The allo-tetraploid common carp is able to biosynthesize endogenous PUFAs. However, selective breeding common carp with high PUFA contents was hindered due to a lack of effective molecular markers. For future breeding common carp capable of producing endogenous PUFAs more effectively, we previously identified the polymorphisms in the coding regions of two duplicated fads2, fads2a and fads2b. However, the polymorphisms in the duplicated elovl5, elovl5a and elovl5b, were not detected. This study screened the genetic variants in the coding regions of elovl5a and elovl5b. Moreover, the joint effects of multiple coding SNPs in fads2b and elovl5b, two major genes regulating the PUFA biosynthesis, were evidenced with the increased explained percentages of the PUFA contents. These polymorphisms in these two genes were used to evaluate the breeding values of PUFAs. These SNPs would be potential markers for future selection to improve the PUFA contents in common carp.PUFAs have an essential impact on human health, but their availability constitutes a critical bottleneck in food production. Although fish is the traditional source of PUFAs, it is limited by the stagnation of fisheries. Many studies aim to increase the PUFA products of fish. Genetic markers are efficient in aquaculture breeding. Fatty acid desaturase 2  in elovl5a associated with the content of one PUFA and two cSNPs in elovl5b with the contents of eight PUFAs. The heterozygous genotypes in these three loci were associated with higher contents than the homozygotes. Together with previously identified two associated cSNPs in fads2b, we found the joint effect of these four cSNPs in fads2b and elovl5b on the PUFA contents with the increased explained percentages of PUFA contents. The genotype combinations of more heterozygotes were associated with higher PUFA contents than the other combinations. Using ten genomic selection programs with all cSNPs in fads2b and elovl5b, we obtained the high and positive correlations between the phenotypes and the estimated breeding values of eight PUFAs. These results suggested that elovl5b might be the major gene corresponding to common carp PUFA contents compared with elovl5a. The cSNP combinations in fads2b and elovl5b and the optimal genomic selection program will be used in the future selection breeding to improve the PUFA contents of common carp.The allo-tetraploid common carp, one widely cultured food fish, is able to produce poly-unsaturated fatty acids (PUFAs). The genetic markers on the PUFA contents for breeding was limited. The polymorphisms in  In  pathway ,8. There content ,10,11. Gd bovine ,13,14. Ifads2 genes (fads2a and fads2b) and identified the polymorphisms in these two genes associated with the PUFA contents in common carp [fads2 but also suggested that the PUFA biosynthesis pathway in common carp was more complex than diploid Cyprininae fish. Although two elovl5 genes (elovl5a and elovl5b) in common carp were sequenced [The allo-tetraploid common carp is widely cultured in the world and is able to convert dietary 18-Carbon PUFAs to LC-PUFAs including arachidonic acid, eicosapentaenoic acid, and docosahexaenoic acid ,15. As amon carp . These fequenced , the queelovl5 genes is the major effect gene is still unknown. Furthermore, how duplicated fads2 genes and duplicated elovl5 genes coordinate to regulate the PUFA biosynthesis is less studied.Recent study revealed that two subgenomes in common carp performed balancing of differential expression in response to different conditions, dampening the stimulus impact to the expression of the duplicated genes . As for elovl5a and elovl5b and detected the polymorphisms in these two genes. With the association study, we identified the SNPs significantly associated with the PUFA contents and found more associated SNPs in elovl5b than elovl5a. We further examined the joint effects of the associated SNPs in fads2b and elovl5b on the PUFA contents. Finally, we obtained the high and positive correlations between the contents and the predicted breeding values of eight PUFAs using the cSNPs in these two genes. These cSNPs would be used as the biomarkers to facilitate the selective breeding of common carp with high PUFA contents. To answer these questions, in this work, we sequenced the coding regions of common carp We collected the juveniles of three bred strains of common carp, including ‘HuangHe’ (HHC) strain, ‘FuRui’ (FRC) strain and ‘Jian’ (JC) strain in May 2018, described in our previous study . These sBriefly, FAs were converted into the fatty acid methyl esters (FAMEs), which were further extracted and purified by thin-layer chromatography following the strategy of Li et al. . A totalelovl5a and elovl5b in common carp (MK893918.1 and MK893919.2), the gene-specific primers were designed to amplify the complete coding sequence (CDS) regions , the expected heterozygosity (He), the minor allele frequency (MAF), and the polymorphism information content (PIC). The former three indicators were measured with the Genepop software 4.7 . The PICp values using the false discovery rate (FDR) method for multiple hypothesis testing. One cSNP was deemed to be significantly associated with one PUFA content when it had a p value < 0.05 in the GLM method and an FDR-corrected p value < 0.05 in the ANOVA. The explained percentage of phenotypic variation (PV) of each cSNP was measured using Tassel 5.To identify the cSNPs in these two genes associated with the PUFA contents, the general linear model (GLM) and the analysis of variance (ANOVA) were used to study the association between each PUFA content and the genotypes, respectively. We ran the GLM model with the parameters of the genetic distance matrix and 100,000 permutations using Tassel 5 . This mefads2a associated with the content of C20:3n-6 PUFA. Another two cSNPs in fads2b were significantly associated with the contents of seven PUFAs and six PUFAs, respectively. Herein, we identified three cSNPs associated with the contents of multiple PUFAs. Since fads2b and elovl5b were two major effect genes on the contents of multiple PUFAs, we hypothesized that the joint analysis of multiple associated SNPs could detect a larger effect than single SNP and identify the optimal genotype combinations associated with higher PUFA contents. Hence, we estimated the joint effects of four significantly associated SNPs (two in fads2b and two in elovl5b) on the contents of multiple PUFAs. We generated different genotype combinations from all these SNPs. If one genotype combination was observed in at least three individuals, this combination was used in the comparison. For each PUFA, we performed the pairwise comparisons of the PUFA contents among different retained combinations using ANOVA in R software. The explained percentages of PV of the genotype combination to the content of each PUFA was estimated with the function of ‘lm’ [Previously, we identified one cSNP in  of ‘lm’  in R.fads2b and elovl5b would be applied into estimating the breeding values (BVs) of the PUFA contents. The cSNPs with MAFs over 0.03 were used to estimate the BVs with BWGS [fads2b and elovl5b were treated as the reference group to train the breeding models and the remaining 10% of all individuals as the validation group. For each PUFA, the BV of each individual in the validation group by each program was represented as the predicted content. Further, in each replicate of one cross validation by each program, we calculated the Pearson correlation coefficient value (CV) between the actual contents and the predicted contents of each PUFA across individuals in the validation group. Second, we calculated the mean BV of each PUFA and its standard deviation (SD) of all individuals predicted by each program during 20 replicates. Third, to estimate the BV accuracy of each program, we calculated the mean CV and its SD of each program for each PUFA during 20 replicates. The mean squared error of prediction (MSEP) and corresponding standard deviation (SD-MSEP) were also computed.Further, we were interested in whether all identified cSNPs in ith BWGS . This paith BWGS , EGBLUP ith BWGS , Ridge rith BWGS , LASSO [ith BWGS , Elasticith BWGS , Bayesiaith BWGS , Bayesiaith BWGS , Bayes Aith BWGS , Bayes Bith BWGS , and Bayith BWGS . We usedelovl5a and elovl5b were successfully sequenced in 204 and 269 individuals, respectively. Both amplified lengths of elovl5a and elovl5b cDNA sequences were 876 bp, corresponding to seven exons. The base contents of A, G, C, and T in the elovl5a CDS were 28.9%, 24.5%, 21.4%, and 25.2%, respectively. For elovl5b, the base contents of A, G, C, and T in the CDS were 25.9%, 25.4%, 23.0%, and 25.4%, respectively. These two reference mRNAs were highly identical with a similarity of 95%, higher than that of common carp fads2a and fads2b (89.86%) [We confirmed that the entire CDS regions of elovl5a  and nine synonymous cSNPs (s-cSNP) were identified in six exons of elovl5a . These celovl5b  and two in elovl5b (E5b.172 and E5b.782), were identified to be associated with the contents of multiple PUFAs. In these three loci, we only observed the homozygous wild-types and the heterozygous genotypes while the homozygous mutations were not found. The synonymous SNP in elovl5a was significantly associated with the C20:5n-3 PUFA content , suggesting that the former might be the major SNP. Two haplotype blocks were detected in each of the CDS regions of ectively . E5b.172elovl5b were associated with more PUFA contents than that in elovl5a, suggesting that elovl5b might be the major effect gene regulating the PUFA biosynthesis in common carp. Previously, we found that fads2b might be another major gene responding to common carp PUFA contents and that two cSNPs in fads2b were associated with the contents of multiple PUFAs [The cSNPs in le PUFAs . fads2b were associated with four n-3 PUFAs  and four n-6 PUFAs . The cSNPs in elovl5b had significant association with four n-3 PUFAs  and five n-6 PUFAs . The cSNPs in these two genes were involved in the contents of eight common PUFAs, suggesting the coordinative regulation of the PUFA biosynthesis through the mutations in fads2b and elovl5b. Hence, we estimated the joint effects of four significantly associated cSNPs in two major effect genes  on the contents of 12 PUFAs. Theoretically, we should observe 16 genotype combinations with four cSNPs. In practice, among 223 individuals having the genotypes of both fad2b and elovl5b, ten types were detected in at least three individuals  having no associated SNPs in these two genes, the genotype combination also contributed to the percentages of PV with a range between 4.24%~13.06%. The increased explained percentages of PV indicated the joint effects of these four cSNPs on the contents of 12 PUFAs.The multi-cSNPs combinations greatly improved the explained percentages of PV compared with the single SNP . For thrfads2b and elovl5b, the genotype combination with higher heterozygous levels corresponded to higher contents of PUFAs. The combination (H8) including four heterozygotes corresponded to the highest contents of five PUFAs including C18:3n-3, C18:3n-6, C22:4n-6, C22:5n-3, and C22:5n-6  and two homoeologues of elovl5 (elovl5a and elovl5b). We explored that fads2b was the major effect gene associated with the common carp PUFA contents compared with fads2a [elovl5 genes makes more contributions to the PUFA biosynthesis has not been answered yet. Elovl5b was associated with more PUFAs and had higher explained percentage of PVs than elovl5a, suggesting that the former might be the major effect gene to improve the PUFA contents. More functional studies including yeast heterologous expression system and expression patterns of these two genes are required for further validation of their effects.The species-specific whole genome duplication and parallel subgenome evolution in common carp generated two homoeologues of h fads2a . Howeverfads2a/fads2b/elovl5a/elovl5b, demonstrated by both PCA and population structure analysis. The individuals from one strain were grouped together with the samples from the other strains, suggesting that they might be sampled from multiple families. The grouping also increased the genetic diversities of the studied population and improved the statistical power of the association analysisIn our current study and previous study , three selovl5a associated with one PUFA content. One possible reason is that this synonymous cSNP might lead to the changes in DNA methylation and gene expression of elovl5a. In humans, the polymorphisms in the FADS region modify the epigenetic methylation, which further regulate the fatty acid metabolism [elovl5a DNA methylation needs further research. Two ns-cSNPs in elovl5b were associated with several PUFA contents. The enzymes activity would be affected by either amino acid mutations in the active sites [We observed one synonymous cSNP in tabolism  and conttabolism . Whetherve sites  or distave sites . Thus, wfads2b [elovl5a and elovl5b. We did not find the homozygotes of the minor alleles in both elovl5a and elovl5b. The heterozygotes of three identified cSNPs corresponded to higher PUFA contents than the homozygotes of the reference bases, suggesting that the heterozygosity of elovl5a and elovl5b might increase their elongase activities. We also revealed the heterozygote advantage in the genotype combination from fads2b and elovl5b. The combinations of H8 and H9 having four or three heterozygotes had higher contents of the PUFAs than the homozygote combination.In our previous study, the heterozygote advantage on the PUFA contents was observed in the polymorphisms of fads2b . Herein,fads2 and elovl5 genes coordinate to regulate the PUFA biosynthesis is less studied. Our studies showed that fads2b and elovl5b were two major effect genes on the PUFA contents compared with fads2a and elovl5a, respectively. The cSNPs in fads2b and elovl5b were associated with the contents of eight common PUFAs. However, in six PUFAs, fads2b.751 made higher explained percentages of PVs than the cSNPs in elovl5b (How duplicated  elovl5b , suggestNone has studied the joint effects of multiple SNPs on the PUFA contents. The explained percentages of PVs by four cSNPs were higher than the sum percentage of each SNP, indicating the additive effects by these cSNPs on the PUFA contents. The coordination to improve the PUFA contents required the simultaneous mutations in these two genes. We also used the cSNPs in these two genes to predict the BVs of the PUFA contents. The best prediction accuracies of the contents of eight PUFAs ranged between 0.3413 and 0.5824. These values were in the BV reliability range from 0.04 to 0.72 for economic traits in sheep, pig, and cattle ,48,49.fads2a, fads2b, elovl5a, and elovl5b. Although fads2 and elovl5 were two rate-limiting enzymes in the PUFA biosynthesis [fads2a and elovl5a were identified and four cSNPs in fads2b and elovl5b explained 21.26~54.95% of PVs for the contents of eight PUFAs, suggesting that there might exist other regulatory elements or genes involved in the common carp PUFA biosynthesis. The SNPs in the promoter region of elovl5 significantly related to fatty acid content in many animals [elovl4, elovl6, and other genes also participated in the PUFA biosynthesis [elovl5a and elovl5b and the genomic regions of the related genes and then identify their associations with the PUFA contents in the future.We comprehensively investigated the polymorphisms in ynthesis , few cSN animals ,52,53,54ynthesis ,54,55. Telovl5, elovl5a and elovl5b. The association study identified three cSNPs in these two genes significantly related to the PUFA contents in common carp. Elovl5b might be the major gene regulating common carp n-3 and n-6 PUFA biosynthesis, and two ns-cSNPs in this gene might be the main effect SNPs. The joint effects of four cSNPs in fads2b and elovl5b improved the explained percentages of PVs of the PUFA contents. The individuals having more heterozygotes of four cSNPs had higher PUFA contents than the ones having the homozygotes, suggesting that the former would be used as the parents for selective breeding of offspring having higher PUFA contents. The cSNPs in these two genes could be applied to estimating the breeding values of the PUFA contents with the optimal tool of the BB program. In sum, our results highlight the importance of the polymorphisms in elovl5a and elovl5b, other critical factors in the PUFA biosynthesis in common carp. These cSNPs would be useful markers for selection to improve the PUFA contents in common carp.We identified the polymorphisms in the CDSs of two duplicated common carp


Selection on complex traits can rapidly drive evolution, especially in stressful environments. This polygenic selection does not leave intense sweep signatures on the genome, rather many loci experience small allele frequency shifts, resulting in large cumulative phenotypic changes. Directional selection and local adaptation are changing populations; but, identifying loci underlying polygenic or environmental selection has been difficult. We use genomic data on tens of thousands of cattle from three populations, distributed over time and landscapes, in linear mixed models with novel dependent variables to map signatures of selection on complex traits and local adaptation. We identify 207 genomic loci associated with an animal’s birth date, representing ongoing selection for monogenic and polygenic traits. Additionally, hundreds of additional loci are associated with continuous and discrete environments, providing evidence for historical local adaptation. These candidate loci highlight the nervous system’s possible role in local adaptation. While advanced technologies have increased the rate of directional selection in cattle, it has likely been at the expense of historically generated local adaptation, which is especially problematic in changing climates. When applied to large, diverse cattle datasets, these selection mapping methods provide an insight into how selection on complex traits continually shapes the genome. Further, understanding the genomic loci implicated in adaptation may help us breed more adapted and efficient cattle, and begin to understand the basis for mammalian adaptation, especially in changing climates. These selection mapping approaches help clarify selective forces and loci in evolutionary, model, and agricultural contexts. Interest in mapping the impacts of selection and local adaptation on the genome is increasing due to the novel stressors presented by climate change. Until now, approaches have largely focused on mapping “sweeps” on large-effect loci. Highly powered datasets that are both temporally and geographically distributed have not existed. Recently, large numbers of beef cattle have been genotyped across the United States, including influential individuals with cryopreserved semen. This has created multiple powerful datasets distributed over time and landscapes. Here, we map the recent effects of selection and local adaptation in three cattle populations. The results provide insight into the biology of mammalian adaptation and generate useful tools for selecting and breeding better-adapted cattle for a changing environment. ST, FLK, XP-CLR) , indicating that the demographic histories of the populations and sampling strategy across the breeds were similar. We removed the link between generation proxy and genotype by randomly permuting the animals’ birth date and on reanalysis of the permuted data we observed PVE to decrease to zero (Table C in S1 Text).We used continuous birth date and high-density SNP genotypes for large samples of animals from three large US beef cattle populations; Red Angus , Simmental , and Gelbvieh  to map loci responding to selection (S2). Additionally, we found that despite birth date being a significantly skewed dependent variable , the GPSM p-values were well calibrated (. Despite the tendency for genome-wide association studies (GWAS) to be biased in its detection of moderate frequency variants [Fig 2J–2L). This suggests GPSM can differentiate drift from selection across the allele frequency spectrum. Rapid shifts in allele frequency create highly significant GPSM signals. For example, rs1762920 on chromosome 28 has undergone large, recent changes in allele frequency in all three populations . The allele frequency changes observed for this locus are extremely large compared to other significant regions, most of which have only small to moderate changes in allele frequency over the last ~10 generations. When we regressed allele frequency  on birth date, the average allele frequency changes per generation (ΔAF) for significant GPSM associations were 0.017, 0.024, and 0.022 for RAN, SIM, and GEL, respectively (Table D in S1 Text). In the analyses of each dataset, GPSM identified significant SNPs with ΔAF < 1.1⨯10−4. The generally small allele frequency changes detected by GPSM are consistent with the magnitude of allele frequency changes expected for selection on traits with polygenic architectures [The GPSM analyses for these three populations identified 268, 548, and 763 statistically significant SNPs , representing at least 52, 85, and 92 genomic loci associated with birth date in RAN, SIM, and GEL, respectively  from wfABC [r = 0.002) and relationship-pruned (r = 0.003) datasets. The slight increase in correlation is likely due to a violation of wfABC’s assumption of random mating, because the correlation between estimated s between the full and relationship-pruned datasets was only 0.432. When we restricted our comparison to significant GPSM SNPs, the correlation between wfABC-estimated s and GPSM betas was higher (r = 0.0675), but still quite low. Interestingly the correlation between estimated selection coefficients and significant GPSM p-values was higher than with the GPSM effect size estimate (r = 0.126).We found that significant GPSM loci go largely undetected when using other selection mapping methods. In these datasets, the genomic regions identified by both TreeSelect and allele frequency trajectory (wfABC) methods for detecting selection were almost entirely different from those identified by GPSM. Further, the estimated selection coefficients . The between-breed allele frequency differences detected by TreeSelect did not overlap at all with GPSM signatures. When used to detect selection within a breed using discrete groupings based on animal birth date, test statistic correlations were also low . Despite generally low correlations between test statistics, TreeSelect with arbitrary population groupings detected some of the same loci that were identified by GPSM. In Red Angus, the within-breed TreeSelect analysis identified selection at six loci that were also significant in GPSM, including the two most significant loci on chromosome 23 near KHDRBS2 and on chromosome 28 near RHOU .TreeSelect and GPSM test statistics were also lowly correlated. A three population TreeSelect analysis that treated each breed as a distinct population to detect between-breed selection showed low correlations between test statistics (Table 2). We suspect that these SNPs are undergoing directional allele frequency changes too small to detect at genome-wide significance, even in this highly-powered dataset. Since GPSM continues to gain power with additional samples, we suspect that future sample size increases will detect more of these signatures of polygenic selection at a genome-wide significance level. Regardless of the number of SNPs used in the drift GRM, the variance associated with drift was consistently minimal (Table 2).We performed a genomic restricted maximum likelihood (REML) analysis to identify how much of the variation in birth date was explained by various classes of GPSM SNPs. We built three GRMs using different SNP sets: One set with GPSM genome-wide significant SNPs (q < 0.1), the second with an equivalent number of the next most suggestive GPSM SNPs outside of significant loci (> 1 Mb from a q < 0.1 significant SNP), and the third with an equivalent number of random, moderate minor allele frequency (MAF > 0.15) SNPs not in the first two variant classes, intended to represent loci randomly drifting in the population. For each population, we observed that nearly all of the variation in birth date was explained by the significant and suggestive GRMs. While genome-wide significant loci explain the majority of genetic variance associated with birth date, an equivalent number of suggestive, but not significant SNPs resulted in only slightly smaller PVEs , ERBB3/am color ), and KIloration ) have noTable E in S1 Text). While GPSM is able to detect Mendelian selection, the overwhelming majority of signatures identified represent selection on complex, quantitative traits. Of the regions identified in multiple populations, many possessed positional candidate genes with production-related functions in cattle . Growth phenotypes  are known to be under strong selection in all three populations [In addition to these three known Mendelian loci, we detected numerous novel targets of selection within and across populations. While the majority of the genomic regions detected as being under selection were population-specific , we identified seven loci that are under selection in all three populations, and fifteen more under selection in two . Howeveulations , but antS3 Table). In each population, we identified numerous biological processes involved in cell cycle control, which are directly involved in determining muscle growth rate [). Enrichments in these tissues did not exist in TSEA of Simmental or Gelbvieh GPSM gene sets, suggesting explicit within-population selection on fertility. Gelbvieh cattle are known for their rapid growth rate and carcass yield. Selection on these phenotypes likely drives the identification of the six biological processes identified which relate to muscle development and function in the Gelbvieh GPSM gene set. Consequently, this gene set is significantly enriched for expression in human skeletal muscle , an enrichment unique to Gelbvieh. A complete list of genomic regions under population-specific selection and their associated candidate genes is in S2 Table.Biological processes and pathways enriched in genes located proximal to GPSM SNP associations point to selection on drivers of production efficiency and on population-specific characteristics . These ecoregions are largely consistent with those represented in previously-published maps from the environmetrics and atmospheric science literature [Figs S1 Text). Since the development of these US populations in the late 1960s and early 1970s, registered seedstock animals from these populations have a small footprint in desert regions with extreme temperatures and low rainfall.Using an equivalent form of model to GPSM, but with continuous environmental variables  or statistically-derived discrete ecoregions as the dependent variable (rather than birth date in GPSM) allows us to identify environmentally-associated loci that have been subjected to artificial and, perhaps in this context more importantly, natural selection . We refeterature , and refTable G in S1 Text). In Red Angus, PVE for ecoregion membership ranged from 0.463 for the Arid Prairie to 0.673 for the Fescue Belt (Table H in S1 Text). We observe similar environmental PVE in both Simmental and Gelbvieh datasets. These measures suggest that genetic associations exist along both continuous environmental gradients and within discrete ecoregions. Permutation tests that shuffled environmental dependent variables, removing the relationship between the environment and the animal’s genotype, resulted in all PVEs being reduced to ~ 0, strongly suggesting that the detected associations between genotype and environment were not spurious. An additional permutation test that permuted animals’ zip codes, such that all animals from a given zip code were assigned the same “new” zip code from a potentially different ecoregion provided similar results, indicating that bias due to sampling at certain zip codes was not producing envGWAS signals. From 10 rounds of permutation, there were no SNP associations with p-values < 1⨯10−5. Consequently, we used this empirically-derived p-value threshold to determine SNP significance in all of the envGWAS analyses. Gene drop simulations suggested that a small portion of the identified associations are likely due to pedigree structure or founder effects . However, in this data, the pedigree structure reflects selection decisions of farmers and ranchers that are not beyond the influence of performance differences relative to environmental differences.Although environmental variables and ecoregions are not inherited, the estimated PVE measures the extent to which genome-wide genotypes change in frequency across the environments in which the animals were born and lived. The proportion of variance explained by SNPs ranged from 0.586 to 0.691 for temperature, 0.526 to 0.677 for precipitation, and 0.585 to 0.644 for elevation . Despite locus-specific signal, principal component analysis (PCA) does not suggest that ecoregion-driven population structure exists in any of the populations . Of these loci, only two overlapped with loci identified in the continuous envGWAS analyses, suggesting that using alternative definitions of environment in envGWAS may detect different sources of adaptation. Of the 18 significant loci, 17 were within or near (< 100 kb) positional candidate genes (Table I in S6 Table), many of which have potentially adaptive functions. For example, envGWAS identified SNPs immediately (22.13 kb) upstream of CUX1 (Cut Like Homeobox 1) gene on chromosome 25. CUX1 controls hair coat phenotypes in mice [CUX1 can be used to differentiate between breeds of goats raised for meat versus those raised for fiber [CUX1 in hair coat phenotypes makes it a strong adaptive candidate in environments where animals are exposed to heat, cold, or toxic ergot alkaloids from fescue stress [In Red Angus, we identified 54 variants defining 18 genomic loci significantly associated with membership of an ecoregion in the discrete multivariate envGWAS analysis . In Gelbvieh, 66 variants identified 33 local adaptation loci . In the analyses of all three datasets, we identified a common local adaptation signature on chromosome 23 (peak SNP rs1023574). Multivariate analyses in all three populations identified alleles at this SNP to be significantly associated with one or more ecoregions . In all three datasets, we identified rs1023574 as a univariate envGWAS association with membership of the Forested Mountains ecoregion. However, the most significant univariate association in Red Angus was with the Arid Prairie region which was excluded from both the Simmental and Gelbvieh analyses due to low within-region sample size. In the multivariate analysis for Red Angus, the associated locus spanned 18 SNPs from  and contained the pseudogene LOC782044. The nearest annotated gene, KHDRBS2  has previously been identified by other adaptation studies in cattle, sheep, and pigs [rs1023574 was significantly associated with temperature, elevation, and humidity variables in Simmental. The KHDRBS2 locus was preferentially introgressed between Bos taurus and domestic yak [Fig 4C), indicating that it may be a target of balancing selection.In Simmental, we identified 11 loci tagged by 39 variants significantly associated with membership of an ecoregion in the multivariate envGWAS analysis . These SNPs tag 17 loci, many of which are within 100 kb of positional candidate genes. Univariate envGWAS identified 23, 17, and 10 variants associated with temperature, precipitation, and elevation, respectively . The most significant multivariate association in Red Angus is located on chromosome 29 within BBS1 (Bardet-Biedl syndrome 1), which is involved in energy homeostasis [BBS1 mutant knock-in mice show irregularities in photoreceptors and olfactory sensory cilia [Table J in S1 Text). For example, SNPs near GRIA4 were implicated in body temperature maintenance in cold stressed Siberian cattle [S6 Table.Using continuous temperature, precipitation, and elevation data as quantitative dependent variables in a multivariate envGWAS analysis of Red Angus animals, we identified 46 significantly associated SNPs (eostasis . BBS1 mury cilia  functionn cattle . SignifiS7 Table) derived from lists of envGWAS positional candidate genes. Pathways in common between populations were driven by largely different gene sets. Across all populations, we identified the “axon guidance” pathway, and numerous other gene ontology (GO) terms related to axon development and guidance as enriched with environmentally-associated loci. Ai et al. (2015) suggested that axon development and migration in the central nervous system is essential for the maintenance of homeostatic temperatures by modulating heat loss or production [While we found minimal candidate gene overlap between populations, we identified multiple shared biological pathways and processes (oduction . In addioduction . These coduction . We idenoduction . The abiC. elegans) both identified brain and nerve tissues as the lone tissues where envGWAS candidate genes show significantly enriched expression . Tissue-specific expression in the brain further supports our observed enrichment of local adaptation pathways involved in neural signaling and development.To further explore the biology underlying adaptive signatures, we performed Tissue Set Enrichment Analysis of our envGWAS candidate gene lists. These analyses using expression data from humans and distantly related worms , or locally different allele frequencies moving towards the population mean . We identified 59, 38, and 46 significant SNPs in Red Angus, Simmental, and Gelbvieh, respectively undergoing ecoregion-specific selection. These represent 15, 21, and 26 genomic loci (> 1 Mb to nearest next significant SNP) . In most cases, these variants have an effect  in only one or two ecoregions . Further, nearly all represent the decay of ecoregion-specific allele frequencies towards the population mean  as opposed to on-going directional selection for ecoregion specific beneficial adaptations .envGWAS detects allelic associations with continuous and discrete environmental variables, but does not address whether selection is towards increased local adaptation, or whether local adaptation is being eroded by the exchange of germplasm between ecoregions via artificial insemination. We used the spatiotemporal stratification of genotyped animals to identify loci undergoing ecoregion-specific selection. We performed GPSM within each sufficiently genotyped ecoregion and identified variants with high effect size heterogeneity (Cochran’s Q statistic) between ecoregions. Variants with significant heterogeneity across regions that were also significant in at least one within-region GPSM analysis implied ecoregion-specific allele frequency change. These changes could have been due either to selection for local adaptation , which is typically a low minor allele frequency.Adaptive alleles at these loci are being driven in frequency towards the population mean allele frequency . In addition to a small number of loci, for which function is known, we identify hundreds of novel signatures of ongoing selection.The envGWAS identified 174, 125, and 130 SNPs associated with both continuous or discrete environmental factors in Red Angus, Simmental, and Gelbvieh, respectively. Using genes found near these environmentally-associated SNPs, we identified a consistent enrichment of pathways and tissues involved in neural development and signaling. These envGWAS associations emphasize the role that the nervous system likely plays in recognizing and responding to environmental stress in mammals, which will be valuable as society and agriculture cope with climate change. In addition to neural pathways, we observe significantly enriched expression of envGWAS genes in the brain tissues of humans, mice, and worms. Other pathways associated with environmental adaptation reveal the importance of mechanisms involved in regulating vasoconstriction and vasodilation, both of which are essential for responses to heat, cold, altitude, and toxic fescue stressors in cattle.KHDRBS2) and pathways previously implicated as domestication-related [The statistical power and wide geographical distribution of the cattle comprising these data highlights that the utilized approaches can be leveraged to understand the genomic basis of adaptation in many other studies and species. While for GSPM there is a clear connection between the model and directional selection, for envGWAS the model is only identifying associations between the environment and SNP genotypes. These associations could be caused by a multitude of factors, one of which is local adaptation. The small allele frequency differences identified by envGWAS are consistent with a polygenic model of local adaptation, likely driven by small changes in gene expression . Further-related . This suFigs 4C). In three independent datasets, we identified a single shared environmentally-associated locus near the gene KHDRBS2. This locus has been identified as introgressed in yak, and exhibits an irregular allele frequency trajectory which suggests that it may be subject to balancing selection [The balance between artificial and natural selection in domesticated beef cattle is quite precarious. If natural selection is for a stressor which occurs nation-wide and is positively correlated with production traits, natural selection can effectively act in concert with artificial selection. The identification of genes involved in the immune system by the GPSM analysis reflects this interplay between artificial and natural selection; in other words, natural selection could be acting in the background in populations under artificial selection. However, if natural selection is acting at a local, ecoregion scale, then natural selection and artificial selection via additive genetic breeding values are likely to be at odds. Artificial insemination in cattle has allowed the ubiquitous use of males which have been found to be superior when progeny performance has been averaged across US environments. Due to limited gene flow and phenotypic selection which would act on loci with genotype-by-environment effects, local adaptation likely occurred prior to the 1980s. Our results suggest that environmental associations are present in cattle populations, but that the widespread use of artificial insemination resulting in gene flow and selection on breeding values, has caused US cattle populations to lose ecoregion-specific adaptive variants . We idenelection . Though We further demonstrate that large commercially-generated genomic datasets from domesticated populations can be leveraged to detect polygenic selection  and locahttps://github.com/troyrowan/gpsm_envgwas.RMarkdown files, snakemake files, and R scripts used in the manuscript are available at Bos taurus beef cattle ranging in density from ~25K SNPs to ~770K SNPs were imputed to a common set of 830K SNPs using the large multi-breed imputation reference panel described by Rowan et al. 2019 [SNP assays for three populations of genotyped al. 2019 . Genomical. 2019 . Genotypal. 2019 , referenal. 2019 , and impal. 2019 . Followiy is an individual’s generation proxy, in our case birth date, and X was an incidence matrix that related SNPs to birth dates within each individual and g was the estimated effect size for each SNP. An animal’s age as of April 5, 2017 was used as the generation proxy in GPSM. We control for confounding population structure, relatedness, and inbreeding with a polygenic term u that uses a standardized genomic relationship matrix (GRM) G [aσ2eand σ2 using restricted maximum likelihood estimation (REML). Here, continuous age served as a proxy for generation number from the beginning of the pedigree. Other than the tested SNP effects, no fixed effects other than the overall mean were included in the model. We tested each SNP for an association with continuous age. To control for multiple-testing, we converted p-values to FDR corrected q-values [To identify alleles that had changed in frequency over time, we fit a univariate genome-wide linear mixed model (LMM) using GEMMA (Version 0.98.1) . Here, wq-values  and usedq-values . Resultsq-values .e) of approximately 100, similar to estimates of U.S. beef cattle populations [e, we simulated populations with effective population sizes of 50 and 250. Based on the chosen genomic architecture, 1000, 500, or 200 purely additive QTL were randomly assigned to segregating sites. Effect sizes for simulated QTL were drawn from either a normal  or a gamma (shape = 0.42) distribution [All simulations were performed in AlphaSimR . Stochasulations . To testribution . Prior tS1 Fig). Sampled individuals were chosen at random, and were not more or less likely to become parents in the next generation. In addition to sampling genotypes each generation, we calculated the allele frequency of simulated QTL each generation to track observed allele frequency changes over the course of selection. This process was performed in replicates of 10 for each scenario, allowing us to calculate descriptive statistics and compare GPSM’s performance across scenarios.After burn-in (generation 0), we performed selection of parents for the next generation in two parallel manners: randomly or using truncation selection on true breeding value. In each scenario, we held the effective population size by selecting appropriate numbers of males and females to be parents each generation. Selection intensity was altered by increasing or decreasing the number of crosses performed . We also varied the number of generations of selection post-burn-in . For each scenario, we extracted 10,000 total simulated individuals for analysis in GPSM. To test the effects of uneven generation sampling that we see in real data, we performed two different strategies for sampling simulated genomes. In one case, we sample an equal number of individuals each generation. In the other, we sample more animals from the most recent generations. The number of sampled individuals is based on a negative exponential distribution that approximates the of ages observed in our real datasets  in GEMMA (v0.98.1) with all SNPs that had a MAF > 0.01. Using GEMMA, we fit the individuals’ true generation number as the dependent variable in a genome-wide linear mixed model. Outputs from GPSM were read, manipulated, and plotted in R using multiple tidyverse packages .Finally, we fit regression models to assess the impact of various parameters on the PVE. For PVE, two regression models were analyzed. In the first, PVE from the random mating simulations was used as the dependent variable with proportion false positives, number of generations, number of crosses, number of QTL, QTL distribution, and number of segregating sites as explanatory variables. In the second, PVE from selection on true breeding value (TBV) was the dependent variable with proportion true positives, number of generations, number of crosses, number of QTL, QTL distribution, and number of segregating sites as dependent variables.To estimate the amount of variation in birth date explained by GPSM significant SNPs, we performed multi-GRM GREML analyses for birth date in GCTA (v1.92.4) . We builWe used the software wfABC (Wright-Fisher Approximate Bayesian Computation)  to generUsing TreeSelect , we arti2 of the continental US were extracted from the PRISM Climate Dataset [K-means clustering of these three variables grouped each 4 km2 of the continental US into 9 distinct ecoregions. Using the reported breeder zip code for each individual, we linked continuous environmental variables to animals and partitioned them into discrete environmental cohorts for downstream analysis. For ecoregion assignments, latitude and longitude were rounded to the nearest 0.1 degrees. As a result, some zip codes were assigned to multiple ecoregions. Animals from these zip codes were excluded from the discrete region envGWAS but remained in analyses that used continuous measures as dependent variables.Thirty-year normals (1981–2010) for mean temperature ((average daily high (°C) + average daily low (°C)/2), precipitation (mm/year), and elevation (m above sea level) for each 4 km Dataset , and useEq 1, but used environmental values as the dependent variable (y) instead of birth date. We also fit a combined multivariate model using all three environmental variables to increase power. To identify loci associated with entire climates as opposed to only continuous variables, we fit univariate and multivariate case-control envGWAS analyses using an individual’s region assignment described in the “Environmental Data” section as binary phenotypes. Proportion of variation explained (PVE), phenotypic correlations, and genetic correlations were estimated for continuous environmental variables and discrete environmental regions using GEMMA’s implementation of REML.To identify loci segregating at different frequencies within discrete ecoregions or along continuous climate gradients, we used longitudinal environmental data for the zip codes attached to our study individuals as dependent variables in univariate and multivariate genome-wide LMMs implemented in GEMMA (Version 0.98.1). We fit three univariate envGWAS models that used 30-year normal temperature, precipitation, and elevation data as dependent variables. These used an identical model to −5) [To ensure that envGWAS signals were not driven by spurious associations, we performed two separate permutation analyses. In the first, we randomly permuted the environmental variables and regions associated with an individual prior to performing each envGWAS analysis, detaching the relationship between an individual’s genotype and their environment. In the second, to ensure that envGWAS signals were not driven by the over-sampling of individuals at particular zip codes, we permuted the environmental variables associated with each zip code prior to envGWAS analysis. These two types of permutation analyses were performed for each dataset and for each type of univariate and multivariate envGWAS analysis. We determined significance using a permutation-derived p-value cutoff (p < 1⨯10−5) .To identify variants undergoing ecoregion-specific allele frequency changes, we performed GPSM analyses within each region with more than 600 individuals. The SNP significance testing effects and standard errors from each of the within-region GPSM analyses were combined into a single meta-analysis for each population using METASOFT (v2.0.1) . We idenBos taurus reference assembly, we located proximal candidate genes near significant SNPs from each of our analyses. We generated two candidate gene lists each from significant GPSM and envGWAS SNPs. Lists contained all annotated genes within 10 kb from significant SNPs. We consolidated significant SNPs from all envGWAS analyses to generate a single candidate gene list for each breed. Using these candidate gene lists, we performed gene ontology (GO) and KEGG pathway enrichment analysis using Clue GO (v2.5.5) [Using the NCBI annotations for the ARS-UCD1.2 (v2.5.5)  implemen(v2.5.5) . We idenC. elegans genes orthologous with members of our envGWAS and GPSM gene lists. We then queried these lists in WormBase’s Tissue Enrichment Analysis tool [C. elegans. We used each tool’s respective multiple-testing correction to determine significance. We deemed an enrichment in a tissue “suggestive” when its p-value was < 0.1.Using the above gene sets, we performed three separate Tissue Set Enrichment Analyses (TSEA) using existing databases of human, mouse, and worm gene expression data. We searched for enriched gene expression with data from the Human Protein Atlas  and Moussis tool ,100 to iSummary data from GPSM and envGWAS analyses are publicly available as a Zenodo repository .S1 TextThis file includes supplementary text, Tables A-J, and SI References.(DOCX)Click here for additional data file.S1 Fig(a) Birth date histograms for complete datasets. (b) Histograms of animal birth dates born before 2000. (c) Q-Q plots of residual error from a GREML analysis of birth date in each population. Points represent individuals, and are colored by the number of years since the animal’s birth date.(TIF)Click here for additional data file.S2 Fig10 for Red Angus branch of across-breed TreeSelect analysis. TreeSelect Manhattan plots for b) oldest ⅓, c) middle ⅓, and d) youngest ⅓ branches in within-breed analysis for the Red Angus population. Red line indicates significance at p < 1 x 10−5. Green points are SNPs that were significant (q < 0.1) in GPSM analysis of Red Angus dataset.a) Single SNP -log(TIF)Click here for additional data file.S3 FigQ-Q plots for envGWAS p-values of (a) a linear model for Forested Mountains ecoregion membership, (b) a linear mixed model for Forested Mountain ecoregion membership, and (c) a multivariate linear mixed model of ecoregion membership. Univariate discrete envGWAS for (d) Forested Mountain linear model, (e) Forested Mountains linear mixed model, (f) Southeast, (g) Fescue Belt, (h) Arid Prairie, (i) High Plains, and (j) Upper Midwest & Northeast ecoregions. In all Manhattan plots the red line indicates an empirically-derived p-value significance threshold from permutation testing (p < 1×10–5). Note the drastically inflated p-values from the linear model in (a). Further, note that associated loci are not consistent between linear model and linear mixed model, highlighting the need to control for geographic dependency with a genomic relationship matrix.(TIF)Click here for additional data file.S4 FigK-means clustering of 30-year normal temperature, precipitation, and elevation. (b) Locations of Simmental animals colored by breeder’s ecoregion and sized by number of animals at that location. (c) Multivariate envGWAS . Univariate discrete envGWAS for (d) Desert, (e) Southeast, (f) Fescue Belt, (g) Forested Mountains, (h) High Plains, and (i) Upper Midwest & Northeast ecoregions. In all Manhattan plots the red line indicates an empirically-derived p-value significance threshold from permutation testing (p < 1×10−5). Maps were plotted using public domain data from the US Department of Commerce, Census Bureau via the R package maps .(a) Nine ecoregions of the continental United States defined by (TIF)Click here for additional data file.S5 FigK-means clustering of 30-year normal temperature, precipitation, and elevation. (b) Locations of Gelbvieh animals colored by breeder’s ecoregion and sized by number of animals at that location. (c) Multivariate envGWAS . Univariate discrete envGWAS for (d) Desert, (e) Southeast, (f) Fescue Belt, (g) Forested Mountains, (h) High Plains, and (i) Upper Midwest & Northeast ecoregions. In all Manhattan plots the red line indicates an empirically-derived p-value significance threshold from permutation testing (p < 1×10−5). Maps were plotted using public domain data from the US Department of Commerce, Census Bureau via the R package maps .(a) Nine ecoregions of the continental United States defined by (TIF)Click here for additional data file.S6 FigPlots for Red Angus (A-D), Simmental (E-H), and Gelbvieh (I-L). Points indicate individuals, colored by their assigned ecoregion.(TIF)Click here for additional data file.S7 Fig−5. Maps were plotted using public domain data from the US Department of Commerce, Census Bureau via the R package maps .Q-Q plots for envGWAS p-values of (a) a linear model for temperature, (b) a linear mixed model for temperature, and (c) a multivariate linear mixed model of temperature, precipitation, and elevation. Geographic distributions colored by (d) temperature, (e) precipitation, (f) elevation. Manhattan plots for univariate envGWAS analysis of (g) temperature, (h) precipitation, (i) elevation. Red lines indicate permutation-derived p-value cutoff of 1×10(TIF)Click here for additional data file.S8 Fig−5. Maps were plotted using public domain data from the US Department of Commerce, Census Bureau via the R package maps .(a) Multivariate envGWAS of temperature, precipitation, and elevation for Simmental cattle. Geographic distributions colored by (b) temperature, (d) precipitation, (f) elevation. Manhattan plots for univariate envGWAS analysis of (c) temperature, (e) precipitation, (g) elevation. Red lines indicate permutation-derived p-value cutoff of 1×10(TIF)Click here for additional data file.S9 Fig−5. Maps were plotted using public domain data from the US Department of Commerce, Census Bureau via the R package maps .(a) Multivariate envGWAS of temperature, precipitation, and elevation for Gelbvieh cattle. Geographic distributions colored by (b) temperature, (d) precipitation, (f) elevation. Manhattan plots for univariate envGWAS analysis of (c) temperature, (e) precipitation, (g) elevation. Red lines indicate permutation-derived p-value cutoff of 1×10(TIF)Click here for additional data file.S10 Fig−5 and significant in at least one region-specific GPSM analysis p < 1×10−5). Each box represents the lead SNP, colored by ecoregion, and sized by Cochran’s Q value (for heterogeneity). (b) Region-specific allele frequency trajectories for lead SNPs since 1980, generated by fitting smoothed loess regression of allele frequency on birth date. Trajectories are colored by ecoregion.(a) PM-plots for lead SNPs of significant within-region GPSM meta-analysis Click here for additional data file.S11 Fig−5 and significant in at least one region-specific GPSM analysis p < 1×10−5). Each box represents the lead SNP, colored by ecoregion, and sized by Cochran’s Q value (for heterogeneity). (b) Region-specific allele frequency trajectories for lead SNPs since 1980, generated by fitting smoothed loess regression of allele frequency on birth date. Trajectories are colored by ecoregion.(a) PM-plots for lead SNPs of significant within-region GPSM meta-analysis Click here for additional data file.S12 Fig(a) PM-plots for lead SNPs of significant within-region GPSM meta-analysis . Each box represents the lead SNP, colored by ecoregion, and sized by Cochran’s Q value (for heterogeneity). (b) Region-specific allele frequency trajectories for lead SNPs since 1980, generated by fitting smoothed loess regression of birth of allele frequency on birth date. Trajectories are colored by ecoregion.(TIF)Click here for additional data file.S1 TableDescriptions of 36 selection scenarios, and the corresponding true and false positive rates for GPSM detecting simulated QTL under selection . Each scenario’s true and false positive statistics were calculated based on 10 replicates starting with different founder populations and selected randomly  or based on true breeding value (true positives). For each scenario, we report the number simulated QTL, the number of total crosses performed using 50 males and 500 females, the distribution from which QTL effects were drawn from, and the number of generations of selection performed. The mean PVE was reported for both selection on true breeding value (TBV) and random mating. We report results when 10,000 simulated individuals were randomly chosen to be genotyped (evenly each generation) and when more recent animals were genotyped more frequently (uneven sampling).(XLSX)Click here for additional data file.S2 TableVariants are significant if GPSM q-value < 0.1. Genomic locations are reported based on coordinates from ARS-1.2 genome assembly. Candidate genes were assigned to a SNP if within 10 kb of a significant SNP.(XLSX)Click here for additional data file.S3 TableCandidate genes were annotated genes < 10 kb to significant GPSM SNPs (q < 0.1). Significant  KEGG pathways and GO biological processes are reported for each breed.(XLSX)Click here for additional data file.S4 Table10 p-value for tissue enriched expression.TSEA from TissueEnrich software using Human Protein Atlas gene expression data. Enrichment analysis carried out for candidate genes within 10 kb of significant envGWAS SNPs. For each test, we report the number of tissue specific genes, their average fold change, and the FDR-corrected log(XLSX)Click here for additional data file.S5 TableEnrichment significance values for four specificity index thresholds (pSI) of 25 human tissues types. Each combination of stringency for enrichment (pSI) and tissue reports a p-value for Fisher’s Exact Test and a Benjamini Hochberg corrected p-value reported in parentheses. Tissue-gene-set combinations that are significant  are highlighted in red, those that are suggestive  are highlighted in green.(XLSX)Click here for additional data file.S6 Table−5. Candidate genes are genes within < 10 kb of significant envGWAS SNPs. We report significant SNPs from all univariate and multivariate analyses for both continuous environmental variables and discrete environments in Red Angus, Simmental, and Gelbvieh populations.SNPs were significant when p < 1×10(XLSX)Click here for additional data file.S7 Table−5). A single gene list for each breed was generated using significant SNPs from all combinations of univariate/multivariate, continuous/discrete envGWAS. Significant  KEGG pathways and GO biological processes are reported, along with the associated genes.Candidate genes were annotated genes < 10 kb to significant envGWAS SNPs Click here for additional data file.S8 Table10 p-value for tissue enriched expression.TSEA from TissueEnrich software using Human Protein Atlas gene expression data. Enrichment analysis carried out for candidate genes within 10 kb of significant envGWAS SNPs. For each test, we report the number of tissue specific genes, their average fold change, and the FDR-corrected log(XLSX)Click here for additional data file.S9 TableEnrichment significance values for four specificity index thresholds (pSI) of 25 human tissues types. Each combination of stringency for enrichment (pSI) and tissue reports a p-value for Fisher’s Exact Test and a Benjamini Hochberg corrected p-value reported in parentheses. Tissue-gene-set combinations that are significant  are highlighted in red, those that are suggestive  are highlighted in green.(XLSX)Click here for additional data file.S10 TableEnrichment significance values for four specificity index thresholds (pSI) of six brain regions and 35 brain cell types. Each combination of stringency for enrichment (pSI) and brain region/cell type reports a p-value for Fisher’s Exact Test and a Benjamini Hochberg corrected p-value reported in parentheses. Combinations that are significant  are highlighted in red, those that are suggestive  are highlighted in green.(XLSX)Click here for additional data file.S11 TableC. elegans gene homologs were generated from Ortholist2, requiring that genes be present in at least three data sources to be included in enrichment analysis. For each breed’s gene list, we include a list of worm tissues with significant enrichment of listed genes .(XLSX)Click here for additional data file.


Oncorhynchus nerka), which exhibits various reproductive ecotypes  that co-occur with each other and/or anadromous O. nerka in some systems across their pan-Pacific distribution. Here, we developed a multi-purpose Genotyping-in-Thousands by sequencing (GT-seq) panel of 288 targeted single nucleotide polymorphisms (SNPs) to enable accurate kokanee stock identification by geographic basin, migratory form, and reproductive ecotype across British Columbia, Canada. The GT-seq panel exhibited high self-assignment accuracy (93.3%) and perfect assignment of individuals not included in the baseline to their geographic basin, migratory form, and reproductive ecotype of origin. The GT-seq panel was subsequently applied to Wood Lake, a valuable mixed-stock fishery, revealing high concordance (>98%) with previous assignments to ecotype using microsatellites and TaqMan® SNP genotyping assays, while improving resolution, extending a long-term time-series, and demonstrating the scalability of this approach for this system and others.The ability to differentiate life history variants is vital for estimating fisheries management parameters, yet traditional survey methods can be inaccurate in mixed-stock fisheries. Such is the case for kokanee, the freshwater resident form of sockeye salmon ( Freshwater fish populations have declined 83% over the past forty years as a result of the cumulative effects of overfishing, habitat degradation, climate change, dams and other migration barriers –3. TodayFor mixed-stock fish populations, traditional survey methods can be inaccurate due to overlaps in morphology between closely-related life history forms . In respGenotyping-in-Thousands by sequencing (GT-seq) is a multiplex amplicon sequencing approach that is able to simultaneously generate genotypes for thousands of individuals at hundreds of single nucleotide polymorphism (SNP) markers . This teOncorhynchus nerka), is a species of economic, ecological, and cultural value. Kokanee are the 3rd most valuable freshwater fishery in British Columbia, Canada, represent a primary prey species for sustaining lucrative rainbow trout fisheries ($555 million/year in British Columbia), and serve important cultural and spiritual roles within some First Nations communities [Salvelinus namaycush) [Kokanee, the freshwater resident form of sockeye salmon  shormunities ; and 3) munities . Populatmunities , 31, as munities , 32–35. maycush) .Here, we developed a GT-seq panel for use in informing kokanee fisheries management at multiple scales . We validated the panel using sensitivity analyses and self-assignment across systems spanning three distinct geographical basins in British Columbia and comprising locations where migratory forms and reproductive ecotypes co-occur or reside in isolation. We subsequently genotyped kokanee individuals that were distributed across British Columbia to evaluate individual assignment accuracy and applied this new tool to assess stock proportions of co-occurring shore- and stream-spawning kokanee in Wood Lake, Canada’s highest-use wild stock kokanee fishery. More broadly, our results highlight important considerations when developing and applying GT-seq panels for fisheries management.RADseq_kokanee) included outlier loci that provide signal to differentiate migratory and reproductive ecotypes, as well as neutral loci that were previously filtered for putative paralogs and loci that deviated from Hardy-Weinberg equilibrium [We used previously published genotypic data at 7,347 SNPs  collecteilibrium . Divergeilibrium  Fig 2)..RADseq_kth and 70th base pairs of the contigs. Moving forward, this step will not be necessary, as the O. nerka reference genome can be used for this purpose, which was not yet available at the time of this study [θ) as implemented in VCFtools [RADseq_kokanee. We then selected a total of 550 informative SNPs from a sequentially ranked list by θ values for each comparison based on previous trends of genetic divergence, with some loci exhibiting high θ across multiple comparisons: 1) geographic basin (Columbia v. Fraser v. Skeena) (200 SNPs); 2) migratory form (anadromous sockeye v. resident kokanee) (100 SNPs); and 3) kokanee reproductive ecotype (stream- v. shore- v. deep-spawning) (250 SNPs)  . All locRADseq_kokanee from [K [K was then combined across ten independent runs with CLUMPP [recom-sim [https://gtseek.com/) for custom locus-specific primer design.To evaluate our initial pool of candidate loci, we used the Bayesian clustering method implemented in STRUCTURE 2.3.3  to test nee from . We used from [K  method a from [K . The opth CLUMPP . We alsoecom-sim –48. AfteRADseq_kokanee baseline in order to minimize high-grading bias. We estimated assignment accuracy (RADseq_kokanee baseline (n = 10) to assess genotyping error between RADseq and GT-seq, with five baseline samples repeated to assess within GT-seq genotyping error . The PCR2 product was quantified with PicogreenTM  and each sample was normalized to a concentration of 10 ng/μL. The pooled library was purified with a MinElute PCR Purification Kit (Qiagen) and eluted into a final volume of 25μl. Test libraries were sequenced using a single lane of Illumina MiSeq paired-end 150 bp sequencing at the McGill University and Genome Quebec Innovation Centre.Extracted DNA was quantified with a Qubit 3.0 Fluorometer and the Qubit dsDNA High Sensitivity DNA Quant Kit (Invitrogen). Library preparation followed the standard GT-seq protocol , with thhttps://github.com/GTseq/GTseq-Pipeline). We removed loci that met these criteria: 1) primers with unequal counts indicating non-specificity of in silico probes; 2) candidate loci exhibiting >2% of the raw read count; 3) loci with no counts indicating off-target amplification or in silico probe variation; 4) loci contributing to potential PCR artefacts; and 5) observed primer dimers. A second test library was prepared with an optimized primer pool and the same samples as library 1 according to the protocols detailed above for sample preparation, sequencing, and primer dropout.Demultiplexed raw sequencing files were processed with the GT-seq pipeline available on GitHub (GTseq_ped_converter3.py (https://github.com/schmanii/GT-seq). Individuals and loci with >30% missing data were removed from downstream analyses with PLINK [genoerrorcalc.py .Raw sequencing files for the same individual were concatenated across libraries. Genotypes were called and compiled into one file that was converted to a .ped file with th PLINK . We thenθ loci  and varying proportions of training individuals .We conducted self-assignment tests using the leave-one-out procedure and the Rannala and Mountain (1997) likelihood estimation method as implemented in ONCOR , 47. AddRADseq_kokanee pure stock populations with the method of Rannala and Mountain (1997) as implemented in ONCOR and the support vector machine algorithm as implemented in assignPOP [We filtered genotypic data by individual and locus with a 30% missing data threshold in PLINK  to accurssignPOP , 47, 50.® genotyping assays . We genotyped these samples using the final optimized GT-seq panel and a Mid Output Reagent Kit (300 cycles) on an Illumina MiniSeq.To compare assignment results across methods, we selected previously extracted DNA samples from  that werdoi.org/10.17504/protocols.io.byvhpw36) and constructed a GT-seq library with the final optimized panel and the methods detailed above.To extend an annual time-series conducted for Wood Lake since 2012, operculum punches were collected from angler surveys during the spring in 2018 (n = 65) and 2019 (n = 100). These samples were collected by personnel from the British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development, which is the management authority charged with issuing permits and managing freshwater fisheries in the Province. Collection procedures also adhered to University of British Columbia Animal Care protocol # A19-0103. We extracted DNA with Chelex  method in ONCOR [K = 2 to assign individuals to the stream or shore cluster following [The Wood Lake samples were sequenced using a partial lane of Illumina HiSeq paired-end 150 bp sequencing at the McGill University and Genome Quebec Innovation Centre. All individuals were genotyped by locus and individual, and filtered at a 50% missing data threshold in PLINK , as the in ONCOR , 47. In in ONCOR , 43. We ollowing . Individollowing .RADseq_kokanee [Mixed stock proportions were estimated for each year with 95% confidence intervals calculated from 10,000 bootstrap replicates using the conditional maximum likelihood-based approach as implemented in ONCOR and reference baseline data from pure stock Wood Lake shore- (n = 33) and stream-spawning (n = 34) kokanee in _kokanee .K = 8, mirroring results conducted from the full set of neutral SNPs from [The finalized SNP pool (n = 547) before primer design included those that exhibited the highest divergence across geographic basin (n = 102), migratory form (n = 87), reproductive ecotype (n = 182), or overlapped across multiple comparisons (n = 176). Bayesian clustering analyses conducted with the initial 547 candidate loci revealed an optimal value of NPs from . Individin silico testing. Following multiplex amplicon sequencing and subsequent primer pool optimization, we retained a final optimized panel of 288 SNPs  estimator . The macAfter filtering for missing data, we retained 56 novel individuals at 261 loci . Assignment accuracy was 100% with the Rannala and Mountain (1997) estimator, and 98% with the support vector machine algorithm to the correct geographic location, migratory form, and reproductive ecotype .® SNP genotyping assays . Overall assignment to Wood Lake shore- or stream-spawning kokanee was 99.3% concordant between ONCOR [We successfully genotyped Wood Lake individuals at 242 SNPs after filtering for missing data  that were previously analyzed in [en ONCOR  and STRUWe successfully genotyped Wood Lake individuals from 2018 (n = 62) and 2019 (n = 92) at 282 SNPs after filtering for missing data . Individual assignment was 98.7% concordant between ONCOR  and STRUThe ability to accurately differentiate life history variants such as migratory form and reproductive ecotype is vital for estimating fisheries management parameters such as recruitment and mortality in mixed-stock fisheries , 52–54. θ signatures for reproductive ecotype, geographic basin, migratory form, or a combination of these categories. However, self-assignment accuracy from the machine learning framework was consistently >90% when analyzing data from the entire panel simultaneously, regardless of the specific level at which information was required; for example, inclusion of loci that were initially selected based on high θ at the among-geographic basin level did not reduce assignment accuracy between co-occurring reproductive ecotypes within single systems . Although this cross-category signal may not necessarily be the case for multi-scaled SNP panels developed in other species or systems, the ability to simultaneously use the full set of 288 SNPs provides enhanced power due to an increased number of loci while limiting filtering steps and facilitating downstream analyses.Regarding initial panel development and optimization, selection of an appropriate population assignment method is important for determining panel composition and overall utility. For example, assignment results from the leave-one-out approach  with the Rannala and Mountain (1997) estimator  were nota priori assumption of location to provide system-specific signal to stock assignment, especially in mixed stock fisheries  may have been affected by noise due to the grouping of ecotypes across geographic basin within this analysis. This result could indicate that the genetic signature of geographic basin across the province is stronger than that of reproductive ecotype, where shore- and stream-spawning kokanee populations within the same lake were found to be more closely related to each other than to their corresponding ecotypes in other lakes . The repSize of initial reference population baselines is another important consideration in GSI that can be effectively addressed using data from GT-seq. Minimum baselines for GSI have been suggested with increased sample sizes found to improve resolution in closely-related groups . GT-seq Comparison of the same SNPs can also be difficult in RADseq due to the combination of restriction enzyme digestion variation and bioinformatic assignment of RADtags, even with alignment to a reference genome. Though capture probes have been integrated into RADseq protocols to allow for sequence capture (e.g. Rapture) , these mO. nerka in Alaska found that a minimum of 43 markers was required to achieve >90% accuracy for population assignment across two creeks, revealing potential lower limits needed for high accuracy [More generally, GT-seq panel designs have revealed potential trade-offs to consider depending on panel intent and species characteristics. For example, a large primer pool designed for optimal panel performance needs to be balanced with the increased likelihood for primer interaction as well as the cost of subsequent primer drop out . This obaccuracy , 54. ComRADseq_kokanee baseline to specific stocks spanning geographic basin, migratory form, and reproductive ecotypes across British Columbia. Moreover, we demonstrated a real-world application to Wood Lake, a valuable mixed-stock fishery of co-occurring shore- and stream-spawning kokanee that has been subject to long-term, genetic stock assessment using a range of different molecular markers, from expressed sequence tag-linked microsatellites to TaqMan® SNP genotyping assays [In addition to considerations related to GT-seq panel composition, optimization and quality control, the accuracy of any GSI tool in real-world applications is of paramount importance. Our multi-scaled GT-seq panel showed high accuracy (100%) to assign novel individuals using the entire g assays . Shore-sg assays , 35, as g assays . Recent g assays . Previoug assays . With oug assays . Moreoveg assays . AdditioRADseq_kokanee). Although it remains unclear the degree to which this panel can be applied to systems outside of British Columbia, at minimum we expect these markers to have the capacity to identify individuals to migratory form and reproductive ecotype given the inclusion of SNPs found to be informative for this purpose from previous studies [O. nerka GT-seq panel to inform temporal and spatial monitoring of mixed-stock kokanee fisheries moving forward.Overall, our results demonstrate that this multi-scaled GT-seq panel is highly informative for guiding kokanee fisheries management within the systems included in the baseline database Click here for additional data file.S2 TablePrevious assignments indicated in greyed boxes. Red text indicates ambiguous assignment, blue text indicates confirmed assignment for previously ambiguous assessment.(XLSX)Click here for additional data file.S3 TableRed text indicates ambiguous assignment.(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.