Genetics and evidence for balancing selection of a sex-linked colour polymorphism in a songbird

Kim, Kang-Wook; Jackson, Benjamin C.; Zhang, Hanyuan; Toews, David P. L.; Taylor, Scott A.; Greig, Emma I.; Lovette, Irby J.; Liu, Mengning M.; Davison, Angus; Griffith, Simon C.; Zeng, Kai; Burke, Terry

doi:10.1038/s41467-019-09806-6

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Published: 23 April 2019

Genetics and evidence for balancing selection of a sex-linked colour polymorphism in a songbird

Nature Communications volume 10, Article number: 1852 (2019) Cite this article

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Abstract

Colour polymorphisms play a key role in sexual selection and speciation, yet the mechanisms that generate and maintain them are not fully understood. Here, we use genomic and transcriptomic tools to identify the precise genetic architecture and evolutionary history of a sex-linked colour polymorphism in the Gouldian finch Erythrura gouldiae that is also accompanied by remarkable differences in behaviour and physiology. We find that differences in colour are associated with an ~72-kbp region of the Z chromosome in a putative regulatory region for follistatin, an antagonist of the TGF-β superfamily genes. The region is highly differentiated between morphs, unlike the rest of the genome, yet we find no evidence that an inversion is involved in maintaining the distinct haplotypes. Coalescent simulations confirm that there is elevated nucleotide diversity and an excess of intermediate frequency alleles at this locus. We conclude that this pleiotropic colour polymorphism is most probably maintained by balancing selection.

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Introduction

Colour-polymorphic species are observed across diverse taxa, but are relatively rare and younger than monomorphic species^1,2. This is probably because any difference in fitness between alternative morphs should result in either the fixation of one of the causative alleles under natural or sexual selection, or lead to sympatric speciation under disruptive selection—potentially increasing the rate of speciation and promoting the transition from polymorphism to monomorphism^2,3,4. Even if there are no fitness differences, genetic drift is expected to remove alternative morphs from a small population³. Therefore, the evolutionary processes that maintain colour polymorphisms in some species over long periods are not well understood. This is partly because the genetic basis of pigmentation, especially for non-melanic pigments such as carotenoids and psittacofulvins, has only recently been elucidated^5,6,7,8.

Gouldian finch (Erythrura gouldiae) head plumage is a classic example of a Mendelian colour polymorphism in which the morphs co-occur in sympatry. The striking head colour morphs of the Gouldian finch reflect variation in both carotenoid and melanin pigmentation^9,10,11; a pigmentation locus on the Z chromosome (hereafter the Red locus, with a recessive Z^r allele and a less frequent dominant Z^R allele) produces melanin-based black (Z^rZ^r in male, Z^r in female) or carotenoid-based red (Z^RZ^R, Z^RZ^r in male, Z^R in female) head morphs in both sexes, as well as differences in feather structure^9,10. Rarely in the wild (<1% of birds), an epistatic interaction of the Red locus with an autosomal locus produces a yellow morph^11,12.

With regards to the black and red morphs, there are remarkable inter-morph differences in stress hormone levels, personalities, social dominance, sperm-length plasticity and sex allocation associated with the colour polymorphism (see Supplementary Table 1 for summary and references). Moreover, pre- and postzygotic incompatibilities have been documented between these morphs in a captive population (Supplementary Table 1), but the same phenomena have not been fully investigated in wild populations¹³.

Despite these differences, red and black morphs coexist in natural populations at stable phenotypic frequencies¹², suggesting that there are mechanisms maintaining this multi-functional colour polymorphism in nature. Given that multiple phenotypes are associated with head colour, one hypothesis is that the Red locus is a supergene, defined as a tight cluster of genes acting as a mechanical unit in particular allelic combinations¹⁴, which is often facilitated by a chromosomal inversion, as recently demonstrated in several avian species^15,16,17,18. Alternatively, the Red locus might be a single pleiotropic locus that can regulate multiple genes¹⁹. We undertake a detailed analysis of the region associated with the colour polymorphism using genomic and transcriptomic tools, and identify the genomic location of the Red locus as a putative regulatory region for follistatin on the Z chromosome. In addition, by using coalescent simulations, we find evidence that balancing selection is the most probable evolutionary mechanism behind the maintenance of the colour polymorphism in the Gouldian finch.

Results and Discussion

Identification of the Red locus

By using high-depth/low SNP density RADSeq²⁰ and low-depth/high SNP density whole-genome sequencing (see Methods), we identified a region on the Z chromosome strongly associated with red–black head colouration (1000 permutations, P < 0.05, Fig. 1a, Supplementary Figs. 1 and 2) within the candidate interval identified in a previous linkage analysis²¹. The genotypes of the wild birds (n_male = 89, n_female = 72) from an allele-specific PCR in this region showed a near-perfect association with head colour, with one exception, in a female (Fisher’s exact test, male: P < 10⁻¹⁵; female: P = 7.52 × 10⁻¹⁰, Fig. 1b, Supplementary Fig. 2). The frequencies of the black-linked allele in this population were similar in females (0.88) and males (0.85) (Fisher’s exact test, P = 0.69). The genotypic frequencies in males did not depart from Hardy–Weinberg Equilibrium (P_permutation = 0.24, Supplementary Table 3). The low frequency of homozygous red males is not significantly different from expectation for a sex-linked locus under random mating (Supplementary Table 3) and a prediction based on the phenotypic frequency⁹.

High LD but no genomic rearrangements around the Red locus

Next, we examined if the multi-functional polymorphism is caused by a supergene. Previously-described supergenes have large genomic blocks of elevated linkage disequilibrium (LD) that harbour multiple genes. We measured the extent of LD around the Red locus, defined as the region with the most significant association with the colour polymorphism and its flanking regions (Fig. 1c), using Sanger sequencing of multiple loci. Intriguingly, the LD block significantly associated with the colour polymorphism was limited to a single intergenic region (where no known protein-coding genes are annotated in zebra finch) that spans ~72 kilobase pairs (kbp) and lies between the coding regions of the genes molybdenum cofactor synthesis 2 (MOCS2) and follistatin (FST) (Fig. 1c). In this region, the density of fixed differences between the Z^R and Z^r alleles was high (6.8–27.8 per kbp; Supplementary Table 5). This region, which is also supported by the analyses of diversity, divergence and simulations (below), is hereafter considered to comprise the Red locus.

Centromeres and genomic rearrangements, particularly chromosomal inversions, may reduce recombination rates and generate significant LD blocks between haplotypes²². However, our previous work showed that the Red locus is not located near the centromere or in a large genomic rearrangement between morphs²¹. We confirmed this finding with a fine-scale test for inversion breakpoints using long-range PCR: our genomic assembly across the Red locus for each genotype contained contiguous homologous sequence, consistent with the absence of an inversion differentiating the Z^R and Z^r alleles (Fig. 1c, Supplementary Table 7).

The Red locus as a candidate regulatory region of FST

Gouldian finch head colour is associated with multiple phenotypic traits (see above). As such, the intergenic location of the Red locus suggests that this locus acts as a regulatory region that controls a pleiotropic gene. From a small-scale transcriptomic analysis of regenerating feather follicles from the scalp, we identified a list of differentially expressed genes belonging to functionally correlated gene ontology (GO) networks with potential roles in feather development and pigmentation (Supplementary Fig. 3, Supplementary Data 1). Although neither MOCS2 nor FST were differentially expressed between morphs, some genes in this network are known to interact with FST (Supplementary Fig. 3, Supplementary Data 1).

The multiple differences in gene expression suggest that the fates of feather precursors are determined by gene(s) that act upstream of the regulatory network at an earlier developmental stage. Previous studies indicate that FST is a strong candidate for regulating the colour polymorphism. Follistatin is known to regulate hair and feather development through its antagonistic action on genes of the TGF-β superfamily^23,24. The genes in this family (e.g. activin, TGF-β and BMP) may control melanocyte differentiation and melanogenesis^25,26. FST and its downstream genes might also be responsible for the structural differences in the feathers found in the Gouldian finch polymorphism as BMP and its alternative antagonist, noggin, are important determinants of the morphogenesis of feather branching^24,27. The TGF-β superfamily and its regulator FST are also involved in the development and regulation of the visual^28,29 and reproductive systems³⁰.

We found no differences in the FST protein-coding sequences between black- and red-headed birds, and the amino-acid sequences were conserved between the Gouldian finch and the zebra finch (Supplementary Fig. 4). However, we identified several evolutionarily conserved sites within the Red locus located upstream of FST (Supplementary Fig. 5), suggesting that this region has functional importance. It is therefore likely that cis-regulatory region(s) in the Red locus determine spatio-temporal variation in the expression of FST that then controls multiple genes in the TGF-β superfamily, which in turn may be responsible for the pigmentation difference and other pleiotropic effects. We note that one of the few divergent regions that distinguish the genomes of hybridizing golden-winged (Vermivora chrysoptera) and blue-winged (V. cyanoptera) warblers has been mapped to the same intergenic region between MOCS2 and FST³¹, suggesting convergent evolution of plumage colouration mechanisms and a possibly important role of the Red locus as a barrier to gene flow in other taxa. However, the fact that this locus has not led to speciation in the Gouldian finch indicates that selection may instead promote the persistence of divergent morphs in this species.

The nucleotide diversity and divergence at the Red locus

If selection has played a role in the origin and maintenance of the Red locus then it may have left a signature in the pattern of nucleotide diversity in this region. We calculated point estimates of summary statistics at the Red locus, as well as in sliding-windows across the ~100-kbp sequenced candidate region that included the Red locus to characterize the pattern of nucleotide diversity and divergence within and between morphs (Fig. 2a–d; Supplementary Table 9). We then compared the Red locus with 24 randomly-chosen intronic loci on the Z chromosome, as a putatively neutrally-evolving reference set (Fig. 2e–h; Supplementary Table 9). Notably, diversity for the combined sample of alleles (Z^R and Z^r together) at the Red locus is elevated compared with the reference loci, as is the level of differentiation between Z^R and Z^r alleles.

The mean nucleotide diversity at the reference loci (π_ref) was 0.0033 and varied little between the alleles of each morph (Fig. 2e). The point estimate of nucleotide diversity for the combined sample at the Red locus (π_total = 0.0102) was above that at all 24 reference loci (Fig. 2a), while diversity within the Z^R (π_red = 0.0004) and the Z^r (π_black = 0.0015) alleles was similar to that at the reference loci.

The estimate of absolute divergence d_xy (the mean number of pairwise differences per site³²) between the Z^R and Z^r alleles at the Red locus (d_{xy Red locus} = 0.019) was again greater than between morphs at all 24 reference loci (d_xy ref = 0.0033; Fig. 2b, f) and the estimate of net divergence d_a (d_xy minus the mean of the two within-morph π values) was not greatly different from d_xy due to the low nucleotide diversity within each allelic class (data not shown). In contrast, the divergence between zebra finch and alleles of each morph was similar at both the Red locus and the reference loci (Fig. 2b, f).

Tajima’s D³³ was moderately negative at the reference loci, both within the alleles of each morph and with both morphs’ alleles combined (D_{Taj black} = −0.599, D_{Taj red} = −0.304 and D_Taj total = −0.818), as well as within the alleles of each morph at the Red locus (D_{Taj red} = −0.844 and D_Taj black = −0.891; Fig. 2g, Supplementary Table 9), consistent with population expansion (see Supplementary Methods). However, D_Taj was positive in the combined sample at the Red locus (D_Taj total = 2.193; Fig. 2c), which is significantly >0 under the standard neutral model (P_coalescent = 0.002) using simulations in DnaSP v5³⁴, and again higher than at all 24 reference loci.

In captive populations of the Gouldian finch, pronounced pre- and postzygotic incompatibilities between morphs have been described, and mixed-morph pairings resulted in reduced survival: 40% of male and 80% of female offspring died before 90 days of age^35,36. If these incompatibilities persisted in the wild, then genome-wide differentiation between the black and red morphs might be expected. To quantify this, we estimated Wright’s fixation index F_ST³⁷ between each morph’s alleles. F_ST between alleles of each morph at the Red locus reached a near-maximal value (F_ST = 0.951) and was significantly >0 (P_permutation = 0.003). This value was greater than F_ST between each morph’s alleles at all the Z-linked reference loci (composite F_ST = 0.038, Fig. 2d, h, Supplementary Fig. 1).

In contrast, we found no genomic differentiation with regards to colour morph across the genome outside of the Red locus in the wild sample. First, a coancestry matrix³⁸ constructed for 32 wild birds (n_black = 22, n_red = 10) using autosomal RADSeq loci, designed to fully exploit the available haplotypic information, failed to identify any clusters that separated the morphs (Supplementary Fig. 6). Second, a STRUCTURE³⁹ analysis between morphs using nine unlinked autosomal microsatellite markers for all the wild birds (n_black = 126, n_red = 35) indicated that the most-likely number of subpopulations was one (K = 1; Supplementary Fig. 7). These results suggest that there is no or little incompatibility leading to any genome-wide differentiation between morphs in the wild.

Test for neutrality

Overall, the patterns of nucleotide diversity and divergence at the Red locus (i.e. elevated π, excess of intermediate frequency alleles and maximal F_ST) are consistent with balancing selection⁴⁰. However, such patterns can be obtained even under neutral evolution if sampling is conditioned on a site associated with the phenotypes of interest, as in this case (e.g. see the simulated patterns of polymorphism close to the focal site presented in Fig. 3). Therefore, to formally test for neutrality we carried out coalescent simulations under a neutral model that incorporated demography, recombination, and accounted for our sampling scheme and asked if the pattern of genetic variation we observed within the 100-kbp-region is compatible with neutral expectations (see Methods and Supplementary Methods, Supplementary Figs. 8–17, Supplementary Tables 11–14 for detailed procedures of the neutrality test).

Our simulations show that the observed patterns of genetic variation at the Red locus cannot be explained by either neutral processes or biases caused by the sampling scheme (Fig. 3, Supplementary Methods). Assuming the Z^R allele is derived (i.e., the derived allele frequency, DAF = 0.144), the observed nucleotide diversity (π_total) and Tajima’s D for the combined sample of Z^R and Z^r alleles at the Red locus (D_{Taj total}) both lie above the 95% confidence intervals (CIs) of the simulations for most windows, as do F_ST and d_xy between the Z^R and Z^r alleles (Fig. 3a–d). In contrast, statistics calculated within the Z^R or Z^r alleles fell within the 95% CIs of the simulated distributions, with the exception of nucleotide diversity within the Z^R allele (π_red, Supplementary Fig. 9). The same pattern was observed when we assumed that the Z^r allele was derived (DAF = 0.856, Supplementary Fig. 10). In particular, the observed region of greater than expected differentiation extended over an ~72-kbp interval, with a rapid drop on either side—contrasting with neutral expectations for a single site on which sampling is conditioned, which results in a gradual decline towards background levels over a 20-kbp interval (Fig. 3d). As we have demonstrated here, there is no evidence of an inversion across this region (Supplementary Table 7). Moreover, we found evidence for the presence of recombination within the Red locus (Supplementary Table 13, Supplementary Methods). Therefore, the highly diverged haplotype blocks are not likely to be maintained by reduced recombination, but rather by selection against recombinants. A sliding-window implementation of the HKA test⁴¹, taking into account nonrandom sampling, demography and recombination at the Red locus, also revealed significant departures from neutrality for multiple windows across the locus (Fig. 3e).

The evolutionary history of the black and red haplotypes

Balancing selection can maintain polymorphism over extended periods of time, resulting in deeply diverged haplotypes that may be even older than the species in which they reside⁴⁰. In a gene genealogy of the M1 region (see Fig. 1c) for 14 species in the family Estrildidae, sequence divergence between black and red haplotypes was deeper than that between other species or even between genera (Fig. 4, Supplementary Fig. 18). Four other Erythrura species formed a monophyletic group (bootstrap support 100%), separate from the Gouldian finch, whereas none of the Gouldian finch haplotypes clustered with the sequences of red-faced finches (E. pealii and E. psittacea) or blue-faced finches (E. tricolor and E. trichroa). Despite the high divergence, the two Gouldian finch haplotypes formed a clear monophyletic group (bootstrap support 100%), distinct from other Erythrura species, suggesting that these haplotypes evolved within the Gouldian finch lineage (Fig. 4). These patterns suggest that the divergent haplotypes at the Red locus are old (nearly half of the divergence between zebra finch and the Gouldian finch), although we did not attempt to estimate the age since balancing selection is likely to affect this estimation. Near the highly divergent M1 region, we identified an insertion with the characteristic feature of a long terminal repeat (LTR) retrotransposon, which is absent in the zebra finch (Fig. 2). Transposable elements are often associated with adaptive polymorphism⁴². Indeed, signals of selection, including the departure of the observed data from expectations under neutrality derived from simulations as well as from the HKA test, were strongest in this region (Fig. 3e, Supplementary Methods). However, comparison of the 3′ and 5′ LTRs in each haplotype suggests that the divergence between the colour-associated haplotypes did not occur until after the insertion of the retrotransposon, which occurred some time after the divergence of Gouldian finch and zebra finch (Supplementary Fig. 19).

In a relative rate test, the red haplotype showed disproportionately longer branch lengths than the black haplotype across the Red locus (Supplementary Figs. 18 and 20), suggesting that the two haplotypes have evolved with unequal evolutionary rates. Although the driver of this asymmetry remains unclear, one possible explanation is that the red haplotype may have undergone more rounds of adaptive substitution, which would also explain the lower diversity of the red haplotype over a long stretch (Supplementary Figs. 9b and 10f).

The origin of divergence and the mode of balancing selection

There are several hypotheses for the origin of haplotypic divergence at the Red locus, including former allopatry followed by secondary contact⁴³ or the lateral transfer of ancestral variation⁴⁴. For example, in northern Australia, the historical Ord Arid Intrusion event resulted in the splitting of many co-distributed taxa into three distinct biogeographical populations, and some became (and remain) distinct subspecies⁴⁵.

Regardless of the initial cause of divergence, our analyses suggest that balancing selection may be responsible for maintaining the divergent haplotypes at the Red locus. There are a few possible mechanisms of balancing selection in this species. First, red males may have an advantage under sexual selection, as females of all morphs of the Gouldian finch show a preference for red males³⁵. Moreover, the dominant status of red males in the social structure⁴⁶ may be beneficial when competing for limited resources, such as nest sites, albeit at the expense of reduced paternal care⁴⁷. Since the red allele is genetically dominant, its allele frequency would be expected to quickly rise to a high level under such a selective advantage. However, the red phenotype is present at low frequency in natural populations¹². Therefore, opposing selection has probably prevented the spread of the red allele in the population. Red males suffer from high stress levels and have significantly poorer reproductive outcomes than black birds when aggressive red males are in high frequency, and are consequently in a more competitive and stressful environment^48,49,50. Thus, frequency-dependent selection may have acted to maintain a low equilibrium frequency of the red individuals. Alternatively, this may be an example of sexual antagonism⁵¹, in which the fitness effects of different colouration are not the same in both sexes, and selection against red females prevents the red haplotype from increasing to a high frequency, despite being favoured in males. Red females fully express red head colour in captive populations (as a result of strong artificial selection by aviculturists). However, those in the wild population are less brightly coloured generally; the proportion of red feathers on their heads is typically lower, and they are often indistinguishable from black females⁹, suggesting selection against redness in females in the wild.

In this study, we have shown that a single locus upstream of FST is associated with colouration. This locus is intergenic, which does not support a role for a supergene in the classical sense in differentiating the red and black colour morphs of Gouldian finches. But it provides an example of a putative regulatory element with downstream effects on multiple phenotypes being maintained in the long-term by balancing selection. This has resulted in the maintenance of markedly divergent morphs, despite gene flow within the species, and in the absence of genomic rearrangements.

During the revision of the current manuscript we were alerted to a study on face colour polymorphism in domesticated Gouldian finches by Toomey et al.⁵². The research presented here confirms, complements, and extends that detailed study in several ways. First, both independent approaches highlight the same genomic region upstream of the follistatin gene, providing strong evidence of its functional role in generating the colour polymorphism. Second, unlike Toomey et al.⁵², our analysis incorporates data from wild birds, making clear the relevance of this region to non-domesticated populations. Finally, our simulations and evolutionary reconstructions add key historical context to how and why this polymorphism has evolved and is maintained in the wild.

The lack of wider genomic divergence between morphs suggests that the Red locus may not, alone, be sufficient to induce the incompatibilities observed in captive populations. This, in turn, suggests that the extended LD in captive populations (e.g. an ~8 Mb region showing zero recombination around the Red locus²¹) might encompass other linked loci underlying these incompatibilities. Therefore, further studies of the region around the Red locus will provide an important opportunity to address novel questions about the evolution of colour polymorphism. In particular, understanding the functional role of Red, a pleiotropic switch gene associated with both melanin and carotenoid colouration, may shed light on the mechanistic basis of sexual selection and genetic incompatibilities between colour morphs, which are both key evolutionary factors in speciation⁵³.

Methods

DNA samples

Blood samples were collected from 161 Gouldian finches (male: n_black = 62, n_red = 27; female: n_black = 64, n_red = 8) in 2008 from a single wild population in Western Australia (within a 50-km radius of Wyndham in East Kimberley). Birds were either caught in mist nets at waterholes or at their nests. The population size was estimated to be around 300 adults. Genomic DNA was extracted using the ammonium acetate technique. The sex of each sample was determined using two sex-linked markers, Z-002D⁵⁴ and Z37B⁵⁵ that amplify alleles on both the Z and W chromosomes. No power analysis was performed prior to the study. All 161 samples were used for the association test and a population structure analysis. A subset of samples was randomly selected and used for RADSeq and subsequent Sanger and MiSeq sequencing and genotyping (see below). Throughout the study, traits were measured blind to treatment and to values of other traits.

For whole-genome sequencing (WGS), blood samples were collected from 24 Gouldian finches (males: n_black = 4, n_red = 4, n_yellow = 3; females: n_black = 3, n_red = 5, n_yellow = 5) that were sampled from a single captive population of domesticated birds in Ithaca, NY, in 2016. Yellow birds have the same allele at the Red locus as the red morph but develop as yellow under the control of an unknown epistatic autosomal locus¹¹. Genomic DNA was extracted from these blood samples using DNeasy kits (tissue protocol; Qiagen, Hilden, Germany).

RADSeq

We genotyped 32 birds from the wild population described above using RADSeq²⁰ in two separate batches. In the first batch, DNA fragment libraries for 20 individuals (n_{black_male} = 10, n_{red_female} = 8 and n_{red_male} = 2) were constructed following restriction digestion with SbfI, and the pooled libraries were sequenced using two lanes of Illumina Genome Analyser IIx. This sampling scheme was to identify the Red locus efficiently with limited sample size in GWAS (see below). In the second batch, a pooled library, including 12 additional black males from the same population, was constructed using the same protocol but sequenced on an Illumina HiSeq2000 instrument.

The quality of the short sequence reads (2 × 100 bp) was checked using FASTQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). We cleaned and deduplicated the sequence reads using the process_radtags and clone_filter utility programmes from STACKS⁵⁶. We then mapped the sequence reads against the zebra finch (Taeniopygia guttata) reference genome assembly (taeGut3.2.4) using SeqMan NGen 4.0 (DNASTAR, Madison, Wisconsin, USA), allowing up to 15% divergence between sequence reads and the reference sequence. Single-nucleotide variants were called using SAMTOOLS v1.2⁵⁷ and filtered based on the minimum depth for each site per individual (>2), minimum SNP quality (>250), maximum of missing genotypes across all individuals (<30%) and minor allele frequency filter (>0.05) using custom R scripts and VCFtools v0.1.12⁵⁸. Although the HiSeq2000 sequencing runs had more coverage than the GAIIx runs, by applying filtering based on missing genotypes we could effectively remove any sites that were found only in the HiSeq2000 run. Mean depth per locus in an individual was different among libraries (GAIIx: L1 = 3.2, L2 = 7.8; HiSeq2000: L3 = 13.2). However, because we mixed equal numbers of black and red birds in L1 and L2, any bias due to the coverage in the inference of structure between morphs should be limited. Females were encoded to have only one allele for the genotypes of Z-linked loci using the vcf-fix-ploidy function of VCFtools. In an F_ST outlier analysis (Supplementary Fig. 1), we included 32 individuals (n_{black_male} = 22, n_{red_female} = 8 and n_{red_male} = 2) whose genotypes at the Red locus were either homozygous or hemizygous, except for the two heterozygous red males.

GWAS

SNPs linked to the causative site at the Red locus are expected to show a recessive homozygous genotype in black males (a/a), to be hemizygous (A) in red females and to be dominant homozygotes (A/A) or heterozygotes (A/a) in red males (see Introduction). In a genome-wide association study (GWAS) in 32 RAD-sequenced samples, we used Fisher’s exact tests with one degree of freedom for a dominant model of penetrance in a 2 × 2 contingency table of phenotype (black or red) by genotype (a/a or not a/a) using PLINK v1.9⁵⁹ (Fig. 1a). We treated the hemizygous Z-chromosome genotypes of females as homozygotes. We corrected for genomic structure among samples using the --within option and the family-wise estimate of the SNP’s significance was determined and corrected for multiple testing using the --mperm option (1000 permutations).

De novo low-coverage whole-genome sequencing

Sequencing libraries for the 24 captive Gouldian finches were generated using the Illumina TruSEQ PCR-free Library Preparation Kit following the protocol for generating 350-bp libraries. These 24 individuals were sequenced on a single lane of an Illumina NextSeq500 instrument to produce 150-bp paired-end reads. We used AdapterRemoval⁶⁰ to collapse overlapping paired reads, trim sequences of “Ns” along the 5′ and 3′ ends of reads, and remove sequences shorter than 20 bp. This resulted in a mean of 33.7 million total reads per individual (range 20.8–49.3 million reads). We used Bowtie 2⁶¹ to map each of the individual reads to the zebra finch genome to a mean coverage of ~3.6×, using the “very sensitive local” set of alignment presets. For SNP discovery and variant calling, we used the UnifiedGenotyper in GATK⁶². We removed possible variants that had “quality by depth” (QD) of <2 and “mapping quality” (MQ) of <30 (the filtering conditions were QD < 2.0, FisherStrand >40.0, MQ < 30.0, HaplotypeScore >12.0, MappingQualityRankSum < −12.5, ReadPosRankSum < −8.0). Variant confidence is a measure of sequencing depth at a given variant site; mapping quality refers to the root-mean-square of the mapping confidence (from Bowtie 2) of reads across all samples. We applied additional filters using VCFtools. First, we coded genotypes with a Phred-scaled quality lower than 8 as missing data. This is a permissive filter, necessary given our low-coverage data, which allows for genotypic calls with as few as three reads at a given site for an individual. We excluded loci with more than 30% missing data and used a minor allele frequency filter of 5%. This resulted in 4,818,133 SNPs distributed across the genome.

To properly correct for the ploidy of genotypes identified on the Z chromosome, we used the vcf-fix-ploidy function of VCFtools to assign one allele at each site for Z-linked loci in females. To increase the chance of detecting the location of the Red locus in an F_ST outlier analysis (Supplementary Fig. 1), we only included data from 17 individuals whose genotypes were known to be homozygous or hemizygous for alternative alleles at the Red locus based on the pedigree (black: n_{black male} = 4, n_{black female} = 3; red: n_{red female} = 5 and n_{yellow female} = 5).

Test for the association with colour phenotype

Several segregating sites associated with head colour were identified in the sequences around a RADSeq locus we found to be associated with head colour (locus Ego165, Supplementary Table 4). To confirm the association with plumage colour, 161 birds were genotyped for alternative two-base pair variants (TG/CA) using an allele-specific PCR with the Multiplex PCR Kit (QIAGEN) and a set of primers (locus M1, see Supplementary Table 2). The conditions for the PCR were 95 °C for 15 min, 30 cycles of 94 °C for 30 s, 64 °C for 90 s and 72 °C for 1 min, followed by 72 °C for 5 min. The PCR products were separated on an ABI 3730 DNA Analyser (Applied Biosystems, Foster City, California, USA) and scored using GENEMAPPER v 3.7 (Applied Biosystems). The association of the genotypes with face colour was tested for each sex using Fisher’s exact tests in R v3.2.5 (R Core Team, 2015, Fig. 1b). In addition to the locus M1, a multiplex marker set was developed including two allele-specific markers to genotype loci at the edges of the region of elevated LD at the Red locus (M2 and M3, Supplementary Table 2, Supplementary Fig. 2). The same condition as above was used in the PCR, except for an annealing temperature of 60 °C. We examined the deviation of the M1 genotypes from Hardy–Weinberg equilibrium (HWE) using Graffelman–Weir’s exact test for sex-linked markers implemented in the HardyWeinberg package⁶³ and obtained the P-value by permuting individual genotypes 10,000 times (Supplementary Table 3).

The extent of LD and sequencing of a candidate gene

To determine the extent of LD across the Red locus and the association of marker loci with the colour trait, the DNA sequences of 26 short fragments in the upstream region and the whole exon of an adjacent candidate gene (follistatin) were obtained (Supplementary Table 4). We sequenced an additional 15 loci on the Z chromosome that are not close to the Red locus as controls (Supplementary Table 4). Genomic DNA from 16 females (n_black = 8, n_red = 8) was amplified and cleaned with ExoSAP-IT (USB, Cleveland, Ohio, USA). After Sanger sequencing with the BigDye Terminator v 3.1 cycle sequencing kit (Applied Biosystems), and precipitation using the ethanol/EDTA method, the products were separated on an ABI 3730 DNA Analyser and the bases were called using CodonCode Aligner v2.0.6 (CodonCode Corporation). Association tests between phenotype and 282 SNPs from 26 fragments, and the estimation of pairwise LD between SNPs, were performed using Haploview v4.2⁶⁴ and visualized using LDheatmap⁶⁵ (Fig. 1c). The coding sequences of follistatin in 16 females were aligned, along with the homologous assembled zebra finch sequence, to examine the differences between species and morphs (Supplementary Fig. 4).

Test for the presence of an inversion

If there is an inversion breakpoint between two neighbouring loci, then the inverted chromosome will not produce amplicons across the breakpoint in a PCR that produces an amplicon in the non-inverted chromosome. To test for an inversion across the Red locus, 19 contiguous, overlapping fragments were amplified in 12 females (n_black = 6, n_red = 6) from the wild population by combining each forward primer with a reverse primer designed within the adjacent amplicon (Supplementary Table 6) and using the LongRange PCR kit (QIAGEN). When the primer sets originally designed from zebra finch sequences failed to produce long amplicons, we designed new primers using the Gouldian finch sequences obtained from previous sequencing efforts to ensure the failure was not due to mismatching sequences in the primer-binding sites (Supplementary Table 6). We confirmed that the long fragments amplified the correct sites by sequencing the boundaries of each long fragment using PCR primers and comparing with the zebra finch reference genome.

Long amplicons across the Red locus from the above PCR experiments and short amplicons for the reference loci (see below) were labelled by individual, pooled and sequenced using the Illumina MiSeq platform. All traces of adapter sequences added to amplicons during paired-end short-read (250-bp) sequencing were removed using a custom Perl script (TagCle v0.80, available upon request from K.-W.K.). De novo assemblies were performed for each individual using NGen. Supercontigs were produced by BLAST searching the contig sequences against the zebra finch reference sequence and comparing the positions of the contigs and the overlapping sequences. The assembled sequences were aligned using UGENE v1.12.3⁶⁶ and manually edited using BioEdit v7.1.11⁶⁷. These data were used to measure DNA polymorphism across the Red locus and in coalescent simulations to test for selection (see below).

Transcriptome analysis

We tested for differential gene expression in the feather follicles between the Gouldian finch morphs. First, we sampled regenerating head feathers from birds held by breeders in Australia during the annual moulting period and stored them directly in RNAlater (Qiagen). The feathers were subsequently frozen solid with liquid nitrogen, homogenized using a pestle and mortar, and RNA was extracted using TRIzol (Thermo Fisher Scientific, Waltham, Massachusetts, USA) and RNeasy Mini kits (Qiagen). The quality of the RNA was measured on a Bioanalyzer (Agilent, Santa Clara, California, USA) and high-quality samples with an RNA integrity value > 7 were used in microarray analysis. We obtained the gene expression profiles of six birds (n_{black_male} = 3, n_{red_male} = 2, n_{red_female} = 1) using the Affymetrix zebra finch gene array ST1.0 that contains probe sets from 18,595 genes. Gene expression was normalized using a robust multi-array average algorithm implemented in the Affymetrix Expression Console v1.3. The expression differences were tested using one-way ANOVA implemented in Transcriptome Analysis Console v2.0 (Affymetrix). Because of the small sample size, no gene passed the threshold required to correct for the false discovery rate. Therefore, we used uncorrected P-values <0.05 and log₂-fold changes ≥1.3 to select 304 potentially differentially expressed (DE) genes. Human orthologues of DE genes were obtained from UniProt (www.uniprot.org). A functionally grouped annotation network for the DE genes was constructed using the Cytoscape plugin ClueGO v2.3.5⁶⁸. Functionally related Gene Ontology (GO) terms for biological processes in human (23 February 2017) were grouped based on a kappa score >0.4 with network specificity of 3–5. The significance was examined using a two-sided hypergeometric test and the false discovery rate was corrected using the Benjamini–Hochberg method⁶⁹.

Evolutionarily conserved elements within the Red locus

We used PhastCons values⁷⁰ to examine the presence of the evolutionarily conserved elements within the Red locus that might represent regulatory regions potentially responsible for the expression of a candidate gene underlying colour polymorphism. The position of human and mouse transcripts for MOCS2 and FST, and the pre-calculated PhastCons values across the Red locus, were obtained from the UCSC Genome Browser Gateway (https://genome.ucsc.edu/cgi-bin/hgGateway). PhastCons values were based on multi-genome alignments among three vertebrates; medium ground finch (Geospiza fortis), human (Homo sapiens) and mouse (Mus musculus), and among five birds: medium ground finch, zebra finch, budgerigar (Melopsittacus undulatus), chicken (Gallus gallus) and turkey (Meleagris gallopavo). The records were visualized using Gviz⁷¹. The genomic position of the Red locus in the genome of the medium ground finch was determined using a BLAT search in the UCSC.

Genomic regions under consideration in the neutrality test

We obtained a 99,669-bp stretch of sequence using the Illumina MiSeq (see above) encompassing the Red locus (46,503,706–46,601,744 bp in zebra finch, Supplementary Table 6) in 12 female birds (n_black = 6, n_red = 6) from the wild population. Because females are hemizygous at the Red locus, these sequences represent six each of the black (Z^r) and red (Z^R) alleles.

In an additional MiSeq run, we also randomly chose 24 loci (henceforth the ‘reference’ loci, Supplementary Table 8) across the Z chromosome, outside of the LD block of the Red locus, to measure the background level of diversity for comparison with that seen at the Red locus. These loci consisted of single introns, sampled in 11–12 female individuals, 10 of which (5 each of black and red) were also represented in our sample of 12 birds sequenced at the Red locus.

Estimates of DNA polymorphism and divergence

In order to compare diversity at the Red locus (combining the Z^r and Z^R alleles) with that at the reference loci using the MiSeq data, we calculated π and Tajima’s D for the whole sample at each locus using a custom R script. We then calculated the same statistics within each allelic class (we define two allelic classes: one each for birds having the Z^r or the Z^R allele at the Red locus, respectively) at the reference loci and the Red locus, in order to compare polymorphism for the combined sample with that within each allelic class. In addition, we calculated F_ST and D_XY between the two allelic classes at the reference and the Red loci using custom R and Python scripts. We used Weir and Cockerham’s definition of F_ST³⁷, and the estimator provided by Hudson et al.⁷² to calculate site-by-site F_ST values (Supplementary Fig. 1). To obtain a composite estimate of F_ST from multiple SNPs for each window, we used equation (6) of Jackson et al.⁷³. In the estimation of F_ST, D_XY and π across the candidate region using sliding-window analyses (window size of 1000 bp with a step of 500 bp; Figs. 2 and 3), we retained sites with missing data to maximise the number of SNPs in each window. However, because Tajima’s D cannot be calculated when data are missing, we removed individuals with missing data on a per window basis for this statistic.

We define statistics that refer to subsets of the data using subscripts: for example, π_red refers to the nucleotide diversity among the alleles of red birds, and π_total refers to nucleotide diversity among the alleles of both black and red birds combined. When conducting point estimates of summary statistics per locus we removed sites with missing data in any individual. To test for the statistical significance of the point estimate of Tajima’s D, we used a coalescent simulation under the standard neutral model implemented in DnaSP v5³⁴.

To examine the genetic structure with regards to colour polymorphism, we constructed a coancestry matrix for samples for which we had RADSeq data that summarises the nearest-neighbour haplotype relationship and provides a more sensitive inference on population structure than the STRUCTURE and PCA approaches^38,44 (Supplementary Fig. 6). We selected autosomal RADSeq loci with ≤5 SNPs (total 18,719 RADSeq loci) and individuals with ≤20% missing data. We used fineRADstructure³⁸ to obtain coancestry coefficients between individuals and data missingness for each individual. Two individuals had significantly high levels of missing data and were excluded from calculation of the coancestry matrix (Supplementary Fig. 6).

To examine the differentiation between morphs in all wild-population individuals, we used nine autosomal microsatellite markers from a set of previously-tested cross-species markers⁵⁵ (Supplementary Table 10) and genotyped 161 birds (n_black = 126 and n_red = 35) using the ABI3730 DNA Analyser. Deviation of microsatellite genotypes from HWE was examined using CERVUS v3.0.3⁷⁴. A model-based clustering method implemented in STRUCTURE v2.3.4³⁹ was used to infer the number of subpopulations (K) and to assign individuals to populations (length of burnin period = 500,000; number of MCMC repeats after burnin = 750,000; 20 iterations for each run, Supplementary Fig. 7).

Considering nonrandom sampling in the test for selection

It is important to take into account nonrandom sampling with respect to the Red locus in simulations to generate polymorphism data under the null hypothesis of neutrality because, even under neutrality, it is possible to obtain high F_ST values, as well as elevated π at a focal site, if the sampling scheme is conditioned on that site, as was the case here. In order to replicate our observed sampling scheme for the purpose of statistical testing, we used the allele frequencies from the genotypes of 161 wild birds at the Red locus for this purpose (Supplementary Table 3). As we do not know which allele (Z^r or Z^R) is ancestral, it was necessary to consider both scenarios in turn; i.e. Z^r is derived (derived allele frequency, DAF = 0.856); or Z^R is derived (DAF = 0.144) (see Supplementary Methods for details about allele frequency estimation). This is, in part, because we cannot be sure which site(s) within the Red locus is causal, and so cannot polarise that site using an outgroup sequence. In addition, we lack outgroup sequence data for a portion of the Red locus that coincides with a putative TE insertion in the Gouldian finch lineage, but which does not exist at the same location in the zebra finch genome. As such, for the majority of the simulations and analyses below, we arbitrarily set the focal site at the centre of the Red locus. However, our results are robust to different choices of this parameter (see Supplementary Methods).

Testing for selection

We asked if the patterns of genetic variation we observed within the 100-kbp region are compatible with neutral evolution, given the fact that variants underlying the trait are in this region, and that our sampling is nonrandom. We employed two widely-used methods to test for evidence of natural selection. First, we used coalescent simulations to generate polymorphism data under neutrality at a locus that is equivalent to the ~100-kbp sequenced region in Gouldian finches in terms of its allele frequencies in nature (see above), the sampling scheme (6 each of red and black birds) and relevant population genetic parameters (the population mutation rate θ = 0.0144 estimated from 24 reference loci, and the population recombination rate ρ = 0.004 estimated from the ~100-kbp sequenced region). Note that the simulations based on the 100-kbp region are robust to the estimate of the recombination rate because significant differentiation was also apparent between the red and black alleles in a simulation using ρ = 0.0002, which is 20× lower than the estimate from the 100-kbp sequenced region (ρ = 0.004) (Supplementary Figs. 11 and 12). The lower value of ρ approximates that inferred by LDhat⁷⁵, both within the red and within the black allelic classes in the 100-kbp region. It should be noted that the strong LD between the alleles prevented us from using LDhat to assay recombination in the combined sample. The conclusions from the simulations were also robust to limiting the range of sequence simulated to the 72-kbp Red locus (Supplementary Figs. 13 and 14). The estimate of ρ from this region was unchanged from the value for the entire sequenced region. By assessing whether the observed polymorphism in the sequenced region is compatible with these simulations, we can accept or reject the null hypothesis of neutrality. We used the programmes SelSim⁷⁶ and mbs⁷⁷ to simulate polymorphism linked to a focal site at which a mutation has arisen and spread by genetic drift to the frequencies we observed at the Red locus (0.144 or 0.856). These programmes allowed us to replicate the allele frequencies, sampling scheme and population genetic parameters (see above) observed at the Red locus and obtain expectations under neutrality. In addition, mbs can model demographic changes. We also used a modified version of the HKA test⁴¹ to test for selection. See Supplementary Methods for more detailed methods.

Gene genealogy and relative rate test

To infer the evolutionary history of the haplotypes at the Red locus we constructed gene genealogies and compared the level of evolutionary divergence between the black and red haplotypes with those of orthologous sequences in other species. First, blood samples of 14 species in the family Estrildidae including red-faced (E. pealii, E. psittacae) or blue-faced (E. tricolor and E. trichroa) Erythrura species were obtained from an aviculturist. Blood samples were collected and stored using Whatman FTA cards (GE Healthcare, Little Chalfont, UK) and DNA was extracted following the manufacturer’s instructions. Sequences of the fragments amplifiable using the M1–M3 primers (Supplementary Table 2) were obtained using Sanger sequencing. For zebra finch, the sequences from the reference genome assembly were used.

We tested alternative nucleotide substitution models using the R package, phangorn v2.3.1⁷⁸, which suggested GTR+Γ+I to be the best model for the sequence of the Red locus. Maximum-likelihood trees were then constructed using this model and tested with 1000 bootstraps using phangorn and visualised using ggtree v1.4.20⁷⁹. To examine if the molecular clock hypothesis applied to the Red locus, we used Tajima’s nonparametric relative rate test implemented in pegas v0.10⁸⁰.

Compliance with ethical standards

Blood sampling of birds was conducted under the approval of the Animal Ethics Committees at the University of New South Wales and Macquarie University (ARAs 2007/037 & 2007/038).

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data that support the findings of this study are available from the corresponding authors on request. PLINK files for RADSeq analysis and sequence alignments of de novo sequencing for the Red locus underlying Figs. 1a and 4b, Supplementary Fig. 1, 2, 6 and 8–19 and Supplementary Tables 9 and 11–13 are available from the Dryad Digital Repository [https://doi.org/10.5061/dryad.9nk3757]. Raw Illumina reads are available at NCBI SRA (SAMN10751906-SAMN10751929) under BioProject PRJNA515277. Microarray data are available on the Gene Expression Omnibus (GEO) under accession number GSE125295. A reporting summary for this article is available as a Supplementary Information file.

Code availability

Custom scripts are available from the corresponding authors on request.

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Acknowledgements

We thank Brian and Deborah Charlesworth for comments on an earlier version of the manuscript. Sarah Pryke and David Harris provided blood samples of the Gouldian finches and other species of Estrildidae. Peri Bolton assisted with the collection of cheek feathers. We thank the Natural Environment Research Council Biomolecular Analysis Facility (NBAF) at the University of Edinburgh for sequencing RAD libraries (NBAF674) and NBAF at the University of Liverpool for gene expression microarray scanning. This study was supported by a University of Sheffield scholarship to K.-W.K., a joint University of Sheffield/Natural Environmental Research Council (NE/H524881/1, NE/K500914/1) Ph.D. studentship to B.C.J., a UK Department for Business, Innovation and Skills/Ministry of Education of China UK–China Scholarships for Excellence studentship to H.Z., an NSERC Banting postdoctoral fellowship to D.P.L.T., an Australian Research Council (DP130100418) grant to S.C.G. and T.B., and a Leverhulme Fellowship to T.B.

Author information

These authors contributed equally: Kang-Wook Kim, Benjamin C. Jackson.

Authors and Affiliations

Department of Animal and Plant Sciences, University of Sheffield, Sheffield, S10 2TN, UK
Kang-Wook Kim, Benjamin C. Jackson, Hanyuan Zhang, Kai Zeng & Terry Burke
Fuller Evolutionary Biology Program, Cornell Lab of Ornithology, Cornell University, 159 Sapsucker Woods Road, Ithaca, NY, 14850, USA
David P. L. Toews, Scott A. Taylor, Emma I. Greig & Irby J. Lovette
Department of Ecology and Evolutionary Biology, Cornell University, Corson Hall, Ithaca, NY, 14853, USA
David P. L. Toews, Scott A. Taylor & Irby J. Lovette
Department of Biology, The Pennsylvania State University, University Park, PA, 16802, USA
David P. L. Toews
Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, 80309, USA
Scott A. Taylor
School of Biology, University of Nottingham, University Park, NG7 2RD, Nottingham, UK
Mengning M. Liu & Angus Davison
Department of Biological Sciences, Macquarie University, Sydney, NSW, 2109, Australia
Simon C. Griffith

Authors

Kang-Wook Kim
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Benjamin C. Jackson
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Hanyuan Zhang
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David P. L. Toews
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Scott A. Taylor
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Emma I. Greig
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Irby J. Lovette
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Mengning M. Liu
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Angus Davison
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Simon C. Griffith
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Kai Zeng
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Terry Burke
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Contributions

K.-W.K., B.C.J., S.C.G., K.Z. and T.B. conceived and designed the study. S.C.G. managed the work on Gouldian finches in the wild in Australia. K.-W.K., M.M.L. and A.D. constructed the RAD library. D.P.L.T, S.A.T., E.I.G and I.J.L. performed whole-genome sequencing. K.-W.K. and H.Z. performed molecular work. K.-W.K. and B.C.J. analysed the data. K.-W.K. and B.C.J. wrote the paper with contributions from all other authors.

Corresponding authors

Correspondence to Kang-Wook Kim, Kai Zeng or Terry Burke.

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The authors declare no competing interests.

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Kim, KW., Jackson, B.C., Zhang, H. et al. Genetics and evidence for balancing selection of a sex-linked colour polymorphism in a songbird. Nat Commun 10, 1852 (2019). https://doi.org/10.1038/s41467-019-09806-6

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Received: 09 April 2018
Accepted: 22 March 2019
Published: 23 April 2019
DOI: https://doi.org/10.1038/s41467-019-09806-6

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