Vgt1, Vgt2 and Vgt3) and new loci. We found several QTLs whose effect depended on the group ancestry of alleles while others interacted with the genetic background. Our GWAS approach provides useful information on the stability of QTL effects across genetic groups and can be applied to a wide range of species.When handling a structured population in association mapping, group-specific allele effects may be observed at quantitative trait loci (QTLs) for several reasons: (i) a different linkage disequilibrium (LD) between SNPs and QTLs across groups, (ii) group-specific genetic mutations in QTL regions, and/or (iii) epistatic interactions between QTLs and other loci that have differentiated allele frequencies between groups. We present here a new genome-wide association (GWAS) approach to identify QTLs exhibiting such group-specific allele effects. We developed genetic materials including admixed progeny from different genetic groups with known genome-wide ancestries . A dedicated statistical methodology was developed to analyze pure and admixed individuals jointly, allowing one to disentangle the factors causing the heterogeneity of allele effects across groups. This approach was applied to maize by developing an inbred “Flint-Dent” panel including admixed individuals that was evaluated for flowering time. Several associations were detected revealing a wide range of configurations of allele effects, both at known flowering QTLs ( Identification of genomic regions involved in genetic architecture of traits has become commonplace in quantitative genetics studies. Genetic structure is a common feature in human, animal and plant species and most current methods target genomic regions whose effects on traits are conserved between genetic groups. However, a heterogeneity of allele effects may be observed due to different factors: a group-specific correlation between the alleles of the tagged marker and those of the causal variant, a group-specific mutation at the causal variant or an epistatic interaction between the causal variant and the genetic background. We propose a new method adapted to structured populations including admixed individuals, which aims to identify these genomic regions and to unravel the previous factors. The method was applied to a maize inbred diversity panel including lines from the dent and the flint genetic groups, as well as admixed lines, evaluated for flowering time. Several genomic regions were detected with various configurations of allele effects, with evidence of epistatic interactions between some of the loci and the genetic background. Quantitative traits are genetically determined by numerous regions of the genome, also known as quantitative trait loci (QTLs). The advent of high density genotyping of single nucleotide polymorphisms (SNPs) has opened the way to the identification of QTLs in diversity panels. These studies, referred to as genome-wide association studies (GWAS), use the linkage disequilibrium (LD) between the SNPs and causal variants at QTLs underlying the traits of interest. The panels evaluated in GWAS often include sets of individuals with complex pedigrees or genetic structure . The latApplying GWAS in a diversity panel including individuals from different groups raises the issue of spurious associations. The stratification of a population into genetic groups generates LD between loci that are differentiated between groups but not necessarily genetically linked. When a given trait is characterized by contrasted group-specific means, all these SNPs will correlate to it and may be detected as false positives. An efficient control of these spurious associations can be done by taking structure and kinship into account in the statistical model , 3. ThisHNF4A gene which was associated with a higher risk of developing type 2 diabetes in Askenazi compared to United Kingdom populations M1 while r, while  detected2M in this study. As with 1M, the existence of a SNP effect can be tested for each group, but 2M also allows one to test the existence of a general and a divergent SNP effects between groups. In our study, this model allowed to test for a dent  and the genetic background (dent/flint/admixed). As shown using simulations  testing an overall SNP effect for SNP with conserved effects accross ancestries and/or genetic backgrounds, and (ii) testing hypotheses for complex configurations between allele effects. When applied to MF, 17 QTLs were detected (20% FDR). While many of these QTLs were previously detected using 1M and 2M, the new hypotheses tested allowed us to discover new interesting regions.We developed model ulations , applyin1M, 2M and 3M (e.g. Δm (Dent) in 1M, 2M and 3M), the lower number of associations detected with 2M and 3M compared to 1M for real traits can be attributed to a different filtering on allele frequencies, the use of an approximate model for 2M and 3M, and to the randomness associated with a particular experiment. Regarding false positive control, the observation of the QQ-plots of the test p-values of 1M, 2M and 3M did not show particular problems, as presented for MF in For equivalent tests in The idea of exploiting admixed individuals has been proposed in the creation of NAM  and MAGIFrom a global perspective, a high number of QTLs have been detected in previous maize studies , 59, 60.Vgt2 [ZCN8 [10, and conversely for hypotheses testing a divergent SNP effect. When simultaneously interpreting all tests, Vgt2 appeared to have an effect that is conserved between genetic groups. Such a QTL can easily be detected in a multi-group population sample using a standard GWAS model [When doing GWAS in a multi-group population, geneticists generally assume that QTL effects are conserved between groups. Such QTLs were detected in our study with the example of the SNP associated with MF in the vicinity of Vgt2  and its t2 [ZCN8 –64 on chVgt1 [ZmRap2.7) that was detected by a SNP located on chromosome 8. High -log10 were observed when testing for a divergent SNP effect between ancestries  was detected by a SNP located on chromosome 4 and had a very similar profile to that of Vgt1. Its position is close (< 700 Kbp) to GRMZM2G126253, a candidate gene for maize flowering time proposed by [When group-specific allele effects are only due to group differences in LD or group-specific mutations at the QTL, the difference in allele effects should be conserved between the pure and the admixed genetic backgrounds. A first QTL matching this situation is Vgt1 , 47, 48 nt group , while Lnt group . Howeverposed by . To valiVgt3 on chromosome 3 [ZmMADS69 [10 was observed for tests that supported this hypothesis: a dent SNP effect in the dent genetic background , which supports the hypothesis of Vgt3 interacting with the genetic background. Recently, [ZmMADS69, the candidate gene of Vgt3, as being an activator of the regulatory module ZmRap2.7—ZCN8, which are the candidate genes of Vgt1 and Vgt2, respectively. The existence of such interactions is consistent with flowering time being controlled by a network of interacting loci, as now well established in model species arabidopis [Group-specific allele effects may also be due to an interaction with the genetic background. A first QTL matching this profile was detected by a SNP in the vicinity of mosome 3 , 55 and ZmMADS69 . This QT at Vgt1  and the ecently,  demonstrabidopis .QTL2.1) and showed a flint effect in the admixed genetic background, while the other QTL was located on chromosome 7 (QTL7.2) and showed an opposite dent effect between the dent and the admixed genetic backgrounds. Such QTLs are interesting as they are mainly revealed when creating admixed genetic material. They also suggest complex epistatic interactions between QTLs for these traits. The position of QTL2.1 is close (< 1.4 Mbp) to ereb197 and the position of QTL7.2 is close (< 100 Kbp) to dof47. Both are candidate genes for maize flowering time proposed by [Other examples of QTLs interacting with the genetic background were identified. Two of them featured a similar profile in the sense that they mainly exhibited a QTL effect in the admixed genetic background. One was located on chromosome 2 Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Figfo estimated on the admixed lines and their expected value fe along each chromosome (|fo − fe|). The expected allele frequencies were computed as the mean of flint and dent allele frequencies estimated on the parental lines by taking into account the contribution of each parent. A cubic smoothing spline was adjusted using the R function “smooth.spline”, and plotted in red.Absolute difference between observed allele frequency of the reference allele (TIF)Click here for additional data file.S4 FigIndividuals were colored depending on their genetic background: dent, flint or admixed.(TIF)Click here for additional data file.S5 Figr2. LD was calculated and averaged for loci pairs characterized by a similar physical distance ranging from 0 to 2 Mbp, considering a sliding window of 1Kbp. A cubic smooth spline was adjusted for each group, using the R function “smooth.spline”.LD extent estimated separately in dent and flint genetic groups using the standard (TIF)Click here for additional data file.S6 Figa) between the r of dent and flint groups, and (b) between the signs of r in the dent and flint groups. LD was calculated and averaged for loci pairs characterized by a similar physical distance ranging from 0 to 2 Mbp, considering a sliding window of 1Kbp. A cubic smooth spline was adjusted for each method, using the R function “smooth.spline”.Conservation of LD phases estimated using the correlation ((TIF)Click here for additional data file.S7 Figa) 1M, (b) 2M and (c) 3M. The size of the grey dots is proportional to the -log10 of the test at the most significant SNP of the region.Position of QTLs detected for FF with a FDR of 20% using ((TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.S9 Fig(TIF)Click here for additional data file.S10 Fig(TIF)Click here for additional data file.S11 Fig(TIF)Click here for additional data file.S12 Fig(TIF)Click here for additional data file.S13 Fig(TIF)Click here for additional data file.S1 Tablej  was computed following: Nk is the number of individuals (genotypes) in genetic background k  and N is the total number of individuals. The mean of each genetic background was computed following: k and the GxE variance k in each trial j were also reported. The heritabilities of each genetic background k were computed as: j.The lines “Row-Column” refer to the modeling of rows and columns as defined by the experimental design. AR1 refers to the autoregressive model AR1, while IID refers to the modeling of rows and columns as being independent and identically distributed among rows and among columns for a given trial. For more information, see the ASReml-R reference manual by . The mea(XLSX)Click here for additional data file.S2 Tableμk and, the genetic variance k and k′, and the error variance k ∈ {D, A, F}. The genetic correlations rkk′ between genetic backgrounds were also reported, with The parameters included the mean (XLSX)Click here for additional data file.S3 Table10 of the test (obtained from the approximate model for 2M and 3M), and the FDR for which it was declared significant.Information regarding significant SNPs for MF using all GWAS strategies: the name of the SNP, the chromosome on which it is located, its position in bp along the chromosome, the frequency of the allelic state observed in the dataset in which it was tested, the GWAS model applied, the hypothesis tested, the estimated values of the contrast (Delta), the Wald statistics and the -log(XLSX)Click here for additional data file.S4 Table10 of the test (obtained from the approximate model for 2M and 3M), and the FDR for which it was declared significant.Information regarding significant SNPs for FF using all GWAS strategies: the name of the SNP, the chromosome on which it is located, its position in bp along the chromosome, the frequency of the allelic state observed in the dataset in which it was tested, the GWAS model applied, the hypothesis tested, the estimated values of the contrast (Delta), the Wald statistics and the -log(XLSX)Click here for additional data file.S5 Tabley = 1μ + g + ge + e, where y is the vector of phenotypes, 1 is a vector of 1, μ is the global intercept, g is the vector of additive genetic values with K is the kinship matrix computed following ge is the vector of global epistatic deviations with e is the vector of errors with I is the identity matrix, K ∘ K is the Hadamard product of the kinship matrix with itself. This model can be seen as a simplified version of the one proposed by [ge.The existence of epistasis can be investigated using a test based on variance components. The epistatic variance component between pairs of loci was estimated on the joint dent, flint and admixed dataset using a model neglecting genetic structure: posed by , as pure(XLSX)Click here for additional data file.S1 AppendixThis appendix shows that (PDF)Click here for additional data file.S2 AppendixIn this appendix, the properties of the new GWAS models were evaluated in terms of false discovery rate and statistical power of the tests.(PDF)Click here for additional data file.


The objective of this study was to estimate the genetic parameters for worm resistance (WR) and associated characteristics, using the linear-threshold animal model via Bayesian inference in single- and multiple-trait analyses.Data were collected from a herd of Santa Inês breed sheep. All information was collected with animals submitted to natural contamination conditions. All data  were collected on the same day. The animals were weighed individually on the day after collection (after 12-h fasting). The WR trait was defined by the multivariate cluster analysis, using the FEC, HCT, BCS, and FS of material collected from naturally infected sheep of the Santa Inês breed. The variance components and genetic parameters for the WR, FEC, HCT, BCS, and FS traits were estimated using the Bayesian inference under the linear and threshold animal model.A low magnitude was obtained for repeatability of worm-related traits. The mean values estimated for heritability were of low-to-high (0.05 to 0.88) magnitude. The FEC, HCT, BCS, FS, and body weight traits showed higher heritability  in the multiple-trait model due to increased information about traits. All WR characters showed a significant genetic correlation, and heritability estimates ranged from low  to high .Therefore, we suggest that FS be included as a criterion of ovine genetic selection for endoparasite resistance using the trait defined by multivariate cluster analysis, as it will provide greater genetic gains when compared to any single trait. In addition, its measurement is easy and inexpensive, exhibiting greater heritability and repeatability and a high genetic correlation with the trait of resistance to worms. Worm diseases are those that affect sheep and cause productive and economic damage in sheep breeding. The selection of sheep genetically resistant to worms (WR) is a promising alternative in the control of the disease since this trait is inheritable and can be improved in a herd. The WR is the animal ability to avoid infection, reduce parasitic burden, or recover from an infection . This abHaemonchus contortus is the main cause of anemia in sheep ) and were renamed as LFEC.To define the resistance trait, as proposed in this study, the animals were grouped into three classes  and multivariate analysis of the characteristics FEC, HCT, BCS, and FS was performed. The classification was performed with the standardized data, using the K-means algorithm (skmeans package) available in the R language . After t1) and dry season (Aug–Dec), with smaller food supply (ECO2). The birth months were grouped into two periods (EN1 and EN2), similarly to what was done with the collection seasons. Regarding age, the animals were grouped into four age classes: less than one year (CI1); between one and two years (CI2); between two and four years (CI3); and greater than four years (CI4). As for the physiological status, the animals were grouped into six categories (CAT): matrices in gestation ; matrices in peripartum ; dry matrices ; pups aged less than six months (CAT4); pups aged six months to one year (CAT5); and adult males (CAT6).The database was edited and formatted, using the statistical  softwareSex, birth season, age class, year of birth (ANON/YB), and CAT were considered to form the contemporary groups (CG). The effects considered in the CG were tested to be significant (p<0.05). Groups with less than 3 animals were excluded. After consistency analysis, a file containing information about animal, father, mother, CG, collection year and station, FEC, HCT, BCS, FS, and body weight (BW) was edited. After formatting, the file contained 2,753 observations and 427 animals in the numerator matrix for the Wright’s kinship coefficients.a-posteriori distribution with 10,000 samples, from which inferences were made.The variance components and genetic parameters for the WR, FEC, HCT, BCS, and FS traits were estimated using Bayesian inference under the linear and threshold animal model, using the THRGIBBSF190 application  in uni- The values for burn-in and sampling interval were defined based on preliminary analyses, in which convergence and distribution of samples were evaluated using the POSTGIBBS1F90 program . In thisy = Xβ+Zα+Wγ+ɛ (1); where: y is the observation vector of the study traits; X is the n×f incidence matrix , relating observations to systemic effects; β is the vector for systemic effects of contemporary groups ; Z is the n×N incidence matrix, relating direct additive genetic effects, where n is the total number of observations and N is the number of individuals in the numerator matrix for Wright’s kinship coefficients (427); α is the vector of direct additive effects in each animal ; W is the n×N matrix of mean permanent environment effects; γ is the mean permanent environment effect vector; and ɛ is the residual random error vector associated with each observation.The animal model can be represented in matrix notation as follows: y) and data variance matrices and permanent and residual environments, respectively; A is the numerator matrix of Wright’s kinship coefficients and I is the identity matrix of order equal to the number of animals with observations. For β, non-informative a priori and p(β) ∝ constant were assumed.where Convergence was monitored using the criteria of Geweke  and erroU is the vector of the base scale of origin r; θ  is the vector of the location parameters of order s with a (direct additive random effect); W is the incidence matrix of order r×s; and For the threshold model, FS, BCS, and WR traits were assumed. Normal distribution was also assumed for the underlying scale, which is represented as: yi falls into category j , given the vectors β, a, and t , is represented as:The conditional probability that a priori distribution) was defined for all initial variances.Since residual variance is not estimable in threshold models, the value 1 was assigned to it in the parameterization process to obtain identifiability in the likelihood function . Such anHaemonchus was predominant in all collections . Trichostrongylus was the second most prevalent genus (12%) followed by Oesophagostomum (5%), and other trichostrongylids were not found. Prevalence of the genus Haemonchus was confirmatory to the study because anemia indicator-parameters were evaluated, and this parasite is the main causative agent of anemia in sheep [Haemonchus sp., which can survive to high temperatures with high fecundity and may resist to antiparasitic drugs. These characters indicate that this species has a large potential for incidence in the edaphoclimatic conditions of the region where this study was carried out.The coproculture analyses showed that the genus The mean values for all these variables are presented in The mode value for the BCS trait  indicateThe mode values were normal and even higher than the mean values for each variable, indicating that variation occurred in the results as a function of a small number of animals . AccordiIn the resistance level classification, which was obtained using clustering analysis, two groups showed significant differences. Among all parameters analyzed, the animals that presented the best results  were classified as resistant. The other animals, which presented opposite results, were classified as sensitive. The third group presented varying results: FEC values equal to those of the resistant group, BCS values equal to those of the sensitive animals, and HCT and FS differing from both groups. Thus, as these animals presented characteristics common to both groups, they were classified as partially resistant .a-posteriori estimates of marginal distributions since the Geweke’s criterion [a posteriori distribution of the heritability coefficient are summed and change in the value magnitude of this estimate  is not observed variance and heritability components of the study traits  due to tThe repeatability coefficients observed in the genetic parameter estimates of the study traits using multiple-trait analysis  varied iBy using the values estimated by multiple-trait analysis, heritability values with low-to-high (0.10 to 0.88) magnitude were observed. The FEC, HCT, BCS, FS, and BW presented low heritability values , whereasDifferent heritability values were obtained due to various  factors. Greater control over the environmental factors can reduce the environmental variance, increasing the percentage of phenotypic variance explained by the additive genetic variance .The low heritability estimated for HCT, as obtained in this study, was also reported by Gauly et al . These lRegarding the WR trait, the value for heritability obtained in the multiple-trait analysis (0.88) was higher than that obtained in the uni-trait analysis (0.44) . The varOn the other hand, an increase in values for permanent-effect variance occurred in the BCS and FS variables. This occurred because all information about the study trait is used in the multiple-trait model, but the BCS and FS are strongly influenced by the parasitic load ,26. ThisThe genetic correlations between the study traits are shown in The FEC values showed low genetic correlations with the HCT, FS, BCS, and BW variables, in agreement with Lôbo et al  who evalThe genetic correlations between BW and the other variables were unfavorable. These results indicate that improvement for WR in this herd will cause few changes in the BW of adult animals. The inverse situation also occurs, i.e., genetic progress for WR will not be observed if selection for BW is performed. This may explain the frequent occurrence of sanitary problems with verminosis in herds of Santa Inês sheep, since for a long time the objective of cattle breeders was to select animals by their ponderal performance and body size, and these traits contributed very little to the genetic improvement of resistance to gastrointestinal parasites.On the other hand, FS showed favorable correlation with all variables, and the correlation with the WR trait was the largest of them. Thus, as FS is highly correlated with the WR and HCT traits and a mean correlation with BCS, it becomes an interesting variable to be included in genetic improvement programs with a view to select worm-resistant sheep. In addition, this trait is still of easy and inexpensive measurement.The high correlation (−0.87) between FS and HCT indicates that genes related to mucosal color and globular volume are strongly associated, and the Famacha method represents well the clinical anemia in sheep. The FS and BCS presented a mean genetic correlation (−0.54). Values for genetic correlation between these variables were not found in the literature, and some authors have reported the existence of a negative (low-to-middle) sample correlation between these variables –30. ThisThe moderate genetic correlation (0.54) between BCS and WR character indicates that genetic gain for WR will occur in the sheep herd when BCS, which is commonly included as a criterion for animal selection for carcass quality, is used in selection programs.The worm resistance trait was determined by multivariate clustering analysis and showed a high estimated heritability, which can be improved by selection. The Famacha score is recommended for inclusion as a criterion for genetic selection of sheep in order to improve their resistance to endoparasites, since its measurement is easy and inexpensive, presents greater heritability and repeatability, and a high genetic correlation with the worm resistance trait.


The difficulty in finding causative mutations has hampered their use in genomic prediction. Here, we present a methodology to fine-map potentially causal variants genome-wide by integrating the functional, evolutionary and pleiotropic information of variants using GWAS, variant clustering and Bayesian mixture models. Our analysis of 17 million sequence variants in 44,000+ Australian dairy cattle for 34 traits suggests, on average, one pleiotropic QTL existing in each 50 kb chromosome-segment. We selected a set of 80k variants representing potentially causal variants within each chromosome segment to develop a bovine XT-50K genotyping array. The custom array contains many pleiotropic variants with biological functions, including splicing QTLs and variants at conserved sites across 100 vertebrate species. This biology-informed custom array outperformed the standard array in predicting genetic value of multiple traits across populations in independent datasets of 90,000+ dairy cattle from the USA, Australia and New Zealand. Genomic prediction of phenotype may be improved by using DNA mutations with functional, evolutionary, and pleiotropic consequences. Here the authors describe a method for genome-wide fine-mapping of QTLs and develop a genotyping array for improved prediction of genetic values for cattle traits. Markers from genotyping arrays can map causal variants to a region of the genome, but to identify the causal variants requires whole-genome sequence since the causal variants should be included in the sequence data4. Even with whole-genome sequence data, it is difficult to identify the causal variants because their effects are typically small, and there are too many variants in linkage disequilibrium (LD)5. These difficulties in identifying causal variants may be reduced by statistical analysis that fits all variants simultaneously6 and by the use of information on the likely function of genomic sites9. When these methods are applied to a small genomic region, it is known as fine-scale mapping10. However, it would be desirable to fit all sequence variants simultaneously across the genome, i.e., genome-wide fine-mapping. Causal variants often affect more than one trait, i.e. they are pleiotropic, so power to identify them might be gained by multi-trait analysis11.Genome-wide association studies (GWAS) have been widely used in humans13. The resulting prediction is known as a polygenic risk score (PRS) in humans and a genomic estimated breeding value (gEBV) in livestock17. The low effective population size (Ne) in many breeds of livestock causes extensive LD and hence makes this prediction possible with modest numbers of SNPs18. However, the predictions are usually far from 100% accurate in humans or livestock. The accuracy depends on a number of factors including the accuracy with which the individual SNP effects are estimated and the extent to which these SNPs explain the genetic variance by their LD with causal variants. The proportion of the genetic variance explained by SNP panels has been estimated to vary from 33 to 90%19 and this represents a limitation on the accuracy of PRS or gEBV. The accuracy of prediction is typically low when the target population in which the prediction is to be used is different from the population in which the prediction equation was derived, i.e., out-of-sample prediction20. For instance, if the populations differ in LD between the causal variants and the genotyped SNPs then the apparent effect of the SNP will not be the same in both populations. Even within a breed LD will change over time due to selection and genetic drift eroding the accuracy of the prediction. The accuracy with which individual SNP effects are estimated is dependent on the size of the training population. Therefore, it would be desirable to maximise the size of the training data, for instance, by combining the training data from multiple populations. Single-step genomic prediction methods21 that effectively use genotyped and ungenotyped animals can also increase accuracy. However, the gain is offset by the differences in LD between the populations. This problem could be eliminated if the variants that are used in the prediction were the causal variants or SNPs in consistent LD with them across all populations. In this case, the prediction would be more accurate and more robust because the SNPs would explain all of the genetic variance and their effects would be unaffected by changes in LD. In theory we could use whole-genome sequence, or use high-density panels such as HD (~800 K markers)22 to genotype every animal. However, whole-genome or high-density genotyping of large populations is very expensive. Therefore, a panel with a modest marker number such as 50k, enriched with potentially causal variants that provide a similar genomic prediction power to high-density panels, would be optimal for large-scale genotyping.Prediction of genetic or breeding value does not need the causal variants to be identified provided variants (e.g. single-nucleotide polymorphism or SNPs) in LD with the causal variants are genotyped and their effects estimatedh2 of 0.42 ± 0.04 in bulls and 0.16 ± 0.03 in cows). First, we reduce the number of variants from 17 to 1.7 M by carrying out a multi-trait GWAS using single variant regression incorporating the Functional-And-Evolutionary Trait Heritability (FAETH) score, a publicly available ranking of cattle sequence variants based on their functionality and predicted heritability7. The set of 1.7 M variants is further reduced to 165k by variant clustering and pruning for LD. Then we carry out an analysis that fits all remaining variants using Bayesian methods. Finally, we derive a set of informative variants that we chose to be designed on a custom 50K array that will enable better genotyping and more accurate prediction of genetic merit in many cattle.The identification of causal variants and accuracy of prediction would be enhanced by a large training dataset representing multiple populations and recorded for multiple traits; (imputed) genome sequence data; a statistical method that fitted all variants simultaneously and which used functional data on these sequence variants. Unfortunately, this is too computationally demanding with current resources. In this paper, we describe a method that approximates this ideal and applies it to data on 44,000 dairy cattle from three breeds  with records on 34 traits including milk production, fertility, management and body conformation  score7 where a high score indicates the high functional and evolutionary significance of these 17.7 M variants. Our genome-wide fine-mapping in Holstein , Jersey (2059 ♂/6174 ♀), mixed breed (0 ♂/2850 ♀) and Australian Red breeds (125 ♂/424 ♀) had five major steps as described in Fig. Our genome-wide fine-mapping analysis utilised two major sources of information: first, the GWAS effects of 17.7 million sequence variants on 34 Cholesky-decorrelated traitsp-values divided by their FAETH score7. Variants that had low multi-trait p-value and high FAETH score, i.e., pleiotropic and functional, would be top-ranked. Then, those variants ranked within the top 10% of the FAETH adjusted multi-trait p were selected. This led to 1,757,104 variants in bulls with the average multi-trait p being 0.028. In cows, 1,756,637 variants were left with the average multi-trait p being 0.022(±1.4e−05). This top 10% of variants in bulls and cows were LD pruned using Plink 1.925 to r2 < 0.95 within 5 Mb sliding-windows, leading to 317,804 variants in bulls and 313,760 in cows.The 17.7 million sequence variants were first ranked by their multi-trait i and j within a 5 Mb window, we calculated:If two variants are in LD with the same causal variant, they are likely to show the same pattern of associations with the 34 traits and to be in LD with each other. Therefore, for each pair of variants t values (beta/se from GWAS described above) of varianti and variantj; rLD was the LD assessed by the correlation between the genotypes of variant i and variant j. ρ was computed for all variant pairs within 5 Mb sliding-windows for variant clustering (see ‘Methods’). Within each cluster, the top 50% of SNPs were selected based on their ranking of the FAETH adjusted multi-trait p values. The selection of top variants in each cluster led to 165,214 unique variants remaining in bulls and 164,965 variants in cows. Figure Where If each cluster represents a single, independent QTL then when the most significant variant from a cluster is fitted in the statistical model, the significance of other variants in that cluster should drop while variants in other clusters should not. We tested this by repeating the multi-trait GWAS but jointly fitting the top 100 variants (tagging up to 90 clusters) from the 165k variant prioritisation. The results in a region of chromosome 5 Fig.  and acro6 to model the effects of all 165k selected variants simultaneously with their functional priors, where the variants were divided into three categories defined by their FAETH score ‘high’ (top 1/3 of the FAETH score), ‘medium’ (middle 1/3) and ‘low’ (bottom 1/3) (see ‘Methods’). If BayesRC is applied to the same data as used to select the top 165k variants, this can result in bias because the non-significant variants are missing from the BayesRC analysis. To avoid this bias of variant preselection, we analysed the 165k variant set discovered in the bull analysis in the cows and vice versa  in bulls with the maximum variance being 0.062. In cows the average local gEBV variance was 1.3e−06(±2.9e−08) with the maximum variance being 0.048. Note that the trait variance was close to 1 after Cholesky transformation. The local gEBV variance for each trait is summarised in Supplementary Table If a chromosome segment contained causal mutations for a quantitative trait, the predicted breeding value using this segment would display a large between-individual variance. The variance of local gEBV across individuals has previously been shown to be a useful metric for prioritising informative genomic regionsThe bias that occurs when the same animals are used for selection of the most significant variants and for the BayesRC analysis occurs in the estimated mixing proportions of the BayesR model. Therefore, an unbiased BayesR analysis in the bulls using the variants selected in the bulls can be obtained if the mixing proportions needed for the BayesR model are estimated in the cows. This approach is used in the next section.19 from the BayesRC runs in the opposite sex . The other set was estimated by BayesR where the variant-predictors were trained in the sex with which the variant-predictors were prioritised , but using the Dirichlet prior (α) for the distribution of variant effectsglocal) was the local gEBV variance, r2 was the squared correlation between the vector of local gEBV  and the vector of the genotype allele count of the variant (x). The sum across 34 traits, p-value of these 80k variants are shown in Supplementary Fig. We wish to identify a reduced panel of variants that could be used to predict the breeding value of all 34 traits. To do this we looked for variants within each chromosomal segment that were highly correlated with the local gEBV for all traits by calculating:mentclass2pt{minimMYH9-CSF2RB  of BayesRC and the metric of 2RB Fig. . Previou7 based variant selections showed competitive performances in prediction accuracies, while GWAS based top variant selections had the worst prediction accuracies . The average gap between markers was 57.1 ± 0.4 kb, which was comparable to 65 ± 0.4 kb for the Standard-50K panel. As shown in Supplementary Fig. The prioritized 80k variants were used as a base for the array design as developed using the systematic approach described , including categories  of ‘coding.related’, ‘noncoding.related’, ‘splice.sites’, ‘UTR’, ‘gene.end’, and ‘intron’7. Around 30%  were putative regulatory variants from our FAETH analysis33, including milk fat metabolite QTLs (mQTLs), splicing QTLs (sQTLs), allele-specific expression QTLs (aseQTLs), exon expression QTLs (eeQTLs), gene expression QTLs (geQTLs) and variants under ChIP-Seq peaks in multiple tissues. Around 5% (2158) were involved in within and/or across species evolutionary processes, including 578 variants under selection and/or young and 1604 variants within sites conserved across 100 vertebrate species. Over 80% of the markers  had an effect on more than 1 out of 34 traits in at least one sex  of non-zero effects across 34 traits >0.9 for BayesRC mapping.The final selection included 45,226 SNPs and 1285 INDELs , in the New Zealand cows compared to the standard-50K markers in all scenarios . Note that cow phenotypes usually have high error variances and low heritabilityios Fig. . Averageays Fig. .Fig. 5Co29. The US study used 2.7 M imputed sequence variants from Run 5 of the 1000 Bull Genomes Project34 and excluded intergenic and intronic variants29. Thus, from the US study variants, there were 27.7k variants overlapping markers on the XT-50K and these were used to predict 3 milk production traits of 28.2k Australian cows and of 21.2k New Zealand cows  associated with up to 36 USA traits29. Only 19 of 36 US traits were found in the 34 Australian traits that were used to prioritise the XT-50K markers .The third validation of the XT-50K markers and standard-50K markers was to test for their enrichment in pleiotropic variants in the US GWAS study  using data of 6 traits recorded in 42.2k Australian cows across 3 breeds , GGP-F250 (up to 250k SNPs)22 and HD that contain hundreds of thousands of markers. This implies that by using the XT-50K panel in future routine genotyping, good prediction accuracy can be achieved with a lower cost genotyping platform that is affordable to farmers. Our results based on real cattle data contrast the results from Karaman et al.18 using simulated human data which suggested that the selection of informative variants has little benefit in increasing genomic prediction accuracy. Significant differences in the types of genomic and phenotype data, models, and design used between the two studies could lead to different conclusions. However, our conclusion is supported by extensive discovery and validation analyses using data of over 100k cattle with multiple traits across breeds and countries. The use of the dairy cattle data in our study takes advantage of large datasets from this sector, like previous studies19. The methods presented in the current study can also be applied to other species.In this paper, we have demonstrated a method that fine-maps genome-wide informative sequence variants with pleiotropic effects and functional significance. Tested using global datasets, we show that a selection of <50k markers from these informative variants can be used to predict multiple traits in populations quite different from the one used for training the prediction equations. Genomic prediction tested in this study using our XT-50K panel which contains up to 50k potentially causal markers, is as powerful as dense panels such as GGP-F25024, variant clustering with ρij and variance of local gEBV summed over 34 traits). Also, we select variants that had consistent rankings based on their functionality and multi-trait associations between two sexes within each chromosome segment. This leads to the selected informative variants being evenly distributed across the genome, instead of being clustered in some parts of the genome.The main feature of our variant prioritisation is the incorporation of functional information of variants not necessarily correlated with their size of effects on complex traits. Besides, variants were selected if they were associated with at least one trait  and a multi-trait meta-analysis of single-trait GWAS to select 1.7 M variants out of 17 M. The most significant variants from a GWAS are not necessarily causalIn step 2, we clustered variants that had a similar pattern of effects across traits and were in LD. By selecting some variants from every cluster, we attempted to prevent QTL of small effect from being neglected in the selection of variants to include in the Bayesian analysis.In step 3, a set of 165k variants were fitted simultaneously in a Bayesian analysis. However, if the Bayesian analysis was based on variants that had been selected using a GWAS from the same data, the estimated effects of these variants would likely be biased. This potential bias was overcome by performing the Bayesian analysis in the cows (bulls) using the pre-selected variants from bulls (cows) GWAS. BayesR assumes that variant effects follow a mixture of four normal distributions with increasing variance. The preselection of variants by GWAS causes bias in the BayesR estimated mixing proportions if the same data is used. Therefore, another way we overcame the bias was to analyse the same data in the BayesR as in the GWAS but using the Dirichlet prior (α) for the distribution of variant effects from another dataset.In step 4, a set of 80k variants was selected from the 165k used in the Bayesian analysis. For a set of causal or highly informative markers to well predict complex traits across populations/breeds, they need to cover the whole genome and include small-effect variants. Our genome-wide fine-mapping was designed to meet these needs by prioritising variants region-by-region, including clusters (selecting the top 50%) and 50 kb segments for local gEBV calculation (selecting the top 3). Such ‘localised’ mapping maximised the chance of finding potential causal variants representing genome-wide QTL Fig.  and weigi and j calculated from two analyses, e.g., (1) local gEBV of trait i based on variants pre-selected in bulls and the prediction equation trained in cows, and (2) local gEBV of trait j based on variants pre-selected in bulls and the prediction equation trained in bulls with Dirichlet α from cows  training dataset to predict traits for Australian Jerseys and Reds and New Zealand Jerseys, respectively  of cattle complex traits requires more investigation in the future. Nevertheless, even though only around half of the traits overlapped between the Australian and the US data, putative pleiotropic variants affecting up to 36 US traits were strongly enriched in the Australian XT-50K panel but barely so for the Standard-50K panel  and no further live animal experimentation was required for our analyses. A set of 11,923 bulls and 32,347 Australian cows were used for the main study and this data set has been previously described7. Briefly, phenotypes for Australian bulls (N = 5354) were obtained from DataGene as daughter trait deviations: i.e. the average trait deviations of a bull’s daughters pre-corrected for known fixed effects by DataGene, Australia for the official release of National bull breeding values. Phenotypes for the non-Australian bulls  were derived from their Interbull MACE breeding values (https://interbull.org/ib/interbullactivities) deregressed on the Australian scale and converted to the scale of the daughter trait deviation. Only those bulls’ phenotypes which were based on records from more than 15 daughters were included. Cow phenotypes were also processed and corrected for known fixed effects by DataGene. Animal breed origins included Holstein , Jersey (2059 ♂/6174 ♀), mixed breed (0 ♂/2850 ♀) and Australian Red breeds (125 ♂/424 ♀). The 34 traits were ordered by their number of non-missing records and decorrelated by Cholesky transformation24 separately in each sex, so that the traits had minimal phenotypic correlations with each other. After transformation, the Kth trait can be interpreted as the Kth original trait corrected for the preceding K−1 traits and each Cholesky trait had a variance of close to 1  to pre-phase genotypes41. The sequence reference set for imputation was Run 6 of the 1000 Bull Genomes Project34. All imputed sequence variants with a Minimac3 R2 imputation score < 0.4 were removed. Variants with minor allele frequency (MAF) >0.001 were used. All these sequence variants had a Functional-And-Evolutionary Trait Heritability (FAETH) score where a higher value indicates their stronger functional and evolutionary significance7. The genome-wide fine-mapping in our study is described in the following five major steps:Most bulls were genotyped with a medium-density SNP array (50 K) or a high-density SNP array  while most cows were genotyped with a low-density panel of which approximately 6.9k SNPs overlap with the Standard-50K panel . The low-density genotypes were first imputed to the Standard-50K panel and then all 50K genotypes were imputed to the HD panel using Fimpute v2.2p-value from the multi-trait meta-analysis of GWAS of 34 decorrelated traits and (2) their FAETH score. These variants were ranked based on their multi-trait p-value adjusted (divided) by their FAETH score and the variants within the top 10% of such ranking in each sex were prioritised. The prioritised sets of over 1.7 million variants in bulls and cows were LD pruned using Plink 1.925 where one of each pair of variants within 5000 kb windows were removed if LD r2 > 0.95.This step used two metrics to rank over 17.7 M sequence variants: (1) p-value was based on the meta-analysis of 34 single-trait GWAS using the multi-trait χ2 statistic for varianti:11. ti was a K (number of traits = 34) × 1 vector of the signed t-values of varianti effects, i.e., beta/se, for the K traits; ti′ was a transpose of vector ti(1 × K); and V−1 was an inverse of the K × K correlation matrix where the correlation was calculated over the all estimated variant effects  of the two traits. The χ2 value of each variant was examined for significance based on a χ2 distribution with K degrees of freedom to test against the null hypothesis that the variant had no significant effects on any one of the K traits.The multi-trait 42:y = vector of phenotypes for bulls or cows, breedi = three breeds for bulls  and four breed groups for cows ; bx = regression coefficient b on variant genotypes x; a = polygenic random effects ~N where G = genomic relatedness matrix based on all variants. The same GWAS model as above, but without including variants in the model (bx), was applied to estimate variance components for the calculation of decorrelated trait heritability.The single-trait GWAS of 34 decorrelated traits in each sex fitted a linear mixed model implemented in GCTAρij was that the combined measure of the genotype correlation and effect correlation between variant pairs was expected to capture sequence variants tagging the same QTL. The reason for choosing 5 Mb as the size of the sliding-window was that it was likely to be large enough to see the differentiation of distributions of LD between sequence variants and to potentially include several QTL. ρ was computed for all variant pairs within 5 Mb sliding-windows (step was 2.5 Mb). Within each window, variant pair ρ values were clustered using graph-based Random Walks determining densely connected subgraphs (clusters)43 implemented in igraph44. Up to 50 clusters were retrieved in each window and in total, 30,514 clusters were retrieved in bulls and 30,440 were retrieved in cows. There were 1–98 (average 11.9) variants per cluster in bulls and 1–92 (average 11.7) variants per cluster in cows. The Random Walks algorithm was chosen because of its clustering capability for a dense network with high computational efficiency43. The Random Walks algorithm can be operated at an agglomerative mode which learns the hierarchy of a network, and this was similar to the conventional hierarchical cluster algorithms which tend to be much less efficient. The hierarchical tree for each sliding-window was cut to form individual clusters by assuming that up to 50 clusters could be reasonably formed to represent QTL within each 5 Mb window. Within each cluster, unique variant members were determined and ranked based on their multi-trait p-value adjusted by the FAETH score. Then variants ranked within the top 50% (inclusive) were chosen as the prioritized variants. The R codes of variant clustering with test datasets are available at https://github.com/rxiangr/SNP_cluster_ranking.This step was based on the metric 45. Thirty-four single-trait GWAS were re-run conditioned on variants ranked within top 100 (out of the 165k variants after prioritisation within sliding-window clusters) for their adjusted p-value by the FAETH score in both sexes. The meta-analysis of the conditional GWAS also used Eq. . Similar to BayesR, BayesRC modelled the variant effects as mixture distribution of four normal distributions including a null distribution, 46 using a single genomic relationship matrix made of all 17 M sequence variants. For each trait, the BayesRC model fitted in the training dataset was:y was the vector of each decorrelated trait; X was the design matrix allocating phenotypes to fixed effects; b was the vector of fixed effects, such as breeds; W was the design matrix of marker genotypes; centred and standardised to have a unit variance; v was the vector of variant effects, distributed as a mixture of the four distributions (described above); e = vector of residual errors.The across-sex design was used to maximise the power from separated male and female cattle and to reduce bias as much as possible by not training and predicting using the same population. Those 165k variants prioritised from bulls as described above were trained by BayesRC in cows. Accordingly, those 165k variants prioritised from cows were trained by BayesRC in bulls. The BayesRC algorithmc (c = 3): variants that were ranked ‘high’ (top 1/3), medium (middle 1/3) and low (bottom 1/3) for their FAETH score. Within each category c, an uninformative Dirichlet prior (α) was used for the proportion of effects in each of the four normal distributions of variant effects: ac = . ac was updated each iteration within each category: βc was the current number of variants in each of the four distributions within category c, as estimated from the data.The C component of BayesRC had three categories 6. Across 34 single-trait BayesRC, each variant had PPi where i ~ 1:K (K = 34 in this study). Then, a multi-trait PP of each variant to have a non-zero effect for all traits was calculated asAfter each single-trait BayesRC run, the posterior probability (PP) of having a non-zero effect was obtained for each variant by summing the proportion of iterations the variant was allocated to each of the 3 mixture distributions with non-zero variance26), except that the variants used to calculate the local gEBV were from each 50 kb segment:l, n within the segment l, and 26. The local gEBV was calculated using each segment for each one of the 34 traits within each sex.The local gEBV was calculated using the conventional gEBV methods for each trait  was determined by the variant effect distribution (mixing proportion) previously estimated from the opposite sex with BayesRC mapping. According to the implementation of BayesR19, in each iteration, the proportions of variant effects in each one of the four effect distributions was updated as: P ~ Dir(α + β)19 where β was the current number of variants in each of the four distributions. This meant that a predetermined Dirichlet prior α informed by the mixing proportions estimated in the opposite sex with BayesRC will influence each iteration of the BayesR learning, i.e., the mixing proportion of the current BayesR modelling will lead towards the previous mixing proportion of the BayesRC modelling.In the above described BayesRC modelling scenario, variants prioritised from one sex were trained in the opposite sex to avoid bias. However, it was also possible for variants prioritised from one sex were trained in the same sex, but the prior of the variant effect distribution  within each sex: one set was generated from the BayesRC scenario where variants were prioritised (discovered) in one sex , trained in the opposite sex  and estimated the local gEBV in that sex . The other set was generated from the BayesR scenario where variants were prioritised (discovered) in one sex , trained in the same sex  and estimated the local gEBV in the opposite sex . Therefore, for each segment, a K × K asymmetric variance-covariance matrix can be built , to each variant, based on the squared correlation (r2) between the value of local gEBV  and the genotype of the variants (x) within the segment where the local gEBV was estimated. The use of the squared correlation was to prevent the negative value of the r which would reduce the sum of https://github.com/rxiangr/SNP_correlation_local_gEBV_variance.The variance of local gEBV estimated with BayesRC scenario across individuals was calculated for each segment for each trait within each sex. Then, Eq. , \documehttps://sapac.illumina.com/informatics/sample-experiment-management/custom-assay-design.html) according to the manufacturer’s instructions. Only designable markers with a design score >0.4 were kept. Where possible Infinium I design (requiring 2 probes), were swapped with designable Infinium II (requiring 1 probe) mates with LD r2 > 0.9 in the 80k list and design score >0.4.The final goal of this study was to use the selected list of 80k markers to design a custom XT genotyping panel with up to 50k variants for the Dairy industry (XT-50K). In this instance, the selected variants were to be added to an existing XT array of ~9000 variants. The steps for the design of the Infinium XT-50K (Illumina Inc) beadchip are detailed in Supplementary Fig. https://www.dairynz.co.nz/) and were merged into the Australian cow database by DataGene, enabling the new Australian cows and the NZ cow data to be processed jointly by DataGene with all the national Australian data to produce deregressed proof phenotypes (phenotypes corrected for herd-year-season-age) for milk, fat and protein yields47. The New Zealand cows consisted of 8.6k pure Holsteins, 3.9k pure Jerseys and 8.7k mixed breed of Holstein and Jersey.Phenotypes from an additional 28.2k Australian cows with no overlap with the above described 44k bulls and cows were provided by Datagene and processed in the same way as described above for the main set of cows. The new Australian cows consisted of 24.4 Holsteins, 2.5k Jerseys and 1.2k Australian Reds. The raw phenotype data of an additional 21.2k New Zealand cows were obtained from DairyNZ  was used as the proxy for the prediction accuracy.In the first validation test, three sets of markers, (1) XT-50K markers, (2) standard-50K markers and (3) the combined markers from (1) and (2) were trained by BayesR in the new Australian cows and predicted into the New Zealand cows. This training used the same model as Eq.  and the 29. The summary statistics included the beta, p-values and coordinates of ~2.7 million sequence variants that were imputed from Run 5 of the 1000 Bull Genomes Project34 with the imputation cut-off set by the authors29. The authors29 also excluded those variants annotated as intergenic and intronic variants by Ensembl VEP31. These 2.7 M US variants had an overlap of 2.3 M variants with the 17.7 M Australian variants, an overlap of 36.2k with the standard-50K markers, and an overlap of 27.7k with the 46.5k Australian XT-50K markers.In the second validation test, we used published single-trait GWAS summary statistics for 36 traits of 27.1k US Holstein bulls46 with the option of -sbv b.The estimated variant effects from the US GWAS that overlapped with the Australian XT-50K and standard 50 K markers were used to predict (similar to Eq. ) milk yi29) with the method of Bolormaa et al.11. This analysis generated a multi-trait p-value for each variant presented in the US GWAS, which was tested against the null hypothesis that each variant had no effect on any one of the 36 US traits. This meant that a variant with small a multi-trait p-value would be associated with many traits. Then, for 100 thresholds of the multi-trait p-value, the significance of the enrichment of significant pleiotropic variants within the XT-50K markers and Standard-50K markers were tested. At each multi-trait p-value threshold, e.g., p = 0.05, the following counts of variants were obtained: nXT&sig: the number of variants with multi-trait p < 0.05 and were also the XT-50K markers and nSTD&sig: the number of variants with multi-trait p < 0.05 and were also the Standard-50K markers. Other constant numbers counted included: nXT: the total number of XT-50K markers presented in the US GWAS; nSTD: the total number of Standard-50K markers presented in the US GWAS; nsig: the total number of variants with multi-trait p < 0.05; and nu: the total number of unique variants presented in the XT-50K, Standard-50K and US GWAS. In R (v3.5.1), the hypergeometric test was conducted for these numbers at each multi-trait p threshold as implemented in phyper; for XT-50K markers: In the third validation test, we conducted a multi-trait meta-analysis of 36 US single-trait GWAS (Supplementary Table Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


COL6A3 gene was the most significant SNP related to DM. Hens carrying the G allele including GA and GG produced higher DM (P < 0.01) levels than those carrying the other genotype AA. Our results showed that DM is a medium-inheritable trait and that COL6A3 could be a potential candidate gene that regulates DM level in chickens.Dropping moisture (DM) refers to the water content of feces. High DM in chickens could be disadvantageous to pathogen control and fecal treatment in chicken farms. DM can be affected by environment, nutrition, disease, and genetics. In the present study, significant individual differences were presented in the DM of Rhode Island Red (RIR) chicken population, indicating that genetics could contribute to DM in the chickens. Subsequently, we estimated the genetic parameters of DM and conducted a genome-wide association study (GWAS) to find the potential genomic regions related to DM. The results showed that the heritability of DM ranged from 0.25 to 0.32. Furthermore, 11 significant loci on chromosome 7 were found to be associated with DM levels by the GWAS. The SNP rs15833816 within the  The cloaca in laying hens is a passage at the end of the digestive, urinary, and reproductive systems and includes the urethra, ejaculation canal, and anal canal. Because of the unique excretory organ of chickens, feces and urine are mixed together and excreted through the cloaca, and the moisture content of hen feces is typically higher than that of some other animals.3, H2S, and other harmful gases that threaten the biological safety of laying hens (Excess dropping moisture (DM) can severely affect the economic benefits of the laying hen industry. First, high DM can reduce the digestibility of chicken. Next, a higher DM will cause problems for fecal cleanup and fecal composting, thus eventually increasing labor costs . In addiing hens .Escherichia coli  chickens, and performed a genome-wide association study (GWAS) to identify the potential genomic regions that regulate DM in the chickens.Our experimental RIR hens were obtained from a layer breeding company in China. All hens were raised in three-tier H-shaped single cages. In total, 2,500 hens from 112 half-sib families were used to record the DM for each chicken. Observers started to record the DM when the RIR hens were 45 weeks old. Because Salmonella Pullorum (SP) and avian leukocyte virus (ALV) are common in China, we checked for the SP antibody and ALV p27 antigen titers in our birds. We confirmed that our chickens were all free of the two pathogens.Because it is difficult to detect the real water content of the feces for each of the chickens, we developed an easy and efficient method to measure the relative DM levels. By this method, the DM levels were evaluated by the appearance of the feces. The lower the DM levels, the drier were the feces. Therefore, we recorded the DM levels by numbers from 1 to 4, each corresponding to the water content of the feces, i.e., normal, slight, medium, and severe DM, respectively . The graIn order to study the relationship of DM to cage height and chicken performance, cage height, egg number (EN), and egg weight (EW) at age 48 weeks were recorded for further analysis.Two strategies were used in our study to estimate the genetic component variance. Strategy 1: The variance component of DM level was estimated based on the daily record; observer was included in module as a fixed variable. The variance component was estimated by Eq. 1; Strategy 2: To avoid the artificial bias, DM level numbers were added for the five times to represent the severity of DM (SDM) for each chicken, consider that SDM is not normal distribution as SDM is an integer between 5 and 20, SDM was divided into norm (SDM ≤ 6), slight (7 ≤ SDM ≤ 9), medium (10 ≤ SDM ≤ 14), and severe (SDM ≥ 15) according to the SDM distribution , and the2]), [pe]∼N, and [e]∼N. The component estimation was performed using the DMU (v6) package . The samples were filtered based on the call rate (mind > 0.9). In filtration, 356,008 variants and all the 48 chicken samples passed quality control. Then, a chi-square-based genome association analysis was performed for all samples. Bonferroni correction was applied to account for multiple testing. Consider that many SNPs fall within regions of strong linkage disequilibrium (LD) (“blocks”), the suggestive and significant threshold was set at 1/LD blocks number and 0.05/LD blocks  Co., Ltd.], DNA quality was tested by NanoDrop 2000 , and all the DNA samples were tested as qualified . Then thD blocks . The LD A total of 2,500 hens were phenotyped as candidate experimental individuals in our study . AccordiP < 0.01). The first-level cages not only exhibited a lower proportion of slight DM but also higher proportions of middle and severe DM .On the other hand, we counted the EN and randomly selected the eggs to measure the average EW at the age of 48 weeks. The EN ranged from 160 to 180, and the EW ranged from 52 to 66 g. One-way ANOVA analysis showed that EN nor EW was correlated with DM  and 2.68 × 10–6 , respectively. The case-control GWAS was performed for all individuals, and the results are presented in COL6A3, MLPH, and RAB17 gene  gene. Then gene ontology (GO) was employed for other autosome gene which overlap with significant SNP, but no GO item was enriched, and the gene set and SNPs set was present in We selected 25 chickens with a total SDM of five in the case group and 23 with total SDM of 20 in the control group to perform GWA. The top-3 principal components explained more than 60% of the variance between the individuals. No significant population stratification was found in our experiment . A totalB17 gene . The detThe estimation of variance component is largely dependent on the quality and quantity of data. DM level can be quantified by measuring the water content of feces of each bird. However, it was not feasible to measure the feces of each bird in our experiment. Therefore, we regarded DM as a threshold trait by assigning numbers from 1 to 4 according to the feces shape of each chicken. A similar strategy has been used in other animal studies, such as feather peaking  and bodyA previous study reported that cage height affected the behavior of hens leading to head scratching, and body shaking . In thisWith the development of sequencing technology and microarray technology, the cost of genome-wide genotyping is becoming lower. GWAS, an effective method to identify important molecular markers, has been widely used in animal breeding.CLO6A3, MLPH, and RAB17). Considering that the most significant SNPs occurred in the COL6A3 gene, this gene is most likely associated with DM in chickens. The COL6A3 gene starts at 4,808,221 bp on 7 chromosome and ends at 4,861,524 bp in Gallus_gallus-5.0, and its full length is 53,303 bp. As reported in a previous study, mutations in the COL6A3 gene can lead to poor muscle development in humans and cause Down’s syndrome (MLPH gene is melanophilin, which has been found to be related to the feather color in chickens (RAB17 gene, full name is member RAS oncogene family, which is a cancer- and immunity-related gene.In our study, we identified a marker that is significantly related to DM level and three candidate regulatory genes (syndrome  and muscsyndrome . The fulchickens . RAB17 gCOL6A3 could affect the DM level in chickens. This provides theoretical basis for subsequent functional verification and insight into genetic selection for the DM level to help improve the economic efficiency of layer farms.Dropping moisture is a medium heritable trait and a genetic marker exhibited significant association with it. Furthermore, we found that the main candidate gene https://figshare.com/articles/GENETICS_OF_DROPPING_MOISTURE/12027936.The datasets that support the findings of this study are available on FigShare The animal study was reviewed and approved by the Committee for Animal Care and Use of China Agricultural University. Written informed consent was obtained from the owners for the participation of their animals in this study.TZ and T-YZ performed the experiments and data analysis and wrote the manuscript. JW and ZG contributed to sample collection. XZ provided test poultry farms. YJ, YC, LW, XL, WY, and LQ designed the experiments and coordinated the study. LQ and ZN reviewed and revised the manuscript. All authors approved the submitted version.XZ was employed by the company Hebei Dawu Poultry Breeding Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Lolium perenne L., using a set of 385 accessions from the natural diversity of this species collected right across Europe and provided by genebanks of several countries. For each of these populations, genotyping provided the allele frequencies of 189,781 SNP markers. GWAS were implemented for over 30 agronomic and/or putatively adaptive traits recorded in three climatically contrasted locations . Significant associations were detected for hundreds of markers despite a strong confounding effect of the genetic background; most of them pertained to phenology traits. It is likely that genetic variability in these traits has had an important contribution to environmental adaptation and ecotype differentiation. Genomic prediction models calibrated using natural diversity were found to be highly effective to describe natural populations for almost all traits as well as commercial synthetic populations for some important traits such as disease resistance, spring growth or phenological traits. These results will certainly be valuable information to help the use of natural genetic resources of other species.The natural genetic diversity of agricultural species is an essential genetic resource for breeding programs aiming to improve their ecosystem and production services. A large natural ecotype diversity is usually available for most grassland species. This could be used to recombine natural climatic adaptations and agronomic value to create improved populations of grassland species adapted to future regional climates. However describing natural genetic resources can be long and costly. Molecular markers may provide useful information to help this task. This opportunity was investigated for  The objectives were i) to evaluate the natural variability of agronomic and putatively adaptive phenotypic traits, ii) to identify SNP markers linked to trait variation (GWAS) that could be afterward used to screen larger diversity by targeted sequencing, iii) to estimate the potential of GP models to accurately predict the genetic values of natural populations and to efficiently screen very large collections of in-situ material, iv) to test whether such GP models could be optimized notably by using a limited number of representative accessions in the calibration set .By contrast, the present study is based on a set of 385 genebank accessions representing natural populations of perennial ryegrass from almost the whole region of natural expansion of the species, L. perenne and other Lolium taxa were collected by scientists and plant breeders from a number of agronomic research institutes in Europe and the USDA between years 1960 and 2013. The sampling, regeneration and conservation protocols for the seed lots of collected populations are described in Table S1. Seed lots were regenerated and increased once soon after collection and then once or twice again (each 15 years) for the oldest accessions in field facilities available to genebanks. Regeneration of a population is performed by the panmictic intercross of a number of plants (50 to 200) isolated from pollen flow of other plants of the species. Balanced seed bulks harvested on such intercrossed plants can be considered as a mixture of half-sib family genotypes. The taxonomy (L. perenne L) and the caryotype (diploid) of populations was checked from plant material grown in experimental common gardens . Seeds samples of cultivars were extracted from seed lots produced for seed trade by plant breeders from three generations of intercross in panmixia from 8 to 30 selected founder genotypes. These seed samples can thus also be considered as mixtures of half-sib family genotypes. The list of cultivars with their origin is reported in Table S2.This study used 385 perennial ryegrass accessions from natural populations provided by European genebanks as well as 32 cultivars. The set of natural populations was chosen as to represent the intraspecific diversity across Europe and the Near-East as presented in https://floodlightgenomics.com) to target candidate genes putatively involved in aerial morphogenesis  Genotyping by Sequencing (GBS) described by PstI restriction enzyme was performed on DNA extracted from pooled leaf material  reported in this study are available in the NCBI Short Read Archive (SRA) database through accession SRP136600. The allele frequencies per population are available in Table S4.th of April 2015, Melle (ME) in Belgium  on the 2nd of October 2015 and Lusignan (LU) in France  on the 9th of April 2015. They were sown in three complete blocks in each location. Micro-swards of 1 m2 were sown with 2, 4 or 6 g of seeds according to whether the previously checked germination rate was higher than 80%, between 80% and 60%, or smaller than 60%, respectively. An amount of 2 g m-2 seeds of good germination quality (> 80%) is the seed density commonly used to sow dense perennial ryegrass meadows for forage usage. Trials were monitored until end of 2017 in PO and ME and until end of 2018 in LU. Micro-swards were regularly cut at 7 cm above ground surface. Cutting dates were 16/06/15, 06/08/15, 04/09/15, 12/10/15, 04/03/16, 01/06/16, 13/07/16, 31/08/16, 26/10/16, 10/03/17, 07/06/17, 19/07/17, 01/09/17, 13/10/17 at PO; 13/05/16, 08/07/16, 29/08/16, 13/10/16, 18/04/17, 31/05/17, 13/07/17, 24/08/17, 04/10/17 at ME and 30/06/15, 03/09/15, 30/10/15, 04/02/16, 08/06/16, 26/07/16, 01/02/17, 13/06/17, 07/09/17, 07/06/18, 27/08/18 at LU. Anti-dicotyledon herbicide was applied once in 2015 in each location and a second time in 2016 or 2017 according to location. Nitrogen fertilization was applied with 60 kg N ha-1 two months after sowing and after each aerial biomass cut and with 80 kg N ha-1 after each winter (2015-2016 and 2016-2017) at start of spring growth.The 385 perennial ryegrass populations as well as the 32 cultivars were sown in experimental common gardens in three locations: Poel Island (PO) in Germany  on the 8Weather conditions experienced at each trial location are displayed per season of each year in Table S5. At LU, drought stress was severe during summers of 2016 and 2017. During these summer periods, the average soil water content fell below 20% of the soil water content at field capacity. At PO, no signs of drought stress were detected but the winter periods were colder (mean temperature below 3°) than at the two other locations, especially at the end of the 2015-2016 winter period. At ME, periods of moderate drought stress occurred during summer and autumn, notably during the summer of 2017 when soil water content fell below 27% of the soil water content at field capacity.2 micro-swards. A large set of traits was assessed in order to evaluate both the agronomic and the adaptive capacity of the vast diversity of perennial ryegrass populations. The traits recorded are briefly presented in Scores or measurements of phenotypic traits were recorded at the level of 1mlm” and “Anova” of the R (R Core Team 2018) “car” library.Models of analyze of variance (ANOVA) were used to check the accuracy of the raw data and the significance of the population effect and to compute adjusted means per population. Analyses of variance were performed using the R functions “ij is the observed value of population i in the complete block j, gi is the (genetic) effect of population i, bj is the effect of the complete block j and Eij is the residual effect of the model.For each location-trait combination, the following fixed effect model was used:pvalue < 0.005) for all combinations of traits, locations and record dates (can be different periods of a same year), in agreement with satisfactory accuracy of collected raw data. Adjusted means per location and date were computed using the “emmeans” function of the “emmeans” R library. These adjusted means were used as population values for downstream analyses.F statistics of the population effect were highly significant  using the following model:ijr is the observed value of population i within the complete block r of environment j, gi is the (genetic) effect of population i, envj is the effect of environment j, g × envij is the interaction between population i and environment j, b/envjr is the effect of complete block r nested within environment j and Eijr is the residual of the model.For traits for which it was relevant, an analysis of variance was also performed across tested combinations of locations and dates of record  of the gi effect for all traits recorded in more than one location × record date combination. For these traits, it was thus relevant to use population means across environments for downstream analyses. These were computed using the “emmeans” function. The mean of a trait across environments was identified in downstream analyses by adding to the name of the trait the suffix avg .Analyzing model 2 as a mixed model with gAll the adjusted means of phenotypic traits from models 1 and 2 are available in Table S6.2 indicators analogous to broad-sense heritability were computed by setting the gi effect as random in models (1) and (2) and using only the natural population data.For each phenotypic variable, H2g is the variance of the population (gi) effect, σ2e is the residual effect of the ANOVA and R is the number of replicates of populations per environment.Then for traits recorded in only one environment:2ge is the variance of the g × envij interaction effect and J is the number of environments.And for traits recorded in several environments:distm” function of the R “geosphere” package. Moran indices were then calculated using the “Moran.I” function of the “ape” R package.The Moran’s index (I) is an indicator of spatial autocorrelation for a variable distributed across space. It was used for phenotypic trait values. It varies between -1 (perfect dispersion) and 1 (perfect autocorrelation). Values close to 0 indicate that the spatial distribution of the variable is perfectly random. Spatial distances between sites of origin of populations were calculated from their geographical coordinates using the “mantel” function of the R “ecodist” package with 500 permutations to test the significance level of the correlation. Pairwise distances could either be geographic distances in meters, genetic Euclidian distances or phenotypic Euclidian distances. Such analyses were notably used to investigate phenotypic and genetic isolation by geographical distance.To investigate the correlation between different kinds  of distance matrices, Mantel correlations were computed. The Mantel correlation between two distance matrices was calculated using the “n*p (n populations and p markers) giving the alternative allele frequency of each SNP marker in each population. The genomic relationship matrix (G) was calculated using the following formula:np is the X*np matrix in which each column is centered by the average alternative allele frequency and K is a scaling parameter representing the sum of genetic variance  without accounting for kinship.The “step” R function to predict the phenotypic trait from a set of non-redundant significant SNPs. From this regression model, the total phenotypic variance explained by all non-redundant significant SNPs was assessed.A forward stepwise multiple regression model (starting with the most significant SNP and ending with least significant detected SNP) was finally implemented using the “mixed.solve” function of the “rrBLUP” R package, was used for GP. Either all available SNP markers were used or different sets of SNP markers were used according to preliminary GWAS results. Model parameters were estimated using REML.The RR-BLUP model  were tested.dist” function. This distance matrix was used to cluster populations into k groups with the R “hclust” function. To create a calibration set of k populations, one population per group was selected. This population was the one which had the lowest average distance to the other populations in the group.For the first procedure, we computed an Euclidian genetic distance matrix between populations based on SNP allele frequencies using the R “distm” function of the “geosphere” R package. The process was the same as for the first procedure except that we used spatial distances instead of genetic distances.For the second procedure, we used a matrix of spatial distances (in meters) computed from spatial coordinates (longitude/latitude) of populations with the “hclustgeo” function of the “ClustGeo” R package with the mixing parameter set to 0.5.For the third procedure, we used both spatial and genetic data. The process was similar to that of the first procedure except that the clustering method was a hierarchical genetic clustering with spatial constraints  criterion  and maximum (MaxPA) predictive abilities over 100 randomly selected calibration sets of populations with same sizes as the optimized set were computed. The performance of an optimized calibration set was then calculated as A GWAS which accounted only for kinship was first implemented with the calibration set of populations. The SNP markers were ranked according to increasing p-values in GWAS analyses.GWAS set) were considered the most informative according to the GWAS.SNP markers were added one by one as fixed effects in a multiple regression model predicting the phenotypic trait, starting from the SNP with smallest p-value. SNP markers were added only if the model was significantly improved  and the total number of included SNP markers could not exceed the number of populations in the calibration set so as to avoid overfitting. The SNP markers in the final model .GP models (SNP markers as random effects) including from one to all SNP markers of the GWAS set) were then included to the genomic prediction model one by one, up to 2000, in addition to all markers of the GWAS set.Randomly selected markers . The following steps were then implemented:https://doi.org/10.25387/g3.12504422.All the necessary data required to confirm the findings of this study is available in the supplemental material available at figshare: P < 0.005) for all phenotypic variables. The H2 indicator within environment varied from 0.37 for summer growth rate at ME in 2017 (SGR_me17) to 0.98 for heading date at LU and PO in 2017 (HEA_lu17 and HEA_po17) (Table S7). For a given trait, H2 was variable between trial sites, years and seasons (Table S7). The environment effect and the genotype × environment interaction were both significant (P < 0.05) for all traits observed over multiple environments. H2 computed from the ANOVA across environmental conditions (model 2) varied from 0.46 for vigor after sowing (VAS) scored in 2015 at LU, ME and PO to 0.99 for heading date (HEA) measured in 2016 and 2017 at LU and PO.Variation between populations was highly significant .Pairwise phenotypic distances between natural populations were significantly positively correlated to pairwise geographic and genetic distances for over half the traits . The phePRT_04_me17), lamina width (LMW_avg) or flowering stem density (DST_avg) were strongly correlated (|r|>0.6) with heading date (HEA_avg). Vigor after cutting (VAC_avg) did not show correlation with heading date (r = 0) but was strongly correlated to various measures of spring growth which accounted for heading date differences  and autumn growth (AGR) at different environments. Autumn growth rate at PO in 2017 (AGR_po17) was negatively correlated to the aftermath heading score (AHD_po17) of the same year (r=-0.45), in agreement with an assumed cost of reproductive investment on vegetative growth. Measures of spring growth accounting for heading date in the exceptionally cold conditions of spring at PO in 2016 were poorly correlated to those in other environments (r < 0.35), but were strongly negatively correlated to the winter damage score (WID_po16) recorded in early March 2016 in PO (r=-0.59).Pearson correlation coefficients between traits are displayed in Table S8. Some important correlation patterns are reported hereafter. Many traits measured in spring such as canopy height (CHs500), protein content  is displayed in Figure S2. The average distance between SNP allele frequencies whose correlation was greater than 0.5, 0.25 and 0.1 was 5,351 bp, 13,397 bp and 23,603 bp respectively. The average correlation between SNP allele frequencies whose distance was less than or equal to 1,000 bp, 100 bp and 1 bp was 0.1, 0.15 and 0.24, respectively.The average kinship between populations was close to zero, with 0.49 as the highest value between two populations. The values for the diagonal varied between 0.1 and 0.97 with 50% of the values between 0.23 and 0.42.The quantile – quantile plots  show that the distribution of observed p-values from GWAS models in which kinship was not accounted for, or in which only structure was accounted for, strongly departed from the expected distribution under a null model. This indicated that significance tests using such models were highly inflated and that there was a high level of false positives. This trend was removed by accounting for kinship and applying a q-value threshold. Accounting for both kinship and structure in GWAS models did not reduce the number of false positives as compared to only accounting for kinship. Therefore, we only present results from GWAS models which accounted for kinship only in the next paragraphs.HEA_avg), spring canopy height measures and lamina width (LMW_avg).With a q-value of 10%, a total of 329 significant SNP markers distributed over 180 scaffolds were detected as associated to at least one trait (Table S9). Out of those, 226, 156 and 14 were unlinked to any other marker at correlation thresholds of 0.9, 0.5 and 0.1, respectively. One or more significant marker-trait associations were detected for 49 of the 145 phenotypic variables . Two remCH300h_po16) if kinship was not accounted for in computing the explained variance and up to 7% of the phenotypic variance (for aftermath heading at ME in 2016: AHD_me16) if kinship was accounted for  in all the environments in which it was recorded  seemed to depend on the average daily minimum temperature in the trial location during the winter period preceding the heading date record  as illustrated in recorded . Howeverrecorded . Notablye record . It was RAS) at PO (H2 = 0.42) to 0.88 for heading date (HEA) at PO in 2017 (H2 = 0.98). Prediction abilities were greater than 0.5 for three quarters of the 146 variables (Table S7). Over traits, the Pearson correlation between the predictive ability and its standard deviation across the 100 samples equalled -0.75, indicating that traits accurately predicted were also less dependent on the choice of the calibration set. Among the studied traits, the predictive ability increased with the value of the correlation between genetic and phenotypic pairwise distances (Predictive abilities observed in this study varied from 0.23 for regularity after sowing (istances . Howeveristances .HEA_avg), aftermath heading (AHD_avg), first year heading (HFY_AVG), spring growth (CHs300_lu16), vigor after cutting (VAC_avg) or disease susceptibility (DIS_avg) (Table S7). However, for 58 other traits such as autumn growth or forage quality traits, GP models failed to accurately predict cultivar phenotypes (predictive ability < 0.4). For traits for which more than 5 SNPs were detected by GWAS, the phenotypes of cultivars was well predicted (prediction ability greater than 0.6) by GP models calibrated using the natural populations (Table S7).The ability of GP models calibrated on natural populations to predict the phenotype of cultivars depends on the considered trait (Table S7). The GP models were able to accurately predict cultivar phenotypes (predictive ability > 0.6) for 56 of the 145 traits, including heading date (AMH_po17 (autumn maximum canopy height) to 0.1 for WSC_04_me17 (spring water soluble carbohydrates content). As such, the accuracy of a GP model was highly dependent on the choice of populations used to calibrate it. Several procedures were implemented to optimize this choice. Genetic and spatial optimization of the calibration set tended to perform similarly (SMH_lu16) to above 1 for soil coverage variation during the summer of 2017 at LU (SCD_su17_lu) and was greater than 0.5 for 90% of the traits. The performance of optimized calibration sets was expressed relatively to a random choice of the calibration set. As such, it was generally greatest when the size of the calibration set was small  to 1,663 SNP markers for water soluble carbohydrates content in spring at ME in 2017 (WSC_04_me17). It was less than 200 SNP markers for 80% of the traits, notably for those for which significant  SNP markers were detected by GWAS (Table S7). For over three quarters of the traits, fewer SNP markers were required to meet 95% of the maximum predictive ability if they were not randomly drawn but rather if they included SNP markers found significantly associated with the trait in a preliminary GWAS , the GWAS detected very few SNP markers even though the heritability was high . However13C  to dry matter yield (r > 0.8) recorded at the same date during the spring vegetative growth, which confirms that canopy height could be used as an indicator of biomass production .2 values observed for most traits (Table S7) indicated that great inter-population variance was revealed by the field trials implemented for this study. For example, heading date heritability was greater than 0.97 in all environments, which is higher than values reported in previous studies . This could be explained by the marked differences of climate between the three experimental sites. For example, the spring growth (weekly records of canopy height) in the exceptionally cold conditions at PO in 2016 (Table S5) was poorly correlated to that observed in other locations or in other spring season at PO (Table S8). Adaptation to frost stress was likely revealed for some populations that grew best in spring 2016 at PO since they also suffered the less damage in the preceding winter (2015-16). A previous study which evaluated perennial ryegrass natural populations in experimental sites located in France also revealed strong population × environment interactions even for phenological traits such as heading date . The values of cultivars were often tightly distributed closer toward an interesting extreme for forage production. However, for all traits, the range of values shown by the cultivars was within (or close to) the range of values shown by the natural populations. This was even the case for productivity traits such as spring canopy heights or autumn growth rates and for forage quality traits such as lignin content. A previous study on natural perennial ryegrass populations also revealed that even though natural populations showed on average lower agronomic performances, their range of variation was much larger . Such reDrosophila melanogaster .The four following reasons could explain the poor capacity of GWAS to detect significant makers for a number of traits. (i) Even if the number of markers used was high, some QTL could have been missed due to the fast LD decay. (ii) Because of confusion between genetic kinship and phenotypic similarity, accounting for kinship in GWAS models could have hampered the detection of QTL. For natural diversity in which diversifying selection and gene flow are strong, it has been proposed that only a small number of large effect QTL that shape inter-population trait variability may show high levels of allelic differentiation . A largeFor all traits, there are cases of phenotypically similar populations that are spatially and genetically distantly related even if genetic distance is calculated using the allele frequencies of the SNPs most significantly associated to the trait . This suggests that the causal polymorphism can vary according to geographical regions. This shows that the search for natural genetic resources to improve a given trait, or adaptation to a given climatic constraint, should generally not focus on a single restricted geographical region. Original genotypes recombining valuable alleles at various loci could be designed through transgressive segregation  interactions should be taken into account when implementing GWAS. Many phenotype-SNP associations were environment-dependent (Table S10), a likely common issue in association studies that is often ignored , which could be related to the genetic architecture of predicted traits. For example, heading date is thought to be under the control of few large effect genes (Arabidopsis (The use of GP models to describe genebank accessions was already evaluated for various domesticated plant species such as wheat (Describing a non-phenotyped portion of genetic resources by the mean of GP models calibrated with phenotypic data recorded on a another portion of these genetic resources leads to query about which resources should be phenotyped and what genotyping effort should be made.Optimization of calibration sets for GP models, based on stratified sampling was previously shown to be effective (Regarding the genotyping effort, it could be considered to optimize the number and choice of SNP markers to use. Many previous studies showed, as we did, that the predictive ability of genomic models improves as the number of included SNP markers increases until a saturation is reached (In the present study, the populations were sampled across the natural geographic range of perennial ryegrass so that most of the genetic diversity of the species may be represented. Hence, if GP models calibrated on a sub-set of the sampled populations can accurately predict the remaining populations, they should also accurately predict any other natural genetic material already present in a genebank or collected in its native environment. Consequently, this study shows that GP can greatly improve capabilities to mine the whole natural genetic diversity of a species for many traits of agronomical and/or ecological importance. Furthermore, the objective of choosing an optimized set of populations for the calibration of GP models, as presented in this study, is quite similar to the objective of choosing an optimized core collection representative of the genetic diversity to be safeguarded in a genebank. Setting up such core collections is relevant to maximize the ratio of preserved diversity to conservation costs but also to study the diversity of a species with a representative sample of limited size (