Body weight (BW) is an economically important trait in the broiler (meat-type chickens) industry. Under the assumption of polygenicity, a “large” number of genes with “small” effects is expected to control BW. To detect such effects, a large sample size is required in genome-wide association studies (GWAS). Our objective was to conduct a GWAS for BW measured at 35 days of age with a large sample size.The GWAS included 137,343 broilers spanning 15 pedigree generations and 392,295 imputed single nucleotide polymorphisms (SNPs). A false discovery rate of 1% was adopted to account for multiple testing when declaring significant SNPs. A Bayesian ridge regression model was implemented, using AlphaBayes, to estimate the contribution to the total genetic variance of each region harbouring significant SNPs (1 Mb up/downstream) and the combined regions harbouring non-significant SNPs.Gallus gallus autosomes , with the strongest associations on GGA4 at 65.67–66.31 Mb . The association of these regions points to several strong candidate genes including: (i) growth factors ; (ii) leptin receptor overlapping transcript (LEPROT)/leptin receptor (LEPR) locus (GGA8), and the STAT3/STAT5B locus (GGA27), in connection with the JAK/STAT signalling pathway; (iii) T-box gene (TBX3/TBX5) on GGA15 and CHST11 (GGA1), which are both related to heart/skeleton development); and (iv) PLAG1 (GGA2). Combined together, these 25 genomic regions explained ~ 30% of the total genetic variance. The region harbouring significant SNPs that explained the largest portion of the total genetic variance (4.37%) was on GGA4 (~ 65.67–66.31 Mb).GWAS revealed 25 genomic regions harbouring 96 significant SNPs on 13 To the best of our knowledge, this is the largest GWAS that has been conducted for BW in chicken to date. In spite of the identified regions, which showed a strong association with BW, the high proportion of genetic variance attributed to regions harbouring non-significant SNPs supports the hypothesis that the genetic architecture of BW35 is polygenic and complex. Our results also suggest that a large sample size will be required for future GWAS of BW35.The online version contains supplementary material available at 10.1186/s12711-021-00663-w. Poultry meat represents a major component of human nutrition . At the Knowledge of the genetic variants that underlie the variance observed in traits can amplify the breeding efficiency. For example, accuracy of genomic prediction can be increased by using markers that are strongly linked to the causative loci in genomic prediction models. Furthermore, information on the genetic architecture that underlies growth in meat type poultry will help to unravel the genes and pathways that are involved and enhance our understanding on such complex developmental processes.2 or advanced inter-crosses between lines of chicken that have been selected for egg laying, which are relatively slow growing, and fast growing broiler lines [Quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS) have been used to improve BW in chicken –9. However lines , 10, 11.er lines , 13.Moreover, a large part of the genetic variance might be due to rare variants, or variants that are highly correlated/linked with other variants . If thisThe routine use of genomic selection in broiler breeding makes large GWAS possible. As part of the routine implementation of genomic selection over the past decade, Aviagen has accumulated both single nucleotide polymorphisms (SNP) array genotype and phenotype data for BW on 157,674 individuals from one of its lines.Our objective was to conduct a GWAS, with a large sample size, for BW measured at 35 days of age (BW35) in broilers, which is a typical age at which broilers are slaughtered for meat production. After editing routinely collected data from a commercial broiler line, we analysed a dataset consisting of 137,343 broilers with phenotypes and 595,299 imputed SNPs.P < 0.000001), and that were located on the Gallus gallus (GGA) autosomes 1 to 28 (except GGA16) were retained. After quality control, 137,343 birds and 392,255 SNPs remained for the analysis. The Galgal4 assembly in the Ensembl Genome Browser (version 85) was used to map the SNP positions on the genome (www.ensembl.org).In total, we used 157,674 broilers spread across 15 generations of a pedigree for which BW35 and SNP array genotype data were collected as part of the routine commercial broiler breeding program . The line used in this study was a female line . The birds were genotyped with SNP arrays of different densities: 600 k SNPs for 1690 birds, 50 k SNPs for 59,773 birds, 42 k SNPs for 1507 birds, 3 k SNPs for 72,221 birds and 384 SNPs for 2152 birds. The development of these arrays is described in detail in . Of thesVariance component and heritability estimates were based on a pedigree-based model using ASReml.v3 :1\documeμ is intercept, qqman package [https://www.ensembl.org/Tools/VEP) program using the Ensembl database and the Galgal4 assembly. Moreover, genes located 1 Mb up/downstream of the top SNP in each genomic region that contained significant SNPs were annotated using the BioMart tool of the Ensembl database and the Galgal4 assembly (http://www.ensembl.org/biomart/martview/).The GWAS was conducted by single SNP regression while simultaneously correcting for the background polygenic effect using the GEMMA software :2\documeVanRaden  and \docant SNPs . Manhattant SNPs  with the package . AnnotatBased on the GWAS results, the genome was partitioned into different regions that harboured significant and non-significant SNPs. Regions that contained significant SNPs were defined by considering the region 1 Mb upstream and 1 Mb downstream from the SNP with the highest p-value in each region. Due to closely located GWAS signals on GGA13 (13a and 13b) and GGA14 (14a and 14b), the two regions on each of these chromosomes were merged. To reduce the computational cost, we used all the significant SNPs from the 600 k Affymetrix Axiom chip and among the non-significant SNPs only those that overlap between the 50 k and 600 k Affymetrix Axiom chips. To estimate the variance explained by each region, a Bayesian ridge regression model was implemented using AlphaBayes  and the The summary statistics and variance components of the raw data are presented in Fig. A summary of the results of the GWAS is in Table On GGA1, four regions harboured significant SNPs, denoted 1a to 1d, at positions ~ 54.68 (1a), ~ 134.49 (1b), ~ 184.46 (1c) and ~ 193.81 Mb (1d). On GGA2, two regions harboured significant SNPs, denoted 2a and 2b, at ~ 103.15 (2a), ~ 111.28 Mb (2b)  locus, (iv) the JAK/STAT signalling pathway, (v) the T-box genes (TBX3/TBX5), (vi) other candidate genes for BW35, and (vii) implications for breeding programs.Previous studies have shown that the genomic architecture of BW is age- and population-dependent , 9, 37. http://www.animalgenome.org/QTLdb) revealed a number of QTL for BW35 on GGA1 to 5, 7, 10–11, 15, 18, 20 and 27 . In particular, there is strong evidence in the literature for QTL associated with BW35 on GGA1 to 4.A search on the animal QTL database  is located in region 1a. The second most significant SNP detected in this region is located within TXNRD1 . Although TXNRD1 is not considered as a growth factor, studies on salivary adenoid cystic carcinoma [insulin like growth factor 1  gene is located within a 1-Mb region from TXNRD1. IGF1 has a major role in the body size of dogs [TXNRD1 and IGF1 have been associated with BW35 and BW41, respectively [IGFBP4 (insulin like growth factor binding protein 4) gene has been identified as a candidate gene for broiler BW in a study that analysed a subset of the population used in our work [IGFALS  gene, is located ~ 8 kb downstream of the region 14a. In addition, on GGA1, near the region 1b (~ 134.49 Mb), we identified the TGFBRAP1  gene.Growth factors, such as the transforming growth factor-β (TGF-β) and the insulin-like growth factor-1 (IGF1) are known to be key regulators of several traits related to body composition, growth, and development in chicken , 43. Ourarcinoma  have fou of dogs , 46, wit of dogs  and heig of dogs . In broi of dogs , and IGF of dogs . In a reectively . Moreoveour work . This geFGF5  and BMP3 . The FGF20 gene, which is a key regulator of skin development in chicken [FGF2 gene at ~ 53.00 Mb is close to the 4c region (~ 52.73 Mb). We detected another component of the growth factor pathway on GGA8, with the SNP in region 8a being located within the intronic region of the EPS15  gene. The region 13b is located between NDF1P1  and another growth factor, FGF1 .On GGA4, the region 4a (~ 44.84 Mb) contains two genes with growth factor activity:  chicken , is alsoLEP and LEPR genes with obesity [LEPROT  gene. Moreover, the leptin receptor  and the JAK1  genes are located near this genomic region. It should be noted that LEPROT is not in any way homologous to the leptin receptor gene, but its expression has been associated with muscle development in turkey [cytokine receptor, growth hormone receptor (GHR) and LEPR genes.Leptin (LEP) is a well-known hormone that is strongly related to appetite, through regulation of the brain satiety centres . Its eff obesity , 59, fee obesity , 61, gro obesity , 63, and obesity , 65 in a obesity –68. In on turkey  and it i, and therefore for the growth of poultry. The JAK/STAT pathway is related to the generation of spermatogonial stem cells in chicken [STAT3  gene, and more precisely is within an intron of the ATP6V0A1  gene  gene. The ADAD1  and TRPC3  genes are located in the same region, and are involved in spermatid development and single fertilization. Interestingly, another spermatogenesis linked gene, SPATA18 (~ 65.75–65.77 Mb), is located on GGA4 in the region with significant associations in our study , that harbors two significant SNPs (at ~ 65.75 Mb) downstream of the gene  gene. The results of an experimental study in pigs that compared the expression level of MAPKAPK3 in mini and large-type Diannan small-ear pigs, indicated that MAPKAPK3 might have an important role in growth and development [LEMD2 (LEM domain containing 2), located on GGA26, as a strong candidate gene for BW and it is considered to have an important role during embryonic development in mice by regulating the MAPK signalling pathway [Apart from the effect of  and Luo  have sugelopment . Intereselopment  suggeste pathway .TBX4 and TBX5) in the development of the chicken limb, heart and embryo [TBX5 . The TBX3 gene is adjacent to TBX5. TBX4 is located on GGA19 at ~ 7.59–7.61 Mb but the region that we detected on this chromosome is located further down at ~ 6.62 Mb, in the intron of the SYNRG  gene. In the same region on GGA19 (within 1 Mb) are found the TBX2 (~ 7.63–7.64 Mb) and HNF1B  genes. Interestingly, Tarsani and colleagues [TBX21 gene and several members of the homeobox family (HOXB1-9 and HOXB13), and Moreira et al. [HOXB2,4,7,9 and HOXB13 with BW35 in an F2 cross. However, it should be noted that all these genes are located on GGA27 at more than one Mb from our top SNP on that chromosome. In our analysis, another limb morphogenesis gene was detected in region 1a, with the SNP at 54,681,614 bp being within an intron of the CHST11 (carbohydrate (chondroitin 4) sulfotransferase 11; ~ 54.54–54.74 Mb) gene  [PLAG1, LYN , RPS20 , and CHCHD7 (coiled-coil-helix-coiled-coil-helix domain containing 7) [PLAG1 has been reported to have a major contribution to stature in modern cattle [PLAG1, LYN, and RPS20 genes. Our top SNP on GGA2 was located 3.5 kb downstream of the IMPAD1 (inositol monophosphatase domain containing 1) gene, but the second most significant SNP in the region was a synonymous variant in PLAG1 (Table PLAG1 as a very good candidate for BW35 in broilers.nd dogs) . In cattining 7) –84, is tn cattle . More imn cattle  and to rn cattle , 88. TheCCKAR (cholecystokinin A receptor) gene is located at ~ 72.8 Mb on GGA4. Decreased expression of this gene is associated with increased BW and growth in chicken and it has been suggested that modern chicken breeds might have been selected during the early domestication process for the high-growth haplotype [LCORL-NCAPG locus, at ~ 74.0 Mb [CCKAR and even further away from the LCORL-NCAPG locus. Whereas a long-range enhancer might be involved, a more conservative hypothesis indicates another gene. The top SNP in the region 4e detected here was ~ 400 bp upstream of the CWH43  gene, which encodes the PGAP2-interacting protein. This SNP also had the highest positive effect, indicating an effect that increases BW35 , DCUN1D4 , OCIAD1 (OCIA domain containing 1), SLAIN2 (SLAIN motif family member 2), TEC (tec protein tyrosine kinase), NFXL1 , and CORIN . In the same region, five more genes are present, namely FRYL (FRY like transcription coactivator), SLC10A4 (solute carrier family 10 member 4), TXK (tyrosine kinase), NIPAL1 (NIPA like domain containing 1) and CNGA1 . Among all these genes, SLAIN2 was recently reported as a strong candidate gene regulating BW in broilers [In line with previous studies , 89, 90,aplotype . Other s 74.0 Mb , 89, whi 74.0 Mb –96 and i 74.0 Mb . Howeverbroilers . Moreovebroilers .group-specific component (GC) gene, which is located ~ 19 kb from the region (Table GC and BW35 is consistent with recent studies that found that 25(OH)D supplementation increases breast meat yield in broilers [Concerning the second region 4b on GGA4, we identified the on Table . GC , which is near the region containing the C2orf40 gene. The SNP in region 1c was located within the MTMR2  gene. In region 1d, several genes are mapped. The significant SNP located in region 1c was in an intron of the STIM1 gene , which is close to DGAT2 , a gene that is related to fatty acid metabolism and associated with changes in carcass and meat quality characteristics in domestic pigeons [Although the important role of GGA1 was previously reported in the literature, we detected only weak associations in our study, significant at 1% FDR. The top SNP in region 1b (~ 134.49) was located within the 3ʹ untranslated region of the  pigeons .IMPACT  and HRH4  genes. On GGA3, the significant SNP was located within the FBX028  gene.On GGA2 two significant regions were detected, namely at 103.15 and 111.28 Mb. The closest genes in region 2a were PPCDC  gene, which is involved in the biosynthesis of coenzyme A. Coenzyme A is essential for energy production of the body. The association on GGA11 was located between the CRISPLD2  and HNF4beta  genes. On GGA13, two regions were identified and because of the “two-peak” pattern in the Manhattan plot . On GGA14, in region 14b the significant SNP was located within the PDIA2 gene . Finally, of particular interest are the signals on GGA27, on which apart from the STAT3/STATB5 locus, we detected the CACNB1  gene, which has been reported as a strong candidate for BW35 in broilers [We detected significant associations on nine other chromosomes . The association on GGA10 points to the broilers .Combined together, the 25 genomic regions that contain 96 significant SNPs explained ~ 30% of the total genetic variance. This implies that the genetic architecture of BW35 is polygenic and complex, and therefore genomic prediction , rather than targeting specific genes via marker-assisted selection, will be more effective to improve BW35 in broilers. However, the region that contains significant SNPs and explains the largest proportion of the total genetic variance, is region 4e (~ 65.67–66.31 Mb), which explains 4.368% of the total genetic variance, and taken together the regions with significant SNPs on chromosome GGA4 explain ~ 8.6%  = position of the SNP on the chromosome in base pairs on the Galgal4 assembly; MAF = minor allele frequency; EFF = allele substitution effect; SE = standard error of the effect; P-value = P-values from the single SNP regression while simultaneously correcting for the background polygenic effect; P-valueFDR = false discovery rate P-value; LOG = the −log10 of the P-value; VSNP = variance due to the SNP ; VA = additive genetic variance; VP = phenotypic variance; VAsnp (%) = percentage of additive genetic variance explained by each SNP; VPsnp (%) = percentage of phenotypic variance explained by each SNP. 1Of the 96 significant SNPs 18 are proprietary to Aviagen and therefore their base pair location has been excluded.


Oryctolagus cuniculus) has led to a multi-purpose species that includes many breeds and lines with a broad phenotypic diversity, mainly for external traits  that are valued by fancy rabbit breeders. As a consequence of this human-driven selection, distinct signatures are expected to be present in the rabbit genome, defined as signatures of selection or selective sweeps. Here, we investigated the genome of three Italian commercial meat rabbit breeds  and 12 fancy rabbit breeds  by using high-density single nucleotide polymorphism data. Signatures of selection were identified based on the fixation index (FST) statistic with different approaches, including single-breed and group-based methods, the latter comparing breeds that are grouped based on external traits (different coat colours and body sizes) and types (i.e. meat vs. fancy breeds).Domestication of the rabbit , coat structure (LIPH), and body size  in rabbits and that characterize the investigated breeds. Their identification proves the suitability of the applied methodologies for capturing recent selection events. Other regions included novel candidate genes that might contribute to the phenotypic variation among the analyzed breeds, including genes for pigmentation-related traits  and body size, with a strong candidate for dwarfism in rabbit (COL2A1).We identified 309 genomic regions that contained signatures of selection and that included genes that are known to affect coat colour (O. cuniculus. These results enhance our knowledge about the major genetic loci involved in rabbit external traits and add novel information to understand the complexity of the genetic architecture underlying body size in mammals.We report a genome-wide view of genetic loci that underlie the main phenotypic differences in the analyzed rabbit breeds, which can be useful to understand the shift from the domestication process to the development of breeds in The online version contains supplementary material available at 10.1186/s12711-022-00696-9. Oryctolagus cuniculus), usually simply referred to as rabbit, is the only species that has been domesticated exclusively in western Europe. Its domestication started from the wild populations in the South of France that originally derived from the wild populations of the O. c. cuniculus subspecies spread in the Iberian Peninsula, which experienced a postglacial expansion (reviewed in , OCU5 [cadherin 13 (CDH13)], OCU8 [endothelin receptor type B (EDNRB)], OCU9 [protein tyrosine phosphatase non-receptor type 2 (PTPN2)] and OCU14 (LIPH) ST values across the genome ranged from 0.092  to 0.231 . Using Method 2, these same breeds also constituted the extremes . FST values obtained from the two methods showed strong and significant correlation   are reported in Additional file ST values from the two methods suggested that Method 2 better highlighted genetic differences that may characterise the tested breed against all other breeds, as its FST values were always higher than those obtained with Method 1.In the analyses that compared one breed against all other breeds Table  using MePCAdapt analysis. The details for each breed are shown in Additional file ST analysis with the single-breed approach (Method 1 and Method 2) are in Additional file ST values against all other breeds averaged over SNPs in the window.In these analyses considering all 15 breeds, 210 and 1050 candidate selection signature windows were identified by each method based on the 99.8th (14 windows × 15 breeds) and 99.0th (70 windows × 15 breeds) percentile thresholds, respectively. The percentages of overlapping genomic windows obtained by Method 1 and Method 2 considering all breeds were 48% (99.8th percentile) and 54% (99.0th percentile), respectively. By merging the results obtained in the analysed breeds, Method 1 identified 190 (99.8th) and 869 (99.0th) unique candidate windows and Method 2 identified 193 (99.8th) and 883 (99.0th) unique candidate windows. After merging and expanding windows detected by the two methods by ± 200 kb (Windows-M1 ∪ Windows-M2), 78 (99.8th) and 400 (99.0th) unique overlapping genomic regions were identified, comprising 47% (99.8th) and 51% (99.0th) of the overlapping genomic regions identifed by Method 1 and Method 2. Figure TYR , a gene located on OCU1 with variants that are responsible for the allele series of the albino locus and characterise the breeds for which this region was highlighted , which was identified in the Rhinelander breed in which it determines the eJ allele of the extension locus that causes the classical tricolour phenotype of this breed . The signal on OCU5, harbouring the CDH13 gene, was also detected in the PCAdapt analysis  were located on nine chromosomes  and on several unassigned scaffolds (Un0030 and Un0366). These windows include several genes related to growth traits and regulators of developmental processes. For example, the most extreme window, on OCU12, harbours the ogenesis , and then humans . The nexn humans  [cadheri species , 66–68.  species . The genmitochondrial ribosomal protein L33 (MRPL33) gene, which has been reported to be a candidate gene for muscle development in ducks [coiled-coil serine rich protein 1 (CCSER1), which has been suggested to be involved in fat deposition in pigs [protein tyrosine phosphatase non-receptor type 2 (PTPN2), confirms the results of the PCAdapt analysis  is located in an identified genome window on the unassigned scaffold Un0030. In humans, mutations in this gene are associated with severe obesity [The signatures of selection that were identified by combining the three meat rabbit breeds overlapped partially with those obtained in the comparisons based on groups of rabbits of similar body size. Outlier regions were identified on nine chromosomes and on two unplaced scaffolds  and were detected for each of the five tested groups of breeds identified three over-represented biological features  for comparisons involving the two silver/greying breeds and the two checkered spotted breeds .–76.73–76In this study, we investigated different fancy rabbit breeds that have characteristic phenotypes, i.e. coat colours, colour patterns, and body size and morphology. In addition, three pure breeds used to produce a three-way crossbred commercial meat line, were analysed.ASIP, MC1R and TYR; [LIPH; [LCORL/NCAPG, COL11A1 and HOXD; [As a proof of concept to demonstrate that the applied methodologies can capture recent signatures of selection, several of the results that we obtained highlight previously identified genes that affect coat colour , coa; [LIPH; ), and bond HOXD; ) in rabbIn spite of the insights obtained from our results, it is important to note a few limitations and how to overcome them, at least in part. Based on how the rabbit breeds have been constituted, with bottleneck effects, genetic drift, and introgression potentially playing important roles, a genome region that possesses a pattern of differentiation between breeds does not necessarily prove that the region has been under selection and has functional roles. The function of the annotated genes in the identified regions, which usually derives from what is known from other species, can be useful in some cases to interpret the obtained results. However, not all genes are functionally well described in the literature, which leaves many uncertainties, especially for complex traits or when multiple pathways are involved in the breeds’ differential phenotypes. Therefore, to extract meaningful biological features, for group-based results, we applied enrichment approaches that attempted to improve the insights into the complexity of body size and selection for productive performances. However, this analysis was not very informative, probably due to the heterogeneity of the biological mechanisms, with involvement of a large number of genes that are present in the identified genomic regions. The inclusion of additional animals per breed, additional breeds, and other breed populations, e.g. sampled in more countries  may EDNRB, EDNRA, MITF, and OCA2 that, to date, were not reported to affect coat colour in rabbits, although the role of these genes on coat colour is well known in several other species. We previously excluded a polymorphism in the EDNRB gene to be associated with the English spotting locus [MITF, EDNRA, and OCA2. However, the role of some of these genes in the coat colour of these breeds should be further analysed. The silvering or hereditary greying of hair, which is expected to be caused by a recessive si allele at the Silver locus , was inLCORL/NCAPG, the HOXD gene family, and COL11A1), which have alleles that might contribute to reducing body size in rabbit, were identified in this study, confirming the results already reported in a previous study based on Netherland Dwarf rabbits [HMGA2 gene region was identified in the dwarf/small body size breeds that we analysed in our study, although a large deletion that overlaps the promoter region and the first three exons of this gene has been reported to be the causative mutation of a form of dwarfism [ST analysis used in our study. However, this interpretation is supported by the results obtained from the observed heterozygosity analysis for variants within the HMGA2 gene region, since the average level of heterozygosity was close to 0.5. It is also possible that, in our dwarf breeds, other loci cause this extreme reduction in size. For example, in the Dwarf Lop breed, we identified a strong signature of selection in the region of the COL2A1 gene, which is known to be involved in a broad spectrum of skeletal defects, including dwarfism [HMGA2 gene region in the giant breeds was interesting and was probably due to the presence of alternative alleles for this gene that increase body size. In general, our results based on the comparisons between breeds of extreme body size demonstrate the complexity of the genetic factors that affect this phenotype, which is usually referred as a breed-specific trait in rabbit. Body size, body structure, and stature are quantitative traits for which some major genes have been identified, as already demonstrated in several other mammals [The complexity and heterogeneity of the signatures of selection that were detected in the comparisons involving the dwarf/small body size breeds, the giant breeds, and the three meat breeds indicate that a large number of genes with small effects are involved in determining body size and production performances in the rabbit, similarly to what is reported in other mammals  over time (from 13 to 142 generations ago). Table S4. Single-SNP-based FST distances between pairs of rabbit populations. Table S5. Window-based FST distances between pairs of rabbit populations. Table S6. The most relevant results obtained from the PCAdapt analysis that overlap with those of the FST analyses. Table S8. Pearson’s correlations for genome windows FST values obtained from Method 1 and Method 2 . The reported values are the means of the correlations of the SNPs across all the chromosomes and all the genome windows. Table S9. Statistics on the single-breed window-based FST analyses (Method 1 and Method 2). Table S10. Statistics on the identified genome regions from the single-breed window-based FST analyses. Table S14. List of relevant genes identified with the FST single-marker-based analysis in the single-breed approach based on the two methods also applied in the window-based analyses (Method 1 and Method 2). FST values of the markers at the extreme lower end of the distributions (99.95th percentile) and mapped to genes of interest are presented. Table S17. Genome regions including candidate genes and the total number of genes included in the genome windows identified with the window-based single-breed FST analysis. This table is complementary to Table Table S18. Statistics for the window-based FST analyses in the approach based on groups of breeds. Table S20. List of relevant genes identified with the FST single-marker-based analysis in the approach based on groups of breeds. FST values of the markers at the extreme lower end of the distributions (99.95th percentile) and mapped to genes of interest are presented. Table S26. Gene enrichment analysis. Gene sets (99.0th percentile) related to the group-based FST analyses were tested for over-represented biological features.Additional file 2: Figure S1. Window-based Neighbor Joining tree. Figure S2. Multidimensional scaling plot. The first three components are provided. Figure S3. Scree plot used to identify the number of principal components that describe well the population structure of the investigated rabbit breeds. The plot displays in decreasing order the percentage of variance explained by each principal component. Figure S4. Manhattan plots of the PCAdapt analysis. Each dot represents a 350-kb genome window. The red line identifies the threshold value . Unassembled scaffolds are not reported. Figure S5. Genome regions carrying signatures of selection  identified in the studied breeds. Only the assembled autosomes are presented and unassembled scaffolds are not reported. Figure S6. Manhattan plots of the genome-wide FST analyses based on Method 1 (M1). Each dot represents a 350-kb genome window. The blue line identifies the threshold value (99.8th percentile of the distribution). Unassembled scaffolds are not reported. Figure S7. Manhattan plots of the genome-wide FST analyses based on Method 2 (M2). Each dot represents a 350-kb genome window. The blue line identifies the threshold value (99.8th percentile of the distribution). Unassembled scaffolds are not reported.Additional file 3: Table S7. List of outlier markers identified using PCAdapt. Table S11. Comparative single-breed FST analysis (M1 = Method 1). The genome windows at the extreme lower end of the distributions (99.0th percentile) are presented. Table S12. Comparative single-breed FST analysis (M2 = Method 2). The genome windows at the extreme lower end of the distributions (99.0th percentile) are presented. Table S13. Genome regions identified in the single-breed FST analyses (99.8th percentile). Table S15. List of the markers at the extreme lower end of the distributions (99.95th percentile) identified with the FST single-marker-based analysis for the single-breed approach using Method 1 (M1). SNPs are reported in decreasing order of FST value. Table S16. List of the markers at the extreme lower end of the distributions (99.95th percentile) identified with the FST single-marker-based analysis for the single-breed approach using Method 2 (M2). SNPs are reported in decreasing order of FST value. Table S19. Comparative FST analysis for groups of breeds as defined in Additional file Table S21. List of the markers at the extreme lower end of the distributions (99.95th percentile) identified with the FST single-marker-based analysis using the approach that includes groups of breeds. SNPs are reported in decreasing order of FST value. SNPs are reported in decreasing order of FST value. Table S22. Genotype information for SNPs within the KIT gene region. Table S23. Observed heterozygosity of SNPs within the KIT gene region. Table S24. Genotype information for SNPs within the HMGA2 gene region. Table S25. Observed heterozygosity of SNPs within the HMGA2 gene region.


Antibody response to porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) infection, measured as sample-to-positive (S/P) ratio, has been proposed as an indicator trait for improved reproductive performance during a PRRS outbreak in Landrace sows. However, this result has not yet been validated in Landrace sows or evaluated in terminal sire lines. The main objectives of this work were to validate the use of S/P ratio as an indicator trait to select pigs during a PRRS outbreak and to explore the genetic basis of antibody response to PRRSV. Farrowing data included 2,546 and 2,522 litters from 894 Duroc and 813 Landrace sows, respectively, split into pre-PRRS, PRRS, and post-PRRS phases. Blood samples were taken from 1,231 purebred sows (541 Landrace and 690 Duroc) following a PRRS outbreak for subsequent PRRSV ELISA analysis for S/P ratio measurement. All animals had high-density genotype data available . Genetic parameters and genome-wide association studies (GWAS) for S/P ratio were performed for each breed separately. Heritability estimates (± standard error) of S/P ratio during the PRRS outbreak were moderate, with 0.35 ± 0.08 for Duroc and 0.34 ± 0.09 for Landrace. During the PRRS outbreak, favorable genetic correlations of S/P ratio with the number of piglets born alive (0.61 ± 0.34), number of piglets born dead (−0.33 ± 0.32), and number of stillborn piglets (−0.27 ± 0.31) were observed for Landrace sows. For Duroc, the GWAS identified a major quantitative trait locus (QTL) on chromosome (Chr) 7  explaining 15% of the total genetic variance accounted for by markers (TGVM), and another one on Chr 8 (25 Mb) explaining 2.4% of TGVM. For Landrace, QTL on Chr 7 (24–25 Mb) and Chr 7 (108–109 Mb), explaining 31% and 2.2% of TGVM, respectively, were identified. Some of the SNPs identified in these regions for S/P ratio were associated with reproductive performance but not during the PRRS outbreak. Genomic prediction accuracies for S/P ratio were moderate to high for the within-breed analysis. For the between-breed analysis, these were overall low. These results further support the use of S/P ratio as an indicator trait for improved reproductive performance during a PRRS outbreak in Landrace sows. Recent studies have shown that reproductive performance traits in commercial sows infected with porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) have low heritability estimates . TherefoAntibody response to PRRSV infection, measured as sample-to-positive (S/P) ratio using a commercial ELISA, has been proposed as an indicator trait to improve litter size traits in sows infected with PRRSV. Sus scrofa chromosome (SSC) 7 that together explained 40% of the total genetic variance accounted for by markers (TGVM) for S/P ratio. The two QTL identified by Genomic analyses for S/P ratio following a PRRSV infection are scarce in the literature, with All animal experimental procedures used in this study were followed according to international guidelines on Animal Care under industry standard conditions .The data used in this study were obtained from two commercial purebred herds (Duroc and Landrace) that experienced a PRRS outbreak during the spring of 2018. The PRRS outbreak was identified based on a combination of previous methodologies , as descst, 2018, approximately 54 days after the detection of PRRS  on June 1 of PRRS . Blood sSscrofa 11.1 assembly being used for the SNP location.All animals had follicular hair or ear tissue samples taken and shipped to Neogen GeneSeek  for genotyping using the GGP Porcine HD panel (Neogen GeneSeek) for 45,536 SNPs. The genotype data were processed according to the breeding company’s pipeline, including the removal of non-segregating SNPs, SNPs with a minor allele frequency of less than 0.05, and minimum SNP call rate and animal call rate of 0.9. In addition, missing genotypes were imputed using Fimpute 2.2 . These sGRM) was derived for each breed separately based on Heritability estimates of S/P ratio were estimated using a univariate model, while correlations of S/P ratio with reproductive traits were estimated using a bivariate model. A genomic relationship matrix ; a_j is the animal random effect, assuming eij is the random residual term associated with Yij, assuming I is the identity matrix and where th iteration, resulting in 500 posterior MCMC samples. Results from this analysis are presented as the posterior mean of the %TGVM of non-overlapping 1-Mb regions based on the Sscrofa11.1 genome build. Genomic regions explaining ≥ 2 %TGVM were considered significant. For a given 1-Mb region i including m SNPs, its %TGVM was calculated as:Genome-wide association studies (GWAS) were performed for S/P ratio for each breed separately using the BayesB method . In the %TGVMi represents the vector of calculated %TGVM of the ith 1-Mb region including m SNPs; Mm SNPs included in the ith 1-Mb region of the genome for the jth animal ; and αik represents the column vector of allele-substitution effects of the m SNPs included in the ith 1-Mb region of the genome in the kth stored MCMC sample. For both breeds, the number w of 1-Mb non-overlapping windows was 2,211 and the average (SD) number m of SNPs within each 1-Mb region was 13.5 (8).where c was assumed to be i,000,000c:Mb, and the end of the QTL region as f,999,999c:Mb where Mbi and Mbf represent the Mb where the identified QTL window started and ended, respectively. Thus, for example, if a QTL was identified in a given 1-Mb region r, the position of the QTL was expressed as rMb, such that Mbi = Mbf = r and the QTL encompassed c:r,000,000–r,999,999. In contrast, when closely located 1-Mb QTL regions were combined into a single window, the position of the QTL was expressed as r-r’Mb, such that Mbi = r < Mbf = r’ and the QTL encompassed c:r,000,000–r’,999,999. All analyses were performed using GenSel version 4.4  were combined into one larger window and its %TGVM was recalculated as described in Eq. (2). This strategy was used due to the resolution of Bayesian GWAS methods, where simulations have shown that quantitative loci nucleotides are expected to be located within 3 Mb of the identified QTL regions . For thesion 4.4 . The linsion 4.4 .Sscrofa11.1 genome build. Although two additional SNPs were also associated with S/P ratio in p-value < 0.05, and a trend was declared at 0.05 < p-value < 0.10. Analyses were performed using ASReml 4.0.We investigated the effects of SNPs identified to be associated with S/P ratio in this study and by Genomic prediction analyses for S/P ratio were performed with the model described for GWAS, using BayesB (π = 0.9973) in GenSel v.4.4 . Analyseri is the correlation between the genomic estimated breeding value (GEBV) and phenotypes adjusted for fixed effects (y∗) in the ith validation dataset, weighted by the number of records in each validation dataset (ni); and h2 is the heritability of S/P ratio for the whole dataset of the breed being analyzed.where y∗, divided by the square root of the estimate of heritability based on the whole dataset for the breed used for validation.For the between-breed analysis, all data for one breed were used as training to validate the other breed. Thus, GPA was calculated as the correlation between GEBV and Both the within- and between-breed analyses were performed using three sets of SNPs, as proposed by •All);All 29,799 SNPs across the genome (SNP•MHC);Only SNPs in the QTL that harbors the MHC region (SNP•Rest).All SNPs across the genome, excluding those in the MHC region  of S/P ratio with farrowing traits are shown in Heritability estimates of reproductive traits and genetic correlations  associated with S/P ratio. For Landrace sows, the ASGA0032063 SNP explained 29.1% of TGVM, while all other SNPs explained less than 0.7% of TGVM. Of the five selected SNPs, ASGA0032063, MARC0089437, and ASGA0032151 were significantly  associated with S/P ratio. These results provide information on key SNPs associated with S/P ratio for Duroc and Landrace sows during a PRRS outbreak.The SNPs within the two QTL regions found for each breed were subjected to additional investigation to identify SNPs with large effects that could be responsible for the %TGVM explained for by these regions. From this, we identified one SNP for Duroc  and one for Landrace , both located on the MHC region. These two SNPs were combined with the three previously reported by p-value ≥ 0.370) of these selected SNPs with reproductive traits in Landrace sows during the PRRS phase. For both breeds, most associations were found for the post-PRRS period. Starting with pre-PRRS, the MARC0089437 SNP had a trending association with NBA  in Duroc sows, with heterozygotes showing greater performance than both homozygotes. In Landrace sows, this SNP had a trending association with NW , with BB animals having greater NW than AB animals, while no AA animals were observed in the dataset. The ASGA0032063 SNP had a trending effect  for two mortality traits (NBD and NSB) in Landrace sows during pre-PRRS, with AA animals showing lower NBD and NSB than animals with the other two genotypes. For the PRRS phase in Duroc sows, the ASGA0031860 SNP had a trending association with NBD , with BB sows having greater NBD than AA and AB sows. For the post-PRRS phase, the ASGA0031860 SNP was associated  and had a trending effect  for NW in Landrace and Duroc sows, respectively, with AA animals showing greater performance than the other genotypes for both breeds. The ASGA0032151 SNP also had a trending effect for NW  and NSB  in Landrace sows only, with AA sows showing overall greater performance for both traits. The MARC0089437 SNP was associated with several traits  in Duroc sows in the post-PRRS period. Although AA sows had greater NBM than AB and BB sows, they also had greater TNB, NBA, and NW. However, differences for TNB, NBA, and NW were between BB with AA and AB, indicating that heterozygote animals have overall better reproductive performance than the other genotypes. This same SNP had a trending effect for TNB  in Landrace sows, with AA sows showing greater performance than AB sows. On the other hand, the AA genotype of the MARC0058875 SNP in Duroc sows was associated not only with greater TNB  and NW  but also greater NBM  compared with the other genotypes. These results provide additional information on key SNPs associated with S/P ratio in Duroc and Landrace sows during a PRRS outbreak and their associations with reproductive performance across PRRS phases.Estimates of the association of the five selected SNPs with reproductive performance are shown in All, then SNPMHC, and then SNPRest. These GPAs were 0.73 ± 0.06 for Landrace and 0.60 ± 0.08 for Duroc (SNPAll), 0.60 ± 0.05 for Landrace and 0.50 ± 0.09 for Duroc (SNPMHC), and 0.41 ± 0.10 for Landrace and 0.45 ± 0.07 for Duroc (SNPRest), indicating that genomic selection for S/P ratio, regardless of the genomic information used, is feasible. On the other hand, for the between-breed analysis, GPAs were low and sometimes negative. For SNPAll, SNPMHC, and SNPRest, these were −0.03, −0.32, and 0.10, respectively, when training on Duroc and validating on Landrace, and 0.08, 0.09, and 0.03, respectively, when training on Landrace and validating on Duroc. These results indicate that between-breed genomic selection has limited usefulness.Genomic prediction accuracies (GPA ± standard deviation) were moderate to high for the within-breed analyses , showingHeritability estimates of S/P ratio were overall lower than in Other studies have also estimated the heritability of S/P ratio. Estimates of genetic correlations of S/P ratio with litter size traits during the PRRS outbreak were, overall, consistent with the results of As reported in Under the conditions described in this study, For instance, based on the results reported by In summary, antibody response to PRRSV, measured as S/P ratio, was shown to be moderately heritable in Landrace and Duroc sows during a PRRS outbreak. In combination with the high genetic correlation of S/P ratio with NBA in Landrace (0.61) and the negative genetic correlations with mortality traits, our results validate and further support the use of S/P ratio as an indicator trait for improved reproductive performance under a PRRS outbreak in Landrace populations. However, in Duroc, the weak genetic correlation estimates and the large standard errors do not allow us to make conclusions for this population.In this study, the QTL for S/P ratio identified on SSC7 (24–25 Mb) for S/P ratio in Duroc and Landrace sows is located within the MHC region, as previously reported by Sscrofa10.2). The QTL identified by these authors around 130 Mb on the draft genome assembly (Sscrofa10.2) was not identified in our study, but these authors indicated that this region shows high LD. However, there are some errors in the Sscrofa11.1 assembly in the part of SSC 7 corresponding to 128–132 Mb in the Sscrof10.2 assembly, with most of the missing content now located on an unplaced scaffold (AEMK02000452) in the Sscrofa11.1 assembly , a protein-coding gene that has been associated with immune response in humans by regulating the cytokine production of T cells and macrophages  in Duroc and on SSC7 (108–109 Mb) in Landrace sows. However, they explained a much smaller proportion of TGVM, 2.4 and 2.2%, respectively. The QTL at 108–109 Mb on SSC 7 had two candidate genes associated with reproduction or immune response. The G protein-coupled receptor 65  in Landrace sows but a much higher frequency (0.41) in Duroc. In fact, only one Landrace sow had the AA genotype, and this individual was removed from the analysis. We found a similar situation for the ASGA0032063 SNP identified in the GWAS for Landrace, for which the B allele had a very low frequency (0.05) in Duroc sows, but a much greater frequency (0.69) in Landrace sows. Heterozygote Landrace sows had a greater S/P ratio than homozygote sows, indicating an overdominance effect for this SNP. This has some limitations in breeding schemes in the nucleus, as selection for improved performance results in fixation of the favorable allele, limiting the number of AB animals for this SNP. Nonetheless, AA Landrace sows had a greater S/P ratio than BB sows for this SNP, which indicates that selection for an increase in the frequency of the A allele in purebred Landrace sows may increase the S/P ratio of the population.Furthermore, the direction of the effects for this SNP was opposite in the two populations. Although a greater S/P ratio was obtained in Duroc sows with the AB genotype at the MARC0058875 SNP had a lower S/P ratio than homozygous animals, indicating a negative dominance effect for this SNP. AB and BB compared with AA sows at the MARC0058875 SNP. This contrasting result indicates a complex relationship between this marker and the QTL in these distinct populations. For ASGA0032151, the BB genotype had a greater S/P ratio than AA in Landrace animals. This is partially in accordance with BB sows than AB genotypes, whereas no differences in S/P ratio were observed in our study between AB and BB sows. In addition, the B allele was highly frequent in both populations, with 0.46 and 0.41 in For the SNPs selected based on the results of Given the hypothesis of S/P ratio being an indicator trait for reproductive performance in PRRSV-infected  and healAB and BB genotypes. For reproductive traits, AB sows had greater NBA pre-PRRS and TNB, NBA, and NW post-PRRS for this SNP. These results indicate that selection for heterozygotes at the MARC0089437 SNP may increase not only S/P ratio but also TNB, NBA, and NW in sows not facing a PRRS outbreak. At the MARC0058875 SNP, AA and AB animals had greater S/P ratio than AB animals. Although there was under-dominance for this SNP for S/P ratio, AA animals had greater TNB and NW post-PRRS, indicating that selection for fixation of the A allele would result in animals with greater post-PRRS reproductive performance and S/P ratio. Although not significantly associated with TNB and NW in the pre-PRRS phase, the AA genotype showed numerically greater performance than the other genotypes, suggesting that fixation of the favorable allele for this SNP might increase performance even in PRRSV-naïve animals. This is important as it is expected that purebred animals in the nucleus will not go through a PRRS outbreak. Hence, significant associations between the MARC0058875 SNP and pre-PRRS performance would further suggest that this SNP might be important in explaining variation in reproductive performance of purebred sows even in the absence of a PRRS outbreak. Interestingly, the only selected SNP associated with reproductive performance during the PRRS phase was ASGA0031860. Although this SNP was not associated with S/P ratio in Duroc pigs, greater NBD was obtained in BB animals.In Duroc sows, only the MARC0089437 and MARC0058875 SNPs were associated with S/P ratio. However, they were associated with reproductive traits only outside the PRRS phase. At the MARC0089437 SNP, a greater S/P ratio was obtained for the It is worth noting that, based on estimates of genetic correlations, we suggested that S/P ratio might not be an indicator trait for reproductive performance in Duroc pigs. However, the two SNPs associated with S/P ratio in Duroc sows (MARC0089437 and MARC0058875) were also associated with some reproductive traits for this breed, with all favorable genotypes being overall the same for S/P ratio and reproductive traits. Thus, although we could not find strong genetic correlations between S/P ratio and reproductive traits, it could be that these SNPs are capturing pleiotropic QTL(s) for these traits, similar to what AB animals had a greater S/P ratio, followed by AA and then by BB animals. For pre-PRRS NBD and NBS, BB sows also had lower performance but not different than AB sows. Thus, these results suggest that greater reproductive performance might be obtained by increasing the frequency of the AA genotype at this SNP in the population. For S/P ratio, the ASGA0032151 SNP, BB sows had a greater S/P ratio than AA sows in our study, similar to the results of AA animals had better performance in post-PRRS NSB and NW than BB animals, indicating that selection for increased frequency of the A allele at this SNP would result in overall better improved reproductive performance. On the other hand, a greater S/P ratio was obtained in AB animals at the MARC0089437 SNP. Interestingly, although sows with this genotype had lower pre-PRRS NW (9.1 ± 0.33) than BB sows (9.7 ± 0.19), the same direction of effects for S/P ratio was observed in post-PRRS TNB, in which AB animals had greater performance than BB animals. Finally, although the ASGA0031860 SNP was not associated with S/P ratio in Landrace sows in our study, AA animals had greater post-PRRS NW than BB animals, bringing the possibility of selection for increased NW based on this SNP regardless of S/P ratio.For Landrace, the three SNPs associated with S/P ratio were ASGA0032063, ASGA0032151, and MARC0089437. However, these SNPs were only associated with reproductive performance in the absence of PRRS. At the ASGA0032063 SNP, The lack of associations between these selected SNPs for S/P ratio and reproductive performance during the PRRS outbreak in this study was unexpected. Moreover, the fact that associations of S/P ratio SNPs with reproductive performance were only found in the pre-PRRS and post-PRRS phases could be considered contradictory to the proposed use of S/P ratio as an indicator trait during a PRRS outbreak. Therefore, these issues must be addressed. To begin with, previous studies have associated polymorphisms in the MHC region with reproductive performance in healthy animals . Given tThe statistical power to detect these associations during the PRRS outbreak could be lower than in the absence of infection. The residual variance of traits under selections measured during challenge conditions is expected to be greater than in the lack of challenge . In factThe greater number of associations in the post-PRRS phase than in the others could be due to two factors. First, a greater number of sows and litters were used for analyses in the post-PRRS phase than the others  were used in our study. For their first strategy, the GPAs were 0.33, 0.24, and 0.09 for SNPAll, SNPMHC, and SNPRest, respectively. For their second strategy, these were 0.45, 0.40, and 0.10, respectively. These GPAs reported by All, SNPMHC, and SNPRest, respectively, similar to those obtained in our study.Studies on genomic prediction of antibody response to PRRSV are scarce in the literature. To the best of our knowledge, All and SNPMHC, whereas the one for SNPRest when training in Duroc and validating in Landrace (GPA = 0.09) was very similar to the result found in MHC when training in Duroc and validating in Landrace. Interestingly, we obtained a negative GPA (−0.32). However, we observed a small positive GPA for this SNP set when Landrace was used for training and Duroc for validation (GPA = 0.09). These two contrasting results could suggest a combination of events. For example, it could be that the LD between SNPs and a major QTL in the MHC for Duroc and Landrace are in opposite phases between the two breeds. Also, it could be that some of the QTL effects captured by SNPs using Duroc sows are not segregating in Landrace sows. Finally, when Landrace is used for training, SNPs could be capturing few small effects of QTL on the same phase in both breeds, explaining the small and positive GPA for this scenario, whereas this was not observed when training using Duroc pigs. Nonetheless, the between-breed results show that genomic selection for S/P ratio should not be performed across breeds based on our results. The high within-breed genomic prediction accuracies for S/P ratio indicate that genomic selection within a breed is an efficient strategy to change S/P ratio within Landrace and Duroc populations. However, the sample size used in this study was still limited to obtain very accurate estimates for the measures reported. Therefore, a larger sample size would be needed to validate these results.With regard to the between-breed scenario, our results were much worse than those in Antibody response to PRRSV infection, measured as S/P ratio, was shown to be moderately heritable in Landrace and Duroc sows following a PRRS outbreak. In combination with a high estimated genetic correlation of S/P ratio with NBA (0.61) in Landrace sows, our results validate and further support the use of S/P ratio as an indicator trait for improved reproductive performance during a PRRS outbreak. However, this seems to work only for Landrace populations. In Duroc, the weak estimates of genetic correlations of S/P ratio with reproductive performance and their large standard errors do not allow us to propose using S/P ratio as an indicator trait in this breed. Our genomic analyses further validated the major histocompatibility region as the major QTL for S/P ratio during a PRRS outbreak in Landrace, showing that this QTL also plays a major role for S/P ratio in Duroc pigs. In addition, we identified novel small-effect QTL on SSC7 (108–109 Mb) in Landrace and on SSC8 (25 Mb) in Duroc sows. We also provided information on specific SNPs within the major histocompatibility region in both populations, providing the opportunity of marker-assisted selection for increased S/P ratio and reproductive performance. Finally, the high genomic prediction accuracies for S/P ratio indicate that genomic selection within a breed is an efficient strategy to change S/P ratio in Landrace and Duroc populations.The datasets presented in this article are not readily available because the data that support the findings of this study are not publicly available. Data may be available from authors upon reasonable request and authorization from the company that generated the data.The animal study was reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) at Iowa State University (protocol number 6-17-8551-S). Written informed consent was obtained from the owners for the participation of their animals in this study.FH performed statistical analyses, interpreted results, prepared figures and tables, and drafted the manuscript. JB, LK, and JD were involved in the interpretation and discussion of results. YH and KG provided the data, led the collection of blood samples, and interpreted results. LS provided help and guidance for statistical analyses, being involved in the discussion of results as well. NS assisted with data analysis, interpretation of results, and drafted the manuscript. All authors read and approved the final version of the manuscript.The authors declare that this study received funding from Smithfield Premium Genetics, NC, United States. The funder had the following involvement with the study: providing performance and genotype data and collection of blood samples. In addition, YH and KG are employed by the company Smithfield Premium Genetics, NC, United States. The remaining 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Salvelinus alpinus) is an anadromous salmonid and the most harvested fish species by Inuit people, including in Nunavik , one of the most recently deglaciated regions in the world. Unlike many other anadromous salmonids, Arctic Char occupy coastal habitats near their natal rivers during their short marine phase restricted to the summer ice‐free period. Our main objective was to document putatively neutral and adaptive genomic variation in anadromous Arctic Char populations from Nunavik and bordering regions to inform local fisheries management. We used genotyping by sequencing (GBS) to genotype 18,112 filtered single nucleotide polymorphisms (SNP) in 650 individuals from 23 sampling locations along >2000 km of coastline. Our results reveal a hierarchical genetic structure, whereby neighboring hydrographic systems harbor distinct populations grouped by major oceanographic basins: Hudson Bay, Hudson Strait, Ungava Bay, and Labrador Sea. We found genetic diversity and differentiation to be consistent both with the expected postglacial recolonization history and with patterns of isolation‐by‐distance reflecting contemporary gene flow. Results from three gene–environment association methods supported the hypothesis of local adaptation to both freshwater and marine environments . Our results support a fisheries management strategy at a regional scale, and other implications for hatchery projects and adaptation to climate change are discussed.Distinguishing neutral and adaptive genetic variation is one of the main challenges in investigating processes shaping population structure in the wild, and landscape genomics can help identify signatures of adaptation to contrasting environments. Arctic Char ( We then used genotyping by sequencing (GBS) to determine whether Arctic Char populations from different regions of Nunavik (ii) are genetically differentiated, (iii) differ in their genetic diversity, and (iv) follow patterns of isolation‐by‐distance. Additionally, as migration in Arctic Char has been found to be predominantly coastal Moore, , we test22.1Arctic Chars were sampled in 23 water bodies across Nunavik, southern Baffin Island, and Labrador Figure . In Nuna2.2n = 10–34 per site). The entire mitochondrial control region was amplified with primers Tpro2  alignment tool with parameters set to default and the closest matching haplotype was reported.To assess whether the Arctic and Atlantic glacial lineages hybridized in Nunavik, we sequenced mtDNA for a subset of individuals from sampling sites in the eastern part of our study area  at IBIS, Université Laval  on Ion Torrent sequencers with p1v3 chips and a median target of 80 million single‐end reads per chip. Each library was sequenced on three separate chips for each batch of 96 individuals, and the volume of DNA from each sample was adjusted after the first chip to reduce the unbalanced representation of individuals in sequences.Ten (10) µl of DNA was normalized to a concentration of 10 ng/µl using Quant‐iT Picogreen dsDNA Assay Kit (Invitrogen) for precision quantification. GBS libraries were prepared with a modified version of the Abed et al.  two‐enzyhttps://github.com/enormandeau/stacks_workflow) built around STACKS 2.5  under −0.2 were removed. For SNP calling and quality control, we combined our samples with 119 other samples from three sampling sites that were not included in our final dataset, as they either had too few samples, had no access to sea, or targeted a population reintroduced from a hatchery brood.We processed the data and filtered the SNP dataset using a RADseq workflow  and Ungava Bay (n = 85). We classified SNPs as showing a statistically supported association with sex when they loaded with more than 2.5 standard deviation from the mean (p = 0.012) and removed those markers from the dataset. Finally, we measured pairwise relatedness . The first 13 PCs were used, based on visual evaluation of PCA scores and scree plots, and SNPs with minor allele frequencies under 0.05 were excluded from the analysis. We then used BayeScan with prior odds of 1000 and other parameters set to default. For both tests, SNPs with a false detection rate  under 0.05 were considered putatively under selection.Markers potentially under selection were identified using two methods: (1) pcadapt  and 95% C. I. were estimated with Neestimator v2.01  ranging from 1 to 20. We considered the value of K yielding the lowest cross‐validation error to be the most likely number of genetic groups. Based on this K value, we identified contiguous sampling sites where most individuals shared a common cluster membership and repeated the ADMIXTURE analysis within those sites with K ranging from 1 to 6. Considering that adult Arctic Char is expected to stray to nearby rivers, we regrouped sampling sites in connecting rivers and estuaries for the following analyses on genetic diversity and isolation‐by‐distance, with the exception of cases where ADMIXTURE showed strong population structure.We used a principal coordinate analysis (PCoA) in the R package glmmTMB  on a linear mixed‐effect model with a random effect of the sampling site, in the R packages s Lenth, .2.6FST were calculated with the R package StAMPP ; Slatkin, ResistanceGA  with a linear mixed‐effect model. Linearized  Barton,  for mode2.7https://www.aviso.altimetry.fr/). Marine variables represent sea‐surface values for factors of potential biological importance for Arctic Char and were aggregated in a 20 km radius around each river mouth and within 5 km from the coast, as to best represent the local coastal environment based on existing knowledge of Arctic Char marine habitat use from other geographical regions , zebrafish (Danio rerio), or other organisms.Candidate SNPs associated with environmental variables were identified using the complete genomic dataset with a combination of two redundancy analyses (RDA) and latent factor mixed models (LFMM). Genes within 10,000 base pairs were recorded using bedtools Quinlan,  for cand2.7.1vegan , and SNPs were defined as outliers if their maximal absolute Z‐score was over 3.5 (p < 0.0002).Spatial factors were first submitted to a backward model selection procedure with the function ordistep in 2.7.2lfmm  and regularization parameters (lambda) were respectively set to 9 and 1e−5, to minimize predictor error estimated by a cross‐validation method, as advised in the lfmm manual. p‐Values were calibrated using the genomic control method, and false discovery rate  was calculated following the Benjamini–Hochberg procedure in the qvalue R package  or Arctic  lineage. Mitochondrial haplotypes from Nunavik and Baffin Island samples (n = 114) all corresponded to haplotypes from the Arctic lineage, primarily ARC19 (n = 85), but also ARC22, ARC25, and ARC26  had 2.217 million reads on average. A total of 30,773 SNPs were called and passed basic filtering in STACK2. Among these, 7244 SNPs were categorized as duplicates and removed , 170 of those had higher FST values than expected under neutrality, suggesting divergent selection, and 103 had lower FST values than expected, suggesting balancing selection. Pcadapt identified 186 outliers , including three SNPs commonly identified by both genome scan methods, for a total of 456 markers putatively under selection. The remaining 17,676 SNPs were considered neutral for subsequent analyses.Using GBS, we sequenced a total of 745 samples, of which 95 did not pass our filtering criteria. Retained individuals . The first axis of the PCoA reflected the longitude of the sampling sites, while the second axis differentiated populations in Western Ungava Bay from all other sampling sites . Multiple sampling locations sometimes clustered together, this was the case for the two sites in Labrador, two groups of three neighboring sampling sites in Northern and Southern Hudson Bay, and all pairs of rivers with a common estuary except the Leaf (FEU) and Bérard (BER) rivers. ADMIXTURE best supported K = 1 within each of those groups. Even if cross‐validation errors for K = 2 were in some cases only slightly higher . In Ungava Bay, eastern sampling sites had a diversity similar to Labrador and higher diversity than western sampling sites.Individual mean proportion of heterozygous markers varied regionally Figure . Notably3.2FST calculated by pairs of sampling sites ranged between 0.000 and 0.319 across neutral loci and between −0.003 and 0.544 across putatively adaptative loci . All comparisons using neutral loci were statistically supported at p = 0.01, except for a pair of sampling sites on rivers sharing an estuary (pCHR‐VOL = 0.568) and a pair of sampling sites on a river and its estuary (pBDE‐FRM = 0.015). These results indicate that all unconnected systems harbor genetically distinct populations, although their extent of differentiation is highly variable.i Figure . FST foFST and marine distances , MEM4 (p = 0.21). MEM1 was highly correlated with PC3 (r = 0.71) and MEM2 was linked to PC1 (r = 0.74). We conducted a redundancy analysis (hereafter referred to as “RDA”) which excluded the spatial covariables MEM1 and MEM2 (thereby excluding factors correlated to the environment). A partial RDA (“pRDA”) that included the spatial covariables MEM1 and MEM2 was also conducted. Spatial covariables explained 34.8% of the genetic variance, 29.0% of which was shared with environmental factors, resulting in the RDA and pRDA having respective adjusted R2 of 0.438 and 0.148.The distance‐based Moran's eigenvector map produced 12 eigenvectors reflecting negative autocorrelation and four eigenvectors for positive autocorrelation  and were thus used for outlier detection. The RDA identified only 14 outliers on the first axis and respectively 112 and 109 on the second and third axes  or PC2 (n = 90). LFMM identified a total of 173 outlier SNPs . Thus, 15 candidate SNPs were associated with PC1 , 61 were associated with PC2 , and 17 were associated with PC3 (tides). We identified a total of 75 named genes within 10,000 bp of those candidate SNPs, covering a wide range of biological functions, including muscle contraction, development, and circadian rhythm  were also among the loci considered as potentially under selection by either genome scan methods . This relatively low proportion is not unexpected, given that genome scans are mostly sensitive to large‐effect loci, while RDAs have the power to detect polygenic selection   with diversity and differentiation decreasing in populations far from the Iberic glacial refugia . Nevertheless, using those habitat‐specific components, we did find through a constrained ordination that both habitats independently explained significant proportions of the genetic variation. For instance, measures of air temperature (as a surrogate for river temperatures) and sea temperatures were highly correlated, but likely caused selective pressures at different life stages. Several studies have found temperature to be a driver of genetic structure and local adaptation in salmonids, with most studies focusing on freshwater measures (Bourret et al., Recent studies of local adaptation in salmonids have focused on tributary‐specific variation in freshwater conditions within a single catchment (e.g., the Columbia River; Hand et al., Regardless of the geographic scale being studied, an ideal sampling design for detecting local adaptation should maximize the environmental variation while minimizing its collinearity with neutral genetic patterns. For example, Lotterhos and Whitlock  suggeste4.4Genomic data have the potential to improve the definition of management and conservation units by accounting for neutral genetic structure as well as local adaptation (Bernatchez et al., Also, there is growing interest in Arctic Char hatchery projects in Nunavik, both for supplementation and for reintroduction of Arctic Char in traditional fishing locations George, . The genAs the Arctic warms at a greater pace than any other regions on earth (Cohen et al., No conflict of interest has been declared by the authors.Figures S1‐S11Click here for additional data file.Tables S1–S7Click here for additional data file.


How ecological divergence causes strong reproductive isolation between populations in close geographic contact remains poorly understood at the genomic level. We here study this question in a stickleback fish population pair adapted to contiguous, ecologically different lake and stream habitats. Clinal whole-genome sequence data reveal numerous genome regions (nearly) fixed for alternative alleles over a distance of just a few hundred meters. This strong polygenic adaptive divergence must constitute a genome-wide barrier to gene flow because a steep cline in allele frequencies is observed across the entire genome, and because the cline center closely matches the habitat transition. Simulations confirm that such strong divergence can be maintained by polygenic selection despite high dispersal and small per-locus selection coefficients. Finally, comparing samples from near the habitat transition before and after an unusual ecological perturbation demonstrates the fragility of the balance between gene flow and selection. Overall, our study highlights the efficacy of divergent selection in maintaining reproductive isolation without physical isolation, and the analytical power of studying speciation at a fine eco-geographic and genomic scale. How ecological divergence causes reproductive isolation between populations in close contact remains poorly understood at the genomic level. This study presents a clinal investigation based on whole-genome sequencing to characterize reproductive isolation between threespine stickleback adapted to contiguous but ecologically different lake and stream habitats. At the genomic level, reproductive isolation is typically studied through the marker-based comparison of populations that diverged relatively recently into ecologically different habitats8. Genome regions showing exceptionally strong population differentiation are inferred to harbor loci important to adaptive divergence that potentially also restrict the exchange of genetic material across larger chromosome segments, or the genome as a whole9. This common analytical approach generally pays insufficient attention to the fine-scale eco-geography of speciation; our understanding of the genomics of reproductive isolation can benefit greatly from investigating diverging populations across their contact zones at a high geographic resolution15. One reason is that such a clinal focus can reveal over what distance gene flow between populations occurs. Moreover, if marker resolution is sufficiently high – ideally whole-genome resolution, we can learn to what extent gene flow differs among genome regions. These details are crucial for evaluating the strength and genetic architecture of reproductive isolation. Another benefit is that fine-grained clinal analyses facilitate recognizing a possible link between reproductive isolation and ecological transitions, and hence the role of divergent adaptation in speciation16.Deciphering the origin of species requires understanding the nature of reproductive isolation between diverging populations13. Here, we present such an investigation in a threespine stickleback fish (Gasterosteus aculeatus) population pair residing in parapatry  in Misty Lake and its inlet stream 18 . This divergence has almost certainly arisen in the face of gene flow, as opposed to reflecting secondary contact after evolution in isolation. The reason is that temporally stable spatial associations of concurrent phenotypic and genetic discontinuities of different magnitudes with lake-stream habitat transitions are ubiquitous in stickleback29. By contrast, the conditions for the evolution of neighboring lake and stream populations in initial physical isolation must be rare in general, and appear entirely implausible in watersheds within which divergent lake and stream populations have evolved repeatedly in a spatio-temporal sequence30. The small scale of the Misty system in particular makes the initial physical isolation of the lake and inlet stream habitat appear improbable hydro-geographically. Given the absence of physical dispersal barriers and the potential of stickleback to disperse over hundreds of meters in a few days (even against water current32), potent reproductive barriers between the lake and stream population must exist.Nevertheless, research combining a clinal perspective with the analytical power of whole-genome sequence data is largely lacking)18 Fig. . This syTo characterize reproductive isolation between Misty Lake and stream stickleback at the genomic level, we perform a clinal investigation based on pooled whole-genome sequencing at a fine geographic scale. Combined with individual-based simulations tailored to this system, our study offers a fine-grained illustration of how divergent natural selection can drive and maintain reproductive isolation between populations in direct contact.To initiate our investigation, we sampled ~56 stickleback individuals from each of 11 sites across the Misty Lake and inlet stream transition, with a distance of < 2 km between the most distant sites , thus confirming the strong overall genomic differentiation between the lake and stream population indicated previously by sparser marker data25. However, numerous genome regions exhibited much stronger differentiation between the two most distant samples, and dozens of SNPs proved fixed for alternative alleles  Overall, our comparison of a single site pair makes clear that genomic divergence in Misty Lake-stream stickleback is strong and highly polygenic; divergent selection likely targets hundreds of loci, as inferred in other lake-stream stickleback systems36.This genome scan revealed a median differentiation of 0.35 across all SNPs  at the selected SNPs across all 11 clinal sites uncovered a dramatic genetic shift over a physical stream distance of merely 45 meters, starting at the transition of the marsh to the stream  or backcrosses. Our evolutionarily young study system thus offers no support for the idea that steep genetic clines and the underlying strong reproductive isolation reflect primarily intrinsic, ecology-independent genetic incompatibilities having become spatially coupled with clines at loci under  divergent ecological selection46.Together, these spatially fine-grained analyses reveal a remarkably tight association between ecology, phenotype, and genome-wide genetic variation. Divergent selection thus not only maintains adaptive divergence, but simultaneously generates strong barriers to gene flow across the entire genome. What are these barriers? A major contribution to reproductive isolation must arise directly from local adaptation, in the form of reduced performance of migrants and hybrids between the habitatsHaving uncovered reproductive isolation at the habitat transition, we next asked how strong this isolation is. An informative pattern in our clinal genomic data was that for all three SNP categories , the frequency of the stream allele increased substantially upstream of the marsh-stream transition – but only up to sampling site S4, that is, over c. 150 m  was generally differentiated only trivially from the lake across most of the genome, hence producing chromosomal differentiation profiles closely resembling those from the within-lake (L1-L2) site comparison  exhibit substantially elevated recombination rates near the chromosome peripheries compared to the chromosome centers52. Under this recombination rate distribution, theory predicts that polygenic divergent selection with gene flow causes relatively elevated population differentiation in chromosome centers54. The reason is that in the chromosome centers, maladaptive foreign chromosome segments recombining into the locally favored genomic background will tend to be longer and hence to harbor a greater number of locally maladaptive alleles, thus making their selective elimination more efficient. We thus quantified the magnitude to which genetic differentiation is elevated across chromosome centers relative to the peripheries  for all pairwise combinations of neighboring samples. This bias proved greatest for the S1-S2 sample comparison  deme was specified to be larger than all other (stream) demes together. The two habitats were under polygenic divergent selection, with fitness being a function of genetic variation at 100 loci. All loci were polymorphic in the beginning of the simulations, thus mimicking standing genetic variation well known to underlie adaptive diversification in stickleback59. After 1000 generations of evolution, we examined clinal patterns in the frequency of the stream allele  resulting from different combinations of dispersal rates between the demes and strengths of divergent selection between the habitats.We have inferred from empirical patterns that reproductive isolation between parapatric stickleback populations reflects a by-product of adaptive divergence. To support the plausibility of our interpretation theoretically, we tailored individual-based simulations to the Misty stickleback system. We assumed nine demes in a linear array, with dispersal occurring in the beginning of every generation between contiguous demes only  established easily across the habitat transition, even for combinations of relatively small selection coefficients and high dispersal rates Fig. . Specifi62. However, in Misty Lake-stream stickleback, we found no indication of divergence in inversion polymorphisms. Importantly, the three large inversions often involved in adaptive divergence between pelagic and benthic stickleback ecotypes57 proved monomorphic across our samples. Not denying the importance of chromosomal rearrangements in adaptive divergence in some study organisms, our whole-genome clinal investigation highlights that polygenic selection per se, without any particular physical arrangement of the targeted loci, can be sufficient for the emergence of strong divergence and reproductive isolation in the face of gene flow49.The simulations support our empirically based conclusion that adaptive divergence from abundant standing genetic variation has produced strong reproductive isolation in the absence of physical barriers. Our study thus provides insights into the genomic architecture of adaptive divergence: previous theory has emphasized that in the face of gene flow, adaptive divergence is promoted by the physical linkage of adaptive alleles, as produced by inversions22. Similarly, the frequency of the stream allele at our selected SNPs was slightly elevated in the marsh relative to the lake samples . This raised the possibility that the marsh might allow a modest degree of genetic mixing between the lake and the stream population despite being strongly dominated by lake fish. If true, we hypothesized that changes in the level of dispersal from the lake or the stream into the marsh, as mediated by a physical perturbation of the system, should drive a measurable shift in the genetic composition of the marsh fish.Our clinal phenotypic data and allele frequencies in all SNP categories revealed that the stickleback inhabiting the marsh (sites M1 and M2) are genetically very similar to the true lake fish . Nevertheless, in a previous study, microsatellite loci designed to discriminate Misty Lake and inlet stream fish indicated hybridization in the marshEvaluating this hypothesis was made possible by exceptionally intense rainfall during our main sampling period, causing an unusual rise in inlet stream discharge and lake water level that for a few days flooded the marsh that normally is above water level . Sample sizes ranged from 40 to 62 individuals per site  and foraging (number of gill rakers) from another subset of our sites  studied in a previous phenotypic study of Misty Lake and inlet stickleback67. All these phenotypic data were mean-centered and standardized to allow visualization on the same scale. All animal work in this study was conducted in accordance with the Animal Use Protocol from McGill University.To allow qualitatively linking genomic to phenotypic differentiation along the geographic gradient, we performed a geometric morphometric body shape analysis. For this, 40 individuals from a subset of our sites  were photographed on their left side with a standard scale using a digital camera . All photographs were digitized with tpsDIG2 (life.bio.sunysb.edu/morph/) by the same investigator (KBO) in haphazard order by placing 14 landmarks used in previous studies in the same system68.DNA was extracted individually from the dorsal spine clip of each of the 701 total stickleback using the Quick-DNA TM Miniprep Plus Kit , following the manufacturer protocol. For enzymatic tissue digestion, the spines were minced with micro spring scissors to maximize DNA yield. Following DNA quantification using a Qubit fluorometer , individuals were pooled to equal molarity without PCR-enrichment to obtain a single DNA library per sampling site (and per time point in the case of the marsh site M1). The 13 total libraries were paired-end sequenced to 151 base pairs on 10 total lanes of an Illumina HighSeq2500 instrument, producing a median read depth per base pair of 103x across the libraries , we first combined nucleotide counts from the two lake samples (L1 and L2) and from the two most upstream inlet samples (S6 and S7) into lake and stream pools. Genomic positions then qualified as SNPs for further analysis if they exhibited a read depth between 50 and 400x within each pool (to exclude poorly sequenced and repeated regions), and a minor allele frequency (MAF) of at least 0.25 across the two pools combined (to ensure a high information content71). Throughout the study, allele frequencies were calculated directly from raw nucleotide counts. The 1,920,596 SNPs passing these filters were genotyped in all 13 samples separately.Raw sequences reads were parsed by sampling site and aligned to the third-generation assembly33, although a few key analyses were repeated by using FST. We considered only nuclear markers, and ignored the sex chromosome (XIX) because it was enriched for high-differentiation SNPs relative to the autosomes, as expected from its reduced population size and hence stronger drift. Including the sex chromosome, however, always had a trivial influence on the results. If multiple high-differentiation SNPs were < 50 kb apart, they were considered a cluster from which only one SNP was chosen at random to ensure statistical independence, resulting in a panel of 50 independent selected SNPs. As a resource for future investigations, we retrieved from the reference genome annotation all genes located within a 50 kb window centered at each of the selected SNPs, producing a gene compilation (provided as Supplementary Data smooth.spline R function (band width 0.1). Genome-wide median differentiation for these site comparisons, and for all comparisons between neighboring sites, is given in Supplementary Fig. FST.Genetic differentiation along the lake-stream gradient was quantified by two approaches. The first relied on the frequency of the stream allele at selected, neutral, and loDiff SNPs. The selected SNP category comprised markers showing genetic differentiation ≥ 0.97 between the geographically most distant samples L1 and S7). Throughout the study, we quantified genetic differentiation by the absolute allele frequency difference AFD and S7. 37. We here used the sampling site-specific frequencies of the stream allele, the total nucleotide counts underlying these frequencies, and the geographic locations as input data, set allele frequency intervals to the observed maximum values, and assumed two independent tails . We considered all 50 selected SNPs for modeling, and random subsets of the same size from the neutral and loDiff SNPs. For each SNP, cline fitting was run in 10 replicates, and the median maximum likelihood estimate of cline center and cline width across these replicates was recorded. We then compared these key cline features among the SNP categories based on the median values across SNPs and the associated 95% bootstrap compatibility intervals . The raw data distributions are provided in Supplementary Fig. As a numerical complement to our visual cline analyses, we fit our allele frequency data to classical geographic cline models implemented in the HZAR R packageOur visual analysis revealed stable allele frequencies over several hundred meters in the upper reach of the stream for all three SNP categories Fig. . By contn = 178; median AFD difference between the comparisons L1-S1 and L1-L2: 0.132), considered regions under strong divergent lake-stream selection, and for the same number of windows chosen at random as control , we then calculated mean AFD in the L1-S7 comparison.While inspecting the comparison L1-S1, we observed that some genome regions showed remarkably strong genetic differentiation compared to the overall undifferentiated genome-wide background. This led us to speculate that in these specific regions, genetic variants particularly strongly favored in the stream were maintained at elevated frequency at the S1 site, while the remainder of the genome was overwhelmed by gene flow from the lake. Assuming particularly strong divergent selection on these genome regions, we predicted that they should exhibit exceptionally strong differentiation between the lake and the stream population in general, including in the comparison L1-S7. To evaluate this idea, we first subtracted the mean AFD value in the L1-L2 comparison (considered the differentiation baseline within the lake habitat) from the corresponding value in the L1-S1 comparison for each genome-wide 10 kb sliding window (5 kb overlap) containing at least 5 SNPs. For the most highly differentiated 0.5% of these windows  loci (non-HDL) at which the stream allele (1) occurred at a frequency specified by a random draw from the uniform distribution bounded between 0.05 and 0.5 . This simulation was replicated 25 times.Because the HDW by definition exhibited elevated differentiation in the L1-S1 comparison, somewhat stronger L1-S7 differentiation in these windows relative to random windows was expected even if the HDW were strongly differentiated in the former comparison just by chance. Comparing the two categories of windows thus required a benchmark, which was obtained by individual-based simulation. We here constructed Finally, we calculated the difference in median AFD in the L1-S7 comparison between the empirical HDW and non-HDW, and the median difference in AFD achieved during simulated evolution at the HDL and non-HDL across the 25 replicates, both referred to as deltaAFD. Uncertainty around these point estimates was obtained by bootstrapping windows  and replicates (simulated data) 10,000 times. Elevated empirical relative to simulated deltaAFD would indicate that regions exhibiting the strongest differentiation in the L1-S1 comparison also show exceptionally strong differentiation in the L1-S7 comparison relative to the genome-wide baseline, consistent with these regions being targets of particularly strong divergent selection between lake and stream stickleback.54). To explore the strength of selection-gene flow antagonism along the geographic gradient, we thus quantified to what extent genetic differentiation was biased toward chromosome centers for all 10 pairwise comparisons of neighboring samples. For this, we defined the outer 5 Mb on either side of a chromosome as high-crossover rate periphery and the remainder of the chromosome as low-crossover rate center52. Next, we divided median AFD across all central SNPs by median AFD across the peripheral SNPs for each chromosome within each site pair. For each pair, we then treated the median across these ratios as CCBD, and graphed this metric along the lake-stream gradient by using the midpoint between the neighboring samples as geographic location. As a robustness check, this analysis was repeated by using as site pairs each of the samples L2 to S7 combined with L1, which produced very similar results supporting the same conclusion  represented the (larger) lake population while all other demes (each n = 200) represented stream sites. In the beginning of each generation, a fraction of m individuals was chosen at random from each deme to migrate into the neighboring deme on either side (juvenile migration). A total fraction of 2m thus emigrated from each deme, except for the demes located at the endpoints of the array, for which this fraction was m. The migration phase was followed by selection and reproduction. We modeled polygenic divergent selection by assuming a total of 100 biallelic unlinked loci under divergent selection, with one allele favored in the first deme and the other allele favored in all other demes. The simulations started with the frequency of both alleles set to 0.5 in all demes, to minimize the probability of the stochastic loss of adaptive variation54. Loci contributed additively to fitness; each maladaptive allele reduced an individual’s fitness from the local fitness optimum of one by s, the selection coefficient. Individual fitness was then scaled by the mean population fitness and determined an individual’s probability to be drawn for reproduction. Individuals reproduced as hermaphrodites and were allowed to mate more than once, each mating producing one offspring. Mating was repeated until the number of offspring re-established initial local deme size. The offspring cohort then replaced the parental deme (discrete generations) and entered the migration phase.To explore theoretically how selection can drive and maintain reproductive isolation in the presence of gene flow, we conducted individual-based forward simulations using a diploid stepping-stone expansion of the model in Berner and Roestiion Fig. . The firWe explored a range of combinations of migration rates (0.005−0.1) and selection coefficients (0.0005−0.0075). All simulations were run for 1000 generations, a time span shown by preliminary runs over up to 7000 generations to allow approaching migration-selection balance ; and greater population size imbalance by setting the lake deme ten times (as opposed to 1.25x) larger  than all stream demes combined (n = 125 per deme).The robustness of the standard model described above was assessed by a number of additional simulations, presented in Supplementary Fig. For the marsh site (M1), three temporally replicated samples were available: before, during, and one year after a flood. The former represents the sample also used in all previous analyses; the latter two samples were processed in exactly the same way. To maximize the sensitivity for detecting gene flow, we here considered only SNPs highly differentiated (AFD ≥ 0.75) between the lake pool (sites L1 and L2 combined) and the stream pool (sites S6 and S7), and sequenced to a minimum read depth of 50x within each temporal sample. For the 49,677 SNPs thus obtained, we visually compared the frequency of the stream allele among the samples.65.Because this analysis indicated massive dispersal of lake stickleback into the marsh during the flood, we explored by simulation how much of such dispersal was needed to drive the observed change in allele frequencies. For the SNPs above, we here averaged allele frequency data from the marsh before the flood and those from the nearest lake site (L2), considering a wide range of relative proportions of the latter (10%−100%). Comparing visually the resulting (mixed) allele frequency distributions to the one observed during the flood allowed a qualitative assessment of the proportion of lake dispersers into the marsh during the flood. This proportion was additionally explored using Approximate Bayesian Computation (ABC) (details given in Supplementary Fig. Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary SoftwareReporting Summary