Under the neutral theory, genetic diversity is expected to increase with population size. While comparative analyses have consistently failed to find strong relationships between census population size and genetic diversity, a recent study across animals identified a strong correlation between propagule size and genetic diversity, suggesting that r-strategists that produce many small offspring, have greater long-term population sizes. Here we compare genome-wide genetic diversity across 38 species of European butterflies (Papilionoidea), a group that shows little variation in reproductive strategy. We show that genetic diversity across butterflies varies over an order of magnitude and that this variation cannot be explained by differences in current abundance, propagule size, host or geographic range. Instead, neutral genetic diversity is negatively correlated with body size and positively with the length of the genetic map. This suggests that genetic diversity is determined both by differences in long-term population size and the effect of selection on linked sites. Theory suggests that neutral genetic diversity is determined by census population size, but this is not observed empirically. Here, the authors show that in butterflies, neutral genetic diversity correlates with both body size and chromosome number, suggesting that linked selection is also an important factor. Given that census population size varies widely across the tree of life, much of the variation in genetic diversity between species should be due to differences in census size. However, correlates of census size, such as geographic range, have repeatedly been found to be poor predictors of genetic diversity7. In addition, genetic diversity seems to vary remarkably little overall, given the wide range of population sizes seen in nature. Although the extremely narrow ranges of genetic diversity reported by early comparative studies based on allozymes6 are partly explained by balancing selection8, diversity at nearly neutral sites is also restricted to a narrow range of two orders of magnitude4 (with some notable exceptions9). While the fact that there are only four alternative states for a nucleotide site implies a hard upper bound on the possible level of nucleotide site diversity 10, the levels of neutral genetic diversity seen in natural populations remain far below this.The genetic diversity segregating within a species is a central quantity; it determines its evolutionary potential, and is, in turn, the outcome of its selective and demographic past. Under the neutral theoryNe and μ11, and the second seeks reasons why Ne shows such little variation between species12. Given the lack of firm evidence for large differences in mutation rate among species with different levels of variability, recent comparative studies have focused on identifying factors that determine long-term Ne and hence genetic diversity13.The observation that genetic diversity does not correlate with measures of census size, known as Lewontin’s paradox, has intrigued evolutionary biologists for nearly half a century. Proposed solutions to the paradox are generally of two types: the first proposes that there may be a negative relationship between 12 or deleterious variants15. Because the efficacy of selection depends on the product Nes, selection is expected to be more efficient and therefore remove more neutral linked sites in species with large Ne. Recently, Corbett-Detig et al.16 have shown that the proportional reduction of neutral diversity due to selection at linked sites does indeed correlate with measures of census size such as geographic range and (negatively) with body size. While Corbett-Detig et al.16 argue that this can explain “… why neutral diversity does not scale as expected with census size”, a reanalysis of their data17 concluded that the effect of selection on linked neutral diversity is too small to provide a general explanation for the narrow range of genetic diversity seen in nature.One explanation for the narrow range of genetic diversity observed in nature is that natural selection continuously removes neutral diversity linked to either beneficial5 and Chen et al.18 have uncovered a striking negative correlation between propagule size and genetic diversity across the animal kingdom: species that are short-lived and invest little into many offspring (r-strategists) tend to have higher genetic diversity than long-lived species with few offspring and large parental investment (K-strategists). They argue that K-strategists may be able to avoid extinction at low population sizes, while r-strategists require much larger populations to buffer against environmental fluctuations. While it is striking that propagule size explains 70% of variation in genetic diversity across animal phyla, this finding is unlikely to explain variation in genetic diversity in taxa with similar life-history strategies. However, if robustness to fluctuations in population size is the ultimate determinant of genetic diversity—as Romiguier et al.5 argue—one would expect other life-history traits to correlate with genetic diversity. In particular, more specialized species may be able to avoid extinction in spite of small census sizes and thus have reduced long-term Ne. Alternatively, if the efficacy of selection determines intraspecific genetic diversity then variation between species may be best explained by differences in recombination rate and the density of selective targets in the genome.An alternative  explanation is that variation in genetic diversity is constrained by fluctuations in long-term population size. This would imply that genetic diversity should correlate with life-history traits that affect a species susceptibility to environmental fluctuations. Romiguier et al.19, and are characterised as r-strategists given their short life span and high fecundity20, with little variation in reproductive strategy. Butterflies, in particular European species on which we focus, are arguably the best studied group of insects. Thanks to centuries of study by scientists and amateur naturalists together with numerous recording schemes, butterfly taxonomy, geographic ranges and life-histories are known in great detail. This offers a unique opportunity not only to revisit potential correlates of genetic diversity that have proven difficult to quantify across large taxonomic scales (e.g. geographic range and abundance), but also test the effects of ecological traits. In particular, while niche breadth is difficult to quantify across distantly related taxa and has not so far been considered in comparative analyses of genetic diversity, accurate data for the number of larval host plants (LHP) exist for European butterflies.Here we address these questions using butterflies (Papilionoidea) as a model system. Papilionoidea share a common ancestor ~119 million years ago (MYA)πD4) and non-synonymous diversity was estimated at zero-fold degenerate sites (πD0), i.e. sites where any nucleotide change leads to an amino acid difference. While synonymous sites are subject to codon usage bias22, they are not directly affected by other forms of selection. Although selection intensities on codon usage and biased gene conversion at synonymous sites have been little studied in Lepidoptera23, recent population genomic studies of Drosophila species suggest that the product of Ne and selection coefficients for such sites is generally of the order of 1 or less, so that they can be treated as nearly neutral24. Our rationale for modelling πD4 and πD0 jointly was to better understand the nature of the underlying forces at the population level: theory predicts that any correlate of neutral genetic diversity (πD4) that increases Ne should correlate less strongly with diversity at non-synonymous sites (πD0)25. This is because any increase in diversity due to reduced genetic drift is counteracted by the removal of diversity due to more efficient selection. In contrast, any trait that affects non-synonymous genetic diversity (πD0) via the absolute strength of selection s should be more strongly correlated with diversity at non-synonymous sites (πD0) than synonymous sites (πD4), which are only indirectly affected.We estimated genetic diversity from de novo transcriptome data for 38 butterfly species , body size, voltinism, egg volume (relative to body size) and LHP breadth (Source Data) using a generalized linear mixed model. In addition, we test whether genome size and recombination rate affect genetic diversity. In the absence of detailed recombination maps, we use the number of chromosomes as a proxy for the length of the genetic map. This assumes an average map length of 50 cM per chromosome in male meiosis, and takes into account the lack of crossing over in female meiosis of Lepidoptera. This assumption is supported by a linkage map for the butterfly Heliconius melpomene27 as well as the silkmoth Bombyx mori28. We find that neutral genetic diversity across butterflies varies over an order of magnitude. Perhaps surprisingly, this variation cannot be explained by differences in current abundance, propagule size, host or geographic range. Instead, genetic diversity correlates negatively with body size and positively with the length of the genetic map. This suggests that levels of genetic diversity are determined both by long-term population size and the effect of selection on linked sites.We investigate the relation between average nucleotide site diversity5 (Supplementary Data πD4 varies over an order of magnitude across this set of butterfly species: from 0.0044 in Pieris brassicae (the cabbage white) to 0.0428 in Spialia sertorius (the red-underwinged skipper)  is typical of insects18. Assuming neutrality and a per-site per-generation spontaneous mutation rate of μ = 2.9 × 10−9 29, this corresponds to Ne on the order of 105–106 individuals, a much lower range than that reported for distantly related animal taxa18. While Romiguier et al.5—sampling across the entire animal kingdom—found that species in the same taxonomic family have similar genetic diversity, we observed no significant family effect in butterflies . More generally, phylogeny was a poor predictor of neutral genetic diversity in butterflies  Fig. . However30, we expect diversity at these sites to be greatly reduced compared with synonymous sites. Within this set of butterfly species, πD0 and πD4 typically differ by an order of magnitude as commonly found in insects. Under the nearly neutral theory31 and assuming a gamma distribution for the distribution of mutational effects on fitness (DFE), the slope of the negative linear relationship between ln(πD0/πD4) and ln(πD4) is equal to the shape parameter, β25. The slope we estimate in butterflies . This is higher than the estimates for Heliconius butterflies (0.08–0.28)18, but compatible with previous estimates of the DFE for Drosophila based on the site frequency spectrum32.Since directional selection will purge (or fix) mutations at non-synonymous sitesπD0 are almost certainly underestimates of genome-wide non-synonymous diversity 38. Several comparative studies have shown that mt diversity is uncorrelated with measures of abundance and nuclear diversity39. We find that across European butterflies, mt diversity at the COI barcode locus is only very weakly (and not significantly) correlated with both πD4  and πD0  genes are an easily accessible source of variation data and have been extensively used to infer the phylogeographic history of species and populationsπD0 and πD4  on πD4  breadth has no significant effect on πD4 or πD0  and ln(πD0) in terms of ln(πD0/πD4). This ratio is weakly and positively correlated with body size , suggesting that selection is more efficient in smaller species.Only one trait, body size, is significantly and negatively correlated with 0D Table  than π4DπD4 correlates positively and significantly with chromosome number .While Pieris brassicae, nc = 15, and Melanargia ines (the Spanish marbled white), nc = 13, Fig. p = 0.117).Although (unsurprisingly) the effect of chromosome number we find depends disproportionately on the two species with the fewest chromosomes  changes in Ne in response to past climatic events. Population bottlenecks affect π, but correspond to a sudden burst in coalescence rather than a change in its long-term rate44. Population bottlenecks would also affect our interpretation of πD0/πD4 as a measure of the efficacy of selection. Since πD0 recovers more quickly than πD4 after a bottleneck45, one would expect taxa that have undergone recent changes in Ne to fall above the line of best fit in the relationship between ln(πD4) and ln(π0/πD4)  population. In fact, only seven species have a higher variance in S than expected for a population of constant size  and uncorrelated with body size   and Coenonympha arcania (the pearly heath) . Interestingly, both species fall above the line of best fit in Supplementary Fig. πD0/πD4 is higher) in these species. The presence of different locally adapted subspecies or populations could further increase πD0/πD4. For both species, several ecotypes/subspecies exist in the Iberian peninsula, but their exact distribution and status is uncertain. In contrast, the migratory species Vanessa atalanta  is an outlier in the opposite direction and has lower diversity at non-synonymous sites (πD0) than expected given its neutral diversity (πD4)  propagule size is more strongly correlated with genetic diversity than body size and explains 70% of the variation in genetic diversity across animals5. We find the opposite pattern in butterflies, that is, πD4 is much more strongly correlated with body size than (absolute) propagule size and there is no significant relation between relative propagule size and neutral diversity. This suggests that the correlates of genetic diversity are strongly dependent on phylogenetic scale. Clearly, life-history traits that determine differences in genetic diversity over very large taxonomic scales, are likely to be conserved within major groups of taxa, and so unlikely to explain the still considerable variation in genetic diversity seen within them.We show that neutral genetic diversity in European butterflies varies over an order of magnitude, and that this variation is neither significantly correlated with current abundance nor key life-history traits. In particular, we find little support for the idea that generalist species have larger long-term 47 and therefore long-term Ne. This relationship is not exclusive to butterflies, and has been found in mammals48 and across animals5 more widely.Across European butterflies, we find that body size and chromosome number are the only significant correlates of neutral genetic diversity, and together explain 45% of the variation in genetic diversity. The negative correlation between body size and genetic diversity is consistent with body size limiting population density50. Thus, unlike previous comparative studies which have shown that selection merely constrains variation in genetic diversity16, our analyses suggest that the effect of selection on linked neutral diversity may explain some of the variation in neutral genetic diversity between taxa that differ in the length of the genetic map.As we show below, the positive correlation between chromosome number and neutral genetic diversity is an expected consequence of selection and mirrors the nearly ubiquitous intraspecific correlation between genetic diversity and recombination rateπD4, we find mirror results of previous studies7 and suggests that current abundance does not reflect long-term Ne in butterflies. While the distribution of heterozygosity across the genome suggests that it is unlikely that variation in genetic diversity across butterflies is due to drastic demographic events during the Pleistocene . Interestingly, analysis of RAD-seq data from the closely related species P. rapae  suggests a population expansion ≈20,000 yBP  followed by divergence into subspecies 1200 yBP when Brassica cultivation intensified51. It is therefore possible that, by contrast, the ancestral P. brassicae population remained small after the glacial maximum and only expanded as recently as ≈1200 yBP.The lack of any correlation between estimates of census size and πD4. However, given that LHPs vary drastically in geographic range and density, the number of LHPs may be a very crude predictor of a species long-term census size: a species with a single LHP may have very large populations if its host is widespread. Conversely, a generalist such as Celastrina argiolus (the holly blue), one of the most widespread and generalist (>100 LHPs) species in our set, may have low long-term Ne and hence low genetic diversity (πD4 = 0.0095) due to other biotic factors.If variation in carrying capacity shapes genetic diversity in butterflies, it is perhaps surprising that niche breadth, the number of larval host plants (LHPs), is uncorrelated with Ne which we have not considered: in particular, how (in what life-cycle stage) and where species hibernate, the rate of parasitoid attack and the degree of migratory versus sedentary behaviour. Exploring whether these correlate with genetic diversity will require larger sets of taxa.There are several potential life-history traits that might have large effects on long-term 15 and recurrent selective sweeps53 can plausibly explain the observed relationship between diversity and chromosome number, it is helpful to consider analytic predictions for the reduction in neutral diversity due to selection at linked sites. We take as a starting point the expression of Coop (2016) ] 15, where U is the per base-pair rate of deleterious mutations per diploid genome. Thus both the effects of BGS and positive selection depend on the rate of mutational input relative to recombination. We can scale the rates of deleterious mutations and selective sweeps per genome (rather than per bp). Assuming that the number of selective targets is fixed across species and that there is a linear relationship between recombination rate and map length, the expressions for BGS and positive selection are functions of the number of chromosomes, nc: ν/r ≈ 4νT/nc and B ≈ exp(−4UT/nc), where νT and UT are the genome-wide rates  of selective sweeps and new deleterious mutations respectively (Supplementary Note We have assumed linear relationships between body size and chromosome number and genetic diversity without paying any attention to the causative forces at the population level. To gain some insight into whether the genome-wide effects of background selection (BGS)nc ≤ 31), BGS can only have a modest effect on neutral diversity: even if we assume a rate of UT = 1 deleterious mutation per genome, the reduction in diversity due to BGS, B, only ranges between 0.73 and 0.88 for our dataset. Ignoring the effect of BGS, we have:One immediate conclusion from the above is that, given the large number of chromosomes in butterflies (13 ≤ ln(πD4) and nc  and nc (the estimate in Table nc).We can use Eq.  to ask hnc Table . In the  νT, Eq.  implies mentclass2pt{minimπD4 across species. If we assume that N0 depends linearly on body size, a spontaneous mutation rate of μ = 2.9 × 10−9 29 and J = 10−5 (which is consistent with estimates of Ne and s in Drosophila32), we can co-estimate both the correlation between N0 and body size and νT (Supplementary Software). The best fitting selection regime implies an extremely high rate of sweeps of (νT ≈ 0.133 per generation). However, this approximate model of the effect of selective sweeps on π predicts a much narrower range of πD4 than is observed  are derived from multiple ancestral syntenic blocks, suggesting a series of fission events that was followed by the creation of a novel chromosome organisation through fusions. Given that P. napi returned to a karyotype close to the ancestral nc = 31 of butterflies, there may be some selective advantage in organising the genome this way. If this is the case, chromosome rearrangements that produce karyotypes distant from nc = 31 may only be tolerated in populations dominated by drift. While the forces driving karyotype evolution in butterflies are currently not understood, chromosomal fusions accumulate in small populations59 and in selfing plants60. Thus, an alternative explanation for the positive correlation between chromosome number and genetic diversity we find is that species with low Ne accumulated mildly deleterious chromosome rearrangements through genetic drift. Pieris brassicae (nc = 15) and Melanargia ines (nc = 13), which have probably undergone relatively recent chromosomal fusions (given that in both cases relatives in the same genus have higher nc), are consistent with this. As no species in our set has nc >> 31, we cannot test whether the relationship between genetic diversity and chromosome number is quadratic, and thus consistent with a model where reduced Ne may lead to both increases and decreases in nc. Interestingly, species in the genus Leptidea, which have undergone a recent explosion in chromosome number (nc ranges between 26 and 12061), appear to have very low genome-wide diversity 62, consistent with the idea that extreme karyotypes arise during periods of low Ne.Hill et al.63 have put forward analogous arguments for the evolution of genome sizes: genomes may expand in populations with low Ne, if selection against transposable element proliferation and intron expansion becomes inefficient. While the large genome size and TE content of Leptidea species62 is consistent with this, we find no support for any relationship between genome size and neutral diversity across our set of species. Instead, our analyses clearly show that genome size has significant phylogenetic signal across butterflies  and so must evolve slowly, whereas variation in genetic diversity has little phylogenetic structure  from those that determine the strength of selection itself. Rather than focusing on pairwise π, the most drastic summary of genetic variation, such inferences will require methods that make use of the rich information contained in genomic data. Furthermore, comparative analyses that are based on whole-genome (rather than transcriptome) data and high quality genome assemblies are required to exploit the extra information about selection and genetic drift that is contained in the genomic context . Given the detailed knowledge of their taxonomy, ecology, geographic range and their relatively compact genomes, butterflies are one of the best test cases for attempting a reconstruction of the evolutionary processes that result in Lewontin’s paradox.While we have only considered a small number of life-history traits and genomic parameters, and have modelled neither the effects of selection nor demography explicitly, it is encouraging that we have identified two simple determinants, which together explain a substantial fraction of the variance in genetic diversity across butterflies. However, a more complete understanding of the processes that shape genetic diversity and how these correlate with life-history will require modelling both the demographic and the selective past explicitlyCOI, primers LepF and LepR41) and comparison against a reference database for Iberian butterflies41 for the following species: Carcharodus alcae, Coenonympha arcania, Euphydryas aurinia, Melitaea deione, Thymelicus acteon and T. sylvestris.Butterflies were hand-netted at various locations across four regions in Iberia . RNA-seq datasets for Melitaea athalia, M. cinxia, M. didyma, M. parthenoides, and Thymelicus lineola—previously analysed in ref. 5—were retrieved from the European Nucleotide Archive (ENA).RNA was extracted using a TRIzol (Ambion) protocol according to the manufacturer’s instructions. TruSeq stranded polyA-selected RNA libraries were prepared by Edinburgh Genomics and strand specific 75b paired-end reads were generated on a HiSeq4000 Illumina instrument. Raw reads are deposited at the European Nucleotide Archive  in both individuals of each species as well as their proteins being single-copy orthologues (SCOs) across all analysed species. The resulting loci were subjected to variant calling. A super-matrix maximum likelihood phylogeny was inferred based on SCOs as described in Supplementary Methods.πb, i.e. the genetic diversity between the two individuals A and B sampled for each species :nA, nB are the numbers of heterozygous sites unique to A and B, nAB is the count of shared heterozygous sites and nfix is the number of fixed differences. Calculations were carried out separately for fourfold degenerate (πD4) and zerofold degenerate (πD0) sites using the script bob.py (www.github.com/DRL/mackintosh2019). Estimates of π for COI locus of each species were calculated as described in Supplementary Methods.To minimize the confounding effect of population structure (and inbreeding), we calculated 65. Models were bivariate, that is, included two responses, ln(πD4) and ln(πD0), which were assumed to covary and follow a Gaussian distribution. Only the 32 species with data for all seven predictors were included. Fixed effects were z-transformed when continuous so that estimated effect sizes were comparable for a given response. Phylogeny was included in the model as a random effect based on the inverse matrix of branch lengths in the maximum likelihood species phylogeny  effect.Phylogenetic mixed models were constructed using the R package MCMCglmmeny Fig. . For the66, with some minor modifications. In short, head tissue of butterflies (frozen fresh and preserved at −80 °C) were ground in Gailbraith’s buffer and filtered through a 40-μm mesh, resulting in a suspension of free nuclei with minimal cell debris. The solution was centrifuged at 350/500 × g for 1 min, then the pellet of nuclei was resuspended in 300 µl propidium idodide  and RNAse A  for staining and removal of RNA. After 1–2 h, fluorescence was measured using a BD LSR Fortessa running Diva v8.0.1. DNA content of cells were evaluated by propidium iodide binding using a 561 nm excitation laser and fluorescence emission at 600–630 nm. Each butterfly sample was measured alongside a sample of female Drosophila melanogaster  to establish a reference genome position. Single nuclei were identified by plotting area versus width for the DNA labelling with 5–50 k positive nuclei recorded. For analysis, G0/1 peaks were gated for both the D. melanogaster and butterfly cells and relative intensities were then used to determine the genome size of the butterfly species using FlowJo v9.6.To estimate the size of the genome for each species we followed the protocol outlined by ref. 68. Density estimates were calculated as the mean number of individuals of each species seen per transect where that species is found, per year. The area range of each species was estimated from GBIF occurrence data (see Supplementary Data rbgif69 was used to retrieve occurrence records—human observations with complete latitude and longitude information—for each species. Convex polygon areas (km2) were calculated using the function eoo in the R package red70. For species with large ranges, this was done separately for each land mass (to avoid including large bodies of water).Current census sizes were estimated as the product of geographic range and density. All species in this study can be found in the region of Catalonia, Spain, where butterfly monitoring has been taking place since 199471 and HOST database72. Species were characterised as monophagous when LHPs were limited to one family or polyphagous when LHPs represented multiple families. Mean forewing length  reported in ref. 73 was used as a proxy for adult body size. The mean between sexes was used for statistical analysis. Estimates of egg volume were retrieved from ref. 74, haploid chromosome number from ref. 73 and information on voltinism from ref. 71. Since the number of generations can vary within species, we only classified species as monovoltine if they had strictly one generation per year throughout their European range and polyvoltine if otherwise. In species with variable chromosome numbers, the mean was used for statistical analyses. All data can be found in the Source Data file.A list of larval host plants (LHP) for each species was compiled from ref. Field sampling of butterflies was conducted in compliance with the School of Biological Sciences Ethics Committee at the University of Edinburgh and the ERC ethics review procedure. Permissions for field sampling were obtained from the Generalitat de Catalunya (SF/639), the Gobierno de Aragon (INAGA/500201/24/2018/0614 to Karl Wotton) and the Gobierno del Principado de Asturias (014252).Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Software 1Source Data


To explore the effect of aging, a cohort study is being performed on Sado Island, which is located in the Sea of Japan. Sado Island is close to the eastern coast of Japan, yet its population speaks the western Japanese dialect. Consequently, the genetic background of the population of Sado Island is of interest. Based on Nei’s genetic distance, we compared the allele frequencies of people from Sado Island to those of people from Nagahama and Miyagi, which are located in the western and northeastern parts of Honshu, respectively. The results showed that the populations of Miyagi and Nagahama are genetically closer to each other than to the population of Sado Island. Because the Sado and Honshu Islands are isolated by a channel, it is possible that genetic drift occurred within Sado Island, which would explain the uniqueness of the people of this region. To explore the effect of aging, a cross-sectional study was performed, which was a subanalysis of the Project in Sado for Total Health (PROST), a hospital-based cohort study in Sado City, Niigata Prefecture, Japan (http://square.umin.ac.jp/prost/). PROST, a cohort study targeting outpatients (age ≥20 years) of Sado General Hospital2, began in June 2008 and is currently ongoing.Japan is experiencing a “super-aging” society3. Figure 4. We hypothesized that the cultural difference reflects a difference in the ancestors of these populations. In this study, we investigated the genetic distances between the population of Sado Island and populations in eastern and western Japan.Sado Island is located 30 km off the coast of Niigata City, which is on the eastern coast of Honshu, mainland Japan. Despite their proximity, the dialect spoken on Sado Island is different from that spoken in Niigatahttps://www.megabank.tohoku.ac.jp/wp/wp-content/uploads/2018/05/2017-1014.pdf).This project was part of the PROST study, and it received the approval of the ethics committee of Niigata University School of Medicine (No. G2015-0811) as well as the ethics committee of Tohoku Medical Megabank Project (No. 2018-4-78). The samples used were obtained from the cohort participants, who gave their written consent. In addition, genotype data were securely controlled under the Materials and Information Distribution Review Committee of Tohoku Medical Megabank Project, and data sharing with researchers was discussed with the review committee. ; we excluded three control samples with an overall call rate of <99%. We recalled the remaining 1747 samples with Genotyping Console 4.2.0.26 software (Affymetrix). We performed LD pruning using PLINK 2.00 alpha with the option “–indep-pairwise 50 10 0.1.” We also excluded loci when their minor allele frequencies (MAF) were lower than 5% and when the loci failed (P < 0.05) the Hardy–Weinberg test. In total, 5,078 loci passed these filters.We used the Japonica SNP array6, which contains the genotype counts of participants in The Nagahama Prospective Genome Cohort for the Comprehensive Human Bioscience7. In the present study, this dataset is referred to as Nagahama. As a representative of the eastern Japan population, we used iJGVD8, which contains data obtained from the Miyagi prefecture9. This dataset is referred to as Miyagi in this study. The locations of Miyagi and Nagahama are shown in Fig. As a representative of the western Japan population, allele frequencies were obtained from the Human Genetic Variation Database10 was calculated among the populations using all loci, and 99% confidence intervals of the Dst were calculated by bootstrap resampling, with 5,000 replications.Nei’s genetic distanceTable In addition, Table t-test, P > 0.05). In other words, the size of the population on Sado Island is not overly small.To estimate the degree of isolation of the Sado population, we estimated the inbreeding coefficients (IBC) based on the SNP homozygosity of samples using plink version 1.90b6.9. The same set of SNPs originally selected for PCA was used (Supplementary Fig. This study shows that geographic distance and linguistic similarity do not reflect the genetic differences among these populations. In addition, genetic drift may be more substantial than the linguistic shift.Supplementary Fig. 1


Shandong indigenous pig breeds are an invaluable source of data on genetics in Chinese pigs. However, information on the genetic basis of these breeds remains limited. In this study, we used specific-locus amplified fragment sequencing to conduct whole-genome screening to investigate genetic diversity in Shandong indigenous breeds and Western pig breeds. The results showed that Duroc pigs (DD) had clear genetic relationships with Dapulian pigs  and Laiwu pigs , and DPL and LW were relatively close genetically (Fst = 0.2334). In general, Shandong indigenous breeds showed greater genetic variety than the Western breeds. Both neighbor-joining trees and principal components analyses were able to differentiate the breeds, but population structure analyses indicated that the Western breeds genetically influenced the Shandong indigenous breeds to some extent. A total of 162 differentially selected regions (DSRs) with 841 genes and 157 DSRs with 707 genes were identified in DPL and LW, respectively. Gene annotation of the selected regions identified a series of genes regulating immunity and fat deposition. Our data confirm the rationality and accuracy of the current classification of pig breeds in Shandong province. Our results point to candidate genes in Shandong indigenous pig breeds and further promote the importance of follow-up research on functional verification. Pigs are a very important domesticated species in China. The extreme climate and geographical conditions in China have contributed to the development of 88 indigenous species, many of which have unique characteristics . With anAssessing the genetic variation and population structure in Chinese indigenous pig breeds is critical in animal genetics research and government germplasm conservation. After years of effort, genetic variation and phylogenetic relationships in Chinese indigenous pig breeds have been explored morphologically, cytogenetically, and biochemically . With thAll animal care and treatment procedures were approved by the Animal Ethics Committee of Shandong Agricultural University, China, and performed in accordance with the Committee's guidelines and regulations .Ear-punch samples of 80 individuals representing 10 pig breeds were used in the study. Eight pigs were sampled from each breed except HG (n = 7), WL (n = 7), and LC (n = 10). Samples for the seven Shandong breeds were collected from conservation farms, and those for the three Western breeds were collected from a pig breeding farm in Shandong province. The number of samples per breed was determined according to the lineage of the breed. To ensure that samples covered the entire lineage of each pig population, we collected a boar from each lineage. We extracted total genomic DNA from ear-punch samples using the standard phenol-chloroform method .RsaI and HaeIII restriction enzymes was ultimately used  to digest pig genomic DNA into 1,010,652 SLAF tags 314 to 364 bp in length. Then, a single nucleotide (A) overhang was added to the 3′ region of the SLAF tags. To ligate dual-index sequencing adapters to the A-tailed tags, we performed restriction-ligation reactions with T4 DNA ligase (NEB). Then polymerase chain reaction (PCR) amplification was performed that included the diluted restriction-ligation DNA samples of each pig breed, dNTP, Taq DNA polymerase (NEB), and primers containing barcode 1. The PCR products were purified and run out on a 2% agarose gel. Fragments 314 to 364 bp (with indexes and adaptors) were separated. Barcode two was then added to the isolated fragments by PCR amplification. These products were purified in gel and then diluted for pair-end sequencing using an Illumina HiSeq 2500 system  according to the manufacturer's instructions.Analyses of genomic DNA were based on SLAF-seq . The pigSLAF-seq data were processed by computer. We mapped all raw SLAF pair-end reads to the pig reference genome (Sscrofa 11.1) using BWA . In genevia the ITOL website (http://itol.embl.De/). The population structure was constructed with admixture v1.22 (2) between pairwise SNPs was computed with Haploview , respectively , is a measure of population differentiation based on genetic polymorphism data . We usedre v1.22 . We hypore v1.22 , which caploview . It is gectively . In addiectively . The covTo identify the regions that were selected during domestication, we combined the three Western pig breeds  into a single domestic gene pool called DLY. We used a genome-wide sliding window method to scan the selected regions with the greatest differences in genetic diversity (π log-ratio DLY/DPL and DLY/LW) and extreme divergence in allele frequency between Western commercial and Chinese indigenous pig breeds in Shandong province. To evaluate nucleotide diversity (π) and Fst, we calculated π and Fst using 100 kb windows with 10 kb steps among genomes. The top 5% π and Fst values (ftp://ftp.ensembl.org/pub/release-91/fasta/sus_scrofa/dna/) and NCBI database. In addition, we used Panther bioinformatics (www.Pantherdb.Org) for the gene function enrichment analysis.To examine whether the candidate selective scanning regions had excessive singleton polymorphisms, we calculated Tajima's D for each pig breed using the sliding window method mentioned previously. Genes in these selective regions were identified through the Sscrofa 11.1 assembly  were removed because the sample call rate did not meet criteria (< 95%). Nest quality control was performed on the remaining 77 samples. Of the 77 samples analyzed, 53 were from Shandong province, and 24 represented pig breeds originating outside of China Figure 1Oryza sativa indica was used as a control for evaluating the sequencing data, the efficiency of paired-end comparison and digestion of control were 96.97% and 95.54%, respectively, which indicates that the process was normal and available.The Sscrofa genome served as the reference genome for predicting electron enzymes and identified a restriction fragment length of Rsal + EcoRV-HF from 314 to 364 bp, which was ultimately defined as a SLAF tag. To acquire the actual SLAF marker analyzed in our study, we performed SLAF-seq in seven WL, eight DPL, eight LW, five HG, eight YTL, seven YTW, 10 LC, eight YY, eight LL, and eight DD using the same enzyme combination as in the silico restriction experiment. As shown in http://www.ebi.ac.uk/ena/data/view/SAMEA1557437; ERR173222: http://www.ebi.ac.uk/ena/data/view/SAMEA1557421). The average integrity of SNPs was 44.73% (range = 29.98%–53.02%). A previous study involving SNPs (MAF < 0.05) was biased in quantifying genetic connectivity in the study of population genetic evolution , expected heterozygous number (He), Nei diversity index (Nei), polymorphism information content (PIC), and MAF were used to determine genetic diversity. As shown in To explore genome-wide relationships and the divergence between the Shandong indigenous breeds and Western breeds, we constructed neighbor-joining trees based on pairwise genetic distance  and SNP-based Fst estimation were calculated to determine positively selected regions, defined as regions with shared differential selection signals among populations. We combined the three Western pig breeds  into a single gene pool and compared them to DPL and LW. A total of 162 differentially selected regions (DSRs) were shared between DPL and the commercial breeds. A further 841 unique known genes were found that contained a large number of genes related to immunity, such as nd so on 5A. GeneNR4A1, MSMO1, and so on  to participate in signal transduction and induce antiviral and anti-tumor functions . EYA1, a homolog of Drosophila eyes absent, is expressed in the mesenchyme  in the regulation of actin cytoskeleton, and a novel gene designated 2-acylalycerol O-acyltransferase 2-A in fat digestion and absorption, were discovered in pathway analyses. Other studies have shown that the DH domain of TIAM1 plays an important role in cell deformation and cytoskeletal reorganization .WC and YZ conceived and designed the research. WC, MQ, CL, and ZL performed the research. MQ, WC, and CL analyzed the data. YZ contributed reagents and materials. MQ wrote the manuscript. WC and YZ provided substantial comments and revised the manuscript. All authors read and approved the final version of the manuscript.This study was supported financially by the National Natural Science Foundation of China (No. 31401055), the Funds of Shandong “Double Tops” Program (No. SYL2017YSTD12), the Shandong Provincial Modern Pig Technology and Industry System Project (No. SDAIT-08-02), Shandong Provincial Natural Science Foundation .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Homarus americanus) as a case study. First, we found that GBS, Rapture, and Pool‐seq approaches gave similar allele frequency estimates  and all three revealed the same weak pattern of population structure. Yet, Pool‐seq data showed FST estimates three to five times higher than GBS and Rapture, while the latter two methods returned similar FST estimates, indicating that individual‐based approaches provided more congruent results than Pool‐seq. We conclude that despite higher costs, GBS and Rapture are more convenient approaches to use in the case of species exhibiting very weak differentiation. While both GBS and Rapture approaches provided similar results with regard to estimates of population genetic parameters, GBS remains more cost‐effective in project involving a relatively small numbers of genotyped individuals . Overall, this study illustrates the complexity of estimating genetic differentiation and other summary statistics in complex biological systems characterized by large population size and migration rates.Unraveling genetic population structure is challenging in species potentially characterized by large population size and high dispersal rates, often resulting in weak genetic differentiation. Genotyping a large number of samples can improve the detection of subtle genetic structure, but this may substantially increase sequencing cost and downstream bioinformatics computational time. To overcome this challenge, alternative, cost‐effective sequencing approaches, namely Pool‐seq and Rapture, have been developed. We empirically measured the power of resolution and congruence of these two methods in documenting weak population structure in nonmodel species with high gene flow comparatively to a conventional genotyping‐by‐sequencing (GBS) approach. For this, we used the American lobster ( To date, there is no study that have already explored and compared their relative efficiency in resolving weak population structure in nonmodel species. In this context, the goal of this study was to assess the consistency of two cost‐effective sequencing approaches, Pool‐seq and Rapture, in documenting genetic population structure as well as estimating allele frequency and derived statistics in a high gene flow and nonmodel species, the American lobster  across the Northeast Atlantic .Individual GBS library was prepared following Mascher, Wu, and Amand  and detaFST values per sampling site in order to compare with statistical estimators derived from individual‐based datasets. Pool‐seq libraries were digested using the same restriction enzymes and protocol described above for GBS‐based libraries. We sequenced 16 pool libraries in a first sequencing Ion Proton chip containing four sampling sites  and then six pool libraries in a second chip composed of the two remaining sampling sites .For Pool‐seq library preparation, we used 2 µl DNA of equimolar concentrations for each individual of a given location. The 48 individuals of a given sampling site were pooled and barcoded using the same nucleotide sequence to identify sample origin. Here, the sample size of pooled DNA samples is a critical parameter, which will ultimately influence the accuracy of allele frequency estimates  with a median target of 80 million single‐end reads (50–220 pb) per chip. Two rounds of sequencing  were conducted for all libraries. GBS libraries were normalized after the first round in order to reduce the unbalanced sequence representation of individuals by adjusting DNA volumes for each sample. Pool‐seq libraries normalization was not possible because individual information was unavailable, and therefore, balanced contribution of each individual in each pool is assumed. Rapture protocol was also conducted without normalization as the adjustment of DNA volumes on a highly randomized and multiplexed Rapture setup  is very time‐consuming and could substantially increase the risk of inadvertent pipetting errors.2.42.4.1https://github.com/enormandeau/stacks_workflow) according to Benestan et al. , a minimum stack depth of three (m = 3), and a maximum distance for secondary reads N = 5 were allowed in ustacks. Then, reads were aligned de novo to create a catalog of putative loci  and the populations command was run requiring a locus to be present in at least one sampling location and in 50% of all individuals. Finally, this dataset was postfiltered using a custom python script (available at https://github.com/enormandeau/stacks_workflow/00-scripts/05_filter_vcf.py) where we kept SNPs for which a genotype was called in at least 70% of individuals in each sampling site with a Ho < 0.6 and a FIS between [−0.7: 0.7]. A minor allele frequency (MAF) threshold of at least 1% globally or 5% in each sampling locality was also applied and no more than eight SNPs per locus were allowed. Based on this final individual GBS dataset, we then generated a targeted sequences catalog for Rapture. Ultimately, we removed highly similar sequences through a “self‐blast” test and RepeatMasker 4.0 , respectively. The final panel was composed of 16,780 SNPs spread over 9,818 loci.Genotyping‐by‐sequencing sequence data from four locations  were analyzed using the pipeline available at  were then filtered as above, removing all reads with soft clipping . We computed pairwise ockerham . In ordeockerham  also demFST were computed using the stamppFst function from the R package StAMPP 1.5.1 ; Rousset, marmap 0.9.6  of sampling sites with their PC‐based genetic positions. The correlation between Ω matrices was then assessed using a Mantel test. The Pool‐seq variance–covariance matrix was reduced by averaging over pool site replicates in order to perform this latter analysis with similar matrix sizes as the individual matrix .An additional analysis of allele frequency differentiation was conducted with BayPass v2.1 Gautier, . First, 33.1SD = 0.41), 0.46 million (SD = 0.25), and 8.4 million (SD = 1.7) for GBS, Rapture, and Pool‐seq, respectively.The average number of reads per sample among sequenced libraries was 1.3 million , we expected to retrieve roughly 0.4 million reads per sample . Conversely, we multiplexed only 96 samples per chip for GBS sequencing, yielding an expected number of 1.6 million reads per sample. Here, it is noteworthy that GBS sequencing represented the entire genome sequence diversity obtained from restriction enzyme libraries, whereas Rapture libraries represent only a reduced fraction of the GBS libraries . Hence, the useful genomic load  between the two protocols would not be the same. From our catalog of reference containing 9,818 loci, the average proportion of targeted loci recovered (with at least one read) was comparable yet slightly lower for Rapture mapped data (95%) compared to GBS and Pool‐seq . After filtration, the number of SNPs discovered by GBS and Rapture was 16,986 and 13,931, respectively, while SNPs calling from Pool‐seq discovered a total of 10,874 filtered SNPs . Pearson correlation between GBS and Rapture allele frequency for a given population was 0.95 on average . However, no difference in coverage was observed for THE(1) compared to other pool replicates THE(2) and THE(3) , values obtained from Pool‐seq data were three to five times higher (average FST was 0.0060 for Pool‐seq). Analyzing the coefficient of variation (SD/mean) through pairwise FST bootstraps showed a lower variation for Pool‐seq estimates  in comparison with individual‐based methods where a higher level of variation was observed . The Mantel tests of IBD were significant for all datasets  than Pool‐seq .Overall, the genetic differentiation measured by the three methods was weak with an average pairwise r = −0.08 for PC1 vs. latitude, and r = −0.15 for PC1 vs. longitude). On average among all datasets, correlation level between Ω‐PC space and the spatial distribution of samples sites was stronger for PC2 .The analysis of the variance–covariance matrices (hereafter Ω matrix) depicted similar pattern of clustering between the three sequencing methods tested. Figure r = 0.76; Table r = 0.87), while correlations between either GBS or Rapture Ω matrices and Pool‐seq Ω matrices were lower (mean r = 0.67). Correlations between overall versus overlapped datasets among each method tested were still high and significant . This indicates that subsampling overlapped SNPs from overall SNPs dataset generally conserved the genetic relationships among each pair of populations.Mantel tests detected positive and significant correlation among all Ω matrices  to conventional individual GBS libraries are becoming increasingly popular and may represent a well‐suited approach for the analysis of genetic variation in wide natural populations. These alternative approaches appear to represent interesting SNP genotyping strategies in the case of species exhibiting weak genetic differentiation, where large sampling design  will result in further benefits to accurately investigate genetic structure and connectivity  provided more congruent results than Pool‐seq. In the following sections, we discussed the consistency of these three methods in a context of a weak genetic differentiation and we also highlight the cost and benefits for each method tested.4.1FST were well under 0.01, Table FST observed for GBS and Rapture were almost identical and very similar to the level of genetic differentiation previously reported on this species for a larger set of samples with an averaged FST value of 0.0018 across 10,156 SNPs . Thus, like Gautier et al.  rangingr et al. , we obser et al.  did not FST estimator of Hivert et al.  since we used a purely empirical data in which the truth is unknown. A thorough a simulation study would be relevant to compare the three protocols and complement our outcomes. While simulation studies of Pool‐seq and GBS data have already been performed to report sources of bias  as well as bias in population genomic inferences . This calculation is based on the assumption that the average weight of base pair (bp) is 650 Daltons. We also used a sequence size selection (BluePippin™ prep‐Sage Science) to minimize amplicon size variability and limit PCR cycling to 10 cycles during library preparation, two measures that should prevent efficiently the formation of PCR duplicates. Finally, the probability of obtaining PCR duplicates is negatively correlated to the number of targeted markers. Therefore, Ali et al.  with geographic distances depicted a positive signal of IBD, as also identified by Benestan et al.  may help to further reduce the effect of this experimental bias. We further advocate that future empirical Pool‐seq projects would be reinforced with several pool replicates in order to control for experiment reproducibility and data robustness.In conclusion, we found that Pool‐seq and Rapture provided consistent allele frequency estimates and nearly identical patterns of population structure compared to conventional GBS approach. However, despite increasingly accurate None declared.L. Bernatchez and R. Rochette designed and supervised the project. L. Benestan conducted the field sampling. Y. Dorant conducted genomic data analyses and lead on writing the manuscript with contributions from L. Benestan and Q. Rougemont. All authors contributed to analyses and interpretation of the results in their respective expertise and approved the final version of the manuscript. Click here for additional data file.


Canis familiaris) native to Korea, which became perilously close to extinction in the mid-1980s. However, with systematic genetic conservation and restoration efforts, this breed was rescued from extinction and population sizes have been gradually increasing over the past few decades. The aim of this study was to ascertain novel information about the genetic diversity, population structure, and demographic history of the Sapsaree breed using genome-wide single nucleotide polymorphism data. We characterized the genetic profile of the Sapsaree breed by comparison with seven foreign dog breeds with similar morphologies to estimate genetic differentiation within and among these breeds.The Sapsaree is a breed of dog  contains supplementary material, which is available to authorized users. Canis familiaris) is the most phenotypically diverse mammalian species, and one of the first animals to be domesticated by humans [Canis lupus) is the common ancestor of domesticated dogs, which have since been differentiated through artificial selection of the hugely diverse features of modern breeds [The domestic dog  and two distinguishable coat colors: the ‘Chung’, or blue Sapsaree, and the ‘Hwang’, or yellow Sapsaree , 16, 17.Historical evidence suggests that Sapsarees were used as military dogs by nobles of the Silla dynasty. Following the collapse of the unified Silla, Sapsarees were featured in the classical literary works of the Joseon dynasty and have since gained popularity throughout the Korean peninsula. Their disposition is friendly and gentle, and their loyalty has long been recognized , 18, 19.The population size of Sapsaree was substantially decreased and became perilously close to extinction during the Japanese colonial period 1910–1945) and the Korean War (1950–1953). In 1969, a Sapsaree revival was initiated by Kyungpook National University, however the restoration process and systematic genetic conservation begin by 1985 at the Sapsaree Breeding Research Institute in Gyeongsan, South Korea. In 1992, the Sapsaree was registered as a national treasure of Korea and their breeding and sale were strictly regulated to protect the purity of the breed –23. Curr10–1945 ae), and heterozygosity are parameters widely used to understand the genetic diversity of populations [e, which estimates the rate of genetic drift, inbreeding, and the effects of evolutionary forces such as mutation, selection, and migration [e, and heterozygosity would therefore be useful for establishing a breeding program that avoids inbreeding while maintaining the breed purity of Sapsarees. However, there are a limited number of scientific studies on the genetic diversity of Sapsaree populations [Understanding the genetic diversity of domesticated species is important to establish effective conservation decisions and management strategies , 25. Advulations . The evoulations , 29. Anoigration , 31. Hetigration , 32. Infulations , 23, 33.As LD is expected to decay with recombination and increase the physical distance between markers , Fig. 1 e) at t generations ago is shown in Fig. e was lower in the recent past compared with the ancient past  and the lowest Ne was approximately 17 individuals for the Tibetan Terrier .The estimated effective population size , followed by the Lhasa Apso (0.309) and Tibetan Terrier (0.273). The Old English Sheepdog (0.179) and Great Pyrenees (0.232) showed the lowest heterozygosity in the present generation  in Finland, even though the population was founded by four individuals in 1934 and remained isolated from other deer populations. In contrast, the German Leonberger breed had similar experience as which was nearly wiped out during World War I by violence and starvation. Their genetic variation drastically declined but was re-established in 1992 using five females and two males. However, their genetic variation was still low compared with other breeds [Reductions in genetic variability or heterozygosity primarily depend on bottleneck size, rate of population growth, and mutation rate –74. Althr breeds , 69, 75.Admixture, MDS, and phylogenetic analyses showed the unique diversity of the Sapsaree breed. Other studies have also found that native Korean dogs have substantially different genetic patterns than other foreign dog breeds , 55. FurCorrespondingly, Han et al.  showed tThere was evidence of introgression into Sapsaree in the admixture analysis, which might have occurred prior to the restoration process when the population levels were low. Introgression from non-tested breeds could also have contributed to the high levels of genetic diversity noted in the Sapsaree. The admixture and MDS analyses provide compelling evidence that the ancestor of the Sapsaree is related to Tibetan long-haired breeds. The Tibetan Terrier and Lhasa Apso are native to Tibet, where they lived in nobles palaces and Buddhist monasteries as watch dogs, companions, and ‘good luck charms’. There are definitive evidences that which used as a special gift, tokens of esteem and good fortune when spreading the Buddhism –82. Budde of the breed has declined drastically and is currently insufficient to maintain long-term viability of genetic diversity. Therefore, we suggest a strategic and systematic approach to ensure the purity and genetic diversity of the Sapsaree breed, a Korean natural treasure. Admixture analysis revealed a complex pattern of Sapsaree, where major ancestries were not shared with the other breeds analyzed in this study. LD, Ne, genetic diversity, and population structural analyses indicate a relationship between Sapsaree and the long-haired breeds Tibetan Terrier and Lhasa Apso. Introgression from European breeds was also revealed.Our results provide novel information regarding the genetic diversity and population structure of the native Korean dog, Sapsaree. Consistent with previous studies, our results also revealed higher genetic diversity in Sapsarees compared with other breeds. The majority of the breed showed a discrete genetic pattern, while a small fraction was genetically divergent and might be a consequence of recent restoration process. The Nn = 96), Lhasa Apso (n = 15), Great Pyrenees (n = 10), Tibetan Terrier (n = 7), Afghan Hound (n = 7), Old English Sheepdog (n = 9), Soft-coated Wheaten Terrier (n = 10), and Mastiff (n = 22) dog breeds were categorized as ancient or modern breeds according to Vonholdt et al. [All research methods were approved by the Institutional Animal Care and Use Committee of the Rural Development Administration in South Korea. To investigate the genetic origin of the Sapsaree breed, we selected seven foreign dog breeds analyzed in a previous study Shannon et al.  based ont et al.  and Parkt et al. . Based owww.illumina.com). The quality of SNP data was maintained with the use of PLINK 1.9 [P > 0.001) were also excluded [Samples were genotyped using Illumina CanineSNP20 BeadChip. Other breeds were genotyped by  using thLINK 1.9  to filteexcluded . Non-aut2) with inter-SNP distance within 1 Mb, both within a given breed and across all breeds [e) was estimated based on the LD value (r2) using the SNeP V1.1 tool [The extent of LD between markers was measured using the squared correlation coefficient of allele frequencies at pairs of loci  analysis, ancestor’s admixture prediction, and phylogenetic comparisons. To create a matrix representation of interbreed relationships, MDS algorithms of pairwise genetic distances were implemented in PLINK  and depiAdditional file 1:Figure S1. Cross-validation plot of admixture analysis. The x-axis represents the number of clusters (K) in the model and the y-axis represents cross-validation error values. (DOCX 33 kb)Additional file 2:Figure S2. Clustering of animals from Sapsaree and other selected breeds based on multidimensional scaling of genetic distance. Individuals are plotted for the third and fourth dimension. (DOCX 22 kb)Additional file 3:Figure S3. Population structure plots using K = 8 and K = 11 ancestry models. Each colored vertical line represents proportions of ancestral populations for each individual. K inferred the number of estimated ancestors and which differentiated by colors. (DOCX 214 kb)Additional file 4:Figure S4. Ancestry model for Sapsaree including related dog breeds based on the genetic distance. Each colored vertical line represents proportions of ancestral populations for each individual. K inferred the number of estimated ancestors and which differentiated by colors. Optimum K value (K = 16) was determined by Admixture’s cross-validation (CV) procedure. (DOCX 64 kb)Additional file 5:Figure S5. The population structure bar plots generated by STRUCTURE software at K = 8. (DOCX 73 kb)Additional file 6:Figure S6. Heat map of relatedness between the individuals of Sapsaree and other studied breeds. (DOCX 95 kb)Additional file 7:File S7. Genotype information of Sapsaree. (BED 524 kb)Additional file 8:File S8. Genotype information of Sapsaree. (BIM 697 kb)Additional file 9:File S9. Genotype information of Sapsaree. (FAM 2 kb)


Culture  is present in diverse animal species, yet how it interacts with genetic evolution remains largely unexplored. Here, we review the evidence for gene–culture coevolution in animals, especially birds, cetaceans and primates. We describe how culture can relax or intensify selection under different circumstances, create new selection pressures by changing ecology or behaviour, and favour adaptations, including in other species. Finally, we illustrate how, through culturally mediated migration and assortative mating, culture can shape population genetic structure and diversity. This evidence suggests strongly that animal culture plays an important evolutionary role, and we encourage explicit analyses of gene–culture coevolution in nature. The reciprocal interaction between genetic and cultural evolution is well recognised in humans. Here, Whitehead and colleagues review the growing body of evidence that culture is also a major driver of both neutral and adaptive genetic evolution in non-human animals. Genes and culture are both inheritance systems that transmit information between organisms and generate phenotypic change see Box .Fig. 1Pr2. However, recent research has established that animal culture is present in insects, birds, fishes and mammals4, that it can have important impacts on fitness , that it can be stable over many generations , and that it can affect evolutionary dynamics even when transient . Here, we review evidence from across the animal kingdom that the strength and direction of selection, as well as population genetic structure, are influenced by cultural activities in a range of species, not just humans. We define gene–culture coevolution inclusively, as occurring when cultural evolution shapes genetic evolution, often but not always entailing reciprocal interactions between the two. We compare genetics and culture as systems of inheritance, and explain the different ways in which culture modifies genetic selection. These include how culture may select for particular functional genes, whether in populations, species-wide, or in different species, and how culture shapes the structure and diversity of variation in neutral genes.Early reviews assumed that non-human cultures were insufficiently stable to affect genetic evolutionBox 1 GlossaryConformity: A disproportionate adherence to majority behaviour (positive frequency dependence)2, which may override personal adherence to an alternative.Cultural evolution: Evolutionary change in behaviour, knowledge or constructed artefacts arising from social learning and transmission.Cultural hitchhiking: Neutral genetic variants that happen to be carried by individuals who transmit selective cultural traits ‘hitchhike’ along the same cross-generational pathways; matrilineal cultural inheritance can hence constrain mitochondrial genetic inheritance, leading to a reduction in variance93.Cultural intelligence: Cognitive processes supporting and/or dependent on cultural transmission.Cultural transmission: Diffusion of behaviour patterns or knowledge via social learning from others’ actions or their consequences.Culture: (a) broad sense—equivalent to ‘Tradition’ below123; (b) more elaborate sense–a communal complex of multiple traditions56.Ecotypes: Communities within the same species exploiting different ecological niches.Gene–culture (or Culture-gene) coevolution: Cultural processes shape genetic evolution by modifying the selection of genes, often entailing reciprocal interactions and feedbacks.Horizontal transmission: Cultural transmission of behaviour between members of the same generation13.Phenotypic plasticity: The ability of a genotype to produce different phenotypes in different environments.Social learning: Learning that is facilitated by observation of, or interaction with, another animal or its products17.Tradition: A behaviour pattern shared by members of a community that relies on socially learned and transmitted information.Vertical transmission: Cultural transmission of behaviour from parent to offspring13.11. Although genes are transmitted at conception from parents to offspring, social learning occurs throughout the lifespan, from many different individuals. Culture thereby allows for the propagation of phenotypic variants among unrelated individuals, often within timespans significantly shorter than a generation . Such ‘horizontal cultures’, comprising learning among similarly aged peers, can be highly labile; conversely, ‘vertical cultures’, in which offspring learn from parents, can be very stable14. The stability of cultural transmission can be enhanced through conformity , as in the foraging traditions of great tits, Parus major15. This stability allows cultural traits to be maintained as Nash equilibria, generating a ‘cultural inertia’ that can hinder adaptation to changing environmental conditions, and leading to population-specific traditions in behaviour . Through eliciting change in behaviour, often across an entire population, culture can transform the social environment, whereas cultural activities  also modify ecological circumstances. Culture thus encompasses a range of temporal scales, pathways of information flow and impacts on selection.Parallels between genetic and cultural inheritance are well-established, but cultural transmission is mechanistically different from genetic transmission and cannot simply be treated as ‘another class of gene’17. This kind of learning can be applied in an extremely flexible and open-ended manner, including learning from both conspecifics and heterospecifics, which means that animals are not restricted to learning only about environmental features previously encountered by the lineage . Instead, animals can also learn about entirely novel stimuli or events, and devise appropriate responses to them . Via learning, animals can therefore generate adaptive responses to conditions without prior evolution of dedicated traits with suitable reaction norms. In addition to responding appropriately to changing features of their environments, such as the dangers presented by a novel predator, social learning can also generate opportunities for phenotypic change in the absence of any immediate environmental change or stressor .Cultural transmission has a number of additional properties that affect its role in genetic evolution. Culture provides a highly flexible means to adjust to novel conditions and modify selection. Much of social learning is reliant on phylogenetically ancient and widespread associative learning processes, such as classical and operant conditioning21. For instance, among the most-celebrated examples of animal innovation are the invention by primates and cetaceans of new food-processing methods, and new dominance and courtship displays, which spread because of their perceived beneficial qualities and/or are associated with a rise in status or reproductive success23. Learned behavioural innovation is now extensively documented in animals21, and many such innovations are propagated through social learning . Animal social learning is also typically non-random and strategic, with evidence that individuals often disproportionately copy successful individuals and high-payoff behaviour26, enhancing the spread of adaptive variants . Good information, supporting fitness-enhancing behaviour, is far more likely to be propagated than bad information.Culture typically leads to the production and propagation of adaptive behaviour. Learned behavioural innovations  are usually not random but rather novel functional solutions tailored to new challenges or hitherto-unexploited opportunities29 and introduce novelty into phenotype space, generating diverse selection on genes30. Yet, cultural systems are not typically well-captured by standard population genetic or quantitative genetic models of trait evolution, but instead require dedicated theory32.These features, most of which are particularly well-documented in vertebrates, mean that phenotypic accommodation through culture has the potential to be common, rapid and powerful. Cultures can quickly accumulate adaptive features33. In stationary, or slowly changing, unimodal fitness landscapes, learning typically slows evolution by reducing phenotypic differences between genotypes36. This explains how cultural species such as humans and, possibly, bottlenose dolphins (Tursiops spp.) can live in extremely diverse habitats without major genetic adaptations37. However, learning usually accelerates evolution in dynamic environments that cannot be tracked effectively by selection of genes13, or in static multimodal fitness landscapes, where the existence of multiple optima means that populations can become trapped on suboptimal fitness peaks. In the latter case, learning smooths the fitness landscape, increasing the likelihood of a directly increasing path of fitness to the global optimum38 and helping genotypes to locate otherwise difficult-to-find fitness peaks39.A prima facie confusing feature of culture is that it can both speed up and slow down genetic evolution, but these contrasting effects are now well-understood, thanks to theoretical work40. Although, as described above, social learning can buffer selection on genetic variation that would otherwise lead to genetic adaptation, this buffering is unlikely to be perfect, and hence will not preclude selection of alleles that increase the probability of producing the phenotype, or improve it further (a process known as ‘genetic assimilation’41). For instance, the dietary traditions of killer whales, Orcinus orca, have favoured population-specific genes influencing morphology and digestion43.In cases where learning accelerates evolution, phenotypic change (henceforth ‘phenotypic accommodation’) precedes, and then facilitates genetic adaptation by modifying selection on genetic variation (‘genetic accommodation’)8. Birdsong provides another illustration of how cultural change, which is rapid at least with respect to rates of genetic change, can nonetheless be consequential for genetic evolution, influencing patterns of migration and assortative mating, and facilitating speciation46. In sum, theoretical work leads to the expectation that genetic accommodation and genetic assimilation in response to culture could be widespread in animal populations, but this has been little investigated. Further examples of how cultural plasticity can precede and facilitate genetic evolution, and affect evolutionary rates, are considered in more detail in the following section.Social learning can also elicit the selection of genetic adaptation in other traits (genetic accommodation). For instance, mate-choice copying, where the choice of mating partner is influenced by the mate-choice decisions of other individuals, is found in fruit flies, fishes, birds and mammals. Mate-choice copying propagates mating preferences over short time periods, such as a single season, yet population genetic models have shown that it can substantially enhance the strength of sexual selection on male traits47. More generally, diverse agricultural practices are thought to have inadvertently selected for alleles expressed in enhanced metabolism of the increased starch, carbohydrates, alcohol and so forth, found in agricultural diets48. Genomic studies have identified over 100 other variants subject to recent selection for which cultural practices are thought to be the primary source of selection, although the difficulty of demonstrating their causal role precludes certainty48.Among the processes of gene–culture coevolution, attention has overwhelmingly focussed on relationships between the distributions of functional genes within a population and cultural variants. Correlations are expected if cultural innovations alter the selection regimes for particular genes, as individuals with the same genes can have different fitness in different cultural contexts. Given it is impossible to demonstrate the causes of past episodes of adaptive evolution experimentally, the strongest evidence comes from humans, where extensive genetic sampling can be combined with historical and archaeological data. For example, the coevolutionary relationship between dairy farming and adult lactase production is well-established43. Genes associated with the methionine cycle, which is involved in protein synthesis, differ systematically between mammal-eating and fish-eating ecotypes, a contrast presumed to result from different patterns of dietary protein intake42. Although North Pacific and Antarctic mammal-eating ecotypes both had strong signatures of selection in these groups of genes, the precise locations of the variations were different in the two ecotypes, suggesting independent genetic routes to phenotypic change. Given the culturally mediated variation in inter-population variation in chimpanzee (Pan troglodytes) diets49, similar culturally initiated genetic variation in digestion may well be found in this species too.In non-humans, the most-compelling evidence comes from killer whales, in which recent population-genomic studies show that functional genes associated with digestion differ between ecotypes Box  in ways 50. Providing food for these birds is popular in the United Kingdom and recent genomic comparisons of British great tits with those from the Netherlands, where bird feeding is less common, suggest there has been selection on genes involved in beak morphology50. Birds that use feeding stations have larger beaks, perhaps because these individuals are more effective at breaking open the provided seeds. Parid tits became famous for their foraging innovations when they learned to pierce open milk bottles to drink the cream in 1940’s Britain; this behaviour spread too rapidly to be the result of individual learning alone51. Artificial seeding of foraging behaviours in great tits has subsequently demonstrated that detecting and learning how to access bird feeders can spread horizontally and vertically through populations via social learning, enhancing individual learning15. It is therefore probable that social transmission of information about seed feeders increased their use quickly, and consequently altered selection pressure for beak size in great tits50. Furthermore, genetic differences within and between populations could affect culture reciprocally; through their positions in social foraging networks52, larger-beaked individuals may be more likely to spread knowledge of new feeding opportunities53.Culturally transmitted foraging preferences might also have influenced the evolution of functional genes in great tits54. This is also the case with non-humans where, for instance, it is necessary to exclude the theoretical possibility that ‘allele-surfing’  could have contributed to the patterns found in killer whales55.Although genomic scans suggest large-scale culturally driven selection for genes in recent human evolution, with a few exceptions, such as the dairy farming-lactase case, further work is required to pin down particular instances62 cite a range of evidence that cultural inheritance can enhance fitness through the development of greater competence in key behaviours such as foraging and predator avoidance59, and they propose that this in turn will enhance selection pressures for genetically coupled phenotypic traits particularly the neural and life-history variables mentioned above. These effects may in turn lead to more reliance on culture, potentially creating positive ontogenetic-evolutionary feedback loops shaping gene–culture coevolution.A substantial reliance on social learning has been predicted to select for genetic variants that enhance such learning species-wide, shaping supportive neural traits such as encephalisation, energy production or plasticity, or modifying life-history traits such as a longer juvenile period available for learning or enhanced parental support. 'Cultural drive’ or ‘cultural intelligence’ hypotheses63 applied phylogenetic comparative analysis techniques to published databases that span 55 primate genera and 184 species to address relationships between records of social learning and predictor variables. Evidence of greater proclivity for social learning was predicted by both measures of brain size and of reproductive lifespan. The authors concluded that results are ‘consistent with the hypothesis that the evolution of large brains, sociality, and long lifespans has promoted reliance on culture …. in turn driving further increases in brain volume, cognitive abilities, and lifespan in some primate lineages’63.Tests of the cultural intelligence hypotheses have been of two main types. Most common have been cross-species, comparative analyses, addressing the prediction that the scale of cultural inheritance in a species will be associated with selection on supportive phenotypic characters. For example, Street et al.64 and birds65. Reinforcing these correlational analyses, a recent mechanistic model of brain evolution concluded that ‘our results are consistent with aspects of various cultural hypotheses for brain evolution’66.Culture relies not only on social learning but also on intermittent behavioural innovation, and similar comparative analyses have identified relationships between records of innovation and brain size in both primatesPongo abilii) than for the Bornean species (P. pygmaeus)67. Consistent with the cultural intelligence hypothesis, the Sumatran species have brains reported to be 2–10% larger and showed superior performance in cognitive tests conducted in comparable captive environments67.A second approach is to compare closely related species differing in cultural richness, recently explored in a comparison between orangutan species, in which a more extensive cultural repertoire has been described for the Sumatran  increase with age, but continued reproduction would reduce their capacity to assist relatives69. Older female killer whales are known to be repositories of such extensive adaptive knowledge70.Evolutionary effects of culture may explain a further life-history phenomenon, the existence of menopause not only in humans but in whales, where females of matrilineal species may live long after their reproductive spanCorrelational tests of the cultural intelligence hypothesis outlined above have, however, been constrained by relatively crude measures of the scope of cultural intelligence in any given species, often resting on post hoc analysis of publications that report non-standardised measures of social learning. In future, the field will benefit from the development of more comparable and systematic variables to be applied in such analyses.Malurus cyaneus) hosts72, and reed warblers  learn about the identity of cuckoos that mimic more dangerous enemies horizontally from peers73. In reed warblers at least, this socially learned and transmitted information leads to increased detection of cuckoos and strengthens selection against the parasite74. However, common cuckoo (Cuculus canorus) females have a colour polymorphism, likely to be an MC1R variant75, that defeats host culture. Social learning about cuckoos is not generalised across morphs, so knowledge spreads quickly only about the common form. In this case, host culture generates a stronger selective advantage for cuckoos that appear different  than if hosts only learned individually18.One difficulty with demonstrating gene–culture coevolution arises when genes and culture are both spread predominantly through transmission from parents to offspring. When culture changes how species interact, however, it can also influence genetic evolution across species boundaries, removing this potential confound. For example, experimental studies with brood parasitic cuckoos and hosts have established that culture may alter the strength of selection across time differently to when culture is absent. Knowledge of the threat of brood parasites (‘cuckoos’) is maintained in groups via social learning among naïve fairy-wren 80, and the rise of agriculture facilitating coevolution of novel pathogens with humans82. Although evidence for non-human culture altering selection in other species is at present limited, in theory this could influence any type of interaction where social learning in (at least) one party occurs. For example, previously ‘honest’ foraging bumblebees (Bombus terrestris) learn to rob nectar from flowers by observing others83—if this knowledge transmits across generations84, it could shape pollination efficiency and selection on the plant83. The onset of a new foraging culture could also rapidly shift selection on host microbiomes as they adapt to a novel resource, or influence transmission of microbial communities85. Culture influencing genetic change across species boundaries has great potential to demonstrate how gene–culture coevolution can operate.There are many familiar examples where humans are the cultural species; these include the industrial revolution increasing selection for melanic forms of peppered moths (Tursiops aduncus) that are transmitted from mother to daughter have set up a distinctive spatial structure of mitochondrial haplotypes over scales of a few kilometres, which the dolphins could traverse in minutes. Haplotypes characteristic of a feeding type that is used in deep water map onto the greater depths, whereas in the neighbouring shallows other haplotypes predominate86.When cultural knowledge strongly affects habitat use or patterns of dispersal through the social learning of habitat preferences, migration routes or foraging methods that select for different habitats, then cultural knowledge can be unevenly distributed across the range of a species. If neutral genes are being transmitted in parallel with these cultural traits—which would necessitate relatively stable vertically transmitted cultures—persistent geographical patterns of genetic variation can result. For example, in Shark Bay, Australia, foraging techniques of bottlenose dolphins , learned from parents can set up geographical patterns in neutral genetic variation at either or both ends of the route88. Cultural transmission of migration routes is common among birds89, especially long-lived species, and cetaceans, especially baleen whales; in both cases, geographical patterns of genetic variation result90. A particularly remarkable case is that of the beluga whales (Delphinapterus leucas), where strict matrilineal migration cultures have retained distinct mitochondrial haplotype distributions on summering grounds in eastern and western Hudson Bay, reflecting different glacial refuges of maternal ancestors, even though the whales from each side of the bay meet and mate in winter and during migration91. In contrast, some species, such as barnacle geese (Branta leucopsis) and southern right whales , tend to mate within migratory cultures, leading to spatial patterns in nuclear as well as mitochondrial DNA90. Geographical effects reappear in other sections of this review, where we consider how culture may segregate populations, leading to population structure and in some cases seeding speciation, reducing diversity, or selecting for particular functional genes  is reduced to essentially those genes in the original groups with successful cultures93. In culturally mediated migration, culture sets up barriers or divisions within a population, inhibiting dispersal and mating. Then, the diversity of neutral as well as functional genes is reduced through processes such as bottlenecks and selection94. Cultural hitchhiking, as well as culturally mediated migration, require cultures that affect fitness or dispersal, respectively, that are quite stable, and that do not frequently transfer between population segments.Culture can potentially reduce genetic diversity in socially structured populations through two related processes: cultural hitchhiking and culturally mediated migration95. Female sperm whale  populations are partitioned into sympatric, matrilineally based clans with distinct cultures and fitness differences, so that their exceptionally low mitochondrial diversity is consistent with cultural hitchhiking95. The division of killer whales into ecotypes is much deeper77. A strong mutual antipathy of proto-ecotypes is supported by genomic results, suggesting that a form of culturally mediated migration may be behind the killer whales’ low genetic diversity95.Both cultural hitchhiking and culturally mediated migration have been considered drivers of the very-low mitochondrial DNA diversity of five species of whale that have matrilineal social systems in which daughters typically stay grouped with their mothers while both are alive92. However, it is telling that both hominins and those matrilineal whale species with particularly clear evidence that culture affects behaviour and fitness37 have remarkably low genetic diversity. Similar processes may also have affected genetic diversity in birds97, as described below.In all these cases, it is difficult to rule out all non-cultural drivers of low genetic diversity , and several processes may have contributed98, the role culture plays in influencing divergent selection has attracted theoretical and empirical attention, particularly concerning traits and preferences involved in assortative mating99.For speciation there are two central requirements—the separation of an existing species into groups, and reproductive isolation in the face of secondary contact. As culture can establish behavioural differences among initial population divisions more rapidly than genetic inheritance and local adaptation101 (supported by population genetic models103) that the influence of culture on speciation depends on whether only the trait involved in mate choice is learned socially, or whether the preference is also learned. Learned-trait models  indicate that if culture reduces stabilising selection against new alleles affecting learning predispositions, then genetic divergence in mating preferences will arise more quickly than when traits are genetically inherited102. However, if mating traits are learned obliquely and independently of the tutor’s fitness, then in theory selection will be inhibited as the trait is learned from both mated and unmated tutors103. Cultural transmission of traits may even break down incipient speciation if secondary contact occurs because learning can mask genotypic variation104; the trait can be rapidly adjusted through learning  to match the preferences of either population. For this reason, cultural transmission is primarily thought to inhibit sympatric speciation100.Several researchers have argued98 and secondary contact102 scenarios. Learned preferences will increase the strength of divergent selection acting on the trait and prevent recombination, amplifying both cultural and genetic evolution of associated loci102. Biases to learning  may restrict individuals shifting phenotypes to match local preferences and further limit gene flow99.However, where mating signals and receiver preferences are both culturally transmitted  culture will instead promote speciation under both allopatric105, with recent work demonstrating that song learning increases rates of species diversification across clades106. White-crowned sparrows (Zonotrichia leucophrys) song dialects vary strongly over small spatial scales, with restricted gene flow between dialect groups97. Recently, genomic studies of neighbouring subspecies  demonstrated that song dialects likely act as a barrier to gene flow, with the strength of song discrimination correlated with genetic distance between sites96. Among indigobirds (Vidua), which are brood parasites, cultural transmission occurs across species, as males learn the song of their host species and females imprint upon that same song107. Switching host species therefore creates strong isolating barriers that have led to rapid recent speciation in sympatry108. Finally, culture-shaping speciation may even have been captured in action with the recent establishment of an apparently isolated lineage of Galápagos finches (Geospizinae) derived from the hybrid mating of a cactus finch (G. conirostris) immigrant male with a medium ground finch (G. fortis)—termed the ‘Big Bird’ lineage109. Twenty years ago, Grant and Grant44 showed that Darwin’s finches acquire their song-type and preference from their father (or foster-father). Although females do not sing, they mate assortatively according to their culturally inherited song type as well as by imprinting on parental morphology; culture therefore facilitates pre-zygotic reproductive isolation by constraining female mate choice, even in the absence of genetic penalties110. Together with changes in beak size, in only three generations this song culture has most probably contributed to allele segregation and incipient speciation of the Big Bird lineage109, although experiments demonstrating this mechanism remain to be done.Studies of culturally transmitted song in birds have generated the best lines of evidence for the role of culture in speciation77. There are significant genetic divergences between these lineages both in mitochondrial111 and nuclear42 genomes. It is believed that the ecotypes arose from cultural niche specialisation by matrilineal groups, which later developed reproductive barriers, culturally  and/or genetically77. In killer whales, culturally transmitted behaviour has therefore triggered the evolution of multiple lineages considered to be currently undergoing incipient ecological speciation77. Although some have suggested they should now be considered different species111, these ecotypes are not currently recognised as such. The genetic evidence suggests that the present day global diversity of killer whales has arisen in the last 250 kya, or ~8000 generations42, but the genus Orcinus has been present in the fossil record for up to 5 My—so why are estimates of ecotype divergence so relatively recent? The most likely explanation is that the formation of ecotype lineages is a dynamic process, with learning and cultural transmission at its heart, both driving the exploitation of new niches as they arise and leading to increased specialisation risk, resulting in lineage extirpation112. Thus, in killer whales, cultural transmission seemingly underlies adaptive radiation into ecotypes and then incipient species, but can also increase local extirpation risk. The social learning of foraging techniques or habitat preferences by young animals has the potential to trigger genetic radiation in a variety of taxa, but this has been little investigated beyond killer whales.Finally, by coupling changes in population structure and modified selection on functional genes, cultural transmission of foraging traits or preferences could also help facilitate genetic divergence. Killer whale population structure is strongly influenced by culturally transmitted specialisations on particular prey resources, giving rise to different killer whale ‘ecotypes’ that can be distinguished behaviourally and morphologicallyThe precise nature of the interactions between individuals with alternative cultures may be key to speciation, and to other processes of gene–culture coevolution. However, such interactions, almost inevitably, are rarely observed. How conspecifics whose cultures have begun to differentiate respond to each other, for example, by conformist convergence or alternatively by avoidance and conflicts, is a topic that can benefit from complementary observations in the field and experiments.54. Our review extends the reach of this discussion by suggesting that culture may change the nature of the evolutionary process in diverse ways, and for many animals. Culture provides a form of inheritance that is additional to genes and our review indicates it is far from trivial in its consequences for genetic evolution; moreover the two inheritance streams can interact to influence each other’s evolution113.Until now, consideration of gene–culture coevolution has largely focussed on the evolution of functional genes in human populations114, a process for which we now have preliminary evidence at least, in a diversity of other species. There is good evidence for both humans and non-humans that culture can shape geographic patterns of genes, and comparative phylogenetic evidence that it has driven species-wide genetic traits, such as aspects of brains and life-histories. The cultures of killer whales offer a case study of how culture may drive the evolution of functional genes within population segments, and the ecotype-fissured killer whales offer a simpler system than human culture within which to delineate fundamentals of such processes.Human culture, so prolific and dominant across the globe, has had a dramatic impact on genetic evolution in other species93, and was later extended to humans115. Culturally mediated speciation was a topic first introduced for birds44 and whales77, but might also apply to hominins. It is now well-established that Neanderthals, Denisovans and Homo floresiensis coexisted with modern humans and possessed distinct behavioural repertoires116. Culturally transmitted ecological specialisations may have led to radiations comparable to that of killer whales in our own evolutionary past, possibly on multiple occasions.Two processes of gene–culture coevolution were first considered for non-humans, with their relevance for our own species explored later. Cultural hitchhiking was conceived to explain low genetic diversity in cetaceans117  shows that early learning of host preferences has facilitated the evolution of two isolated, potentially speciating, lineages120, and a recent finding that fruit flies (Drosophila melanogaster) can carry culturally transmitted mate preferences across multiple cultural generations121 suggests great potential for experimental evolution of both culture and genes122. The study of gene–culture coevolution beyond humans may be in its infancy, but sufficient evidence now exists to see it as an exciting and significant avenue for further focused empirical research.Another limitation of the current state of knowledge is that a large portion of available evidence is correlational. Experimental studies would add clarity, but many of the best-studied taxa tend not to be well suited for manipulative experiments. Birds are an exception