Icterus galbula) and Bullock’s (I. bullockii) orioles—to investigate this architecture of reproductive isolation. We combine whole genome re-sequencing with data from an additional 313 individuals amplityped at ancestry-informative markers to characterize fine-scale patterns of admixture, and to quantify links between genes and the plumage traits. On a genome-wide scale, we document several putative barriers to reproduction, including elevated peaks of divergence above a generally high genomic baseline, a large putative inversion on the Z chromosome, and complex interactions between melanogenesis-pathway candidate genes. Concordant and coincident clines for these different genomic regions further suggest the coupling of pre- and post-mating barriers. Our findings of complex and coupled interactions between pre- and post-mating barriers suggest a relatively rapid accumulation of barriers between these species, and they demonstrate the complexities of the speciation process.Natural hybrid zones provide powerful opportunities for identifying the mechanisms that facilitate and inhibit speciation. Documenting the extent of genomic admixture allows us to discern the architecture of reproductive isolation through the identification of isolating barriers. This approach is particularly powerful for characterizing the accumulation of isolating barriers in systems exhibiting varying levels of genomic divergence. Here, we use a hybrid zone between two species—the Baltimore ( Genome-wide analyses of admixture between two closely related and sympatric North American oriole species reveals longstanding barriers enforcing reproductive isolation. The relevance of hybrid zones for studying the origin and maintenance of reproductive isolation has been demonstrated through many studies5, but their utility for elucidating the coupling or buildup of barrier effects to hybridization is a more recent research focus8. Research has increasingly shown that the maintenance of species boundaries often relies on the combinatorial interaction of several mechanisms11. This aggregate evolution of multiple reproductive barriers is important in preventing gene exchange, even in recently diverged species8. Accordingly, we can use hybrid zones to explore the idea that the process of speciation hinges not only on the evolution of individual barriers to reproduction between closely related species, but sometimes equally so on the complex pleiotropic interactions that couple these barrier effects together8.Hybrid zones can be used as natural experiments to discover the processes that both facilitate and inhibit speciation. Mounting empirical and theoretical work has shown that hybridization between related species is relatively common in nature, occurring in approximately 25% of flowering plants and approximately 10% of animals15. With existing bioinformatic tools and increased genomic resolution, it is clear that hybridization has played a substantial role in shaping the evolutionary histories of many closely related species18. In recently diverged birds, there has been a wave of literature linking simple pre-mating signals  with reproductive isolation, as indicated by selection for a few divergent regions underlying pigmentation genes across an otherwise largely neutral background of low differentiation21. While these studies have been extremely informative in shaping our understanding of the genomics of reproductive isolation near the onset of the speciation process, there are notably fewer genomic studies that look at the accumulation of barriers in avian hybrid zones with greater levels of genomic differentiation and that are further along the speciation continuum (but see refs. 23). For example, we know that at intermediate levels of divergence, an accumulation of incompatibilities may influence hybrid fitness (through post-mating barriers), but the number of barriers may be insufficient to prevent introgression of some genomic regions23. While these semipermeable barriers to gene flow are important in shaping hybridization outcomes, our understanding of how pre- and post-mating barriers accumulate across the genomic landscape, and for how long following species divergence these regions can be exchanged, remains incomplete.Growing access to genomic data from many taxonomic groups has informed the contributions of ongoing hybridization to the evolution and maintenance of biodiversityIcterus galbula) and Bullock’s (I. bullockii) orioles to investigate the genomic architecture of reproductive isolation between two divergent but actively hybridizing avian species. Although they are not sister species, Baltimore and Bullock’s orioles hybridize extensively along riparian corridors through the Great Plains of North America where they are now in secondary contact26. Based on an argument for extensive hybridization and the presence of viable hybrid offspring24, the two taxa were temporarily lumped into a single species, the ‘Northern Oriole’27. Conversely, later studies have suggested the build-up of both pre- and post-mating barriers of species isolation with evidence for some level of assortative mating in the hybrid zone28 and possible selection driven by divergent climate niches of the parental species, respectively29. The argument that hybrid orioles suffered from some sort of pre- or post-mating fitness detriment later resulted in an official systematics reversal, splitting these orioles back into their two original taxonomic species30.Here we leverage a historically prominent hybrid zone between Baltimore , as well as for genes conferring putative adaptive functions linked to physiology and behavior (post-mating barriers) may play an important role in shaping the genomic landscape between these species. The Bullock’s-Baltimore oriole hybrid zone thereby presents a tractable system for studying the architecture of barriers to reproduction and how speciation progresses on a genomic scale. Moreover, extensive sampling of genetically and phenotypically diverse individuals across a well-studied hybrid transect provides a valuable data set for evaluating the extent and pattern of differential introgression of neutral and barrier loci across space. Taken together, we present a comprehensive examination of the genomics of accumulating reproductive barriers at mid to later stages of divergence through a combination of whole genome and targeted amplicon sequencing methodologies.Despite a long history of study, the few genetic studies of these taxa only characterized general patterns of divergence and introgression between the two oriole speciesST estimate of 0.19. Genome-wide divergence between backcrossed Bullock’s and Baltimore individuals (see methods) was moderately reduced (FST = 0.12) compared to that observed between populations outside of the hybrid zone.Whole-genome resequencing of 55 Baltimore, Bullock’s, and admixed orioles revealed a clinal pattern of divergence corresponding to the phenotypic variation Fig. . AllopatST estimate greater than the 99th percentile of the mean, in aggregate these divergent windows are a very small fraction of the entire genome (~1%). However, genome-wide patterns of divergence differed substantially between the autosomes  compared to the Z chromosome (9.2% of the Z is elevated). Based on variant discovery in allopatric Baltimore and Bullock’s orioles, we additionally selected 150 ancestry-informative markers  across each of the chromosomes  outliers across the Z .Phenotypic signals can function as pre-mating barriers to hybridization. We assessed the underlying genomic basis of plumage traits that distinguish Baltimore and Bullock’s orioles. Genome-wide association mapping for eight plumage traits identified several genes associated with each trait, but the distribution and number of outlier SNPs identified by the GWAS differed by plumage trait. We identified significant SNPs associated with all eight traits, including 310 associated with the ear, 2050 associated with the forehead, 279 associated with the greater coverts, 443 associated with the lesser coverts, 79 associated with the neck, 10823 associated with the tail base, 30 associated with the tail tip, and 6515 associated with the throat. In all cases, a large proportion of the outliers identified by the GWAS were on the Z chromosome . Of the 20529 significant SNPs identified above, 3438 (17%) were shared by two or more plumage traits. From the GWAS outliers, we identified 56 candidate genes putatively linked to melanogenesis  and other regions and three models also included interactive effects between LVRN (linked to phaeomelanin pigmentation36) and other candidate amplicons.In the larger data set of amplityped individuals, we observed a strong overall correlation between genotypes for the melanogenesis candidates  and overall plumage score , fixed amplicons , and inversion-linked amplicons . Regardless of timing, this system offers empirical data and new perspectives into later stages of divergence along the speciation continuum. Here we discuss the possible mechanisms driving the genomic architecture of divergence between Baltimore and Bullock’s orioles and some implications of these complex interactions for avian hybrid zones more broadly.39. Deleterious mutations and gene combinations typically accumulate more rapidly on sex chromosomes compared to the autosomes, thus structural inversions on sex chromosomes may act as effective barriers to reproduction and are thought to play an important role in the early stages of speciation41. This pattern holds true in birds, with observed genomic differentiation being much higher on the Z compared to autosomes across several avian hybrid zones43, supporting the potentially important role of structural inversions as a common isolating barrier. Within the oriole data set, we identified a putative inversion on the Z spanning approximately 44 million base pairs and containing 361 genes. We posit that an inversion of this magnitude likely plays an important role in maintaining the oriole hybrid zone. In fact, of the amplityped male individuals that were identified as putative heterozygotes for the inversion, a high proportion (~82%) were classified as F1 hybrids based on reduced representation sequencing data (see methods). This suggests the potential for strong selection against heterozygotes for the inversion, with later stage hybrids more likely to be homozygous for one orientation or the other. We see some support for this from the mapping of genotypes across chromosomes, where we observed a mosaic of heterozygous and homozygous genotypes across the autosomes compared to notably less admixture across the Z. A gene enrichment test based on these 361 genes revealed several significantly enriched regions including locomotory behavior, retina morphogenesis, and eye photoreceptor cell differentiation. Differential expression of photoreceptors  is thought to play a role in both color discrimination thresholds in vertebrates as well as sensitivity of response to color46. Given the clear links between sexual selection and plumage variation47, one possible hypothesis is that suppressed recombination in the inversion is maintaining gene complexes responsible for visual sensory perception of divergent plumage types in orioles. This link between phenotype and a putative visual preference gene has been previously documented in carrion and hooded crows21, suggesting an important role for this linked selection in maintaining reproductive barriers in avian hybrid zones more broadly. A more thorough investigation into the Z chromosome of this species, via long-read sequencing and targeted sequencing of individuals at the putative inversion, will be necessary to fully understand the role of this region in maintaining hybrid zone dynamics in this system.A striking pattern in the genome-wide comparison of Baltimore and Bullock’s orioles was the highly differentiated region on the Z chromosome, which is indicative of a large inversion on this sex chromosome. Chromosomal inversions act to suppress recombination when paired with the alternate orientation, and inversions on sex chromosomes, in particular, have frequently been implicated in increased divergence between hybridizing lineages20, the association of many genes from different pathways with a single plumage color trait is more challenging to study, though this is likely more common than currently recognized48. In Baltimore and Bullock’s orioles, we would predict changes in melanin-based pigmentation; however, while melanin is often under strong genetic control49, the underlying mechanisms controlling melanin-based traits are not always simple50. For example, in laboratory mice, melanic pigment production has been linked to upwards of 150 different genes from melanocortin pathways51. Moreover, changes in oriole plumage could be a result of complex interactions between carotenoids and melanin, such as color masking (pigments can obscure each other52) or functional redundancy (similar colors are produced by multiple processes51). In fact, chromatographic analyses of pigments in Orchard Orioles (Icterus spurius) suggest that carotenoids present in males are indeed masked by high concentrations of melanin52. While highly informative, a past research focus on the role of well-characterized genes such as the melanocortin-1-receptor (MC1R) or Agouti in melanic avian plumage may have overshadowed the genomic complexity of many melanin-based phenotypes50. The identification of 64 melanin-candidates in our comparison of Baltimore and Bullock’s orioles further highlights this potential complexity underlying seemingly simple and modular phenotypes. Whereas some of our identified candidates have been implicated in plumage coloration in other avian species, including DCP2 (chickens53), FST (Gouldian Finch55), SEMA6A (carrion and hooded crows56), and RGP1 (yellow-shafted and red-shafted flickers57), there are many that have been implicated in coloration in other vertebrate taxa but are less well characterized in birds. We also note here, that over half of these melanogenesis candidates are located on the Z chromosome, which again raises important questions surrounding linked selection for coloration genes and genes underlying visual perception pathways that may be maintained within the large, putative inversion. Thus, while the orioles present a complex architecture of reproductive barriers, they offer a compelling system for research into the co-evolution of coloration and visual perception in avian systems.In addition to the potential post-mating barriers imposed by a putative inversion, we have documented a highly complex architecture of genes underlying plumage characteristics, which appear to be operating pleiotropically to shape phenotypes in Baltimore and Bullock’s orioles. While high throughput sequencing has led to a rapid acceleration in the identification of candidate genes underlying plumage variation in species with phenotypic variation caused by just one or a few loci34. Current hypotheses for this westward shift suggest a possible role for climatic changes in the region and the resulting modification of local habitat29. Empirical examples of climate-mediated hybrid zone movement are becoming more prevalent60, particularly in the Great Plains region of the United States where this pattern has been observed in hybridizing species spanning a suture zone in this region62. This movement of the hybrid zone, and perhaps its existence, is also likely to be associated with the extensive (and non-climate-driven) expansion of mature riparian woodlands in the Platte River Valley over the past century, following anthropogenic changes in river flow regimes, fire suppression, and altered grazing practices63. This increased documentation of hybrid zone movement in nature aids our understanding of past, current, and future distributions of species, from both evolutionary and conservation perspectives, contributing to our understanding of biogeography64. Evidence of this movement is apparent from the genomic data as well, with long tails of introgression extending far to the east of the cline centers of the four different genomic regions . This asymmetry in cline expansion may occur for several reasons, including differences in dispersal rates or fitness between species67, with high rates of dispersal and/or lower fitness for one lineage compared to the other both leading to a potential shift in cline centers65. Moreover, asymmetrical mating barriers can lead to these patterns of cline expansion, with centers shifting toward the species with reduced reproductive isolation68. Curiously, the fixed markers show a notable (though not significant) east-shifted cline center compared to the other three marker types, putting it 37–47 km east of the other cline centers. This difference, which is similar to the distance the hybrid zone has moved, further illustrates the dynamic and complex genetic architecture of this system, suggesting different, and potentially opposing selection pressures in maintaining the oriole hybrid zone. While the melanogenesis and inversion-linked markers may be driving the apparent shift in the hybrid zone, perhaps in response to environmental change63, the fixed markers may be linked to other mechanisms, such as differences in molt and migration34.Previous work with the Bullock’s and Baltimore oriole hybrid zone has also found that the hybrid zone has shifted since it was first studied in the 1950s, with movement estimated between 41–73 km westward over a 60-year period34. Genome-wide data provide a new perspective, however, by allowing for the analysis of different genomic regions  and show limited support for patterns of differential introgression among marker types, perhaps because of differing selective pressures. In later stages of divergence, it is likely that reproductive isolation has accumulated exponentially in orioles as barrier loci continue to couple69, in turn, we expect that these aggregated barriers cause a “snowball effect” on hybrid fitness72. While the divergence time between Bullock’s and Baltimore orioles can be classified as relatively recent (<1–2 million years)73, the level of genome-wide divergence suggests a steady accumulation of barriers over this time. Data on avian hybrid zones suggest a wide range of levels of reproductive isolation between taxa diverging less than 2 million years ago73, with the accumulation of a few strong barriers to reproduction in crows separated by 9,000 years73 to detectable levels of introgression between woodcreepers separated by 2.5 million years23. Our findings of several barriers, genome wide, despite ongoing introgression between orioles further supports previous findings that the buildup of reproductive isolation, and the resulting genomic signatures, is complex. The level of genome-wide divergence observed between Bullock’s and Baltimore orioles over the estimated time since divergence warrants the revisiting of hybrid fitness in the zone of sympatry and a reassessment of the ultimate impacts and consequences of admixture in these populations.These pre-mating (plumage variation) and post-mating  barriers are likely indicative of an accumulation and coupling of barriers over time, as indicated by largely concordant and coincident clines. Our estimates of cline width and center varied slightly, but confidence intervals overlapped suggesting concordant and coincident clines, overall, as well as locations consistent with cline estimates based on previous reduced representation sequencing34. Field work at all sites was approved by local landowners, by Cornell University’s IACUC, and by the US Fish and Wildlife Service at both state , and Federal levels . All specimens collected are currently housed at the Cornell University Museum of Vertebrates. This intensive sampling effort resulted in the collection of 313 vouchered specimens of phenotypic Bullock’s Orioles, phenotypic Baltimore Orioles, and phenotypic hybrids from within the contact zone. From this collection, we sequenced the genomes of 60 male orioles from five genotypic classes, including allopatric Baltimore Orioles (n = 10), allopatric Bullock’s Orioles (n = 10), backcrossed Baltimore Orioles (n = 10), backcrossed Bullock’s Orioles (n = 10), and recent generation hybrids  of 2016–2018 from 12 localities along the Platte River in Colorado and Nebraska, USAFastQC version 0.11.8 (http://www.bioinformatics.babraham. ac.uk/projects/fastqc). An average number of 24.5 million reads were obtained per individual. Library preparation for four samples  failed (i.e. a low number of reads). These four individuals were removed from the data set for all subsequent analyses.Genomic DNA was extracted from all samples using the DNeasy blood and tissue kit . DNA concentrations were quantified using the Qubit dsDNA High Sensitivity Assay Kit (Life Technologies). For whole genome re-sequencing, individual libraries were prepared using the NEBNext Ultra II FS kit . We followed manufacturer protocol for concentrations ≥ 100 ng with the following conditions: fragmentation for 12.5 min and bead-based size selection of 400–600 bp (insert size of 275–475 bp). Libraries were sequenced on three Illumina NextSeq lanes at the Cornell Institute for Biotechnology core facility. Library quality was assessed using n = 30) between Baltimore and Bullock’s orioles, melanogenesis candidates (n = 56) identified through a GWAS (see below), outliers strongly associated with the inversion on the Z , and background variants (n = 30) which are presumably not under selection. We chose background variants following Knief et al.74 with some modification and define these variants as those falling within the 55–65th percentile of the mean FST, 50 kb away from exons, and unlinked (not within 50 kb of each other). Primers were designed using the primer design tool in Geneious Prime 2020.2.3 to amplify a 100–260 bp region including the variant of interest within the first 150 bp. Adapter overhang sequences were added to the forward (5’-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG‐(locus specific seq)) and reverse (5’-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG‐(locus specific seq)) primer sequences. These allowed for addition of the Illumina Nextera barcodes in a second round of PCR.Based on variant discovery in pure Baltimore and Bullock’s orioles (see below), we selected 150 ancestry-informative markers (Supplementary Table 75). This library was then sequenced by the Cornell Institute of Biotechnology Resource Center (BRC) on an Illumina MiSeq instrument (single end 150 bp).We amplified all amplicons for each DNA sample in a single multiplex PCR by preparing a single primer mix containing all 300 primers each at a final concentration of 0.7 uM. First round PCR was performed using the Qiagen Multiplex PCR Plus Kit (206152) with 0.3 ul of the above primer mix and 3–40 ng of DNA in a final volume of 11 ul. Initial denaturation at 95 °C for 5 min was followed with 35 cycles of 95 °C for 30 s, 62 °C for 90 s and 72 °C for 30 s and a final extension step of 68 °C for 10 min. A 10-fold dilution of these products was used as the template for a second round of PCR to add the Nextera i5 and i7 index primers. This second round of PCR was performed in a total volume of 11ul with 2ul of the diluted PCR product, 0.5U Platinum™ Taq DNA Polymerase , 1 X PCR Buffer, 1.5 mM MgCl2, 0.2 mM dNTP and 0.5 uM of i5 and i7 primer. Reaction conditions are as follows: initial denaturation at 95 °C for 2 min, followed by 7 cycles of 95 °C for 30 s, 55 °C for 30 s and 72 °C for 30 s. This was followed by 95 °C for 10 s, 55 °C for 3 min (with a 0.1 deg/second ramp), 72 °C for 30 s and a final extension step of 72 °C for 5 min. PCR products were then pooled equally and cleaned using 1:1 ratio of SPRI beads 76 using the default settings in BWA 0.7.477 and obtained alignment statistics from Qualimap version 2.2.178. The mean percentage of reads mapped to the reference genome was 85% and the mean coverage across all individuals was 4.1x. We used Samtools version 1.979 to convert all resulting BAM files to SAM files and to sort and index files. We used Picard Tools v.2.19.2 (https://broadinstitute.github.io/picard/) to add index groups and mark duplicates. We used the Haplotype Caller module in GATK version 3.8.180 for SNP variant discovery and genotyping for the 56 orioles and used the following filtering parameters to remove variants: QD < 2, FS > 60.0, MQ < 30.0, and ReadPosRankSum < −8.0. We additionally filtered out variants that were not biallelic, had minor allele counts less than 4, mean coverage less than 2X or more than 50X, and more than 20% missing data. This resulted in a total of 11,651,297 SNPs across the five ancestry categories . We removed one additional individual (backcrossed Bullock’s Oriole) due to high relatedness with another male in the data set (r = 0.53) resulting in a final data set containing 55 orioles.For the whole-genome data set, we used FastQC version 0.11.8 (http://www.bioinformatics.babraham. ac.uk/projects/fastqc) and removed adapter sequences using Cutadapt version 2.181. We subsequently removed reads containing at least a single base with a Phred quality score of less than 10 . We additionally removed sequences if more than 5% of the bases had a Phred quality score less than 20. These filtered reads were then aligned to a reference consisting of only the predicted amplified regions (pulled from the Myrtle Warbler reference genome) using Bowtie version 2.3.5.1 with the very-sensitive-local option82. We used Samtools version 1.9 to convert all resulting BAM files to SAM files and to sort and index files. Variants were called using bcftools -mpileup and -call. We removed 31 amplicons due to poor or no amplification. For the remaining 119 amplicons, we outputted only the targeted SNP. This resulted in 120 SNPs (one amplicon contained 2 targeted SNPs). We filtered this dataset to exclude all individuals with a mean depth of less than 2, which removed all negative controls and 8 individuals. Finally, sites with more than 50% missing data were removed and 94 sites (78%) were retained.For the amplicon data, we assessed read quality using VCFtools83. To assess patterns of genetic structure, principle component analysis (PCA) was performed on all genome-wide SNPs using the SNPRelate package in R84.Whole-genome sequences from the 55 male individuals were used to describe the overall landscape of divergence between Baltimore and Bullock’s orioles. First, descriptive statistics, including nucleotide diversity (pi) and Tajima’s D, were estimated for each genotypic class in 50 kb windows using FST in both 50 kb windows and for individual SNPs between allopatric individuals. We additionally calculated FST between backcrossed Baltimore and Bullock’s orioles and only considered putative outlier regions if they were elevated in both allopatric and backcrossed comparisons. Here, we assume that regions of elevated divergence purely due to geographic isolation in allopatry will be eroded by gene flow in the hybrid zone. Thus, regions exhibiting elevated divergence in allopatry as well as the hybrid zone are likely important for maintaining reproductive isolation between Bullock’s and Baltimore orioles. For both comparisons, windows were dropped if they contained fewer than 10 SNPs. We defined elevated regions of divergence as any window exhibiting a mean FST estimate greater than the 99th percentile of the mean and containing at least one individual SNP with an FST of 1. Due to patterns of elevated divergence across the entirety of the Z chromosome that were discordant with autosomal patterns, we only applied this outlier detection method to the autosomes.We calculated lostruct85, which detects regions of abnormal population structure that may be caused by inversions (Supplementary Code). To detect patterns of population structure across the genome, principal component analysis (PCA) was applied to non-overlapping windows 100 SNPs in size. For each PCA, Euclidean distances between matrices for the first two principal components were calculated and mapped using multidimensional scaling. This approach provides a measure of how similar patterns of population structure are across the windows. MDS values were plotted along the Z chromosome and outlier MDS windows were defined as those with absolute values greater than the 99th percentile of the mean. Following Huang et al.86, we expect that for an inversion, a PCA should divide individuals into three groups with homozygotes representing the two orientations and heterozygotes between the arrangements falling into an intermediate cluster. Based on the methods of Huang et al.86, we used SNPRelate to calculate PCAs for all SNPs from the putative inversions identified above. Furthermore, based on the expectation that groups identified in the PCA represent heterozygotes and homozygotes for the inversion orientation, we expect higher heterozygosity in the center group. For each putative inversion, variable sites were retained from outlier MDS windows and individual heterozygosity was calculated in VCFtools. We investigated these patterns in the 55 sequenced individuals, color-coding individuals by known genotypic class  for visualization purposes. Using the inversion-linked SNPs identified through this process, we evaluated the genotype distribution  across the 312 amplityped individuals.A defined region exhibiting elevated divergence between Baltimore and Bullock’s orioles on the Z chromosome was indicative of a putative inversion. Thus, to detect possible structural rearrangements, we used the R package Lumpy87 using information from split and discordant mapped reads. We used the default parameters and included the ten allopatric individuals from each species for this analysis. We then used the R package GenomicRanges88 to find overlapping annotations within the Myrtle Warbler reference genome and to extract genes within the inversion and near the putative breakpoints , 2 in the tail (tail tip and base), and 2 in the wing (greater and lesser coverts). Scores for all plumage traits ranged from 0 to 4, where 0 indicated “pure” Baltimore plumage traits and 4 indicated “pure” Bullock’s Oriole traits .Given the importance of phenotype as a pre-mating barrier to hybridization, we quantified plumage variation in re-sequenced and amplityped individuals to identify underlying genetic mechanisms of plumage color. We quantified phenotypes of adult males by adapting the classic plumage scoring criteria developed by Sibley and Shortgemma91. gemma detects SNPs that are associated with specified traits while controlling for population structure by including a relatedness matrix in the model. Because gemma requires a complete SNP data set, we imputed missing data using beagle v 4.192. We conducted separate univariate linear mixed models for each of the nine plumage traits and used the Wald test (-lmm 1) with a significance threshold of α-value of –log10 = 7 to identify associations between genotype and phenotypes93. Models for supercilium failed to converge, and thus we present results for 8 out of the 9 plumage traits. We visualized results using the manhattan function in the qqman package in R94.To identify the genomic underpinnings of plumage variation in Bullock’s and Baltimore orioles, we employed genome-wide association analyses implemented in the package VCFtools. A series of linear regression models were used to evaluate the contribution of each term in predicting plumage traits. Due to the large number of possible combinations, if putative melanin candidates were found on the same chromosome, we chose one SNP per million base pairs and retained the SNP with the highest FST, resulting in 185 models. Linear regression models were run using the base package in R.To validate the 56 melanin candidates included in the amplicon panel, we first evaluated the distribution plumage score for each trait by individual genotype. For each plumage trait, we fitted SNPs with a significant effect on phenotype as factors to test for a co-dominant (additive) or epistatic (interactive) effect. For all models, genotypes were encoded as Baltimore-like, heterozygote, or Bullock’s-like using the–012 option in 95. For each outlier region, we compiled a list of gene models within 40 kb of each region (flanking either the window for FST outliers or single SNPs for GWAS outliers) and used the R package GenomicRanges to find overlapping annotations within the Myrtle Warbler reference genome. We obtained information on these annotations from the UniProt database (http://www.uniprot.org/).All outlier regions were inspected in Geneious version 9.1.5Admixture v 1.2.396 analyses were run using a filtered dataset  that contained no missing data and was pruned to avoid linkage using the script ldPruning.sh (https://github.com/joanam/scripts/blob/master/ldPruning.sh). Because we were only interested in a signal of admixture between the two species, we investigated two possible population clusters, with 200 bootstrap resampling iterations. We also characterized patterns of introgression across the genome, with a focus on patterns of differential introgression and reduced recombination on the Z chromosome versus the autosomes . A VCF containing the 11.6 million SNPs from the whole genome data set was split into individual VCFs per chromosome. Each per-chromosome VCF was than split into smaller subsets, containing 100,000 SNPs per file. We then ran Admixture on these smaller SNP data sets across the entire genome using the same parameters described above. Admixture proportions were plotted across the genome for each of the 20 F1 individuals. We additionally investigated patterns of fixation and heterozygosity for parental alleles across the genome. We filtered each per-chromosome VCF to retain only fixed SNPs (sites with an FST = 1) and visualized genotypes across the genome using the R package GenotypePlot97.To characterize general patterns of hybridization between Bullock’s and Baltimore orioles, HZAR98 to fit a series of geographic cline models to allele frequencies for each SNP targeted in the amplicons. We estimated clinal transitions of allele frequencies for the genotyped individuals for fixed, background, melanin-linked, and inversion linked SNPs. We fixed the variance and the mean to 0  or 1 (Bullock’s Oriole) and fit 3 possible tail models . Because our background SNPs showed little differentiation among species, we additionally included a “control” cline that was generated from Structure Q-values from a ddRAD data set of the same individuals34 to provide a proxy for a “neutral” cline. For all clines, we compared confidence intervals for cline width and center to assess whether clines were coincident (overlap in cline centers) and concordant .To evaluate patterns of differential introgression across the hybrid zone for the four categories of ancestry informative markers, we used the Metropolis-Hastings Markov chain Monte Carlo algorithm employed in the R package Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


The effective population size (Ne) decreased in the current generation to 55 in stray, 11 in household, 9 in OSH and 7 in SIAM. In the recent generation, the increase in inbreeding per generation (ΔF) reached its maximum values of 0.0090, 0.0443, 0.0561 and 0.0710 in stray, household, OSH and SIAM, respectively. The genomic inbreeding coefficient (FROH) based on ROH was calculated for three length categories. The FROH was between 0.014 (FROH60) and 0.020 (FROH5) for stray, between 0.018 (FROH60) and 0.024 (FROH5) for household, between 0.048 (FROH60) and 0.069 (FROH5) for OSH and between 0.053 (FROH60) and 0.073 (FROH5) for SIAM. We identified nine unique selective regions for stray through genome-wide analyses for regions with reduced heterozygosity based on FST statistics. Genes in these regions have previously been associated with reproduction (BUB1B), motor/neurological behavior , cold-induced thermogenesis , immune system development (TSHR), viral carcinogenesis (GTF2A1), host immune response against bacteria, viruses, chemoattractant and cancer cells , and lifespan and aging . In addition, we identified twelve unique selective regions for OSH containing candidate genes for a wide range of coat colors and patterns  as well as congenital heart defects  and gastrointestinal disorders . Genes in stray that represent unique selective events indicate, at least in part, natural selection for environmental adaptation and resistance to infectious disease, and should be the subject of future research. Stray cats represent an important genetic resource and have the potential to become a research model for disease resistance and longevity, which is why we recommend preserving semen before neutering.Stray non-breeding cats (stray) represent the largest heterogeneous cat population subject to natural selection, while populations of the Siamese (SIAM) and Oriental Shorthair (OSH) breeds developed through intensive artificial selection for aesthetic traits. Runs of homozygosity (ROH) and demographic measures are useful tools to discover chromosomal regions of recent selection and to characterize genetic diversity in domestic cat populations. To achieve this, we genotyped 150 stray and 26 household non-breeding cats (household) on the Illumina feline 63 K SNP BeadChip and compared them to SIAM and OSH. The 50% decay value of squared correlation coefficients ( Data from early Chinese agricultural settlements dated between 5560–5280 B.P. suggest mutualistic commensal relationships between cats and people2. The domestic cat (Felis silvestris catus) is one of the most popular pet species. The Felidae family includes 40–71 recognized domestic cat breeds distributed throughout the world5, with considerable variation in coat, behavioral, and morphological phenotypes6. The spectacular phenotypic diversity of most domestic cat breeds is thought to have arisen recently, within the past 150 years, largely through intensive artificial selection for aesthetic rather than functional traits7.Early evidence for human-cat coexistence comes from a wildcat interred near a human in Cyprus approximately 9500 years ago8. Starting in the mid-twentieth century, modern SIAM became established, characterized by blue almond-shaped eyes, a very slender body, and wedge-shaped head. SIAM are usually very affectionate and intelligent and are known to be natural conversationalists9. Developed from SIAM, Oriental Shorthair (OSH) was first recognized as a distinct breed by the CFA (USA) in 19779. The OSH has a similar body shape to the modern SIAM but comes in various solid colors and patterns such as smoke, shaded, particolor/tortoiseshell, tabby and bicolor10. A study showed that SIAM living on European mainland had mean inbreeding coefficients per birth year ranging from 0.03 to 0.12 in data comprising animals born between 1920 and 2000. Individual inbreeding coefficients were calculated from diagonal elements of the kinship matrix. Average inbreeding coefficients in 1998–1999 were 0.12 and 0.10 in SIAM and OSH, respectively11. Based on Swedish insurance data, SIAM and SIAM-derived breeds had a higher mortality rate compared to other breeds with a median lifespan of 10–14.2 years in SIAM12. OSH and SIAM have similar health problems: neurodegenerative disease, amyloidosis, asthma/bronchial disease, congenital heart defects, crossed eyes, gastrointestinal conditions, hyperesthesia syndrome, lymphoma, nystagmus and progressive retinal atrophy9. The G302R mutation in TYR causes the ‘pointed’ coat color phenotype in SIAM 14. The causative mutation of retinal degenerative disease in CEP290 had a frequency of ∼33% in North American and European SIAM populations15. Neurological disease phenotypes in SIAM arose from polymorphisms within GLB1 (GM1 gangliosidosis)17 and ABCB1 18. Metabolic disorder phenotypes in SIAM are associated with genetic variants in ARSB 19 and SLC7A9 (cystinuria)20.The Siamese cat (SIAM), which originated in the Kingdom of Siam (now Thailand), is one of the first distinctly recognized breeds. The breed was already known in the fourteenth century and became one of the most popular breeds in Europe and North America in the nineteenth century11, SNP-array22 or whole genome sequencing (WGS)23 data. A summary of previous studies is given in Supplementary Table 24. To date, ROH analysis in cats has identified selection signatures that shaped the Persian breed22, and common ROH islands for multiple populations25. An ROH island on chromosome B3 at 25–29 Mb was identified in SIAM, OSH, and other nine domestic cat populations25. The candidate gene ARID3B in this region, known to impact neural crest cell survival, was linked to a domestication syndrome hypothesis26. The measures FIS, FST, and FIT are collectively referred to as F-statistics and are derived from the inbreeding coefficient. They are related to the amount of heterozygosity at various levels of population structure. FIS and the summary statistic di based on pairwise FST have been used to determine selection signatures in more than 26 domestic cat breeds23. A shared candidate region on chromosome D3 (1.25–2.25 Mb) has been identified in SIAM, OSH and Peterbald breeds, which share a slender body shapes as a common trait5.Measures of demographic indicators and genomic architecture of cat breeds have been performed based on pedigreeHo = 61% ± 5%) and the degree of inbreeding level (average fixation Index FIS = 0.14 ± 0.05) had been studied in Australian household-stray cats27. However, a refined assessment based on genome-wide BeadChip array data had not been performed in stray cats. According to cat statistics (http://carocat.eu/), there are more than 480 million stray cats worldwide, accounting for 80% of the total number of cats. Stray cats have an abundant variation in phenotypes in terms of coat type and color, eye color, eye shape, eye pupil, ears, tail, shade, and body28. Stray cats have a high reproductive potential, and tend to transmitting various infectious and parasitic diseases, as well as viruses31. Presumably, stray cats could represent an important genetic resource of various morphological, physiological and adaptability traits that would facilitate animal breeding in the future under challenging conditions. For example, the rexoid mutation, one of the most recent mutations, arose from a random bred population in the United Kingdom to develop the Devon Rex breed33. Characterizing the genomic diversity and possible natural selection signatures of stray cats is essential for optimal genetic resources management.Stray and household cats are collectively referred to as non-breed, random bred or incidentally bred domestic cats. These non-breed cats are usually not artificially selected or bred, but are subject to natural selection  was greater than the expected heterozygosity (HE). In OSH and SIAM, however, HO is smaller than HE. HO in stray, household, OSH and SIAM was 0.714, 0.704, 0.722 and 0.672, respectively. HO in the stray subpopulations ranged from 0.701 for Stuttgart-stray to 0.726 for Leonberg-stray. The percentage of monomorphic SNPs were lower in stray (3.73%) and household (9.62%) compared to that in OSH (22.63%) and SIAM (35.43%).The feline 63 K SNP array contains 62,897 SNPs. After data filtering and quality control, the final dataset contained 53,581 autosomal SNPs with a genotyping rate > 0.95 in 246 cats. The SNP distribution across the autosomal cat genome in 1 Mb window sizes can be considered homogeneous, with a SNP density of 0.02241/Kb. The dataset included 150 stray from 8 residential regions  was calculated in stray (0.052 ± 0.094) and household (0.047 ± 0.12), while a negative mean was obtained for OSH (− 0.031 ± 0.072) and SIAM (− 0.071 ± 0.050). FIS values differed significantly different between non-breed and breed populations (p < 0.0001)  plots in Fig. r2 values in the stray, household, OSH and SIAM were 0.45, 0.50, 0.48 and 0.49, respectively , household (0.25), OSH (0.24) and SIAM (0.25) corresponded to a mean marker distance of 1.12 Kb, 4.55 Kb, 62.50 Kb and 175.07 Kb, respectively  decreased from 1923 to 55 in stray, from 760 to 11 in household, from 286 to 9 in OSH, and from 210 to 7 in SIAM, respectively  reached its maximum values of 0.0090, 0.0443, 0.0561 and 0.0710 in stray, household, OSH and SIAM, respectively , 30 (ROH30) or 60 (ROH60) in the region. Based on individual patterns, we identified 712 (ROH60) to 1645 (ROH5) ROHs in stray Table . ROH5 leFROH5) were 0.020 ± 0.022, 0.024 ± 0.024, 0.069 ± 0.020 and 0.073 ± 0.015 for stray, household, OSH and SIAM, respectively. Corresponding median values of FROH5 were 0.011, 0.015, 0.070 and 0.075. Means and medians of FROH5 were significantly (p < 0.0001) lower in stray and household than in OSH and SIAM  and the lowest for stray from Hohne (0.015). Genomic inbreeding coefficients per population across the different ROH lengths showed that all four cat populations had a very similar in distribution of ROH up to genomic inbreeding coefficients of 0.01  to 3.82% (E2) in stray and from 0.12% (E1) to 7.24% (E2) in household  to 12.15% (F2) and from 0.82% (F1) to 10.20% (D3), respectively. Pearson correlation coefficients between FIS and FROH5 based on different ROH lengths for the four populations ranged from 0.755 to 0.999  to evaluate population structure is shown in Fig. di. We defined scattering unique selective events in stray as the same outliers that appeared in di, stray vs OSH, di, stray vs SIAM, and di, stray vs household, but neither in di, household vs OSH nor in di, household vs SIAM. In this way, we excluded non-unique stray selective events that were also displayed as household selective events. Nine of the 137 significant 1-Mb windows on chromosomes A1, A3, B3, B4, C1 and F2 were identified as unique selective stray candidate regions   Fig. b. We detub) Fig. . Almost ub) Fig. . Whether28. These cats have evolved under difficult conditions, having to resist numerous infectious and parasitic diseases and viruses in order to survive31. Therefore, high genetic variability and natural selection signatures of adaptability were expected in this population. The detection of ROHs allowed us to perform a comparative analysis of genomic diversity, inbreeding and selection signatures in non-breed and breed cat populations.Stray cats represent the largest cat population in the world and have extremely important genetic resources. Without human selection for aesthetic traits, stray cats exhibit a wide variety of phenotypesr2 with corresponding marker distances of 62.50 Kb (at r2 = 0.24) and 175.07 Kb (at r2 = 0.25), respectively. Stray showed a steeper decline in r2 values, reaching the 50% decay point at r2 = 0.23 for SNPs 1.12 Kb apart. Thus, compared to the results for cat breeds, stray belong to a more diverse population. In addition, we compared LD decay in stray with that in randomly bred populations from current and two other studies also using SNP array data34. Household in our study had a point of 50% r2 decay (at r2 = 0.25) at SNPs 4.55 Kb apart. Chinese wild cats and household cats from Germany had a point of 50% r2 decay at r2 = 0.24 with corresponding SNP distances of 36 kb and 19 kb, respectively34. The randomly bred population from the east and west reached their maximum r2 value 0.2 at the point of LD < 50 Kb. This population had a point of 50% r2 decay at r2 = 0.1 with a corresponding SNP distance of about 100 kb24. Compared to these randomly bred populations, the stray population had a faster decay of LD, indicating higher genetic diversity.Here we present the first report on LD in a stray population from Germany. The stray population showed a rapid decay of LD. OSH and SIAM had a point of 50% decay of Ne of OSH (9) and SIAM (7) was lower than in many other cat breeds. In a study using the same DNA array with 13 breeds from Japan and the US, only SIAM from Japan and three other breeds from the US had a similar Ne (~ 10) as OSH and SIAM from Germany35. SIAM from the US had Ne above 100. Here we present the first report on Ne in stray. Ne of stray has dropped sharply from 1,923 to 55 in the last 100 generations, possibly due to the increasing prevalence of neutering surgery to render stray cats infertile. Neutering surgery can reduce the number of unfortunate cats. Considering that stray cats are crucial for future breeding under challenging conditions, as they represent an important genetic resource with various morphological, physiological and adaptive characteristics, we recommend preserving semen before neutering.A small effective population size is a factor for high inbreeding coefficients. Due to the limited number of mating partners, the probability of mating with a related animal increases. The Ho) from 0.49 to 0.74 in OSH populations38. The average Ho for OSH (0.722) based on the feline 63 K SNP array in this study falls into this range. In SIAM populations, the average SNP-based Ho was 0.20 using a panel of 148 evenly dispersed SNPs39, and the average STR-based Ho was from 0.47 to 0.6439. The average feline 63 K SNP array-based Ho for SIAM in this study indicated a higher value with Ho = 0.672 than the low-density SNP-/STR-based Ho results. This may imply that the feline 63 K SNP array may be more effective than low-density SNP sets/STRs. Compared to the studies of Australian household-stray cats (average Ho = 61% ± 5%)27, higher average heterozygosities were measured for stray cats (0.714) and household cats (0.704) from Germany. But this difference may be caused by different research strategies and methods.STR loci analyses showed average observed heterozygosities  and household (0.047) were positive. This could indicate inbreeding and an increase in homozygosity associated with natural selection of non-breed cats, albeit to a small extent. However, the FIS values of stray individuals ranged widely range from -0.14 to 0.41, consistent with that of FROH5, indicating that the population has a high genetic variability. It does not make much sense to look at the median or average value. The mean FIS values < 0 in OSH (-0.031) and SIAM (-0.071) do not indicate a state of hybridization in the breeding populations, but may result from the low degree of variation of alleles in the whole genome caused by intensive artificial selection. In a study based on 10 STR loci, positive inbreeding coefficients were estimated for OSH (FIS = 0.19) and SIAM (FIS = 0.10)37. Of note is that these STR loci were selected for high polymorphism to be used in genetic individualization of domestic cat samples.The mean FROH is indicative for inbreeding. There were significant differences between the FROH5 in non-breed and breeding populations. Low FROH5 values were found in stray (0.020 ± 0.022) and household (0.024 ± 0.024). This is in line with our expectations, because non-breed cats usually do not undergo artificial selection or breeding practices, but breed randomly in a large population. However, we found a few stray in the residential area of Frankfurt (n = 3), Hamburg (n = 1), Hohne (n = 1), Landau (n = 1) and Leonberg (n = 1) as well as one household with more than five ROH > 10 Mb and FROH > 0.07. Stray and household were randomly selected without prior knowledge where they came from, but without any similarity in their appearance with a breed cat. So, we can speculate whether recent inbreeding may had also occurred in stray and household and/or their ancestors were already inbred. As these individuals were seen in different residential areas, this may not be caused by selection of cats for this study. Very long ROH (> 25 Mb) may indicate recent inbreeding. Very few stray individuals with such long ROH were seen in the residential areas of Leonberg, Landau and Hohne. Distribution of cumulative genomic inbreeding coefficients across ROH lengths in stray indicated a steep increase to a value at 0.01 through short ROH segments (< 7.5 Mb) but a slow increase between values from 0.01 to 0.02 through longer ROH segments (> 7.5 Mb). This may suggest that approximately 50% of genomic inbreeding may be attributed to ancestral generations and in more recent generations increase of inbreeding steadily decreased in stray with very few exceptions of individual cats. A contrasting picture showed OSH and SIAM. In these breed cats, genomic inbreeding was caused through continued inbreeding up to recent generations. Breeding cats had significantly higher FROH5, 0.069 ± 0.020 in OSH and 0.073 ± 0.015 in SIAM. These values were within the previously reported FROH range for OSH and SIAM (0.03–0.12)11. The low genetic diversity and high inbreeding coefficients in the breeding cat populations are probably the result of high selection pressure for certain aesthetic traits. We calculated the FROH across each of the three ROH length thresholds in SNPs, as the FROH takes on different values depending on the ROH length used for the calculation. Short ROHs capture ancient or historical inbreeding, and long ROHs reflect recent matings of related individuals as homozygous chromosomal segments are broken down over time by crossing-over in meiosis40. In stray and household, long ROH cover a similar proportion of the genome as short ROH, resulting in a similar FROH . The long ROH make up the majority of ROH. The considerable amount of detectable long ROH in the OSH  and SIAM  breeds suggests that recent rather than distant inbreeding is likely to exist in breeding cat populations. It seems that the higher the inbreeding coefficient, the higher the consensus level of ROHs in the population (SIAM > OSH > household > stray). However, we could not find any ROH with 100% consensus in any of the four populations. It is conceivable that the few effective founders in each of the populations were distantly related and not yet inbred that much even in OSH and SIAM.The genomic inbreeding coefficient di analysis identified nine stray unique selective regions in the genome where significant windows were only observed in stray, but not in household, OSH and SIAM. The region on chromosome B3 at position 114.9 Mb was identified as a signature of selection underlying feline biology and domestication by comparing domestic cat and wild cat FST results (di)23. Next to this region, we detected a stray unique selective region on B3 at 115,036,645–115,982,934 bp using di analysis. It also overlapped with a partial consensus ROH shared by stray from eight different residential regions. The GPHN gene in this region encodes a neuronal assembly protein. Motor defects and hyperresponsiveness were observed in GPHN–/– mice41. Hasan et al. reported that 12 of the 26 studied cat breeds showed a ROH island on chromosome B3 at 25–26 Mb25. In the same region, we found a signature of selection in stray using di analysis. The GABRB3 gene in this region encodes the receptor for a major inhibitory neurotransmitter of the mammalian nervous system42. Considering the more aggressive and defensive behavior of stray cats, GPHN and GABRB3 could be driving factors for the selection signature of stray in these regions. It was intriguing that the analyses in stray suggested high positive natural selection for survival and maintenance of non-breeds in harsh environment. The strongest candidate selective region overlapping 10% consensus ROH blocks is located on chromosome B3 (127 Mb) and contains four coding genes, i.e., DIO2, CEP128, TSHR and GTF2A1. The products of the DIO2 and TSHR genes are essential for cold-induced thermogenesis in brown adipose tissue of small mammals44. GTF2A1 supports basal transcription of the human T-cell leukemia virus type 1 (HTLV-1) promoter and is required for viral carcinogenesis46. Differential TSHR expression has been found during T-cell development in the thymus. Binding of thyrotropin to TSHR acts as a T-cell developmental factor in mice and humans47. Another strong candidate region on chromosome A1 (221 Mb) was also identified by the selective sweep and homozygosity mapping analyses. No coding gene is located in this region. Other candidate genes in other stray-specific selective regions, PLCB2, BAHD1 and TIGAR were proposed to be involved in the host immune response. PLCB2-/- mice showed enhanced chemotaxis of various leukocyte populations and a sensitized in vivo response to bacteria, viruses, and immune complexes. PLCB2 plays a role in the response triggered by chemoattractants48. A bacterial protein targeting the BAHD1 chromatin complex can stimulate a type III interferon − mediated immune response to control bacterial colonization of the host. Murine listeriosis decreased in BAHD1 + /– mice49. TIGAR plays a dual role in cancer cell survival through regulating apoptosis and autophagy50. Furthermore, BUB1B and FGF23 are involved in the regulation of lifespan, aging and reproduction. BUB1B is a central component of the mitotic spindle assembly checkpoint and protects against aneuploidy. Mouse models have revealed functions for BUB1B in the regulation of normal aging51. Its role in reproduction has been demonstrated by infertility in male and female BUB1B mutants52. FGF23-/- mice have a short lifespan and exhibit numerous biochemical and morphological features consistent with the premature aging-like phenotypes53. In summary, we propose that stray cats are subject to selection likely controlled by nature that affects adaptive thermogenesis, reproduction, immune system development, host immune responses to bacteria, viruses and chemoattractants, and lifespan and ageing. These biological processes are thought to play an important role in survival under harsh living conditions with bacteria, viruses, infectious and parasitic diseases in strays.The di. The SIAM-derived OSH has a similar body shape and similar health problems to the modern SIAM, but has a wide range of coat colors and patterns. Consistent with this, five unique selective regions harboring pigmentation-related genes were discovered in OSH. One region on chromosome B4 at 70,056,541–72,975,983 bp falls into the candidate region under selection forming the coat color on Birman cats’ feet (11.25–114 Mb) identified by di estimates5. Of note, both the Birman and OSH breeds were derived from the Siamese, expanding its range of coat patterns and colors. ADAMTS20 in this region was reported as a pigmentation candidate gene54. Mice homozygous for spontaneous or ENU-induced mutations in ADAMTS20 exhibit abnormal coat or hair pigmentation, including a typical white belt phenotype54. Another candidate gene in this region, KITLG, encodes the ligand for KIT, which plays a central role in melanogenesis, melanoblast migration and proliferation. A regulatory region of the KITLG gene has been significantly associated with common blond hair color in Northern Europeans56. The largest OSH-specific selective region was on chromosome D1 at 42,138,904–50,969,224 bp and contained the coat color related gene TYR. The tyrosinase encoded by TYR catalyzes the first two steps and at least one subsequent step, in the conversion of tyrosine to melanin. Two mutations in TYR have been associated with the Siamese and Burmese coat color phenotypes55. Another gene related to color determination, MITF, was not found in the unique selective OSH regions. However, we identified TYRO3 as a candidate gene linked to skin, as it has been reported to function as a MITF regulator in melanoma57. Other candidate genes located in the selective regions have been proposed to be involved in skin or eye pigmentation. GPNMB has been suggested to be important for melanin biosynthesis and development of the retinal pigment epithelium and iris58. Decreased expression of FGF7 was found in pathological hypopigmentation of the skin59. Rab38cht/cht exhibited ocular hypopigmentation60. In addition, four genes PDE4D, PKP2, NLGN1 and ALDH1B1 may be candidate genes for congenital heart defects or gastrointestinal disorders in OSH. Three of these genes were not only located in unique selective OSH regions, but also in partially fixed ROH blocks. This could explain why congenital heart defects and gastrointestinal diseases are common in the OSH breed. Recent reports of PDE4D mutations suggest that functionality is an increased cardiac risk factor as well as increased risk of atrial fibrillation61. Mutations in PKP2 have been reported to underlie familial arrhythmogenic right ventricular cardiomyopathy62. Down-regulated expression levels of neurexin and neuroligin in enteric nervous system may be involved in the pathogenesis of Hirschsprung disease63. ALDH1B1 has become an immunohistological marker for colorectal cancer64.Twelve unique selective regions compared to SIAM have been identified in OSH by We have identified natural selection signatures in stray that are thought to influence adaptive thermogenesis, motor/neurological behavior, reproduction, immune system development, host immune response against bacteria, viruses and chemoattractant, as well as lifespan and aging. Artificial selection signatures in OSH have been found to likely be involved in pigmentation or cause the breed's common heart and gastrointestinal defects. Further research using whole genome sequencing data can foreseeably provide more definitive confirmations of the candidate selective regions presented. The candidate genes we have proposed also require an in-depth molecular investigation to reach satisfying conclusions.Compared to cat breeds, stray cats are a genetically diverse population with a rather low genomic inbreeding coefficient and a reduced increase of genomic inbreeding in recent generations. Ten genes have been identified in the unique selective regions of strays, when compared to household, OSH and SIAM, that are associated with reproduction, cold-induced thermogenesis, motor/neurological behavior, immune system development, viral carcinogenesis, host immune response against bacteria, viruses, chemoattractant and cancer cells, and lifespan and aging. Our results suggest, at least in part, selection for environmental adaptation and resistance to infectious disease in the stray population that is driven by nature rather than humans. In addition, eleven genes have been identified in the unique selective regions of the OSH, compared to the SIAM, that are implicated in the breed's wide range of coat colors and patterns, as well as its common heart and gastrointestinal defects. These selective regions and genes should be the subject of further investigation.In the present study, we used tissue samples from the biobank of our diagnostic lab. The living organisms were not involved in our study. Tissue samples from gonads were obtained from animal shelters where cats were neutered for other reasons. Oral swabs were sent to our diagnostic lab by cat breeders to get diagnostic test results. Therefore no animal welfare committee approval was obtained according to the German Animal Welfare Law .Gonad samples of 150 stray and 26 household cats as well as oral swab samples of 21 unrelated SIAM and 49 unrelated OSH were obtained from the bio-bank of the Institute for Animal Breeding and Genetics at the University for Veterinary Medicine Hannover. These cats were random samples, mainly from Germany. The 49 OSH and 21 SIAM were not related with each other according to their pedigree records up to 4–6 generations. Genomic DNA was extracted using a standard ethanol fractionation with 6 M sodium chloride (NaCl) and 10% sodium dodecyl sulphate (SDS). The concentration of each DNA sample was adjusted to 50 ng/µL and then genotyped with the Illumina Infinium HD feline 63 K SNP BeadChip® Array  containing 62,897 SNPs.www.cog-genomics.org/plink/1.9/)65. All SNPs from sex chromosomes and SNPs that could not be assigned to a chromosome were excluded from the dataset. Individuals with more than 30% missing genotype data were excluded from the analysis using –mind command in PLINK. –maf and –geno were set to their default values, i.e. 0.01 and 1 respectively. The final dataset contained 53,581 autosomal SNPs with a genotyping rate > 0.95 in 246 cats.For the estimation of the genetic diversity measures, quality control was carried out using PLINK v.1.09 (www.cog-genomics.org/plink/1.9/)65, the function–freq was used to calculate (1) the number of monomorphic SNPs, and (2) the mean and standard deviation of minor allele frequency (MAF). The function–hardy was used to calculate (3) the mean and standard deviation of observed heterozygosity, and (4) the mean and standard deviation of expected heterozygosity. The number and frequency of all polymorphic SNPs for a dataset containing all individuals combined was determined using the PLINK function (–freq). The numbers of SNPs within different minor allele frequencies bins are reported.For each population independently, the following summary statistics were calculated using PLINK v.1.09  of SIAM, OSH, stray and household cats was carried out in order to investigate population structure. The stray cats were found in eight regions across Germany . Using 68 implemented in R, v.4.1.066, a NetView analysis was performed to detect fine-scale stray population structure. It is based on a relationship matrix which was derived from SNP marker distances calculated with PLINK (www.cog-genomics.org/plink/1.9/), v.1.969. NetView clusters based on mutual k-nearest neighbors were created to visualize the relatedness between the individuals68. The maximum number of nearest neighbors (K-NN) was analyzed to select suitable K-NN for the animal set. The visualization of the population network was done using Cytoscape, v.3.7.070. To enable a visualization of potential connections between genetic distances and living regions, stray cats were grouped into 8 subpopulations according to their living regions .Using Net View package, v.3.4.1Ne and the increase in inbreeding per generation ∆F were calculated based on the linkage disequilibrium (LD) using PLINK v.1.09 (www.cog genomics.org/plink/1.9/)65. We computed the squared correlation (r2) as a measure of LD between SNP pairs per chromosome. The r2-values for SNP alleles with distances of 1 kb to 33.3 Mb were sorted into distance bins of 0.1 Mb. For every distance bin the mean r2-value was assessed. The Ne was calculated by 71. Regarding the distance c between two SNP markers, we assumed that 100 Mb was approximately 1 Morgan. The number of generations in the past (t) was assessed by ∆F was estimated by 72.The effective population size –homozyg command in PLINK v.1.0965, using a sliding window of ‘‘X’’ SNPs. A ROH in one individual was called if a homozygous stretch contained 5, 30, 60, or more SNPs, with the command –homozyg-snp. We did not allow for heterozygous SNPs and only admit five missing SNP genotypes per 60 SNP-window and three per 30 SNP window73. We determined ROHs that were partially fixed in each of the four populations and therefore common to 10%, 20%, 25%, 30%, 50%, 75% or 90% of all cats in a population. The genomic distribution of partial consensus ROHs in stray subpopulations was calculated using SAS, v.9.4 . Stray partial consensus ROHs were assigned into eight groups, i.e. Poolti, a partial consensus ROH was shared by stray cats from i different living regions . Percentage of each partial consensus ROH in a living region was estimated as nt = total number of stray cats from this region and nr = number of stray cats harboring the ROH. A plot with ROH percentage values for all the eight regions on y-axis and the chromosomal positions on x-axis using ggplot2 package in R, v.4.1.066 was plotted to represent the result.Screening for ROH regions across the genome was conducted with the FROH and fixation index FIS were calculated using the software SAS, v.9.4 . FROH for each cat was estimated as a proportion of the length of all ROHs to the total length of all autosomes covered by SNPs: 74. FIS for each individual (65.The inbreeding coefficient di) was previously developed and implemented based on unbiased estimates of pairwise FST76. Original files (.ped and .map) were first converted into Variant Call Format (vcf) files using –recode-vcf command in Plink v1.969. Pairwise FST was calculated using vcf files and –weir-fst-pop command of vcftools for each SNP between all populations. Subsequent calculations of di values were performed using scripts written in R using the following equation. For each SNP we calculated the statistic FST between populations  from all the 53,581 SNPs. di was averaged over SNPs in nonoverlapping 1-Mb windows. Windows with less than four SNPs were discarded. The 99th percentile threshold was used to decide significance of the calculated di values. Using the same method, stray subpopulation specific selective events were identified by comparing the stray subpopulation to the remaining 7 stray subpopulations FST results .Locus specific divergence . PANTHER 15.0 Supplementary Information.


Fatness-related traits are economically very important in sheep production and are associated with serious diseases in humans. Using a denser set of SNP markers and a variety of statistical approaches, our results were able to refine the regions associated with fat deposition and to suggest new insights into molecular aspects of fat tail selection. These results may provide a strong foundation for studying the regulation of fat deposition in sheep and do offer hope that the causal mutations and the mode of inheritance of this trait will soon be discovered by further investigation.ST and Median homozygosity) or haplotype (iHS and XP-EHH). The results from candidate regions on chromosome 5 and X revealed clear evidence of selection with the derived haplotypes that was consistent with selection to near fixation for the haplotypes affecting fat tail size in the fat tail breed. An analysis of the candidate region on chromosome 7 indicated that selection differentiated the beneficial alleles between breeds and homozygosity has increased in the thin tail breed which also had the ancestral haplotype. These results enabled us to confirm the signature of selection in these regions and refine the critical intervals from 113 kb, 201 kb, and 2831 kb to 28 kb, 142 kb, and 1006 kb on chromosome 5, 7, and X respectively. These regions contain several genes associated with fat metabolism or developmental processes consisting of TCF7 and PPP2CA (OAR5), PTGDR and NID2 (OAR7), AR, EBP, CACNA1F, HSD17B10,SLC35A2, BMP15, WDR13, and RBM3 (OAR X), and each of which could potentially be the actual target of selection. The study of core haplotypes alleles in our regions of interest also supported the hypothesis that the first domesticated sheep were thin tailed, and that fat tail animals were developed later. Overall, our results provide a comprehensive assessment of how and where selection has affected the patterns of variation in candidate regions associated with fat deposition in thin and fat tail sheep breeds.The fat tail is a phenotype that divides indigenous Iranian sheep genetic resources into two major groups. The objective of the present study is to refine the map location of candidate regions associated with fat deposition, obtained via two separate whole genome scans contrasting thin and fat tail breeds, and to determine the nature of the selection occurring in these regions using a hitchhiking approach. Zel (thin tail) and Lori-Bakhtiari (fat tail) breed samples that had previously been run on the Illumina Ovine 50 k BeadChip, were genotyped with a denser set of SNPs in the three candidate regions using a Sequenom Mass ARRAY platform. Statistical tests were then performed using different and complementary methods based on either site frequency (F Identifying the regions of the genome that have experienced substantial selective pressure can provide a powerful tool into the location of functionally important polymorphisms and can help prioritize targets for association mapping ,2. Fat tVarious genomic-based studies have been conducted to understand the genetic basis and genomic architecture of sheep tails and to find the specific causal genomic variant(s) contributing to sheep tail pattern making ,13,14,15Genome scan approaches, such as quantitative trait locus (QTL) and hitchhiking mapping, provide the opportunity for investigating the genetic regions associated with different traits. QTL mapping studies have been applied to examine the genetic basis of various economically important traits, e.g., ,21. HoweHitchhiking mapping starts with a genome scan using an approximately uniformly spaced set of molecular markers followed by a fine scale analysis of the candidate regions . For exaST, three regions on chromosomes 5 , 7  and X  were confirmed in both data sets. Interestingly, these regions have also been supported in recently published studies using two different ways consisting of different breeds of thin and fat tailed sheep breeds through a genome-wide analysis of transcriptomic data [Genomic scans for finding candidate regions associated with fat deposition in thin and fat tailed breeds have been described previously . In briemic data ,31 and smic data ,33, and mic data .ST that has been used in the previous study [Unlike Fus study , tests bus study  and XP-Eus study , are mulus study . Denselyus study . The Illus study . Fine maST and median homozygosity) and haplotype based (iHS and XP-EHH) statistics used in the current study have been shown by previous power analyses to be largely complementary [ST and median homozygosity are single-marker tests and detect highly differentiated alleles between populations, where positive selection in one area causes larger frequency differences compared to neutrally evolving alleles. iHS, a measure of within breed evidence for selection, has a suitable power to detect partial selective sweeps. However, a disadvantage of this approach is that it loses power when the beneficial allele is close to fixation, because fixation will eliminate variation at and near the selected site. In contrast, XP-EHH can detect selected alleles that have risen to near fixation in one but not another population. Grossman et al. [ST and XP-EHH signals peaked more narrowly around the causal variant, making them useful for spatial localization, while iHS is better to distinguish causal variants and contributed little to spatial resolution. These tests were relatively uncorrelated in neutral regions, and only weakly correlated for neutral variants within selected regions [The allele frequency spectrum  and haplotype based (iHS and XP-EHH) statistics, and using this variety of selection sweep tests we were able to obtain some novel results associated with fat deposition in these breeds.In this study, three regions recognized as candidate regions associated with fat deposition in the tail were chosen for further analysis. This was achieved through fine-mapping with additional SNPs, using Sequenom technology. The aim of this study was to confirm the signature of selection in the candidate loci, narrow down the chromosomal region showing the selective imprint and to determine the nature of the selection process occurred in these regions. Our analyses focused on two site frequency . A total of 156, 89, and 78 putative SNPs flanked by 150 bp on each side  were submitted to Sequenom’s primer design software (Mass ARRAY Assay Design 3.1) for the regions on chromosomes 5, 7, and X, respectively, and primers and probes were designed in multiplex format. Of these SNPs, assays were successfully designed for 140 (90%), 75 (84%), and 66 (84%) of all SNPs initially selected for study, and were grouped into 6, 3, and 4 multiplex assays for the three regions respectively. The remaining SNPs failed primer design, primarily due to high repeated content. The majority of these multiplex assays were located in the first and second plex which contained 71, 62, and 57 SNPs for chromosomes 5, 7, and X respectively. These two SNP assays were used for genotyping. Selected SNPs, their locations, PCR primers, unextended primer, and its extension masses, are represented in http://www.sequenom.com/, accessed on 31 May 2017). Experiments were conducted following the Sequenom iPLEX Assay application note [Once the regions of interest were defined, all known SNPs in each region were examined for suitability for genotyping. These included SNPs discovered on both the Solexa and 454 platforms , were excluded [All samples with more than 30% missing data and subsequently all loci with more than 15% missing data were excluded. These rejection thresholds were chosen by plotting numbers of animals or loci against percent missing data and the cutoff point was determined as the curve inflection point, where the rate of change in the number of excluded loci, became linear with every increased percent of missing data . For theexcluded . We combST) was calculated as described by MacEachern et al. [ST can theoretically range from zero (showing no differentiation) to one . For each set of five adjacent SNPs, the average of FST values was calculated and termed windowed FST. This is an approximate method of looking for regions where selection is apparent over multiple markers, rather than one-off high values. A window of five markers was chosen as it appeared to provide the better signal compare to other arbitrary window sizes [ST values were then plotted against genome location. To determine the pattern of positive selection, the basic form of Wright’s fixation index , so that we have only presented FST results based on Wright’s estimator in the present study for an easier comparison to previous reported results [ST), Weir and Cockerham, and Hudson’s fixation indices were written and performed in R v 4.0.2.All analyses presented in this work were also performed using Weir and Cockerham  and Huds results . All scrThe selection for a new beneficial mutation increases the level of homozygosity in the selected allele and neighboring regions due to the hitchhiking effect. Therefore, one method of looking for the region where selection has taken place is to compare median runs of homozygosity between breeds. To do this, the median run of homozygosity for each SNP was calculated following Moradi et al.  in each It should be noted that since the historical effective population sizes of males and females are not the same for sexual and autosomal chromosomes , all anaBos taurus (cattle), Sus scrofa (pig), Equus caballus (horse), Canis familiaris (dog) and Homo sapiens (human) genomes using BLAST . A cross-species megaBLAST of Sequenom® primers was used to discover ancestral alleles for the remaining SNPs [To calculate iHS and XP-EHH, the ancestral allele state of each SNP must be specified. Ancestral alleles for the ovine chip SNP were obtained from international sheep hapmap project (ISGC), and then to obtain the ancestral alleles for the additional Sequenom SNPs, 301 base pairs of sequence  were aligned against ing SNPs . The ancing SNPs . This prA pair of haplotypes was reconstructed for each animal in the sample using fastPHASE version 1.2.3 . This soiHS was calculated as in Voight et al.  and XP-EA or iHHD, depending on whether it is computed for the ancestral or derived core allele. The unstandardized iHS (uiHS) was then calculated as ln (iHHA/iHHD). The uiHS is thus adjusted so that the final statistic has a mean of 0 and a variance of 1, regardless of allele frequency at the core SNP. To do this, the results for each breed were split into 18 equally sized allele frequency bins, from which a mean and standard deviation were calculated. These were used in the following equation [p). Due to the different demographic histories of the X chromosome and the autosomes , we normalized the iHS scores of this chromosome separately from those of the other chromosomes. Results of iHS are presented here as |iHS| for a window of 10 SNPs and then plotted against genome location. We chose a window of 10 SNPs because of the longer extent of LD and SNP spacing in sheep compared to humans, in which the window length used is commonly around 40 SNPs [Briefly and following Voight et al. , the iHSequation :iHS=uiHS 40 SNPs . Large pi in two populations, A and B. In each population, the expected haplotype homozygosity (EHH) [i. The log of the ratio of these integrals, ln(A/IBI), is the abnormal XP-EHH   was inteails see ). XP-EHHhttp://stephenslab.uchicago.edu/software.html, accessed on 1 February 2021), although substantially slower to compute. For selection of core regions and study of haplotype frequencies in selected area of our interested regions, the results of FST, iHS and XP-EHH tests were also used as additional information. Finally, the pattern of haplotype blocks in the regions of interest were constructed and the decay of LD (pairwise r2) was visualized using Haploview [After the aforementioned filtering process and reconstruction of haplotypes for candidate regions using PHASE 2.1 , the hapaploview .http://asia.ensembl.org/biomart/martview, accessed on 1 February 2021), with the reference assembly of the O. aries genome OAR v3.1 [O. aries were also compared to the corresponding areas in B. taurus as its genome is better annotated. Regions chosen for fine mapping onto OARv3.1 and their orthologous coordinates in B. taurus  is shown in Genes located in the genomic regions significantly differentiated between sheep breeds were acquired by the use of the data mining tool Biomart .A total of 32, 26, and 29 SNPs passed the filtering criteria in our regions of interest on chromosome 5, 7 and X respectively . Most ofST was plotted against genomic location for candidate regions , 7  and values > 0.40 on chromosome X . The score of 0.30 is in the 99.9 percentile of autosomal SNPs  and a score of 0.40 is in the 99.0 percentile of chromosome X SNPs (n = 1126).The average of differentiation between Zel (thin tail) and Lori Bakhtiari (fat tail) for the whole genome was 0.024 (SD = 0.036), while this parameter was 0.093 (SD = 0.136), 0.172 (SD = 0.184) and 0.279 (SD = 0.217) at the areas of interest on chromosome 5, 7 and X respectively. As shown in When there is a selection for a causal mutation in one breed and not the other, the breed under selection shows high homozygosity in a genomic interval while the other does not . To furtiHS detects signatures of strong selection in favor of alleles that have not yet reached fixation . The resTo identify selective sweeps in which the selected allele has approached or achieved fixation in a subpopulation, but remains polymorphic in the population as a whole, the standardized cross population extent of haplotype homozygosity scores (XP-EHH) were plotted against genomic locations . The resST.Sabeti et al.  considerTo evaluate the haplotype frequencies and the status of selected alleles , the core SNP alleles, and their haplotype frequencies were investigated in the selected regions .ST and XP-EHH statistics were at their highest. There are 5 genotyped SNPs in this region. The SNPs defined 14 core haplotypes (denoted haplotype 1 to 14) in thin tailed but only 6 core haplotypes in fat tailed sheep. As shown in For chromosome 5 , we defiFor chromosome 7  we definSubsequently, this approach was performed in the candidate region on chromosome X. We defined a core region of 242 k corresponding to 6 genotyped SNPs. The longer length of the core region, in this case is due to the wider SNP spacing on chromosome X. The genotyped SNPs defined seven core haplotypes in the thin tail breed and only two haplotypes in the fat tail breed . The comO. aries or by orthology with B. taurus and their functions are presented in The regions of interest were investigated to determine if any genes related to fat deposition could be identified in sheep or their corresponding areas of the cow genome. The genes obtained in O. aries  and haplotype based (|iHS| and XP-EHH) statistics. These tests were chosen because previous power analyses suggested that these are largely complementary [An increasing number of studies have been conducted to detect signals of recent positive selection on a genome-wide scale in different domestic animals ,59,60; hementary .ST, median homozygosity, and XP-EHH test in a relatively narrow region; while an examination of these regions identified no particular |iHS| peak. With a hypothesis that historically different selection pressures operated in thin and fat tail breeds and somehow selection acted on a variant that was advantageous only in one breed, these results suggest that selection in these regions occurred for mutations affecting fat tail size as the beneficial mutations have risen to near fixation in fat tailed breeds. This suggestion is also supported by the core haplotype frequencies observed in candidate regions on these chromosomes as the common core haplotypes in fat tail breeds were near fixation , while XP-EHH test is more powerful for detecting selective sweeps close to fixation (>80%) ,61. Howe2) was visualized using Haploview [To further test this hypothesis, we constructed the pattern of haplotype blocks in this region and the decay of LD  spaced across whole genome , as seleO. aries and their corresponding area of B. taurus and no particular candidate genes associated with fat deposition were identified [O. aries or their orthologous areas of B. taurus (PPP2CA) has a variety of roles in different biological process such as cellular lipid metabolic, membrane lipid metabolic and sphingolipid metabolic process, while hydroxysteroid (17-beta) dehydrogenase 10 (HSD17B10) and emopamil binding protein (EBP) genes play some roles in lipid metabolic process and androgen receptor (Ar) and synaptophysin (Syp) genes get participate in lipid binding [PPP1CA is highly expressed in TAT. Also, PPP1CA was identified as plausible genes associated with the fat-tailed or fat-rumped phenotype by comparing copy number variations (CNVs) with different tail types [PPP1CC) have been under selection signature in Chinese thin and fat-tailed sheep breed and reported to be associated with tail type [HSD17B12 has also been reported to be associated with fat tail metabolism in thin and fat tailed sheep breeds using deep transcriptome analysis with RNA-Seq data [HSD17B12 (act as elongates) are important genes for controlling the overall balance of fatty acid composition [The candidate regions of interest have previously been studied using OAR v1.0 in entified . In thisentified , defined. taurus . Protein binding . Interes binding  implemenil types . Moreoveail type . HSD17B1Seq data . It is rposition . TherefoPPP2CA gene was amplified and its variation patterns were sequenced in an independent study on Zel and Lori-Bakhtiari sheep breeds [p < 0.05) while, in Zel, the effect of genotypes on carcass fat percentage and triglyceride was significant, so that the T/T genotype had more carcass fat percentage comparing to Del/Del genotypes (p < 0.05). Overall, it seems as the annotation of the ovine genome becomes more complete, all genes located in the candidate regions will be identified and promising targets can then be verified by further experimentation.To confirm the results of our study, the exon 1 of p breeds . Two patTCF) genes have been among highly differentially expressed genes in perirenal adipose tissue (PAT) and identified as being the most likely to account for the fat-tailed phenotype of sheep [TCF7 is involved in the Wnt/β-catenin signaling pathway, and this pathway plays a critical role in regulating sheep [CACNA1F) gene that could be as associated with tail type in these breeds [HSD17B10 [SLC35A2 [AR and TIMP1 [BMP15, WDR13 and RBM3 that belong to the gene families that their closely related genes consisting BMP2 [WDR92 [RBM11 [A result which is irrelevant to the inheritance of the trait, but provides an insight into a possible mechanism of fat deposition in this organ, are the results of Gokdal et al.  who examof sheep . TCF7 isng sheep  and porcng sheep  adipogenng sheep  studied e breeds . In addiHSD17B10 , SLC35A2[SLC35A2 , AR and nd TIMP1 , have being BMP2 ,68,72,73TCF7 on chromosome 5, PTGDR and NID2 on chromosome 7 and finally AR on chromosome X that have a multiple effect on lipid metabolisms, macromolecule metabolic process, organ/gland development or associated with ectopic expression of organs simultaneously.Finally, fine mapping of candidate regions using different sweep statistical tests has enabled us to confirm the signature of selection in these chromosomal regions and better refine the critical regions from 113 kb  to 28 kb  on chromosome 5, from 201 kb  to 142 kb  on chromosome 7 and from 2831 kb  to 1006 kb  on chromosome X. These regions were refined considering all statistical test results . AcquiriPPP2CA and TCF7 (OAR5), PTGDR and NID2 (OAR7), AR, EBP, CACNA1F, HSD17B10, SLC35A2, BMP15, WDR13 and RBM3 (OAR X) as the most promising candidate genes for type of tail traits.Recently published study on the origin of European sheep as revealed by the diversity of the Balkan breeds and by optimizing population-genetic analysis tools  using a PDGFD as associated with the fat tail phenotype [2 crosses to provide independent and causal evidence and verify the mode of inheritance. If these areas are shown to have a significant effect, then further work sequencing either side of the regions can help the search for causal variants.It is obvious that understanding the mechanisms that underlie fat tail inheritance in sheep is difficult to verify solely by selective sweep profiling. While this study has now refined three regions located on chromosome 5, 7 and X, associated with fat tail sheep breeds, it is likely that other genomic regions may also be involved. Some other studies have identified a region on Chromosome 15 for example, close to henotype ,13. DefiOur results provide a comprehensive assessment of how and where selection has affected the patterns of variation in candidate regions associated with fat deposition in thin and fat tail sheep breeds. These results enabled us to confirm the signature of selection in these regions, refine the critical intervals regions, and to identify the most promising candidate genes associated with fat deposition in thin and fat tail sheep. These results may provide a strong foundation for studying the regulation of fat deposition in sheep and do offer hope that the causal mutations and the mode of inheritance of this trait will soon be discovered by further experimentation.