FROH) is associated with a 7% lower probability of ever racing. Moreover, a ROH-based genome-wide association study identified a haplotype on ECA14 which, in its homozygous state, is linked to a 32.1% lower predicted probability of ever racing, independent of FROH. The haplotype overlaps a candidate gene, EFNA5, that is highly expressed in cartilage tissue, which when damaged is one of the most common causes of catastrophic musculoskeletal injury in racehorses. Genomics-informed breeding aiming to reduce inbreeding depression and avoid damaging haplotype carrier matings will improve population health and racehorse welfare.Small effective population sizes and active inbreeding can lead to inbreeding depression due to deleterious recessive mutations exposed in the homozygous state. The Thoroughbred racehorse has low levels of population genetic diversity, but the effects of genomic inbreeding in the population are unknown. Here, we quantified inbreeding based on runs of homozygosity (ROH) using 297 K SNP genotypes from 6128 horses born in Europe and Australia, of which 13.2% were unraced. We show that a 10% increase in inbreeding ( The sRuns of homozygosity (ROH), long stretches of the genome identically inherited from both parents, can occur as the result of active inbreeding or other demographic processes that reduce effective population size . The proF) in the Thoroughbred found a strong negative relationship between inbreeding and racing performance measures , electronic supplementary material, table S3), corresponding to a predicted decrease in the odds of racing by 44% for a 10% increase in FROH. It is also possible to translate these effects into predicted probabilities of racing (FROH = 0.40) had roughly a 13% lower probability of ever racing than a horse with the lowest observed inbreeding coefficient (FROH = 0.18). Horses that were 10% more inbred than average had a 7% lower probability of ever racing, while horses with a FROH 10% lower than the mean had a 4% higher probability of racing . To test for differences in inbreeding depression between EUR and ANZ horses, we also fitted a model with an interaction between FROH and region (EUR and ANZ), but the slopes were very similar and the interaction was not significant . In addition to modelling inbreeding effects on whether a horse had raced at all, we also fitted a model to test whether FROH is linked to a lower number of races among those horses that did race, but here FROH was not statistically significant .To test the hypothesis that inbreeding influences failure to race, we modelled the effects of inbreeding (f racing . Accordi (c)FROH_long (log(OR) [95% CI] = −6.57 ) corresponding to a predicted decrease in the odds of racing by 48% for a 10% increase in FROH_long.Long ROH had a strong negative effect on the probability of racing, while short ROH had no effect ( (d)p = 9.09 × 10−8) for a ROH overlapping the minor allele A at SNP rs1144708552 at position 63883487 bp on ECA14 (log(OR) [95% CI] = −1.82 ; FROH constant. The 64 ROH + AA horses were the progeny of 40 sires. 31.25% of ROH + AA horses were unraced, compared to the sample average 13.1%. Furthermore, ROH + AA horses that did race had on average 13.3 races, compared to the sample average of 16.3 races, suggesting that while some ROH + AA horses may race, they are less durable. Notably, genome-wide inbreeding (FROH) also showed a strong effect on the probability of racing in the same model (log(OR) [95% CI] = −6.39 ; electronic supplementary material, table S9). Consequently, inbreeding depression in the Thoroughbred is largely due to the genome-wide effects of many recessive deleterious mutations in addition to a larger effect recessive mutation expected to be in proximity to SNP rs1144708552.The GWAS uncovered a single significant hit (p = 0.006) overrepresented among breeding stallions (n = 130), where it had a frequency of 15.4% compared to 10.1% in the overall sample.Using phased genotype data, we screened haplotypes surrounding the rs1144708552 SNP. Specifically, we focussed on haplotypes of length 1 Mb, including all SNPs occurring up to 500 kb on each side of rs1144708552. Among ROH + AA horses, 78.1% were homozygous for an identical haplotype , which we refer to as THR14. All but one ROH + AA horse carried at least one exact copy of THR14, and where haplotypes differed from THR14, this was on average by only 2 out of 84 base positions that make up the haplotype. THR14 was significantly ( (e)FER, FBXL17 and EFNA5) in the region was EFNA5 (ECA14: 63365481–63631761 bp), which was also the closest (approx. 250 kb) gene to the rs1144708552 SNP .Since most (approx. 80%) of the ROH in the identified region extend across greater than 2.8 Mb we searched a 3 Mb region surrounding the rs1144708552 SNP (±1.5 Mb) for genes with biological functions that may impact on survival or viability of a horse. The most compelling candidate gene among the three protein-coding genes , which encodes the ephrin ligand, ephrin-A5, is a compelling candidate gene for the negative effect of the THR14 haplotype on racing. Ephrin A5 is a member of the ephrin family of ligands and receptors that are broadly expressed during tissue development and repair [d repair –42. Ephrd repair . Ephrin d repair –49. Gened repair , which id repair ,52. The d repair . Ephrin d repair  and is hd repair  where itd repair . Althougd repair . Pre-exid repair  resultind repair ,60. In tEFNA5 and the haplotype association with the probability of racing that we report here lead to the hypothesis that it may play a role in musculoskeletal injury risk; however, this must be tested in a population of horses with well-defined phenotypes. While the nature of the hypothesized causative mutation on THR14 is not known, we provide here strong evidence for the presence of a mutation with a negative effect on racing located within this haplotype that has not previously been described.There are many reasons that a foal may not become a viable racehorse –20. HoweIn summary, the introduction of genome-enabled breeding strategies to avoid the production of long homozygous stretches in the genome and homozygosity of the THR14 haplotype could improve economic returns for breeders and positively impact animal welfare. Therefore, industry-guided monitoring of genome-wide inbreeding over time will be important to maintain sustainable populations of horses, with particular attention focused on higher resolution information that can be obtained for deleterious haplotypes such as THR14.


Ostrya japonica and O. chinensis) are used to explore what these potential forces might be. A total of 40 individuals from 16 populations across their main distribution areas in China were sampled for genome‐wide resequencing. Population demography analyses revealed that these two sister‐species diverged at 3.06–4.43 Mya. Both population contraction and increased gene flow were detected during glacial periods, suggesting secondary contact at those times. All three parameters  decreased in those regions showing high levels of differentiation (FST). These findings indicate that linked selection and recombination played a key role in the genomic heterogeneous differentiation between the two Ostrya species. Genotype–environment association analyses showed that precipitation was the most important ecological factor for speciation. Such environmentally related genes and positive selection genes may have contributed to local adaptation and the maintenance of species boundaries.Studying the evolution of genomic divergence between lineages is a topical issue in evolutionary biology. However, the evolutionary forces that shape the heterogeneous divergence of the genomic landscape are still poorly understood. Here, two wind‐pollinated sister‐species ( Ostrya species, we revealed that linked selection and recombination played a key role on the genomic heterogeneous differentiation. Besides, the environment‐related genes and positive selection genes may also contribute to local adaptation and the maintenance of species boundary.Based on the whole genome resequencing data of the two sister  On the contrary, O. chinensis is an endemic species in China and only contained sparely records in southern and southwestern regions . To reduce the repeat samples of each population, only two trees at least 50 m apart, we collected healthy fresh leaves and dried them using silica gel. A total of 26 O. japonica trees from 12 populations across its geographic distribution in China  we deleted the SNP sites that locate around the 5 bp region of InDels; (ii) we marked SNPs as missing if their coverage was lower than one‐third of or higher than three time the average depth and removed the SNP sites with a missing rate >20%, and (iii) we removed the SNP sites in repeat regions.2.3Carpinus cordata and C. fangiana individuals were also collected (NCBI BioProject: PRJNA428015) as outgroups and mapped to the O. rehderiana genome using the same method as described above. We used concatenated data sets of SNPs to generate a neighbor‐joining tree. We calculated a pairwise genetic distance matrix and generated the neighbor‐joining tree with the “dnadist” and “neighbor” programs in PHYLIP v 3.69.660  using “smartpca” program within EIGENSOFT v 6.1.4  model  environmental factors using ENMTools  and pairwise genetic divergence(FST) were calculated by VCFtools v 0.1.13  were calculated in population scale by FastEPRR  between the two species was calculated according to the formula  were denoted as dij. All parameters were estimated with the 20‐kb window and 5‐kb step‐size; windows including fewer than 10 SNPs were discarded.The nucleotide diversity (2.7FST (top 1%) across the whole genome were selected and then combined by permutation and empirical approaches  described by David et al. and given by the formula in .The outlier CNGs between 2.9FST of each gene was calculated; we counted the number of SNPs within one population (= N1) and the number of fixed loci (FST ≥ 0.95) between two populations (= N2). For each gene, we performed 2 × 2 contingency table filled with N1(gene), N2(gene), and the genome‐wide average N1(genome) and N2(genome). The null hypothesis was N1(gene):N2(gene) = N1(genome):N2(genome), and we used Pearson's chi‐square test to compare the ratio of N1(gene):N2(gene) to N1(genome):N2(genome). Only genes whose a p‐value ≤.01 from the HKA, and more than one nonsynonymous site which annotated and predicted by SnpEff  from WorldClim (https://www.worldclim.org) and downloaded the soil database from FAO (https://www.fao.org). We collected the relevant environmental data for each individual and calculated correlation coefficients for each environmental predictor using the function pairs.panels in R , we derived seven environmental predictors as follows: subsoil sand fraction (SSF), subsoil pH (Sp), solar radiation (SR), max temperature (MaT), min temperature (MiT), mean diurnal range (MDR), and annual precipitation  and determined those outlier variations loaded in the tails of distributions (three standard deviations) in these constrained axes. Finally, we used the cor, one R function to calculate the correlations of each outlier's variations with the seven environmental predictors.We downloaded climate and weather data  between natural populations was calculated for environment‐associated SNPs and neutral SNPs, respectively. The Mantel method in vegan was used to test for associations between FST/(1 − FST) and geographic and environmental distance.To evaluate the false positives that may due to neutral variations in genotype‐environment associations analysis, we performed the following analyses. A total of 23,622 SNPs located in intergenic regions, which were considered neutral variations. Then the Isolation‐by‐distance analyses and Isolation‐by‐environment analyses  of each species as revealed by the MSMC model showed both species to have a similar maximum Ne size at ~2.0 Mya. This was followed by a dramatic decline in Ne to almost one tenth, during the development of two major glaciations: the Xixiabangma Glaciation  and Naynayxyngla Glaciation . Both species then experienced two effective population size change events. O. japonica and O. chinensis experienced the first bottlenecks during 2.58–0.63 and 1.50–0.43 Mya, respectively, each followed by population expansion, which findings are generally consistent with the MSMC results. We also detected a continuous asymmetric bidirectional gene flow during their divergence, in which there was a stronger gene flow from O. japonica to O. chinensis at the early diverging stage, and in reverse a stronger gene flow from O. chinensis to O. japonica occurred in the more recent period. We also detected extensive stronger gene flow between 2.58 and 0.43 Mya than was seen either in the early divergence stage (3.74–2.58 Mya) or during the more recent period (<0.43 Mya). We further investigated the potential distribution regions of these two species by the ecological niche estimation. We found that O. japonica migrated to the south which was warmer than the north during the LGM. The distributions of O. japonica and O. chinensis were highly overlapping, while when the climate warms up after glacial, O. japonica expanded to the north and O. chinensi continued to expand in south  than observed in O. chinensis . This may be due to the O. japonica Ne being larger than that of O. chinensis in recent time  was found to be 0.347, and the mean absolute genetic divergence (DXY) was 4.59 × 10−3 ± 2.34 × 10−5. The FST values showed a U‐shaped distribution with 461,371 fixed divergence sites accounting for 12.81% of the total number of SNPs in the two species , was negatively correlated with both π and ρ  analyses showed that the ρ value for islands was significantly lower compared to the ρ value for genome background . These were associated with their response to UV, DNA repair, and the cell surface receptor signaling pathway . By permuting all individuals 1000 times and calculating 3.5p < .01) correlated with these genomic variances  cutoff from the mean along each significantly constrained axis. A total 4825 outlier SNPs and 990 outlier CNGs were identified, both of which distinguish the two species by PCA analyses  between the two species, which may relate to the environmental adaptation. Seven environmental predictors with low correlations were selected Figures  and S14,es Table . Both RDes Table . Both sees Table . We alsos Figure , indicats Figure , suggests Figure .p < .01). We also found the RDA1outlier SNPs, which contain the major outliers, showed a much higher FST value than the background, while the RDA2 outliers had no such increase of FST  between natural populations with outlier SNPs and neutral SNPs. The outlier SNPs displayed significant association with geographic and environmental distance, but the neutral SNPs not  and showed a high level of gene flow around 1.5 Mya, suggesting the secondary contact may exist during such geological events and accelerate the gene flows  were significantly negatively correlated , and about 28 genes related to roots ; data curation ; formal analysis ; investigation ; methodology ; validation ; visualization ; writing – original draft . Shangzhe Zhang: Data curation ; formal analysis ; methodology . Zeyu Zheng: Formal analysis ; methodology (supporting). Zhiqiang Lu: Conceptualization ; funding acquisition ; investigation ; methodology ; resources ; writing – review and editing . Yongzhi Yang: Conceptualization ; funding acquisition ; investigation (supporting); methodology (supporting); project administration ; resources ; writing – original draft (supporting); writing – review and editing (supporting).The authors state that there is no conflict of interest.This work was supported by the National Natural Science Foundation of China (Grant No. 31900201) and Ph.D. Programs Foundation of Department of Education of Gansu (Grand No. 2021QB‐007).This article has earned Open Data and Open Materials badges. Data and materials are available at [[insert provided URL(s) on the Open Research Disclosure Form]].Appendix S1Click here for additional data file.


Most of the QTL were linked to previously reported QTLs or candidate genes that have been previously reported to be associated with meat quality, pH and pork color; e.g., VIL1, PRKAG3, TTLL4, and SLC11A1, USP37. CTDSP1 gene on SSC15 has not been previously associated with meat color traits in pigs. The findings suggest any added value of WGS was only for detecting novel QTL regions when the sample size is sufficiently large as with the Combined dataset in this study. The percentage of phenotypic variance explained by the most significant SNPs also increased with WGS compared with medium-density panels. The results provide additional insights into identification of a number of candidate regions and genes for pork color traits in different pig populations.Imputed whole-genome sequence (WGS) has been proposed to improve genome-wide association studies (GWAS), since all causative mutations responsible for phenotypic variation are expected to be present in the data. This approach was applied on a large number of purebred (PB) and crossbred (CB) pigs for 18 pork color traits to evaluate the impact of using imputed WGS relative to medium-density marker panels. The traits included Minolta A*, B*, and L* for fat (FCOL), quadriceps femoris muscle (QFCOL), thawed loin muscle (TMCOL), fresh ham gluteus medius (GMCOL), ham iliopsoas muscle (ICOL), and longissimus dorsi muscle on the fresh loin (FMCOL). Sequence variants were imputed from a medium-density marker panel (61K for CBs and 50K for PBs) in all genotyped pigs using BeagleV5.0. We obtained high imputation accuracy (average of 0.97 for PBs and 0.91 for CBs). GWAS were conducted for three datasets: 954 CBs and 891 PBs, and the combined CBs and PBs. For most traits, no significant associations were detected, regardless of panel density or population type. However, quantitative trait loci (QTL) regions were only found for a few traits including TMCOL Minolta A* and GMCOL Minolta B* (CBs), FMCOL Minolta B*, FMCOL Minolta L*, and ICOL Minolta B* (PBs) and FMCOL Minolta A*, FMCOL Minolta B*, GMCOL Minolta B*, and ICOL Minolta B* (Combined dataset). More QTL regions were identified with WGS ( Sus scrofa chromosomes (SSC) 1, 5, 9, 15, 16 and the X chromosome were identified  and ultimate pH (genetic correlation of -0.37 ± 0.16) . Therefoentified . The regentified . Yang etTo date, most GWAS have used medium-to high-density marker panels to detect the genomic regions associated with carcass and meat quality traits. Use of whole-genome sequence (WGS) is expected to improve identification of associated regions (in terms of both distinct and extended candidate regions and identifying novel genomic regions), because most of the causative variants are expected to be within WGS. The causative SNPs have low MAF (rare variants) and their variance is expected to be captured using WGS. According to simulations, using WGS data for GWAS, the precision of mapping for rare variants increased considerably, which supports the efficiency of WGS in detecting and fine-mapping of low frequency variants simultaneously . IdentifThrough imputation, based on WGS, the missing variants in the target population can be predicted by use of linkage and segregation analysis. Imputation accuracy is an important factor for more accurate detection of associated regions. The use of imputed WGS has been more common in GWAS for pig traits in recent years . Van denWe performed GWAS for 18 meat color traits including Minolta L*, A*, and B* for fat (FCOL), quadriceps femoris muscle (QFCOL), thawed loin muscle (TMCOL), fresh ham gluteus medius (GMCOL), ham iliopsoas muscle (ICOL), and longissimus dorsi muscle on the fresh loin (FMCOL). Analyses were conducted for two datasets: 954Regina, SK, Canada). Phenotypes of 18 meat color traits were available for 1,037 commercial crossbred pigs ). Also, phenotypes of 15 meat color traits were available for 891 purebred Duroc females. The list of the 18 meat color traits and their abbreviations are given in This study was performed using the data provided by Hendrix Genetics  had both phenotypes and genotypes with a custom 61K Genomic DNA extraction from blood and tissue was carried out using the Qiagen DNeasy extraction protocol  by Delta genomics. Extracted DNA was quantified using the Qubit dsDNA HS Assay . 100ng to 1ug of gDNA was sheared using the Covaris S2 focused sonicator (Covaris Inc.) to achieve a fragment size ranging from 300 to 400bp. Sheared DNA fragments were used for library preparation according to respective library preparation protocol that were compatible with Illumina next generation sequencing platform. Quality check and library preparations were done by NEOGEN Canada . Sequencing was done by McGill University and Génome Québec Innovation Centre . Libraries were normalized and pooled and then denatured in 0.05N NaOH and neutralized using HT1 buffer. ExAMP was added to the mix following the manufacturer’s instructions. The pool was loaded at 200pM on a Illumina cBot and the flowcell was run on a HiSeq X for 2 × 151 cycles (paired-end mode). A phiX library was used as a control and mixed with libraries at 1% level. The Illumina HiSeq Control Software was HCS HD 3.4.0.38, and the real-time analysis program was RTA v. 2.7.7. Program bcl2fastq2 v2.20 was then used to de-multiplex samples and generate fastq reads.depth in VCFTOOLS (Sus scrofa 11.1 (https://uswest.ensembl.org/Sus_scrofa/Info/Index), www.ensembl.org/biomart/martview) with BWA mem (BWA 0.7.17) using the default parameters (MarkDuplicates from Picard v2.18.2 (http://broadinstitute.github.io/picard/). Variants (SNPs and insertion-deletions (INDELs)) were called using GenomeAnalysisToolKit-3.8-1-0 (GATK)  . Tool HaintReads . Finallyp-value < 0.01 calculated with Fischer’s exact test, two or more alternative alleles, a MAF <0.025, missing observation of the alternative allele on either the forward or reverse reads, being located within 4 bp of each other, being located within 5 bp of an INDEL, a mapping quality (MQ) score of <40, a phred score <20, a read depth (DP) of less than 10% of median or more than median plus 3 standard deviation of read depth, a quality depth (QD) < 5. We also removed sex chromosomes. After filtering, 11, 946, 148 SNPs on autosomes (SSC1 to SSC18) remained for the 60 animals across the whole-genome , 61K (for 954 CBs) and 660K (for 17 PBs) were as follows: SNPs were excluded if they were duplicated, if they had a MAF <0.01. Furthermore, SNPs with genotype call rate <0.95 and SNPs with unknown map positions were removed. The quality control of genotypes was done for each trait separately, because the number of animals with both genotypes and phenotypes differ among the pork color traits. The numbers of SNPs after these exclusions are indicated in Beagle V5.0  was used2 generated by Beagle, which is defined as the squared correlation between the expected dose  + 2*P(BB)) and the true dose  which are abundant in WGS. Evaluation of imputation accuracy was done in two ways. The first measure of imputation accuracy per SNP was obtained from the allelic DRrue dose . Second,rue dose  to compaTo assess whether a two-step imputation strategy would improve imputation accuracy compared with a one-step imputation strategy, particularly for low MAF SNPs , we perf2) produced by Beagle  using ASReml program V4.0 .When the CBs and PBs were combined for variance component estimations, the heterogeneous genetic and residual variances were fitted in the model. Since CBs contained both males and females individuals, while PBs contained only female individuals, first an animal model was fitted to check the difference between the residual variances in CB and PBs as well as the difference between the residual variances between the male and female individuals. For all traits, the residual variances were different between the two populations as well as between the two sexes. Then, the first model was expanded to check if there was a difference between the genetic variances between the two populations  implemented in GCTA version 1.92.1beta6 . The stae was a vector of random residual effects. It was assumed that Where are tool . GWAS wap-values for each trait were corrected for their corresponding genomic inflation factor   plot for each trait was used to evaluate the inflation of p-values by comparing the genome wide distribution of -log10 of the p-values with the expected median of the corresponding chi squared distribution assuming a degree of freedom of one.The significance threshold of SNP effects was assessed by using a false discovery rate (FDR) of 0.1 . Such th lambda) . Lambda r2, defined as the correlation between alleles of two SNPs harbored at different loci  between SNP pairs were computed for SNPs located within 2000 Kb windows and shorter (LD decay pattern between pairwise SNPs (imputed sequence) was evaluated for both CBs and PBs. The pairwise LD values  regions according to the definition described by than 0.3  when thep and q are the allele frequencies of major and minor alleles, and a is the estimated allele substitution effect. It should be noted that for the Combined dataset, the average of phenotype variance of crossbreds and purebreds was used for computation of variance explained.The percentage of phenotypic variance explained by each SNP was estimated as: Sus scrofa 11.1 (https://www.ensembl.org/info/data/biomart/). Genomic regions associated with the pork color traits were identified using a 1 Mb window (up- and down-stream of significant peak). The ClueGo plug-in  was used in pathway and biological processes investigation. We selected the 5th to the 10th levels of the GO hierarchy and a kappa score of 0.4  and PBs (n = 891) populations, which was computed in Plink using the principal component analysis (PCA) procedure. The common SNPs between 61K and 50K (∼30K) were used for plotting. The blue color shows the PB animals and the red color shows CB pigs. The CB individuals are dispersed across the plot.The distribution of MAF from the 61K and 50K SNP panels were uniform, whereas the distribution of MAF from WGS was U-shaped with a substantial proportion of SNPs with small MAF values (approximately 19% of SNPs had a MAF lower than 0.025) . MAF dis2 from the 61K and 50K SNP panels to sequence imputation before any filtration was 0.80 and 0.90 across all chromosomes, for CBs and PBs, respectively  and SSC4 (0.84), respectively. For PBs, the smallest Beagle DR2 were obtained for SSC2 and SSC12 (0.88) and the largest Beagle DR2 were obtained for SSC4, 8, 13, and 15 (0.92). Across all chromosomes, the average allelic DR2 was larger for PBs than CBs.The average allelic DRectively . After f for PBs . The num2 against MAF for CBs and PBs are shown in 2 reached a plateau at about 0.15 for both CBs and PBs. When we performed filtration on Beagle allelic DR2, most SNPs with a very low MAF (<0.01) were removed. Also, the average imputation accuracy was higher for PBs compared with CBs, which is most likely due to the higher genetic relationships between the sequenced pigs  and the PBs  compared with CBs. Moreover, CBs receive alleles from two other purebred parental lines and these lines are not represented in the reference panel for imputation.The distribution of allelic DRThe average animal-specific imputation reliability across the 55 sequenced PBs  for SSC18 was 0.94 using Beagle V5.0 and 0.91 using FImpute . Since t2) was higher (0.90) for one-step imputation approach compared with the two-step imputation approach (0.85)  in one-step (50K to WGS) and two-step imputation (50K to 660K to WGS) procedures, which are plotted against MAF. As shown, the imputation accuracy of low MAF SNPs (MAF <0.02) remains challenging. The average allelic DR2 across the genome was 0.996 for one-step approach and 0.985 for two-step approach. Due to very small difference in imputation accuracies between the two approaches, we performed the GWAS analyses only for the imputed variants from the one-step method.For all chromosomes, the mean imputation accuracy  . After frocedure . Figure Variance components and heritability estimates obtained from different datasets  for each color trait are in p-values is lower when WGS data was used compared with the medium-density SNP panel.Putative family stratifications were accounted for the GWAS analyses by incorporating the full genomic covariance among animals. Lambda ranged from 0.77 for ICOLB in Combined dataset (61K CBs plus 50K PBs) to 1.00 for FMCOLB in sequenced PBs, and the mean lambda across all traits was 0.91 (results not shown), suggesting that any potential bias and any major effect of population stratification was taken into account in the GWAS analyses. For all traits for which the QTL regions were found and for all datasets , lambda increased slightly as SNP density increased . This shFor most color traits , zero associated regions were detected at FDR of 0.1. Total number of significant SNPs, number of QTL regions, FDR threshold, and genomic inflation factor values are given in For all traits and using the medium-density panels, 22 QTL regions containing 71 significant SNPs at a genome-wide FDR of 0.1 were detected, whereas 58 QTL regions comprising 16,261 significant SNPs were detected at the same significance level using WGS data –6. The tGenerally, the number of QTL regions increased with increasing panel density mainly for PBs and the Combined dataset, and did not change for CBs. For instance, for GMCOLB, the number of detected QTL region was only 1, regardless of what SNP density 61K or WGS) were used. For PBs, the additional QTL regions were located on SSC2 at 142.79–144.77 Mb and on SSC8 at 34 Mb, for FMCOLB, and on SSC10 at 38.13–38.29 Mb for FMCOLL . Of all 1K or WGSFor all datasets  and for all SNP panel densities , the majority of the significant SNPs were on SSC15 –6. For WBesides the increase of the number of QTL regions with WGS, the percentage of the phenotypic variance explained by the most significant SNPs also increased by WGS compared with medium-density panels . Figure PRKAG3 have been previously reported by CNOT9, PRKAG3, CDK5R2, VIL1, TTLL4, CTDSP1, SLC11A1, ZFAND2B, USP37, RNF25, STK36, FEV, WNT6, IHH, WNT10A, NHEJ1, TMBIM1 are involved in the regulation of protein phosphorylation processes, proteolysis, intracellular transduction, and negative regulation of cell communication. In addition, CATIP and ARPC2, VIL1 and BCS1L are involved in actin filament organization. There is evidence in the literature that meat color stability is inversely related to the phosphorylation of sarcoplasmic proteins (Candidate genes (and their functions) located on significant regions and/or nearby regions identified by WGS for meat color traits in different populations (crossbreds (CBs), purebreds (PBs), combined CBs and PBs) are given in proteins . An examIn this study, we first assessed the imputation accuracy to WGS for two pig populations; CBs and PBs, using a small reference population of 60 sequenced PB key ancestors . Then, using the imputed WGS, we investigated whether the use of WGS data in a GWAS for pork color traits will improve the identification of the associated regions with respect to the extended QTL regions and/or detection of novel QTL regions in a sequenced-based GWAS relative to a medium-density SNP panel. The superiority of WGS over SNP panels is because of the existence of causal variants (rare variants responsible for phenotype variation) and rare variants with low LD with the SNPs on a medium-density panel (which most have moderate MAF), as the variance explained by these causal and rare variants can be better captured by WGS. Moreover, due to the relatively small size of our CB and PB populations , the Combined dataset was used in a GWAS, to assess whether enlarging the sample size will improve the potential advantage of WGS and enhance the power of detecting QTLs. Our imputation results showed a relatively high imputation accuracy obtained by Beagle V5.0 for both PBs (0.97) and CBs (0.91) after filtering the less accurate imputed genotypes (<0.8). Of the 18 pork colors, using different datasets, the genetic associations were identified only for a few traits –6, and wversus 61K), PBs imputation accuracies are higher, which is likely due to the larger genetic relationships between the 60 reference sequenced pigs and the female PBs in the validation, as both population are Duroc and results in sharing more and longer haplotypes between the two populations , and 0.93 for MAF above 0.028  with simulated data, and for accuracy ranging from 0.51  for MAF <0.001 to 0.93  for MAF ≥0.028 in pigs. Even though 2, which is reliability, whereas the measure reported by 2 to correlations, our imputation accuracy becomes even higher (r = 0.59 to 0.81 for CBs and r = 0.71 to 0.94 for PBs). This suggests that the overall performance of Beagle V5.0 for imputation of low MAF SNPs was good, even with a small reference population size and small genetic relationship between the CBs and PBs. However, to be more certain about the performance of Beagle V5.0 compared with other imputation programs, we compared imputation accuracies from Beagle V5.0 and FImpute in a leave-one-out cross validation approach  compared with medium-density SNP panels (n = 22). Most of the identified QTL regions with all genotype densities were also reported in other GWAS studies that used the same color traits . Thus, the estimated SNP effects of surrounding variants were not independent due to LD. For all traits where the genomic region on SSC15 was significant, the variance explained by the most significant SNP was higher for WGS compared with medium density panels. The added value of WGS for improving the power of GWAS (with respect to the number of identified QTL) have been shown in several species including dairy cattle . The QTL regions were identified (with FDR of 0.1) only for a few traits including TMCOLA and GMCOLB (CBs), FMCOLB, FMCOLL, and ICOLB (PBs) and FMCOLA, FMCOLB, GMCOLB, and ICOLB (Combined dataset). Generally, we identified more QTL regions with WGS  due to imputation accuracy less than 0.8 were included in the Combined dataset GWAS analysis, and yet the power of GWAS improved. This shows that the imputation error rate is not really a limiting factor for GWAS. Similar results are shown by 2 in our study) is higher than the expected LD between the SNPs on the lower density panel (50K and 61K in our study) and the SNPs in the WGS data, imputation is advantageous, as more information is still added by imputation , they still found that using imputed WGS instead of a lower density SNP panel increased the number of detected QTL (48.9 and 64.4% more for their different lines) and the estimated proportion of phenotypic variance explained by these QTL  from the 60K SNP panel to WGS imputation in layers was 0.64, but they still observed an increase of prediction accuracy of 1% using WGS compared with 60K for number of eggs. All these results show that most likely the accuracy of the imputed genotypes is not a limiting factor for GWAS and genomic predictions.When the combined dataset was used for GWAS, many more QTL regions (11 for medium-density panels and 37 for WGS) were identified, suggesting that the added value of WGS was more for detecting novel regions compared with medium-density SNP panels in larger samples. This could be because with the larger sample size, the effect of causative mutations on polygenic quantitative traits might be estimated more accurately. Also, for the Combined dataset, we filtered the imputed genotypes based on the allelic DRputation . Averageputation . This mahese QTL . Also, Hciliogenesis-associated TTC17-interacting protein (CATIP), villin-1 (VIL1), protein kinase AMP-activated non-catalytic subunit gamma 3 (PRKAG3), tubulin tyrosine ligase like 4 (TTLL4), ubiquitin specific peptidase 37 (USP37), CTD small phosphatase 1 (CTDSP1) and solute carrier family 11 member 1 (SLC11A1) were consistently detected for all color traits reported here, hence they were considered the best candidates’ genes in the QTL region for the color traits. Genes such as VIL1, PRKAG3, TTLL4, and SLC11A1, USP37 have been previously reported to be associated with meat quality, pH and color . The genes reported in this study are involved in actin filament organization, regulation of protein phosphorylation processes, proteolysis, and intracellular transduction. There is evidence in the literature that meat color stability is inversely related to the phosphorylation of sarcoplasmic proteins such as myoglobin  to WGS. Additional QTL regions were detected when using the WGS data compared with a medium-density SNP panels. The performance of WGS relative to the medium-density panels is best when the sample size is the largest (combining cross- and purebreds), suggesting that sample size is a limiting factor to capitalize on the added value of WGS in a GWAS.


G6PC2, HDAC9, KTN1, MYLK2, NTM, SLC16A1 and SYNDIG1, with central roles in muscle, metabolism, and neurobiology, are key drivers of the racing phenotype. Although racing potential is a multifactorial trait, the genomic architecture shaping the common athletic phenotype in horse populations bred for racing provides evidence for the influence of protein-coding variants in fundamental exercise-relevant genes. Variation in these genes may therefore be exploited for genetic improvement of horse populations towards specific types of racing.Selection for system-wide morphological, physiological, and metabolic adaptations has led to extreme athletic phenotypes among geographically diverse horse breeds. Here, we identify genes contributing to exercise adaptation in racehorses by applying genomics approaches for racing performance, an end-point athletic phenotype. Using an integrative genomics strategy to first combine population genomics results with skeletal muscle exercise and training transcriptomic data, followed by whole-genome resequencing of Asian horses, we identify protein-coding variants in genes of interest in galloping racehorse breeds . A core set of genes,  A genome-wide analysis in galloping racehorse breeds identifies protein-altering genetic variants in a core set of genes that appear to determine attributes for racing, including muscle, neurobiological, and metabolic advantages. The quest for wealth and status in racing traverses diverse cultures and geographies and today mounted horse racing is a globally popular sport. Different populations of horse have been developed for racing through a process of selective breeding for attributes required to excel in specific types of competition; these include breeds that compete in harness and trotting races, and others, including the Arabian, Mongolian and Thoroughbred, that are galloping breeds.Horse racing is among the oldest known sports, the general concept of which—horses, either mounted or harnessed, travelling at speed over a certain distance and terrain with the horse finishing first being the winner—has been largely unchanged for millennia3. Mongolian horse populations have relatively high genomic diversity compared to other breeds5, which may reflect the role of the central Asian steppe region as an important centre for horse domestication6. The Mongolian is generally classified as a breed in toto but several phenotypically and genetically distinct subpopulations exist4, which are used for meat, milk, transport, and racing. Racing in Mongolia is celebrated annually during the Nadaam festival of racing where adult horses race over long distances (25–30 km) and harsh terrain. In recent years, Thoroughbred stallions have been imported for crossbreeding intended to improve speed traits in the racing populations.The Mongolian is one of the oldest extant horse populations and although domesticated, most animals are free ranging and experience minimal human intervention7. Bred for millennia and developed by Bedouin nomads for transport and military use, the Arabian has traditionally excelled in long distance endurance racing (80–160 km), often in extreme climatic conditions. More recently there has been strong selection among subgroups for an aesthetic conformation phenotype, which is valued in show competition, and for short-distance track racing (~1600 m). Among track racing Arabians there is evidence of recent Thoroughbred crossbreeding, presumably for the introduction of speed, with some horses having up to 60% Thoroughbred ancestry7. Although considered a highly polygenic trait, sequence variants at several genes have been reported to be directly associated with performance traits in the Arabian breed10.The Arabian is also an ancient breed with high levels of genetic diversity11. Most Thoroughbreds compete in races over much shorter distances (1000–3200 m) on maintained track surfaces and are bred for both speed and stamina attributes12. Originating from a very small number of founders13, and with subsequent restricted gene flow since the formation of the stud book14, the Thoroughbred now has very low levels of genetic diversity despite a large global census population size16. These population demographics, coupled with constant human-mediated selection pressures, have resulted in athletic traits with genetic architectures that are especially amenable to modern genomics, particularly because of the high levels of linkage disequilibrium observed across the Thoroughbred genome17 with haplotypes extending >4 Mb in regions under selection12. As a result, genome-wide association studies (GWAS) have been successfully deployed to identify quantitative trait loci (QTLs) for complex traits using relatively modest sample sizes21. Investigations of genomic targets of selection in the Thoroughbred23, and functional analyses of gene expression profiles in skeletal muscle26, have identified suites of genes and molecular pathways that are enriched for functions in energy metabolism, muscle contraction, haemostasis, organismal growth and development, lipid metabolism, the mitochondrion, fatty acid metabolism, cardiovascular signalling, cellular stress and injury, and neurotransmitters and other nervous system signalling.Compared to the Arabian and Mongolian breeds, the Thoroughbred was developed relatively recently during the last three centuries by crossing native British and Irish mares with stallions imported from the Middle East27. The most well studied and arguably core exercise-relevant gene in athletic horses is the myostatin gene (MSTN), where a SINE insertion promoter polymorphism28 profoundly affects skeletal muscle development30 and distribution of muscle fibre types32. The combined effect of the major MSTN QTL and additive genetic variation across the genome is illustrated by recent work examining the genomic architecture and heritability of optimum race distance in multiple Thoroughbred populations33.The recently developed omnigenic model proposes that phenotypic outcomes for eukaryotic complex traits are directly shaped by core genes that are embedded in highly interconnected tissue-specific gene regulatory networks, which are substantially modulated by very large numbers of genetic variants of small effect at peripheral genes across the entire genome34. Like all athletes, in addition to multiple anatomical, physiological, and metabolic processes, environmental factors and interactions also determine the elite athletic phenotype of racehorses36. Consequently, numerous endophenotypes likely contribute to biological systems relevant to the equine athlete. However, notwithstanding this complex vista, relatively straightforward selection signal detection approaches—without recourse to accurately measured endophenotypes such as hormone levels or other biomarkers—can be used to identify genes or genomic regulatory elements containing sequence variants contributing to recent evolutionary adaptation and important physiological traits in livestock species40.In addition to myriad genetic contributions, understanding the biological basis of complex traits is further challenged by the variation in multi-dimensional system-wide endophenotypes that can be dynamic and influenced by the environment, a concept that emerged initially in neurobiological genomics24 and then interrogated prioritised genomic regions for putative functional protein-coding variants identified from whole genome sequence (WGS) data generated from Asian landrace horse breeds. Based on predicted variant effects and the biological functions of the genes containing them, we hypothesised that these variants may contribute to variation in racing ability among horse breeds. To further explore and validate this hypothesis, we generated genotypes for a panel of these variants in independent sample sets of Thoroughbred and Mongolian Racing horses and in other racing and athletic horse breeds, and examined whether they were associated with racing traits. The overall goal of this research was to identify a set of genetic markers contributing to athletic performance in populations of horses bred for competitive racing. These markers may be used for the improvement of racing populations, including among Mongolian horses, for which there has been less opportunity for systematic pedigree and phenotype selection that is advanced in Thoroughbred and the other more common racing breeds.In the present study, we hypothesised that galloping racing breeds harbour signals of selection that contain genomic loci with sequence variants contributing to racing ability. To refine the selection signals, we assigned functional relevance to SNPs that were in proximity to differentially expressed genes (DEGs) in Thoroughbred skeletal muscleAn overview of the sequential steps of the study is provided in Supplementary Fig. 7 and there has been an introduction of Thoroughbred stallions to Mongolia in recent years, we assessed the contribution of Thoroughbred ancestry in the Arabian and Mongolian Racing populations  were hypothesised to reveal genomic regions contributing to similar genomic architectures that result from convergent evolution towards the racing phenotype and not gene flow.In summary, although there was some shared ancestry among the Racing populations, this was not widespread among individual animals suggesting that the observed Thoroughbred admixture most likely reflects recent breeding practices. Therefore, it is unlikely to influence detection of long-standing selection signals due to persistent selection over relatively extended time frames. Furthermore, the composite selection signals (CSS) approach used in this study combines component signals to detect only strongly selected regions that have a common signal across the constituent testsn = 90) and non-Racing (n = 483) breeds (Supplementary Data FST and ∆SAF statistics39. Genome-wide distribution of the smoothed CSS score test statistics (-log10P) for comparison of the Racing versus non-Racing populations identified 14 genomic regions with signals of selection, defined as clusters of SNPs (>5) among the top 1% SNPs, on ECA1, ECA2, ECA4, ECA5, ECA7, ECA9, ECA14, ECA17, ECA18, and ECA22 7.To identify genomic regions targeted by selection for the racing phenotype, we compared allele frequency distribution variation among two data sets comprising horses from Racing  overlapped with a GWAS peak for measured speed traits in juvenile Thoroughbreds43.There was considerable overlap with selection signals previously reported in a range of athletic horse breeds and the selection signals also overlapped with QTLs identified in GWAS for racing performance traits in Thoroughbreds44 Table . Notably45  are, except for SLC12A2, located at a single locus on ECA14. Nonetheless, there were several exercise-relevant gene ontology terms enriched among the genes that were located on different chromosomes including heart looping , cardiac muscle tissue morphogenesis , cellular respiration , skeletal muscle satellite cell differentiation , and glycolysis/gluconeogenesis   using the DAVID functional annotation toolWe evaluated the overlap between the Racing selection signals and selection signals identified when each of the Racing breeds was analysed separately Table . The oveCOX4I147, PPARGC1A48 and DMRT349; all three of these genes have been identified within runs of homozygosity in several horse breeds50.There was also considerable overlap among the Racing selection signals with selection in the Arabian (only), with seven of the 14 clusters also detected in the Arabian versus other breeds analysis  selection signals, with 15 clusters unique to Mongolian Racing. Three regions unique to Mongolian Racing stood out as having very strong signals of selection  Fig. . The topPPARA, which has a major role in exercise and training and is associated with elite human endurance athlete status62. The highest-ranking SNPs on ECA26 encompassed three genes  all of which may be candidate genes for exercise adaptation68. The highest-ranking SNPs according to the FST statistic were on ECA7 within the NDUFB7 and CACNA1A genes. NDUFB7 encodes a structural subunit of complex I of the mitochondrial respiratory chain and mutations in the gene have been observed to cause hypertrophic cardiomyopathy and lactic acidosis69. In a cancer model, NDUFB7 expression is directly modulated by PPARA70 and mutations in CACNA1A cause congenital ataxia in humans72.The second ranked CSS region contained the top ranked SNP according to the ∆SAF statistic that was 16 kb from the closest gene, r2 = 0.6). Therefore, the most strongly selected regions (and regions of greatest interest) were the largest and in most cases these regions contained sizeable numbers of genes. Proximity of a top ranked SNP  to a gene may be informative to some degree; however, linkage disequilibrium extends across large regions in horse populations17, the equine SNP genotyping arrays exhibit ascertainment bias74 being designed to assay genetic variation among the main European and North American breeds and, as illustrated above, many genes in each detected region exhibit biological functions easily interpretable as affecting exercise physiology. Therefore, to better characterise genes subject to selection for exercise traits, we used additional methods that leveraged transcriptomics data to prioritise SNPs proximal to DEGs in equine skeletal muscle, and WGS data from a cohort of (mostly) Asian horses.It is difficult to rely solely on selection signals arising from SNP genotyping array data to pinpoint the gene(s) that may have been subject to natural or human-mediated selection. Almost half of the selection signals identified in the four analyses spanned >1 Mb; the largest region was for Mongolian Racing , followed by three regions on ECA17, ECA7, and ECA18 for Thoroughbred (3.5 Mb to 2.9 Mb in size); the largest selection signal for Arabian was 1.8 Mb on ECA3 (37.45–39.25 Mb); and the largest region for the Racing breeds was the second highest ranked selection signal on ECA7 (2.4 Mb). For all analyses, there was a positive relationship between CSS score and the size of the region identified  and training  response transcriptomes24. For computational efficiency, the gene lists were refined to include DEGs with Padj. < 10−12 (UE) and Padj. < 10−4 (TR), which resulted in 407 (UE) , the integrative analysis was effective for the two input DEG sets. Using a search window that iteratively increased in size from 10–100 kb up and downstream of the genes of interest, a search space of ±100 kb produced the highest number of significantly enriched SNPs with the lowest probability of being significant by chance when compared to a null distribution of 1,000 sets of SNPs randomly sampled from the CSS dataset. SNPs within ±100 kb of a DEG were therefore selected as the target SNP sets to generate new q-values. Gene loci associated with enriched CSS SNPs are provided in Supplementary Data LPIN1, LRRC3B) were enriched for SNPs (q < 0.05) in the exercise response (UE) gene set and three genes  were enriched for SNPs (q < 0.05) in the training response (TR) gene set. Five genes  were common to both outputs (q < 0.1) and two of these  were located within the selected regions defined by the top 1% CSS SNPs.The R software package gwinteRPPP2R3A, PELO, GLB1) were enriched for SNPs (q < 0.05) in the exercise response (UE) gene set and three genes  were enriched for SNPs (q < 0.05) in the training response (TR) gene set  were common among the two outputs (q < 0.1); however, none of these was located within the selected regions defined by the top 1% CSS SNPs. Four DEGs with localised SNP enrichment were located in the top-ranked CSS region for the UE and TR gene sets  and four were among the other CSS regions . The five most significant SNPs for the TR gene set were upstream and within the TBX15 gene. The most significant SNP for the UE gene set was in PPP2R3A.Since there was less overlap between the Mongolian Racing selection signals and the Racing selection signals than there was for the Thoroughbred and the Arabian, we separately integrated the Mongolian Racing CSS SNPs in the context of the skeletal muscle DEGs to refine the gene sets  with moderate minor allele frequencies (MAF >0.1) as we did not expect this approach to identify rare small-effect variants. In addition, we did not expect to identify severely deleterious mutations, and therefore the search was not limited to variants with a predicted high effect on modifying gene function.PCDH15 and ZWINT. PCDH15 has been associated with lipid phenotypes78, but is best known for association with deafness79 and is not a compelling candidate gene. On the other hand, the ZW10 interactor protein, encoded by ZWINT, functions in neurotransmitter release and in rodents mediates negative behaviour induced by neuropathic pain80, which may be relevant to exercise81. We previously reported a sequence tag <5 kb from ZWINT among the most differentially expressed downregulated transcripts in the training-response skeletal muscle transcriptome in the horse25 implicating the locus as functionally relevant to exercise. The absence of identified gene-specific variants in this region may be explained by the focus here on the identification of common protein-coding variants, which precludes the identification of sequence variants in genomic regulatory elements, copy number variants, and chromatin state modifications that also contribute to the gene regulatory networks underlying complex traits83.For the Racing breeds, the eight highest ranked selected regions and 11 significant regions from the integrative analysis  were used to search for putative functional variants with the WGS data Table . Among tA similar approach was taken to identify putative functional variants within regions identified for the Mongolian Racing analyses. For Mongolian Racing the seven highest ranked selected regions and seven significant regions from the integrative analysis  were prioritised Table . For valIn total, 32 polymorphisms in 27 genes were selected for validation genotyping on the basis that the variants disrupt the sequence of proteins with central roles in exercise physiology—including key functions associated with muscle, heart, angiogenesis/blood, limb development, metabolism, and neurological tissues. The known biological functions of the genes are summarised in Supplementary Note n = 267 horses from six breeds  chosen to represent racing breeds, and n = 249 horses from eleven breeds  representing non-racing breeds (Supplementary Data We hypothesised that genetic variants targeted by selection for the racing phenotype segregate among horse breeds to influence underlying endophenotypic variation. Genotypes for the panel of 23 SNPs were generated for P < 3.57 × 10−3) associated with the racing phenotype (Table SLC16A1) that introduces a stop codon was predicted to have a high effect on the protein. We did not expect to identify loss of function mutations, since we expected here to detect alleles that are advantageous for exercise. The introduction of a stop codon may not always disrupt the function of a protein if there is limited truncation or if there is stop codon read through84.In tests of genetic association, SNPs in nine genes were significantly  were enriched for SNPs among DEGs in the skeletal muscle exercise and training response. A literature search and review of associated gene ontology functions, indicated that this set of genes have roles in muscle , metabolism  and neurobiological  functions that are linked to exercise-relevant phenotypes.The functional relevance of this gene set is supported by the integrative analyses in which three of the genes 24. In humans, HDAC9 gene variants are associated with the maximal oxygen uptake (VO2max) response to training90. Among the racing breeds, the Thoroughbred had the highest frequency (0.65) of the A-allele, which was more than twice the frequency of the allele among the other racing breeds (mean = 0.31) and 3.4× that among the sport horse breeds (0.19). Allele frequencies for all SNPs in each breed are shown in Supplementary Data Skeletal muscle is a highly plastic tissue that responds to exercise and training stimuli by increasing muscle mass and changing fibre type composition with concomitant mitochondrial functional adaptationsMYLK2 encodes a myosin light chain kinase (MYL2) expressed in skeletal muscle. The enzyme has a critical role in muscle contraction, and functions in neuromuscular synaptic transmission, skeletal muscle satellite cell differentiation, regulation of muscle filament sliding and skeletal muscle cell differentiation92. MYLK2 was the most significantly downregulated gene  among the 3,241 DEGs in Thoroughbred skeletal muscle following exercise and ranked 6th following a period of training 24, higher than MSTN , a gene with a well-established functional role in exercise33. In humans, genetic variants in MYLK are associated with phenotypic responses to exercise-induced muscle damage93. Here, the A-allele that was significantly different between racing (0.38) and non-racing breeds (0.25), had, among the racing breeds, the highest frequency in Arabian (0.63) and French Trotter (0.45) and the lowest frequency in Quarter Horse (0.28) and Standardbred (0.28)  and was lowest in Connemara (0.50). The glycolytic requirements for high intensity exercise are likely responsible for the observed variation in this gene among the breeds. The FASTKD1 and PPIG genes are also located in the region exhibiting the selection signal for G6PC2. The FAST kinase domains 1 protein, encoded by FASTKD1, supports mitochondrial homeostasis, and has a critical protective role against oxidant-induced cell death101. However, the strongest association in racing breeds was with the G6PC2 SNP and its well-established biological function in the regulation of glucose suggests that it could underpin the selection signal at this locus.The metabolic properties of skeletal muscle are largely influenced by the proportion of slow (type I) and fast (type II) muscle fibres, which are defined by the myosin heavy chain isoforms and characterised by the different densities and functional properties of mitochondriaSLC16A1 gene encoding the solute carrier family 16 member 1 protein that catalyses the movement of lactate and pyruvate across the plasma membrane102. In humans, genetic variants in the gene are used to predict athletic performance, in particular high-intensity exercise, and power ability104. Here, we have identified a novel variant that is predicted to have a major effect on the resulting protein through the introduction of a stop codon. The value of this variant in prediction of racing performance among Arabian horses requires testing in horses phenotyped for economically relevant racing traits.One of the best characterised genes for racing performance in Arabian horses is the 105. Here we identified SNPs in three genes with functions in neurobiology, KTN1, NTM and SYNDIG1. Of particular note is NTM encoding neurotrimin, which functions in brain development, regulates neural growth and synapse formation, and influences learning and memory111. A GWAS in Thoroughbreds previously identified this locus as the most significantly associated with the number of racecourse starts44. NTM also ranks among the top 10 genes positively selected during horse domestication112 suggesting that equine neurological systems associated with domestication may overlap with adaptive traits that are required for racing. Here, the NTM SNP was the most significantly associated (P = 7.49 × 10−14) with the racing breeds and among all breeds the highest frequency of the racing allele was in the Thoroughbred (0.89).Neurobiological functions have regularly featured in equine exercise transcriptomics and genomics researchKTN1 gene variants are strongly associated with KTN1 gene expression in the putamen and the volume of the putamen113, a region of the forebrain belonging to the basal ganglion that influences motor behaviours including motor planning and execution, motor preparation, amplitudes of movement and sequences of movement120. Here, the KTN1 G-allele was <1% in Thoroughbreds but had an average frequency of 0.22 in the other racing breeds, was 0.34 in the ancestral breeds and 0.21 in the sport horse breeds. Selection for the racing allele in breeds other than the Thoroughbred may be valuable in improving locomotor functions critical to racing. For SYNDIG1, the product of which regulates the development of excitatory synapses123, we observed that selection may already have fixed the exercise-favoured variant in racing breeds; the T-allele was absent in Thoroughbred, Arabian and Akhal Teke and was observed at a low frequency in the other breeds, with the highest occurrence in Connemara (0.29) and Irish Draught (0.25).In humans, GLB1 SNP was significantly (Bonferroni-adjusted P < 0.006) associated with the racing phenotype among Mongolian horses , we also performed tests of genetic association for eight SNPs in a cohort of Mongolian horses selected by herdsmen for racing by comparing the genotypes to a set of Chinese Mongolian horses that are not used for racing (Supplementary Data es Table . The pron = 1134) into three groups: horses classified as elite, horses that had raced but had never won a race, and horses that were unraced (Supplementary Data MYLK2 SNP was significantly (P < 0.005) associated with elite racing performance, but it was not associated with the trait among Australian (P = 0.43) or European (P = 0.47) horses , and although it did not meet the threshold for significance following correction for multiple testing, the occurrence of this locus in a previous GWAS44, and the observation of the highest frequency of the SNP among Thoroughbreds, strongly implicates NTM as an economically important gene in the Thoroughbred.To test for genetic association with racing traits among Thoroughbreds, we partitioned a large archive of samples . The sample of horses genotyped in this study was confirmed to be distributed in the PCA space shared predominantly with racing Arabians  had been previously genotyped5 and included horses originating in USA and Europe , Belgian (n = 30), Clydesdale (n = 24), Caspian Pony (n = 16), Exmoor (n = 24), Fell Pony (n = 21), Franches-Montagnes (n = 19), Hanoverian (n = 15), Miniature (n = 25), Mangalarga Paulista (n = 14), Morgan (n = 43), New Forest Pony (n = 15), Norwegian Fjord (n = 21), North Swedish Horse (n = 19), Percheron (n = 42), Peruvian Paso (n = 21), Paint (n = 25), Saddlebred (n = 25), Shetland (n = 27), Shire (n = 23), Tuva (n = 15)  were obtained from rural localities across China and Mongolia  were collected from Akhal-Teke (n = 5), Arabian (n = 5), Shetland Pony (n = 5), Friesian (n = 2), Clydesdale (n = 2) and Russian Draft (n = 1)  data. Samples from indigenous horses , sport horse  and putatively ancestral to Thoroughbred  populations  were used for validation of the SNP panel for Mongolian Racing.A set of n = 1134 Thoroughbreds partitioned into animals that had (1) won an elite (Group or Listed) race, (2) raced but never won, and (3) never raced, were used for validation of the SNP panel among Thoroughbred racehorses . These data were merged with genotypes generated in this study and SNP QC and filtering were performed across populations on the merged data set. Individual SNPs with >10% missing data and MAF < 0.01 were removed, resulting in 36,767 SNPs for analyses.In total, SNP array-derived genotypes for n = 30 Arabian horses genotyped in this study were combined with publicly available genotypes for n = 378 Arabian horses obtained from the Mendeley Data resource (10.17632/mkk5khxrbp.3)7. After removal of individual SNPs with >10% missing data and MAF < 0.01, 35,292 SNPs remained for PCA analysis.Genotypes for n = 24 Mongolian Racing horses genotyped in this study were merged with publicly available genotypes for n = 100 Chinese Mongolian horses obtained from the Open Science Framework (https://osf.io/2xvqf/quickfiles)4. After removal of individual SNPs with >10% missing data and MAF < 0.01, 60,994 SNPs remaining for PCA analysis.Genotypes for 128. One outlier  was identified and excluded from further analyses. The outlier was separated from the breed cluster and had also been identified as an outlier in the study from which the data were obtained5.To visualise genetic relatedness among the populations, principal component analysis (PCA) was performed using smartPCA from the EIGENSOFT package (version 4.2)129. The model assigns ancestry based on a predefined number of K ancestral populations. Individuals are assigned to K clusters based on allele frequencies and the proportion of ancestry from each population is estimated. The analysis was performed for K ranging from 2–6. Unsupervised modelling was used to predict allele frequencies in four ancestral genetic lineages (K = 4) and each animal’s genome was partitioned and proportionally assigned to one of the four lineages. The outputs from the analysis were visualized using pophelper 2.3.1130.For the analysis of population substructure, model-based clustering was performed using the software package ADMIXTURE132 were performed for four different comparison sets: (1) Racing  versus non-Racing breeds (n = 483); (2) Arabian (n = 30) versus all other breeds (n = 544); (3) Thoroughbred (n = 36) versus all other breeds (n = 537); (4) Mongolian Racing (n = 24) versus all other breeds (n = 549). Details for the comparator cohorts are provided in Supplementary Data Composite selection signals (CSS) analyses133. CSS uses fractional ranks of constituent tests allowing a combination of the evidence of historical selection from different population genetic tests of selection. We used the fixation index (FST), the change in selected allele frequency (ΔSAF) and the cross-population extended haplotype homozygosity (XP-EHH) tests combining each test statistic into one composite CSS statistic for each SNP. FST statistics were computed as the differentiation index between the target population/s of interest (as selected) and the contrasting/reference population/s (as non-selected), and the XP-EHH and ΔSAF statistics were computed for the selected population/s against the reference population (as non-selected).The CSS approach is among several composite approaches to successfully identify genes under selection for monogenic and polygenic traits in livestockn) genome-wide on n SNPs. Ranks were converted to fractional ranks (r´) (between 0 and 1) by 1/ (n + 1) through n / (n + 1). Fractional ranks were converted to z-values as z = Φ−1(r´), where Φ−1(⋅) is the inverse normal cumulative distribution function. Mean z scores were calculated by averaging z-values across all constituent tests at each SNP position and P-values were directly obtained from the distribution of means from a normal N  distribution where m is the number of constituent test statistics. Log-transformed P values  were declared as CSS. To identify significant selection signals CSS scores were plotted against the genomic positions and the individual test statistics were then averaged across SNPs within 1 Mb sliding windows to reduce spurious signals (smoothed CSS score). Clusters of >5 SNPs among the top 1% SNPs were defined as signals of selection.The CSS statistics were computed as follows: For each constituent method, test statistics were ranked  a set of significant and non-significant SNPs (named the target SNP set) was collated across all genes in each gene set at increasing genomic intervals upstream and downstream from each gene inclusive of the coding sequence ; (2) for each genomic region, a null distribution of 1000 SNP sets, each of which contains the same number of total significant and non-significant combined SNPs as the target SNP set, was generated by resampling with replacement from the search space of the total population of SNPs in the CSS SNP data set; (3) the nominal (uncorrected) CSS P values for the target SNP set and the null distribution SNP sets were converted to local FDR-adjusted P values (Padj.) using the fdrtool R package (version 1.2.15)134; (4) to test the primary hypothesis for each observed genomic interval target SNP set a permuted P value (Pperm.) was generated based on the proportion of permuted random SNP sets where the same or a larger number of SNPs exhibiting significant q-values (e.g. q < 0.05 or q < 0.10) were observed; (5) a summary output file of all SNPs in the observed target SNP set with genomic locations and q-values was generated for subsequent investigation.To integrate the SNP data arising from the CSS analyses with gene sets generated from functional genomics data analysesP values that could be used for the integrative genomics analyses. For the integrative analyses of functional genomics outputs with the CSS data, two different subsets of differentially expressed genes (DEGs) in skeletal muscle were used: 1) trained rest (TR) and 2) untrained exercise (UE), representing the Thoroughbred skeletal muscle transcriptomic response to training (TR) and exercise (UE)24. The gene sets that were used were filtered with Padj. <10−4 (TR) and <10−12 (UE) to ensure manageable computational loads, resulting in 230 (TR) and 407 (UE) input genes on autosomal chromosomes for integration with the CSS SNPs. The input gene sets can be found in Supplementary Data For the Racing versus non-Racing CSS data set, there were 36,768 autosomal SNPs with nominal P values that could be used for the integrative genomics analyses. The same DEG lists were used for the integrative analyses.For the Mongolian Racing versus other breeds CSS data set, there were 30,003 autosomal SNPs with nominal n = 70 individuals comprising horses indigenous to Mongolia and China and imported horse breeds. Genomic DNA was extracted following the standard phenol-chloroform extraction procedure. For genome sequencing, a total amount of 1.5 μg of genomic DNA from each sample was used to construct a library with an insert size of ~350 bp. Paired-end sequencing libraries were constructed according to the manufacturer’s instructions . The DNA sample was fragmented by sonication to a size of 350 bp, then DNA fragments were end polished, A-tailed, and ligated with the full-length adapter for Illumina sequencing with further PCR amplification. PCR products were then purified, and libraries were assessed for size distribution using the Agilent 2100 Bioanalyzer and quantified using real-time PCR and sequenced on the Illumina HiSeq PE150 platform (Illumina Inc.).Blood samples were collected from In total ~97 Gb raw sequence data was generated for each sample with the quality of the bases 96% and 90.92% for ≥Q20 and ≥Q30, respectively. Approximately 652 Mb clean paired-end reads with 30× coverage per horse (24–36×) were retained after removing low quality reads  were retained for subsequent analysis.The remaining high quality paired-end reads were mapped to the horse EquCab 3.0 reference genome using BWA (Burrows-Wheeler Aligner) (Version: 0.7.8) with the command ‘mem -t 4 -k 32 –M’137. The process was as follows: (1) HaplotypeCaller generates gvcf files for each animal; gatk HaplotypeCaller -genotyping-mode DISCOVERY -ERC GVCF. (2) GATK CombineGVCFs combines all the generated gvcf files. (3) GATK GenotypeGVCFs performs the genotyping on the combined vcf file. (4) GATK SelectVariants separates the SNPs and indels. A summary of the SNPs and indels called in the 70 samples is provided in Supplementary Data SNP calling was also performed using GATK v4.1.2.0www.animalgenome.org/repository/pub/UMN2018.1003) were used for variant calling validation. A summary of the validation of SNPs called in this study and MNEc2M SNPs is provided in Supplementary Data GATK SelectVariants was used to select a common subset of variants between unfiltered GATK and SAMtools calling result. Approximately 2 million horse SNPs (MNEc2M) available in the public repository  were predicted with the Ensembl Variant Effect Predictor .The functional effects of the variants were predicted according to the Ensembl (version 101) annotation of EquCab3. Functional consequences of the variants  demarked by the integrative analyses. Variants were only considered if they were in known genes. Predicted ‘high’ effect variants included the annotations splice_acceptor_variant&intron_variant, frameshift_variant, splice_acceptor_variant, and stop_gained, and predicted ‘moderate’ effect variants included the annotations missense_variant and inframe_deletion.These data formed the reference files that were screened for putative causative / functional variants in regions / genes of interest. Prioritisation of genes and variants was performed with a particular focus on (1) predicted effect of the variant, where ‘high’ effect was selected where possible, (2) biological function of the gene relevant to exercise, (3) minor allele frequency ≥0.1, and (4) high quality score (most had QUAL = 999)Assay development and genotyping was performed at Neogen Genomics  using the MassARRAY platform and iPLEX GOLD chemistry according to the manufacturer’s protocol . Agena Design Suite software developed by the manufacturer was utilized to design multiplex assays. The variants of interest were among a larger set of 150 markers of interest   that were split into four separate sets of 48, 47, 35, and 20 markers each. The four multiplex assays were run on the genomic DNA provided for the animals, data generated, and quality check metrics applied.141. SNPs with a call rate <80% and samples with a call rate <90% were excluded from the analysis. Case-control tests of genetic association were performed to compare the racing breeds horses n = 267 to non-Racing breeds horses n = 249 and to compare the Mongolian Racing horses n = 46 to non-Racing Chinese Mongolian breeds horses n = 121 7. Overlaps with reported selection signals in other horse breeds are shown in Table The composite selection signals (CSS) approach which combines the statistical outputs from the P values for the target SNP set and the null distribution SNP sets were converted to local FDR-adjusted P values (Padj.) using the fdrtool R package (version 1.2.15)134. To test the primary hypothesis for each observed genomic interval target SNP set a permuted P value (Pperm.) was generated based on the proportion of permuted random SNP sets where the same or a larger number of SNPs exhibiting significant q-values (e.g. q < 0.05 or q < 0.10) were observed. A summary output file of all SNPs in the observed target SNP set with genomic locations and q-values was generated.The R package gwinteR was used for the integration of SNPs with DEGs. A set of significant and non-significant SNPs from the CSS analysis was collated across all genes in the DEG gene set at increasing genomic intervals  upstream and downstream from each gene. A null distribution of 1,000 SNP sets, each of which contains the same number of total significant and non-significant combined SNPs as the target SNP set, was generated for each genomic region by resampling with replacement from the search space of the total population of SNPs in the CSS data set. The nominal (uncorrected) CSS P-values were adjusted for multiple testing by applying a Bonferroni correction. To demonstrate the reproducibility of the results, samples were independent of the discovery sample sets (CSS and WGS), and for the association with racing, racing breeds  that were not included in the discovery sample set were included in the racing cohort.Tests of genetic association were performed by comparing allele frequencies in a chi-square (1df) test for the binary traits, and a Wald test for the quantitative trait. Sex of the horse was included as a covariate. Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1 - 19Reporting Summary


Oplegnathus fasciatus) is an important aquaculture species for offshore cage aquaculture and fish stocking of marine ranching in East Asia. Genomic selection has the potential to expedite genetic gain for the key target traits of a breeding program, but has not yet been evaluated in Oplegnathus. The purposes of the present study were to explore the performance of genomic selection to improve breeding value accuracy through real data analyses using six statistical models and to carry out genome‐wide association studies (GWAS) to dissect the genetic architecture of economically vital growth‐related traits  in the O. fasciatus population. After quality control, genotypes for 16,162 SNPs were acquired for 455 fish. Heritability was estimated to be moderate for the three traits , and results of GWAS indicated that the underlying genetic architecture was polygenic. Six statistic models  showed similar performance for the predictability of genomic estimated breeding value (GEBV). The low SNP density (around 1 K selected SNP based on GWAS) is sufficient for accurate prediction on the breeding value for the three growth‐related traits in the current studied population, which will provide a good compromise between genotyping costs and predictability in such standard breeding populations advanced. These consequences illustrate that the employment of genomic selection in O. fasciatus breeding could provide advantages for the selection of breeding candidates to facilitate complex economic growth traits.Rock Bream ( The GBLUP assumes that all marker effects come from a normal distribution and the genetic relationship matrix is merely calculated using genomic information , simple sequence repeat (SSR), microsatellite markers, and quantitative trait loci (QTL) analysis  to assess the potential of genomic selection for genetic improvement. Furthermore, the impact of variable numbers of SNPs and different SNP sampling schemes based on the position of the genome on genomic prediction were also evaluated for improving the genotyping cost‐efficiency. Finally, a genome‐wide association analysis was carried out on the same datasets to detect individual SNP or chromosome associated with growth traits and identify putative growth‐related genes.22.1O. fasciatus population in our study were from a commercial breeding company, Ningde Fufa Aquatic Breeding . The population came from year‐class 2018, April spawning population. They were offspring of 260 randomly selected broodstocks with a random mating design. The offspring were firstly reared in a 40‐m2 (2.4 m in depth) cement pool and were fed chlorella and rotifers twice a day according to hatchery operation at birth, with aeration at 21 ~ 24°C in flow‐through seawater  under natural photoperiod conditions. After 8 months, the progenies were transferred to the marine cage and treated following guidance in standard culture protocols. At the age of 13 months, a total of 500 individuals were randomly collected and the growth‐related traits including body weight (BW), total length (TL), and body depth (BD) were measured. The dorsal fin of each fish was then collected and stored in anhydrous ethanol for DNA extraction.The 2.2EcoRI and MspI . The P1 adapter with forward amplification primer and a 5 bp barcode was added to the EcoRI overhang, and the P2 adapter with reverse amplification primer was added to the MspI overhang, respectively. The DNA fragments of 300–400 bp were retrieved on E‐Gel (Thermo Fisher Scientific) and then amplified with 20 cycles of PCR with regular forward primer and indexes ligated reverse primers, followed by purifying with AMPure XP Beads (Beckman Coulter). The obtained ddRAD libraries were sequenced on an Illumina Hiseq2000 platform with 150‐ bp pair‐end strategy at Novogene Corporation.Genomic DNA was extracted from dorsal fin using standard phenol–chloroform protocol  by using BWA program   and QQ‐plot were produced in R software.The GWAS were performed for the three growth‐related traits using two approaches. Firstly, population structure analysis was performed before GWAS using PLINK (‐‐cluster ‐‐matrix). Principal component analysis algorithm could estimate the potential genetic relatedness and display population structure of the 455 samples using 16,162 SNPs (~16 K SNP panel). Population structure identified by PCA with the first three principal component factors Figure . Accommop < 0.05, the threshold of suggestive association was arbitrarily set to −log10 (p) > 4.0 because the Bonferroni test (0.05/numbers of SNPs) criterion was extremely strict to be a threshold, considering GWAS was hypothesis generating  criterion is extremely strict to be a threshold. In this study, given that none of the SNPs from GEMMA and GAPIT reached the Bonferroni threshold for 2.5y is the vector of observed phenotypes for different traits; b vector is the fixed effects; X and Z represent the corresponding incidence matrices for fixed effects and genetic effects, respectively; U is the vector of random additive effects with the following distribution N, where G is genomic relationship matrix , where I is a vector of identity matrix and σe2 is the residual variance. Hence, the narrow sense heritability (h2) is computed by the additive genetic variance and total phenotypic variance via the following formula: Analysis of each trait was performed using the R package sommer  and testing population (n = 91) with the ratio of 4:1. The training population was used to build the model, and the phenotype record in the testing population was masked and used to validate the effectiveness of established model. In each replicate, the same combination of training population and testing population was adopted to perform the model prediction, so the results would have sufficient comparability for the all algorithms. The fivefold cross‐validation procedure was repeated 40 times in order to reduce randomly sampling effects, and average values of predictability were calculated. And we evaluated the predictability for the six algorithms based on the full data (~16 K SNP panel), where the predictability was calculated as the correlation coefficient between GEBVs and phenotypes.Predictive abilities of the different models described above  were assessed through a fivefold cross‐validation scheme. The 455 individuals were randomly and evenly split into sequential non‐overlapping training population  the same numbers of SNPs were randomly selected from the ~16 K SNP panel. Finally, (iii) the reduced‐density SNP panels were developed by sampling evenly spaced SNPs from the 16 K SNP panel trying to optimize the genome coverage across the chromosomes. All three scenarios adopted a cumulative approach. For instance, beginning with the smallest subset (50) of SNPs, additional 50 SNPs were added to the previous set each time until reached the biggest  subset. For each SNP subset, the GS model was built and corresponding predictability was calculated through fivefold cross‐validation.The GBLUP model was fitted using different subsets of SNPs of various sizes. We selected three different criteria for evaluating the potential of different marker densities for genomic prediction as described below. The first method (i) was based on the informative SNPs that were selected according to 33.1O. fasciatus. In the procedure of genotyping, three statistical parameters were estimated and listed as follows: Average sequencing depth was 35.3×, average reference genome mapping ratio was 91.6%, and average genome coverage was 1.7%. After filtering both individuals and SNPs with low quality, missing genotype imputation, and searching for tagging SNP, the eventual dataset utilized for the GS and GWAS analysis composed of 16,162 QC‐filtered SNPs genotyped in 455 samples. The growth‐related phenotype  was measured at approximately 1 year post‐hatching. The SNP density on each chromosome was shown in Figure Vg and Ve) had different estimates with distinct methods used  was at moderate level for all traits. Interestingly, the heritability of GBLUP in all traits had the most similar estimates with BayesB methods used.In this study, a total of 10 ddRAD‐seq libraries were constructed in 480 samples of 3.2p = 3.26 × 10−5; Table p‐value, allele frequency, and proportion of genetic variation explained (PVE) for the top markers in three traits were also given in Table O. fasciatus reference genome regions based on the above 14 SNPs. From these 14 SNPs, six, thirteen, and eleven genes were annotated corresponding to BW, TL, and BD, respectively (Table NRAC) and kinesin‐like protein KIF26A (KIF26A) were identified on Chr3. Besides, E3 ubiquitin‐protein ligase HACE1 (HACE1) was identified on Chr14, which involved in early embryonic development , unique cartilage matrix‐associated protein (UCMA), and regulator of G‐protein signaling 12 (RGS12) located in chromosome 3, 20, and 24, respectively  performed equally well as GBLUP method for the real dataset of O. fasciatus. These five methods had good stability and were qualified to predict the heritability of the population accurately. These estimates are the first heritability estimates of weight/length for rock bream with genomics matrices.In selective breeding projects for 4.2In the current study, no evidence of major quantitative trait loci was found for the three analyzed traits in rock bream. There were no SNPs surpassed the stringent genome‐wide significance threshold of all traits. Our results are consistent with previous studies in which very few obviously associations had shown for growth‐related straits by GWAS. For example, polygenic regulation of growth traits in the giant grouper was estimated that only 1.38% and 0.95% of genetic variance were explained by the main 20 SNP windows for body weight at 10 and 13 months . Using MAS with such an architecture would not supply satisfactory consequence given that only a small proportion of variance can be explained by individual SNPs. In order to provide useful GWAS data toward breeding for growth traits in rock bream, it will be necessary first to massively increase the sample sizes and use a substantially higher density of SNPs, such that adequate power is facilitated to detect at least part of the slightly larger effects segregating in the aim breeding population.HACE1 plays an important role in early developmental processes in Xenopus laevis, which control body axis elongation through regulation of convergent extension ; SNPs were randomly selected from all available SNPs and spaced evenly SNPs across the genome, respectively. Prediction models using ~1000 SNPs provided predictive abilities almost equivalent to using all available SNPs for all traits, and obvious difference was observed using different sets of SNPs. The predictive abilities using informative SNPs sorted by GWAS show a more stably upward trend compared with the predictability estimated by evenly spaced SNPs or randomly sampled SNPs on genome as the SNP numbers increasing. Reasons for this major divergence are comprehensible due to informative SNPs by GWAS which p‐value was sorted in ascending order. However, the other two strategies did not essentially follow the principle of p‐value sorted.Compared to livestock, in the breeding candidates of aquaculture species, it is not worthy to apply costly high‐density genotyping approaches. The use of lowly cost sparse genotyping to improve predictability in a breeding program is important for implementation of GS. Indeed, SNP density and distribution are crucial for the performance of statistical models. It has been reported that SNPs chosen randomly across the genome might affect the stability of genomic prediction  is sufficient for accurate prediction on the breeding value for the three growth‐related traits in the current studied population, which will provide a good compromise between genotyping costs and predictability in such standard breeding populations advanced. The results from the present study demonstrate that GS has potential for substantial improvement in predictability, and ddRAD‐seq combined with GWAS seems to be an effective channel for examining and promoting a polygenic trait in the rock bream breeding population.So far, this is the first study that contributes to experimental data supporting the positive prospects of genomic selection and GWAS to assess complex growth traits in The authors declare that they have no conflict of interest.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Fig S4Click here for additional data file.Fig S5Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.