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An Ancestral Balanced Inversion Polymorphism Confers Global Adaptation

May 2023
Molecular Biology and Evolution 40(6)

May 2023
40(6)

DOI:10.1093/molbev/msad118

License
CC BY 4.0

Authors:

Martin Kapun

Naturhistorisches Museum Wien

Esra Durmaz Mitchell

University of Southern Denmark

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Since the pioneering work of Dobzhansky in the 1930s and 1940s, many chromosomal inversions have been identified but how they contribute to adaptation remains poorly understood. In Drosophila melanogaster, the widespread inversion polymorphism In(3R)Payne underpins latitudinal clines in fitness traits on multiple continents. Here, we use single-individual whole-genome sequencing, transcriptomics and published sequencing data to study the population genomics of this inversion on four continents: in its ancestral African range and in derived populations in Europe, North America, and Australia. Our results confirm that this inversion originated in sub-Saharan Africa and subsequently became cosmopolitan; we observe marked monophyletic divergence of------- inverted and non-inverted karyotypes, with some substructure among inverted chromosomes between continents. Despite divergent evolution of this inversion since its out-of-Africa migration, derived non-African populations exhibit similar patterns of long-range linkage disequilibrium between the inversion breakpoints and major peaks of divergence in its center, consistent with balancing selection and suggesting that the inversion harbors alleles that are maintained by selection on several continents. Using RNA-seq we identify overlap between inversion-linked SNPs and loci that are differentially expressed between inverted and non-inverted chromosomes. Expression levels are higher for inverted chromosomes at low temperature, suggesting loss of buffering or compensatory plasticity and consistent with higher inversion frequency in warm climates. Our results suggest that this ancestrally tropical balanced polymorphism spread around the world and became latitudinally assorted along similar but independent climatic gradients, always being frequent in subtropical/tropical areas but rare or absent in temperate climates.

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any medium, provided the original work is properly cited. 1

An Ancestral Balanced Inversion Polymorphism Confers Global Adaptation

Martin Kapun1,2,3,4,*, Esra Durmaz Mitchell1,2,5, Tadeusz J. Kawecki1, Paul Schmidt6, and

Thomas Flatt1,2,*

1 Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland

2 Department of Biology, University of Fribourg, Fribourg, Switzerland

3 Division of Cell and Developmental Biology, Medical University of Vienna, Vienna, Austria

4 Natural History Museum Vienna, A-1010 Vienna, Austria

5 Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense,

Denmark

6 Department of Biology, University of Pennsylvania, Philadelphia, USA

ORCID IDs:

MK: 0000-0002-3810-0504

ED: 0000-0002-4345-2264

TJK: 0000-0002-9244-1991

PS: 0000-0002-8076-6705

TF: 0000-0002-5990-1503

*Co-corresponding authors: Martin.Kapun@nhm-wien.ac.at; thomas.flatt@unifr.ch

Running title: Genomics of a Balanced Inversion Polymorphism

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Abstract

Since the pioneering work of Dobzhansky in the 1930s and 1940s, many chromosomal

inversions have been identified but how they contribute to adaptation remains poorly

understood. In Drosophila melanogaster, the widespread inversion polymorphism In(3R)Payne

underpins latitudinal clines in fitness traits on multiple continents. Here, we use single-individual

whole-genome sequencing, transcriptomics and published sequencing data to study the

population genomics of this inversion on four continents: in its ancestral African range and in

derived populations in Europe, North America, and Australia. Our results confirm that this

inversion originated in sub-Saharan Africa and subsequently became cosmopolitan; we observe

marked monophyletic divergence of inverted and non-inverted karyotypes, with some

substructure among inverted chromosomes between continents. Despite divergent evolution of

this inversion since its out-of-Africa migration, derived non-African populations exhibit similar

patterns of long-range linkage disequilibrium between the inversion breakpoints and major

peaks of divergence in its center, consistent with balancing selection and suggesting that the

inversion harbors alleles that are maintained by selection on several continents. Using RNA-seq

we identify overlap between inversion-linked SNPs and loci that are differentially expressed

between inverted and non-inverted chromosomes. Expression levels are higher for inverted

chromosomes at low temperature, suggesting loss of buffering or compensatory plasticity and

consistent with higher inversion frequency in warm climates. Our results suggest that this

ancestrally tropical balanced polymorphism spread around the world and became latitudinally

assorted along similar but independent climatic gradients, always being frequent in

subtropical/tropical areas but rare or absent in temperate climates.

Keywords: inversion, balanced polymorphism, balancing selection, clines, adaptation

Introduction

Chromosomal inversions are structural mutations that cause the gene order of a chromosomal

segment to be reversed (Sturtevant 1917, 1919, 1921). Because inversions suppress crossing-

over (but not gene conversion events) in heterozygous state, they can cause an effective barrier

to genetic exchange (‘gene flux’) between inverted and non-inverted (‘standard’) chromosomes

(Rozas and Aguadé 1994; Navarro et al. 1997; Griffiths et al. 2000; Schaeffer and Anderson

2005; Kirkpatrick 2010; Charlesworth 2016; Crown et al. 2018; Korunes and Noor 2019; Kapun

and Flatt 2019; Durmaz et al. 2020). This pervasive effect of inversions on patterns of

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recombination can have major evolutionary consequences. For example, inversions can

contribute to (i) speciation by allowing mutations involved in reproductive isolation to

accumulate; (ii) genetic divergence between the sexes by accumulating on sex chromosomes;

and (iii) adaptation by capturing beneficial alleles at multiple loci and binding them together

(Dobzhansky 1948, 1949, 1950; Charlesworth and Charlesworth 1973; Rieseberg 2001; Noor et

al. 2001; Navarro and Barton 2003; Kirkpatrick and Barton 2006; Hoffmann and Rieseberg

2008; Kirkpatrick 2010; Charlesworth 2016; Fuller et al. 2016, 2017; Charlesworth and Barton

2018; Wellenreuther and Bernatchez 2018; Faria et al. 2019; Fuller et al. 2019; Kapun and Flatt

2019; Durmaz et al. 2020; Charlesworth and Flatt 2021; Mackintosh et al. 2022).

Since the discovery of inversions in the early 20th century by Sturtevant (1917, 1919, 1921),

their role in adaptation has attracted great interest among evolutionary geneticists (Dobzhansky

1948, 1950; Krimbas and Powell 1992; Hoffmann et al. 2004; Kirkpatrick and Barton 2006;

Hoffmann and Rieseberg 2008; Kirkpatrick 2010; Guerrero et al. 2012; Wellenreuther and

Bernatchez 2018; Faria et al. 2019; Kapun and Flatt 2019). For instance, theory suggests that

linked selection can cause the spread of an initially rare inversion when it captures a locally

adaptive haplotype, protects it from recombination load and/or maladaptive gene flow from

neighboring populations, and then ‘hitchhikes’ with it to high frequency; alternatively, a new

inversion might be favored by direct positive selection when the breakpoints of the inversion

fortuitously induce a beneficial mutation (Charlesworth and Charlesworth 1973; Charlesworth

1974; Kirkpatrick and Barton 2006; Kirkpatrick 2010; Guerrero et al. 2012; Charlesworth and

Barton 2018; Kapun and Flatt 2019; Durmaz et al. 2020; Mackintosh et al. 2022). Indeed,

beginning with Dobzhansky’s seminal observations in Drosophila pseudoobscura (Dobzhansky

1943, 1947, 1948, 1950; Wright and Dobzhansky 1946), many inversion polymorphisms subject

to spatially and/or temporally varying selection have been identified, from plants to humans

(Krimbas and Powell 1992; Hoffmann et al. 2004; Stefansson et al. 2005; Hoffmann and

Rieseberg 2008; Lowry and Willis 2010; Kapun et al. 2016a; Wellenreuther and Bernatchez

2018; Faria et al. 2019; Kapun and Flatt 2019; Machado et al. 2021; Lange et al. 2022).

Despite over 100 years of research, however, many fundamental questions about the

adaptive role of inversions remain poorly understood (Kirkpatrick and Barton 2006; Kirkpatrick

and Kern 2012; Kapun and Flatt 2019). Does adaptive divergence between inverted and

standard chromosomes accumulate after an initially rare inversion has become established in a

population? For instance, if an inversion has direct fitness consequences because it causes a

deletion or gene expression changes near the breakpoints, we might expect that adaptive

divergence between the chromosomal arrangements postdates the initial establishment of the

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inversion. Alternatively, do adaptive haplotypes predate the mutational origin of an inversion and

then get captured by it (Kirkpatrick and Barton 2006; Kirkpatrick and Kern 2012; Guerrero et al.

2012; Charlesworth and Barton 2018; Schaal et al. 2022; Mackintosh et al. 2022)? What forms

of balancing selection maintain inversion polymorphisms (Faria et al. 2019; Kapun and Flatt

2019)? And what are the genic targets of selection carried by adaptive inversions?

A promising, tractable model for tackling some of these major questions is the vinegar fly

Drosophila melanogaster: it harbors several apparently balanced inversion polymorphisms that

form parallel latitudinal clines on multiple continents (Mettler et al. 1977; Knibb et al. 1981;

Knibb 1982, 1983; Lemeunier and Aulard 1992; Fabian et al. 2012; Kapun et al. 2014, 2016a,

2020; Kapun and Flatt 2019). The best studied inversion in this species is In(3R)Payne, a 8-Mb

large paracentric inversion that spans roughly one third of the right arm of the third chromosome

(3R; encompassing ~1200 genes) and whose frequency varies latitudinally on several

continents, most prominently along the North American and Australian east coasts (Ashburner

and Lemeunier 1976; Mettler et al. 1977; Knibb et al. 1981; Knibb 1982, 1986; Lemeunier and

Aulard 1992; Sezgin et al. 2004; Fabian et al. 2012; Rane et al. 2015; Kapun et al. 2014, 2016a,

2020; Kapun and Flatt 2019). The 3R Payne inversion originated in sub-Saharan Africa >120

kya (Corbett-Detig and Hartl 2012); it thus predates the out-of-Africa expansion of D.

melanogaster ~4-19 kya and its subsequent colonization of other continents (Lachaise et al.

1988; David and Capy 1988; Li and Stephan 2006; Keller 2007; Kapopoulou et al. 2018, 2020;

Arguello et al. 2019; Sprengelmeyer et al. 2020). Several lines of genetic and phenotypic

evidence – including patterns of latitudinal clinality – suggest that this chromosomal

polymorphism is adaptive (Rako et al. 2006; Kennington et al. 2006, 2007; Fabian et al. 2012;

Rane et al. 2015; Kapun et al. 2014, 2016a, 2016b, Durmaz et al. 2018; Kapun and Flatt 2019;

Kapun et al. 2020).

The evolutionary history of this adaptive inversion raises several interesting questions. Given

its parallel clinal distribution on multiple continents, being frequent (~40-50% or higher) in

subtropical and tropical climates but rare or absent in high-latitude, temperate areas around the

world (Kapun and Flatt 2019), did this inversion adapt independently – and hence convergently

– to similar climatic gradients on several continents? Under such a scenario of local adaptation,

the allelic content of the inversion might vary among different geographical areas (Dobzhansky

1949; Schaeffer et al. 2003; Kirkpatrick and Barton 2006). Alternatively, selection might act

uniformly across a broad geographic range: if so, did the inversion capture a pre-existing

adaptive haplotype in its ancestral range and then invade the rest of the world, with climatic

selection favoring parallel but non-convergent spatial assortment of this polymorphism on

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multiple continents? With appropriate data we might be able to distinguish between these

scenarios. And, given its effects on multiple fitness traits, what are likely genic targets of

selection harbored by the 3R Payne inversion?

Here we address these fundamental questions by investigating the evolutionary genomics of

the 3R Payne inversion polymorphism on four continents: in its ancestral range in Africa and in

derived populations in Europe, North America, and Australia. First, we seek to elucidate the

adaptive genetic basis of this inversion by combining new phased sequencing data for3R Payne

inverted and standard karyotypes isolated from North American populations in Florida and

Maine with published sequencing data from the African ancestral range as well as from Europe

and Australia. We use these data to investigate patterns of phylogeography, nucleotide

variability, linkage disequilibrium, karyotypic divergence, and allele sharing across populations.

Second, to identify potential targets of selection spanned by the inversion, we combine FST

outlier analyses with transcriptomic analysis of karyotypes from a derived Florida population;

because 3R Payne has been implicated in climate adaptation, we performed RNA-sequencing

across two developmental temperatures.

We discuss our results in the light of theoretical predictions about expected patterns of

variation and divergence of inversions (Navarro et al. 2000; Guerrero et al. 2012) and balancing

selection (Zeng et al. 2021) and with regard to two commonly invoked models for adaptive

inversions, Dobzhansky’s epistatic coadaptation model (Dobzhansky 1948, 1949, 1950, 1951;

Charlesworth and Charlesworth 1973; Charlesworth 1974; Charlesworth and Flatt 2021) and

Kirkpatrick’s and Barton’s model of ‘local adaptation’ (i.e., local selection in the face of

maladaptive gene flow; Kirkpatrick and Barton 2006; Charlesworth and Barton 2018;

Mackintosh et al. 2022). Under both models, a possible consequence is that the same inversion

is highly locally adapted and thus contains distinct sets of adaptive alleles in different

populations (Dobzhansky 1948, 1950; Prakash and Lewontin 1968, 1971; Kirkpatrick and

Barton 2006),

Consistent with either model, our results suggest that In(3R)Payne captured adaptive alleles

in the ancestral African range that predate the origin of the inversion. Yet, contrary to the above-

mentioned corollary, we find relatively weak differentiation among inverted chromosomes across

continents. These results indicate that the adaptive allelic content of the inversion might be

ancestral and shared among populations: selection appears to have favored the spatial

assortment of this ancestral polymorphism on multiple continents in a parallel fashion, resulting

in qualitatively identical latitudinal clines and mediating ‘global’ (species-wide) adaptation.

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Results and Discussion

Table S1 (Supplementary Material online) gives an overview of the genomic data analyzed and

indicates which data subsets were used in the different analyses presented below. Table S2

(Supplementary Material online) provides information

3R Payne is of monophyletic African origin and shows weak out-of-Africa divergence

Given that 3R Payne is a cosmopolitan adaptive polymorphism (cf. Kapun and Flatt 2019) of

sub-Saharan African evolutionary origin (Corbett-Detig and Hartl 2012), we first sought to study

its phylogeography. For example, major divergence of inverted chromosomes among continents

could indicate that the inversion adapted independently (i.e., convergently) to similar conditions

on different continents.

Divergence-based age estimation suggests that 3R Payne has originated ~146,000 years

ago; polymorphism-based estimation indicates a median estimate of ~129,000 years (5%

confidence limit [CL]: ~80 kya, 95% CL: ~196 kya; Corbett-Detig and Hartl 2012). Taking the

latter estimate and assuming a generation time of ~15 generations per year (Pool 2015), this

inversion is thus at least ~1.95 x 106 generations old, i.e., roughly twice the ancestral effective

population size Ne (~1.0 x 106 - 1.5 x 106; Kreitman 1983, Matzkin et al. 2005; Shapiro et al.

2007; Campos et al. 2017; Kapopoulou et al. 2018). The polymorphism is therefore probably

sufficiently old for homogenizing flux between inverted and standard karyotypes to have

occurred via gene conversion or double crossovers: flux rates Φ have been estimated to be

~10-4-10-5 for the central regions of D. melanogaster inversions (Payne 1924; Chovnick 1973;

Navarro et al. 1997, 2000).

The age of 3R Payne is relevant because for sufficiently old inversions (age >> Ne

generations) that have captured an adaptive haplotype we might expect major peaks of

divergence between inverted and standard chromosomes in the center of the inversion, which

are due to the interplay of homogenizing flux and divergent selection opposing recombination

(Guerrero et al. 2012; also see below). Consistent with this expectation, we have previously

found major peaks of divergence in the center of In(3R)Payne in North American samples

(Kapun et al. 2016a). In further support of a selective role, latitudinal frequency clines of 3R

Payne in Europe and North America deviate from neutral expectations (Kapun et al. 2016a,

2020), and inverted and standard karyotypes differ in their effects on several major fitness traits

including body size, cold shock survival and lifespan (Rako et al. 2006; Kapun et al. 2016b;

Durmaz et al. 2018).

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To study the phylogeography of 3R Payne, we investigated phylogenetic relationships among

karyotypes using sequencing data from 485 strains across four continents, including data from

the ancestral African range (Siavonga, Zambia; Pool et al. 2012; Lack et al. 2015, 2016;

Sprengelmeyer et al. 2020) and from several derived populations in Europe (n=3), North

America (n=3) and Australia (n=2) (fig. 1A; supplementary table S1, Supplementary Material

online). Our analyses complement those of Corbett-Detig and Hartl (2012) who had examined

the phylogenetic history of In(3R)Payne and other inversions using several African populations

and single populations from Europe (France) and North America (North Carolina, USA). Based

on the average number of pairwise nucleotide differences per site (nucleotide diversity π; Nei

and Li 1979) in 100 kb non-overlapping windows, we constructed a neighbor-joining haplotype

network of inverted and standard chromosomes using the Neighbor-Net method (Bryant and

Moulton 2004; fig. 1B). In contrast to neighbor-joining trees, Neighbor-Net allows one to

represent conflicting signals in the data, for example due to recombination.

Inverted karyotypes clustered monophyletically within Africa, irrespective of their worldwide

sampling location (fig. 1B; table 1), confirming the finding that 3R Payne arose in sub-Saharan

Africa (Corbett-Detig and Hartl 2012). This differs markedly from the pattern observed when

analyzing a network based on a random set of third-chromosome SNPs at a distance > 200 kb

from In(3R)Payne (and from the second major inversion on chromosome 3, In(3L)P; see

Materials and Methods): here, the network structure mainly reflects geography, not 3R Payne

karyotype (fig. 1C; table 1). However, there is nonetheless a weak signal of clustering of

inverted chromosome in this analysis, suggesting that the effect of In(3R)Payne on genetic

variation might go well beyond its breakpoints (cf. Corbett-Detig and Hartl 2012).

In addition to the dominant signal of monophyletic divergence between inverted and standard

karyotypes, we also found a weaker signal of geographical substructure within the inverted and

standard clusters of chromosomes, indicating some divergence within karyotypes among

continents (fig. 1B; table 1). The observation of substructure within the inverted karyotype bears

on the question of whether 3R Payne inverted chromosomes might be locally adapted. Under

both the ‘local adaptation’ model and the epistatic coadaptation model mentioned above, loci

within the inverted karyotype may be differentiated among populations if gene flow among

populations disrupts locally adapted haplotypes and generates maladaptive genotypes (Prakash

and Lewontin 1968, 1971; Schaeffer et al. 2003). The fact that inverted 3R Payne chromosomes

exhibit some divergence among continents is consistent with this expectation (but see results

and discussion below).

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Patterns of variation are consistent with a balanced inversion polymorphism

According to coalescent models by Navarro et al. (2000) (also cf. Zeng et al. 2021), a newly

arisen inversion subject to balancing selection eliminates substantial amounts of variation

across a large chromosomal segment via a partial selective sweep as it increases in frequency;

during the subsequent slow process of convergence to mutation-drift-flux equilibrium, variation

at the breakpoints is greatly reduced as compared to the central region of the inversion where

variation is restored. This is because the rate of gene flux in the form of crossing over is very

low in regions close to the breakpoints and hence the effect of the partial sweep is greater.

[Generally, pairing in heterokaryotypes is strongly reduced at the breakpoints, with

recombination rates being very low (<<10-4; Navarro et al. 1997, 2000); for an inversion

heterokaryotype in D. subobscura, Rozas and Aguadé (1994) estimated a value of 10-7 near the

breakpoints.] By contrast, old inversions that have reached mutation-drift-flux equilibrium can

exhibit greater variation at the breakpoints as compared to the inversion body (Navarro et al.

2000; cf. Wallace et al. 2013; Charlesworth 2023). This is because, over time, genetic

differences between inverted and standard karyotypes become homogenized by gene flux, but

this effect is much stronger in the central regions of the inversion than at the breakpoints where

flux is effectively suppressed. At least 107 generations are required to reach mutation-drift-flux

equilibrium (Navarro et al. 2000). We thus sought to examine π inside and outside of the

inverted region among inverted and standard 3R chromosomes and compare our data to the

expectations of Navarro et al. (2000) and Zeng et al. (2021).

Nucleotide variability on 3R was markedly higher in the African population sample from

Zambia as compared to the samples from derived population, consistent with the out-of-Africa

bottleneck (Li and Stephan 2006; Lack et al. 2016; Arguello et al. 2019; Kapopoulou et al. 2020;

Kapun et al. 2020, 2021) (fig. 2; supplementary fig. S1, Supplementary Material online). Inside

the inverted region of African chromosomes, π was higher in standard relative to inverted

chromosomes, but not different between arrangement types in derived populations (fig. 2;

supplementary fig. S1, Supplementary Material online; see below).

In both Africa and derived populations, π was markedly reduced in the breakpoint regions as

compared to the inversion body, resulting in a dome-shaped pattern (supplementary fig. S1,

Supplementary Material online). This dome-shaped pattern agrees qualitatively well with the

predictions of Navarro et al. (2000) for an inversion subject to balancing selection and which

might not have reached equilibrium (i.e., inversion age < 107 generations). On the other hand,

consistent with equilibrium (long-term) balancing selection, π was higher for African standard

chromosomes inside as compared to outside the inverted region (fig. 2; also see supplementary

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fig. S1 B, Supplementary Material online). Assuming that the frequency of the inversion is

substantially lower than that of the standard arrangement, such a pattern might be expected

under an equilibrium model of balancing selection with recombination (Zeng et al. 2021). Under

such a scenario, the presence of the inversion would increase diversity due to the accumulation

of new mutations that distinguish inverted and standard chromosomes; the coalescent time

would be somewhat increased for standard alleles, due to the partial population subdivision

created by the inversion, while the coalescent time would be reduced for inversion alleles (cf.

Zeng et al. 2021). Recent calculations by Charlesworth (2023), which are based on our data in

Fig. S1B, are consistent with 3R Payne representing a balanced polymorphism which has

reached mutation-drift-recombination equilibrium with respect to neutral or nearly neutral

variants (see Navarro et al. 2000); this also suggests that 3R Payne might be older than

previously estimated (see above).

The absence of clear differences in π for non-African 3R chromosomes may be due to the

interplay of sufficient time for gene flux having homogenized variation between karyotypes

(already in Africa), selection, and the out-of-Africa bottleneck. The fact that levels of variation in

derived populations are very similar between standard and inverted chromosomes could imply

that a substantial number of individuals carrying 3R Payne has migrated out of Africa during the

range expansion.

We next examined patterns of Tajima’s D to test for departures of the site frequency

spectrum from an equilibrium standard neutral model (Tajima 1989). Relative to standard

neutral expectation (D=0), positive values of D indicate an excess of intermediate frequency

variants and might be consistent with a bottleneck or balancing selection; by contrast, negative

values of D indicate an excess of rare variants which may result from a recent population

expansion, from purifying selection, or from the affected genomic region having recovered

variation after a selective sweep (Innan and Stephan 2000; Wallace et al. 2013; Fijarczyk and

Babik 2015). Figure 2 shows average D, estimated separately for the two karyotypes;

supplementary fig. S2 (Supplementary Material online) displays D along the chromosome,

separately for each karyotype as well as for the pooled sample of inverted and standard

chromosomes.

Average levels of D were positive and significantly higher for inverted as compared to

standard chromosomes within populations on all continents, both inside and outside the inverted

region (fig. 2; also see supplementary fig. S2, Supplementary Material online). Inverted

chromosomes therefore harbor a greater frequency of intermediate variants than standard

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chromosomes. Positive D values for inverted chromosomes could arise from a bottleneck

affecting the inversion within the ancestral range prior to the range expansion. But this seems

unlikely as such a bottleneck should have genome-wide effects beyond the inversion; yet the

positive values of D in the inverted region deviate markedly from the average value of D 0 on

chromosome 3R and the genome-wide average of D for populations in Africa, Europe and North

America (Kapun et al. 2020). Also, given that a new inversion is initially genetically invariant,

one would expect more low-frequency variants as the inversion accumulates new mutations.

Other possibilities might involve an incomplete sweep, or even a balanced polymorphism,

among inverted chromosomes; the latter could account for the relatively high diversity of

inverted chromosomes. Finally, associative overdominance (AOD), reflecting reduced

recombination experienced by the inversion overall, could be involved; AOD might generate a

pattern of pseudo-overdominance (Frydenberg 1963; Sved 1968; Ohta 1971; Zhao and

Charlesworth 2016; Charlesworth and Charlesworth 2018; Becher et al. 2020; Gilbert et al.

2020; Berdan et al. 2021; Waller 2021; Charlesworth and Jensen 2021; Charlesworth 2022).

However, under AOD, low-recombination regions still exhibit a skew towards low-frequency

variants (Becher et al. 2020), so that this scenario seems improbable.

In the pooled sample of inverted and standard chromosomes (supplementary fig. S2,

Supplementary Material online), we did not find evidence for positive D values consistent with

balancing selection. Thus, our analyses of D are somewhat difficult to interpret; a complication

with interpreting Tajima’s D is that it can be strongly influenced by sample size, the number of

segregating sites, and by demography.

Nevertheless, several lines of evidence strongly support the notion that In(3R)Payne

represents a balanced polymorphism, including our analyses of nucleotide variability above. For

example, consistent with some form of balancing selection, In(3R)Payne segregates at

intermediate frequencies in subtropical/tropical populations around the world: for example, the

inversion attains an average frequency of ~45% in subtropical southeastern North America and

~60% in tropical Australian populations (Lemeunier and Aulard 1992; Rako et al. 2006; Rane et

al. 2015; Kapun et al. 2016; see meta-analysis in Kapun and Flatt 2019). In Afrotropical

populations, the average inversion frequency is ~10-13%, with the highest value (~64%) in

tropical Ivory Coast (Aulard et al. 2002; Kapun and Flatt 2019). Temperate, high-latitude

populations, by contrast, are fixed for the standard arrangement (Lemeunier and Aulard 1992;

Kapun et al. 2016; Kapun and Flatt 2019; Kapun et al. 2020). These frequency clines,

presumably in the face of sufficient gene flow to homogenize arrangement frequencies, suggest

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that 3R Payne represents a balanced polymorphism driven by selection in / across

heterogeneous environments (Levene 1953).

The fact that different low-latitude populations exhibit different intermediate inversion

frequencies is consistent with epistatic coadaptation: under such a model, there exist multiple

equilibria and quasi-equilibria for the frequency of the inversion, and the frequency which it will

ultimately attain will depend on the history, the initial conditions of the population, and/or the

local environment (Charlesworth 1974; also see Dobzhansky and Pavlovsky 1957; Lewontin

1974). Although the model of Charlesworth (1974) assumes constant fitness values, it leads to

apparent frequency-dependent selection. Interestingly, Nassar et al. (1973) found that negative

frequency-dependent viability selection operates on In(3R)Payne under crowded larval

conditions, giving further credence to a scenario of balancing selection.

Some studies have reported that In(3R)Payne can locally reach near-fixation or fixation in

some Australian populations (Knibb et al. 1981; Anderson et al. 2005; Umina et al. 2005), an

observation that seems to be at odds with a balanced polymorphism. However, the sample size

in the study of Knibb et al. (1981) was extremely low. Moreover, drift can cause the fixation of

one variant and loss of the alternative variant despite balancing selection (Robertson 1962;

Ewens and Thomson 1970). Also, the selective factors favoring the polymorphism might be

environmentally sensitive, so that balancing selection could break down in some locations.

Overall, the data available to date indicate that In(3R)Payne segregates at intermediate

frequencies in the majority of low-latitude populations around the world and that fixation of the

inversion is rare (Kapun and Flatt 2019) – this pattern and our above results are thus broadly

consistent with balancing selection and/or spatially varying selection (Levene 1953) maintaining

this polymorphism.

Patterns of LD are compatible with linked selection maintaining the inversion

Next, we examined patterns of linkage disequilibrium (LD). Three aspects of LD can be

distinguished: (i) LD among markers without reference to karyotype; (ii) LD between a marker

and inverted vs. standard arrangements; and (iii) LD between markers within inverted or within

standard chromosomes. Because inversions strongly reduce the products of recombination in

heterozygous state, heterokaryotypes (or pools of inverted and standard chromosomes) should

exhibit increased LD as compared to homokaryotypes (aspect ii); for sufficiently old inversions

that evolve neutrally we might expect that LD decreases towards the center of the inversion due

to gene flux between the karyotypes, even though such a pattern is difficult to distinguish from

direct positive selection at the breakpoints (Navarro et al. 1997; Guerrero et al. 2012). Within the

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class of inverted chromosomes (aspect iii), LD can be higher than within standard

chromosomes because of a smaller Ne of the inversion.

LD between the 3R Payne inversion and marker loci has been previously studied by Kojima

et al. (1970), Langley et al. (1974), Voelker et al. (1978), Sezgin et al. (2004) and Kennington et

al. (2006); such LD between the inversion and marker loci might be due to hitchhiking of a

neutral variant initially associated with the inversion by chance (Ishii and Charlesworth 1977) or

due to subsequent new mutations that differentiate the karyotypes. More recently, Rane et al.

(2015) examined LD associated with In(3R)Payne in Australian samples using RAD-sequencing

data. Here, we sought to use phased genomic data to compare patterns of LD in the region

spanned by 3R Payne in African, European, North American and Australian samples with single

nucleotide resolution (fig. 3; supplementary fig. S3, Supplementary Material online).

As compared to standard chromosomes, inverted arrangements showed significantly higher

levels of short-range LD (r2) within the region spanned by In(3R)Payne and a slower decay of

LD with physical distance (fig. 3A). A plausible explanation is that this pattern is due to drift

within the two semi-isolated subpopulations of inverted vs. standard chromosomes, with

inverted chromosomes exhibiting both lower recombination and lower Ne. The pattern of decay

was similar for inverted chromosomes across continents, except for the Portuguese sample,

perhaps due to the rather small number of sampled chromosomes and the overall lower

frequency of 3R Payne in Europe (Kapun et al. 2020). By contrast, the pattern of decay for

standard chromosomes differed markedly among continents: while in the African sample LD

leveled off to r2 < 0.1 within a few hundred base pairs (bp), the decay of LD in standard

arrangements from North America (Florida) and Australia (Queensland) closely resembled that

of inverted chromosomes (fig. 3A), probably reflecting bottlenecks in the derived samples. The

patterns for the derived populations were generally less clear than for the Zambian population,

presumably due to the out-of-Africa bottleneck.

Next, we examined long-range LD (fig. 3B; supplementary fig. S3, Supplementary Material

online). We first analyzed LD within each karyotype. For both standard and inverted

arrangements, LD levels did not exceed r2 > 0.1 within distances of a few kbp, revealing long-

range linkage equilibrium within karyotypes. In marked contrast, when jointly analyzing the pool

of inverted and standard karyotypes from Florida (Fig. 3B), we observed strong long-range LD

within the inverted region, involving SNPs several million bp away from each other and

suggesting that major associations among loci are driven by hetero- not homokaryotypes.

These patterns were similar for the other continents, with major LD between but not within

karyotypes (supplementary fig. S3, Supplementary Material online). Likewise, no strong LD was

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seen within Australian karyotypes; this is contrary to Rane et al. (2015) and likely due to a

misclassification of karyotypes in that study (see below; Materials and Methods; supplementary

fig. S4, Supplementary Material online).

Notably, in European, North American and Australian samples we found large clusters of

SNPs in the center of the inversion that are in strong LD with each other and the proximal and

distal breakpoints, interspersed by regions of low or no LD (fig. 3B; supplementary fig. S3,

Supplementary Material online). For Australia, our data agree well with those of Kennington et

al. (1997) who found LD among marker loci within and near In(3R)Payne and between these

loci and the inversion itself, including marked associations in the center of the inversion. In the

African sample, we also observed LD between the breakpoints and center regions of elevated

LD, yet these central clusters of high LD were much less prominent than in the derived

populations (supplementary fig. S3, Supplementary Material online). These patterns of long-

range LD almost certainly reflect the strong divergence between inverted and standard

arrangements (cf. Zeng et al. 2021); the clearer patterns seen for non-African populations might

be due to lower diversity which tends to sharpen up divergence patterns (Nordborg et al. 1996).

Associations between an inversion and loci within the inverted region can have several

causes that are difficult to distinguish (Strobeck 1983; Navarro et al. 1996): the inversion might

have become associated with neutral alleles when it formed (Ishii and Charlesworth 1977; Nei

and Li 1980), or it might be linked to neutral loci subject to drift (Nei and Li 1975; Strobeck

1983); or selection might maintain associations between selected loci spanned by the inversion

and the inversion itself despite flux between arrangements (see above; also see Prakash and

Lewontin 1968; Charlesworth and Charlesworth 1973; Charlesworth 1974; Ishii and

Charlesworth 1977; Schaeffer et al. 2003; Guerrero et al. 2012; Fuller et al. 2017). The extent of

such associations depends on the flux rate, the effective number of inverted and standard

chromosomes, and the inversion age (Ishii and Charlesworth 1977; Nei and Li 1980). Theory

suggests that the half-life of decay of an association between a neutral locus and an inversion is

on the order of the reciprocal of the flux rate in heterokaryotypes (Ishii and Charlesworth 1977;

Nei and Li 1980). Selection can retard this decay considerably, but only when the neutral locus

is very closely linked to one of the selected loci involved in maintaining the polymorphism (Ishii

and Charlesworth 1977).

How do our data compare to these predictions? Assuming a gene flux rate Φ of ~10-5 in the

center of the inversion (Chovnick 1973), the timescale for the decay of the association would be

on the order of ~105 generations (~7000-10,000 years, assuming 10-15 generations per year).

Given that In(3R)Payne is at least ~129k years old and globally quite frequent (Kapun and Flatt

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2019: average global frequency ~15%, based on 530 samples from 34 independent studies

spanning >50 years of data), and given that Ne is large (~106), gene flux should have had ample

opportunity to break down strong LD associated with this inversion. Our data are thus consistent

with the selective maintenance of the center peaks inside the inversion. On the other hand, D.

melanogaster has undergone an expansion from southern-central Africa and a major out-of-

Africa bottleneck; began to spread from the Middle East into Europe ~1800 years ago; and

colonized North America and Australia ~100-150 years ago (Hoffmann and Weeks 2007; Keller

2007; Sprengelmeyer et al. 2020): demographic events such as bottlenecks, drift and/or founder

effects can therefore not be ruled out as having influenced LD associated with In(3R)Payne.

Major peaks of divergence inside the inversion are shared across continents

To study chromosome-wide patterns of differentiation as a function of 3R Payne karyotype we

used FST, a normalized measure of pairwise allele frequency differentiation (Weir and

Cockerham 1984). In the context of karyotypic differentiation, it would be more accurate to call

this quantity FAT, a measure of variation between allelic classes at a polymorphic locus;

Charlesworth et al. 1997. We were particularly interested in determining whether there might be

major peaks of divergence between standard and inverted chromosomes in the center of the

inverted region, away from the breakpoints. For sufficiently old inversions, and assuming the

existence of targets of selection within the inversion, coalescent theory predicts that selection

might lead to peaks inside the inversion body, which are centered on the adaptive loci and

selectively maintained in the face of homogenizing flux between standard and inverted

chromosomes (Guerrero et al. 2012). This pattern is not unique to inversions: any form of

balancing selection will lead to a peak of divergence and LD around the target of selection at

equilibrium (Hudson and Kaplan 1988; Kaplan et al. 1988; Guerrero et al. 2012; Zeng et al.

2021). For old inversions, this leads to a characteristic pattern of divergence between the

karyotypes (Guerrero et al. 2012; Kirkpatrick 2017): the pattern of divergence resembles the

cables of a suspension bridge with peaks of divergence both at the breakpoints (where

recombination is greatly reduced) and in the center of the inversion (where selection opposes

recombination). Such center peaks of divergence could arise from either the Kirkpatrick-Barton

model or the epistatic coadaptation mechanism (Guerrero et al. 2012; Charlesworth and Barton

2018; Kapun and Flatt 2019; Durmaz et al. 2020; Charlesworth and Flatt 2021); sweeps within

inverted or standard chromosomes could also generate such peaks. We have previously found

such peaks in pool-sequencing data for North American samples (Kapun et al. 2016a), and

Rane et al. (2015) had examined such peaks in Australian data using RAD-sequencing.

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Here, we sought to revisit these results and to extend them to African and European

samples. Secondly, we aimed to assess the contribution of 3R Payne to divergence across

latitudinal clines in Europe, North America and Australia (Kolaczkowski et al. 2011; Fabian et al.

2012; Rane et al. 2015; Kapun et al. 2016a, 2020). To this end, we studied the effects on

divergence of ‘karyotype’ (‘K’, comparing inverted vs. standard arrangements within the same

low-latitude populations), ‘geography’ (‘G’, comparing standard chromosomes between low- and

high-latitude populations) and ‘geography plus karyotype’ (‘G+K’, comparing low-latitude

inverted chromosomes with high-latitude standard chromosomes) (see Materials and Methods).

Figure 4 shows patterns of FST for these effects as a function of position on 3R, including

estimates of LD between SNPs in the region spanned by the inversion and the inversion itself.

Inspection of these patterns revealed several interesting findings.

First, we observed marked divergence in the region spanned by the inversion between

inverted and standard chromosomes on all four continents (effect of ‘K’), with pronounced peaks

in the breakpoint regions (fig. 4, black lines). For derived populations, where 3R Payne exhibits

latitudinal clines on different continents, this divergence is similar when contrasting inverted and

standard chromosomes from within the same low-latitude populations (effect of ‘K’) and when

comparing low-latitude inverted with high-latitude standard arrangements (effect of ‘G+K’,

comparing karyotypes between the cline ‘ends’; fig. 4, light grey lines). By contrast, divergence

is low when comparing standard chromosomes between low- and high-latitude populations in

Europe, North America and Australia (effect of ‘G’; fig. 4, dark grey lines). This is also quantified

for derived populations in fig. 5 and table 2, for both the region inside and outside of

In(3R)Payne.

These results indicate that 3R Payne karyotype is the major determinant of divergence on

chromosome arm 3R in all populations examined, and that clinal divergence in non-African

populations is predominantly caused by the divergence between inversion karyotypes, not by

geography; geographic differentiation inside the inverted region is much weaker than karyotypic

differentiation, despite very large geographical distances (~2600-3900 km) between the

‘endpoints’ of the clines on different continents (Kapun et al. 2016a). By contrast, outside the

inverted region, patterns of divergence are consistent with isolation by distance. These results

agree well with previous pool-sequencing analyses of In(3R)Payne in North America and

Europe (Kapun et al. 2016a, 2020). However, for Australia our findings differ from those of Rane

et al. (2015) who found no major effect of karyotype on divergence in the Queensland low-

latitude population sample. Our reclassification of karyotypes in this dataset suggests that this

previously reported pattern was due to a partial misassignment of karyotypes. Using our new

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classification, we found major karyotypic divergence in the Queensland sample (fig. 4; fig. 5;

supplementary fig. S4, Supplementary Material online; table 2), which is fully consistent with our

analyses of European and North American karyotypes and our analyses in fig. 1.

Second, coarse-grained patterns of karyotypic divergence and LD, especially for derived

populations, are highly congruent across continents, including Australia (fig. 4; fig. 5;

supplementary fig. S4, Supplementary Material online). The parallel divergence due to

In(3R)Payne is underscored by strong correlations between levels of FST with respect to

karyotype across continents, including Africa (supplementary fig. S5, Supplementary Material

online). This might reflect that most SNPs are neutral and in LD with the inversion; on the other

hand, it is also consistent with parallel clinal adaptation to similar environmental gradients

around the world. Together with our phylogenetic analysis, this speaks against a scenario of

‘strict’ local adaptation whereby the same inversion is genetically differently adapted to distinct

local conditions – under such a scenario one might expect larger geographical differentiation

among inverted chromosomes (Dobzhansky 1951; Prakash and Lewontin 1968, 1971;

Schaeffer et al. 2003; Kirkpatrick and Barton 2006; see below and supplementary fig. S6,

Supplementary Material online).

Third, on a fine-grained scale, we observed major peaks of divergence in the inversion center

that are shared among all non-African populations (fig. 4; supplementary fig. S4, Supplementary

Material online). Most prominently, there is a massive central peak of divergence of ~200-300

kb length (position on 3R: ~20.9 - 21.2 Mbp) that is common to derived populations in Europe,

North America and Australia, with alleles in this peak being in strong LD with the breakpoints

(fig. 4; also see fig. 3). These shared peaks, as well as the consistency of LD structure among

populations on several continents, are consistent with the idea that linked selection maintains

non-random associations between the center peaks and the breakpoints despite homogenizing

flux between arrangements (see above; Charlesworth 1974; Guerrero et al. 2012; cf. Prakash

and Lewontin 1968, 1971; Lewontin 1974). However, the history of these derived populations is

not independent, and bottleneck events (or a strong selective sweep within inverted

chromosomes) cannot be ruled out as alternative explanations.

Fourth, although these major center peaks seem to be absent in the African sample (top

panel in fig. 4), preliminary results by Brian Charlesworth (personal communication) suggest

that the observed FST between karyotypes of ~0.1 in the African sample for sites away from the

breakpoints agrees qualitatively well with expected neutral divergence between karyotypes (FST

= 0.13), assuming an equilibrium balanced polymorphism under an island model of population

structure (subdivision with neutral FST = 0.05; inversion frequency = 0.1; rate of exchange = 10-6

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per site per generation). The pattern in the Zambian sample might thus be compatible with 3R

Payne representing a long-term balanced polymorphism (also see discussion of π above; see

discussion in Charlesworth 2023). This prompted us to take a closer look at inversion-

associated alleles within the ancestral African sample.

The inversion appears to have captured adaptive alleles in its ancestral range

Several models of adaptive inversion evolution posit that a new inversion might capture a pre-

existing adaptive haplotype, i.e., a set of selected loci that are in loose LD (Dobzhansky 1949,

1950, 1951; Charlesworth and Charlesworth 1973; Charlesworth 1974; Kirkpatrick and Barton

2006; Charlesworth and Barton 2018; Charlesworth and Flatt 2021; Schaal et al. 2022; also cf.

Kimura 1956); alternatively, adaptive divergence between inverted and standard arrangements

might have accumulated after the inversion was established. In the former case, we might

expect that standard chromosomes in the ancestral African range still carry some of the

presumably adaptive, pre-existing alleles that were captured by the inversion when it first arose

(Kirkpatrick 2017).

Consistent with differentiation among karyotypes not being the result of (continent-specific)

local adaptation but having arisen prior to the out-of-Africa migration, we failed to observe

elevated divergence within the inversion body among inverted chromosomes from different

continents (supplementary fig. S6, Supplementary Material online).

To further explore this idea, we quantified the frequency of inversion-specific alleles, defined

as SNPs with FST ≥ 0.9 between inverted and standard chromosomes in the North American

sample from Florida, among African (and for comparison also among European) standard and

inverted chromosomes (fig. 6).

This analysis revealed that alleles that are highly ‘inversion-specific’ outside of Africa are

polymorphic among both African standard and African inverted chromosomes (fig. 6A), possibly

indicating that they represent ancestral alleles that have been captured by the inversion. The

major enrichment of ‘inversion-specific’ alleles among African inverted relative to standard

chromosomes (frequency difference of ~45% between inverted and standard karyotypes; fig.

6A) might also be consistent with the inversion having captured these alleles before the out-of-

Africa expansion. If so, this would speak against a scenario whereby the inversion spread to

some appreciable frequency by drift and then gained adaptive variants via influx from the

subpopulation of standard chromosomes by recombination or through new mutations, with the

inversion driven to high frequency by hitchhiking.

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Repeating the analysis in fig. 6A by using highly inversion-specific alleles (FST ≥ 0.9) as

defined based on the Zambian population (instead of defining them, as above, based on the

Florida sample) also revealed major frequency differentiation between African inverted and

standard alleles in derived populations, consistent with the notion that African alleles underpin

the divergence of In(3R)Payne karyotypes in derived populations (supplementary fig. S7,

Supplementary Material online).

Figure 6B shows the distribution of ‘inversion-specific’ SNPs (as defined using the Florida

sample) in the African sample with a frequency difference ≥ 50% between standard and inverted

chromosomes: the resulting pattern delineates the breakpoints clearly, indicating that

divergence in the African sample is driven by suppressed recombination at the breakpoints.

Also note the two ‘mini-peak’ regions away from the breakpoints (a larger one at ~19 Mbp; and

a smaller range of peaks at ~21 Mbp) where flux is expected to be much higher than at the

breakpoints: the locations of these mini-peaks correspond well with those of the major central

peaks seen in European, North American and (for the second peak region) Australian samples

(fig. 4). Because levels of diversity are very similar between standard and inverted

chromosomes in the derived populations, it seems improbable that these peaks are due to very

low Ne of inverted chromosomes leaving Africa. Nonetheless, we cannot rule out that these

peaks might have become more pronounced during the range expansion, potentially due to the

out-of-Africa bottleneck and/or drift, perhaps in addition to selection.

Genetic divergence between inversion karyotypes is shared among continents

Because patterns of karyotypic divergence and LD looked very similar across continents (fig. 4),

especially for derived populations, we were interested in quantifying the geographical overlap in

the number of inversion-associated candidate genes and SNPs (fig. 7; candidates defined by

SNPs with FST ≥ 0.9 between inverted and standard karyotypes; see Materials and Methods).

Overall, we observed significant sharing of candidate genes and SNPs across continents (fig.

7). However, the inclusion of the Australian data resulted in very low levels of sharing, perhaps

because this dataset is based on reduced-representation RAD-sequencing with low resolution;

we therefore excluded the Australian data from the analysis (fig. 7). Independent of whether the

Australian data were excluded or not (not shown), we identified major overlap of candidates

between Europe and North America (fig. 7), probably because of the demographic and genetic

similarity of populations on these continents. Importantly, when excluding the Australian data,

we found a highly significant overlap of 174 candidate genes and 34 SNPs that are shared

between Africa, Europe and North America (fig. 7A, fig. 7B) – these loci might thus underlie the

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shared pattern of karyotypic divergence across continents (supplementary table S3;

supplementary table S4; Supplementary Material online; see below).

When examining the putative functional effects of these candidate SNPs, we found a

significant deficiency of SNPs in the intergenic region of African, European and North American

samples and a significant enrichment in the 2 kb upstream region of genes in European and

North American samples (see supplementary fig. S8; supplementary table S4; Supplementary

Material online). These findings suggest that several candidate SNPs might influence gene

regulation and gene expression patterns.

The inversion affects gene expression in a temperature-dependent manner

A handful of studies has examined clinal differences in gene expression in Australian and North

American D. melanogaster (Levine et al. 2011; Chen et al. 2012; Zhao et al. 2015; Juneja et al.

2016; Clemson et al. 2018), but whether 3R Payne contributes to these patterns remains

unknown. To investigate the effects of this inversion polymorphism on differential expression

(DE), and to complement our genomic analyses, we analyzed RNA-Seq data from adult female

whole-body transcriptomes of different In(3R)Payne karyotypes. Our dataset consisted of 9

biological replicates each for Florida inverted (FI), Florida standard (FS) and Maine standard

(MS) homokaryotypes (isochromosomal lines), with each group reared at two developmental

temperatures (18°C, 25°C) prior to RNA extraction (3 karyotypes x 2 temperatures x 9 replicates

= 54 samples in total). Because the 3R Payne inversion is involved in climate adaptation (Kapun

et al. 2016a; Kapun and Flatt 2019), this design allowed us to examine whether developmental

temperature interacts with karyotype and/or geographic origin in affecting expression

(supplementary table S5, Supplementary Material online; see Materials and Methods).

Approximately 60% of all analyzed genes genome-wide (n=9724) showed significant DE in

response to temperature (n=5841; Benjamini-Hochberg [BH]-corrected p < 0.05; supplementary

table S5, Supplementary Material online), in agreement with previous work reporting high levels

of expression plasticity across different rearing temperatures (Chen et al. 2015). Inversion

karyotype had a much weaker effect: only 0.49% of all genes were differentially expressed

between karyotypes (Florida inverted vs. standard; n=46; BH-corrected p < 0.05) and 0.45% in

response to the effect of karyotype plus geography (Florida inverted vs. Maine standard; n=44;

BH-corrected p < 0.05) (supplementary table S5, Supplementary Material online).

Interestingly, we failed to identify any DE in response to the effects of geography alone

(Florida standard vs. Maine standard; BH-corrected p > 0.05; see supplementary table S5,

Supplementary Material online); the effects of karyotype plus geography thus seem to be driven

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by karyotypic differences, not geography. We were thus interested in comparing our

transcriptomic candidates to the RNA-seq data of Zhao et al. (2015) who had examined DE

between populations from Panama (low latitude) and Maine (high latitude) at two growth

temperatures (21°C, 29°C). This analysis revealed significant overlaps between the effects of

karyotype plus geography in our data and differentially expressed genes (DEG) identified by

Zhao et al. (2015) (SuperExactTest; p < 0.05; supplementary table S6; also see supplementary

table S3, Supplementary Material online); overlaps between DEG found by Zhao et al. (2015)

and the effects of karyotype in our data were marginally non-significant. These results, together

with the analyses of Zhao et al. (2015), suggest that In(3R)Payne makes a major contribution to

latitudinal differentiation in gene expression patterns.

We also found pervasive interactions between inversion karyotype and growth temperature:

temperature had a major influence on both the magnitude of DE and the number of DEG

between karyotypes (supplementary table S5, Supplementary Material online). While 648 genes

were differentially expressed between inverted and standard arrangement females that had

developed at 18°C, we did not find any gene exhibiting significant DE between karyotypes at

25°C (fig. 8A; supplementary table S5, Supplementary Material online). This suggests that

variants associated with the inverted arrangement might be more sensitive to lower

temperatures, maybe due to a loss of buffering or because of increased compensatory plasticity

at 18°C (cf. Huang et al. 2022), lending further support to the role of 3R Payne in climate

adaptation (also see Pool et al. 2016).

Similar to other D. melanogaster inversions (In(2L)t, In(3R)Mo, In(3R)K; see Lavington and

Kern 2017; Said et al. 2018), In(3R)Payne karyotype affected DE across the entire genome (at

18°C; see fig. 8B; supplementary table S5, Supplementary Material online). These ‘non-local’

effects on DE suggest that the 3R Payne inversion exerts major trans-acting regulatory effects

(cf. Said et al. 2018), which is also consistent with the significant enrichment of DEG for gene

ontology (GO) terms related to regulation of gene expression (fig. 8C). Despite these genome-

wide transcriptional effects, DEG were enriched within the region spanned by the inversion (108

and 540 genes in- and outside 3R Payne, respectively; Fisher’s exact test (FET), p < 0.001). By

contrast, we failed to find enrichment for effects of temperature (459 and 5382 genes in- and

outside 3R Payne, respectively; FET, p = 0.75). Beyond DEG involved in regulating expression,

GO analysis revealed that the inversion polymorphism also affects the expression of genes

involved in growth, development, and reproduction (fig. 8C), as might be expected given the

multifarious effects of 3R Payne on fitness traits such as body size, survival upon starvation,

cold shock survival and lifespan (Rako et al. 2006; Kapun et al. 2016b; Durmaz et al. 2018).

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Since inversions can have a large impact on the expression of genes in the breakpoint

regions (Lavington and Kern 2017; Said et al. 2018), we also asked whether the 108 DEG within

the inverted region might be enriched in the breakpoint regions (breakpoints plus a region of up

to ± 2 Mb proximal and distal to each breakpoint): there was no evidence for an uneven

distribution of DEG as compared to expectations based on non-candidate genes (FET; p =

0.83). Given that 3R Payne affects DE inside the inversion body as well as across the entire

genome, variants in the breakpoints cannot fully account for the transcriptional effects of the

inversion. These results agree well with the conjecture that inversions such as In(3R)Payne

affect gene expression as a consequence of linked allelic variation maintained by selection for

suppressed recombination (see Said et al. 2018).

To identify links between allelic variation and DEG with respect to karyotype, we compared

genomic and transcriptomic candidates (supplementary table S3, supplementary table S7,

Supplementary Material online). We first quantified the amount of overlap between karyotypic

DE at 18°C (FIFS18 = Florida inverted vs. Florida standard reared at 18°C) and gene-wise FST

without applying significance thresholds since arbitrary thresholds might constrain the ability to

identify overlaps. Using rank-rank hypergeometric overlap tests (RRHO; Cahill et al. 2018)

applied to all genes ranked either by FST or by DE, we found that only genes with high FST

values exhibited highly significant overlap with strongly differentially expressed genes (fig. 9A).

This analysis identified a core set of 86 overlapping genes (see top right corner of fig. 9A) which

are all located within In(3R)Payne or in close proximity to it (fig. 8B; supplementary table S3,

supplementary table S7; Supplementary Material online). Similar results were obtained when

repeating the analysis with the data based on DE between karyotypes irrespective of rearing

temperature (FIFS = Florida inverted vs. Florida standard; see supplementary fig. S9,

Supplementary Material online). This provides further evidence that allelic variation inside the

genomic region spanned by the inversion has a major functional impact on patterns of gene

expression (cf. Said et al. 2018).

We subsequently focused on 470 candidate SNPs on 3R with FST ≥ 0.9 between inverted

and standard arrangements in Florida and 317 transcriptomic candidates with significant DE

between karyotypes at 18°C (FIFS18). Comparison of these two groups of candidates revealed

a significant overlap, comprising 74 genes (SuperExactTest; expected overlap: 37 genes; p <

0.001; fig. 9B; supplementary table S3, supplementary table S7, Supplementary Material

online). Similarly, when considering 55 candidates exhibiting DE with respect to karyotype

irrespective of developmental temperature (FIFS), we found a significant overlap of 19 genes

(SuperExactTest; expected overlap: 4 genes; p < 0.001; supplementary fig. S9; supplementary

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table S3, supplementary table S7, Supplementary Material online). Although neither the 74 nor

the 19 overlapping candidate loci were enriched for GO terms, several of them have well-known

biological functions (supplementary table S3, Supplementary Material online; also see gene

information on FlyBase at http://flybase.org/).

A comprehensive database of In(3R)Payne-associated candidate loci, based on our

genomic, transcriptomic and overlap analyses, is provided in supplementary table S3

(Supplementary Material online). In addition to listing many novel candidates, this dataset

contains and corroborates numerous genes previously associated with either latitudinal clinality

and/or with In(3R)Payne (Hoffmann and Weeks 2007; Kolaczkowski et al. 2011; Fabian et al.

2012; Chen et al. 2012; Zhao et al. 2015; Kapun et al. 2016a, 2016b, 2020). These candidates

include several loci with established mutant effects on fitness-related traits (e.g., size,

reproduction, lifespan, stress resistance; cf. Kapun et al. 2016b; Durmaz et al. 2018; Kapun and

Flatt 2019). Our database of candidates associated with 3R Payne thus provides rich grounds

for future work aiming to dissect the genetic architecture of this balanced inversion

polymorphism.

Conclusions

Here we have sought to refine our understanding of the adaptive nature of a common

cosmopolitan chromosomal inversion polymorphism in D. melanogaster, In(3R)Payne, on four

continents: in its ancestral African range and in derived populations in Europe, North America

and Australia. Based on our population genomic and transcriptomic analyses, we offer the

following conclusions and conjectures:

(1) Our data confirm that the 3R Payne polymorphism is monophyletic, consistent with a single

mutational origin in Africa at least ~129 kya (see Corbett-Detig and Hartl 2012). Despite

some genetic (geographical) divergence both within inverted and standard chromosomes

among continents, inverted arrangements always cluster together, independently of their

geographical provenance, and the same is true for standard arrangements.

(2) Phylogenetic analysis and patterns of divergence and LD support a scenario whereby

differentiation between inverted and standard chromosomes worldwide is due to ancestral

variants that differentiate the two karyotypes. This interpretation is supported by (1)

significant sharing among continents of loci that are strongly differentiated between the

karyotypes, both in the breakpoint regions and the center of the inversion, and (2) an

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absence of pronounced genetic divergence among inverted chromosomes from different

continents. Analyses of inversion-specific alleles that are nearly or completely fixed in non-

African populations within the African population sample suggest that the inversion has

captured adaptive alleles in its ancestral range prior to the inversion having migrated out of

Africa and become cosmopolitan.

(3) Patterns of nucleotide variability, genetic divergence and LD are consistent with (potentially

long-term) balancing selection maintaining the inversion polymorphism (cf. Charlesworth

2023), but the exact type of balancing selection remains to be elucidated. Given its

intermediate frequency in low-latitude populations and its absence in high-latitude locales

around the world, 3R Payne appears to have spread out of its ancestral tropical range and

then become assorted by spatially varying selection in a parallel manner, causing the

formation of similar clines on several continents (Kapun and Flatt 2019). This scenario is

consistent with theoretical expectations suggesting that inversion frequencies can be

maintained by balancing selection at local equilibria that change clinally (Faria et al. 2019);

this could promote inversion polymorphism across large geographical scales and lead to

parallel, stable large-scale clines (Westram et al. 2022).

(4) Our results and previous work (cf. Kapun and Flatt 2019) suggest that 3R Payne is involved

in parallel or ‘global’ (species-wide; cf. Booker et al. 2021) adaptation to similar latitudinal

gradients around the world. It is noteworthy in this context that in Ethiopia 3R Payne occurs

at much lower frequency in a cold high-altitude locale as compared to a warm low-altitude

habitat (Pool et al. 2017). Similarly, Aulard et al. (2002) found negative (albeit non-

significant) correlations between In(3R)Payne frequency and altitude in African populations.

Because 3R Payne does typically not fix under warm conditions but appears to be selected

against under cool conditions, it is an intriguing possibility that the loci captured by the

inversion provide some form of balancing (e.g., negative frequency-dependent) selection

independent of temperature yet happen to render it less tolerant to cool temperatures.

(5) RNA-seq analyses of inverted and standard chromosomes in a sample from North America

(Florida) reveal pronounced effects of inversion karyotype on gene expression that depend

on developmental temperature: expression levels are higher for inverted chromosomes at

low temperature, perhaps due to a loss of buffering or compensatory plasticity (cf. Huang et

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al. 2022) and consistent with the notion that 3R Payne is susceptible to cool conditions (see

above; cf. Kapun et al. 2016a; Pool et al. 2017; Kapun and Flatt 2019).

(6) Although the inversion body is enriched for differentially expressed genes, the 3R Payne

inversion has pervasive genome-wide effects on gene expression, consistent with trans-

acting regulatory effects. Functional effects of this inversion are thus unlikely explained by

lesions at the breakpoints alone. Together with analyses of divergence and LD, our results

support the idea that 3R Payne maintains non-random associations among adaptive loci

(Said et al. 2018). Yet, whether the linked loci are subject to epistatic balancing selection or

to another selective mechanism is an open question. Likewise, the precise identity of the

adaptive loci associated with the inversion remains unknown – our database of candidate

loci might serve as a fruitful starting point for addressing this important question in future

work.

Materials and Methods

Fly strains and their maintenance

To investigate karyotype-specific patterns of genetic variation and differentiation between

karyotypes we established isofemale lines from populations in North America (Homestead,

Florida, and Bowdoin, Maine, collected by Paul Schmidt) (see supplementary table S1,

Supplementary Materials online). Lines were maintained under standard laboratory conditions

(25°C; 60% relative humidity, 12h:12h light:dark cycle). We karyotyped these lines for six major

chromosomal inversion polymorphisms (In(2L)t, In(2R)NS, In(3L)P, In(3R)C, In(3R)Mo,

In(3R)Payne; see Lemeunier and Aulard 1992) using codominant PCR markers following the

approach of Corbett-Detig et al. (2012). By combining PCR-based karyotyping and experimental

crosses we generated lines that were isochromosomal for the third chromosome as described in

Kapun et al. (2016b). In brief, we crossed wild-type males to females carrying a compound

(second and third chromosome) balancer (SMB6; TM6B; Bloomington Drosophila Stock Center

[BDSC] stock #5687) in an ebony (e1) mutant background. F1 offspring, which were

heterozygous for the wild-type chromosome and the balancer, were visually selected based on

the dominant tubby (Tb1) marker mutant phenotype and backcrossed to the balancer strain to

amplify the wild-type chromosomes. Using PCR markers (Matzkin et al. 2005; Corbett-Detig et

al. 2012) we determined the karyotype status of successfully isolated lines with respect to

In(3R)Payne (see Kapun et al. 2016b for details). Whenever possible we selected against the

balancer to establish isochromosomal lines. However, for a subset of standard and inverted

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chromosomes we failed to obtain homokaryons, likely due to recessive homozygous lethal

alleles segregating among these chromosomes – this is not surprising given that typically ~30-

55% of wild third chromosomes are homozygous lethal (Simmons and Crow 1977; Mukai and

Nagano 1983; our unpublished observations). In these cases, isolated wild-type chromosomes

were propagated as heterozygotes over the balancer chromosome. During the propagation of

the compound balancer line used for isolating wild third chromosomes, we occasionally

observed that the visual marker of the second chromosome balancer (SM6B; DuoxCy)

segregated independently of the visual marker of the third chromosome balancer (TM6B; Tb1).

Since we had not consistently selected for both visual markers during the isolation process, we

could not rule out that the wild-type second chromosomes might occasionally have recombined

with those of the lab strain. We therefore excluded sequencing data from all chromosomal arms

other than 3L and 3R from downstream genomic analyses.

Sample preparation for DNA re-sequencing

We generated single-individual (phased) sequencing data using a subset of North American

lines from Florida and Maine (see supplementary table S1, Supplementary Material online) to

investigate patterns of linkage disequilibrium (LD) and haplotype structure with respect to

karyotype and geographic origin. To obtain phased haploid sequencing data, we employed a

‘hemiclone’ approach, as described in Kapun et al. (2014) (supplementary fig. S10;

Supplementary Material online). To this end, we crossed females from isochromosomal lines, or

from strains with wild third chromosomes maintained over the balancer, to males of a highly

inbred, inversion-free isofemale reference strain from Nigeria (line NG9 from the Drosophila

Population Genomics Project [DPGP]; Lack et al. 2015). For each cross, we sequenced a single

F1 hemiclonal male (supplementary fig. S10; Supplementary Material online). To

bioinformatically discriminate between wild-type alleles and alleles segregating in the paternal

NG9 reference (i.e., ‘bioinformatic phasing of alleles’) we pool-sequenced all reference strain

males used for the crosses as a single pool (also see below).

DNA extraction and library preparation

For each of the DNA libraries, we jointly homogenized whole tissue by bead beating (Zirconia

beads; ⌀1.2 mm; 2 x 30 min at 6500 rpm) and incubated the homogenate in lysis buffer (100

mM Tris-Cl, 100 mM EDTA, 1% SDS, 1 mg/ml Proteinase K) for 30 min at 56°C and 30 min at

70°C. The lysate was treated with RNAse A (3 mg per 250 μl aliquot) at 37°C for 30 min prior to

adding 39 μl of 8 M potassium acetate, followed by another incubation step for 30 min on ice to

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precipitate protein. After centrifugation at 14,000 rpm for 15 minutes, we mixed the supernatant

with one volume of phenol-chloroform-isoamyl alcohol (ratio 25:24:1). The aqueous phase was

further washed with 0.75 volume of chloroform prior to precipitation of DNA by adding 3 volumes

of ice cold 100% ethanol. After incubation at 4°C for 2h, followed by centrifugation at 14,000

rpm for 15 minutes, we washed the pellet with 70% ethanol, dried it at room temperature and

then resuspended the DNA in 50 μl of TE buffer. gDNA libraries of each sample were sheared

using a Covaris instrument (Duty cycle 10%, intensity 5, cycles/burst 200, time 50s) and

prepared for paired-end sequencing at the Lausanne Genomic Technologies Facility (GTF),

using the Illumina TruSeq Nano Library preparation kit (Illumina, San Diego, USA). Samples

were sequenced on a HiSeq 2000 Illumina Sequencer to 100 bp paired-end reads.

Differential gene expression assays with RNA-seq

Given that In(3R)Payne is involved in climate adaptation (e.g., Kapun et al. 2016a, 2020, Kapun

and Flatt 2019), and given that some the inversion’s phenotypic effects depend on growth

temperature (Durmaz et al. 2018), we sought to examine the effects of 3R Payne karyotype

and/or of developmental temperature on gene expression and to identify potential candidate

transcripts / genes associated with In(3R)Payne. To do so, we performed RNA-seq assays on

isochromosomal lines carrying either the inverted or standard arrangement from Florida, where

the inversion is polymorphic, and on lines carrying the standard arrangement from Maine, where

the inversion is absent (see Kapun et al. 2016b for details of sampling locations; see Durmaz et

al. 2018 for details of isochromosomal lines; also see supplementary table S1, Supplementary

Material online). Each of these three groups was replicated 9-fold and exposed to two

developmental temperatures (18°C, 25°C; see below) to account for interactions between

genotype (karyotype) and environment (temperature). Prior to sampling for transcriptomic

assays, flies were kept under common garden conditions (~21°C, ~50% relative humidity;

10h:14h L:D) for three generations. The experimental generation was reared at two growth

temperatures during development until 5-7 days of adulthood (18°C vs. 25°C, 12:12h LD, 60%

relative humidity, on a cornmeal/ sugar/ yeast/agar diet). Total RNA was extracted from 5-7

days-old snap-frozen adult females from each isochromosomal line, with each sample being

prepared from 5 individuals (3 karyotypes x 2 temperatures x 9 isochromosomal lines = 54

samples) using the MagMAX™-96 Total RNA Isolation Kit (ThermoFisher Scientific, Waltham,

MA, USA) on a MagMAX™ Express Magnetic Particle Processor (ThermoFisher Scientific,

Waltham, MA, USA), following the manufacturer’s protocol. Prior to library preparation RNA

quality was measured using Fragment Analyzer (Advanced Analytical) analysis. Single-end 101

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bp long reads were sequenced on a Illumina HiSeq 2000 sequencer, following library

preparation using the TrueSeq stranded library preparation kit. For details of bioinformatic

analyses of these RNA-seq data see below.

Bioinformatics pipeline for genomic analyses

The bioinformatics pipeline used for our population genomic analyses (see below for details),

including scripts, is available here: https://github.com/capoony/In3RPayne_PopGenomics.

Mapping pipeline

FASTQ reads from DNA and RNA sequencing data were examined for sequencing quality with

FASTQC (v.0.10.1; http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and then

trimmed and filtered with cutadapt (v.1.8.3; Martin 2011) to remove low-quality bases (base

quality ≥ 18; sequence length ≥ 75 bp) and sequencing adapters. For DNA sequencing data, we

only retained read pairs for which both reads fulfilled our quality criteria after trimming for

mapping with bbmap (v.0.7.15; Li 2013) with default parameters against a compound reference

genome consisting of the genomes of D. melanogaster (v.6.12) and common pro- and

eukaryotic symbionts of Drosophila, including Saccharomyces cerevisiae (GCF_000146045.2),

Wolbachia pipientis (NC_002978.6), Pseudomonas entomophila (NC_008027.1),

Commensalibacter intestine (NZ_AGFR00000000.1), Acetobacter pomorum

(NZ_AEUP00000000.1), Gluconobacter morbifer (NZ_AGQV00000000.1), Providencia

burhodogranariea (NZ_AKKL00000000.1), Providencia alcalifaciens (NZ_AKKM01000049.1),

Providencia rettgeri (NZ_AJSB00000000.1), Enterococcus faecalis (NC_004668.1),

Lactobacillus brevis (NC_008497.1), and Lactobacillus plantarum (NC_004567.2), to avoid

paralogous mapping. We filtered mapped reads for a mapping quality ≥ 20 and used Picard

(v.2.17.6; http://picard.sourceforge.net) to remove duplicate reads and re-aligned sequences

flanking insertions-deletions (indels) with the Genome Analysis Toolkit, GATK (v3.4-46;

McKenna et al. 2010).

Variant calling in DNA sequencing data

We combined mapped reads in BAM file format from each of the sequenced F1 hybrid

individuals and from the sequenced pool of sires into a single mpileup file using samtools

mpileup (v.1.3; Li et al. 2009) without base quality recalibration (parameter -B). Next, we

reconstructed the identity of the maternal wild-type allelic state (‘bioinformatic phasing of

alleles’) by contrasting polymorphisms present in the F1 larvae with the reference alleles from

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the sires based on the bioinformatics pipeline described in Kapun et al. (2014). We only

considered positions that were homozygous in the reference pool (minimum minor allele

frequency < 10%) and retained wild-type alleles with a minimum count of 10 across all

sequenced F1 individuals. To avoid false positives, we excluded alleles whose counts fell

outside the limits of a 90% binomial confidence interval based on an expected frequency of 50%

at a heterozygous site in a given diploid F1 library. We further excluded positions with either (1)

minimum coverage < 15 to reduce false negatives due to large sampling errors or (2) maximum

coverage > the 95th coverage percentile for the corresponding sample and chromosome to

avoid false positives due to paralogous mapping. For positions with more than one wild-type

allele, we only considered the most frequent allele.

Sequencing data availability

All newly generated sequencing data are available under NCBI BioProject ID PRJNA928565

(http://www.ncbi.nlm.nih.gov/bioproject/928565).

Additional sequencing data from other continents

To complement the above-mentioned sequencing data, we used previously published

sequencing data from D. melanogaster lines from Africa, Europe and Australia, all with known

inversion karyotype (see supplementary table S1, Supplementary Material online):

(i) African data. The African strains were collected in Siavonga (Zambia) and sequenced as

haploid embryos to obtain fully phased sequences; they were bioinformatically karyotyped for

various inversions as part of the DPGP (Drosophila Population Genomics Project) resource (see

Lack et al. 2015, 2016). We focused on 21 lines that are known to segregate In(3R)Payne and

randomly selected an equal number of strains with standard arrangement third chromosomes.

Consensus sequence files were downloaded from the DGN (Drosophila Genome Nexus)

website (http://www.johnpool.net/genomes.html), filtered for polymorphic sites and merged into

a single VCF file using custom-made software. Genomic coordinates from D. melanogaster

reference v.5 were converted to v.6.

(ii) European data. We used phased sequencing data from wild-type strains collected in

Póvoa de Varzim in Portugal (Kapun et al. 2014; Franssen et al. 2015). In addition to 7 strains

carrying In(3R)Payne, we randomly picked an equal number of strains with standard

arrangement on the third chromosome and obtained the genomic data from Dryad

(http://doi.org/10.5061/dryad.403b2). In addition to this European low-latitude sample from

Portugal, we integrated phased sequencing data from 14 non-inverted strains from a high-

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latitude population in Umeå (Sweden) into our analyses, which were sequenced as haploid

embryos (Kapopoulou et al. 2020), similar to the African samples mentioned above.

(iii) Australian data. Sequence data for the Australian continent were obtained from Rane et

al. (2015) who had investigated population samples that approximate the endpoints of the

latitudinal cline along the Australian east coast and sequenced these samples with reduced

library representation RAD-tag sequencing. All 55 strains from Innisfail (tropical Queensland)

(19 strains carrying 3R Payne plus 18 carrying the standard arrangement) and Yering Station

(temperate Victoria) (18 standard lines) had been screened by the authors for 3R Payne using

PCR markers (Rane et al. 2015). We obtained a VCF file containing high-confidence SNPs for

all lines from Dryad (https://doi.org/10.5061/dryad.5q0m8) and converted genomic coordinates

from D. melanogaster reference v.5 to v.6 prior to downstream analyses. Note that this dataset

does not include genomic information for the first 12 million bp on chromosome arm 3R.

Re-analysis of DNA sequences of Australian isochromosomal lines

Rane et al. (2015) reported patterns genetic differentiation and LD with respect to In(3R)Payne

in Australia that deviate from observations based on single-individual sequencing data of African

flies (Corbett-Detig and Hartl 2012) and pool-seq data from North American flies (Kapun et al.

2016a). In contrast to the studies of Corbett-Detig and Hartl (2012) and Kapun et al. (2016a),

Rane et al. (2015) found that Australian flies from tropical Queensland (where the polymorphism

is segregating) do not exhibit elevated genetic differentiation between inverted and standard

karyotypes within the genomic region spanned by In(3R)Payne. Yet, these authors found a

pattern of strong, highly localized divergence between flies from Queensland and temperate

flies from Victoria (where the inversion is very rare or absent), irrespective of In(3R)Payne

karyotype. To explore why the patterns in the Australia data might differ from those observed in

Africa and North America, we compared the 55 Australian libraries (Queensland: 19 inverted

karyotypes, 18 standard karyotypes; Victoria: 18 standard karyotypes) to high-confidence

sequencing data from 42 isogenic lines from Siavonga (Zambia, Africa; see above) which had

previously been characterized for the presence or absence of In(3R)Payne (Lack et al. 2015,

2016). Our goal was to use these data to determine whether Australian and/or African lines

cluster according to their In(3R)Payne karyotype and/or their geographic origin. Because 3R

Payne is of monophyletic African origin (Corbett-Detig and Hartl 2012, and analyses herein), we

expected to find marked clustering of inverted chromosomes of African and Australian origin.

Since gene flux due to double crossing overs is strongly suppressed or absent between the

karyotypes in the breakpoint regions, we focused on 240 and 262 SNPs that were polymorphic

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both in Africa and Australia, respectively, and which were located within 200,000 bp around the

proximal and distal breakpoints. We used custom-made software to combine and convert the

allelic data from African and Australian lines to the NEXUS file format and calculated unrooted

phylogenetic networks based on the Neighbor-Net inference method (see below). Since the

karyotype-specific clustering of African strains and Australian lines from Queensland was

inconsistent when using the karyotype classification of Rane et al. (2015) (supplementary fig.

S4, Supplementary Material online), we used a panel of highly diagnostic, experimentally

validated marker SNPs for In(3R)Payne (see Kapun et al. 2014 for details; also cf. Kapun et al.

2016a, 2020) to bioinformatically determine the karyotype status of the sequenced lines. Four of

the 19 inversion-specific SNPs had sufficient coverage in the RAD-Seq data of most lines

reported by Rane et al. (2015), thus allowing us to re-classify the karyotypes of the Australian

lines. Notably, our new classification of karyotypes was highly consistent with the results of the

clustering analysis of African samples. We therefore decided to use this new karyotype

classification for all downstream analyses of the Australian data. Our analysis using inversion-

specific marker SNPs also indicated that several Australian lines were not fixed for either the

inverted or standard karyotype but appeared to be heterokaryotypic (supplementary table S8,

Supplementary Material online). We thus excluded all apparently heterokaryotypic and/or

ambiguous strains and only retained unambiguous homokaryotypes for downstream analyses.

Phylogenetic relationships among In(3R)Payne karyotypes

Phylogenetic relationships among a total of 450 D. melanogaster strains from Africa, Europe,

North America and Australia were analyzed with respect to In(3R)Payne based on a compilation

of sequencing data from the above-mentioned sources (see supplementary table S1,

Supplementary Material online). First, to investigate phylogenetic relationships among strains

within the region spanned by In(3R)Payne, we analyzed 3766 SNPs located within the

breakpoints of In(3R)Payne. Secondly, we reconstructed genome-wide patterns of phylogenetic

relationships among these samples independent of chromosomal inversions by focusing on

4849 SNPs that were randomly drawn from the left and right arms of the third chromosome in

200 kb distance from the breakpoints of In(3L)P and In(3R)Payne. Using custom-made

software, we combined and converted allelic data from all lines to the NEXUS file format and

calculated unrooted phylogenetic networks based on the Neighbor-Net inference method

(Bryant and Moulton 2004) with Splitstree (v.4.14.6; Huson 1998), using the Jukes-Cantor

model for computing genetic distances. Importantly, unlike the Neighbor-Joining (NJ) method,

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Neighbor-Net can represent conflicting signals in the data, e.g., due to recombination (Bryant

and Moulton 2004).

Population genetic analyses

Analysis of nucleotide diversity and Tajima’s D

We quantified genetic variation using the software packages vcftools (v.0.1.16) to obtain SNP-

wise estimates of nucleotide diversity π and Tajima’s D in samples with phased sequencing

data. Because vcftools provides window-wise estimates based on total window size but does

not account for positions in a given window that do not fulfill the same quality criteria as the

polymorphic sites, we obtained average values in 100 kb non-overlapping windows using

custom-made software. We first generated mask files where positions for which more than 50%

of individuals did not fulfil heuristic quality criteria (based on minimum and maximum coverage,

as defined above) were flagged with a ‘0’, whereas all other positions that passed were flagged

with a ‘1’. We then calculated window-wise averages of π and Tajima’s D separately for inverted

vs. standard chromosomes using the information in the mask files for population samples with

phased sequencing data. To test for differences in genetic variation with respect to (1)

geography, (2) karyotype and (3) genomic region we analyzed samples from Siavonga (Zambia,

Africa), Póvoa de Varzim (Portugal, Europe), Homestead (Florida, USA) and Innisfail

(Queensland, Australia). We considered all window-wise averages of π and Tajima’s D between

positions 3R:16,432,209 and 3R:24,744,010 as being located ‘inside’ In(3R)Payne. To define a

representative ‘control’ region ‘outside’ of In(3R)Payne we choose a random sample of equal

size, composed of average estimates of π from 3L and 3R located outside the interval ranging

from 3R:14,232,209 to 3R:26,744,010. To account for potential long-range effects of

In(3R)Payne, we extended the actual length of the inversion by 2 Mb on both ends. Using R we

performed a three-way analysis of variance (ANOVA) of the form yi = O + K + G + O ´ K + O ´ G

+ G ´ K + O ´ G ´ K+ εi, where yi is the continuous dependent variable π or Tajima’s D in the ith

sample, O denotes the categorical factor ‘origin’ with four levels (Africa, Europe, North America,

Australia), K represents the factor ‘karyotype’ with two levels (inverted, non-inverted) and G

stands for the factor ‘Genomic region’ with two levels (in- vs. outside of the inversion), followed

by all possible interactions, and where ε represents the error term. Based on the coefficients

estimated from this model, we calculated planned contrasts using the R package emmeans to

test for significant differences in genetic variation between karyotypes inside the inverted region.

To search for a potential signature of balancing selection (as indicated by positive D values) we

also estimated Tajima’s D for pooled samples of inverted and standard chromosomes (see

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supplementary fig. S2, Supplementary Material online). We note that estimating D for pools

consisting of equal numbers of inverted vs. standard chromosomes (50:50 ratio; see

supplementary fig. S2, Supplementary Material online) vs. estimating D using pools with the

numbers of inverted vs. standard chromosomes being proportional to their population

frequencies (not shown) did not make a qualitative difference.

Analysis of linkage disequilibrium (LD)

We estimated LD within and among karyotypes from low-latitude populations in Siavonga

(Zambia, Africa), Póvoa de Varzim (Portugal, Europe), Homestead (Florida, USA) and Innisfail

(Queensland, Australia) for which phased sequencing data were available. Squared allele

frequency correlations (r2) (Hill and Robertson 1968) were calculated among pairs of 5000

randomly drawn SNPs on 3R and between all polymorphic SNPs and In(3R)Payne using

custom-made software as described in Kapun et al. (2014). Since the r2 statistic can be affected

by large variance due to rare alleles, and because this might confound analyses of LD patterns

(Hedrick 1987), we restricted analyses to SNPs with minor allele frequencies ≥ 0.1. To compare

the decay of LD with physical distance we focused on 5000 SNPs located within the region

spanned by In(3R)Payne and restricted analyses to pairwise r2 among SNPs within 100 kb

distance. Following the approach in Remington et al. (2001) and Marroni et al. (2011), we used

our LD estimates to fit the following equation from Hill and Weir (1988):

which allows modeling the expected reduction of r2 with physical distance. Here, E(r2) is the

expected value of r2, n represents the sample size, and C is the product of the population-

scaled recombination rate (ρ = 4Ner) and the distance in base pairs, which we estimated using

nonlinear regression in R. For each population sample we employed the R function nls which

fits a model based on nonlinear least squares across both karyotypes. Subsequently, we fitted

the same model but additionally accounted for karyotype as a grouping factor using the function

nlsList in the R package nlme (Pinheiro et al. 2021). To infer significant variation in the decay of

LD as a function of In(3R)Payne karyotype we tested for differences in the goodness-of-fit of the

two nested models by analysis of deviance, using the function anova_nlslist in the R package

nlshelper (Duursma 2017).

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Analysis of genetic differentiation

To quantify the amount of genetic differentiation between samples for single SNPs and for

averages in 100 kb non-overlapping windows, we estimated pairwise FST for every SNP based

on the method of Weir and Cockerham (1984), using vcftools (v.0.1.16). We first investigated

how In(3R)Payne affects genetic differentiation within and among populations. We focused on

population samples from the endpoints of latitudinal gradients in Europe, North America and

Australia for which the low-latitude populations harbored In(3R)Payne at appreciable

frequencies and for which the inversion was absent in high-latitude populations. Our LD

analyses mentioned above revealed elevated LD inside inverted karyotypes, indicating that

adjacent SNPs do not evolve independently. We thus compared average FST in 100 kb non-

overlapping windows within the inversion breakpoints (‘inside’) to a similarly sized ‘outside’ set

of average FST values that were randomly chosen from the third chromosome in 2 Mb distance

from the breakpoints of In(3L)P and In(3R)Payne, as defined above. For the regions defined as

‘inside’ and ‘outside’ the inversion, and for each continent separately, we tested for differences

in average pairwise FST values using the following comparisons as input data: (1) samples from

the same low-latitude population with different karyotypes (factor level: ‘Karyotype’; e.g., Florida

inverted vs. Florida standard [FIFS]), (2) samples with standard arrangement from different

populations at the endpoints of a given continental latitudinal gradient (factor level: ‘Geography’;

e.g., Florida standard vs. Maine standard [FSMS) and (3) samples with different karyotypes

from different geographical populations within the same continent (factor l. evel: ‘Geography +

Karyotype’; e.g., Florida inverted vs. Maine standard [FIMS]; i.e., pairwise FST estimates for

which the effects of karyotype and geography might be confounded). To analyze these data we

used one-way ANOVA of the form yi = C + εi, where yi represents pairwise FST in the ith genomic

window, C is the categorical factor ‘pairwise comparison’ with three levels (‘Karyotype’,

‘Geography’, ‘Karyotype + Geography’) and ε represents the error term. To determine which of

the three levels of C differ from each other we performed Tukey’s HSD post-hoc tests. The

above-mentioned between-karyotype FST estimates (i.e., estimates of FAT, Charlesworth et al.

1997) were obtained for pools consisting of equal numbers of inverted vs. standard

chromosomes (50:50 ratio); we note that estimating karyotypic divergence using pools with the

numbers of inverted vs. standard chromosomes being proportional to their population

frequencies did not make a qualitative difference (not shown). In addition to FST, we also

estimated genetic differentiation between karyotypes using KST (Hudson et al. 1992; also cf. Nei

1973 and Charlesworth 1998) – these analyses yielded qualitatively identical patterns as those

for FST (results not shown). Finally, we also examined whether FST was elevated within the

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breakpoints of 3R Payne when comparing inverted chromosomes between continents. We

focused on SNPs shared across populations and calculated FST in 100 kb non-overlapping

windows in all pairwise combinations across inverted chromosomes from Zambia, Portugal,

Florida (USA) and Queensland (Australia) using vcftools (v.0.1.16). FST values were plotted

against their genomic positions for all comparisons in R using the ggplot2 package.

Identification of candidate genes and SNPs associated with In(3R)Payne

To identify candidate genes and SNPs in the region spanned by by In(3R)Payne, we focused

again on the samples from Siavonga (Zambia, Africa), Póvoa de Varzim (Portugal, Europe),

Homestead (Florida, USA) and Innisfail (Queensland, Australia) and isolated SNPs positions

that exhibited FST ≥ 0.9 between In(3R)Payne inverted and standard individuals within a given

population; we considered all genes as candidate loci if at least one candidate SNP with FST ≥

0.9 was located inside or within 2 kb proximity of the 3’ and 5’ ends of a given gene, since these

regions harbour regulatory elements (Down et al. 2007; Nègre et al 2011). Long genes have a

higher probability to harbor candidate SNPs by chance, and this might result in a bias towards

gene ontology (GO) classes that are enriched for long genes. To account for this potential bias,

we used Gowinda (Kofler and Schlötterer 2012) in order to test for overrepresentation of GO

terms associated with karyotype-specific SNP candidates. Gowinda first generates an empirical

null distribution of gene abundance in a given GO category based on a set of randomly chosen

SNPs of equal size as the candidate set; Gowinda then estimates the significance of

overrepresentation for each GO category and accounts for multiple testing by using Benjamini-

Hochberg (BH) correction of p-values (Benjamini and Hochberg 1995). Next, we examined the

extent to which candidate SNPs and genes are shared among continents. Using the R package

SuperExactTest (Wang et al. 2015), which allows assessing the significance of intersections

among multiple sets of similar data, we tested for overlaps between the sets of candidate SNPs

and genes. Since SuperExactTest estimates predicted intersections based on the size of

statistical background populations from which all sets are sampled, we only included SNPs that

were polymorphic in all four datasets. Because the Australian samples were sequenced with

RAD-Seq, only a limited number of SNPs could be recovered when comparing across all four

populations (fig. 7). We thus excluded the Australian samples and performed genome-wide

comparisons among the three remaining populations / continents. Based on the annotations

assigned to each SNP with SNPeff (Cingolani et al. 2012), we tested for enrichment of

candidates with Fisher’s exact test (FET) using a custom-made python script. We focused on

the 8 most common SNP categories (i.e., intergenic_region, upstream_gene_variant,

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5_prime_UTR_variant, intron_variant, synonymous_variant, missense_variant,

3_prime_UTR_variant and downstream_gene_variant). To build contingency tables for

category-specific FETs, SNPs were classified as candidate vs. non-candidate and as belonging

to a given category or not. To account for multiple testing p-values were Bonferroni-corrected.

RNA-seq data analysis

Prior to mapping, we trimmed and filtered raw reads for a base quality ≥ 18 and read lengths ≥

75 bp using cutadapt, as explained above. Next, we used kallisto (v.0.44.0; Bray et al. 2016) for

pseudo-alignments of each library against the D. melanogaster transcriptome (v. 6.17, obtained

from http://flybase.org/), using the following parameters: -l 101 (average fragment length = 101

bp); -s 10 (average standard deviation of fragment length = 10); -b 100 (number of bootstrapped

samples = 100); --rf-strand (reads are strand-specific, with the first read being reversed); --

single (reads are single-ended). We focused on gene-specific expression patterns and summed

up all transcript-specific read counts for each gene using custom-made software following the

approach in Soneson et al. (2015). We first transformed the raw absolute read counts to relative

counts per million (cpm) and normalized data using the ‘trimmed means of the M-value’ (TMM)

approach implemented in the R package edgeR (3.20.9) (Robinson et al. 2010, 2011). Lowly

expressed or non-expressed genes were excluded from downstream analyses by removing

genes with less than or equal to 2 CPM in more than or equal to 9 samples from the dataset. To

identify differentially expressed genes affected by karyotype, developmental temperature or

both, we fitted linear models to the expression data using the R package limma (Smyth 2005;

Ritchie et al. 2015). By employing the model.matrix function of the limma package we set up a

design matrix of the form: ~0 + G, where 0 indicates that the model is fitted without intercept and

where G is a grouping factor with six levels (FI-18, FI-25, FS-18, FS-25, MS-18, MS-25) based

on the geographic origin and karyotype of the samples (i.e., Florida inverted [FI], Florida

standard [FS], Maine standard [MS]) and the two rearing temperatures (18°C, 25°C). To

account for potential line effects, we included replicate line identity as a random effect nested in

karyotype. Using the voom function of limma, precision weights were calculated for log2-CPM-

transformed read counts to account for the relationship between mean and variance in RNA-

Seq data when fitting linear models to expression data (Law et al. 2014). We used the eBayes

function to improve the accuracy of gene-wise variance estimates by empirical Bayes

moderation which integrates information on variation across all genes in the dataset. Based on

the parameter estimates for each of the six levels of the grouping factor G, we calculated

contrasts to test for the effects of karyotype, geography, and karyotype + geography, averaging

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across both temperatures. In addition, we calculated contrasts for the two developmental

temperatures separately. We also employed contrasts to examine interactions between

temperature and karyotype, geography and karyotype + geography. To account for multiple

testing, we used BH p-value correction and only considered genes with a p < 0.05 to be

differentially expressed. For each of the candidate gene lists obtained from differential

expression analysis with limma, we tested for enrichment of specific GO categories using the R

package topgo (Alexa and Rahnenführer 2009). After correcting significant sets of GO terms for

hierarchical clustering using GO-Module (Yang et al. 2011), we visualized the remaining set of

GO terms with REVIGO (Supek et al. 2011) and Cytoscape (Shannon et al. 2003). Enrichment

of candidates according to their position relative to In(3R)Payne was tested by creating

contingency tables based on candidate and non-candidate genes located either in- or outside

the region spanned by the breakpoints of In(3R)Payne and using Fisher’s exact tests in R. We

further compared our candidates to the candidates reported by Zhao et al. (2015), who had

identified genes that exhibit differential expression between a low-latitude population in Panama

and a high-latitude population in Maine at two rearing temperatures (21°C, 29°C), and tested for

significant overlaps using SuperExactTests (Wang et al. 2015).

Overlap between genomic and transcriptomic candidates

To refine our set of candidate loci associated with 3R Payne we compared genomic and

transcriptomic candidates. Genomic plus transcriptomic data were only available from North

American populations (i.e., inverted and standard karyotypes from Florida; standard karyotypes

from Maine). We tested for significant overlaps between FST-based and differentially expressed

candidate genes using SuperExactTests (Wang et al. 2015); as the background set for these

analyses we only considered third chromosome genes identified in both datasets. Since the

significance of overlaps across sets can be confounded by the choice of significance thresholds

in the individual datasets, we also employed a comparison based on rank-rank hypergeometric

overlaps of ranked gene lists using the R package RRHO (Plaisier et al. 2010). RNA-seq

candidates were ranked based on adjusted p-values, whereas genomic candidates were ranked

based on average FST of the 10% top most highly differentiated SNPs located within 2 kb

proximity of a given candidate gene. RRHO tests for significant overlaps between gene lists are

based on hypergeometric tests calculated while sliding across all possible thresholds in the two

ranked lists. Besides visual representation of changes of significance with decreasing rank,

RRHO allows to define an ‘optimally’ overlapping gene set. We used this ‘optimal’ set to test for

enrichment of GO categories using topGO in the R package limma.

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Acknowledgements

We are indebted to Brian Charlesworth for helpful comments on previous versions of the

manuscript and for sharing unpublished results; to Mark Kirkpatrick for suggesting the analysis

in figure 6; and to two anonymous reviewers for their valuable feedback. We also acknowledge

the Lausanne Genomic Technologies Facility (GTF) and the Vital-IT Bioinformatics Facility at

the University of Lausanne for sequencing and bioinformatics support. Our research was funded

by the Swiss National Science Foundation (SNSF grants 31003A-182262, PP00P3_165836,

PP00P3_133641/1 to TF), the Austrian Science Fund (FWF grant P32275 to MK), the

Department of Ecology and Evolution at the University of Lausanne, and the Department of

Biology at the University of Fribourg. TF also received support from a Mercator Fellowship of the

German Research Foundation (DFG), held as an EvoPAD Visiting Professor at the Institute for

Evolution and Biodiversity, University of Mnster (Germany).

Author contributions

Contributions following CRediT (Contributor Roles Taxonomy; https://casrai.org/credit):

MK: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation,

Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing –

review & editing; ED: Formal Analysis, Investigation, Methodology, Software, Validation,

Visualization, Writing – original draft, Writing – review & editing; TJK: Funding acquisition,

Writing – review & editing; PS: Resources, Writing – review & editing; TF: Conceptualization,

Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation,

Writing – original draft, Writing – review & editing.

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Tables

Table 1. Effects on patterns of genetic variation. (A) F-values from a three-way ANOVA testing

for differences in π and Tajima’s D with respect to geographic origin, In(3R)Payne karyotype,

and genomic position relative to the inversion (inside vs. outside). (B) Planned contrasts based

on estimated coefficients from ANOVA, testing for differences in π and Tajima’s D between

inverted and standard chromosomes with respect to geography and genomic position (inside vs.

outside), using the emmeans package in R. * p < 0.05; ** p < 0.01; *** p < 0.001. Also see fig. 1;

see Materials and Methods for further details.

(A) Factor

π: ANOVA F-value

D: ANOVA F-value

Origin

F3,1312 = 505.27 ***

F3,1952 = 1809.03 ***

Karyotype

F1,1312 = 14.72 ***

F1,1952 = 546.38 ***

Genomic position

F1,1312 = 21.61 ***

F1,1952 = 178.14 ***

Origin x Karyotype

F3,1312 = 9.74 ***

F3,1952 = 16.68 ***

Origin x Genomic position

F3,1312 = 3.28 *

F3,1952 = 12.93 ***

Karyotype x Genomic position

F1,1312 = 5.16 *

F1,1952 = 169.35 ***

Origin x Karyotype x Genomic position

F3,1312 = 8.1 ***

F3,1952 = 24.12 ***

(B) Geographic origin

Genomic position

π: t-value

D: t-value

Africa (Zambia)

inside

-8.237 ***

20.362 ***

outside

-0.857

3.341

Europe (Portugal)

inside

0.037

6.593 ***

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outside

-1.841

6.320 ***

North America (Florida)

inside

-0.469

10.979 ***

outside

-0.495

-0.051

Australia (Queensland)

inside

0.032

13.527 ***

outside

0.978

5.044 ***

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Table 2. Effects of In(3R)Payne karyotype and/or geographic origin on pairwise FST differences. F-values from a one-way ANOVAs testing for

differences in divergence using pairwise FST comparisons as input see Materials and Methods for details). ANOVAs were performed separately

for each continent and genomic region (inside vs. outside inversion) with respect to In(3R)Payne. To determine which of the three levels

(Karyotype; Geography; Karyotype + Geography) differ from each other we performed Tukey’s HSD post-hoc tests. * p < 0.05; ** p < 0.01; *** p

< 0.001. Also see fig. 5; see Materials and Methods for further details.

Tukey’s HSD statistic

Origin

Position

ANOVA F-value

Karyotype vs. Geography

Karyotype + Geography vs.

Geography

Karyotype + Geography vs.

Karyotype

Europe

Inside

F2,366 = 543.85 ***

0.33***

0.34***

0.01

Outside

F2,780 = 8.4722 **

0.01

0.02***

0.01

North America

Inside

F2,366 = 221.02 ***

0.21***

0.23***

0.02

Outside

F2,792 = 119.6 ***

-0.03***

0.02***

0.05***

Australia

Inside

F2,366 =170.35 ***

0.14***

0.2***

0.06***

Outside

F2,255 = 77.524 ***

-0.07***

-0.02***

0.05***

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Figure legends

Fig. 1. Distribution of samples and phylogenetic relationships among In(3R)Payne karyotypes

across four continents. (A) Geographic origin of the samples used in this study. The color code

indicates the continent where flies were sampled (Africa, Europe, North America, Australia). The

outline of the circles indicates whether the samples contain chromosomes with In(3R)Payne (in

cyan) and/or with the standard arrangement (in black); the size of the circles indicates whether

samples were used only for phylogenetic reconstruction (small circles) or in addition also for

karyotype-specific genomic analyses (large circles). (B) Haplotype network constructed from

3766 SNPs within the breakpoints of In(3R)Payne; cyan edges represent samples with

In(3R)Payne, whereas black edges represent samples with the standard arrangement. (C)

Haplotype network based on 4849 randomly drawn SNPs at a distance of >200 kb from In(3L)P

and In(3R)Payne (see Materials and Methods). See table 1 for statistical analyses. Note that

several haplotypes from Florida cluster with the NG9 reference strain (see fig. 1B, fig. 1C). This

may be an artifact of our bioinformatic method for haplotype reconstruction (see Materials and

Methods); we therefore excluded these samples from downstream analyses.

Fig. 2. Patterns of nucleotide variability (π) and Tajima’s D in the region spanned by

In(3R)Payne. (A) Average values of nucleotide variability π, calculated in 100 kb non-

overlapping windows, with respect to geographic origin and genomic position relative to

In(3R)Payne, separately for inverted and standard arrangement chromosomes. (B) Average

values for Tajima’s D, calculated in 100 kb non-overlapping windows, separately for the two

arrangement types. See table 1 for details of ANOVA results for π and Tajima’s D; asterisks

(***, p < 0.001) represent significant p-values from planned contrasts. Also see supplementary

fig. 1 and supplementary fig. 2 (Supplementary Material online).

Fig. 3. Patterns of short- and long-range linkage disequilibrium (LD) in the region spanned by

In(3R)Payne. (A) Distribution and decay of LD as estimated by r2 within 100 kb distance, based

on 5000 randomly drawn SNPs inside the region spanned by In(3R)Payne for standard (grey)

and inverted (cyan) chromosomes from different geographic samples. Significant p-values in the

top right corners of the plots indicate differences in the decay of LD among karyotypes as

inferred from analyses of deviance applied to non-linear regression models (see Material and

Methods for details). (B) Triangular heat maps with estimates of r2 for 5000 SNPs randomly

drawn from chromosomal arm 3R in samples from North America (Florida). We restricted our

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analyses to subsamples of 5000 SNPs due to computational reasons: n = 5000 SNPs implies

n(n-1)/2 = 12,497,500 pairwise comparisons, with much larger numbers becoming

computationally prohibitive. In the upper triangle, r2 was estimated jointly for inverted and

standard chromosomes (see fig. S3, Supplementary Material online, for similar plots for the

other continents). The bottom left and right plots show separate r2 estimates for inverted and

standard chromosomes, respectively.

Fig. 4. Chromosome-wide patterns of genetic differentiation (FST) due to In(3R)Payne karyotype

and/or the effects of geography. Line plots show the distribution of FST in 100 kb non-

overlapping windows along chromosome arm 3R. For the non-clinal African population sample

from Zambia (top panel), the line plot shows FST between inverted and standard chromosomes

within the Siavonga population sample. For the non-African populations from Europe, North

America and Australia, which are all situated along latitudinal gradients, the different lines depict

the effects of ‘karyotype’ (black line; pairwise differences between standard and inverted

chromosome from within a given low-latitude population), ‘geography’ (dark grey line; pairwise

comparisons between standard arrangement chromosomes from low- vs. high-latitude

populations, i.e. from the cline ‘ends’); and of ‘geography plus karyotype’ (‘G+K’; light grey line;

pairwise comparisons of inverted and standard chromosomes between the endpoints of a given

cline). Heat maps beneath each line plot show r2 between each SNP and In(3R)Payne. Note

that genomic information for the first 12 million bp is not available for the Australian data.

Fig. 5. Genetic differentiation (FST) as a function of In(3R)Payne karyotype and/or geography.

Bar plots show average values of FST in 100 kb non-overlapping windows in different genomic

regions relative to In(3R)Payne (inside vs. outside the inverted region) for the three non-African

continents (Europe, North America, Australia) which include clinal (low- vs. high-latitude)

populations samples. The different bars represent pairwise FST comparisons for (i) geographic

differentiation (‘G’, comparing standard arrangement chromosomes from populations at the

endpoints of clines), (ii) karyotypic differentiation (‘K’, comparing inverted and standard

arrangement chromosomes sampled from within the same low-latitude populations), and (iii)

geographic plus karyotypic differentiation (‘G +K’, comparing inverted chromosomes from low-

latitude populations with standard chromosomes from high-latitude populations). See Materials

and Methods for details; also see fig. 4 and table 2 for statistical analyses.

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Fig. 6. African origin of inversion-specific alleles. Panel A shows median allele frequencies of

inversion-specific alleles from North America (FST ≥ 0.9) in inverted and standard arrangement

chromosomes in population samples from Africa, Europe and North America. Panel B shows

that highly differentiated SNPs in the Zambian population (exhibiting a frequency difference ≥

0.5 between standard and inverted chromosomes) are mostly clustered around the inversion

breakpoints, with some smaller clusters (‘mini-peaks’) of SNPs also visible around positions ~19

Mbp and ~21 Mbp. Also see supplementary fig. S7 (Supplementary Material online). The

analyses above are based on 1786 SNPs in total, and 277 SNPs in the Zambia sample.

Fig. 7. Overlap of In(3R)Payne-associated candidate genes and SNPs among continents. Bar

plots show the counts of overlapping candidate genes (A) or SNPs (B) in Africa, North America

and Europe, as indicated by black dots underneath each bar plot. The total number of

candidates for each dataset is shown on the right side of the black dots. The color of the bars

corresponds to the significance of the overlaps, as inferred by the R package SuperExactTest

and as indicated by the color gradient in the legend. The grey overlays at the bottom of the bars

indicate the amount of expected overlap. Also see supplementary table S3 and supplementary

table S4 (Supplementary Material online); also see Materials and Methods.

Fig. 8. Transcriptomic analyses of In(3R)Payne karyotypes. (A) Q-Q plot comparing p-values

from differential expression (DE) analyses with limma voom between North American 3R Payne

inverted and standard chromosomes at 18°C and 25°C, respectively. Since karyotype-specific

DE was much stronger at 18°C, we focused on this dataset for downstream analyses. (B)

Manhattan plot depicting -log10(p)-values for each gene relative to its average genomic position.

Candidate genes with karyotype-specific expression irrespective of temperature are highlighted

in red; those showing karyotype-specific expression at 18°C only in blue; and those that are

candidates in both datasets are highlighted in purple. Candidates of both datasets are enriched

within the region spanned by 3R Payne. (C) Significant gene ontology (GO) terms based on

differentially expressed genes among karyotypes at 18°C with Benjamini-Hochberg-adjusted p-

values < 0.05. Also see supplementary table S5 (Supplementary Material online) and Materials

and Methods for further details.

Fig. 9. Overlap between genomic and transcriptomic candidate loci associated with

In(3R)Payne. (A) Summary of results of rank-rank hypergeometric overlaps (RRHO). The dark

red area indicates the highly significant overlap between genomic and transcriptomic

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candidates. A core set of 86 candidate loci located in the top right corner of the heatmap in (A)

is tightly clustered inside the inversion or in close proximity to it (inversion highlighted in cyan).

(B) Significant overlap between genomic candidates (based on candidate SNPs with FST ≥ 0.9

between karyotypes in Florida; in light yellow) and transcriptomic candidates (based on

significant differential expression between inverted and standard karyotypes from Florida reared

at 18°C = FIFS18; in light blue); p-value estimated using SuperExactTest in R. Also see

supplementary figure S7, supplementary table S3 and supplementary table S7 (Supplementary

Material online).

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Chromosomal inversions harbour excess mutational load in the coral, Acropora kenti, on the Great Barrier Reef

Preprint

Full-text available

Feb 2024

The future survival of coral reefs in the Anthropocene depends on the capacity of corals to adapt as oceans warm and extreme weather events become more frequent. Targeted interventions designed to assist evolutionary processes in corals require a comprehensive understanding of the distribution and structure of standing variation, however, efforts to map genomic variation in corals have so far focussed almost exclusively on SNPs, overlooking structural variants that have been shown to drive adaptive processes in other taxa. Here we show that the reef-building coral, Acropora kenti (syn. tenuis) harbors at least five large, highly polymorphic structural variants, all of which exhibit signatures of strongly suppressed recombination in heterokaryotypes, a feature commonly associated with chromosomal inversions. Based on their high minor allele frequency, uniform distribution across habitats, and elevated genetic load, we propose that these inversions in A. kenti are likely to be under balancing selection. An excess of SNPs with high impact on protein coding genes within these loci elevates their importance both as potential targets for adaptive selection and as contributors to genetic decline if coral populations become fragmented or inbred in future.

Inversions Can Accumulate Balanced Sexual Antagonism: Evidence from Simulations and Drosophila Experiments

Preprint

Full-text available

Oct 2023

Chromosomal inversion polymorphisms can be common, but the causes of their persistence are often unclear. We propose a model for the maintenance of inversion polymorphism, which requires that some variants contribute antagonistically to two phenotypes, one of which has negative frequency-dependent fitness. These conditions yield a form of frequency-dependent disruptive selection, favoring two predominant haplotypes segregating alleles that favor opposing antagonistic phenotypes. An inversion associated with one haplotype can reduce the fitness load incurred by generating recombinant offspring, reinforcing its linkage to the haplotype and enabling both haplotypes to accumulate more antagonistic variants than expected otherwise. We apply a purpose-built forward simulator to examine these dynamics under a tradeoff between viability and male display. These simulations indeed generated long haplotypes where alleles of opposing effects occupied alternate chromosomal arrangements. Antagonism increases with time, and can ultimately yield karyotypes at predictable frequencies, and notable genotype frequency differences between sexes and between developmental stages. To test whether this model may contribute to abundant inversion polymorphism in Drosophila melanogaster, we tracked inversion frequencies in laboratory crosses to test whether they influence male reproductive success or survival. We find that two of the four tested inversions show significant evidence for the tradeoff examined, with In(3R)K favoring survival and In(3L)Ok favoring male reproduction. Additionally, all inversions show survival differences between sexes, and paternal success depends on maternal genotype. Based on this work, we expect that balancing selection on antagonistically pleiotropic traits may provide a significant and underappreciated contribution to the maintenance of natural inversion polymorphism.

Evolution of Chromosomal Inversions across an Avian Radiation

Article

May 2024
MOL BIOL EVOL

Chromosomal inversions are structural mutations that can play a prominent role in adaptation and speciation. Inversions segregating across species boundaries (trans-species inversions) are often taken as evidence for ancient balancing selection or adaptive introgression, but can also be due to incomplete lineage sorting. Using whole-genome resequencing data from 18 populations of 11 recognized munia species in the genus Lonchura (N = 176 individuals), we identify four large para- and pericentric inversions ranging in size from 4 to 20 Mb. All four inversions cosegregate across multiple species and predate the numerous speciation events associated with the rapid radiation of this clade across the prehistoric Sahul (Australia, New Guinea) and Bismarck Archipelago. Using coalescent theory, we infer that trans-specificity is improbable for neutrally segregating variation despite substantial incomplete lineage sorting characterizing this young radiation. Instead, the maintenance of all three autosomal inversions (chr1, chr5, and chr6) is best explained by selection acting along ecogeographic clines not observed for the collinear parts of the genome. In addition, the sex chromosome inversion largely aligns with species boundaries and shows signatures of repeated positive selection for both alleles. This study provides evidence for trans-species inversion polymorphisms involved in both adaptation and speciation. It further highlights the importance of informing selection inference using a null model of neutral evolution derived from the collinear part of the genome.

The fitness consequences of genetic divergence between polymorphic gene arrangements

Article

Full-text available

Dec 2023
GENETICS

Brian Charlesworth

Inversions restrict recombination when heterozygous with standard arrangements, but often have few noticeable phenotypic effects. Nevertheless, there are several examples of inversions that can be maintained polymorphic by strong selection under laboratory conditions. A long-standing model for the source of such selection is divergence between arrangements with respect to recessive or partially recessive deleterious mutations, resulting in a selective advantage to heterokaryotypic individuals over homokaryotypes. This paper uses a combination of analytical and numerical methods to investigate this model, for the simple case of an autosomal inversion with multiple independent nucleotide sites subject to mildly deleterious mutations. A complete lack of recombination in heterokaryotypes is assumed, as well as constancy of the frequency of the inversion over space and time. It is shown that a significantly higher mutational load will develop for the less frequent arrangement. A selective advantage to heterokaryotypes is only expected when the two alternative arrangements are nearly equal in frequency, so that their mutational loads are very similar in size. The effects of some Drosophila pseudoobscura polymorphic inversions on fitness traits seem to be too large to be explained by this process, although it may contribute to some of the observed effects. Several population genomic statistics can provide evidence for signatures of a reduced efficacy of selection associated with the rarer of two arrangements, but there is currently little published data that are relevant to the theoretical predictions.

A cosmopolitan inversion facilitates seasonal adaptation in overwintering Drosophila

Article

Full-text available

Dec 2023
GENETICS

Fluctuations in the strength and direction of natural selection through time are a ubiquitous feature of life on Earth. One evolutionary outcome of such fluctuations is adaptive tracking, wherein populations rapidly adapt from standing genetic variation. In certain circumstances, adaptive tracking can lead to the long-term maintenance of functional polymorphism despite allele frequency change due to selection. Although adaptive tracking is likely a common process, we still have a limited understanding of aspects of its genetic architecture and its strength relative to other evolutionary forces such as drift. Drosophila melanogaster living in temperate regions evolve to track seasonal fluctuations and are an excellent system to tackle these gaps in knowledge. By sequencing orchard populations collected across multiple years, we characterized the genomic signal of seasonal demography and identified that the cosmopolitan inversion In(2L)t facilitates seasonal adaptive tracking and shows molecular footprints of selection. A meta-analysis of phenotypic studies shows that seasonal loci within In(2L)t are associated with behavior, life history, physiology, and morphological traits. We identify candidate loci and experimentally link them to phenotype. Our work contributes to our general understanding of fluctuating selection and highlights the evolutionary outcome and dynamics of contemporary selection on inversions.

How chromosomal inversions reorient the evolutionary process

Article

Full-text available

Nov 2023
J EVOLUTION BIOL

Inversions are structural mutations that reverse the sequence of a chromosome segment and reduce the effective rate of recombination in the heterozygous state. They play a major role in adaptation, as well as in other evolutionary processes such as speciation. Although inversions have been studied since the 1920s, they remain difficult to investigate because the reduced recombination conferred by them strengthens the effects of drift and hitchhiking, which in turn can obscure signatures of selection. Nonetheless, numerous inversions have been found to be under selection. Given recent advances in population genetic theory and empirical study, here we review how different mechanisms of selection affect the evolution of inversions. A key difference between inversions and other mutations, such as single nucleotide variants, is that the fitness of an inversion may be affected by a larger number of frequently interacting processes. This considerably complicates the analysis of the causes underlying the evolution of inversions. We discuss the extent to which these mechanisms can be disentangled, and by which approach. Abstract Inversions often play key roles in adaptation and speciation, but the processes that direct their evolution are obscured by the characteristic that makes them so unique (reduced recombination between arrangements). In this review, we examine how different mechanisms can impact inversion evolution, weaving together both theoretical and empirical studies. We emphasize that most patterns are overdetermined (i.e. can be caused by multiple processes), but we highlight new technologies that provide a path forward towards disentangling these mechanisms.

Female meiotic drive shapes the distribution of rare inversion polymorphisms in Drosophila melanogaster

Article

Aug 2023
GENETICS

Spencer Koury

In all species, new chromosomal inversions are constantly being formed by spontaneous rearrangement and then stochastically eliminated from natural populations. In Drosophila, when new chromosomal inversions overlap with a pre-existing inversion in the population, their rate of elimination becomes a function of the relative size, position, and linkage phase of the gene rearrangements. These altered dynamics result from complex meiotic behavior wherein overlapping inversions generate asymmetric dyads that cause both meiotic drive/drag and segmental aneuploidy. In this context, patterns in rare inversion polymorphisms of a natural population can be modeled from the fundamental genetic processes of forming asymmetric dyads via crossing-over in meiosis I and preferential segregation from asymmetric dyads in meiosis II. Here, a mathematical model of crossover-dependent female meiotic drive is developed and parameterized with published experimental data from Drosophila melanogaster laboratory constructs. This mechanism is demonstrated to favor smaller, distal inversions and accelerate the elimination of larger, proximal inversions. Simulated sampling experiments indicate that the paracentric inversions directly observed in natural population surveys of Drosophila melanogaster are a biased subset that both maximizes meiotic drive and minimizes the frequency of lethal zygotes caused by this cytogenetic mechanism. Incorporating this form of selection into a population genetic model accurately predicts the shift in relative size, position, and linkage phase for rare inversions found in this species. The model and analysis presented here suggest that this weak form of female meiotic drive is an important process influencing the genomic distribution of rare inversion polymorphisms.

The effects of inversion polymorphisms on patterns of neutral genetic diversity

Article

Full-text available

Jun 2023
GENETICS

Brian Charlesworth

The strong reduction in the frequency of recombination in heterozygotes for an inversion and a standard gene arrangement causes the arrangements to become partially isolated genetically, resulting in sequence divergence between them and changes in the levels of neutral variability at nucleotide sites within each arrangement class. Previous theoretical studies on the effects of inversions on neutral variability have either assumed that the population is panmictic or that it is divided into two populations subject to divergent selection. Here, the theory is extended to a model of an arbitrary number of demes connected by migration, using a finite island model with the inversion present at the same frequency in all demes. Recursion relations for mean pairwise coalescent times are used to obtain simple approximate expressions for diversity and divergence statistics for an inversion polymorphism at equilibrium under recombination and drift, and for the approach to equilibrium following the sweep of an inversion to a stable intermediate frequency. The effects of an inversion polymorphism on patterns of linkage disequilibrium are also examined. The reduction in effective recombination rate caused by population subdivision can have significant effects on these statistics. The theoretical results are discussed in relation to population genomic data on inversion polymorphisms, with an emphasis on Drosophila melanogaster. Methods are proposed for testing whether or not inversions are close to recombination-drift equilibrium, and for estimating the rate of recombinational exchange in heterozygotes for inversions; difficulties involved in estimating the ages of inversions are also discussed.

The effects of inversion polymorphisms on patterns of neutral genetic diversity

Preprint

Full-text available

Feb 2023

Brian Charlesworth

The strong reduction in the frequency of recombination in heterozygotes for an inversion and a standard gene arrangement causes inversion and standard haplotypes to become partially isolated, resulting in sequence divergence between them and to changes in the levels of neutral variability at nucleotide sites within each arrangement class, as is also the case for other types of balanced polymorphisms. Previous theoretical studies have either assumed that the population is panmictic or that it is divided into two populations subject to divergent selection. Here, the theory is extended to a model of an arbitrary number of demes connected by migration, using a finite island. Recursion relations for mean pairwise coalescent times are used to obtain simple approximate expressions for diversity and divergence statistics relevant to an inversion polymorphism at equilibrium under recombination and drift, and for the approach to equilibrium following the sweep of an inversion to a stable intermediate frequency. The effects of an inversion polymorphism on patterns of linkage disequilibrium are also examined. The reduction in effective recombination rate caused by population subdivision can have significant effects on these statistics, and hence on estimates of the ages of inversions. The theoretical results are discussed in relation to population genomic data on inversion polymorphisms, with an emphasis on Drosophila melanogaster . Methods are proposed for testing whether or not inversions are close to recombination-drift equilibrium, and for estimating the rate of recombinational exchange in heterozygotes for inversions; difficulties involved in estimating the ages of inversions are also discussed.

Inversion invasions: When the genetic basis of local adaptation is concentrated within inversions in the face of gene flow

Article

Full-text available

Jun 2022

Across many species where inversions have been implicated in local adaptation, genomes often evolve to contain multiple, large inversions that arise early in divergence. Why this occurs has yet to be resolved. To address this gap, we built forward-time simulations in which inversions have flexible characteristics and can invade a metapopulation undergoing spatially divergent selection for a highly polygenic trait. In our simulations, inversions typically arose early in divergence, captured standing genetic variation upon mutation, and then accumulated many small-effect loci over time. Under special conditions, inversions could also arise late in adaptation and capture locally adapted alleles. Polygenic inversions behaved similarly to a single supergene of large effect and were detectable by genome scans. Our results show that characteristics of adaptive inversions found in empirical studies (e.g. multiple large, old inversions that are F ST outliers, sometimes overlapping with other inversions) are consistent with a highly polygenic architecture, and inversions do not need to contain any large-effect genes to play an important role in local adaptation. By combining a population and quantitative genetic framework, our results give a deeper understanding of the specific conditions needed for inversions to be involved in adaptation when the genetic architecture is polygenic. This article is part of the theme issue ‘Genomic architecture of supergenes: causes and evolutionary consequences’.

Inversions and parallel evolution

Article

Full-text available

Jun 2022

Local adaptation leads to differences between populations within a species. In many systems, similar environmental contrasts occur repeatedly, sometimes driving parallel phenotypic evolution. Understanding the genomic basis of local adaptation and parallel evolution is a major goal of evolutionary genomics. It is now known that by preventing the break-up of favourable combinations of alleles across multiple loci, genetic architectures that reduce recombination, like chromosomal inversions, can make an important contribution to local adaptation. However, little is known about whether inversions also contribute disproportionately to parallel evolution. Our aim here is to highlight this knowledge gap, to showcase existing studies, and to illustrate the differences between genomic architectures with and without inversions using simple models. We predict that by generating stronger effective selection, inversions can sometimes speed up the parallel adaptive process or enable parallel adaptation where it would be impossible otherwise, but this is highly dependent on the spatial setting. We highlight that further empirical work is needed, in particular to cover a broader taxonomic range and to understand the relative importance of inversions compared to genomic regions without inversions. This article is part of the theme issue ‘Genomic architecture of supergenes: causes and evolutionary consequences’.

Gene Regulatory Evolution in Cold-Adapted Fly Populations Neutralizes Plasticity and May Undermine Genetic Canalization

Article

Full-text available

Apr 2022

The relationships between adaptive evolution, phenotypic plasticity, and canalization remain incompletely understood. Theoretical and empirical studies have made conflicting arguments on whether adaptive evolution may enhance or oppose the plastic response. Gene regulatory traits offer excellent potential to study the relationship between plasticity and adaptation, and they can now be studied at the transcriptomic level. Here we take advantage of three closely-related pairs of natural populations of Drosophila melanogaster from contrasting thermal environments that reflect three separate instances of cold tolerance evolution. We measure the transcriptome-wide plasticity in gene expression levels and alternative splicing (intron usage) between warm and cold laboratory environments. We find that suspected adaptive changes in both gene expression and alternative splicing tend to neutralize the ancestral plastic response. Further, we investigate the hypothesis that adaptive evolution can lead to decanalization of selected gene regulatory traits. We find strong evidence that suspected adaptive gene expression (but not splicing) changes in cold-adapted populations are more vulnerable to the genetic perturbation of inbreeding than putatively neutral changes. We find some evidence that these patterns may reflect a loss of genetic canalization accompanying adaptation, although other processes including hitchhiking recessive deleterious variants may contribute as well. Our findings augment our understanding of genetic and environmental effects on gene regulation in the context of adaptive evolution.

The effects of weak selection on neutral diversity at linked sites

Article

Full-text available

Feb 2022
GENETICS

Brian Charlesworth

The effects of selection on variability at linked sites have an important influence on levels and patterns of within-population variation across the genome. Most theoretical models of these effects have assumed that selection is sufficiently strong that allele frequency changes at the loci concerned are largely deterministic. These models have led to the conclusion that directional selection for selectively favorable mutations, or against recurrent deleterious mutations, reduces nucleotide site diversity at linked neutral sites. Recent work has shown, however, that fixations of weakly selected mutations, accompanied by significant stochastic changes in allele frequencies, can sometimes cause higher diversity at linked sites when compared with the effects of fixations of neutral mutations. The present paper extends this work by deriving approximate expressions for the mean conditional times to fixation and loss of mutations subject to selection, and analysing the conditions under which selection increases rather than reduces these times. Simulations are used to examine the relations between diversity at a neutral site and the fixation and loss times of mutations at a linked site that is subject to selection. It is shown that the long-term level of neutral diversity can be increased over the purely neutral value by recurrent fixations and losses of linked, weakly selected dominant or partially dominant favorable mutations, or linked recessive or partially recessive deleterious mutations. The results are used to examine the conditions under which associative overdominance, as opposed to background selection, is likely to operate.

A Population Genomic Assessment of Three Decades of Evolution in a Natural Drosophila Population

Article

Full-text available

Dec 2021

Population genetics seeks to illuminate the forces shaping genetic variation, often based on a single snapshot of genomic variation. However, utilizing multiple sampling times to study changes in allele frequencies can help clarify the relative roles of neutral and non-neutral forces on short time scales. This study compares whole-genome sequence variation of recently collected natural population samples of Drosophila melanogaster against a collection made approximately 35 years prior from the same locality—encompassing roughly 500 generations of evolution. The allele frequency changes between these time points would suggest a relatively small local effective population size on the order of 10,000, significantly smaller than the global effective population size of the species. Some loci display stronger allele frequency changes than would be expected anywhere in the genome under neutrality—most notably the tandem paralogs Cyp6a17 and Cyp6a23, which are impacted by structural variation associated with resistance to pyrethroid insecticides. We find a genome-wide excess of outliers for high genetic differentiation between old and new samples, but a larger number of adaptation targets may have affected SNP-level differentiation versus window differentiation. We also find evidence for strengthening latitudinal allele frequency clines: northern-associated alleles have increased in frequency by an average of nearly 2.5% at SNPs previously identified as clinal outliers, but no such pattern is observed at random SNPs. This project underscores the scientific potential of using multiple sampling time points to investigate how evolution operates in natural populations, by quantifying how genetic variation has changed over ecologically relevant timescales.

Drosophila Evolution over Space and Time (DEST) - A New Population Genomics Resource

Article

Full-text available

Sep 2021

Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome datasets from natural populations of this species have been published over the last years. A major challenge is the integration of disparate datasets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a probabilistic variant caller (SNAPE-pooled). We use this pipeline to generate the largest data repository of genomic data available for D. melanogaster to date, encompassing 271 previously published and unpublished population samples from over 100 locations in > 20 countries on four continents. Several of these locations have been sampled at different seasons across multiple years. This dataset, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental meta-data. A web-based genome browser and web portal provide easy access to the SNP dataset. We further provide guidelines on how to use Pool-Seq data for model-based demographic inference. Our aim is to provide this scalable platform as a community resource which can be easily extended via future efforts for an even more extensive cosmopolitan dataset. Our resource will enable population geneticists to analyze spatio-temporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail.

On the fixation or nonfixation of inversions under epistatic selection

Article

Full-text available

Jul 2021

Several recent publications have stated that epistatic fitness interactions cause the fixation of inversions that suppress recombination among the loci involved. Under this type of selection, however, the suppression of recombination in an inversion heterozygote can create a form of heterozygote advantage, which prevents the inversion from becoming fixed by selection. This process has been explicitly modelled by previous workers.

Broad geographic sampling reveals the shared basis and environmental correlates of seasonal adaptation in Drosophila

Article

Full-text available

Jun 2021
eLife

To advance our understanding of adaptation to temporally varying selection pressures, we identified signatures of seasonal adaptation occurring in parallel among Drosophila melanogaster populations. Specifically, we estimated allele frequencies genome-wide from flies sampled early and late in the growing season from 20 widely dispersed populations. We identified parallel seasonal allele frequency shifts across North America and Europe, demonstrating that seasonal adaptation is a general phenomenon of temperate fly populations. Seasonally fluctuating polymorphisms are enriched in large chromosomal inversions and we find a broad concordance between seasonal and spatial allele frequency change. The direction of allele frequency change at seasonally variable polymorphisms can be predicted by weather conditions in the weeks prior to sampling, linking the environment and the genomic response to selection. Our results suggest that fluctuating selection is an important evolutionary force affecting patterns of genetic variation in Drosophila .

Effects of Selection at Linked Sites on Patterns of Genetic Variability

Article

Nov 2021

Patterns of variation and evolution at a given site in a genome can be strongly influenced by the effects of selection at genetically linked sites. In particular, the recombination rates of genomic regions correlate with their amount of within-population genetic variability, the degree to which the frequency distributions of DNA sequence variants differ from their neutral expectations, and the levels of adaptation of their functional components. We review the major population genetic processes that are thought to lead to these patterns, focusing on their effects on patterns of variability: selective sweeps, background selection, associative overdominance, and Hill–Robertson interference among deleterious mutations. We emphasize the difficulties in distinguishing among the footprints of these processes and disentangling them from the effects of purely demographic factors such as population size changes. We also discuss how interactions between selective and demographic processes can significantly affect patterns of variability within genomes.