MC1R locus associated with chestnut coat color may have been targeted by selection for a different mutation much earlier on than the recent selection for chestnut color. We also identified regions that show signatures of selection specific to Thoroughbreds. In addition, we found that intense inbreeding early in the history of the Thoroughbred breed and also before the formation of the breed has a significant impact on the genomic architecture of modern Thoroughbreds. Our study demonstrates that the horse 670k array can be utilized to gain important insight into the domestication process of horses and to understand the genetic basis of the phenotypic diversity in horses.The domestication process of plants and animals typically involves intense inbreeding and directional selection for various traits. Here, we genotyped 370 Japanese Thoroughbred horses using the recently developed 670k SNP array and performed various genome-wide analysis also using genotype data of other horse breeds. We identified a number of regions showing interesting patterns of polymorphisms. For instance, the region containing the  Humans have been selectively breeding several plants and animals for a wide range of desirable traits throughout history. This domestication process offers an excellent opportunity to understand the genetic basis of phenotypic variation, and also to understand how forces such as directional selection, population bottleneck, and inbreeding shape the genome , 2. A nuDespite the economical value of Thoroughbreds, genetic information has traditionally not been considered in the breeding of Thoroughbreds. Even with the progress in DNA sequencing in the recent years and the availability of a reference genome , genomicHere, we investigated the genetic variation of Japanese Thoroughbred racehorses by genotyping 370 Japanese Thoroughbreds using the 670k SNP array. Our study provides insight into the impact of inbreeding and artificial selection on the genetic makeup of Thoroughbred horses. Our study also provides a platform to identify genomic loci associated with various traits which should help improve the breeding of Thoroughbreds.hsphase [r2 in our study is slightly lower and drops below 0.2 at ∼70kb ..K = 14 i [et al.  are highhsphase for the half-sib families, we were able to infer the haplotype blocks in the half-sibs inherited from the sires and the recombination events that occurred during meiosis. Examples of three chromosomes of the largest half-sib family are shown in The presence of several half-sibs sharing the same sire makes it possible to accurately phase the chromosomes of each half-sib, and also infer the phased chromosomes of the sire even though the sires were not genotyped. This allows us to examine how the homologous chromosomes of the sire are recombined and passed on to each offspring. Based on the results of Thoroughbreds are highly inbred with a closed breeding system where foals with a non-Thoroughbred sire or dam cannot be registered as Thoroughbreds. Very strong inbreeding (e.g. between first cousins) is generally avoided at least in recent years. All modern Thoroughbreds can be traced back to a small number of founder stallions and mares, indicating severe bottleneck during the establishment of the breed –10, 18. F for each individual based on the pedigree of the past 5 (FPED5) and 10 (FPED10) generations (FPED5) was low for most individuals, with most horses having FPED5 <0.03. The inbreeding coefficients were much larger when calculated based on pedigrees of the past 10 generations (FPED10), even though these estimates should be underestimates due to the incompleteness of the pedigree record. We then calculated the inbreeding coefficient F from the genome-wide SNP data based on runs of homozygosity (ROH) of >0.5 Mb (FROH>0.5Mb) and >5 Mb (FROH>5Mb)  . ROH is pedigree . Each hopedigree . The avepedigree . The amoGray locus (STX17) on chromosome 25 have a gray coat color [Extension locus (MC1R) on chromosome 3 have a chestnut coat color [Gray locus on chromosome 25 showed an extremely strong association with gray coat color, whereas a large region on chromosome 3 including the Extension locus showed an extremely strong association with the chestnut phenotype  is often used to identify candidate regions associated with traits of interest. To examine the utility of our dataset for GWAS, we first performed GWAS for 2 known color coat loci, gray and chestnut. It is known that all Thoroughbred horses (excluding very rare white horses) with a mutation in the at color , and of at color . Our samat color , which fhenotype . Althoughenotype . This ren humans . One pren humans .MC1R gene  responsible for the chestnut coat color showed reduced levels of diversity in Thoroughbreds and in the other breeds   was calcbreds of , and allocessing , 48. Polocessing . Other gocessing .MC1R gene may have been the target of a selective sweep much earlier on [Population genomic studies have proven extremely useful in identifying genomic loci associated with agronomically important traits and also to understand the domestication process of these species , 2. Hererlier on , 14, 42,FROH and FPED was also observed for the other horse breeds  [Our results offer interesting insights regarding the effect of historical inbreeding in Thoroughbreds. We found that the degree of inbreeding estimated based on pedigree information considerably underestimates the degree of inbreeding observed in the genome as measured by segments of ROH. This is likely due to the excess of historical inbreeding that is not captured by the recent pedigree. One recent study examined the pedigree-based inbreeding coefficients in 135,572 Australian Thoroughbreds by compiling an extensive pedigree dating back to the founders of the population . They foctively) , suggestHere, by performing population genetic analyses, we identified a number of regions that are likely to be associated with various important traits in Thoroughbreds, and showed that historical inbreeding has played an important role in shaping the genome of modern Thoroughbreds. Our results demonstrate the utility of horses as an interesting system to study genetics thanks to the presence of a high-density SNP array, detailed pedigree record, a large number of half-sibs, and several breeds with high phenotypic diversity. Future studies with a larger number of samples and detailed records of various phenotypic traits should enable us to understand the genetic basis of various phenotypic traits.https://www.ebi.ac.uk, Project: PRJEB32686, Analyses: ERZ936110 at European Variation Archive). The genomic coordinates based on the equCab2 assembly is used throughout this article. 54 samples with an average genotype call rate of <98% were removed, resulting in 370 samples that were used in this study for genome-wide analyses. SNPs that could not be genotyped in >1% of the samples were removed, resulting in 536,448 SNPs that were used in this study. For phasing, hsphase [hsphase and the unphased genotypes of the remaining samples were combined into a single VCF file, and BEAGLE v4.1 [This research was approved by the Animal Care and Use Committee at Hidaka Training and Research Center. Blood samples were collected from 424 Thoroughbred racehorses owned by the Hidaka Training and Research Center . All horses were born between 2009 and 2015. DNA was extracted by Maxwell 16 (Promega) with the Maxwell 16 LEV Blood DNA Kit. SNP genotyping was performed using the Axiom Equine Genotyping Array (Axiom MNEC670) which includes a total of 670,796 SNP markers. The data file containing all 670,796 SNP calls of the 370 samples are available as a VCF format file from EMBL-EBI  was estimated by calculating r2  between r2 . Out of r2  were retr2  of the cth MEGA7  and was th MEGA7 . Bootstrth MEGA7  with a mMC1R on chromosome 3.The heterozygosity was calculated for each site separately for the 370 Thoroughbreds of this study, 24 Thoroughbreds of , and theFST values [et al. [FST values were then transformed to obtain estimates of the population divergence time T in units scaled by the population size following [T = −log(1 − FST). For each genomic window this value was calculated between the Japanese Thoroughbreds and the Thoroughbreds of Schaefer et al. (TJTST), the Japanese Thoroughbreds and the non-Thoroughbred horses (TJTnT), and the Thoroughbreds of Schaefer et al. and the non-Thoroughbred horses (TSTnT). The length of the branch leading to the Japanese Thoroughbreds was then calculated asThe population branch statistic (PBS) was calculated over an overlapping sliding window of 1 Mb with an increment of 200 kb  following  in orderT values  were cal [et al. , the Jap [et al. , and the [et al.  and all ollowing  as T = −FPED [www.netkeiba.com. Segments of runs of homozygosity (ROH) of >0.5 Mb and >5 Mb were detected using PLINK v1.07 [FROH were calculated as the proportion of the genome covered by ROH segments assuming that the length of the genome is 2243 Gb.The pedigree-based inbreeding coefficients FPED  for eachNK v1.07  with thewww.netkeiba.com. Association with the gray coat color was tested using the 18 gray horses as cases and the remaining 352 non-gray horses as controls. Association with the chestnut coat color was tested using the 94 chestnut horses as cases and the 258 non-gray, non-chestnut horses as controls. Association with racing performances was done by classifying high-performance horses and low-performance horses based on their lifetime earnings at JRA races. The racing records until the end of 2018 for all horses born before 2015 were obtained from www.netkeiba.com. Horses that had earned ≥30 million JPY were classified as high-performance horses and horses that had earned <5 million JPY despite having raced at least 3 times were classified as low-performance horses. In the JRA racing system, a win at a maiden race (a race between horses that have not won a race yet) earns ∼5 million yen. Horses that cannot win until the end of the age of 3 lose their eligibility to enter maiden races and often end up retiring. Thus, most if not all horses classified as low-performances will not be able to earn any more at JRA races. Genome-wide association studies were performed using GEMMA [The coat color of all 370 horses were obtained from ng GEMMA  by firstS1 Figr2 is plotted as a function of distance in kilobase up to 1 Mb. The average r2 values over each non-overlapping 1 kb bin are shown.(EPS)Click here for additional data file.S2 FigSNPs from a randomly selected subset of 10 Thoroughbreds genotyped in this study and the 311 samples from 20 breeds of  were use(EPS)Click here for additional data file.S3 FigSNPs from the 370 Thoroughbreds genotyped in this study and the 311 samples from 20 breeds of  were use(TIF)Click here for additional data file.S4 FigSNPs from a randomly selected subset of 10 Thoroughbreds genotyped in this study and the 311 samples from 20 breeds of  were use(TIF)Click here for additional data file.S5 FigThe two homologous chromosomes of the sire are shown at the top in red and blue, and the chromosomes inherited from the sire in each genotyped half-sib are shown below. Results of all chromosomes for each half-sib family with 10 or more half-sibs are shown.(EPS)Click here for additional data file.S1 TableProtein-coding genes in the top 10 regions with the highest PBS values are listed.(XLSX)Click here for additional data file.


Increasing popularity of high-throughput phenotyping technologies, such as image-based phenotyping, offer novel ways for quantifying plant growth and morphology. These new methods can be more or less accurate and precise than traditional, manual measurements. Many large-scale phenotyping efforts are conducted to enable genome-wide association studies (GWAS), but it is unclear exactly how alternative methods of phenotyping will affect GWAS results. In this study we simulate phenotypes that are controlled by the same set of causal loci but have differing heritability, similar to two different measurements of the same morphological character. We then perform GWAS with the simulated traits and create receiver operating characteristic (ROC) curves from the results. The areas under the ROC curves (AUCs) provide a metric that allows direct comparisons of GWAS results from different simulated traits. We use this framework to evaluate the effects of heritability and the number of causative loci on the AUCs of simulated traits; we also test the differences between AUCs of traits with differing heritability. We find that both increasing the number of causative loci and decreasing the heritability reduce a trait’s AUC. We also find that when two traits are controlled by a greater number of causative loci, they are more likely to have significantly different AUCs as the difference between their heritabilities increases. When simulation results are applied to measures of tassel morphology, we find no significant difference between AUCs from GWAS using manual and image-based measurements of typical maize tassel characters. This finding indicates that both measurement methods have similar ability to identify genetic associations. These results provide a framework for deciding between competing phenotyping strategies when the ultimate goal is to generate and use phenotype-genotype associations from GWAS. As image-based methods for quantifying plant phenotypes grow in popularity, they present the ability to measure phenotypes that previously could not be easily quantified as well as an alternative way to measure phenotypes that previously had to be manually quantified. The types of novel phenotypes enabled by image analysis include fractal dimension  involve measuring a phenotype, usually in a replicated population of hundreds to thousands of genetically distinct individuals, then scanning for statistical associations between individuals’ phenotype and their genotype at numerous genetic loci. In such studies, the accuracy and precision with which a phenotype is measured will have a direct impact on the ability to detect genetic associations by GWAS.At its core, GWAS involves testing for a difference in phenotype between individuals with different genotypes at a particular single nucleotide polymorphism (SNP). This process is repeated separately for hundreds of thousands of SNPs across the genome. Ideally, SNPs within or near genes that have some effect on the phenotype of interest will result in strong statistical associations. As such, the precision and accuracy of phenotypic measurements influence the ability to detect statistical differences between groups of individuals with different alleles. The heritability of a phenotype, defined as the ratio of genotypic variance to phenotypic variance, is a useful way to quantify the proportion of phenotypic variability that is attributable to genetic differences between individuals. All other components held equal, heritability will increase as precision of phenotypic measurement increases, due to decreasing apparent phenotypic variability from measurement error. Two methods of measuring the same ‘true’ phenotype with differing precision will have different heritability, and thus different power to detect SNPs that are statistically associated with the ‘true’ phenotype of interest. For the remainder of this study we will refer to the ‘true’ phenotype of an individual as its character, and will refer to different measurements of a character as traits.In addition to heritability, another parameter that affects power in GWAS is the number of loci that control a character. For two characters measured with the same heritability, one controlled by fewer loci will have on average a larger proportion of variance explained by each locus. Power to detect an association at a particular locus is positively related to the proportion of phenotypic variance explained by that locus . Thus, pe.g., Increased throughput of image-based phenotyping methods can make it possible to collect measurements of more traits on more individuals than by manual measurement, which makes image-based phenotyping an attractive way to generate phenotypic data for GWAS. We can consider the manual measurement and the image-based measurement of an individual character to be two traits with differing heritability but the same exact set of causative loci. As in the NIRS example above, researchers may sometimes prefer a less precise method for measuring a character because it is cheaper, faster, or otherwise more desirable. It is unclear just how much loss in precision (decrease in heritability) can occur before GWAS results begin to suffer. Part of the answer to this question lies in the goals and risk tolerance of the researcher: if the goal of the experiment requires identification of few, strong signals then perhaps lower power to detect associations will still be tolerable; if instead the goal is to identify many small-effect loci, then even small reductions in heritability could negatively impact the outcome of the study. In this experiment, we use simulations to investigate the relationship between trait heritability and the ability to detect genetic regions associated with a character. We use receiver operating characteristic (ROC) curves to characterize detection of causative loci. Previous studies have used ROC curves or similar visual aids to evaluate the efficacy of different GWAS methods (The Wisconsin Diversity panel (WiDiv-942) is a set of 942 inbred maize lines that reach grain physiological maturity in the upper Midwest region of the United States , and represents an expanded version of the 503 line diversity panel described by Hirsch and colleagues . SNP data contained 30% missing data, which were imputed using fastPHASE , to simulate traits controlled by differing numbers of variants, and the heritability of simulated traits. The NCL was set to 10, 100, and 1000, and e.g., principal component scores) were included in the model. Compression was turned off by setting the group.to and group.from parameters to 9999. GWAS was run separately for each of the 270 simulated traits.Genome-wide association studies (GWAS) were conducted with the software GAPIT . The samroc function in the R package pROC  of each SNP was used as the predictor variable. A separate empirical ROC curve was fitted to the GWAS results for each of the simulated traits. A representative ROC curve was estimated from all 10 replicates of each parameter combination by calculating the mean of the sensitivities and specificities along the curve.Receiver operator characteristic (ROC) curves are typically used to assess the ability of a particular method for identifying the true, binary status (case or control) of an individual based on various threshold levels of a continuous predictor variable. The 10 from GWAS, so a nonparametric approach is needed instead.The ROC curves for different traits can be compared quantitatively by testing for differences in their AUCs. The GWAS ROC curves described in the preceding section are not independent of each other, because for each comparison the causative and non-causative loci are identical. Some of the original methods for testing differences between non-independent ROC curves relied on the assumption that the populations being compared by the ROC curve were both normally distributed  should not have significantly different AUCs. Therefore, the empirical distribution of Z when the difference in heritability (D) between two traits equals zero is used as the distribution of Z under the null hypothesis that the AUCs of two ROC curves are the same. Thresholds for significance at α=0.05 were calculated empirically from the 2.5th and 97.5th percentiles of the null distribution of Z.In the context of the AUCs from our GWAS of simulated traits, the AUC is theoretically equivalent to a Mann-Whitney statistic where the two samples being compared are the p-values of causative and non-causative loci. This equivalency holds even when the two samples being compared are in violation of the assumption that they follow the same distribution , as long as the sample sizes are large enough . For AUCroc.test function in pROC with the method argument set to ‘delong’ , we predicted the value of Z for each character at different NCL using the fitted regressions. Using the thresholds for significance derived from the empirical distribution of Z when D = 0, we tested the null hypothesis that the two traits, measured by different methods, have the same AUC for each character at each NCL.https://doi.org/10.25386/genetics.7097813.Genotypic data are available from doi: 10.5061/dryad.6tg35t6. Phenotypic data for tassel traits are available in Supplemental File S3 from Empirical phenotypic data used in this study comes from morphological measurements of the male inflorescence of maize, known as the tassel. Four tassel morphological characters were measured manually and by image analysis in the Wisconsin Diversity panel, a population of 942 diverse inbred maize lines (WiDiv-942). The manually measured traits were tassel length (TL), spike length (SL), branch number (BN), and tassel weight (TW), and their image-based counterparts are referred to as TLp, SLp, BNp, and TWp, respectively. TL, SL, and BN were measured in replicated experiments over three years, while the other five traits were measured in a replicated experiment in one year. Best linear unbiased predictors (BLUPs) for manually measured traits are highly correlated with BLUPs for the corresponding image-based traits, with Pearson’s correlation coefficients ranging from 0.81 to 0.9  Gage et  on GWAS results. We first simulated a number of traits with different heritability and NCL. Phenotypes were simulated as being controlled by single nucleotide polymorphisms (SNPs) from the WiDiv-942, which was genotyped at 529,018 SNPs discovered by RNA sequencing. Simulated phenotypes were controlled by varying NCL: 10, 100, or 1,000 randomly selected SNPs were randomly assigned effect sizes drawn from a normal distribution. For each of the three values for NCL, traits were simulated with heritabilities ranging from 0.1 to 0.9 in increments of 0.1. Each combination of NCL and heritability was simulated ten times. GWAS were performed on all simulated phenotypes, and empirical ROC curves were created with the results from each GWAS. ROC curves plot the proportion of true positives  against the proportion of false positives , as the threshold for labeling an observation as positive moves from stringent to more liberal. The ROC curve for a test with very good ability to identify true positives without too many false positives will rise steeply from the origin and approach the point , before flattening out and continuing on to the point , producing an AUC close to 1. A test that is no better than randomly guessing which observations are positives will yield an ROC curve that follows a line with slope equal to one from the origin to , producing an AUC of 0.5.10 for each SNP from GWAS.ROC curves are typically constructed by classifying a number of individuals as either cases or controls, based on some continuous predictor variable. The TPR and FPR are calculated at different levels of the predictor variable to create the curve. To create ROC curves for GWAS results each SNP is treated as an individual, the true status of which is either causative (case) or non-causative (control). The continuous predictor variable is the –logOur empirical results show that for any given NCL, simulated traits with higher heritability generally had better ROC curves, as measured by AUC . This waFor any given heritability, the NCL plays an even larger role in the shape of the ROC curve, with traits controlled by more loci having worse ROC curves than those with fewer loci . This reIn order to evaluate whether the difference in heritability (D) between two traits has a significant effect on GWAS results, we used a nonparametric method for testing the null hypothesis that two correlated ROC curves have the same AUC  for NCL = 10,  for NCL = 100, and  for NCL = 1,000. Regardless of NCL, predictions of Z for all four tassel traits fall within the thresholds for significance , 0.14 (BN), 0.16 (TL), and 0.17 (SL), with the manually measured trait always having higher heritability than the image-based trait . The preificance . TherefoIn this study, we use AUCs of ROC curves constructed from GWAS results of simulated traits to test for significant differences in the ability to detect common genetic signal underlying traits with differing heritability. Our results show that as D increases, the test for differences between the traits’ AUCs becomes more significant. Though there is a strong relationship between D and Z, there is also substantial variability for Z scores at a given value of D. We predicted Z scores for real tassel morphological traits using the relationship between D and Z of simulated traits. Because each tassel trait was measured by manual and image-based methods, we predicted Z using D from the estimated heritabilities of the two measurement methods. Regardless of NCL, the predicted values of Z for real tassel traits were within the thresholds for significance that were calculated from the null distribution of Z. Based on these results, we conclude that there is unlikely to be a significant difference between AUCs of measurements made by different methods for any of the four tassel morphological phenotypes studied.et al. , as posited in the omnigenic model (The use of AUC as a metric to quantify the success of GWAS is also accompanied by assumptions about the goals of GWAS. ROC curves, and thus the AUC, consider both power and type I error, as measured by true and false positive rates. Depending on the goals of the GWAS study, power and type I error may not both be of equal importance. For genomic prediction or marker assisted selection, a high type I error rate is not particularly concerning as long as power is high and trait prediction is accurate. On the other hand, studies using GWAS to choose genes for further molecular characterization have a large financial incentive to minimize type I error.Using AUC to quantify the effectiveness of GWAS assumes that the entire ROC curve is of interest. When NCL is low this assumption may be true, but as NCL increases, it may be the case that only the beginning of the ROC curve is of practical interest. The simulated ROC curves for NCL = 1,000  are closIn this study, we use AUC of ROC curves to characterize and quantitatively compare GWAS results from different traits. Overall, our findings show an expected relationship between NCL, heritability, and AUC of ROC curves. Greater NCL and lower heritability both reduce the AUC, while lower NCL and higher heritability can increase AUC. Results suggest that there is no significant difference between AUCs from GWAS using manual and image-based measurements of typical maize tassel characters. Creation of more nuanced simulation models and consideration of partial ROC curves may enable improvement upon the results presented in this study. Additionally, these results are specific to additive traits in diversity panels of inbred lines. Further studies are required to determine whether different population structures and/or incorporation of non-additive effects to the simulated traits will change the relationship between heritability and AUC. The results presented here provide a foundational framework that may facilitate decision-making for researchers weighing the benefits of different phenotyping methods.


Basic bovine behavior is a crucial parameter influencing cattle domestication. In addition, behavior has an impact on cattle productivity, welfare and adaptation. The aim of the present study was to infer quantitative genetic and genomic mechanisms contributing to natural dual-purpose cow behavior in grazing systems. In this regard, we genotyped five dual-purpose breeds for a dense SNP marker panel from four different European countries. All cows from the across-country study were equipped with the same electronic recording devices. In this regard, we analyzed 97,049 longitudinal sensor behavior observations from 319 local dual-purpose cows for rumination, feeding, basic activity, high active, not active and ear temperature. According to the specific sensor behaviors and following a welfare protocol, we computed two different welfare indices. For genomic breed characterizations and multi-breed genome-wide association studies, sensor traits and test-day production records were merged with 35,826 SNP markers per cow. For the estimation of variance components, we used the pedigree relationship matrix and a combined similarity matrix that simultaneously included both pedigree and genotypes. Heritabilities for feeding, high active and not active were in a moderate range from 0.16 to 0.20. Estimates were very similar from both relationship matrix-modeling approaches and had quite small standard errors. Heritabilities for the remaining sensor traits  and welfare indices were lower than 0.09. Five significant SNPs on chromosomes 11, 17, 27 and 29 were associated with rumination, and two different SNPs significantly influenced the sensor traits “not active” (chromosome 13) and “feeding” (chromosome 23). Gene annotation analyses inferred 22 potential candidate genes with a false discovery rate lower than 20%, mostly associated with rumination (13 genes) and feeding (8 genes). Mendelian randomization based on genomic variants  was used to infer causal inference between an exposure and an outcome. Significant regression coefficients among behavior traits indicate that all specific behavioral mechanisms contribute to similar physiological processes. The regression coefficients of rumination and feeding on milk yield were 0.10 kg/% and 0.12 kg/%, respectively, indicating their positive influence on dual-purpose cow productivity. Genomically, an improved welfare behavior of grazing cattle, i.e., a higher score for welfare indices, was significantly associated with increased fat and protein percentages. NPY5R), which regulates appetite and feeding behavior in beef cattle. Similar mechanisms for polymorphisms of the melanocortin 4 receptor gene (MC4R), i.e., influences on feed intake capacity and feeding behavior, were reported in Korean Hanwoo cattle [The current fundamental interest in dairy cattle research addresses a deeper understanding of the role of genetics in phenotypic expressions of behavior traits. Behavior is an essential part of biological regulations and influences the production and welfare of farm animals. However, the underlying genetic mechanisms explaining the relationships between cattle behavior and productivity are unclear. Currently, consumer demands strongly influence animal husbandry and management decisions, e.g., a wish towards the utilization of natural and cow friendly production systems. Furthermore, there are increasing concerns, critically addressing the high yielding Holstein Friesian breed and suggesting local dual-purpose cattle as a breed alternative. Against this background, a better understanding of the genetic mechanisms of animal behavior allows for the implementation of local dual-purpose cattle selection strategies for specific environments, e.g., for specific grazing conditions. Hohenboken  listed bo cattle . NeverthBos taurus) already reached r2 > 0.3 [In the process of animal husbandry system intensifications, domestication and artificial selection via specific mating plans were major driving components contributing to extensive linkage disequilibrium (LD) across the bovine genome ,11. Consr2 > 0.3 , while lr2 > 0.3 . This phr2 > 0.3 . In detar2 > 0.3 ,14. Hencr2 > 0.3 . With thThe current study is based on SNP data from five dual-purpose cattle breeds located in Germany , Poland , Slovenia  and Switzerland . Genotyped cows were phenotyped based on 24 hours of continuously recorded behavior data in grazing systems. The overall hypothesis is that electronically recorded natural behavior of cows for feeding (FEED), ruminating (RUM), resting / non-active (NACT), basic activity (BACT) and high activity (HACT) and digital ear surface temperature (ET) contributes to the detection of significant SNP markers and associated potential candidate genes across the bovine genome. Additionally, for population structure analyses, we considered genotypes from the dual-purpose Red and White breed from Germany (DE_DN) and from German Holstein (DE_HF) and Slovenian Holstein (Sl_HF) subpopulations. We assume that different breeds with a similar breeding history share ancestral mutations and recombination events. Accordingly, Gutiérrez-Gil et al.  identifiThe present study is based on dense genomic marker data and longitudinal behavior traits from different dual-purpose cows across European country borders. Such unique data can be used i) to infer the population structure for European dual-purpose and dairy cattle breeds; ii) to estimate genetic parameters for behavior traits based on pedigree and genomic information; iii) to detect associated SNP and potential candidate genes significantly influencing cattle behavior; and iv) to infer causal trait associations.When plotting the first and the second principal components , two distinctly diverged clusters of genetic origin were detected . The HolThe breed assignment  identifiA) or the combined relationship matrix (H) matrices   threshold, seven SNP markers were significantly associated with behavior traits . One of .36E-08) . Five SN.36E-08) , and ano.36E-08) . A more The SNP coverage was examined by counting the number of SNPs in consecutive windows of 1 Mb on each chromosome. The mean SNP coverage per Mb considering the 29 autosomes was 14.2, ranging from zero to 27 SNP per Mb. At least one Mb window without a SNP was identified on ten chromosomes .DGAT1 gene. Hence, the multi-breed GWAS identified the most prominent candidate gene in dairy and dual-purpose cattle.With regard to the GWAS for fat percentage (Fat%), two significant SNPs above the Bonferroni corrected threshold on BTA14  were ideP-values of all SNPs, i.e., GWAS output, 22 potential candidate genes were identified (BTBD1) is located on BAT21, and two are on at BAT27 (THAP1 and RNF170). The remaining ten potential candidate genes are located on at BAT29. Both dependent variables for RUM (DRP and MEAN) were associated with eight putative candidate genes . With regard to the DRP for RUM, we identified two potential candidate genes without clear biological functions on BTA29 (the novel gene ENSBTAG00000000776 and MRGPRG). The SNP Hapmap48998-BTA-104140  suggested PPM1E as a potential candidate gene for BACT. With regard to the DRP of FEED, we identified seven potential candidate genes on BAT11 . The two neighboring SNPs ARS-BFGL-NGS-80066 and ARS-BFGL-NGS-111955 on BTA23 indicated one putative candidate gene (SLC25A27) for FEED.Based on the entified . All of The regression coefficients for the variety of trait associations are summarized in The identified breed clusters from the principal component analysis (PCA) reflect the geographical origin of the European cattle breeds. The optimization criterion for PCA is the maximization of variation in the genomic relationship matrix considering the first principal components , which aBreed assignment results gave deeper insights into the pattern of genetic diversity and principles of historical evolutionary processes in dual-purpose cattle populations. All breeds depicted at least 57.83% of ancestral allele proportion to European cattle breeds, affirming their European origin. Nevertheless, for the European dual-purpose cattle genotypes, exotic ancestral allele proportions from Sheko, Zebu Bororo, Gir or Arabic Zebu were identified. Despite the fact that cattle are ascribed to two major geographic types, i.e., taurine  and indicine , the same ancestors were identified more than 250,000 years ago . AncientEarly breeders who spread from the Fertile Crescent towards North-West Europe used two different migration routes . One rouThe PCA results as well as the breed assignment analyses indicate the European origin of dairy (DE_HF) and dual-purpose breeds  and reflect selection according to geographic and breeding goal characteristics. The evolutionary formative events contributed to the establishment of different genetic variants in cattle breeds in different regions. Moreover, they influenced the differentiation of allele frequencies among populations  and the A matrix) were very similar or slightly smaller compared to the H matrix approach . This result is in agreement with other studies focusing on genetic parameter estimations based on different genetic relationship matrices [Apart from BACT, genetic parameter estimates from the pedigree-based approach  ,38. SchöThe low heritability estimates for ET indicate partial genetic control of temperature regulation mechanisms but a stronger impact due to environmental effects and production levels . HeritabTo our knowledge, this is the first study considering dense sequences of longitudinal behavior measurements of dual-purpose cows from grazing systems across countries, combined with high-throughput genomic marker data. On the basis of a multi-breed GWAS, we gained new insights into the genetic control of dual-purpose cattle behavior under grazing conditions, and we located some interesting chromosomal segments. Nevertheless, for the detection of causal functional mutations in ongoing studies, it is imperative to use denser SNP data or even sequence data and a larger sample of genotyped cows ,46. RegaDGAT1 gene on BTA14 supported our a priori hypothesis that  the multi-breed GWAS is an appropriate approach to identify putative causative variants and candidate genes. Using an FDR of 20%, the number of identified significant genetic variants, including SNP and potential candidate genes, was larger compared to the stricter Bonferroni correction. However, the risk of detecting false positive SNPs also increased. The consideration of accumulated effects from a set of SNPs ±50 Kb of a gene (set-based association) was very powerful for detecting potential candidate genes, as suggested in previous studies [The identification of the  studies . Some of studies , humans  studies  or mice  studies .RNF170 were associated with autosomal dominant sensory ataxia in humans [RPS6KB2 is involved in innate immune response mechanisms in indigenous and crossbred cattle [RPS6KB2 was differentially expressed in Angus cattle selected for low and high residual feed intake [RPS6KB2 with embryonic development in cattle [PTPRCAP gene is an additional identified potential candidate gene that is associated with RUM behavior. In humans, PTPRCAP is involved in defense response mechanisms and is a key regulator of lymphocyte activation [Based on the five significant SNPs with FDR < 20%, we detected 13 potential candidate genes for RUM. Mutations of the identified potential candidate gene n humans . The putd cattle . In addid intake  and in bd intake . Other fn cattle . The PTPtivation .CaBP4, coding for a neuronal Ca2+-binding protein, was expressed in photoreceptors in mice and regulated synaptic terminals [CaBP4−/− mice have behaviors similar to those in patients with incomplete congenital stationary night blindness. Generally, CaBP4 is involved in the process of signal transduction [The putative candidate gene erminals . Haeseleerminals  concludesduction  and visusduction .TMEM134 influences obesity and atherosclerosis in adults [TMEM134 is involved in the prototypical inflammatory nuclear factor-κB (NF-κB) signaling pathway [NF-κB signaling is the most important characteristic for innate immune programming in chronic inflammation [PITPNM1 is associated with retinal degeneration and hypopyon in humans and is involved in pathways of metabolism and glycerophospholipid biosynthesis [The identified potential candidate gene n adults . Further pathway . The modammation . The ideynthesis .LCN15 for FEED is involved in the transport of glucose and other sugars, bile salts and organic acids, metal ions and amine compounds as well as the transport of vitamins and nucleosides [LCN2 influences obesity and diabetes in humans [LCN15 physiologically interacted with high glucose levels in enterocytes [LCN15).The potential candidate gene leosides . As a men humans . Furthererocytes . Extendeerocytes , implyinSLC25A27 is part of a recently identified genetic network associated with economically important traits in Wagyu x Limousin crossbred cattle [SLC25A27 contributes to long chain fatty acid uptake [SLC25A27 gene is involved in thermoregulatory heat production and metabolism in the brain [The potential candidate gene d cattle . Additiod uptake  and contd uptake , oxidatid uptake , and fasd uptake . The mithe brain .PPM1E) influenced BACT behavior in dual-purpose cattle. Accordingly, the dephosphorylation gene (PPM1E) was associated with feeding behavior in Danish Duroc boars [PPM1E is mediated by 5’AMP-activated protein kinase (AMPK), which plays a key role in controlling energy balances. The enzyme AMPK is involved in hypothalamic glucose and nutrient sensing. Hence, due to the identified impact of PPM1E on activity traits in dual-purpose cattle and due to the strong correlation between feeding and activity  implies intensification of rumination time (RUM). Behavior-related feed intake had negative genetic impacts on BACT, enhanced BACT (HACT), and resting/sleeping (NACT) . An incrCows with 1% higher levels for RUM and FEED produced 0.10 kg and 0.12 kg more milk , respectGenotype data were provided from the national breeding organizations. Phenotypes reflect the standard trait pattern from official milk recording schemes. Behavior recording was non-invasive.The five dual-purpose cattle breeds with phenotypic sensor behavior data were from Germany (DE_DSN), Poland , Slovenia  and Switzerland  . In Germ® [For the electronic recording of behavior traits, dual-purpose cows were equipped with sensors implemented in ear tags (Dutch CowManager system Agis Automatisering BV). The validation and testing phase of ear tag sensors under grazing conditions covered a period from 1 May 2016 until 31 June 2016 . After o®   were available for 329 cows from Germany, Poland and Switzerland. Descriptive statistics of the sensor traits, welfare indices and test-day traits are listed in Illumina Bovine 50K Bead chip version 2, with the Illumina Bovine 50K Bead chip version 3, and with a customized bovine 50K SNP chip (IDB V3), according to the Illumina Infinium assay protocol . Quality controls of the genotype data were conducted using PLINK software [p < 0.00001. All SNPs had a call rate larger than 85%, and SNPs located on sex chromosomes were excluded. Cows with a call rate lower than 80% for all loci were excluded. Whenever the genomic relation between two animals was larger than 0.95, one animal was excluded. Sporadic missing SNPs were imputed by the BEAGLE version 3.3.2 [The five dual-purpose breeds, two additional breeds from Germany (DE_DN and DE_HF) and one from Slovenia (Sl_HF) were genotyped with the software , defininon 3.3.2 . After SThe data used in the present study is available as supplementary file Bos taurus) as well as zebus (Bos indicus) [PCA was conducted to account for potential population stratification prior to GWAS and to explore the genetic diversity of the European cow dataset. PCA based on the genomic relationship matrix was generated in GCTA . In a seindicus) . The allindicus) . A convey was the observation vector for sensor traits and indices ; b was the vector of fixed effects including the combined breed-farm effect, the year-month effect for the measuring date, and the age of cows as a fixed linear regression; a was the vector for additive genetic effects; p was the vector for permanent environmental effects for the cows with repeated measurements; e was the vector of random residual effects, and X, Z1, and Z2 were incidence matrices for b, a, and p, respectively. The assumed variance-covariance structure was a ~ N  [Gw was calculated as follows:A22 is the submatrix of the pedigree-based relationship matrix for genotyped animals. Estimated breeding values (EBV) from model A matrix were the databases for the calculation of DRP according to Garrick et al. [For the estimation of genetic parameters, genomic and pedigree relationship matrices were combined. In additional analyses, only the pedigree relationship matrix was considered. The pedigree consisted of 8,798 animals and was traced back as far as possible. The oldest ancestors in the pedigree were born in 1944 for Germany, in 1981 for Poland, in 1990 for Slovenia, and in 1917 for Switzerland. Variance components of sensor traits were estimated via univariate animal models using the AIREML procedure, as implemented in the DMU software package . The stak et al. . Only ank et al. .A matrix was defined as follows:y was the observation vector for MY, Fat%, Pro%, and SCS; b was the vector of fixed effects including the breed-farm and calving-year-season effects, and the lactation curve modeled via Legendre polynomials of order three for days in milk; a was the vector for additive genetic effects based on the A matrix; p was the vector for permanent environmental effects for the cows with repeated measurements; e was the vector of random residual effects, and X, Z1, and Z2 were the incidence matrices for b, a, and p, respectively. Again, EBV were de-regressed to obtain DRP for test-day production traits and SCS.The genetic-statistical model  used forloco) option. Dependent variables  were MEAN and DRP. For testing single-locus SNP effects, the following statistical model  = 0.05/35,826 = 4.47 x 10−7. The FDR as introduced by Benjamini and Hochberg [Single-trait multi-breed GWAS was performed using the software package GCTA . In thisal model  was usedHochberg  was a fufastBAT option [P-values considering the set of SNPs within the window were used simultaneously for candidate gene detection. The P-values of genes were adjusted according to FDR (significance threshold < 20%). In the last step, physiological functions and positions of candidate genes were inferred based on information from the Ensembl [The associated potential candidate genes were identified via a gene-based test in GCTA and applying the T option . The datT option . A totalT option ,85,86).  Ensembl , NCBI [8 Ensembl , UniProt Ensembl  and Gene Ensembl  databasez and uncorrelated residuals when regressing y on x and y and x as phenotypes, respectively. The variance of x as a fixed regression to explain y; n was the sample size; x; and x explained by z, which equaled y; x and trait y; p was the allele frequency, and x. The test statistic In epidemiology, Mendelian randomization uses genetic variants as instrumental variables to test for the causal inference between an exposure and an outcome . Hence, b^yx was :b^yx=co−p)⋅b^xz2where vaowed χ12 , which wTo fulfill the precondition of database ,86. A toP-value of the SNP lower than 0.05 to meet the assumption of Afterwards, the GWAS estimates for the 1,011 SNP were transmitted into a self-modified version of the GSMR package  in R to S1 FigThe red line is the significance threshold line for the Bonferroni correction of 5%, and the green dots represent significant SNP according to the false discovery rate of 20%.(PDF)Click here for additional data file.S2 FigThe red line is the significance threshold line for the Bonferroni correction of 5%, and the green dots represent significant SNP according to the false discovery rate of 20%.(PDF)Click here for additional data file.S3 FigThe red line is the significance threshold line for the Bonferroni correction of 5%, and the green dots represent significant SNP according to the false discovery rate of 20%.(PDF)Click here for additional data file.S4 FigThe red line is the significance threshold line for the Bonferroni correction of 5%, and the green dots represent significant SNP according to the false discovery rate of 20%.(PDF)Click here for additional data file.S1 Table1, NCBI2, UNIPROT3, and GeneCard4.RUM = rumination; FEED = feeding; BACT = basic active; BTA = Bos taurus chromosome; DRP = de-regressed proof; MEAN = mean of observations; functions derived from Ensembl(DOCX)Click here for additional data file.S2 TableCorrelations estimated from bivariate models with the same fixed and random effects as model  and stan(DOCX)Click here for additional data file.S1 File(ZIP)Click here for additional data file.


The heritability of most complex traits is driven by variants throughout the genome. Consequently, polygenic risk scores, which combine information on multiple variants genome‐wide, have demonstrated improved accuracy in genetic risk prediction. We present a new two‐step approach to constructing genome‐wide polygenic risk scores from meta‐GWAS summary statistics. Local linkage disequilibrium (LD) is adjusted for in Step 1, followed by, uniquely, long‐range LD in Step 2. Our algorithm is highly parallelizable since block‐wise analyses in Step 1 can be distributed across a high‐performance computing cluster, and flexible, since sparsity and heritability are estimated within each block. Inference is obtained through a formal Bayesian variable selection framework, meaning final risk predictions are averaged over competing models.We compared our method to two alternative approaches: LDPred and lassosum using all seven traits in the Welcome Trust Case Control Consortium as well as meta‐GWAS summaries for type 1 diabetes (T1D), coronary artery disease, and schizophrenia. Performance was generally similar across methods, although our framework provided more accurate predictions for T1D, for which there are multiple heterogeneous signals in regions of both short‐ and long‐range LD. With sufficient compute resources, our method also allows the fastest runtimes. Ideally, multivariate regression would be used to estimate weights adjusted for LD. Unfortunately, however, this is complicated for two reasons. The first is that privacy concerns and the logistics of sharing data on such a large scale mean that meta‐GWAS typically only conduct one‐at‐a‐time association tests of each variant, based on simple summaries shared between the cohorts. The final univariate results ignore correlations among the variants, and consequently signals tagged by multiple correlated variants would be overrepresented in a polygenic risk score constructed according to a simple threshold on statistical significance. This issue is usually dealt with by pruning variants until they are approximately independent, although information is inevitably discarded  and empirical Bayes  of indices for whether each variant has a nonzero effect, that is, included in the model. Denoting the proportion of variants with nonzero effects as π, sparsity is induced via a β prior distribution:To avoid over‐fitting in the context of potentially many genetic effects, we use a Bayesian sparse regression framework to draw inference under the model described by Equation 1/λP, and therefore decreases with the total number of variables P, whereas the global prior odds of any effect is constant at 1/λ , which we have used previously in an ′omics setting , which is a selection of variants in Step 1 and corresponding parameter values, θ(0). To sample the next model and set of parameters, which we denote by γ(1) and θ(1), we propose moving from the current state to another model and/or parameter values, γ* and θ*, using a proposal function q. The proposed model and parameters are accepted with probability equal to the Metropolis‐Hastings ratio:D is the observed data, and P is the multivariate likelihood described by Equation P(γ⁎) is the β‐binomial model space prior defined in Equation P(θ*|γ*) is the prior on the parameters conditional on (i.e. included in) the model. The proposed model and parameter values are therefore accepted with a probability proportional to both their likelihood and prior support. If this new set of values is accepted, we set γ(1) = γ* and θ (1)=θ*, otherwise they are discarded and the current values are retained; γ(1) = γ(0) and θ(1) = θ(0). It can be shown that this produces a sequence of parameter/model samples, which converge to the target posterior distribution , we average to obtain the final weighted polygenic risk score across all variants, β^. This may also be interpreted as the vector of posterior mean variant effects, averaged over the posterior distribution of models.We cannot derive analytical expressions for the posterior of C Green,  to sampln Green, . After o2.1.5X′X. This is due to the necessary inversion during inference .In step one, the β^G, could then be constructed by simply appending the block‐specific scores:In the absence of correlations across blocks, an unbiased genome‐wide polygenic risk score, with weights which we denote by across blocks, since they were ignored in Step 1. To account for cross‐block correlations, we introduce a second layer of multivariate weights, δ = , which adjust the “marginal” block‐specific scores for one another. Specifically, we seek to estimate the “block‐adjusted” risk score:However, this risk score will be biased in the presence of correlations B covariates, and view δ as the multivariate vector of effects, we would obtain from a regression of y on an n × B matrix of the block‐specific scores, S. For clarity, the element of S corresponding to individual i and block b is:xi,b,p is their genotype at variant p in block b, and βˆb,p is the corresponding weight from the block‐specific score as estimated in Step 1. It transpires that the estimation of δ is straightforward, by reapplying the same methodology used to estimate multivariate SNP weights for each block in Step 1, except now we wish to adjust the “marginal” block‐scores for the block‐block correlation structure S′S. The analogy of Equation It is instructive to consider the block‐specific scores as a set of X, we also obtain a plug‐in estimate for S′S. In the same way X′y for Equation S′y from the marginal block‐score “effects” and the column means of S. By construction, the “marginal” effect of each block‐specific score is 1; a unit increase in each risk score is associated with the same unit increase in the trait y. Intuitively, all scores have equivalent unit effects on y because the variant‐specific effects from which they are composed are on the same scale as per Equation δˆ after multiplying both sides of Equation S′S to obtain a linear regression analogous to Equation δˆ completes our genome‐wide polygenic score in which correlations are accounted for both within and across regions:By applying Equation σ2, as well as the proportion of “causal” variants, θ, in practice we found no difference compared to estimating each block‐specific score, β^b, independently. By running the block‐specific analyses independently, we could take advantage of large numbers of CPU cores available to us via a high performance computing cluster (HPC). In each of the following real data applications we ran our algorithm for 200,000 iterations in the Step 1 block‐specific analyses.Note that the first step of our algorithm is why it is highly parallelizable. Although, in principle, joint analysis of multiple blocks would help inform estimation of the residual variance, 33.12 95% using the Plink software package  versus 0.65 (95% CI: 0.62–0.68), and T1D, AUC of 0.87 (95% CI: 0.85–0.89) versus 0.83 (0.80–0.85). Our two‐step JAM method appeared the most robust, generally resulting in performance on par with the best performing of LDPred and lassosum. Runtimes for our two‐step approach ranged between 3 and 4 min when running different prior settings and chromosomes in parallel on different computing nodes, with block parallelization across the 16 cores of each compute node. These runtimes were similar, though slightly faster than lassosum, which typically took an extra minute to run. Conversely, LDPred took several hours. Therefore, with a large numbers of compute cores available, our parallelizable approach was the fastest, while offering typically better predictive performance. However, in terms of total computational cost, LassoSum is the most efficient method, achieving these runtimes when only a single compute node is available.3.2n = 4,715) and T1D . For the latter, the 1958 birth cohort was removed since these samples were used as controls in the T1DGC. For schizophrenia, testing data comprised genotypes measured using the Affymetrix 6.0 array in 5,334 samples from the Molecular Genetics of Schizophrenia (MGS) study , CAD , and schizophrenia . Summary statistics from the T1DGC study were available for both genotyped SNPs (Illumina 550K) as well as imputed SNPs unique to the older Affymetrix 500K array. The imputation, originally conducted to facilitate meta‐analysis with the WTCCC, was based on a substantial subset of controls for which both arrays were available. Details of the original QC, imputation method, and meta‐analysis can be found in Barrett et al. . The carp‐value thresholding approach, as well as a combined clumping and p‐value thresholding approach, which selectively removes less significant SNPs to reduce LD  compared to 0.85 (95% CI: 0.83–0.86) for LDPred, and 0.82 (95% CI: 0.81–0.84) for Lassosum. We suspect the improved performance from JAM is due to the large number of correlated signals in regions of both short‐ and long‐range LD within the major histocompatibility complex (MHC) on chromosome 6, for which a more sophisticated model search and averaging algorithm should offer greater accuracy. To confirm, we reran JAM, LDPred, and lassosum after excluding chromosome 6 from the training data. As expected, performance was considerably diminished for all three methods but was indistinguishable between JAM, LDPred, and lassosum , indicating that JAM's improved performance for T1D is indeed driven by a more flexible model for the MHC. For schizophrenia and CAD, where the polygenic signal is weaker and more dispersed through the genome, all methods performed similarly  complex, and it is those diseases with HLA associations where our method, and others that account for LD, did best. There is a particularly strong and complex HLA signal for T1D, consisting of multiple heterogeneous signals in regions of both short‐ and long‐range LD, and it was here, in both the WTCCC and T1DGC case studies, that performance gains were greatest compared to LDPred and lassosum, which only model short‐range LD. A further novel feature of our method, compared to LDPred and lassosum, is that we allow for different genetic models, that is, signal sparsities, across different blocks. This is achieved by treating sparsity and local heritability as random quantities rather than fixed hyperparameters, which should offer increased statistical robustness over repeated replication studies, as well as inference on where highly polygenic predictive signals are concentrated  is available to many researchers today, however, our parallelization approach can take advantage of even more resources as they inevitably become available over the coming years, since many of the individual block analyses are still being run sequentially in Step 1. With sufficient computing resources, in principle, every single 100 SNP block could be run be run in parallel, which would reduce runtimes to well under a minute in our three case studies. Relatedly, while we found the use of 100 SNP blocks in Step 1 worked well in practice, this choice is likely to be dependent on marker density, which will determine the average genomic length of these blocks. Using microarray data, as in our case studies, the 100 SNP blocks span genomic lengths the order of 100 kb for microarray data, but, when, for example, using imputed sequence data larger blocks may lead to better performance. We intend to explore this in more detail in future work.https://github.com/pjnewcombe/R2BGLiMS. We have included scripts demonstrating the JAMPred syntax, and how to distribute a genome‐wide analysis on an HPC, in the Supporting Information.We have incorporated our algorithm “JAMPred” into our existing fully documented R package for Bayesian model selection “R2BGLiMS”, which also contains the original “JAM” software, and is freely available to download via github Supporting informationClick here for additional data file.


Arctocephalus gazella) pups. We specifically developed a panel of 13 microsatellites from expressed immune genes and genotyped these together with 48 neutral loci in 234 individuals, comprising 39 pups that were classified at necropsy as having most likely died of bacterial infection together with a five times larger matched sample of healthy surviving pups. Identity disequilibrium quantified from the neutral markers was positive and significant, indicative of variance in inbreeding within the study population. However, multilocus heterozygosity did not differ significantly between healthy and infected pups at either class of marker, and little evidence was found for fitness associations at individual loci. These results support a previous study of Antarctic fur seals that found no effects of heterozygosity at nine neutral microsatellites on neonatal survival and thereby help to refine our understanding of how HFCs vary across the life cycle. Given that nonsignificant HFCs are underreported in the literature, we also hope that our study will contribute toward a more balanced understanding of the wider importance of this phenomenon.Numerous studies have reported correlations between the heterozygosity of genetic markers and fitness. These heterozygosity–fitness correlations (HFCs) play a central role in evolutionary and conservation biology, yet their mechanistic basis remains open to debate. For example, fitness associations have been widely reported at both neutral and functional loci, yet few studies have directly compared the two, making it difficult to gauge the relative contributions of genome‐wide inbreeding and specific functional genes to fitness. Here, we compared the effects of neutral and immune gene heterozygosity on death from bacterial infection in Antarctic fur seal ( These heterozygosity–fitness correlations (HFCs) have been documented for a wealth of traits in many animal and plant species David,  and suggTwo main mechanisms have been proposed to explain HFCs. First, early studies tended to assume that more homozygous individuals were less fit due to inbreeding depression  that was separated from adjacent breeding sites by a cliff on the east side, open sea on the west and rocky outcrops to the north and south. An elevated scaffold walkway , during the austral summers of 2000/2001–2013/2014 (hereafter breeding seasons are referred to by the year in which they began). The study population was located at a small cobblestone breeding beach . Piglet ear notching pliers were used to collect a small skin sample from the interdigital margin of the foreflipper, which was stored individually in 20% dimethyl sulphoxide (DMSO) saturated with salt  placed in the trailing edge of the foreflipper .A total of 96 microsatellite loci were selected for testing, comprising twenty that appeared polymorphic in silico as described by Hoffman and Nichols  plus a f2.4n = 39 sampled over ten seasons) together with a five times larger matched sample of randomly selected healthy pups that survived until the end of the respective field seasons (n = 195). The sample of healthy individuals was matched in the sense that we endeavoured wherever possible to select these animals from the same years as infected individuals in order to maximize comparability. This was achieved in all years apart from 2003, where two individuals died of bacterial infection but high‐quality DNA could only be obtained for two healthy individuals. Consequently, six additional healthy individuals were genotyped from 2008 in order to match the total sample sizes.We selected for further genotyping all pups born during the study period for which the most likely cause of death was determined at necropsy to be bacterial infection . The resulting PCR products were subsequently resolved by electrophoresis on an ABI 3730xl and allele sizes were called relative to the LIZ size standard (1:150 dilution) using GeneMarker. To ensure genotype quality, all of the traces were manually inspected by V.L. and J.I.H and any incorrect calls were adjusted accordingly.Total genomic DNA was extracted from the skin samples using a standard phenol–chloroform protocol  using Genepop version 4.2 Rousset, . For eac2.5.2g2 , which quantifies the proportion of loci that are heterozygous while weighting the contribution of each locus by the expected heterozygosity at that locus  and included year as a random effect to control for any variation in survivorship attributable to interannual variation. The following GLMMs were considered:survival ~ 1 + (1|year)survival ~ sMLH  + (1|year)survival ~ sMLH (immune microsatellites) + (1|year)survival ~ sMLH  + (1|year)survival ~ 1 + birth weight + (1|year)survival ~ sMLH  + birth weight + (1|year)survival ~ sMLH (immune microsatellites) + birth weight + (1|year)survival ~ sMLH  + birth weight + (1|year)To test for associations between microsatellite heterozygosity and death from bacterial infection, we constructed several alternative generalized linear mixed‐models (GLMMs) incorporating relevant predictor variables and quantified their relative support using AICc value, which compares weights as a measure of the likelihood of a particular model  as well as models that included sMLH combined over all loci or calculated separately for the neutral versus the immune loci. Models five to eight also included pup birth weight (in kg) to incorporate any potential effects of body size on survivorship. All of the models were specified using the glmer function of the package “lme4” . If we assume that our estimates of variability are not biased by elements of marker development, this difference is significant . Furthermore, the neutral markers tended to carry more alleles when they were cross‐amplified from other species as opposed to developed in Antarctic fur seals . Finally, cross‐species amplification rates were significantly higher for microsatellites developed in the Antarctic fur seal from immune versus neutral genomic regions .Figure g2) based on the neutral loci. Our estimate . To test for effects of heterozygosity on death from bacterial infection, we then used a multimodel inference approach to compare alternative models, including null models with no genetic terms as well as models containing sMLH calculated respectively for the neutral loci, the immune loci, and all of the loci combined . The best supported model contained only the intercept together with year fitted as a random effect, although three other models, the first containing birth weight as a fixed effect, the second containing neutral marker sMLH, and the third containing overall sMLH, were ranked within two ΔAICc units of the best model, indicating that they have a similar level of statistical support . In addition, all but one of the 61 markers had effect sizes with 95% CIs overlapping zero (Figure To quantify the variance in inbreeding within the Antarctic fur seal population, we calculated the two‐locus disequilibrium g based on4Heterozygosity–fitness correlations are widespread across the animal kingdom but remain poorly understood because most studies still use too few genetic markers to capture variation in inbreeding (Hansson & Westerberg, 4.1By focusing our microsatellite development effort on immune loci residing within expressed transcripts, we generated a new marker panel that captures genetic variation across predominantly functional regions of the genome. Reassuringly, genetic variability expressed as allelic richness was lower for this immune marker panel, consistent with the notion that stronger selection on functional genomic regions constrains microsatellite evolution, regardless of whether the microsatellites themselves have direct functional effects or whether they are in linkage disequilibrium with nearby functional polymorphisms (Li, Korol, Fahima, Beiles, & Nevo, We also found that neutral microsatellites cross‐amplified from other pinniped species tended to show higher levels of variability in Antarctic fur seals than microsatellites developed in the focal species. This observation may help to explain two previous HFC studies that found stronger associations between heterozygosity and fitness using markers cloned from other species (Hoffman, Hanson, Hanson, Forcada, Trathan, & Amos, 4.2g2 estimate, which is indicative of variance in inbreeding within the study population. This is to be expected given the highly polygynous breeding system of Antarctic fur seals (Hoffman, Boyd, & Amos, We deployed a large panel of neutral microsatellites to test for inbreeding effects on mortality due to bacterial infection. In line with recent studies based on both microsatellites (Stoffel et al., 4.3We found no effect of multilocus heterozygosity on pup survival, in contrast to studies of several other pinniped species including grey seals (Bean et al., Given that bacterial infection status may have a genetic basis and is likely to be associated with variation in immune activity, we compared and contrasted fitness effects at neutral and functional genomic regions related to immunity. Previous studies of other species using a similar approach have uncovered mixed results, with some studies finding comparable HFC effect sizes at neutral and functional loci (Da Silva et al., 4.4post hoc power analyses as a means of interpreting tests with nonsignificant results. However, the approach of calculating the “observed power” given the value of a test statistic is flawed because there is a one to one relationship between the p‐value and the power to reject the null hypothesis of no effect: in other words, the higher the p‐value, the lower the power (Steidl, Hayes, & Schauber, p‐value should cause nothing to change about our interpretation of the p‐value”.Heterozygosity–fitness correlation effect sizes are often small, with multilocus heterozygosity typically explaining only around 1–5% of trait variance (Chapman et al., post hoc power calculations do not provide a meaningful way of evaluating negative results, many authors have advocated providing confidence intervals for effect sizes (Steidl et al., As A further caveat is that the term “bacterial infection” could potentially encompass one main type of bacterial pathogen or a number of different pathogens. Unfortunately, precise data on the bacterial strains present in pups that died of bacterial infection are lacking. We therefore cannot rule out the possibility that an HFC for bacterial infection could not be detected due to confounding interactions between host heterozygosity and multiple bacterial strains. Nevertheless, regardless of the exact cause of death, our results in combination with those of Hoffman et al.  suggest 4.5Setting our findings into a broader context, correlations between heterozygosity and fitness have been reported in diverse organisms, suggesting that HFCs are a genuine and biologically important phenomenon. However, the field of HFCs as a whole suffers from publication bias, with around 10% of all HFC effect sizes going unreported in the literature (Chapman et al., 5Although inbreeding occurs in our study population and can be detected with our panel of neutral markers, neither neutral nor immune microsatellite heterozygosity was associated with neonatal mortality due to bacterial infection in Antarctic fur seals. Taken together with the results of a previous study (Hoffman et al., The authors declare no competing interests.JIH and VL conceived the study. JF collected and transported the samples. VL and MO conducted the laboratory work under the supervision of JIH. VL and JIH scored the genotypes. MO, VL, and LH analyzed the data. JIH and VL wrote the manuscript, and all of the authors edited and approved the final manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


P < 5.00E-07) were detected for six growth traits. Of these, 22 SNPs with pleiotropic effects were shared by DGE and SGE. For multi-locus GWAS, a total of 72 and 110 putative QTNs were detected for DGE and SGE, respectively. Of these, 5 SNPs with pleiotropic effects were shared by DGE and SGE. It is noteworthy that 2 SNPs (SSC8: 16438396 for DGE and SSC17: 9697454 for SGE) were detected in single-locus and multi-locus GWAS. Furthermore, 15 positional candidate genes shared by SGE and DGE were identified because of their roles in behaviour, health and disease. Identification of genetic variants and candidate genes for DGE and SGE for socially affected traits will provide a new insight to understand the genetic architecture of socially affected traits in pigs.Socially affected traits are affected by direct genetic effects (DGE) and social genetic effects (SGE). DGE and SGE of an individual directly quantify the genetic influence of its own phenotypes and the phenotypes of other individuals, respectively. In the current study, a total of 3,276 Large White pigs from different pens were used, and each pen contained 10 piglets. DGE and SGE were estimated for six socially affected traits, and then a GWAS was conducted to identify SNPs associated with DGE and SGE. Based on the whole-genome re-sequencing, 40 Large White pigs were genotyped and 10,501,384 high quality SNPs were retained for single-locus and multi-locus GWAS. For single-locus GWAS, a total of 54 SNPs associated with DGE and 33 SNPs with SGE exceeded the threshold ( Social cooperation among pigs (such as group behaviour) would result in positive effects, whereas the competition (such as tail biting) may result in negative effects. Considering the social interaction in the genetic evaluation model, the phenotypic value should be decomposed into a direct effect due to its own characteristic and a social effect originating from others5. Both effects can be further decomposed into a heritable component and a non-heritable component. The heritable component of the social effect its called the social genetic effect (SGE)7. Griffing et al. (1967) first reported that the social effects among group individuals distinctly affected the selection processes in a group selection5. Amelie Baud et al. (2017) reported that the social genetic effect explained up to 29% of phenotypic variance for health and disease traits in mice, that showed the contribution of the social genetic effect was more than the direct genetic effect (explained 21% of phenotypic variance) in some cases8. However, the classical quantitative genetic model ignored the SGE among group members in the past several decades. In that case, the genetic variance for the socially affected trait would be incorrectly estimated due to the ignorance of SGE8.Social interactions between individuals are plentiful, which could contribute to pig’s growth and welfare4 and that their genetic parameters for important traits were estimated in pigs9. For average daily gain (ADG), the genetic correlation between direct genetic effect and social effect was low to moderate (0.24) in pigs10. The model ignored the pen effects and showed that the heritability of SGE was approximately were about 0.00311 and 0.000712 for ADG. The reported social heritabilities were 0.007, 0.001, and 0.003 for body weight, carcass backfat depth, and carcass lean meat content in heavy pigs13, respectively. Moreover, Rostellato et al. (2015) reported that the social genetic effect contributed largely to total genetic variance for the carcass traits (ranging from 33.2% to 35%) in pigs13. Canario et al. (2017) showed that the heritability of SGE was 0.001 to 0.004 for pig growth rate and its genetic correlations among direct and SGE were −0.28 to 0.12 in a Swedish pig population3.Previous studies have been demonstrated that the SGE plays an important role in genetic evaluation14, feed intake, backfat and muscle depth7. Several models that considered different fixed factors were built to estimate SGE. The animal model was first extended to SGE in Japanese quail15. This model incorporated the competitive effects in classical BLUP (best linear unbiased prediction), and substantially resulted in an extra selection response. Muir16 and Bijma et al.18 presented a full model that simultaneously contained both direct genetic effects and social genetic effects. They estimated SGEs for growth traits and found that SGE contributed the main part of genetic variance in growth rate and feed intake in pigs. In summary, the previous studies on SGE mainly focused on the development of an evaluation model and the estimation of genetic parameters. The genetic architecture and causal pathway for SGE are still unknown.In general, the pigs were grouped into different pens, and the social effects among members strongly affected pig productive performance. In pigs, the estimation of the social genetic effect was mainly focused on ADGPig growth traits are affected by direct genetic effects (DGE) and SGE. To investigate the genetic architecture of DGE and SGE, this study conducted a GWAS for DGE and SGE of six growth traits using the whole-genome re-sequenced data from the Large White pig population. The purpose of this study was to estimate the DGE and SGE for six socially affected traits, and reveal the genomic variation and candidate genes for DGE and SGE of six growth traits in pigs.Y = Xb + ZDaD + ZSaS + Wl + Vg + e , the DGE and SGE were estimated for each socially affected trait in Large White pigs. Table Using this full model A single marker test was performed to identify SNPs associated with DGE of 6 growth traits. The Table P < 5 × 10−8), 1 SNP was associated with dgeADG  was located in the PRKG1 gene. For dgeB100, the most significant chromosome region was located in SSC6: 39.75–39.79 Mb, and the top SNP  was located in POP4 and the PLEKHF1 gene.At the genome-wide threshold , 9 SNPs were associated with dgeADG, 19 with dgeD100, 6 with dgeB100, 2 with dgeADFI, 4 with dgeFCR, and 12 with dgeRFI  was located in LOC110259270 gene for dgeD100. In addition, a clear peak was observed within the region SS16: 23.20–23.23 Mb for dgeD100, however, no candidate gene was identified in this region.At the suggestive threshold , 4 identified SNPs were associated with SGE of days to 100 kg (sgeD100), 7 with sgeRFI. At the lenient threshold (P < 5 × 10−7) for suggestive associations, 1 SNP was associated with SGE of average daily gain (sgeADG), 14 with sgeD100, 2 with sgeADFI, 4 with sgeFCR, and 1 with sgeRFI, but no association was found for sgeB100  was distributed on SSC14 (Table PRKG1) was found for sgeADG within the ±20 kb region of the top SNP (SSC14: 98711408 bp).For sgeADG, only one suggestive SNP  was 1.03 × 10−8. Furthermore, a total of 14 suggestive significant SNPs (P < 5 × 10−7) for sgeD100 was characterized on SSC2, SSC3, SSC4, SSC8, SSC9, SSC16 and SSC17. On SSC2, the SNP (SSC2: 14306048 bp) was located within the region of the uncharacterized gene (LOC110259270). Two genes  near the SNP  were found for sgeD100.The results of sgeD100 are shown in Table P < 5 × 10−7) on SSC8 and SSC14 were detected for sgeADFI  were found.A total of 2 suggestive significant SNPs (FI Table . In thesP < 5 × 10−7) on SSC1 and SSC6 (Table P = 2.83 × 10−7) was located in the GABRR2 gene.For sgeFCR, a total of 4 associations reached the suggestive significant threshold (C6 Table . In the P < 5 × 10−8). The top SNPs (SSC16: 43805766) with the lowest P-value of 3.72 × 10−9 showed the strongest association effect in the region of 43.79–43.83 Mb. In the significant region of SSC16: 43.79–43.83 Mb, five consecutive SNPs and one promising candidate gene (ADAMTS6) were identified for sgeRFI. The second top SNP (SSC1: 312947811) with a low P-value of 1.18 × 10−8 was found in the region of 312.93–312.97 Mb. In addition, one candidate gene (LPAR1) related to disease and healthy were found for sgeRFI in the chromosome regions of SSC1: 251.97–252.01 Mb.A total of 8 SNPs were found on SSC1 and SSC16 for sgeRFI Table . Two SNPPRKG1 gene for dgeADG and sgeADG. In the region of SSC2: 14.29–14.33 Mb, the top SNP (SSC2: 14306048 bp) was located in the LOC110259270 gene, and in the region of SSC8:112.41–112.45 Mb, the top SNP (SSC8: 112431517 bp) was located in GAR1 and the CFI gene for dgeD100 and sgeD100, respectively.A total of 15 significant SNPs were shared by DGE and SGE that were distributed on SSC1, SSC2, SSC3, SSC4, SSC7, SSC9, SSC14 and SSC16  described by Bergsma et al.23 to estimate DGE and SGE of six growth traits. In this model, on one hand, the fixed effects contained sex, tested year and month, the random litter effect and the random group effect were corrected. On the other hand, additional 3236 phenotypic records for ADG, D100, B100, ADFI, FCR and RFI and pedigree information were added into mixed model equations, which improved the prediction accuracy of DGE and SGE for these traits.Many studies have reported that the social genetic effect are widespread in hens8. If SGE contributes to the studied traits, the study of genetic architecture should simultaneously consider DGE and SGE for socially-affected traits. However, few studies have focused on the genetic architecture of SGE. This study simultaneously quantified the SGE and DGE, and performed GWAS to reveal the genetic architecture of DGE and SGE for six growth traits in Large White pigs. Based on the Illumina PorcineSNP60 v2 BeadChip panel, Hong et al.24 identified several genomic regions and candidate genes associated with SGE for ADG in Landrace pigs. In their study, the individual ADG and the average ADG of unrelated pen-mates were directly used as DGE and SGE, respectively. In our study, the SGE and DGE were estimated for six growth traits, using the full animal model. Then, (1) pseudo phenotype values including DGE and residual effects were used to perform GWAS for DGE of six growth traits; and (2) for SGE of six growth traits, pseudo phenotype values including SGE and residual effects were used to perform a GWAS. Using these pseudo phenotype values, this study revealed the genetic architecture of DGE and SGE for six growth traits in Large White pigs. This study detected 151 and 205 SNPs for DGE and SGE, respectively. These results did not confirm the results with Hong et al.24. In the present study, the power of QTL detection could be limited by the low estimated heritability of SGE and the small population size in this study. Furthermore the assessment of SGE was different between our study and the previous study24. In a previous study, the average ADG of unrelated pen mates was used to detect genetic variants of SGE. However, in our study, the full model Y = Xb + ZDaD + ZSaS + Wl + Vg + e  were used to estimate the SGE of each trait, and then the estimated SGE wasused to perform GWAS for SGE of each trait. Our study offered a new insight and effective method for the genetic architecture of socially affected traits in pigs.Currently, the genomic information has increased the understanding of complex traits, but the majority of studies have focused only on the DGE of studied traits. Published studies demonstrated that ignoring SGE may severely bias estimates of DGE, and the genetic basis of SGE may be different from the genetic basis of DGE25, FarmCPU26 and FASTmrEMMA28 software. Due to the multiple tests, the single-locus GWAS required Bonferroni correction29. However, the quantitative traits are controlled by numerous polygenes with large or minor effects, and the Bonferroni correction is overly conservative to identify important loci and may result in false negative results27 for complex traits. Recently, some multi-locus GWAS were proposed to detect significant SNPs for complex traits31. The significant threshold of the multi-locus GWAS is less stringent than single-locus GWAS. Because of the multi-locus and shrinkage natures of this method, a less stringent selection criterion is used in the multi-locus GWAS31. Although the multi-locus GWAS used a less stringent criterion, the study used simulated and real data to demonstrated that the multi-locus GWAS using the FASTmrEMMA model is more powerful and has less bias of QTN effect estimation than other methods32. The FASTmrEMMA method with high power, low false-positive rate and low computing time in QTN detection were used to perform multi-locus GWAS. Using GEMMA, 54 and 33 SNPs reached the threshold of 5.00 × 10−7 for DGE and SGE, respectively. Using FarmCPU, 51 and 101 SNPs reached the threshold of 5.00 × 10−7 for DGE and SGE, respectively. For FASTmrEMMA, a total of 46 and 71 putative QTNs were detected for DGE and SGE, respectively. Clearly, different methods provided different results. However, all these results show that numerous identified SNPs are associated with pleiotropic effects for multiple socially affected traits. Importantly, two SNPs were detected in three methods. Among them, one SNP (SSC8: 16438396 bp) was located in the region 16.42–16.46 Mb for dgeB100. In this region, four reported QTLs were associated with the B100 trait in pigs34. One SNP (SSC17: 9697454 bp) was detected in the region of 9.68–9.72 Mb for sgeD100. A previous study reported that numerous QTLs were associated with body weight35, behaviour36, immune capacity37 and disease38 in pigs. This SNP may contribute to weight, behaviour, immune and disease traits, and further affect socially affected traits. Thus this study suggested that the SNP SSC17: 9697454 bp was an informative causal locus for SGE in socially affected traits.To minimize the number of false positives and obtain credible results, the SNPs with MAF > 0.1 were used to perform single-locus and multi-locus GWAS using GEMMA39. To date, no genes with their biological functions were reported to associate with SGE in animals. Thus, it is essential to reveal the candidate genes and understand the genetic architecture of DGE and SGE for future pig breeding programmes. In the current study, using a GWAS study, many candidate genes associated with DGE and SGE were found. A total of 20 genes for DGE and 9 genes for SGE were found using single-locus GWAS. A total of 42 and 54 genes were found for DGE and SGE using multi-locus GWAS, respectively. All of these genes were first reported to be associated with SGE in pigs. Additionally, a total of 15 candidate genes associated with pleiotropism for socially affected traits. Among them, 4 genes for single-locus GWAS  and 11 genes for multi-locus GWAS  were shared by DGE and SGE, which may imply the pleiotropism of these shared genes for socially affected traits in pigs. These candidate genes were play a role in behaviour, disease and health.To investigate the genomic features that contribute to SGE and DGE in pigs, 40 Large White pigs were re-sequenced and 6 socially affected traits were studied. Based on the whole-genome re-sequencing data, this GWAS revealed the genetic architecture for 6 socially affected traits. The previous study only reported a total of 5 QTLs related to socially affected traits in pigsPRKG1 gene is a protein coding gene that acts as a key mediator of the cyclic guanosine monophosphate (cGMP) signalling pathway and plays an important role in the cellular signal transduction process. In platelets, hippocampal neurons, smooth muscle and cerebellar Purkinje cells, the PRKG1 gene was found to be strongly expressed41. This gene is known to be associated with behaviour41, circadian rhythms42, sleep42, learning42 and memory41. In humans, the PRKG1 plays an important role in stress response related traits43.The SORCS3 is an orphan receptor of the VPS10P domain receptor family, a group of sorting and signalling receptors central to many pathways in the control of neuronal viability and function. SORCS3 plays an important role in the nervous system. This gene was associated with behavioural activities in mice44.GABRR2 was found to be associated with dgeFCR and sgeFCR in pigs. GABRR2 is a protein coding gene that encodes a receptor for gamma-aminobutyric acid (GABA). The GABA receptor regulates the neurotransmitter in the brain45, and plays an important role in the behavioral stress response and physiology in animals and humans48. In mice, the GABA was associated with the aggressiveness and sociability towards conspecifics47. The performance and physical condition were affected by GABA in heat-stressed Roman hens49.On the region of SSC1: 57.25–57.29 Mb, P = 4.85 × 10−9) was located in a disintegrin and metalloproteinase with thrombospondin motifs 6 (ADAMTS6). The ADAMTS6 gene is a member of the ADAMTS protein family and is regulated by the cytokine TNF-alpha50. The functions of this gene were still not clearly elucidated and mainly reported about aetiology and played a role in the turnover of the extracellular matrix51. ADAMTS6 was expressed differently in each tissue, but the accurate function and the substrates of ADAMTS6 protein were not clearly demonstrated by a previous study. In past decades, numerous association studies have found the ADAMTS6 gene to be associated with complex traits in humans. Inguinal hernias52 and central corneal thickness and keratoconus53 were both identified ADAMTS6 in a GWAS, which suggested that the ADAMTS6 gene would affected the collagen homoeostasis in tissues and disorders and lung function. A previous study also suggested that this gene was related to osteosarcoma54. Some studies also demonstrated that the ADAMTS6 gene was significantly upregulated in cancer cells and stromal cells55. Importantly, this gene affected the intelligence and growth development, and the balanced translocation disruption of the ADAMTS6 gene was resulted in short stature and intellectual disability56. Given previous studies, ADAMTS6 gene was associated with growth and disease in biological organisms. The ADAMTS6 gene was first identified to associate with the sgeRFI. The sgeRFI was not only affected the feed efficiency, but also affected the growth and health of livestock. Considering the pleiotropic effect of this gene, the ADAMTS6 gene with a potential effect on socially affected traits would be identified as a prominent candidate gene.The strongest association  and the Institutional Animal Care and Use Committee of the Sichuan Agricultural University under permit number DKY-B20140302.1) and the ended at about 110 kg (BW2) in the feeding trial.The phenotypic data was collected in 2017, including ADG, days to 100 kg (D100), backfat thickness to 100 kg (B100), average daily feed intake (ADFI), residual feed intake (RFI) and feed conversion ratio (FCR). Initially, the tested pigs were selected between 120 and 130 day of age, with the body weight (BW) about 60 kg. Then a total of 40 Large White pigs from 4 pens were used for this research, containing 27 female and 13 male pigs, and each pen contained 10 piglets homogenous in body weight and age in this study. In this test, these pigs were grouped in the standard commercial pens, which were fed ad libitum by an automatic feeder (The feed intake recording equipment of OSBOREN). The test began at about 65 kg :58:The average metabolic body weight (AMW) was calculated for each individual using the following classical formula16:Pi is the phenotypic value of individual i, AD,i is the direct breeding value of individual i, ED,i is the direct non-genetic effect of individual i, AS,j is the social breeding value of the group member j, ES,j is the social non-genetic effect of the group mate j, and the n is the group size. The ED,i and For a socially affected traits, the phenotypic value of an individual can be denoted using following modelY is the vector of phenotypic observations; b is the vector of fixed effects, including sex, tested year and month; aD is a vector of DGE; aS is a vector of SGE; l is vector of random litter effects; g is vector of random group effects; e is random residual vector; X, ZD, ZS, W and V are incidence matrix of b, aD, aS, l and g, respectively. The variance-covariance matrix of aD and aS is denoted as:A is additive genetic correlation matrix; ith row of ZS, the group members of individual i were set to 1 and the others were set to 0. For improving the estimation accuracy of DGE and SGE, other 3,236 phenotypic records about ADG, D100, B100, ADFI, FCR and RFI that were obtained from previous performance test also were added into this analysis. These traits were analyzed using the average information restricted maximum likelihood (AI-REML) algorithm by DMU software59. Supplementary Table To investigate the direct and social genetic effects, the following full model was built for each trait in this study.60. The mapped reads were realigned by GATK software61. A total of 21,104,245 variants were identified using GATK software61. In order to minimize the number of false positives in this limited population size, the SNP with MAF > 0.1 were retained for further analysis. Thus, the nucleotide variants were filter based on the quality requirement with minor allele frequency (MAF > 0.1), Missing rate (Miss < 0.1), Hardy-Weinberg equilibrium (HWE < 1.0 × 10−6), and read depth (dp > 6) using VCFtools 4.262. Then, the SNPs on the sex chromosome and scaffolds were removed. After the quality control, a total of 10,501,384 SNPs across 18 autosomes were analyzed for association study.The pig’s ear tissue was collected from Large White pigs and stored in 75% alcohol. The genomic DNA was extracted by the standard phenol/chloroform method. The Nanodrop-2000 spectrophotometer was used to control the quality of genomic DNA. To obtain nucleotide polymorphism information, a total of 40 Large White pigs were re-sequenced using the Illumina HiSeq PE150 platform, then about 2,000 Gb sequence data was obtained in total. The average sequencing depth of these samples were close to 20×. All the sequence reads were filtered for data quality and mapped to the Sscrofa11.1 reference sequence using BWA software25 with a univariate liner mixed model were used to perform the single-locus association for DGE and SGE. The analysis models was written as follows:y is a vector of DGE or SGE contained residual effects from previous model (1), X is the incidence matrix of SNP effects, m is the vector of SNP effects, W is the incidence matrix of residual polygene effects, a is the vector of residual polygene effects, e is the random residual vector. Notably, where y is a vector that phenotype value was corrected by fixed effect, DGE (for SGE), SGE (for DGE), random litter and random group effects.The genome-wide efficient mixed-model analysis (GEMMA)63, False discovery rate64, and Sidak correction method65. In published literatures, the traditional Bonferroni correction method was routinely adopted, but this threshold was not absolute. When identified SNPs exceeded the accepted genome-wide statistical significance threshold P < 1.0 × 10−8, the GWAS results are most reliable67. The identified associations (5.00 × 10−8 < P ≤ 5.00 × 10−7) also could be replicated from subsequent studies28. The proper significant threshold is various due to different populations, different traits, and different genotypic data70. Furthermore, the Bonferroni correction is too conservative due to the ignorance of linkages between SNPs, especially for the sequencing data where many adjacent SNPs are highly linked. In the sequencing data, the number of markers used in the Bonferroni correction should be more relevant to the number of segments of chromosome. Thus, the whole-genome significance threshold 5.00 × 10−8 was established72 and the suggestive significant threshold was set at 5.00 × 10−7 in this study73.Recently, several statistical methods have been contributed to determine the significance threshold, such as Bonferroni correction26 and the fast multi-locus random-SNP-effect EMMA (FASTmrEMMA)74. FarmCPU iteratively used fixed effect model and random effect model. Using the FASTmrEMMA, this study performed a GWAS for DGE and SGE for six traits in pigs. In this multi-locus GWAS, the random-SNP-effect and multi-locus model methods were used to improve the power and decrease the false-positive rate. At the first stage, the putative quantitative trait nucleotides (QTNs) with P ≤ 0.005 were selected for further analysis. Then the selected putative QTNs were analyzed using a multi-locus model for true QTN detection.The multi-locus association analysis were performed by FarmCPUhttp://www.ensembl.org/) and NCBI (https://www.ncbi.nlm.nih.gov/) were implemented to screen the candidate genes located the significant loci. Only the genes located at the region of ±20 kb around the significant SNPs were considered in this study.The bioinformatics databases BioMart (Supplementary informationSupplementary Information