duplication is common on the D. simulans X.

thumbnail
Fig 3. Nucleotide diversity, θπ as a function of distance from new mutations in D. simulans for putatively neutral intronic SNPs (black) and for tandem duplications (red) by chromosome with lowess smoothing.

Duplicates show a reduction in mean diversity approaching duplications on the D. simulans autosomes and X chromosome, whereas neutral SNPs show no reduction in diversity. Plots exclude centromeric regions and the 4th chromosome which have atypical nucleotide diversity. Chromosome 3L is strongly affected by a cluster of duplications at roughly 8.5Mb, which is excluded from the plot, but the effect is still significant without this region.

https://doi.org/10.1371/journal.pone.0132184.g003

It is possible that tandem duplications whose breakpoints lie within gene sequences may have different phenotypic impacts from tandem duplications that capture whole genes and do not interrupt or otherwise modify gene sequences. We compare θπ for windows centered around tandem duplications that capture solely intergenic sequence with those that capture whole genes and do not create chimeric constructs with those that create chimeric genes or recruit non-coding sequence. In D. simulans, tandem gene duplications that do not create chimeric genes have reduced diversity (W = 17880, P = 0.0123) in comparison to mutations that capture intergenic mutations (Fig 4C–4D). Such results are consistent with selection driving an excess of whole gene duplications in D. simulans [4]. However, tandem duplications whose breakpoints lie within gene sequences thereby forming chimeric genes do not show similar overabundance (W = 5020, P = 0.5755). Relationships in D. yakuba are not significant for chimeric gene mutations (W = 2656, P = 0.8226) or whole gene duplications that do not create chimeric genes (W = 6387, P = 0.8847). Based on a binomial test, we observe a marginally significant overrepresentation of tandem duplications that capture gene sequences vs. solely non-coding sequences in D. yakuba (P = 0.0291) and highly significant for D. simulans (P = 9.044 × 10−5).

thumbnail
Fig 4.

Histogram of nucleotide diversity, θπ, (A) For Intergenic mutations (yellow) and duplications that capture gene sequences but do not create chimeric constructs (blue) in D. yakuba. (B) For Intergenic mutations (yellow) and duplications that create chimeric genes (red) in D. yakuba. (C) For Intergenic mutations (yellow) and duplications that capture gene sequences but do not create chimeric constructs (blue) in D. simulans. (D) For Intergenic mutations (yellow) and duplications that create chimeric genes (red) in D. simulans.

https://doi.org/10.1371/journal.pone.0132184.g004

Populations of both D. yakuba and D. simulans show negatively skewed Tajima’s D for neutral SNPs, suggesting recent population expansion in both species (Figures B-K in S1 File), and similar results have been identified in D. melanogaster [55]. While demography and neutral evolutionary forces can result in shifts of diversity and site frequency spectra, these forces should affect sequences across individual chromosome arms and act similarly on intronic SNPs. Hence, demography is unlikely to explain the observed differences between duplicates and intronic SNPs. Further, gene conversion might putatively alter the SFS, while divergence of paralogs would artificially increase observed diversity for regions immediately surrounding tandem duplications. These forces would artificially skew statistics away from selection, leading to underreporting of the adaptive impacts of chimeric genes. Thus, we would expect many of the high frequency variants reported here to be strong candidates for ongoing selective sweeps. We further observe large numbers of singleton variants among tandem duplicates in comparison with intronic SNPs in D. yakuba and D. simulans autosomes. Copy number variants are subject to purifying selection in D. melanogaster [5, 56], and we observe large numbers of singleton variants in excess of neutral expectations, indicating negative selection preventing variants from rising to higher frequency. Hence, while some variants are likely to offer a means of adaptive change, many are likely to ultimately be lost from the pool of standing variation. We suggest that tandem duplications are likely to confer phenotypic impacts that are on average large enough to surpass the threshold of nearly neutral effects in Drosophila.

Limits of standing variation in natural populations

We observe hundreds of segregating tandem duplicates in D. yakuba and D. simulans, spanning 2.6% of assayable the genome (i.e X and 4 major autosomal arms) in D. yakuba and 1.8% of the assayable genome in D. simulans. If evolutionary trajectories depend on tandem duplications to effect beneficial phenotypic changes, then these trajectories may be constrained if the population does not contain the desired variants as standing variation. We estimate the number of variants present in the entire population based on the observed sample variation in order to determine the extent to which selection will be limited by mutation. We estimate that the population contains at most 6800 segregating tandem duplications in D. yakuba and 4,500 in D. simulans (Tables E-F in S1 File), corresponding to 13.4% of major chromosome arms in D. yakuba and 9.7% of major chromosome arms in D. simulans. Estimates using rarefaction estimators free from assumptions of neutrality [57] are comparable (Table E in S1 File). Thus, the standing variation for tandem duplications will be insufficient to offer tandem duplications for every potential mutation for the majority of the genome (≈ 85%). If a tandem duplication is required for adaptation, evolutionary trajectories must then by definition rely on new mutations. Tajima’s D is negative in both D. yakuba and D. simulans suggesting recent population expansion and greater census size than effective population size. Even under expectations of large population sizes of 108 after population expansion, we estimate that there are at most 8550 duplications segregating in the population at large for D. yakuba and 5700 for D. simulans (Table E in S1 File), still far from expectations required to span the entire genome (Table F in S1 File).

We calculate population level mutation rates θπ (4Ne μ) of 0.00277 per gene per generation for whole gene duplications, 0.00082 for recruited non-coding sequence and 0.00088 for chimeras in D. yakuba. Population level mutation rates in D. simulans are slightly higher for most types of mutations with 0.00291 per gene per generation for whole gene duplications, 0.00117 for recruited non-coding sequence but a lower population level mutation rate of 0.00041 for chimeras. In comparison, we calculate θπ for putatively neutral intronic SNPs of 0.0138 for D. yakuba and 0.0280 for D. simulans. We use these estimates of θ to calculate the likelihood of adaptation from alleles among the standing variation rather than new mutation for a population (Psgv) [18] assuming variants with a large selection coefficient of 1% under an additive genetic model. With such low levels of θπ the likelihood of adaptation from a tandem duplication among the standing variation is 2.2% in D. yakuba and 2.6% in D. simulans (Table 1), a strikingly low likelihood that standing variation offers a sufficient substrate for adaptation. Even with a massive selective coefficient of s = 0.20 [24], the likelihood of adaptation from standing variation rather than new mutation is 3.1% for duplicates in D. yakuba and 3.4% in D. simulans. Chimeras are even more extreme with less than a 1% chance of fixation from standing variation (Table 1). In comparison, intronic SNPs have a likelihood of adaptation from standing variation of 12.1% in D. yakuba and 24.6% in D. simulans given s = 0.01, and 15.7% in D. yakuba and 30.1% in D. simulans, given extreme selection coefficients of s = 0.20 (Table 1). Thus, the limits of standing variation are expected to be far more severe for complex gene structures than for SNPs and will not offer sufficient standing variation to provide for adaptation via tandem duplication for any randomly selected gene.

Drosophila have large effective population sizes and should offer large absolute numbers of tandem duplications in comparison to many other multicellular eukaryotes. However, the dynamics of standing variation may be drastically different for organisms with varying effective population sizes. We estimate time to loss given loss (sometimes called the sojourn time) [58], population mutation rates, and maximum nearly-neutral selection coefficients [59] for organisms with different population sizes in order to determine the extent to which dynamics observed in Drosophila might be applicable to other organisms. For organisms with very low Ne, we expect to observe fewer mutations due to lower population level mutation rates, but a greater tolerance for extreme variation as more variants are subject to nearly neutral dynamics though sojourn times are largely unaffected (Table I in S1 File). Under such a scheme, evolution will still be severely limited by mutation with only small numbers of mutations appearing in the population. Given small Ne, however, an extreme variant needed for adaptation to extreme environmental change is more likely to be tolerated among the standing variation. Thus, there may be higher variance in adaptive outcomes for organisms with small Ne due to the limits of mutation and in extremely rare cases organisms with small Ne might be able to adapt to sudden and drastic shifts in selective pressures.

Long waiting times for new mutations

We calculate the per generation effective mutation rate μ per gene for whole gene duplications, considering duplicates that capture 90% or more of gene sequences, in agreement with previous methods [10]. We estimate a whole gene duplication rate of 1.17 × 10−9 per gene per generation for D. yakuba and 6.03 × 10−10 per gene per generation for D. simulans (Fig 5, Table 1). These estimates are in general agreement with surveys of duplicates in the D. melanogaster reference genome of 3.68 × 10−10 per gene per generation [8, 10]. The rate of recruited non-coding sequence is 3.46 × 10−10 in D. yakuba and 2.42 × 10−10 in D. simulans and the rate of chimeric gene formation is equally low with 3.7 × 10−10 in D. yakuba and 8.52 × 10−11 in D. simulans (Fig 5, Table 1). We observe more tandem duplications in D. yakuba in spite of its lower Ne, yielding a duplication rate per gene in D. yakuba two-fold higher than that of D. simulans. New mutations are often unable to spread through populations as low frequency variants can be lost through stochastic drift especially if recessive [60]. Therefore, the time to a sweep on a new mutation is expected to be substantially longer than the time until new mutations appear in populations [19, 20].

thumbnail
Fig 5. Genomewide population mutation rates for all duplications (θ), population sizes (Ne), and per gene mutation rates (μ) for gene structures produced by whole gene duplication, recruitment of non-coding sequence, and chimeric genes by species.

Low mutation rates and mutation limited evolution leads to low levels of parallel recruitment of tandem duplications.

https://doi.org/10.1371/journal.pone.0132184.g005

Given these estimates of θπ for each class of mutations, we estimate Te, the time to establishment of a deterministic sweep from new mutations in a population such that variants overcome the forces of drift [19, 20]. We assume that beneficial mutations appear at strongly selected sites at a rate equivalent to the genome-wide effective mutation rate. In reality not all mutations are beneficial and the true rate of adaptive substitution is likely to be less common than those discussed here. These estimates therefore represent a lower bound on the time to adaptation through new mutation. With a modest selection coefficient of s = 0.01 similar to that previously observed for duplicates and chimeras [12], in D. yakuba Te would be 7270 generations (600 years at 12 generations per year) for SNPs, 36,000 generations (3000 years) for whole gene duplications, and over 100,000 generations (≥ 9500 years) for chimeric genes (Table 1). For D. simulans, these numbers point to a greater disparity between SNPs and duplicates with Te of 3580 generations (300 years) for SNPs, 34,400 generations (2800 years) for whole gene duplications, and 243,000 generations (20,000 years) for chimeric genes (Table 1). These estimates of effective mutation rates for whole gene duplications and complex gene structures point to long waiting times for new mutations and a disparity in the response of duplicates and SNPs in the face of strong selective pressures. Although the differences in effective mutation rates appear to be modest, they can result in additional thousands of years in the waiting time for selective sweeps to establish with new mutations, resulting in limited ability to adapt to shifting selective pressures.

Under more extreme selection coefficients, given the assumption that the beneficial mutation rate matches the mutation rate per site, waiting times may be shorter, allowing for adaptation at SNPs in hundreds of generations (decades) and thousands of generations (centuries) for gene duplications (Table 1). Under expanded census sizes 100X larger than Ne with extreme selection coefficients, waiting times for new mutations may potentially approach the range of full availability of mutations (Table H in S1 File). However, such extreme dynamics are unlikely to reflect the range of selection coefficients or the rate of adaptation genomewide [61, 62] and are well outside selection coefficients previously estimated for duplicate and chimeric genes of ≈ 1% [12]. Thus, we would not expect such estimates based on selection coefficients of 20% to be broadly applicable.

Parallel evolution for tandem duplications

We find 56 genes are partially or wholly duplicated both in D. yakuba and in D. simulans, 11% of duplicated genes in D. simulans () and less than the number of genes duplicated multiple times in D. simulans alone, suggesting that there is little concurrence in the standing variation of the two species. That 56 genes would be shared across the two species is greater than expected given the limits of available standing variation of 478 duplicated genes in D. simulans and 875 in D. yakuba based on uniform chance (P = 2.812 × 10−8, binomial test) pointing to mutational or selective pressures on similar genes (SI Text). Fewer annotated gene models are available for D. simulans w501 reference [63] leading to smaller absolute numbers of genes even though proportions are similar. Furthermore, a comparison to duplicate genes in D. melanogaster[64] shows only 5 genes that exist among the segregating variation of tandem duplications in all three species. The mutations described here have been polarized with respect to ancestry, and are segregating meaning that they are expected to have formed very recently. As such, shared variants are the product of independent mutation in the two species, not shared ancestry. We find that 13.4% of the genome is present but unsampled in D. yakuba and 9.7% in D. simulans, indicating that the likelihood of shared, unsampled variation is low. Such unsampled alleles will be at low frequency and are unlikely to be able to establish selective sweeps. Hence, the portion of variation available for selective sweeps that is shared across species will be low, resulting in a rarity of evolution through parallel recruitment of tandem duplicates.

Some genes within the genome are captured by as many as 6 independent tandem duplications in D. yakuba and 32 independent tandem duplications in D. simulans. There are 10 genes in D. yakuba and 12 genes in D. simulans that are captured by more than three independent tandem duplications, and these have been excluded from mutation estimates (Table G in S1 File). Some of these variants are chorion proteins known to experience somatic duplications in follicle cells [65], and certain of these hotspots may therefore represent cases of somatic mutation rather than inherited variation. These “hotspots” within the genome may have duplication rates high enough that evolution will not be subject to the same limitations with respect to standing variation and sweeps on new mutations.

Rapid Evolution

Biases in the rates at which tandem duplications form in different genomic regions or a greater propensity for selection to favor duplications in specific functional classes can result in a bias in gene ontology categories among duplicated genes. We previously used DAVID gene ontology analysis software to identify overrepresented functions among duplicate genes in D. yakuba and D. simulans [4]. Here, we compare the agreement in gene ontology categories in D. yakuba and D. simulans with expectations based on random sampling to determine whether such convergence at the level of functional categories is significant. Notably, among randomly selected subsets of genes for D. yakuba and D. simulans there is no agreement in functional categories at an EASE cutoff of 1.0 for any biochemical function or domain, and no functional category was significant at an EASE cutoff of 1.5, a stark contrast with what is observed for duplications (see S2 File). Among genes captured by tandem duplications, immune response and toxin metabolism, chitin cuticle formation and chemosensation are overrepresented in both species (Fig 6A, Table D in S1 File). Such overrepresentation is not identified in randomly selected subsets of genes for D. yakuba and D. simulans, suggesting a greater level of convergence than is expected based on chance alone (Table D in S1 File). To determine whether selection is favoring these functional classes, we identified duplications outside centromeric regions that lie in windows at or below the 5% tail of θπ, consistent with selection reducing diversity. Among these genes in regions with reduced diversity we identify genes in both D. yakuba and D. simulans with functions in chorion or oogenesis, mating behavior, immune response and defense against bacteria, olfactory response, chitin metabolism, xenobiotics and toxin metabolism, and sperm development (Supplementary Information). The presence of genes with these functional categories is consistent with a portion of the overrepresentation in gene ontologies across all duplicates being driven at least in part by selection.

thumbnail
Fig 6.

A) Gene ontology classes overrepresented by species among singly duplicated genes or among multiply duplicated genes. B) Number of genes duplicated by species. Most variants are species specific, with small numbers of parallel duplication of orthologs across species.

https://doi.org/10.1371/journal.pone.0132184.g006

Discussion

We have described the prevalence of tandem duplications in natural populations of D. yakuba and D. simulans, their frequencies in the population, and the genes that they affect. We find that tandem duplications show a bias towards gene ontologies associated with rapid evolutionary processes and that they commonly affect the X chromosome in D. simulans in comparison to the autosomes. In spite of their strong role in adaptation, we find low rates of parallel recruitment of tandem duplications across species due to low formation rates and mutation limited evolution.

Widespread positive selection on the X chromosome in D. simulans

We observe an excess of high frequency tandem duplications on the D. simulans X chromosomes in comparison to neutral intronic SNPs as well as signs of reduced diversity surrounding tandem duplications on the D. simulans X, consistent with widespread selection. Background selection [66] and hitchhiking [67] are not expected to act differently on duplications in comparison to SNPs and cannot explain the patterns observed. Yet, we observe significant differences between the SFS of duplicates and putatively neutral SNPs, pointing to a role for adaptation through tandem duplication. We also observe reduced nucleotide diversity surrounding tandem duplications on the D. simulans X, consistent with selection favoring duplicates. Hence, the overabundance of high-frequency tandem duplications on the X is likely to be driven by selection and these represent strong candidate loci for ongoing selective sweeps. Based on the newly assembled D. simulans reference, X vs. autosome divergence indicates faster evolution on the X chromosome at non-synonymous sites, long introns, and UTRs [63]. This pattern is distinct from observations at synonymous sites as well as general patterns of differential evolution on the autosomes [63], further evidence of more frequent selective sweeps on the X chromosome. Roughly 25% of tandem duplications in each species are flanked by repetitive sequence and there is an overabundance of tandem duplicates associated with repetitive sequence on the D. simulans X [4]. It is possible that such repeats contribute to the formation of these mutations, thereby influencing the evolution of the D. simulans X.

The X chromosome is thought to evolve rapidly due to sexual conflict, intragenomic conflict, and sexual selection [68] and thus multiple selective forces may facilitate the spread of duplicates on the X. The X chromosome in D. simulans houses an excess of duplicates in comparison to all autosomes, as well as a strong association with repetitive sequence and tandem duplications on the X [4]. Therefore, the X chromosome appears to be subject to particularly rapid evolution of duplicate content in D. simulans. Previous work has identified signals of adaptation through duplication on the D. melanogaster X chromosome [69, 70], suggesting parallel evolution through duplication in these species. However, we do not observe similar patterns in D. yakuba, suggesting that the X may either be evolving under different selective pressures in the two different clades or that selective pressures on the D. yakuba X chromosome are of lesser magnitude. Stronger sexual selection, greater selection for X-chromosome related traits, sympatric associations with competitor species with reinforcement for mating aversion, or a greater instance of driving X chromosomes might potentially drive these species differences in X-chromosome evolution, and elucidating the nature of selection on these sex chromosomes may help explain the adaptive (or selfish) role of tandem duplication on the X.

Mutation limited evolution

While both D. simulans and D. yakuba house a rich diversity of duplicated sequences, only a few percent of the genome will be covered by tandem duplications. With lower mutation rates for duplications [8, 10, 71], there may be long waiting times to achieve any single new mutation, and the landscape of standing variation will shape evolutionary outcomes. As such, any evolutionary path that is dependent upon tandem duplications of any specific genomic sequence will be severely limited by the small likelihood that the necessary mutation is among the standing variation. Drosophila represent organisms with large effective population sizes (Fig 5) [22, 72] and are expected to host large numbers of duplications as standing variation in comparison to other multicellular eukaryotes. We have shown that the number of tandem duplications segregating in the population is substantially smaller than the number of mutations needed to guarantee a duplicate of any desired genomic region. However, when population level mutation rates are small, standing variation is unlikely to offer a sufficient substrate for selective sweeps and systems will be stuck waiting for new mutations that are slow to materialize [18]. We observe population level mutation rates θ per gene for tandem duplications on the order of 0.00277 in D. yakuba and 0.00291 in D. simulans (Table 1, Fig 5) resulting in low probabilities that standing variation offers the major source of adaptation and long waiting times to sweeps on new mutations on the order of hundreds to thousands of years. While retrogenes might provide additional sources of duplicated sequences, their rates of formation are exceptionally limited [8, 10] and they are therefore not expected to contribute more substantially than tandem duplications to genomic variation and will not suffice to overcome these limitations of low mutation rates. Thus, we conclude that outside of a small number of mutational hotspots evolution through duplication is mutation limited even in Drosophila which have large Ne, and that these limits are expected to be even more severe for many other multicellular eukaryotes, especially vertebrates. In some rare cases, organisms with small Ne may harbor more extreme variation under nearly neutral dynamics, but the absolute number of such mutations will be very limited. Thus, cases of adaptation from standing variation of extreme variants in organisms with small Ne are possible, but rare.

The majority of tandem duplications identified in D. yakuba and D. simulans appear to be at extremely low frequency, with an excess of singleton variants in comparison to neutral intronic SNPs, suggesting that large numbers of tandem duplications are detrimental, consistent with previous work in other species [5]. It has previously been argued that the accumulation of duplications is the product of small Ne and inability of selection to purge nearly neutral alleles from the population [17, 59]. However, we show that duplicates are less likely to be neutral in comparison to putatively neutral intronic SNPs suggesting that they often have phenotypic effects larger than the limit near-neutrality. We have shown that both positive and negative selection will affect the fixation or loss of duplications and that simplified nearly neutral theories are unlikely to explain the patterns observed across species. Rather, selection is expected to play an appreciable role in the evolution of tandem duplications and their contribution to genome content. Previous work has shown different patterns for young and old duplicate genes, with many young duplicates and chimeras forming in tandem but more duplicates and chimeras preserved over long periods that are where paralogs are dispersed from one another [8, 10, 73] even though variants found in tandem show signals of selection. Two factors are likely to explain this disparity. First, genome shuffling and syntenic breaks are common over time [74, 75]. Second, it is likely that forces leading to adaptation are likely to be distinct from forces that lead to gene preservation as preservation requires that genes remain essential over long periods of time, especially if selective pressures on non-essential genes are transient [10, 12].

Likelihood of parallel recruitment of tandem duplications across species

Convergent evolution is often interpreted of a signal of adaptation in experimental evolution and in natural populations [29, 30]. Here, we show that for tandem duplications, parallel recruitment of genes for duplication and diversification independent from shared ancestry will be very rare in spite of convergence in functional categories represented. Thus, the reliance on genetic convergence to establish natural selection in natural populations will underreport selected alleles and result in significant underestimation of the number and types of alleles that are selected. Though convergence is common in experimental evolution of both prokaryotic systems and multicellular eukaryotes with shared ancestry [30], these results suggest that these systems are unlikely to reflect the frequency of convergent evolution in natural populations of independently evolving species of multicellular eukaryotes that have little shared standing variation. We observe an excess of variants with gene ontologies consistent with similar rapid evolutionary processes both in D. yakuba and in D. simulans (Fig 6A). However, few genes (∼ 11%) are duplicated in both species and only a handful have been identified in D. simulans, D. yakuba, and D. melanogaster (Fig 6B). Moreover, none of the high frequency variants in the in D. yakuba and D. simulans capture orthologous sequences. Hence, in spite of parallel selective pressures on rapidly evolving phenotypes, there is little parallel recruitment of the same genetic solutions with respect to duplication. Given the limited genomic span of standing variation in the population (Table F in S1 File), and low rates of new mutation (Fig 5, Table 1), as well as the low frequency of a large fraction of variants, parallel fixation of tandem duplications in the same genes will be extremely rare even among genera with large effective population sizes facing similar selective pressures.

Convergence depends on the waiting time of new mutations to enter populations and establish selective sweeps. We show that the average waiting time for a new mutation given a selection coefficient of s = 0.01 is hundreds of years for SNPs. Here, we find that tandem duplications display signals of reduced heterozygosity in the surrounding regions as well as an association with gene ontologies indicative of rapidly evolving phenotypes, and an overrepresentation of shared tandem duplicates across species for specific genes given the limits of standing variation, consistent with widespread adaptation through tandem duplication. However, the average waiting time for a deterministic sweep to establish in a population will be thousands of years for tandem duplications and tens of thousands of years for chimeric genes given a modest selection coefficient of s = 0.01. Such strongly selected sites are expected to be rare throughout the genome and beneficial mutations are likely to appear less often than the actual mutation rate [61, 62]. Thus, these waiting times given strong selection provide a lower bound to the waiting time for a selected sweep. We therefore expect that mutation will severely limit evolution through whole gene duplication and chimera formation. To the extent that adaptation depends on tandem duplications, the ability of organisms to adapt to changing environments will be hindered by a lack of variation. Thus, even when a given tandem duplication is needed for adaptation, we expect that the limits of mutation will lead to low levels of convergence and scarcity of shared genetic solutions.

Duplicate genes and rapidly evolving phenotypes

Both D. simulans and D. yakuba have an overabundance of genes with ontology classifications involved in immune function, chemosensory processing or response, and drug and toxin metabolism that is significantly greater than expectations based on random chance (Table D in S1 File). These phenotypes are strongly associated with rapid evolution due to host-parasite interactions, predator-prey coevolution, and sexual conflict [7679]. Previous work has observed similar bias toward rapid amino acid substitutions in olfactory genes, and chitin cuticle genes in D. melanogaster and D. simulans [80], selection for gene family evolution in and selection for toxin resistance is common in D. melanogaster [78, 81] suggesting that associated phenotypes may be under widespread selection in multiple species.

Host pathogen systems as well as arms races in pesticide and toxin resistance operate under Red Queen dynamics in which conflicts between organisms result in repeated selective sweeps [82]. Organisms that lack the genetic means to adapt to rapidly changing systems will be at a distinct disadvantage in the face of selective events. Additionally, the overrepresentation of tandem duplications in cytochromes and drug or toxin metabolism genes confirms rapid evolution in copy number seen in comparison of reference genomes [75] as well as recent studies of insecticide resistance and viral resistance in natural populations [3, 81, 83]. Large amounts of divergence driven by selection among non-synonymous sites and UTRs in D. simulans [84] and high rates of adaptive substitutions [80, 85] point to widespread selective pressures acting in D. simulans, and it is likely that these same pressures influence the current diversity and frequency of copy number variants. If rapidly evolving systems rely heavily on complex mutations or if selection coefficients are modest, profiles of standing variation will place strong limits on outcomes in response to selection.

Shifting selective pressures such as those found in rapidly evolving systems or gross ecological change require a pool of genetic variation to facilitate adaptation. We observe standing variation and mutational profiles that will limit evolutionary trajectories and would expect these limits to be even more severe for rapidly evolving phenotypes. Repeated sweeps are expected to purge genetic and phenotypic diversity, and recovering such diversity after sweeps can take thousands of generations [86]. Thus, during rapid evolution, selection will potentially purge diversity that is needed for subsequent steps in the adaptive walk. Hence, although tandem duplications are key players in rapid evolution, their limited rates of formation combined with low frequencies due to commonly detrimental impacts will hinder evolutionary outcomes or force alternative adaptive trajectories precisely when variation is urgently needed. Hence, we do not observe convergence across individual loci in spite of substantial convergence across functional categories.

Materials and Methods

Tandem duplications

Tandem duplications were identified using paired-end Illumina sequencing of genomic DNA for 20 strains of D. yakuba and 20 strains of D. simulans as well as the reference genome of each species as described in Rogers et al. (2014). The dataset describes derived, segregating tandem duplications that span 25 kb or less. These sequences exclude ancestral duplications as well as putative duplications in the resequenced reference genomes. The resulting list of variants describes segregating variation for newly formed tandem duplicates across the full genome in these two species of non-model Drosophila. All data files are available via http://molpopgen.org/Data and http://www.github.com/ThorntonLab/DrosophilaPopGenData-Rogers2014. Aligned bam files were deposited in the National Institutes of Health Short Read Archive under accession numbers SRP040290 and SRP029453. Sequenced stocks were deposited in the University of California, San Diego (UCSD) stock center with stock numbers 14021-0261.38- 14021-0261.51 and 14021-0251.293–14021-0251.311.

Identifying duplicated coding sequence

Tandem duplications were previously identified using a combination of paired-end read mapping and coverage changes in 20 isofemale lines of D. yakuba and 20 isofemale lines in D. simulans generated via 9–12 generations of sibling mating from wild-caught flies. We sequenced 10 isofemale lines of D. yakuba from Nairobi, Kenya, and 10 isofemale lines from Nguti, Cameroon as well as 10 isofemale lines of D. simulans from Nairobi, Kenya and 10 isofemale lines from Madagascar. Duplications were identified through divergently oriented reads and coverage changes in comparison to reference genomes. We identify 1415 tandem duplications in D. yakuba and 975 tandem duplications segregating in D. simulans that span 845 different gene sequences in D. yakuba and 478 different gene sequences in D. simulans [4]. Gene duplications were defined as any divergent read calls whose maximum span across all lines overlaps with the annotated CDS coordinates. D. yakuba CDS annotations were based on flybase release D. yakuba r.1.3. Gene annotations for the recent reassembly of the D. simulans reference were produced by aligning all D. melanogaster CDS sequences to the D. simulans reference in a tblastx (http://genomics.princeton.edu/AndolfattoLab/w501_genome.html). Percent coverage of the CDS was defined based on the portion of the corresponding genomic sequence from start to stop that was covered by the maximum span of divergent read calls across all strains. Using the representation of gene sequences in D. yakuba of we use a binomial test to calculate the likelihood of 56 shared variants among the genes duplicated in D. simulans.

Estimated number of segregating tandem duplications

We compared the estimated total number of duplications expected in a population to estimates of diversity based on our sample of 20 strains, correcting S for a 3.9% false positive rate (Table E in S1 File). Under a standard coalescent model [50, 87, 88]:

  1. Where a in a sample of size n (in this case n = 20):

  2. When 2Ne is large:
  3. Hence:
  4. We can use similar methods to estimate the variance in the number of segregating sites in the population.
  5. When 2Ne is large:

Alternatively, we can use rarefaction estimators, which are free of population genetic assumptions to estimate the total number of duplications. Using the Chao estimator [57] for , where S1 and S2 are the number of variants at a frequency of 1 and 2 resepectively. The estimated variance is then .

Gene Ontology

Overrepresented functional categories were identified using DAVID gene ontology software with an EASE threshold of 1.0. as previously described [4]. We observe several functional categories indicative of rapid evolution that are shared between the two species (Table D in S1 File). In order to determine whether such agreement at the level of functional category is greater than expected by chance, we selected a random subset of 845 genes for D. yakuba and 478 genes from D. simulans, and performed ontology analysis for a comparison.

Proportion of the genome represented by segregating duplicates

To determine the number of duplications necessary to span the full range of the genome, we simulated chromosomes with a length determined by the number of base pairs with non-zero coverage in our reference strain. We then simulated random draws from the distribution of duplication lengths for each chromosome, placing duplication start sites at random and recorded the number of duplications necessary to cover 10%, 25%, 50%, and 90% of sequence length for each chromosome in each trial. Simulations were repeated for 1000 trials for each chromosome.

These simulations do not account for mutational biases that might result in clustering of duplications in particular regions while other regions remain static, nor do they require that new duplications reach an appreciable frequency so that they are immune to stochastic loss through genetic drift. They do not require that duplications capture sufficient sequence to have functional impacts or require that breakpoints not disrupt known functional elements. Furthermore, simulating individual chromosomes separately decreases the likelihood of resampling particular sites thereby lowering the estimated number of duplications needed to cover the entire genome. Hence, these estimates put a highly conservative lower bound on the minimum number of mutations necessary to capture the full genomic sequence.

To estimate the expected proportion of the genome spanned by all duplicates in the population, we resampled 6700 duplicates from the observed size distribution of D. yakuba with replacement and 4000 duplicates from the observed size distribution of D. simulans, placing duplications at random positions across the chromosome. We performed 100 replicates of sampling and report the mean across all replicates for each species. In D. simulans we observe one case with 19 independent whole gene duplications of a single ORF [4], suggesting up to 1000-fold variation in mutation rates over the genome average. Estimates of population level variation and genome wide effective mutation rates ignore mutation rate variation where some regions may be highly prone to duplications whereas others remain static, which would reduce the likelihood of unobserved tandem duplications outside of mutational hotspots. Hence, these estimates represent a lower bound on the number of duplications necessary to span the entire genome.

Effective mutation rates and waiting times for duplicates

We estimate average heterozygosity (θπ) and effective mutation rates (μ) per gene for D. yakuba and D. simulans for gene duplications that capture at least 90% of gene sequence (in agreement with previous estimates [10]), for genes that recruit non-coding sequence, and for chimeric genes. Heterozygosity estimates used to calculate effective mutation rates were corrected for ascertainment bias (see SI Text) and excluded genes that were captured by 4 or more independent mutations, a signal of hotspots and mutation rate heterogeneity. Heterozygosity per gene is estimated given 16,082 gene sequences in D. yakuba and 10,786 coding sequences in D. simulans (Table 1). Like all estimates of mutation rates, these will exclude lethal varinats or variants that produce sterility or early-life pathogenic effects. However, they should accurately reflect the amount of variation that can be observed among standing variation, including even moderately deleterious mutations that are destined for eventual loss. Given estimates of θπ, we estimate the probability of adaptation from standing variation under an additive genetic model for neutral variants, Psgv = 1 − eθπ*ln(1+2Ne s) [18] and the time to establishment (Te) of a deterministic sweep from new mutations, such that new mutants escape the stochastic forces of drift, [19, 20]. These estimates are provided for two strong selection coefficients of s = 0.01 similar to what is observed in Drosophila for chimeric and duplicate genes [12] and s = 0.20 modeling abnormally strong selection on a single locus consistent with Karasov et al. [24]. Estimates assume that a given site of interest is under strong selection and that the beneficial mutation rate is equal to the mutation rate per site per generation providing an upper limit on the ability of new mutations to facilitate adaptation. In reality, strongly selected sites will be rare throughout the genome [61, 62] and these waiting times given strong selection will not accurately reflect the expected number of selective sweeps throughout the genome. We additionally estimate the time to loss given loss for alleles in the population according to 2ln(Ne) [58], the nearly neutral selection coefficient [59] as well as population level mutation rates θ for alternative effective population sizes to determine the broader applicability of these results to other organisms.

Intronic SNPs

In order to produce a neutral proxy for sequence change in each species, we identified SNPs for short introns 100 bp or less, focusing on sites 8–30 which are generally subject to little constraint [8991]. Reads containing indels were re-aligned using GATK [92]. SNPs were identified across strains using samtools v1.18 mpileup [93] disabling probabilistic realignment (-B) and outputting genotype likelihoods in BCF format (-g). The resulting BCF used to create a VCF using bcftools, calling bases using Bayesian inference (-c) calling genotypes per sample (-g) with a scaled mutation rate of 1% (-t.01) under a haploid model (ploidy = 1). SNPs were required to have minimum Illumina coverage depth of 20 reads, maximum coverage of 250 reads, MQ ≥ 20, and GQ ≥ 30 and invar GQ ≥ 40. We excluded SNPs identified in the reference, which are indicative of either assembly errors or residual heterozygosity. We performed hierarchical cluster analysis in R using all SNPs by chromosome to evaluate population structure.

The ancestral state for each SNP was established through comparison with the nearest sequenced reference genome as an outgroup, D. erecta for D. yakuba sequences and D. melanogaster for D. simulans sequences. Orthologs between each species and its outgroup were identified using reciprocal best hit criteria in a BLASTn at an E-value cutoff of 10−5. Full gene sequences for each ortholog were then aligned using clustalw, keeping only genes which aligned with 85% or greater nucleotide identity. Divergence between the two species, Divx, y, was defined based on alignments of intronic sites from bases 8–30 between each species and the outgroup reference genome, excluding gapped sequences, for aligned orthologs with 85% or nucleotide identity. The ancestral state was defined based on the corresponding sequence in the outgroup genome (D. melanogaster for D. simulans and D. erecta for D. yakuba). We excluded sites where the outgroup reference was in disagreement with both the D. yakuba reference and D. yakuba SNPs, as well as triallelic SNPs, sites with reference sequence of ‘N’, or SNPs identified in the VCF for the reference, suggesting inaccuracies in reference assembly or residual heterozygosity in the reference. These resulted in a total of 7158 intronic SNPs in D. yakuba and 5504 intronic SNPs in D. simulans. The resulting unfolded SFS was then corrected for the probability of independent mutations in both reference genomes leading to incorrect inference of the ancestral state.

Parallel mutations occurring independently at nucleotide sites in different species can obscure evolutionary relationships and artificially skew SFS. We corrected SFS for SNPs prior to performing comparisons for tests of selection. Given net divergence Dnet = Divx, yπx, the probability of identical independent mutations occurring in the outgroup reference genome is reflected by either the probability of an independent transition (ts) at the site of a transition mutation, or by 1/2 the probability of a transversion (tv) at the site of a transversion polymorphism. Thus, (1)

Empirically, in Drosophila . Thus, .

The unfolded SFS for intronic sites was corrected for the likelihood of independent mutations in the reference, k. The probability of independent mutations occurring in both genomes is equal to the probability of either two independent transitions or two independent transversions occurring in both genomes. We calculated πx as the average heterozygosity per intronic site.

Given a likelihood of independent identical mutations of . (2) (3) Substituting Eq 3 into Eq 2, we obtain (4)

Correcting Duplicates for Ascertainment Bias

Tandem duplications, unlike SNPs, cannot be identified using paired-end reads in individual strains except through comparison to the reference genome. Moreover, variants that are segregating at high frequency in populations are substantially more likely to be present in the reference, and therefore are substantially less likely to be identified in sample strains [5]. We corrected site frequency spectra according to the model developed previously [5]. (5)

Here, xi is the true proportion of alleles at frequency i in the population, and yi is the observed proportion of alleles at frequency i in a sample of n strains (here 21). The correction for ascertainment bias lowers estimates of the proportion found a low frequencies and increases estimates of the proportion at high frequency. For estimates of population site frequency spectra, we removed all variants with divergently oriented reads in the reference strain, as these would not be identified in an accurately annotated reference.

Residual heterozygosity

Some isofemale lines contained regions of residual heterozygosity in spite of over 10 generations of inbreeding in the lab. To detect regions of residual heterozygosity, we called SNPs as above under a diploid model. Segments with residual heterozygosity were detected using an HMM (“HMM”;http://cran.r-project.org/web/packages/HMM/).

  1. Prior probabilities on states were set as:
  2. Transition probabilities were set to:
  3. and emission probabilities set to:

Where ε = 0.001 and θ = 0.01. The most likely path was calculated using the Viterbi algorithm, and heterozygous segments 10kb or larger were retained. Heterozygous blocks within 100kb of one another in a sample strain were clustered together as a single segment to define the span of residual heterozygosity within inbred lines.

Differences in Site Frequency Spectra

If different classes of duplications have different selective impacts, we should observe clear differences in site frequency spectra, with more positively selected duplications showing fewer singleton alleles and more high frequency variants. Site frequency spectra are not normally distributed, nor can they be normalized through standard transformations, and thus require non-parametric tests. We used a two-sided Wilcoxon rank sum test to determine whether site frequency spectra were significantly different. For each comparison, we excluded tandem duplications that are present in the reference genomes as well as putative ancestral duplications, as these are likely to display biases with respect to size, propensity to capture coding sequences, and association with repetitive content. We compared site frequency spectra of the following groups within each species: duplications on the X and on the autosomes and all pairwise combinations of SNPs and duplicates on the X and autosomes. We also performed Kolmogorov-Smirnov test for comparison. In D. simulans, we used a χ2 test to determine whether high frequency alleles are overrepresented among duplications on the X relative to intronic SNPs. Data for the χ2 test was binned using a cutoff to compare the proportion of variants as at a sample frequency of .

Tandem duplicates that lie in regions with residually heterozygous segments extending 1kb upstream or downstream were excluded from the SFS, resulting in unequal sample sizes for different variants. Samples with fewer than 15 strains remaining were excluded from the SFS. The SFS for intronic SNPs and for duplicates was then scaled to a sample of size 17 in D. simulans and 15 in D. yakuba according to Nielsen et al. [48].

Segregating Inversions

In order to check for population substructure, we aligned all SNPs in intronic sequences from 8–30 bp as a neutral proxy [8991] and performed hierarchical clustering in R using hclust. These SNPs were intended solely to differentiate strains and were not polarized with respect to the ancestral state or otherwise filtered. We observe little evidence for population structure in D. simulans (Figure L in S1 File). However, we identify structure on chromosome 2 in D. yakuba (Figure M in S1 File), consistent with known polymorphic inversions prohibiting recombination on chromosome 2 [53, 54]. Strains do not strictly cluster with respect to geography but rather are reticulated amongst other groups. Moreover, among duplicates we do not observe an excess of moderate frequency alleles as one would expect under population substructure given our sampling scheme (Figure A in S1 File). Thus, these strains constitute a single admixed population.

Some strains retained residual heterozygosity even after 9 generations of inbreeding, with greater residual heterozygosity in D. yakuba than in D. simulans, consistent with inversions segregating in D. yakuba. These regions of residual heterozygosity can result in incorrect estimates of SFS by artificially increasing chances of observing variation. Site frequency spectra were calculated across all strais by correcting sample frequencies for ascertainment bias, excluding regions of residual heterozygosity and then projecting frequencies onto a sample size of 15 in D. yakuba and 17 in D. simulans according to [48]. As a neutral comparison we calculated SFS for intronic SNPs (as above) and projected the SFS down to a sample size of 15 in D. yakuba and 17 in D. simulans (Figure A in S1 File).

Likelihood of shared variation through ancestry

The likelihood of shared variation through shared ancestry can be obtained through a coalescent approach. The probability that an allele does not coalesce in the time period from the present back to the speciation event that separated D. yakuba and D. simulans is . This can be approximated using . We estimate θpi for putatively neutral 8–30 bp from short introns using libsequence [94], ignoring sites that are heterozygous and sites with missing data. For neutral intronic SNPs, θpi = 0.0138 in D. yakuba and θpi = 0.0280 in D. simulans. Using the mutation rate of 5.8 × 10−9 [95], we find Ne = (0.0138)/(4 × 5.8 × 10−9) = 5.93 × 105 in D. yakuba and Ne = (0.0280)/(4 × 5.8 × 10−9) = 1.21 × 106 in D. simulans. Using t = 12MY [28] and 12 generations per year, and Ne = 1.2 × 106 from D. simulans, we obtain a probability of shared ancestry for an allele of 9 × 10−27, vanishingly small. We have polarized all mutations against the putative ancestral state using outgroup reference genomes, focusing solely on derived mutations [4]. Furthermore, the expectation of shared variation for any two alleles through shared ancestry for D. yakuba and D. simulans is expected to be low. Even large samples are not expected to harbor shared variation over such timescales [96]. Thus, we expect shared variants described here to result from independent mutations, not from long standing neutral polymorphism.

Diversity surrounding SNPs and duplications

We estimate θπ, θW, and Tajima’s D for all SNPs in the D. yakuba and D. simulans genomes, removing sites with missing or ambiguous data as well as heterozygous sites using libsequence [94]. We calculate θπ, θW, and Tajima’s D3 for 5 kb windows moving in a 500 bp slide across the genome. For each window, we divide estimates by the number of sites per window with a minimum Illumina coverage depth of 20 reads, maximum coverage of 250 reads, MQ ≥ 20, consistent with the threshold used to identify SNPs, in order to estimate θπ and θW per site. We compare θπ per site for regions surrounding derived, segregating tandem duplications with regions surrounding derived, segregating, putatively neutral intronic SNPs from 8–30 bp of short introns, excluding windows with less than 4000 bp out 5000 bp that could be assayed for SNPs. We exclude second SNPs in a single 5 kb window, and exclude SNPs and duplicates that are found in the centromeric regions, which have unusually low diversity (Figures B-K in S1 File). We scaled diversity estimates by chromosome mean and standard deviation to produce a unit normal distribution and allow data to be combined across chromosomes. We then compared diversity at the 5000 bp window immediately surrounding SNPs to diversity for the window immediately surrounding duplications and the X chromosome using a single tailed Wilcoxon rank sum test (Table C in S1 File). Chromosome 2 in D. yakuba houses multiple segregating inversions [53, 54], which can cause atypical evolutionary dynamics and abnormal signals of diversity. Thus, we assayed data for chromosomes 2, 3 and X separately in D. yakuba. For D. simulans, data was combined across all autosomes, and the X chromosome in D. simulans. We plotted lowess smoothed regressions of diversity using a smoothing factor of from 50 kb upstream to 50kb downstream of a mutant (Figs 23). Tests of nucleotide diversity and plots of diversity surrounding duplicates and SNPs exclude a cluster of multiple duplications from 8.45 Mb-8.55 Mb which has abnormally low diversity (Figure I in S1 File).

Supporting Information

S2 File. Contains Supplementary Text A-J.

Text A: chromosome names vs number identifiers for D. simulans. Text B: DAVID gene ontology results for D. simulans. Text C: list of duplicated genes by chromosome for D. simulans. Text D: DAVID gene ontology results for D. yakuba. Text E: list of duplicated genes by chromosome for D. yakuba. Text F: chromosome names vs number identifiers for D. yakuba. Text G: list of fly stocks. Text H: Readme file. Text I: List of duplications for D. simulans. Text J: List of duplications for D. yakuba.

https://doi.org/10.1371/journal.pone.0132184.s002

(ZIP)

Acknowledgments

The authors would like to thank Nigel F. Delaney, Tim Bassinger, Elizabeth G. King, Anthony D. Long, and Alexis S. Harrison, and Trevor Bedford for helpful discussions as well as the anonymous reviewers whose comments substantially improved the manuscript.

RLR, JMC, KRT, and PA performed analyses. LS generated Illumina sequencing libraries. TTH provided gene annotations for D. simulans. RLR, JMC, PA and KRT designed experiments and analyses.

Author Contributions

Conceived and designed the experiments: RLR JMC PA KRT. Performed the experiments: LS. Analyzed the data: RLR JMC TTH PA KRT. Contributed reagents/materials/analysis tools: RLR JMC PA KRT. Wrote the paper: RLR KRT PA JMC.

References

  1. 1. Conant GC, Wolfe KH. Turning a hobby into a job: how duplicated genes find new functions. Nat Rev Genet. 2008 Dec;9(12):938–950. pmid:19015656
  2. 2. Ohno S. Evolution by gene duplication. London: George Alien & Unwin Ltd. Berlin, Heidelberg and New York: Springer-Verlag.; 1970.
  3. 3. Magwire MM, Bayer F, Webster CL, Cao C, Jiggins FM. Successive increases in the resistance of Drosophila to viral infection through a transposon insertion followed by a Duplication. PLoS Genet. 2011 Oct;7(10):e1002337. pmid:22028673
  4. 4. Rogers RL, Cridland JM, Shao L, Hu TT, Andolfatto P, Thornton KR. Landscape of Standing Variation for Tandem Duplications in Drosophila yakuba and Drosophila simulans. Mol Biol Evol. 2014 Jul;31(7):1750–1766. pmid:24710518
  5. 5. Emerson JJ, Cardoso-Moreira M, Borevitz JO, Long M. Natural selection shapes genome-wide patterns of copy-number polymorphism in Drosophila melanogaster. Science. 2008 Jun;320(5883):1629–1631. pmid:18535209
  6. 6. Cardoso-Moreira M, Emerson JJ, Clark AG, Long M. Drosophila duplication hotspots are associated with late-replicating regions of the genome. PLoS Genet. 2011 Nov;7(11):e1002340. pmid:22072977
  7. 7. Dopman EB, Hartl DL. A portrait of copy-number polymorphism in Drosophila melanogaster. Proc Natl Acad Sci USA. 2007 Dec;104(50):19920–19925. pmid:18056801
  8. 8. Zhou Q, Zhang G, Zhang Y, Xu S, Zhao R, Zhan Z, et al. On the origin of new genes in Drosophila. Genome Research. 2008 Sep;18(9):1446–1455. pmid:18550802
  9. 9. Katju V, Lynch M. On the formation of novel genes by duplication in the Caenorhabditis elegans genome. Molecular Biology and Evolution. 2006 May;23(5):1056–1067. pmid:16500928
  10. 10. Rogers RL, Bedford T, Hartl DL. Formation and longevity of chimeric and duplicate genes in Drosophila melanogaster. Genetics. 2009 Jan;181(1):313–322. pmid:19015547
  11. 11. Hahn MW, Han MV, Han SG. Gene Family Evolution across 12 Drosophila Genomes. PLoS Genetics. 2007 Nov 9;3(11):e197. pmid:17997610
  12. 12. Rogers RL, Hartl DL. Chimeric genes as a source of rapid evolution in Drosophila melanogaster. Mol Biol Evol. 2012 Feb;29(2):517–529. pmid:21771717
  13. 13. Jones CD, Begun DJ. Parallel evolution of chimeric fusion genes. Proceedings of the National Academy of Sciences, USA. 2005 Aug 9;102(32):11373–11378.
  14. 14. Shih HJ, Jones CD. Patterns of amino acid evolution in the Drosophila ananassae chimeric gene, siren, parallel those of other Adh-derived chimeras. Genetics. 2008 Oct;180(2):1261–1263. pmid:18780749
  15. 15. Long M, Langley CH. Natural selection and the origin of jingwei, a chimeric processed functional gene in Drosophila. Science. 1993 Apr 2;260(5104):91–95. pmid:7682012
  16. 16. Zhang J, Dean AM, Brunet F, Long M. Evolving protein functional diversity in new genes of Drosophila. Proceedings of the National Academy of Sciences, USA. 2004 Nov 16; 101(46):16246–16250.
  17. 17. Lynch M, Conery JS. The evolutionary fate and consequences of duplicate genes. Science. 2000 Nov;290(5494):1151–1155. pmid:11073452
  18. 18. Hermisson J, Pennings PS. Soft sweeps: molecular population genetics of adaptation from standing genetic variation. Genetics. 2005 Apr;169(4):2335–2352. pmid:15716498
  19. 19. Maynard Smith J. What use is sex? Journal of theoretical biology. 1971;30(2):319–335.
  20. 20. Gillespie JH. 5. In: The Causes of Molecular Evolution. Oxford University Press; 1991. p. 232.
  21. 21. Kreitman M. Nucleotide polymorphism at the alcohol dehydrogenase locus of Drosophila melanogaster. Nature. 1983;304(5925):412–417. pmid:6410283
  22. 22. Bachtrog D, Thornton K, Clark A, Andolfatto P. Extensive introgression of mitochondrial DNA relative to nuclear genes in the Drosophila yakuba species group. Evolution. 2006 Feb;60(2):292–302. pmid:16610321
  23. 23. Andolfatto P, Wong KM, Bachtrog D. Effective population size and the efficacy of selection on the X chromosomes of two closely related Drosophila species. Genome Biol Evol. 2011;3:114–128. pmid:21173424
  24. 24. Karasov T, Messer PW, Petrov DA. Evidence that adaptation in Drosophila is not limited by mutation at single sites. PLoS Genet. 2010 Jun;6(6):e1000924. pmid:20585551
  25. 25. Carneiro M, Hartl DL. Colloquium papers: Adaptive landscapes and protein evolution. Proceedings of the National Academy of Sciences, USA. 2010 Jan 26;107 Suppl 1:1747–1751.
  26. 26. Cui Y, Wong WH, Bornberg-Bauer E, Chan HS. Recombinatoric exploration of novel folded structures: a heteropolymer-based model of protein evolutionary landscapes. Proceedings of the National Academy of Sciences, USA. 2002 Jan 22;99(2):809–814.
  27. 27. Giver L, Arnold FH. Combinatorial protein design by in vitro recombination. Current Opinion in Chemical Biology. 1998 Jun;2(3):335–338. pmid:9691067
  28. 28. Tamura K, Subramanian S, Kumar S. Temporal patterns of fruit fly (Drosophila) evolution revealed by mutation clocks. Mol Biol Evol. 2004 Jan;21(1):36–44. pmid:12949132
  29. 29. Gould SJ, Lewontin RC. The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme. Proc R Soc Lond, B, Biol Sci. 1979 Sep;205(1161):581–598. pmid:42062
  30. 30. Stern DL. The genetic causes of convergent evolution. Nat Rev Genet. 2013 Nov;14(11):751–764. pmid:24105273
  31. 31. Chen L, DeVries AL, Cheng CH. Convergent evolution of antifreeze glycoproteins in Antarctic notothenioid fish and Arctic cod. Proc Natl Acad Sci USA. 1997 Apr;94(8):3817–3822. pmid:9108061
  32. 32. Shapiro MD, Summers BR, Balabhadra S, Aldenhoven JT, Miller AL, Cunningham CB, et al. The genetic architecture of skeletal convergence and sex determination in ninespine sticklebacks. Curr Biol. 2009 Jul;19(13):1140–1145. pmid:19500990
  33. 33. Brodie ED. Convergent evolution: pick your poison carefully. Curr Biol. 2010 Feb;20(4):R152–154. pmid:20178758
  34. 34. Khadjeh S, Turetzek N, Pechmann M, Schwager EE, Wimmer EA, Damen WG, et al. Divergent role of the Hox gene Antennapedia in spiders is responsible for the convergent evolution of abdominal limb repression. Proc Natl Acad Sci USA. 2012 Mar;109(13):4921–4926. pmid:22421434
  35. 35. Wittkopp PJ, Williams BL, Selegue JE, Carroll SB. Drosophila pigmentation evolution: divergent genotypes underlying convergent phenotypes. Proc Natl Acad Sci USA. 2003 Feb;100(4):1808–1813. pmid:12574518
  36. 36. Tanaka K, Barmina O, Kopp A. Distinct developmental mechanisms underlie the evolutionary diversification of Drosophila sex combs. Proc Natl Acad Sci USA. 2009 Mar;106(12):4764–4769. pmid:19255422
  37. 37. Zhen Y, Aardema ML, Medina EM, Schumer M, Andolfatto P. Parallel molecular evolution in an herbivore community. Science. 2012 Sep;337(6102):1634–1637. pmid:23019645
  38. 38. Riehle MM, Bennett AF, Long AD. Genetic architecture of thermal adaptation in Escherichia coli. Proc Natl Acad Sci USA. 2001 Jan;98(2):525–530. pmid:11149947
  39. 39. Chan YF, Marks ME, Jones FC, Villarreal G, Shapiro MD, Brady SD, et al. Adaptive evolution of pelvic reduction in sticklebacks by recurrent deletion of a Pitx1 enhancer. Science. 2010 Jan;327(5963):302–305. pmid:20007865
  40. 40. Moxon E, Lenski R, Rainey P. Adaptive evolution of highly mutable loci in pathogenic bacteria. Perspectives in Biology and Medicine. 1998;42(1):154–155. pmid:10766604
  41. 41. Burke MK, Dunham JP, Shahrestani P, Thornton KR, Rose MR, Long AD. Genome-wide analysis of a long-term evolution experiment with Drosophila. Nature. 2010 Sep;467(7315):587–590. pmid:20844486
  42. 42. Woods R, Schneider D, Winkworth CL, Riley MA, Lenski RE. Tests of parallel molecular evolution in a long-term experiment with Escherichia coli. Proceedings of the National Academy of Sciences. 2006;103(24):9107–9112.
  43. 43. Tenaillon O, Rodriguez-Verdugo A, Gaut RL, McDonald P, Bennett AF, Long AD, et al. The molecular diversity of adaptive convergence. Science. 2012 Jan;335(6067):457–461. pmid:22282810
  44. 44. Orozco-terWengel P, Kapun M, Nolte V, Kofler R, Flatt T, Schloetterer C. Adaptation of Drosophila to a novel laboratory environment reveals temporally heterogeneous trajectories of selected alleles. Molecular ecology. 2012;21(20):4931–4941. pmid:22726122
  45. 45. Colosimo PF, Hosemann KE, Balabhadra S, Villarreal G, Dickson M, Grimwood J, et al. Widespread parallel evolution in sticklebacks by repeated fixation of Ectodysplasin alleles. Science. 2005 Mar;307(5717):1928–1933. pmid:15790847
  46. 46. Martin A, Papa R, Nadeau NJ, Hill RI, Counterman BA, Halder G, et al. Diversification of complex butterfly wing patterns by repeated regulatory evolution of a Wnt ligand. Proc Natl Acad Sci USA. 2012 Jul;109(31):12632–12637. pmid:22802635
  47. 47. Reinhardt JA, Kolaczkowski B, Jones CD, Begun DJ, Kern AD. Parallel geographic variation in Drosophila melanogaster. Genetics. 2014 May;197(1):361–373. pmid:24610860
  48. 48. Nielsen R, Bustamante C, Clark AG, Glanowski S, Sackton TB, Hubisz MJ, et al. A scan for positively selected genes in the genomes of humans and chimpanzees. PLoS Biol. 2005 Jun;3(6):e170. pmid:15869325
  49. 49. Tajima F. Evolutionary relationship of DNA sequences in finite populations. Genetics. 1983 Oct;105(2):437–460. pmid:6628982
  50. 50. Watterson GA. On the number of segregating sites in genetical models without recombination. Theor Popul Biol. 1975 Apr;7(2):256–276. pmid:1145509
  51. 51. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics. 1989 Nov;123(3):585–595. pmid:2513255
  52. 52. Coop G, Ralph P. Patterns of neutral diversity under general models of selective sweeps. Genetics. 2012 Sep;192(1):205–224. pmid:22714413
  53. 53. Lemeunier F, Ashburner MA. Relationships within the melanogaster species subgroup of the genus Drosophila (Sophophora). II. Phylogenetic relationships between six species based upon polytene chromosome banding sequences. Proc R Soc Lond, B, Biol Sci. 1976 May;193(1112):275–294. pmid:6967
  54. 54. Llopart A, Lachaise D, Coyne JA. Multilocus analysis of introgression between two sympatric sister species of Drosophila: Drosophila yakuba and D. santomea. Genetics. 2005 Sep;171(1):197–210. pmid:15965264
  55. 55. Mackay TF, Richards S, Stone EA, Barbadilla A, Ayroles JF, Zhu D, et al. The Drosophila melanogaster Genetic Reference Panel. Nature. 2012 Feb;482(7384):173–178. pmid:22318601
  56. 56. Cridland JM, Thornton KR. Validation of rearrangement break points identified by paired-end sequencing in natural populations of Drosophila melanogaster. Genome Biol Evol. 2010;2:83–101. pmid:20333226
  57. 57. Chao A. Nonparametric estimation of the number of classes in a population. Scandinavian Journal of statistics. 1984;p. 265–270.
  58. 58. Hartl DL, Clark AG. In: Principles of population genetics. Sunderland, Mass.: Sinauer Associates; 2007. p. 652. Available from: http://www.loc.gov/catdir/toc/ecip074/2006036153.html.
  59. 59. Lynch M. In: The Origins of Genome Architecture. Sunderland, Mass.: Sinauer Associates; 2007. p. 494. Available from: http://www.loc.gov/catdir/toc/ecip077/2007000012.html.
  60. 60. Haldane JBS. A mathematical theory of natural and artificial selection V: Selection and Mutation. Proceedings of the Cambridge Philosophical Society. 1927;23:838–844.
  61. 61. Jensen JD, Thornton KR, Andolfatto P. An approximate bayesian estimator suggests strong, recurrent selective sweeps in Drosophila. PLoS Genet. 2008;4(9):e1000198. pmid:18802463
  62. 62. Andolfatto P. Hitchhiking effects of recurrent beneficial amino acid substitutions in the Drosophila melanogaster genome. Genome Res. 2007 Dec;17(12):1755–1762. pmid:17989248
  63. 63. Hu TT, Eisen MB, Thornton KR, Andolfatto P. A second-generation assembly of the Drosophila simulans genome provides new insights into patterns of lineage-specific divergence. Genome Res. 2013 Jan;23(1):89–98. pmid:22936249
  64. 64. Zichner T, Garfield DA, Rausch T, Stutz AM, Cannavo E, Braun M, et al. Impact of genomic structural variation in Drosophila melanogaster based on population-scale sequencing. Genome Res. 2013 Mar;23(3):568–579. pmid:23222910
  65. 65. Claycomb JM, Orr-Weaver TL. Developmental gene amplification: insights into DNA replication and gene expression. Trends Genet. 2005 Mar;21(3):149–162. pmid:15734574
  66. 66. Charlesworth B, Morgan MT, Charlesworth D. The effect of deleterious mutations on neutral molecular variation. Genetics. 1993 Aug;134(4):1289–1303. pmid:8375663
  67. 67. Smith JM, Haigh J. The hitch-hiking effect of a favourable gene. Genet Res. 1974 Dec;89(5–6):391–403.
  68. 68. Presgraves DC. Sex chromosomes and speciation in Drosophila. Trends Genet. 2008 Jul;24(7):336–343. pmid:18514967
  69. 69. Thornton K, Long M. Rapid divergence of gene duplicates on the Drosophila melanogaster X chromosome. Mol Biol Evol. 2002 Jun;19(6):918–925. pmid:12032248
  70. 70. Thornton K, Long M. Excess of amino acid substitutions relative to polymorphism between X-linked duplications in Drosophila melanogaster. Mol Biol Evol. 2005 Feb;22(2):273–284. pmid:15483321
  71. 71. Lynch M, Conery JS. The evolutionary demography of duplicate genes. Journal of Structural and Functional Genomics. 2003;3(1–4):35–44. pmid:12836683
  72. 72. Eyre-Walker A, Keightley PD, Smith NG, Gaffney D. Quantifying the slightly deleterious mutation model of molecular evolution. Mol Biol Evol. 2002 Dec;19(12):2142–2149. pmid:12446806
  73. 73. Zhang YE, Vibranovski MD, Krinsky BH, Long M. Age-dependent chromosomal distribution of male-biased genes in Drosophila. Genome Res. 2010 Nov;20(11):1526–1533. pmid:20798392
  74. 74. Bhutkar A, Schaeffer SW, Russo SM, Xu M, Smith TF, Gelbart WM. Chromosomal rearrangement inferred from comparisons of 12 Drosophila genomes. Genetics. 2008 Jul;179(3):1657–1680. pmid:18622036
  75. 75. Drosophila Twelve Genomes Consortium. Evolution of genes and genomes on the Drosophila phylogeny. Nature. 2007 Nov 8;450(7167):203–218. pmid:17994087
  76. 76. Lazarro BP, Clark AG. Rapid evolution of innate immune response genes. In: R S Singh JX, Kulathinal RJ, editors. Rapidly Evolving Genes and Genetic Systems. Oxford: Oxford University Press; 2012.
  77. 77. Beckerman AP, de Roij J, Dennis SR, Little TJ. A shared mechanism of defense against predators and parasites: chitin regulation and its implications for life-history theory. Ecol Evol. 2013 Dec;3(15):5119–5126. pmid:24455141
  78. 78. Ffrench-Constant RH, Daborn PJ, Le Goff G. The genetics and genomics of insecticide resistance. Trends Genet. 2004 Mar;20(3):163–170. pmid:15036810
  79. 79. Panhuis TM, Clark NL, Swanson WJ. Rapid evolution of reproductive proteins in abalone and Drosophila. Philos Trans R Soc Lond, B, Biol Sci. 2006 Feb;361(1466):261–268. pmid:16612885
  80. 80. Begun DJ, Holloway AK, Stevens K, Hillier LW, Poh YP, Hahn MW, et al. Population genomics: whole-genome analysis of polymorphism and divergence in Drosophila simulans. PLoS Biol. 2007 Nov;5(11):e310. pmid:17988176
  81. 81. Schmidt JM, Good RT, Appleton B, Sherrard J, Raymant GC, Bogwitz MR, et al. Copy number variation and transposable elements feature in recent, ongoing adaptation at the Cyp6g1 locus. PLoS Genet. 2010 Jun;6(6):e1000998. pmid:20585622
  82. 82. Van Valen L. A new evolutionary law. Evolutionary theory. 1973;1:1–30.
  83. 83. Bass C, Field LM. Gene amplification and insecticide resistance. Pest Manag Sci. 2011 Aug;67(8):886–890. pmid:21538802
  84. 84. Haddrill PR, Bachtrog D, Andolfatto P. Positive and negative selection on noncoding DNA in Drosophila simulans. Mol Biol Evol. 2008 Sep;25(9):1825–1834. pmid:18515263
  85. 85. Andolfatto P, Wong KM, Bachtrog D. Effective population size and the efficacy of selection on the X chromosomes of two closely related Drosophila species. Genome Biol Evol. 2011;3:114–128. pmid:21173424
  86. 86. Kaplan NL, Hudson RR, Langley CH. The “hitchhiking effect” revisited. Genetics. 1989 Dec;123(4):887–899. pmid:2612899
  87. 87. Wakeley J. In: Coalescent Theory: An Introduction. Roberts & Company Publishers; 2009. p. 97.
  88. 88. Ewens WJ. A note on the sampling theory for infinite alleles and infinite sites models. Theor Popul Biol. 1974 Oct;6(2):143–148. pmid:4445971
  89. 89. Halligan DL, Keightley PD. Ubiquitous selective constraints in the Drosophila genome revealed by a genome-wide interspecies comparison. Genome Res. 2006 Jul;16(7):875–884. pmid:16751341
  90. 90. Parsch J, Novozhilov S, Saminadin-Peter SS, Wong KM, Andolfatto P. On the utility of short intron sequences as a reference for the detection of positive and negative selection in Drosophila. Mol Biol Evol. 2010 Jun;27(6):1226–1234. pmid:20150340
  91. 91. Clemente F, Vogl C. Unconstrained evolution in short introns?—an analysis of genome-wide polymorphism and divergence data from Drosophila. J Evol Biol. 2012 Oct;25(10):1975–1990. pmid:22901008
  92. 92. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010 Sep;20(9):1297–1303. pmid:20644199
  93. 93. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009 Aug;25(16):2078–2079. pmid:19505943
  94. 94. Thornton K. libsequence: a C++ class library for evolutionary genetic analysis. Bioinformatics. 2003;19:2325–2327. pmid:14630667
  95. 95. Haag-Liautard C, Dorris M, Maside X, Macaskill S, Halligan DL, Houle D, et al. Direct estimation of per nucleotide and genomic deleterious mutation rates in Drosophila. Nature. 2007 Jan 4;445(7123):82–85. pmid:17203060
  96. 96. Rosenberg NA. The shapes of neutral gene genealogies in two species: probabilities of monophyly, paraphyly, and polyphyly in a coalescent model. Evolution. 2003 Jul;57(7):1465–1477. pmid:12940352