Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios . Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs. Global wheat production needs to be increased by 60% to meet the demand of a projected population of 9 billion by 2050  is a recent approach that utilizes genome-wide marker data to select individuals superior for complex traits in the early breeding cycle to increase the genetic gain per unit of time , and heritability of traits, predictive ability also depends on the choice and optimization of statistical models  interactions necessitate the evaluation of breeding lines for multiple traits over multiple environments. Thus, the extension of MT approaches to account for a G × E interaction could improve the model for genomic prediction accuracy in breeding programs. Montesinos-López et al.  proposedAlthough different GS approaches have been tested for predicting complex traits in wheat breeding programs, only few studies have reported the application of GS in actual yield trials where lines are evaluated over several environments  using a total of 314 winter wheat genotypes. The genotypes included breeding lines from 2018 to 2019 and 2019 to 2020 wheat advanced yield trials (AYTs) and elite yield trials (EYTs) from the South Dakota State University (SDSU) winter wheat breeding program and well-adapted check cultivars. Most of the genotypes were either F−2 at all the locations. Recommended agronomic practices were followed for proper growth and yield.The experimental plots were planted under a no-till system at five locations in South Dakota  in both −1), grain protein content (PROT) (%), test weight (TW) (kg hL−1), plant height (HT) (cm), and days to heading (HDs) (Julian days). YLD was determined after harvesting the plots upon maturity using a plot combine . PROT, TW, and moisture content were measured using InfratecTM 1241 Grain Analyzer . YLD from plot and PROT were adjusted to 13% moisture content equivalence. HT was recorded as the distance from the soil surface to the tip of the fully emerged spike, excluding any awns if present. HDs were recorded as the Julian days required for 50% of heads to emerge from the boot in each plot.Five agronomic traits were measured in this study, namely, grain yield (YLD)  for individual environments. The model used for estimation of the genotypic BLUEs for individual environments was as follows:ij is the trait of interest, μ is the overall mean, Ri is the effect of the ith replicate, Gj is the effect of the jth genotype, and eij is the residual error effect associated with the ith replication and jth genotype. The replicates correspond to the complete blocks.where yFor the across environment estimation of best linear unbiased estimates (BLUEs) and best linear unbiased predictions (BLUPs), the statistical model was modified, as shown below:ijk is the trait of interest, μ is the overall mean, Ei is the effect of the ith environment, R j(i) is the effect of the jth replicate nested within the ith environment, Gk is the effect of the kth genotype, GEik is the effect of the genotype × environment (G × E) interaction, and eijk is the residual error effect associated with the ith replication and jth genotype. The environment corresponds to the individual locations and replicates correspond to the complete blocks. The genotype was assumed as a fixed effect, whereas the environment and block nested within the environment were assumed as random effects.where yH2) of a trait of interest in an independent environment was assessed as follows:The broad-sense heritability  method  of adjusted means (BLUEs) from n genotypes for a given trait; μ is the overall mean; Z is the design matrix (n × p) with known values of p markers for n genotypes; u is a genotypic predictor with u ~N ; μ is the means vector; Z represents the incidence matrix of order [(n × t)p]; ut)p][. The matrix G represents the positive semi-definite matrix obtained from markers. The residuals of the MT model are represented by the vector ε, with ε ~N. The matrices ∑ and R are the variance-covariance matrices for depicting the genetic and residual effects, respectively, for each individual in all traits, estimated with the Gibbs sampler with 5,000 burn-in and 25,000 iterations in R package “MTM” ; X is the design matrix for environmental effects of order n × l, whereas β is the matrix of beta coefficients of order l × t. Z1 is the incidence matrix of genotypes of order j × n, and b1 is the matrix of genotypic random effects of order n × t. Z2 is the incidence matrix of genotype × environment interaction of order j × ln and b2 is the random effect of genotype × environment × traits of order ln × t. We assume that b1 is distributed under a matrix variate normal distribution as b1 ~MN, where G is of order n × n, obtained from SNP markers using “A.mat,” which is an additive relation matrix function in rrBLUP, and ∑t is the unstructured variance-covariance matrix of traits of order t × t. The b2 is assumed to be distributed under a matrix variate normal distribution as b2 ~MN, where ⊗ denotes a Kronecker product and ∑E is the unstructured variance-covariance matrix of l × l. The matrix ε is the matrix of residuals of order j × t distributed as ε ~MN. A detailed account of this model and prior distributions can be found in Montesinos-López et al. , where the population was equally divided into five subpopulations, with four subpopulations (80%) as the training population (phenotyped and genotyped) to train the model and one subpopulation (20%) as the testing population (genotyped only) for prediction. The single-trait model with cross-validation scheme 1 (designated as ST-CV1 hereafter) was implemented in the “rrBLUP” R package  of the MT model was estimated using two cross-validation schemes, as described in Lado et al.  Supplem. SimilarAs the multi-trait multi-environment model showed a potential in predicting different agronomic traits the using cross-validation approach, we evaluated the possible application of this method in the breeding program to reduce phenotyping efforts and per plot costs. As discussed earlier, we evaluated ~40 elite lines and ~110 advanced lines each year in multiple environments. Per plot costs and phenotyping efforts could be reduced if we can successfully determine the genomic estimation of breeding values (GEBVs) of the advanced lines in fewer locations rather than testing these lines in all available locations. The MTME model can estimate the environmental effect based on elite lines evaluated in all locations and the genotypic effect of advanced lines from fewer locations. To test this, we used the MTME model in an allocation design where we used the phenotypic data of elite lines from five testing environments; however, we used phenotypic records of advanced lines from three environments only. We predicted five traits in the remaining two environments in both the growing seasons. The model was fitted using the R package “BMTME”  was estimated for all the five agronomic traits in each environment . The PA of various models for the five traits is presented in The multi-trait model MT-CV2, which includes phenotypic data for secondary agronomic traits from individuals to be predicted, showed an overall higher prediction accuracy for YLD in both growing seasons. In 2018–19, the PA for YLD using the MT-CV2 model ranged from 0.15 to 0.56, outperforming the single-trait (ST-CV1) model by an average of 26% , 5. SimiLikewise, we observed a marginal to moderate improvement in PA for other agronomic traits using MT-CV2 model in both of the growing seasons , 5. In 2The multi-trait multi-environment MTME model generalizes the multi-trait model to consider the correlation among the environments on top of the genetic correlation between the traits. In 2018–19, the MTME model did not show a significantly different PA over the ST-CV1 model for YLD (0.18–0.36) and PROT (0.13–0.46). The performance of the MTME model for these two traits likely relates to the lower genetic trait correlations and lower correlation among the environments for these traits in 2018–19 . AnalogoIn contrast to 2018–19, we observed higher genetic correlations among the five traits and higher environmental correlations in 2019–20 . As a reBased on the cross-validation results, we evaluated the efficacy of the MTME model in reducing phenotypic efforts in the breeding program. We used the MTME model to estimate the GEBV of advanced lines in environments where only elite lines are evaluated. In the tested allocation design, we used phenotypic data of EYTs from five environments and AYTs from three environments to predict GEBVs of AYTs in remaining environments . Two envIn recent years, genomic prediction has been intensively evaluated in wheat breeding programs to select and advance lines for several traits of interest  is one of the most often used single-trait prediction models. The rrBLUP has an advantage over Bayesian models in predicting complex traits governed by several loci with small effects . In this study, the similarity in performance of the MT-CV1 and ST-CV1 models might be contributed by the moderate to high heritability estimated for most of the traits and the small size of the training population.We evaluated the multi-trait model using two cross-validation schemes. The first scheme (MT-CV1) conducts multi-trait prediction for new un-phenotyped individuals, and the testing set has not been phenotyped for any of the traits. In the second cross-validation scheme (MT-CV2), phenotype information for the predicted trait is missing, whereas phenotype information for the secondary traits is available in the testing set  that generalizes a multi-trait model to consider the correlations among multiple environments. Recently, two studies reported an increase in the PA of agronomic and end-use quality traits in wheat using the BMTME approach  of a trait is another crucial factor for improving PA  in winter wheat yield trials could help predict YLD. This would permit trials on a large number of locations  but harvesting only in a limited number  of locations. Similarly, evaluating secondary traits  could facilitate the prediction of other complex traits such as end-use quality. Finally, GS holds a tremendous potential for improving the selection accuracy of complex traits in wheat breeding; however, we believe GEBVs will complement phenotyping efforts rather than replacing them. Future breeding strategies should focus on increasing the efficiency of breeding programs by maximizing genetic gain.In conclusion, this study evaluated the PA of univariate and multivariate GP models for five agronomic traits in advanced winter wheat breeding lines. We compared two different cross-validation strategies mocking practical breeding scenarios. Overall, the results supported the practical implementation of multivariate GS models in predicting complex traits. We found a significant advantage of using MT and MTME models when correlated traits and/or environments are included in the models. The results suggest that the inclusion of correlated traits and environments in prediction models can offset the limitation of a small training population, allowing the use of advanced breeding lines to predict preliminary breeding lines in the same year or the following one. It will be interesting to further study the inclusion of different combinations of secondary traits in the MT model to increase the PA of YLD. We envision that the evaluation of secondary traits such as plant height, tillers/mThe datasets presented in this study can be found in online repositories. The names of the repository/repositories can be found in the article/SS and HG conceptualized the experiment and designed the methodology. SS, HG, CH, NB, JH, and JZ performed the field trials. HG and SS performed the data curation, performed the data analysis, software implementation, and visualization, and wrote the original manuscript. PA, AB, and GB performed genotyping and SNP discovery. TR assisted in data analysis and software implementation. GB, PA, EO, BT, JH, and SA contributed to the interpretation of results and revision of the manuscript. SS obtained the funding for the project. All authors approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Population stratification is a confounder of genetic association studies. In analyses of rare variants, corrections based on principal components (PCs) and linear mixed models (LMMs) yield conflicting conclusions. Studies evaluating these approaches generally focused on limited types of structure and large sample sizes. We investigated the properties of several correction methods through a large simulation study using real exome data, and several within- and between-continent stratification scenarios. We considered different sample sizes, with situations including as few as 50 cases, to account for the analysis of rare disorders. Large samples showed that accounting for stratification was more difficult with a continental than with a worldwide structure. When considering a sample of 50 cases, an inflation of type-I-errors was observed with PCs for small numbers of controls (≤ 100), and with LMMs for large numbers of controls (≥ 1000). We also tested a novel local permutation method (LocPerm), which maintained a correct type-I-error in all situations. Powers were equivalent for all approaches pointing out that the key issue is to properly control type-I-errors. Finally, we found that power of analyses including small numbers of cases can be increased, by adding a large panel of external controls, provided an appropriate stratification correction was used. When dealing with rare variants, single variant association test used in GWAS are underpowered, unless very large number of samples are available3. To overcome this problem, most approaches use an aggregation strategy within a genetic unit, usually a gene. These gene-based tests can be divided into two main categories: burden and variance-component tests5. Population stratification occurs when study subjects, usually cases and controls, are recruited from genetically heterogeneous populations. This problem is well known in association studies with common variants, causing an inflation of the type I error rate and reducing power. Several statistical approaches can be used to account for population stratification in GWAS. The most widely used are based on Principal Components (PC) analysis7 and Linear Mixed Models (LMM)11.Genetic association studies focusing on rare variants have become a popular approach to analyzing rare and common diseases. The advent of next-generation sequencing (NGS) and the development of new statistical approaches have rendered possible the comprehensive investigation of rare genetic variants, overcoming the shortcomings of classical genome-wide association studies (GWAS)14. However, it remains unclear whether the same correction methods can be applied to rare variant association studies15, particularly as rare and common variants may induce different types of population structure16. Many studies have investigated the bias introduced by population stratification in the analysis of rare variants and have highlighted the need for corrective approaches to obtain meaningful results18. The performance of the correction method depends on the study setting and the method used to analyze the variants22. PC has been widely investigated26 and shown to yield satisfactory correction at large geographic scales, but not at finer scales21. LMM have also been studied27 and shown to account well for stratification if variance-component approaches are used to test for association20. Most of these studies used simulated genetic data that did not completely reproduce the complexity of real exome sequences, and limited types of population structures. In addition, they used large numbers of cases , which may not always be possible in practice, particularly in studies focusing on rare diseases.Population stratification also affects association studies including rare variants28 and our in-house cohort, with data for > 5000 exomes29. We focused on two population structure scenarios: within-continent stratification (recent separation) and between-continent stratification (ancient separation). We also considered different sample sizes, including situations with as few as 50 cases, which have, to our knowledge, never been extensively investigated in this manner. We focused on a classic genetic model for a rare disease with a phenotype driven by rare deleterious variants well suited for a burden test, such as the cohort allelic sums test (CAST)4. We tested two classical correction methods, PC and LMM, a promising novel correction method called adapted local permutations (LocPerm)30 and considered an uncorrected CAST-like test as a reference. Our global objective here is to provide useful practical insights into how best to account for population stratification in rare variant association studies.We aimed at addressing such limitations of classical comparative studies with the comprehensive evaluation study proposed in this article. We investigated the main correction methods for rare variant association studies in the context of the study of rare disorders for which many aspects still have to be unraveled. As a matter of fact, association studies aiming at understanding rare diseases are usually performed on small cohorts and specific genetic models. We therefore focused on limited sample sizes and modeled our disease phenotype in an appropriate manner as described hereafter. For an accurate assessment of the different approaches, we used real NGS data from two sources: 1000 Genomes data31. The first dataset used was our HGID (Human Genetic of Infectious Diseases) database, containing 3104 samples of in-house whole-exome sequencing (WES) data generated with the SureSelect Human All Exon V4 + UTRs exome capture kit (https://agilent.com). All study participants provided written informed consent for the use of their DNA in studies aiming to identify genetic risk variants for disease. IRB approval was obtained from The Rockefeller University and Necker Hospital for Sick Children, along with a number of collaborating institutions, and all research was performed in accordance with relevant guidelines and regulations. The second dataset used was the 2504 whole genomes from 1000 Genomes phase 3  reduced with the same capture kit. We merged all the exomes from these two databases into a single large dataset before selecting samples. We performed quality control, retaining only coding variants with a depth of coverage (DP) > 8, a genotype quality (GQ) > 20, a minor read ratio (MRR) > 0.2 and call-rate > 95%32. We then excluded all related individuals based on the kinship coefficient (King's kinship 2K > 0.1875)34, resulting in a final set of 4,887 unrelated samples. From these samples, we created two types of samples, as comparable as possible to those used in practice in association studies. The first sample, the “European” sample, consisted of samples from patients of European ancestry, and was used to assess stratification at the continental level. The second, the “Worldwide” sample, consisted of samples from European individuals together with North-African, Middle-Eastern, and South-Asian samples, for the assessment of intercontinental stratification.For a realistic comparison of the correction approaches, we used two real exome datasets rather than program-based simulated exomes. Simulated data tend to provide erroneous site frequency spectra or LD structuresi and j is calculated as PCCV is the matrix of principal components calculated on common variants and k-th principal component i.e. variants present in only one sample , South-Asian (sample NA20847 from the GIH population of 1000 Genomes), Middle-Eastern and North-African (samples from our in-house sample with a reported and verified Middle-Eastern or North-African ancestry) ancestry Fig.  online. i.e.with the whole European or Worldwide sample). We used the same three stratification scenarios for both samples. In each scenario, we considered a fixed proportion of 15% cases and 85% controls. Thus, in all our scenarios, the case/control ratio was unbalanced, as is often the case in practice. Comparison studies generally consider balanced scenarios with large numbers of cases and controls, corresponding to the ideal situation for most correction approaches, and their performance in more realistic conditions may therefore be overestimated. We considered a first scenario without stratification (No PS), in which we randomly selected 15% of the samples in each subpopulation as cases, the rest being used as controls. The second scenario corresponded to moderate stratification (Moderate PS), with the cases selected mostly from certain subpopulations. The third scenario was an extreme situation (High PS), in which all the cases were selected from a single subpopulation. The distribution of cases for the European and the Worldwide samples is shown, for each scenario, in Supplementary Table We first assessed the various correction approaches on case/control samples with large sample sizes (28), UK10K35, and UK Biobank36 cohorts. We decided to investigate such strategies, by considering several scenarios with 50 cases and various numbers of controls of similar or different ancestries . For large samples, we used the full European and Worldwide datasets composed of independent individuals. For each replicate, we simulated an independent vector of phenotypes according to the population structure by randomly assigning the case and control status according to the stratification proportions provided in Supplementary Table G is the total number of genes tested. We decided to provide an adjusted prediction interval (PI), accounting for the number of methods investigated, with the type I error rate as suggested in previous studies20. The bounds of this interval are For each type of sample and stratification scenario, the type I error rate was estimated under the null hypothesis of no association between a gene and the phenotype (H1). We selected a subset of 10 genes for the power analysis. All these genes had a cumulative frequency of rare variants (i.e.with i.e. ~ 20% of carriers) and at least 10 mutation carriers. In addition, we considered ~ 50% of the rare variants of each gene to be causal, with the same direction of effect, and used the presence of at least one of these variants to define the binary genetic score described in the Statistical method section. This implies that there was no cumulative effect of carrying several causal variants, and that the relative risk is defined at the gene level. Supplementary Table Power was estimated under the alternative hypothesis of an association between a gene and the phenotype , where yi is the status of individual i coded 0  or 1 (affected). We call X = (xij)i=1…n, j=1…p the n × p genotype matrix for n individuals and p markers. Each term xij corresponds to the genotype of sample i at marker j and is coded 0, 1 or 2 according to the number of minor alleles. We also introduce the normalized genotype matrix Let us now consider an association study including 39. Considering our focus on a small number of cases with phenotypes driven by the presence of at least one causal variant, the most appropriate approach is that based on the CAST method4. This approach collapses variants into a single genetic score that takes a value of 0 if there are no rare variants in the region or 1 if there is at least one variant. Considering a given genetic region g, in our case a gene, the score for this region is denoted Zg = , where zgi = I , I being the indicator function.Several routine statistical tests are available for assessing the association between rare variants and a phenotypeThe corresponding association test can be expressed in a logistic regression framework.zgi is the weighted sum of the number of minor alleles with the weight for each variant being a function of its minor allele frequency. For a variant k, we defined a weight 38 for the weighted sum statistic (WSS) and denoted this approach WSS. We also considered the variance-component “sequence kernel associated test” (SKAT)5, as implemented in the R package SKAT.For some scenarios, we also considered a quantitative genetic score in the logistic regression framework where H of pH SNPs, a normalized similarity matrix H. Each term sik, i=1…n, k=1…n represents the genetic similarity between samples i and k based on the SNPs of set H.Certain methods, including PC and LMM, account for population stratification by using a large number of single-nucleotide polymorphisms (SNPs) to derive genetic similarity matrices . Considering a set 25. We will consider four categories of variants, based on the MAFs calculated for the total sample: rare variants H and are derived from the singular vector decomposition of the normalized similarity matrix SH. After adjustment for the first m principal components, the corresponding logistic model becomes:PC analysis creates new variables from SNP data, the principal components, corresponding to axes of genetic variation. These variables can be included, as covariates, in a statistical model, such as the one described above to adjust for population stratification. Principal components CV, for example, indicates that the first three PCs based on common variants were used.Under the null hypothesis of no association SH and an additional variance parameter CV indicating that common variants were used.Linear mixed models were initially developed to alleviate the effect of familial relatedness in association analyses, and have also been used to correct for population stratification in GWAS. This regressive approach considers both fixed and random effects and uses a genetic similarity matrix to improve estimation of the parameters of interest. Using the previous CAST regression framework, the LMM model becomes:p-values when the 'true' null distribution of the test statistic T0 is unknown40. This is the case for population stratification, which creates a bias that cannot be numerically derived. The rationale behind permutation procedures is to simulate several test statistics  under the null hypothesis, to derive an approximated distribution as close as possible to the unknown true null distribution, and to use these statistics to estimate a p-value. With the classical permutation approach, the simulation of test statistics under H0 is achieved by randomly resampling phenotypes . Adapted local permutations are based on the observation that, in the presence of population structure, not all phenotypes are exchangeable30. However, this assumption is no longer valid in the presence of population structure. A given sample has a higher chance of sharing its phenotype with another sample of the same ancestry. Few approaches have been proposed for handling confounding factors such as population stratification in permutations. Epstein and colleagues41 proposed to estimate the odds of disease conditional on covariates under a null model of no genetic association and to resample individual phenotypes using these conditional disease probabilities as individual weights to obtain permuted data with a similar PS. However, subsequent studies showed that this procedure was less efficient than regular PC correction for dealing with fine-scale population structure21. Recently, a general new method, the conditional permutation test, for testing the conditional independence of variables X and Y given a potentially high dimensional random vector Z that may contain confounding factors was proposed42. The test permutes entries of X non-uniformly, to respect the existing dependence between X and Z and thus to account for the presence of these confounders. However, like the conditional randomization test of Candès et al.43, the test relies on the availability of an approximation to the distribution of X/Z and sensitivity analysis to the misspecification of the distribution parameters showed that the method suffered from a type I error inflation increasing with the misspecification level42.Permutation strategies have been designed to derive i.e. a set of relatively close samples with whom it is reasonable to exchange phenotypes. Each sample neighborhood is based on a genetic distance derived from the sample coordinates along the first 10 principal component axes calculated on common variants:PCCV is the matrix of principal components calculated on the set of common variants and k-th principal component N and select permutations that ensure that each phenotype is drawn from the N nearest neighbors. Such permutations are called local permutations and are selected with a Markov chain Monte Carlo sampler described in30. We focus on a number of N = 30 neighbors as it has been proposed to be a reasonable choice from a sensitivity analysis described in30. Permutations can then be performed for each sample, within its neighborhood.More recently, we proposed a simpler method based on adapted local permutations. The principle is to establish, for each sample, a neighborhood, p-value for the permutation test is to assess the quantity B is the number of permutations. This method is dependent on the number of permutations computed, and a large number of permutations is required for the accurate estimation of small p-values. Mullaert et al. proposed an alternative semi-empirical approach, in which a limited number of resampled statistics are used to estimate the mean (m) and standard deviation (σ) of the test statistic under H0. The previously described CAST-like LRT statistics are used, through their square roots with a sign attributed according to the direction of the effect, 2) distribution parameters and then calculate the p-value. We evaluated both the semi-empirical approach using 500 local permutations and the full-empirical approach using 5000 local permutations. These two approaches yielded very similar results. We therefore present here only the results for the semi-empirical approach.A straightforward empirical way to derive a https://www.R-project.org/) to code the comparison pipeline and implement the logistic CAST, logistic WSS and permutation models. The SKAT test used was implemented in the SKAT R package. Principal components and similarity matrices were obtained with Plink2 software (https://www.cog-genomics.org/plink/2.0/), and GEMMA was used for the LMM method44.We used R software  and of SKAT under both moderate and high stratification scenario . Overall, PC3 correction performed similarly for WSS and SKAT, as compared with CAST, and we focused on CAST for subsequent analyses.We further investigated the impact of the PC correction on type I errors rates in the European sample when increasing the number of PCs and for alternative association methods (i.e. WSS and SKAT). With CAST, increasing the number of PCs did not improve the correction, a result consistent with previous findings reported by Persyn et al. (2018). The use of 50 PCs resulted in an inflation of type I error rates whatever the level of stratification, probably due to an over-adjustment of the regression model , in addition to the unadjusted CAST. Only powers derived from a correct type I error rate under H0 are presented in the main text. Adjusted powers accounting for inflated type I error rates are provided in the Supplementary figures online for information. The results of the power study for the European sample are presented in Fig. CV, and LMMCV, respectively. It is also interesting to compare the power of each method, separately, between the different stratification scenarios , an inflation of type I errors was observed: 1) with PC3 (about 0.0015 at α = 0.001) when the number of controls was low (100), and, to a lesser extent, with CAST (about 0.0012 at α = 0.001), and 2) with LMM (about 0.002 at α = 0.001) when the number of controls was high (1000 or 2000). In the presence of stratification (i.e. a large number of controls with an origin different from that of the cases), a strong inflation of type I error rates was observed for CAST. This was also the case for LMMCV, albeit to a lesser extent, particularly for stratification within Europe or when the cases came from the Worldwide sample and the controls from Europe only. Both PC3CV and LocPerm provided correct type I error rates in all the scenarios considered with small numbers of cases and a large number of controls.The results of the simulation study under the null hypothesis for a small sample size, based on 50 cases, are presented in Table CV and LocPerm with small numbers of cases  and a gene-testing phase (see Supplementary Table e.g. for different MAF thresholds). PC3CV and LMMCV had similar pretreatment times, markedly shorter than that for LocPerm, which also requires the calculation of permutation matrices. However, the need to calculate these matrices only once decreases the relative disadvantage of the LocPerm method. In terms of gene-testing time, LMMCV was the fastest approach when used with GEMMA, but this may not be the case for other programs that have not been optimized. A comparison of the methods implemented with R showed that the adjustment on PCs and LocPerm took 1.4 × and 2.5 × longer, respectively, than the unadjusted test. These comparisons were run on a 64-bit Intel Xeon Linux machine with a CPU of 3.70 GHz and 64 GB of RAM.We also assessed the computing time required for the different approaches. While the unadjusted CAST method does not imply the computation of any particular matrix, the same covariance matrix is necessary for PC326 although one reported better performances for PC based on RVs15. However, this study was based on simulated NGS data, which may have led to an unrealistic rare variant distribution. Our results also indicate that CVs or ALLVs were the best sets of variants for the LMM approach applied to CAST, confirming the results of Luo et al. based on the SKAT test20. Variant selection remains an area in which there are perspectives for improving the corrections provided by strategies such as PC or LMM27, although the use of CVs appeared to be a good choice in most situations.We performed a large simulation study based on real exomes data to investigate the ability of several approaches  to account for population stratification in rare-variant association studies of a binary trait. In our simulation study, the efficiency of PCs and LMM to correct for population stratification was dependent on the type of variant used to derive the similarity matrices, the best performance being obtained with CVs. It was generally not possible to correct the stratification bias with RVs, even with the exclusion of private variants for the calculation of the matrices. Private variants have very sparse distributions, which may lead to difficulties in calculation, and their inclusion resulted in an even lower efficiency of correction for population structure (data not shown). Other studies evaluating different types of variants reached the same conclusions45, which is often the case with rare variants. For large sample sizes, both PC and LMM controlled for stratification better at larger geographic scales than at finer scales. In small samples (50 cases and 100 controls), PC approaches gave inflated type I errors even in the absence of population stratification, as previously reported46. This inflation disappeared when the sample included additional controls, whatever their ethnic origin, even with a highly unbalanced case–control ratio. By contrast, the type I error of LMM was inflated in samples with highly unbalanced case–control ratios, whatever the level of population stratification, as previously noted in the context of GWAS47. Finally, the adapted local permutations procedure recently proposed by Mullaert et al.30 gave very promising results, as it fully corrected for population stratification, regardless of the scale over which the stratification occurred, sample size and case–control ratio. When valid under H0, the three correction methods had similar powers. For a given setting, power was similar in the different stratification situations, indicating that the correction method could maintain the power it would have in the absence of stratification. These results are in partial agreement with several studies reporting a small loss of power for PC-adjusted logistic regression in the presence of stratification relative to an absence of stratification21.With the optimal set of variants, PC generally corrected for population stratification more efficiently than LMM. This is consistent with benefits of the PC approach over LMM observed in the presence of spatially confined confounderse.g. in terms of kit or technology used), the conclusions drawn here still apply. Overall, these results validate a strategy of using additional external controls to increase the power of a study, provided that an efficient stratification correction approach is used.We also investigated the specific situation in which only a very small number of cases are available, which is particularly relevant in the context of rare disorders. In this setting, we showed that PC and LocPerm provided correct type I errors when the number of controls was large, regardless of the ethnic origin of the controls. In addition, the strategy of adding controls, even of worldwide origin, provided a substantial gain of power for PC and LocPerm when few cases were available. This is an important finding, highlighting the potential interest of using publicly available controls, such as those of the 1000G project, to increase the power of a study with a small sample size. We also investigated an additional scenario in which all cases were strictly from our in-house HGID cohort and the controls were obtained from both the HGID and 1000 Genomes cohorts (data not shown). This scenario gave identical results to those presented here, indicating that, even in the presence of heterogeneity in the types of exome data considered for cases and controls  the key issue is to properly control type I errors as powers are comparable, (2) population stratification can be corrected by PC3CV in most instances, unless there is a high degree of intracontinental stratification and a small sample size, (3) LocPerm proposes a satisfying correction in all instances, and (4) strategies based on the inclusion of a large number of additional controls (e.g. from publicly available databases) provide a substantial gain of power provided that stratification is controlled for correctly.We focused on the investigation of rare diseases caused by a few deleterious variants, for which the CAST-like approach is particularly appropriate. Additional studies are required to investigate more complex genetic models, such as the presence of both risk and protective variants of a given gene, for which other association tests, such as variant-component approaches, may be more appropriate. Different results can be expected, as the effect of population stratification differs between testing strategiesSupplementary Information 1.


Given the extremely rapid developments of our field in the past decades, usually a textbook of behavior genetics is outdated by the time it is released. Yet, the timing of the 2nd edition of ‘How Genes Influence Behavior’ feels neat since fairly recently the behavior genetics field has made some major steps. These steps include novel developments in molecular genetic analyses and statistical applications, and, most of all, lots of exciting new results (or breakthroughs as you wish). The breadth of knowledge that is required to prepare a new generation of behavior geneticists is certainly present in the 2nd edition of ‘How Genes Influence Behavior’.Reading ‘How Genes Influence Behavior’ often feels like sitting at the kitchen table of one of the authors who tells you various entertaining anecdotes about his long and interesting academic life in genetics. When one of the authors (KSK) describes how he collected family data in Irish families with schizophrenia in the previous century, and as such ‘came to know well  the bed and breakfast houses (..) of the western part of the Republic’, for example. Or, when an elderly grandmother of one of those families, wondering why KSK collects those data, clarifies that ‘everybody knows that being daft runs in families’, adding, with indignation, ‘What will these crazy Americans think of next?’. The other parts of the book are also written in a style that is informal and engaging. The historical sketch of behavior genetics, outlining the contradicting views of Mendelians versus Biometricians, is written as ‘four acts of a drama’, and indeed, it reads as a thrilling course of events. The edition further starts off typically with telling how family, twin and adoption data were used at the start of behavior genetics to study the ‘nature versus nurture’ questions and how this revealed the sources of individual differences for a variety of traits.Subsequently, the book provides a clear section on (molecular) genetic terminology, fortunately taking into account the confusing habit in behavior genetics of using multiple terms for the same subject. What follows is an introduction into the molecular biology of genetic variation, the methods that are currently used for genotyping and sequencing, an outline of epigenetics and ‘omics’ technologies and the specific queries about inheritance that these techniques address. The authors then touch upon linkage and (candidate) genetic association analyses before fully covering genome wide association study (GWAS) developments and all ins and outs of this contemporary gene finding approach.Having provided the readers at this point with all information needed to understand the concept of heritability, and the methodologies and results of studies in behavior genetics, the next chapters are devoted to a variety of psychological and psychiatric human traits. The authors use the different genetic architectures of these traits to address in detail the full spectrum of behavioral and molecular genetic findings, varying from family-based heritability and genetic correlations, to common and rare genetic variation, SNP heritability, and polygenic risk scores, to copy number variants, chromosomal abnormalities, mosaicism and de novo mutations. We learn that new methods, like LD regression score analysis, confirm what family studies already taught us in the early days. For example, the heritability between traits differs, and many traits are genetically correlated, sometimes in a surprising direction, like a positive genetic correlation between autism and intelligence. Another surprising finding in that context is the replicated genetic correlation of 1 between major depression and general anxiety disorder, illustrating that ‘genes often disrespect diagnostic boundaries between psychiatric disorders’.The authors then switch gears by turning to model organisms. Studies in rodents, worms, and flies teach us a lot about reverse genetics (i.e. take the gene as starting point to investigate behavior), genetic engineering (e.g. CRISPR-Cas), and courtship (in flies). Much attention is paid to the phenotype of circadian rhythms. We get to know the ‘period gene’, and so-called clock mutants with names as ‘timeless’, ‘Clock’ and ‘shaggy’. The unusual thing about circadian rhythms is that for this phenotype large genetic effects have been detected , and the book comprehensively explains how model organism studies have contributed to this knowledge.Since readers at this point in the book might become slightly fatigue, the authors here cleverly introduce some appealing subjects such as genes for aggression, monogamy and real estate. Again, animal studies  are important in understanding how genes play a role in these complex behaviors. The part on animal studies ends with a section on supergenes illustrated with an anecdote of author JF, who tries to dissuade a phalanx of black ants on his kitchen floor. No need to say who owns the supergenes.–8 is a value that all complex trait geneticists now have tattooed on their hearts’. Apart from statistical and methodological guidance throughout the book, Chapter 21 discusses extensively ‘how we know a finding is true’ and an excellent Appendix completes the education on genetics methodology.The importance of statistical and methodological rigor in contemporary genetic studies is a returning theme in almost each chapter. Apart from golden standards in measuring behavior, and applying the correct statistical tests, the problem of multiple testing is obviously addressed. Reassuring, the authors state that ‘The p-value of 5 × 10In sum, the 2nd edition of ‘How Genes Influence Behavior’ is an easy to read, comprehensive, up to date textbook on the exciting field of behavior and psychiatric genetics. I am sure the entertaining writing style will enthuse many new students, as will the low price of only €43. The authors Flint, Greenspan and Kendler are to be applauded for sharing with us their fascinating journey through the world of genetic research , and as such delivering a great contribution to the behavior genetics literature.


Epidemiological investigations have established unhealthy lifestyles, such as excessive leisurely sedentary behavior  and breakfast skipping, increase the risk of type 2 diabetes (T2D), but the causal relationship is unclear. We aimed to understand how single nucleotide variants contribute to the co-occurrence of unhealthy lifestyles and T2D, thereby providing meaningful insights into disease mechanisms.N = 422218), breakfast skipping (N = 193860) and T2D (N = 159208) in European pedigrees, we conducted comprehensive pairwise genetic analysis, including high-definition likelihood (HDL-method), cross-phenotype association studies (CPASSOC), GWAS-eQTL colocalization analysis and transcriptome-wide association studies (TWAS), to understand the genetic overlap between them. We also performed bidirectional two-sample Mendelian randomization (MR) analysis for causal inference using genetic instrumental variables, and two-step MR mediation analysis was used to assess any effects explained by body mass index, lipid traits and glycemic traits.Combining summary statistics from genome-wide association studies (GWAS) on TV watching  and skipping breakfast . CPASSOC identifies eight independent SNPs shared between T2D and TV watching, including one novel shared locus. TWAS and CPASSOC showed that shared genes were enriched in lung, esophageal, adipose, and thyroid tissues and highlighted potential shared regulatory pathways for lipoprotein metabolism, pancreatic β-cell function, cellular senescence and multi-mediator factors. MR showed TV watching had a causal effect on T2D , but no significant results were observed between breakfast skipping and T2D. Mediation analysis provided evidence that body mass index, fasting glucose, hemoglobin A1c and high-density lipoprotein are potential factors that mediate the causal relationship between TV and T2D.HDL-method showed that T2D shared a strong genetic correlation with TV watching (Our findings provide strong evidence of shared genetics and causation between TV watching and T2D and facilitate our identification of common genetic architectures shared between them. The strongest positive genetic correlation was observed between TV watching and type 2 diabetes.Cross-trait meta-analysis identifies eight independent genomic loci shared between type 2 diabetes and television watching, one of which is novel.Implicated genes suggest potential treatment targets and signaling pathways for type 2 diabetes and television watching.Transcriptome-wide association studies and cross-trait meta-analysis support the role of lipoprotein metabolism, cellular senescence and multi-mediator factors may account for the shared metabolic pathway and causes between TV watching and T2D.IVW = 0.629, PIVW = 1.80×10-10).Mendelian randomization study showed TV watching had strong causal effect on T2D (βType 2 diabetes (T2D) is a global epidemic that affects more than 463 million people and is a leading cause of morbidity and mortality worldwide. Family-based studies have shown that T2D is highly heritable, with an estimated heritability range of 20%-80% , 2. CurrA growing body of evidence from observational studies suggests that the risk of T2D is positively associated with prolonged TV watching –6 and brEvidence from observational studies is limited for making causal inferences, as such associations may be due to  confounding and/or reverse causality . Considehttps://data.mendeley.com/datasets/mxjj6czsrd/1), the Common Metabolic Diseases Knowledge Portal (CMDKP) website (for exposures) and the Diabetes Genetics Replication And Meta-analysis (DIAGRAMv3) Consortium (for type 2 diabetes). To further identify genomic loci shared between T2D and exposures, we used cross-phenotype association (CPASSOC) analysis and transcriptome-wide association (TWAS) studies to explore shared genetic components among these complex phenotypes.Therefore, to increase our understanding of potential causality and shared genetic architecture between TV watching, breakfast skipping and T2D, we conducted a comprehensive genetic analysis. We performed a bidirectional MR and mediation analysis using summary statistics from public external URL (N = 422218)  , which irg) between T2D and TV watching and breakfast skipping. HDL-method extends the LDSC method by modeling the relation between covariances among Z statistics for pairs of traits across multiple SNPs and a full matrix of cross-SNP LD scores. As the HDL-method yields more precise estimates of genetic correlations than LDSC, we chose the HDL-method as the primary result. The HDL-method uses the LD reference computed from 335265 genomic British individuals in the UKB.The more recent high-definition likelihood (HDL-method)  method arg > 10% and Pbonferroni < 0.05 from HDL-method. CPASSOC combines effect estimates and standard error of GWAS summary statistics to test the hypothesis of association between a SNP and two traits and assumes that effects may exist only within a subset of traits   method tf traits . We usedf traits .-5 have an r2 greater than 0.4 and less than 500 kb away from the peak will be assigned to that peak’s clump. We identified all genes falling within each clump region. A P value of 2.5×10-8 (5×10-8/2) was used as genome-wide significance level for cross-trait meta-analysis to account for 2 meta-analyses. SNPs with a meta-analysis P value less than 2.5×10-8 and trait-specific P value less than 1×10-5 were selected for downstream analysis.We applied PLINK1.9 clumping function (parameters: –clump-p1 2.5e-8 –clump-p2 1e-5 –clump-r2 0.4 –clump-kb 500) to determine index loci that are independent of each other, i.e., variants with P value less than 1×10To investigate whether the shared index SNPs from CPASSOC and their expression quantitative trait loci (eQTLs) co-localized with candidate causal variants, we performed colocalization analysis, COLOC, which uses Bayesian posterior probability to assess colocalization . We extrPBonferroni < 0.05). To increase the significance of the TWAS results, we used the most recent and authoritative summary data for T2D obtained from DIAGRAM. This study was performed in 2018 by Mahajan et al., who mined additional novel T2D susceptibility SNP loci by combining data from 898130 (including UKB sample) individuals of European descent  to identify genes whose cis-regulated gene expression was associated with the corresponding traits. Then, we further evaluated shared tissue-gene pairs between different traits. We performed TWAS analysis using FUSION software and its precomputed transcript expression reference weights, as well as eQTL data from GTEx v.7 . Bonferr descent .Finally, we implemented a bidirectional MR using TwoSample MR package to test the causal relationship between T2D and unhealthy lifestyles, where the associations for IV-exposure and IV-outcome came from two nonoverlapping groups of participants. Since different MR methods have different degrees of explanation and contexts of application and differ in statistical efficiency, we adopt many MR methods to estimate causal effects. The causal effect estimates from the multiplicative random effects inverse variance weighted (IVW) model were used as the primary result. We conducted a range of sensitivity analyses using multiplicative random effects inverse variance weighted heterogeneity test, weighted median, MR–Egger regression, MR-Steiger, MR-Robust Adjusted Profile Scores (MR-RAPS), MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) analysis and leave-one-out cross-validation analysis. The weighted median approach provides consistent and robust estimates even if more than 50% of the IVs are invalid . The inthttps://www.ebi.ac.uk/gwas/) to exclude IVs with genome-wide significance for potential confounding traits  and reran the MR to obtain more robust MR estimates. For TV watching, breakfast skipping and T2D, independent genetic instruments were selected at GWAS p value < 5×10-8 and LD r2 < 0.001 based on the 1000 Genomes European phase 3 reference panel. Given the multiple comparisons, in this study, we considered a P threshold < 0.05 as suggestive significance, while Bonferroni-corrected P threshold was used as statistically significant (P<0.05/6 = 0.008).Furthermore, the effect allele frequency reported in the corresponding GWAS was used to detect and exclude all palindromic SNPs to determine the corresponding strand between two GWAS in harmonization section. For trait pairs with significant causal relationships, we searched the GWAS catalog (P threshold (P<0.05/11) was used as statistical significance accounting for the 11 mediation analyses.To further assess the direct effects of TV watching on T2D, we performed two-step MR mediation analysis. We selected body mass index (BMI), 4 lipid traits , and 6 glycemic traits  as potential mediators of liability to TV watching in T2D. Two-step MR is based on the coefficient product method to calculate indirect (or mediator) effects Figure 1rg = 0.26; P = 1.63×10-29) and skipping breakfast . The results suggested a potential common genetic basis and thus warranted further investigation of the underlying mechanisms using cross trait meta-analysis and instrumental variable analysis (T2D showed a strong positive genetic association with TV watching (Pmeta < 2.5×10-8 and single-trait P < 1×10-5). However, we did not find any shared loci between T2D and breakfast skipping. GWAS-eQTL colocalization analysis had no significant results, but it identified a specific region at 12q14.3 that might be an expression quantitative trait locus between T2D and TV watching . Two of our CPASSOC index SNPs are located at the 12q14.3 region mapping to HMGA2 gene. HMGA2 encodes a protein belonging to the non-histone chromosomal high-mobility group (HMG) protein family, and the protein contains structural DNA-binding domains and may act as a transcriptional regulating factor. Significantly higher expression of HMGA2 mRNA in white adipose tissue has been reported in patients with T2D . CFL1 is a typical protein-coding gene that encodes cofilin-1, an intracellular actin regulatory protein that plays an important role in regulating the organization of the actin cytoskeleton. Phosphorylated (inactive) cofilin-1 is upregulated in diabetic glomeruli, suggesting alterations in actin dynamics . The mapped gene APOC1 (apolipoprotein C1) is a protein-coding gene engaged in the inhibition of cholesteryl ester transfer protein (CETP). A study showed that APOC1 was highly expressed in clear cell renal cell carcinoma . CENPW encodes a centromere protein that plays a central role in the assembly of kinetochore proteins, mitotic progression and chromosome segregation. The association between CENPW and T2D has been reported in previous genome-wide meta-analysis . ARAP1 encodes protein that is thought to regulate the cell-specific trafficking of a receptor protein involved in apoptosis. Findings suggest that ARAP1 engages in islet insulin content and secretion and is thus likely to mediate the effects on diabetes susceptibility  gene encodes the blood group ABO systemic transferase and is ubiquitously expressed in many tissues and cell types (ATG16L2 (11q13.4) is a protein-coding gene whose function is not fully understood, and it has been shown to play a unique function in autophagy. Analysis of transcriptomic data shows that autophagy plays a major role in the molecular pathology of T2D and AD     . Given tach SNP)  to obtaial bias) .IVW = 1.86 , P = 1.80×10-10; ORWM = 1.82 , P = 1.12×10-6; ORMR-RAPS = 1.78 , P = 3.13×10-11; OR MR-PRESSO : Outlier-corrected = 1.84 , P = 1.22×10-11], with all P values reaching the Bonferroni-corrected threshold and without any evidence of pleiotropy (PMR-Egger-intercept = 0.41). This causal effect became more significant in the sensitivity analysis excluding 16 SNPs associated with potential confounders , P = 3.74×10-11; ORWM = 1.82 , P = 3.27×10-6; ORMR-RAPS = 1.78 , P = 3.13×10-11; OR MR-PRESSO : Outlier-corrected = 1.84 , P = 1.22×10-11]. The confounding traits associated with the 16 SNPs can be found in P > 0.05). The leave-one-out cross-validation analysis showed that the overall estimates were not overdriven by any particular SNP . With the current sample size of T2D and the phenotypic variance of TV watching explained by IVs , which in turn promotes HDL cholesterol esterification and increases HDL levels  that they could not select enough IVs to improve the statistical power (number of IVs = 6 in their study) , the estimation accuracy obtained by this method is higher than that obtained by multivariate Mendelian randomization (MVMR) . HoweverOur comprehensive genetic analysis identified shared genetic similarities between TV watching and T2D, suggesting a strong intrinsic genetic link between this trait pair. We further used MR to find convincing evidence supporting a putative causal role between TV watching and T2D, but mediation analyses suggest that this effect is largely mediated by BMI, HbA1c, FG, and HDL. As obesity, hyperglycemia, and hyperlipidemia are recognized as established risk factors for T2D, our findings underscore the importance of actionable prevention strategies for T2D. However, to date, the complex interactions between TV watching and T2D do not appear to be fully understood, and further studies are needed to deepen our understanding of the biological pathways by which TV watching influences T2D.R scripts used to generate results in this study can be made available upon requestThe original contributions presented in the study are included in the article/The study was conducted using publicly available summary-level genetic data, and no ethical approval was requested.JJ and TH conceived and designed the study. DC and HW performed the data preparation and statistical analysis. DC and HW wrote the manuscript. DC and HW contributed equally to this article. All authors helped interpret the data, reviewed and edited the final paper and approved the submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Twin and multiple births are rare in cattle and have a negative impact on the performance and health of cows and calves. Therefore, selection against multiple birth would be desirable in dairy cattle breeds such as Holstein. We applied different methods to decipher the genetic architecture of this trait using de-regressed breeding values for maternal multiple birth of ~ 2500 Holstein individuals to perform genome-wide association analyses using ~ 600 K imputed single nucleotide polymorphisms (SNPs).LHCGR and FSHR). Analysis of whole-genome sequence data by linkage-disequilibrium estimation revealed a regulatory variant in the 5ʹ-region of LHCGR as a possible candidate causal variant for the identified major QTL. Furthermore, the identified haplotype showed significant effects on stillbirth and days to first service.In the population studied, we found no significant genetic trend over time of the estimated breeding values for multiple birth, which indicates that this trait has not been selected for in the past. In addition to several suggestive non-significant quantitative trait loci (QTL) on different chromosomes, we identified a major QTL on chromosome 11 for maternal multiple birth that explains ~ 16% of the total genetic variance. Using a haplotype-based approach, this QTL was fine-mapped to a 70-kb window on chromosome 11 between 31.00 and 31.07 Mb that harbors two functional candidate genes (QTL detection and subsequent identification of causal variants in livestock species remain challenging in spite of the availability of large-scale genotype and phenotype data. Here, we report for the first time a major QTL for multiple birth in Holstein cattle and provide evidence for a linked variant in the non-coding region of a functional candidate gene. This discovery, which is a first step towards the understanding of the genetic architecture of this polygenic trait, opens the path for future selection against this undesirable trait, and thus contributes to increased animal health and welfare. In dairy cattle production, herd profitability is heavily influenced by the number of calves born alive and by the length of calving intervals. Intensive selection for milk yield in dairy cattle has led to a decline in female fertility, due to unfavorable genetic correlations between milk yield and female fertility , 2. ImprGenome-wide association studies (GWAS) have become a useful tool to partially reveal the genetic architecture of complex traits. For several traits related to female fertility, such as non-return rate at 56 days and interval from first to last insemination, quantitative trait loci (QTL) have been detected e.g. in Holstein  and BrowCattle are generally a monotocous species and pregnancies typically result in the birth of singletons. Multiple births are rare, with multiple birth rates (MBR) ranging from 1.02 to 9.6% depending on the breed and study  and are For dairy cattle, twins and multiple births are undesirable for several reasons. Multiple births are generally associated with increased health problems for both the dam and the calves. Occurrence of remained placenta, metabolic disorders, displaced abomasum, and ketosis are some of the direct negative effects on the dam –21. The There are different non-genetic factors which might influence MBR. Several studies have shown that the parity of the cow influences MBR significantly, as well as other environmental factors such as season and herd , 26, 27.Different QTL mapping studies have analyzed maternal multiple birth in North American Holstein, Israelian Holstein or Norwegian cattle using family-based microsatellite interval mapping approaches , 31–38 aIn this study, our aim was to conduct a comprehensive genetic analysis of the trait multiple birth in Swiss Holstein cattle. To estimate breeding values, we used phenotypic data for single and multiple birth cases that have been recorded over several decades through the national animal recording database and combined them with pedigree data. In addition, we used large-scale genotype data that were obtained during routine genomic selection of males and females to identify associated QTL by GWAS and haplotype regression analysis. A fine-mapping approach was carried out to define a critical genomic region and potential candidate causal variants. Finally, the detected association was validated by evaluating the effect of the identified haplotype on available birth and fertility traits.Large-scale phenotypic recording of birth records was carried out routinely through the Swiss national animal recording database between 2006 and 2018. Here, we focused on data from only one breeding organization: swissherdbook . The raw dataset contained 3,977,467 birth records mainly from the Holstein and Simmental breeds. Data analysis and preparation for breeding value estimation were performed with an inhouse software written in R  using RSA mixed linear model was fitted to the phenotypic data described as follows:gram vce .Breeding values were predicted with the MiX99 program  using thBreeding values were standardized to have a mean of 100 and a standard deviation of 12. We defined base animals, the 8- to 10-year old Holstein sires (Red Holstein and Holstein), which is similar to the definition of base animals for the calving ease trait used in Switzerland.Estimated breeding values (EBV) for the direct (mbd) and maternal (mbm) multiple birth traits were de-regressed according to Garrick et al.  Table 22. De-regRoutine SNP genotype data generated for genomic selection were available for ~ 60,000 animals. Animals were genotyped using several routinely available array chips that include between 3 and 150 k SNPs. The available genotype archive was used in a two-step imputation approach and was imputed first to a density of 150 k. Subsequently, imputation to HD-density was carried out using 150 k data. The reference dataset for the 150 k array included 1688 Holstein and 1511 Simmental animals and the reference dataset for the HD-density array included 703 Holstein and 663 Simmental cattle. FImpute software was used with default parameters for both steps . In eachAA, AB, and BB, respectively. Genome-wide single marker association studies were carried out using the mixed model approach and the software snp1101 . Only dap matrix  and \docFitting one genotype at a time can easily lead to biased results due to stratification and linkage disequilibrium (LD) . FittingA subsequent fine-mapping approach was used to detect possible causal variants. To identify the haplotype that encompasses the putative causal QTL, we performed a haplotype regression analysis limited to the previously identified genomic windows that explained a significant proportion of the total genetic variance. Following an approach described by Pausch et al. , our aimUsing the plink software package  and deco2 ≥ 0.7) with the identified associated haplotype were analyzed.To obtain supporting evidence for a putative causal role of the variants, an analysis of sequence homology among 20 mammals was performed with the UCSC genome browser. All variants that were in high LD  encodes the luteinizing hormone/choriogonadotropin receptor and the FSHR gene  encodes the follicle stimulating hormone receptor.Subsequently, we inspected the gene content of the significantly associated QTL region on chromosome 11, and interestingly, we identified only two genes including their regulatory flanking regions in this segment, according to the NCBI Annotation Release 106: We selected a 2-Mb-segment on chromosome 11 that starts at 30.5 Mb for haplotype association analyses based on the association results shown above. As discussed, this region encompasses two candidate genes including their regulatory regions. The 36 top-associated haplotypes according to their significance level from the association test are in Table 2) between the haplotype and all the detected variants in the VCF file for the 880-kb region of the top-associated haplotypes  with the haplotype   indicates a low level of conservation Table . The ove2 ≥ 0.8) with the top-associated haplotype for all breeds that had at least 50 records. Interestingly, the frequency of this variant  was highest for the Deutsches Schwarzbuntes Niederungsrind, the founder breed of modern Holstein cattle  and stillbirth maternal (SBM), showed a significant association at the 5% level Table . The assLHCGR and FSHR, which directly affect the female reproduction cycle. We used the top-associated haplotype to identify variants in high LD with this region in the WGS data of hundreds of Holstein genomes. We found only one variant that was located in the 5ʹ-regulatory region of the LHCGR gene and represented a potential causal candidate.Decreasing female fertility is an acknowledged issue in high-performance dairy production and the occurrence of multiple births has long been an undesired trait. In this study, which is based on large-scale phenotyping and genotyping data, we have detected for the first time genetic factors that contribute to this trait. The estimated breeding values for maternal and direct multiple births were used as phenotypes in association studies. Two alternative GWAS approaches were applied and identified a major QTL on bovine chromosome 11 in a region that harbours two plausible candidate genes, The values of the estimated genetic parameters  were similar to those in the literature , 15. TheIn the recent past, no clear genetic trend for the two studied traits (mbd and mbm) has been observed in the Swiss data in contrast to previous findings in Norwegian cattle  that shoIGF1. The region on chromosome 5 containing IGF1 does not overlap with the suggestive QTL detected in our analysis. However, one can speculate that a larger and/or global dataset might improve such analyses and reveal additional QTL in Holstein and/or other cattle breeds.In our dataset, we found only one clear QTL for the maternal trait, which underlines the essential role of the maternal component for the complex multiple birth trait. This QTL on chromosome 11 was detected by two GWAS approaches: single SNP regression and a window-based BayesB approach. Although single marker detection can be useful to detect many associations, only a small fraction of the genetic variance of quantitative traits can be significantly highlighted and identified with this approach –59. The LHCGR and FSHR, which are located in the identified QTL region, are obvious candidate genes for multiple birth since they encode receptors of three essential hormones for female reproduction: luteinizing hormone (LH), choriogonadotropin, and follicle stimulating hormone (FSH). This is the first study that detects these genes as candidates for multiple birth in cattle.The two genes, LHCGR gene that is expressed in the testis and ovary lead to disorders of the development of the male secondary sexual character, including familial male precocious puberty, also known as testotoxicosis, hypogonadotropic hypogonadism, Leydig cell adenoma with precocious puberty, and male pseudohermaphroditism with Leydig cell hypoplasia (OMIM 152790). In females, an acute increase in LH (LH peak) triggers the ovulation by initiating meiosis II in the oocyte at the point of ovulation and leads to follicle rupture and subsequent development of the corpus luteum [LHCGR gene might alter the expression of the encoded receptor in the ovary cell and thereby influence the ovulation rate explaining the effect on the studied multiple birth trait.Human chorionic gonadotropin is a hormone that is involved in the maternal recognition of pregnancy and is produced by trophoblast cells that surround the growing embryo, whereas LH is a hormone that is produced by gonadotropic cells in the anterior pituitary gland under the regulation of the gonadotropin-releasing hormone from the hypothalamus. Mutations in the human s luteum , 66. Ints luteum . TherefoFSHR gene cause ovarian dysgenesis type 1, and also the ovarian hyperstimulation syndrome (OMIM 136435). In the ovary, the FSH receptor is necessary for follicular development and is expressed on the granulosa cells and during the luteal phase in the secretory endometrium of the uterus. Although this gene can also represent a plausible functional candidate for multiple birth, the genetic findings presented here and the genomic localization of the identified potential causal variant provide stronger support for the LHCGR gene. However, an effect on FSHR expression cannot be fully ruled out. Interestingly, a study of the Flemish and Dutch human population provided some evidence that the same homologous region on chromosome 2 carrying the candidate genes FSHR and LHCGR had an effect on multiple birth [FSHR has been reported in a single family [FSHR have been detected in one woman with two twin pregnancies [FSHB and the SMAD3 genes [The FSH receptor is a transmembrane receptor that interacts with FSH and represents a G protein-coupled receptor that is expressed in the ovary, testis, and uterus. Mutations in the human le birth . In addie family , and twognancies . Howevergnancies . RecentlD3 genes , and sevD3 genes . In gene2 ≥ 0.7) that formed a 160-kb block. This was not unexpected due to the known relatively long-range LD pattern in cattle populations [LHCGR gene. Since the candidate variant  segregates in various breeds at low frequency, it represents an old variation that most likely occurred before the formation of modern breeds. Ideally, a cross-breeding validation analysis should be performed. For most of the breeds analyzed here, neither phenotypes nor genotypes are available to evaluate a possible effect on multiple birth traits. In the data that was available for Brown Swiss and Simmental cattle, this variant showed low minor allele frequencies, which could be the reason why we were not able to confirm an effect in those populations (data not shown). Hence, we speculate that the pattern observed here is similar to that previously reported for the DGAT1 p.Lys232Ala mutation that determines fat content in milk in Holstein and Brown Swiss, for which multiple variants showed a similar effect [Unfortunately, LD-based filtering of sequence variants within the critical segment did not filter out a single variant in perfect LD with the top-associated haplotype on chromosome 11. However, several non-coding variants showed high values of LD  as described in previous studies is required to make a definitive statement about the causality of the reported candidate location [LHCGR gene expression levels and our trait of interest (mbm). The three key assumptions for a genetic variant to qualify as an IV are shown in Fig. LHCGR gene expression. This means that the nodes of this genetic variant are connected to the intermediate phenotype. Third, conditional on confounders and exposure, the genetic variant and the outcome (mbm) are independent. Hence, if we know the levels of the exposure and the confounders, the genetic variant does not provide any additional information to the outcome trait (mbm). To test these three assumptions, we would need to have the observed values for the exposure of the intermediate phenotype which in our case are the expression levels of LHCGR, in addition to the genotypes of the genetic variant and the recorded events of our outcome trait (mbm). Unfortunately, the data for the intermediate phenotypes are not available in this study. The additional collection of intermediate phenotypes, such as the expression levels of LHCGR in order to be able to make a more definite statement about the causes behind the outcome trait of mbm, requires further research.Although the association reported for the SNPs found in this study strongly suggests a close connection to the location , 78. In 2 value ≥ 0.7 was low, which is expected since our observed segment is an intergenic and non-coding region. Regarding the single variants, we did not observe any variants that showed high homology scores. Therefore, no further information can be obtained from this analysis regarding a potential causal role.We compared the sequence homology of the six variants identified in high linkage with the associated haplotype between 20 mammalian species, which resulted in an homology score that is a measure of supporting evidence for a variant. The overall mean of the homology for the whole region for variants with an rThe detected associated effect of the identified haplotype on the studied trait mbm was negative. Hence, female haplotype carriers are expected to have a decreased incidence for multiple birth. Evaluation of the possible effects of the haplotype on other available birth and fertility traits revealed significant effects on days to first service and maternal stillbirth Table . We founLHCGR gene was linked to the top-associated haplotype. This suggests that selecting against multiple births is possible, but a putative negative effect on other female fertility traits needs to be considered. These findings improve our understanding of the genetic architecture that underlies multiple birth in mammals and female fertility, in general, but further studies are needed to strengthen the evidence for a causal relationship between the detected LHCGR locus and the phenotype.Our aim was to undertake a comprehensive genetic analysis of multiple births in Swiss Holstein cattle. By analyzing large-scale genotype and phenotype data, we have detected, for the first time, a major QTL for maternal multiple birth on chromosome 11. One non-coding most likely regulatory variant located in the 5ʹ-region of the