Genome-wide association studies (GWAS) have mapped thousands of susceptibility loci associated with immune-mediated diseases. To assess the extent of the genetic sharing across nine immune-mediated diseases we apply genomic structural equation modelling to GWAS data from European populations. We identify three disease groups: gastrointestinal tract diseases, rheumatic and systemic diseases, and allergic diseases. Although loci associated with the disease groups are highly specific, they converge on perturbing the same pathways. Finally, we test for colocalization between loci and single-cell eQTLs derived from peripheral blood mononuclear cells. We identify the causal route by which 46 loci predispose to three disease groups and find evidence for eight genes being candidates for drug repurposing. Taken together, here we show that different constellations of diseases have distinct patterns of genetic associations, but that associated loci converge on perturbing different nodes in T cell activation and signalling pathways. Immune related diseases have been found to have overlapping genetic susceptibility loci. Here the authors perform cross-disorder genetic analysis to uncover three genetic groups of immune diseases that converge on the same immune cells and pathways. It is well documented that these diseases co-occur within families and that multiple immune diseases are likely to occur in the same individual5. For example, the major histocompatibility complex locus is associated with most autoimmune diseases6. Another example is a locus containing CTLA4 which is associated with multiple immune diseases including rheumatoid arthritis (RA), coeliac disease (CeD), type 1 diabetes (T1D) and Hashimoto thyroiditis (Ht)10. Targeting the CTLA-4 pathway has been successful in tumour immunotherapy, however in more than 60% of patients, CTLA-4 blockade leads to multiorgan autoimmune reaction11. In contrast, the property of CTLA-4 to bind the costimulatory molecules is extensively used as a treatment for RA12.Genome-wide association studies (GWAS) have identified thousands of susceptibility loci associated with immune-mediated diseases, many of which have been observed in multiple diseasesRGS113. Similarly, a study using local genetic correlation showed widespread sharing across traits14. For example, T1D and Systemic Lupus Erythematosus (SLE) shared 18 loci. Another study assessed the regulatory activity of immune disease-associated SNPs and showed that shared genes were highly connected and were involved in immune pathways15. Although it has been established that immune phenotypes have a shared genetic predisposition, further detailed and systematic analysis is necessary to understand the causes and structure of such sharing. In particular, it is unclear whether sharing is equally distributed across immune diseases  or whether there are subgroups of immune diseases that are more similar to each other than the rest.Understanding the pleiotropy of genetic associations is critical, as it can reveal common disease mechanisms and pathogenic pathways. A cross-disorder genomic analysis could identify shared mechanisms and potential targets for drug repurposing. By combining cases and controls across immune diseases, recent work identified 224 shared associations, improved fine-mapping, and revealed shared disease genes such as 16 to GWAS data. This revealed three groups of diseases: the first consisted of diseases affecting the gastrointestinal tract, the second consisted of rheumatic and systemic disorders and the third group represented allergic diseases. Each group had unique genetic architecture and only a limited number of loci were in common among the groups. Collectively, our results provide new insights into shared mechanisms of genetic risk for immune-mediated diseases and prioritise drug targets that could be used for multiple immune disorders.In this work, we sought to investigate common factors representing general risk across immune-mediated diseases. To examine the genetic architecture of nine immune-mediated diseases we applied genomic structural equation modelling (genomic SEM)17 to estimate genetic correlations among nine diseases  , we were able to show that the genetic correlation structure was well described by a model using three factors  Fig. . To uncost) Fig. 16. By usgut, 60 for Faid and 67 for Falrg   Fig. . In addiSNP heterogeneity statistics (Methods). In short, QSNP allows us to identify SNPs that plausibly do not affect individual diseases exclusively by their associations with the latent common factors16. In other words, if the QSNP heterogeneity statistic is significant, it implies that the tested SNP acts at least partially independently of the latent factors. Our results show that only 9% of loci were significant for QSNP heterogeneity (18/194) , rs416603 (the closest gene TNP2 involved in the regulation of protein processing) and rs13335254 . Similarly, Faid had two independent signals which colocalized with T1D. The locus that was shared across all three groups of diseases is located on chromosome 4  and encompasses a potent regulator of T and B cell proliferation IL21.Due to the challenges of the HLA region, we removed genomic regions encompassing Taken together, we identified independent signals between factors and determined how each of the factors relate to individual diseases and their likely causal genes.19, and for each lead SNP we retrieved the top prioritised gene based on the Variant-to-Gene (V2G) score. To test whether these genes are enriched in specific pathways, we performed pathway enrichment with gProfiler2 (Methods). This showed that the factor-associated genes were enriched in cytokine signalling, differentiation of T helper cells, immune diseases and response to pathogens  data from 982 donors and 1.27 million peripheral blood mononuclear cells (PMBCs). We showed that there is an enrichment of Fgut, Faid, and Falrg-associated loci in memory CD4+, CD8+ and unconventional T cells in all three disease groups  and colocalized with factor-associated loci using the Bayesian framework coloc27. We identified 46 colocalizations in Fgut, 49 in Faid and 20 in Falrg with PP4 ≥ 0.9  derived from PMBCs from the OneK1K cohortits Fig. , with anER4 Fig. . In this29 and investigated whether colocalizing genes are known drug targets  Taken together, our data show that understanding the pleiotropy of genetic associations can reveal common disease mechanisms, identify novel drug targets and offer evidence for drug repurposing.17.In this work, we used genomic SEM to investigate the common genetic factors predisposing to multiple immune-mediated diseases. We identified three broad categories of immune-mediated diseases: diseases affecting the gastrointestinal tract, rheumatic and systemic disorders, and allergic diseases. Surprisingly, underlying factors affecting the pathogenesis of each of these disease groups had a highly specific pattern of genetic associations, with only 13/301 loci being shared across these groups. This suggests that there is a genetic similarity between diseases within a group, but that the associated loci are highly distinct across groups. Importantly, as LDSC and genomic SEM control for the sample overlap in GWAS studies, disease groupings are not confounded by sharing of the samples32. Similarly, ~70% of patients with PSC have IBD, with UC being the most prevalent33. Our study shows that there are common genetic mechanisms driving the pathogenesis of these diseases and suggests that creating cross-disorder cohorts of immune diseases could increase the power to identify causal pathogenic processes.The identified groups agree with previous epidemiological findings. For example, T1D was grouped with rheumatic diseases including RA, which is in line with reports that patients with T1D but not T2D have an increased risk of RA (OR = 4.9)gut and Faid and Falrg-associated loci were enriched in the JAK-STAT signalling pathway, although there were only three overlapping genes driving the pathway enrichment in each of these groups. Similarly, out of 53 genes that are enriched for lymphocyte activation, only 7 were shared across at least two factors. Therefore, one can speculate that perturbations at different nodes which regulate T cell activation and cytokine signalling are partially responsible for driving different disease outcomes. Recent advances in CRISPR editing in T cells and its subpopulations35 will be instrumental to elucidate the differential effects of perturbing each node within shared pathways.Importantly, over 90% of identified loci acted via common factors, rather than independently on each of the diseases. Therefore, we sought to identify transdiagnostic risk pathways to uncover biological processes whose perturbation affects each of the disease groups. Our study showed that despite associated loci being highly factor specific, they converged on perturbing the same pathways involved in T cell activation, differentiation and cytokine signalling. F36. Therefore, understanding how trans-diagnostic variants regulate gene expression can help to identify novel drug targets or provide additional evidence for existing trials. Here we colocalized the factor-associated loci with sc-eQTL derived from the OneK1K cohort. To date, OneK1K is the largest study containing single-cell RNA sequencing (scRNA-seq) data from 982 donors and 1.27 million PMBCs. We showed that eight of these colocalizing genes are known drug targets offering further genetic support for their potential therapeutic effect. In addition, given that the assessed variants are pleiotropic, our results imply that identified drugs could be repurposed for diseases within the same group. For example, our data shows that the increase in expression of a key immune regulator CTLA4 is protective against the Faid group of diseases. The property of CTLA-4 to regulate the immune system has long been exploited in the treatment of RA12. Similarly, an inhibitor for Integrin Subunit Alpha 4, ITGA4 has been trialled in UC and CD (Open Targets database). Our data gives further genetic evidence that an increase in ITGA4 expression leads to an increased risk for Fgut diseases, and therefore it is plausible that inhibiting ITGA4 would be beneficial not only in CD and UC but should also be trialled in PSC. However, one limitation of this study is that we identified colocalization events for 37 out of 301 loci. This highlights the urgent need for larger cohorts, which will be better powered to detect eQTLs, as well as large-scale genetic studies in immune disease patients.Finally, it has been widely demonstrated that supporting preclinical data with genetic evidence can significantly increase the chance of developing successful drugs17. While no studies have to date validated the behaviour of genomic SEM in similar settings, we would expect that the use of the GWAS datasets originating from different ancestries may lead to spurious results. Therefore, we believe that the analysis should be conducted per ancestry rather than combining GWAS of different ancestries. As the representation of global populations in immune disease GWAS increases, follow-up studies will be required to test whether our observations are fully transferable to different ancestral groups.A limitation of our study is that it only focussed on GWAS performed on populations of European ancestry. This is because genomic SEM and LD score regression require the samples to be drawn from the same ancestry, as linkage disequilibrium blocks and thus LD scores are ancestry-dependentIn conclusion, our work underscores that three groups of immune-mediated diseases do not share similarities in their genetic predisposition, but show associated loci which converge on perturbing different nodes of a common set of pathways, including in lymphocyte activation and cytokine signalling.7, RA8, JIA37, SLE38, CD39, UC39, AST40, ECZ41 and PSC42 (Supplementary Data https://utexas.app.box.com/s/vkd36n197m8klbaio3yzoxsee6sxo11v/file/576598996073). Where necessary, chromosomes X and Y were removed and the standard error of logistic betas was calculated based on Odds Ratio confidence intervals. Summary statistics were formatted with the munge function from Genomic SEM R package v.0.0.5, (with default parameters) which removes all the SNPs not present in the reference file, filters out SNP with MAF < 1% and flips the alleles according to the reference file and computes z-scores. The HapMap3 reference file is provided in the Genomic SEM repository https://utexas.app.box.com/s/vkd36n197m8klbaio3yzoxsee6sxo11v/file/805005013708.We downloaded GWAS summary statistics from published studies on the most common autoimmune disorders: T1DThe sum of the effective sample sizes for GWAS that was meta-analysed was calculated by retrieving the information about the cohorts from the respective publications (Supplementary Data EffNc of each contributing cohort to compute the sum of the effective sample size:c are contributing cohorts (as described at https://github.com/GenomicSEM/GenomicSEM)43. To estimate genetic correlation we used the ldsc function in Genomic SEM, using the LD reference panel provided in the Genomic SEM repository (https://utexas.app.box.com/s/vkd36n197m8klbaio3yzoxsee6sxo11v/folder/119413852418).Next, we calculated the cohort-specific sample size as follows:16. By using the disease clustering threshold (genetic correlation 0.4) and the model fit statistics thresholds (CFI > 0.95 and SRMR < 0.10) we excluded the models with one and two factors. The four-factor model was instead excluded as it partitioned the variance of PSC, CD and UC into two separate latent factors, where UC is both in factor 1 and factor 4 reducing the interpretability of a four-factor model.To assign immune-mediated diseases in groups, we used the genetic correlation ≥0.4 between disease pairs, resulting in three observed disease groups. To uncover the latent factors which represent shared variance components across diseases, we modelled the genetic variance-covariance matrix across traits using genomic SEM. We computed four confirmatory factor analyses guided by the exploratory factor analysis: a) a common factor model b) a two-factor model, where one factor was loading into CD, UC, PSC, JIA, SLE, RA and T1D while the other factor was loading into Ecz and Ast. c) A three-factor model where F1 was loading into CD, UC, PSC; F2 was loading into T1D, SLE, JIA, RA, and F3 loading into Ecz and Ast; d) A four-factor model, F1 was loading into CD and UC, F2 was loading into T1D, SLE, JIA, RA, F3 was loading into Ecz and Ast and F4 was loading into PSC and UC. The fit of the models was assessed by estimating the comparative fit index (CFI) and the standardised root mean square residual (SRMR) parameters. We used CFI > 0.95 and SRMR < 0.10 as a measure of a good fithttps://utexas.app.box.com/s/vkd36n197m8klbaio3yzoxsee6sxo11v/file/576598996073) which is used to standardise the effect sizes and SE and format the summary statistics  with the sumstats function in Genomic SEM with default parameters. SNP-specific effects of the 4,994,803 SNPs that were shared among all the nine GWAS were estimated with the userGWAS function with default parameters using the weighted least squares (WLS) estimation method. To evaluate whether the calculated SNP effects were acting through our three-factor model, we performed the QSNP heterogeneity tests. The heterogeneity test returns a χ2, whose null hypothesis suggests that the SNP is acting through the specified model. Therefore, rejecting the null hypothesis means that the SNP acts through a model that is different from the specified one44.Before estimating the SNP-specific effect, we aligned the summary statistics to the reference file , flip the alleles according to the reference file, remove the SNPs which are not in the reference file , exclude the SNPs with betas or standard errors equal to 0.Before running conditional analysis and colocalization, summary statistics (traits and factors) were processed with the Bioconductor MungeSumstats package46. The stopping criterion was that all conditional p-values were larger than 1 × 10−4. This led to a set of independent SNPs using all SNPs within the genomic region boundary (±100 kb). For each SNP, a conditional dataset was produced where SNPs in the genomic region were conditioned to all identified independent SNPs apart from the target one. We then considered as true signals those with p value < 10−6 or those for which the SNP with the lowest p-value was lower than 5 × 10−8 in the original GWAS.The genomic regions defined in the previous steps are based on genomic position, but multiple association signals may be present within each genomic region. To this end, we developed a statistical approach which first divides each GWAS-significant genomic region into its component signals and then uses colocalization across different traits to group similar association signals. First, in each genomic region for each GWAS, we performed stepwise forward conditional regression using COJO47 but instead of using the step-wise conditioned datasets, it uses an ‘all but one’ approach.This procedure was repeated on all the traits which had a significant association in the considered genomic region. We thus obtained for each trait a set of conditional datasets covering all the SNPs in the genomic region. This procedure is similar to that used by Robinson et al.27 analysis between all pairs of loci specific for each trait. Loci which colocalized with PP4 ≥ 0.9 were grouped in a single locus. We excluded the genomic regions in the HLA locus  from this analysis.To understand which loci were pleiotropic between traits, we ran colocalization using coloc22. pQTL results were obtained from DECODE genetics30. For each genomic region, we first identified if cis-eQTLs or cis-pQTL were present. For each identified eQTL we performed the decomposition of the locus as described above and the identified loci were colocalized with factor-associated GWAS signals. For pQTL, we did not perform the conditional analysis prior to colocalization as we did not have a reference LD panel for the Icelandic population. Attempts of using a different LD reference set resulted in hundreds of putatively independent loci, which are likely false positives. Therefore we tested only the single main effect. To identify a colocalizing signal we required PP4 ≥ 0.9. To identify the direction of the effect of the increase in gene expression for the colocalizing loci, we used Mendelian Randomization using the Wald ratio method (TwoSampleMR R package48). We used the SNP with the smallest p-value in the conditional analysis as an instrument variable. Significant MR results  were reported. This procedure was performed per cell type.We downloaded eQTLs from the OneK1K cohort20 and S-LDSC49.To identify cell types underlying identified factors we used CELL-type Expression-specific integration for Complex Traits (CELLECT). CELLECT quantifies the association between GWAS signal and gene expression specificity using well-established models for GWAS enrichment MAGMA19 for the lead SNPs within the conditionally independent loci. To calculate a prioritisation score for candidate genes, the V2G pipeline takes into account molecular phenotypes , chromatin interactions, functional predictions and distance to the transcription start site. To identify enrichment in KEGG pathways and GO terms we used the R package gprofiler2 (v0.2.1)50, with default parameters. Pathway was considered significant if p-adj < 0.05. We used the R package pathview (v1.34.0)51 to represent the KEGG pathways and to highlight factor-specific genes. The diagram shown in Fig. Candidate genes were retrieved by interrogating the Variant-to-Gene (V2G) pipeline in the Open Targets Platform29 (v.22.06) was used to identify drug targets for eQTL genes. This website was queried on (29th August 2022).Open Targets PlatformFurther information on research design is available in the Supplementary informationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Reporting Summary


PHENIX which imputes missing data using kinship and correlated traits, and we screened insertions and deletions (InDels) from the recently whole-genome sequenced Sorghum Association Panel for putative loss-of-function effects. Candidate loci from genome-wide association results were screened for potential loss of function using a Bayesian Genome-Phenome Wide Association Study (BGPWAS) model across both functionally characterized and uncharacterized loci. Our approach is designed to facilitate in silico validation of associations beyond traditional candidate gene and literature-search approaches and to facilitate the identification of putative variants for functional analysis and reduce the incidence of false-positive candidates in current functional validation methods. Using this Bayesian GPWAS model, we identified associations for previously characterized genes with known loss-of-function alleles, specific genes falling within known quantitative trait loci, and genes without any previous genome-wide associations while additionally detecting putative pleiotropic effects. In particular, we were able to identify the major tannin haplotypes at the Tan1 locus and effects of InDels on the protein folding. Depending on the haplotype present, heterodimer formation with Tan2 was significantly affected. We also identified major effect InDels in Dw2 and Ma1, where proteins were truncated due to frameshift mutations that resulted in early stop codons. These truncated proteins also lost most of their functional domains, suggesting that these indels likely result in loss of function. Here, we show that the Bayesian GPWAS model is able to identify loss-of-function alleles that can have significant effects upon protein structure and folding as well as multimer formation. Our approach to characterize loss-of-function mutations and their functional repercussions will facilitate precision genomics and breeding by identifying key targets for gene editing and trait integration.High-throughput genomic and phenomic data have enhanced the ability to detect genotype-to-phenotype associations that can resolve broad pleiotropic effects of mutations on plant phenotypes. As the scale of genotyping and phenotyping has advanced, rigorous methodologies have been developed to accommodate larger datasets and maintain statistical precision. However, determining the functional effects of associated genes/loci is expensive and limited due to the complexity associated with cloning and subsequent characterization. Here, we utilized phenomic imputation of a multi-year, multi-environment dataset using  Genome-Wide Association Studies (GWAS) have served as powerful tools for genotype-phenotype mapping of genomic regions that can be used as breeding targets since their initial application . As neweThe decreased costs of high-throughput sequencing have led to the exponential growth of genomic data  includinvia tissue culture and genetic transformation represent pivotal steps in the complete characterization of novel loci. Even in maize, characterization via tissue culture involves considerable effort. Both transformation and gene editing are limited in scale by the work required for tissue culture and plant regeneration, which takes several months  mapping  or GWAS l months . As suchl months .in silico  model that offers improvements over existing genotype-phenotype mapping approaches in its ability to identify pleiotropic and putative knockdown/out effects of given loci n silico . This mon silico . Addition silico . Predictenotypes . By perfTo demonstrate the value of these approaches, we apply these models to the Sorghum Association Panel (SAP) . The SAPp matrix of random effects and ϵ is a N×p matrix of residuals and are modeled using matrix Gaussian distributions as followsPhenomic data constituting 234 traits measured on the SAP  were obtIN is the identity matrix of size N×N. PHENIX uses a Bayesian MPMM to fit a low-rank model for U, such that U = S β, whereMN represents a matrix normal distribution with mean zero, IP is an identity matrix of size p×p, τ is a regularization parameter, and a Wishart prior (Wi) is used for the residual precision matrix E−1p + 5 degrees of freedom and scale In this model K is the N×N kinship matrix between accessions (or row-wise covariance), B is the p×p matrix of genetic covariances between phenotypes (or column-wise covariance), E is the p×p matrix of residual covariances between phenotypes, and https://github.com/jlboat/PHENIX).The model is fit using Variational Bayes methods resulting in sample posteriors with multivariate normal distributions, and missing data are imputed using the posterior mean. In summary, PHENIX uses known kinship and trait covariance to better predict missing phenotypic data . As the https://github.com/jlboat/BGPWAS) using the model described below.Genomic data were obtained and processed as described in . In shor < -8)  and impu < -8) . Princip < -8)  to preve < -8) . While a < -8) . The numy represents numerically encoded variant data , X represents the matrix of predictors including traits (124) and LOCO-based principal components 3) as described above (see Genomic Data), and ϵ represents the residual variance. Coefficients are estimated using a horseshoe prior  posterior interval estimates from MCMC draws did not overlap zero. Posterior intervals were plotted for each variant using bayesplot .https://github.com/jlboat/BGPWAS).Variant data used in the BGPWAS model were selected from the full set of InDels using ranges for known loci and novel associations in concert with BEDTools  or BCFtohttps://github.com/sokrypton/ColabFold) (https://colab.research.google.com/). AlphaFold two was executed using the global superposition metric template model (TM) for protein structure prediction. Output from AlphaFold two includes PDB formatted structures sorted by average predictions of side-chain χ angles and per-residue accuracy of the structure estimated using the predicted local distance difference test (pLDDT) where complexes are sorted by the predicted TM score, plots of the model quality , pLDDT, and sequence identity to query coverage), and multiple sequence alignment files   availablnt files .The BGPWAS was designed using a horseshoe prior to effectively shrink non-relevant coefficients . This alMa1 and Ma3), tannin content , and plant height . We performed BGPWAS analysis of InDels falling within these genes as a means of assessing model accuracy. For maturity genes, many maturity-dependent traits were associated - particularly traits mediating both biomass and grain yield   . The affnd zinc) .Ma1 with a single-base deletion at Chr06_40312436 corresponded with a loss-of-function mutation that affects the protein product via a frameshift mutation . Similarly, for the well-documented Dw2 gene, we identified a loss-of-function mutation that significantly affects the translated protein via a frameshift mutation that results in a product less than one-quarter the original size , which can be a limiting factor at larger scales. We minimized computational requirements both by focusing analyses on known QTL and by limiting associations to InDels that resulted in frameshift mutations. This not only reduces the total number of tests necessary but also shifts focus to those InDels most likely to result in loss-of-function mutations. Similarly, the BGPWAS was not written in multi-response form both to reduce computational requirements and avoid complications in sampling. Sampling of multi-response models resulted in a significant number divergent transitions and significantly longer sampling times to overcome low effective sample sizes. Instead, running variants individually allowed for parallelization of runs and circumvented issues with highly correlated variants, which are exceptionally common when examining variants falling within a single locus. Importantly, issues with generating estimates from complex, multi-response models are not unique to Bayesian models, but rather the Bayesian model makes explicit the issues inherent in generating estimates from complex models that derivative-based maximum likelihood approaches do not.via indigestible protein–tannin complexes  domain. IDRs increase the functional versatility of proteins by facilitating interactions between the structural domains of other proteins, and IDRs are frequently targeted for post-translational modifications that affect the functional state of the protein , we were able to identify a loss-of-function mutation in Dw2. Along with the other major dwarfing genes, Dw2 was introgressed into breeding lines during the SCP due to its ability to confer short stature which facilitates combine harvesting. This particular gene exhibits a large effect on plant height and was the second most significant QTL identified for plant height based on whole-genome sequencing of the SAP  designated as Dw4 (Dw4 locus (on Chr4) (Among the known dwarfing genes in sorghum ( the SAP . As the d as Dw4 . Importaon Chr4) . The onlon Chr4) . The Araon Chr4) . Sink-toon Chr4) . As suchon Chr4) .TTG1 (a WD-repeat protein), TT2 (an R2R3-MYB transcription factor (TF)), and TT8 (a basic helix-loop-helix (bHLH) TF). These three genes work in a complex and directly activate BANYULS expression (Y1) and TT8 (Tan2) exhibited significant binding affinity as that observed for the orthologs in Arabidopsis. Iteration of the various protein isoforms all resulted in ipTm values less than 0.25, well below our significance threshold of 0.75. Similarly, the multimer of Tan1, Tan2, and Y1 exhibited ipTm values Y1 allele used was incorrect for multimer formation. We additionally examined the potential for Tan1 (a WD-repeat protein like TTG1) and Tan2 proteins to generate a heterodimer. Interestingly, the tan1b allele exhibiting a high predicted multimer score with the annotated Tan2 allele at an ipTm Tan1 serving the homologous role of Arabidopsis TTG1 (or maize PAC1) and Tan2 as the TT8 ortholog. This interaction is consistent with observations that mutations in either gene mask effects of a dominant allele at the other locus  gene. Significantly, CCR genes have been shown to profoundly affect soluble phenolic pools in tomato , and botca napus . Conversca napus , this acca napus , marker-ca napus , or anotin silico alternative to the traditional methods for phenotypic characterization.As an additional example of model accuracy, we assessed a random selection of 30 genes - three per chromosome - representing 322 variants - both SNPs and InDels. In total, 10 of the 30 genes had no associations, but the remaining 20 genes were associated with 65 traits. Here, we highlight some interesting associations. One of the gene examined was the sorghum gene Sobic.007G018550 . While tin silico characterization of loci, this model could serve to expedite the process of moving from novel QTL to their functional characterization and the introgression of desired loci in a breeding program. Further, by serving as a quick in silico alternative to existing cloning and validation procedures, this model may serve as a vital tool for identifying key functional targets to act upon for improvement across all species and alleviate some of the current bottlenecks in functional genomics.Here, we demonstrated the value of our BGPWAS as a proof of concept approach to identify breeding targets in the form of genes with gain/loss of functions for given traits and to identify putative pleiotropy of associated loci/variants. We further show that even previously characterized sorghum genes possess major InDels that directly affect protein folding and interactions during multimer formation. As a high-throughput approach for


The objectives of this study were to estimate the heritability, genetic and phenotypic trends for the livability of Israeli Holstein cows; estimate the genetic and environmental correlations between livability and the nine traits included in the Israeli breeding index; estimate the effect of the inclusion of livability in the Israeli breeding index on expected genetic gains; and compute a genome-wide association study (GWAS) for livability. Seven data sets were analyzed. All data were derived from the database of the Israeli dairy cattle herd-book. The mean livability for the complete data set of 523,954 cows born from 2000 through 2016 was 89.6%. Pregnancy reduced livability by 15%. Livability generally increased with parity and days in milk within parity. Heritability of livability was 0.0082. Phenotypic and genetic trends over the 14-year period from 2000 through 2013 were −0.42% and −0.22% per year. If livability is included in the Israeli breeding index, accounting for 9% of the index, livability would increase by 1.3% and protein production would decrease by 11 kg over the next decade, as compared to the current index. A marker in proximity to the oxytocin–vasopressin locus had the greatest effect in the GWAS. Oxytocin activity in cattle affects calving-associated pathologies and maternal death. Inclusion of livability in the Israeli breeding index is not recommended. Dairy cow survival, or herd life, is an economically important trait that is now included in the selection indexes of most commercial dairy-cow populations . The maiMortality rates (MORT) in the US Holstein population increased from 1998 through 2007 ,7. AnnuaOwing to the significant additional loss that results from cow death, as compared to culling, “cow livability (LIV),” the inverse of death, has been included in the major US selection indexes since 2016 , even thComputation of a genome-wide association study (GWAS) in dairy cattle requires computation of genetic evaluations for animals with genotypes. To compute unbiased genetic evaluations, it is first necessary to correct for environmental factors that may affect the phenotypic values of the traits analyzed. The objectives of this study were therefore: (1) to determine environmental factors affecting the LIV of Israeli Holsteins, (2) to estimate the heritability and herd-year-season’s effects on LIV by a restricted maximum likelihood (REML) analysis based on the individual animal model (IAM), (3) to compute genetic evaluations for LIV for all Israeli Holstein cows with valid records since 2000, (4) to estimate genetic and phenotypic trends for LIV, (5) to estimate the genetic and residual correlations between LIV and the nine traits included in the Israeli breeding index (PD20), (6) to estimate the effect of inclusion of LIV in the Israeli breeding index on expected genetic gains and (7) to compute a GWAS for LIV based on Israeli Holstein bulls with genotypes and genetic evaluations.http://www.icba-israel.com/, accessed on 2 January 2023). The first data set included valid exit records of 523,954 cows born from 2000 through 2016, with up to six parities, and no more than 30 months in milk at the last parity. This data set was used to estimate the effects of parity, exit month, days in milk at exit date, pregnancy status and milk production in the final lactation. The second data set included 475,512 cow records from data set 1 with valid first parity records for the 9 traits included in the Israeli breeding index. This data set was used to compute phenotypic correlations between LIV and the traits included in the current Israeli breeding index. The third data set included 174,106 cow records selected from data set 1 born from 2004 through 2009 with known sires of the Holstein breed. These data were used to compute REML variance components for LIV based on the “individual animal model” (IAM), and also included parents and grandparents of cows with LIV records. The fourth data set included 145,023 cows born from 2003 through 2009 with known Holstein sires, valid records for LIV and first parity records for 8 of the 9 traits included in PD20. For herd life, only a single record is generated per cow. The index traits are listed in As in , in all ijklmn is the LIV record for cow n with milking status i, pregnancy status j, lactation number k, exit month l and herd-year-season m; nmi is the milking status—either a valid record was recorded for milk production in the last parity or not; milk and milk2 are linear and quadratic effects of predicted 305-day milk production in the last lactation for cows with at least 35 days in milk; pregj is pregnancy status j on the date of cow exit, either pregnant or open; lack is parity number k at exit; monl is calendar month l of exit; HYSm is herd-year-season m; dim is days in milk at the exit date; idim is the inverse of dim; preg×lac, dim×lac and idim×lac are the corresponding interactions; and eijklmn is the random residual. Since the last lactation was generally incomplete, predicted milk production to 305 days was computed as described previously [Data set 1 was analyzed by PROC GLM of SAS   based on the genetic and residual variance components was computed using the following equation [b = the vector of breeding index coefficients listed in G = the genetic variance matrix; and P = the phenotypic variance matrix, computed as the sum of the genetic and residual variance matrices. As noted previously, VanRaden et al. [The vector of expected genetic changes over 10 yr (equation :(3)Φ= ibn et al.  estimaten et al. , the indn et al. . jc) with and without inclusion of calf survival was computed as:jb = the index coefficient for trait j; jg = the genetic standard deviation for trait j; J = total number of traits; and abs = absolute value. In addition, Equations (3) were also computed with a coefficient of 5 for LIV. In this case, the contribution of LIV would be 1% of the total index, as proposed by [The contribution of each trait to the total selection index  and BeadChips  on hair or semen samples collected in Israel by SION, the Israeli AI institute . A bull’s genotype was considered valid if at least 85% of the SNPs had valid genotype calls. In addition, for males, >90% homozygosity for markers on the X chromosome was required. As genotyping of these bulls was performed using several SNP chip platforms with different qualities and coverage, we performed imputation using 2114 Holstein bulls with genomic evaluations. A total of 50,392 genetic markers were selected for further analysis, according to [https://aipl.arsusda.gov/software/findhap/, accessed on 2 January 2023).The GWAS analysis included 965 Holstein bulls from data set 6 with genotypes and phenotypes, born in or after 1991. All genotyping was performed by Neogen . The response variable was the sires’ transmitting abilities  for LIV. The allele substitution effects and the nominal probabilities for the hypothesis of no effect were computed using EMMAX software, version EMMAX-intel64-20120205 [http://csg.sph.umich.edu/kang/emmax/, accessed on 2 January 2023). We generated a relationship matrix based on the identity by state matrix calculated using the emmax-kin-intel64 algorithm, using the -v -s -d 10 flags. Then, using the -v -d 10 –t flags, the relationship matrix and the –k argument, GWAS was computed. The nominal probability values were corrected for multiple testing by computation of the false discovery rate, as proposed by Benjamini and Hochberg [p < 0.05. Using the relatedness matrix, we also assessed the variance explained by all SNPs by the EMMAX algorithm for genomic REML. The GWAS files were prepared and formatted as described in ,24 using20120205   and the gtf file with the annotated bovine genome from Ensembl . Both files are based on the bovine reference genome assembly ARS-UCD1.2 [https://cran.r-project.org/web/packages/GALLO/index.html, accessed on 2 January 2023) was applied to annotate the SNP markers found for LIV and the genes spanning them. Gene enrichment analysis was performed using the Enrichr server [https://maayanlab.cloud/Enrichr/, accessed on 2 January 2023), which can identify gene enrichment for several terms and data sources, including diseases, pathways, Gene Ontology (GO) terms and tissue expression. QTL annotation was performed as follows: first, the annotation file containing the quantitative trait locus (QTL) database for cattle from the Animal QTLdb  using the values listed in p-values ≤ 0.05 (p = 1.82 × 10−42), is located on chromosome 13, in close proximity to the bovine gonadotropin-releasing hormone 2 (GNRH2), oxytocin/neurophysin I prepropeptide (OXT) and arginine vasopressin (AVP) genes. OXT and AVP are important to smooth muscle contraction during parturition and lactation and are associated with endometritis, as summarized in GeneCards [The Manhattan plot for the GWAS results for LIV is given in s ≤ 0.05 . The SNPeneCards .2 values for genetic linkage. About 85% of the marker pairs with R2 > 0.9 are within 150 kb distance from each other, as we found recently in this population [We performed QTL and gene enrichment analyses to annotate the significant markers’ possible roles in LIV. The confidence interval for each significant marker harbors several genes and QTL that have been previously reported. We thus analyzed the distribution of the genomic distances between markers as a function of the Rpulation . We therOXT and vasopressin pathways results in severe pathology at parturition that can finally lead to maternal death [Gene enrichment analysis of the genes spanning the significant markers for LIV revealed significant enrichment to the vasopressin and the opioid prodynorphin pathways, and for the GO interleukin-17 (IL-17) signaling process . IL-17s al death . This fiAs noted in the introduction, it was necessary to determine the effects of environmental factors on LIV, in order to compute unbiased evaluations for LIV, which were required for the GWAS analysis. In general, nongenetic effects on LIV, including HYS, culling month, months in milk at culling, final parity, pregnancy status at culling and milk production in the final lactation were similar to those in previous studies ,7,9,10. According to selection index theory, if selection is based on genetic evaluations, maximum economic progress is obtained when the selection index coefficients correspond to the economic values of the traits included in the index. Thus, a selection index that includes the correct economic value for LIV should result in greater total economic gain as compared to an index with a coefficient of zero. Based on the value of $1200 per cow death, a gain of 1.3% in LIV has an economic value of $16/cow. It is unlikely that milk producers would be willing to forgo an 11 kg increase in protein production to achieve the predicted LIV gain. It should be noted, though, that the prediction given in p-values ≤ 0.05 (DGAT1 gene (diacylglycerol O-acyltransferase 1), which is known to affect fat concentration. Freebern et al. [A total of seven SNPs passed the threshold of adjusted s ≤ 0.05  in the Gn et al.  found sip = 1.82 × 10−42) and is located in close proximity to the genes OXT and AVP, which are both part of the vasopressin pathway. The bovine OXT plays an essential role in maternal lactation, but apparently also in parturition. The disruption of the bovine OXT in the transgenic mouse model resulted in severe pathology at parturition [OXT in parturition, previously, AVP and OXT were shown to each have a role in regulating postpartum estrous behavior in dairy cows, and possibly to be involved in the calving interval and timing of the first insemination postpartum [The functional analysis of the significant markers pointed to the IL-17 signaling and the vasopressin pathways. The importance and implications of the IL-17 cytokines in health and disease were demonstrated in cattle. For instance, activation of IL-17 is required upon infection with paratuberculosis and Johne’s disease, in Salmonella-induced gut inflammation and in pathogens-derived mastitis . In additurition , suggeststpartum . In accoEven though LIV has significant economic value, inclusion in the Israeli breeding index is not recommended. A marker in proximity to the oxytocin-vasopressin locus had the greatest effect in the GWAS analysis. Previous studies show that oxytocin activity in cattle affects calving-associated pathologies and maternal death. It may be possible to determine the causative mutation by sequencing of individual bulls for the chromosomal segment of interest.


Family-based genome-wide association studies (GWAS) have emerged as a gold standard for assessing causal effects of alleles and polygenic scores. Notably, family studies are often claimed to provide an unbiased estimate of the average causal effect  of an allele, on the basis of an analogy between the random transmission of alleles from parents to children and a randomized controlled trial. Here, we show that this interpretation does not hold in general. Because Mendelian segregation only randomizes alleles among children of heterozygotes, the effects of alleles in the children of homozygotes are not observable. Consequently, if an allele has different average effects in the children of homozygotes and heterozygotes, as can arise in the presence of gene-by-environment interactions, gene-by-gene interactions, or differences in LD patterns, family studies provide a biased estimate of the average effect in the sample. At a single locus, family-based association studies can be thought of as providing an unbiased estimate of the average effect in the children of heterozygotes . This interpretation does not extend to polygenic scores, however, because different sets of SNPs are heterozygous in each family. Therefore, other than under specific conditions, the within-family regression slope of a PGS cannot be assumed to provide an unbiased estimate for any subset or weighted average of families. Instead, family-based studies can be reinterpreted as enabling an unbiased estimate of the extent to which Mendelian segregation at loci in the PGS contributes to the population-level variance in the trait. Because this estimate does not include the between-family variance, however, this interpretation applies to only (roughly) half of the sample PGS variance. In practice, the potential biases of a family-based GWAS are likely smaller than those arising from confounding in a standard, population-based GWAS, and so family studies remain important for the dissection of genetic contributions to phenotypic variation. Nonetheless, the causal interpretation of family-based GWAS estimates is less straightforward than has been widely appreciated. The approach does not only infer the direct genetic effects that are often of primary interest, however . Instead, estimates from population-based GWASs may also include indirect genetic effects of parents and other relatives, as well as absorb genetic and environmental confounding . In the Because for many traits, there are a large number of GWAS associations or loci, each of which explains only a tiny proportion of variance, researchers often focus on aggregate properties of the GWAS loci, such as genetic correlations, or predict individual trait values by combining estimated effect sizes across loci into a ‘polygenic score’ (PGS). The issues of confounding can be more pronounced for these aggregate measures, as systematic biases are compounded ,b.The possible contribution of non-direct genetic effects—in particular of environmental confounding—has motivated a turn towards family-based GWASs, which overcome the limitations of population-based GWASs by taking advantage of the randomness of Mendelian segregation from parent to child . By holdIn causal inference, the effect of interest is often that of a treatment, which can be defined in terms of a hypothetical manipulation, in which each person is moved from untreated to treated . In realIn many contexts, it cannot be assumed that the treatment effect is identical across individuals. In the presence of heterogeneous treatment effects, the question becomes whether the effect has internal validity, i.e., whether it provides an unbiased estimate of the mean effect for the entire sample—what in social sciences is known as an average treatment effect or ATE—or an unbiased estimate for a well-defined subsample, that is a “local” ATE or LATE . For insaverage treatment effects, there has been little or no discussion of the impact of heterogeneity in effect sizes on family-based GWAS estimates. Yet in genetics and quantitative genetics, the importance of heterogeneous genetic effects has long been recognized and, in contrast to many other contexts in which ATEs may be obtained, its possible sources are understood . In plants and animals in which environments and/or genotypes can be controlled, such gene-by-environment (G×E) and gene-by-gene (G×G) effects are ubiquitous. Similarly, studies in a wide range of species have established norms of reaction, in which the phenotypic effect of a genotype depends on the context Concerned about indirect effects as well as environmental and genetic confounding, the investigator aims to use the randomness of Mendelian segregation in families in order to more cleanly estimate the average causal effect of an allele or polygenic score on the trait in the offspring generation. They may be also interested in using the causal effect estimated in the sample to learn about the population from which the sample is drawn. They may also want to use the estimated causal effect in order to construct a genetic intrument, with the goal of identifying other causal relationships with the phenotype, as in Mendelian Randomization designs . To make2.1To generate intuition for the influence of G×E (and G×G) interactions on population and family-based GWAS estimates, we initially focus on a single bi-allelic locus, at which the two alleles have a direct causal effect on the trait of interest. We later consider the case where the alleles at the genotyped locus do not causally affect the trait, but instead tag alleles at a nearby causal locus. Label the two alleles definition of the causal effect of allele Given this phenotypic model, we consider two questions. First, what is a sensible It is common to define causal effects in terms of counterfactual manipulations . To thisWe begin with a simple thought experiment in which we randomly reassign genotypes at the locus to individuals in the sample, independent of their environments or genetic backgrounds. This thought experiment resembles an RCT. Since, in expectation, there is zero covariance between periment In turn, to define a causal effect of We can similarly define the causal effect of In general, the definitions  and 3)  3) need We can define a counterfactual manipulation that effectively randomizes alleles across environmental backgrounds and thus reconciles the randomization and allele-flipping definitions of the causal effect. We first note that the expected effect of the =α,see . Thus, wNext we consider whether various GWAS designs provide an unbiased estimate of the average causal effect. The genotype of each offspring can be written as the average of their parental genotypes If we perform a family-based association study by regressing the trait values of offspring on their genotypes at the locus, controlling for their parental genotypes, we obtain an effect-size estimate for allele al locus . This real locus . When paWe note that the estimate in among families to the population slope, while the second term is the contribution from the genotypic variance within families (due to random segregation). The first term  if the As a simple illustration of how LD differences between heterozygous and homozygous parents can influence the various association study designs, consider two populations that differ in both the degree of LD between and the allele freqencies at the marker and causal loci. In a sample drawn from across these two populations, the effect size estimated at the marker locus by a family-based association study will more strongly reflect the degree of LD in the population with greater heterozygosity at the marker locus, whereas an unconfounded population-based association study will instead reflect differences in the haplotype frequencies across the two populations .2.2To study the influence of G×E in applications of polygenic scores (PGSs), we focus on the general case of the relationship between a PGS for trait An investigator has access to effect-size estimates If individual We are interested in the causal relationship between the PGS for the trait In practice, a PGS is constructed using estimated rather than true effect sizes. Specifically, it reflects the signal of causal effects partially (and noisily) captured by the genotyped loci on which the PGS is based. Additionally, the effect-size estimates may be biased due to confounding in the GWAS. We ignore these complications here. For simplicity, we further assume that the set of loci included in the polygenic score is a subset of the loci that causally affect the trait of interest As in the single locus case, we can again consider two thought experiments to understand casual effects in the PGS case, the first involving randomization and the second involving genetic manipulation. First, we imagine randomly assigning genotypes at the loci in It is more complicated to define the causal effect of the PGS in terms of hypothetical genetic manipulations, in which we imagine experimentally flipping alleles at the PGS loci: with many loci contributing to the PGS, a desired change in the value of the trait-X PGS can be achieved via many different manipulations, and these different manipulations will, in general, not have the same average effect on trait haplotypes of a given length around each PGS locus. Under this definition, the When the PGS loci do not themselves causally affect the trait, but instead tag ungenotyped causal loci, we can still define a causal effect of the PGS via manipulation, just as we did for the case of a single marker locus above, with the investigator flipping Family-based designs for estimating the effect of the PGS rely on the regressionSince we can write theorem .In the presence of G×E interactions, and under the simplifying assumptions laid out above concerning the sets of PGS and causal loci  locus l .As this expression makes clear, the estimate from a family-based GWAS will differ systematically from the randomization or allele-flipping effect of the PGS if parents heterozygous at any locus in the PGS are non-randomly distributed across environments Without information about parental genotypes, a population-based study of the effect of the PGS effect is based on the regressionIn addition to our assumption that there is no genetic confounding in the sample—that is no long distance LD between PGS loci and other causal loci—we also make a further ‘best case’ assumption that there is no environmental confounding, i.e., no covariance between environmental effects Even in the absence of genetic or environmental confounding, the estimated effect of the PGS is a mixture of between- and within-family effects. Thus, while the family-based estimate In the presence of G×E, the family-based estimate of the causal effect of the PGS, families .At any given locus, there exists a set of individuals, i.e., the offspring of heterozygotes, for whom a family-based GWAS provides an unbiased estimate of an average causal effect, i.e., a LATE cf. . HoweverThese conclusions extend to the case where the PGS loci are not causal themselves, but instead tag nearby causal loci. In this case, the within-family estimate of the PGS effect will reflect the level of LD in heterozygotes for each marker , the property of a single G×E multiplier After computing the within-family slope of the PGS, PGS loci . In lighThe estimate mponents .The population variance of the PGS can be decomposed into contributions from within and between families:This approach is internally coherent, with the fraction properly interpretable as the contribution to phenotypic variance arising from Mendelian segregation within families at the PGS loci. This interpretation is valid because families are weighted the same in the calculation of These results establish a solid basis for causal statements about the proportion of phenotypic variance explained by within-family PGS variance. In the absence of assortative mating or other sources of long-range signed linkage disequilibrium, the within-family variance of the PGS is expected to be half the sample-wide variance A caveat is that we have not considered the multiple covariates often included in family studies beyond parental genotypes, which will further complicate the interpretation of causal effects . Clearly3While the random inheritance of parental alleles by children offers a natural experiment , the genaverage treatment effects reflects the fact that heterogeneity in causal effects is ubiquitous. Genetics is no exception, with many well studied sources of heterogeneity, including G×G and G×E interactions, and for marker loci, varying patterns of LD. For the specific issues with family studies raised here to be a problem, however, requires two conditions to be met: (i) genetic effects must be heterogeneous across environments and (ii) genotypes must be non-randomly distributed across the relevant environments. Given extensive evidence for heterogeneity in the effects of alleles due to G×E (and G×G) in settings where experimental manipulations are possible, the first condition seems highly plausible. In turn, the evidence of environmental and genetic confounding in human GWAS studies for an increasing number of traits shows that SNP genotypes are non-randomly distributed across environmental and genetic backgrounds. Thus, it seems important to consider what happens to estimates of family-based GWASs in this setting.That so many fields of study focus on reliably estimating In doing so, we have focused primarily on G×E as the source of heterogeneity in genetic effects. As noted above, however, the effect of an allele could also systematically differ across families In addition to interactions with the environment or other loci, differences between homozygotes and heterozygotes in the degree of LD between marker loci and the causal loci they tag can also give rise to variation in effect sizes at the marker loci. As we have shown , these dTo provide an intepretation of the effect size being estimated at a tagging—rather than causal—locus, we proposed a thought experiment in which a haplotype of a given length is experimentally swapped. Under the assumption of tight linkage between the causal and marker locus and no LD between causal loci, the effect estimated in family studies corresponds to the average effect of this manipulation in the offspring of heterozygotes at the marker locus. In the presence of LD between causal loci, the family-based effect-size estimate at the marker locus absorbs the effects of any causal loci on its chromosome that are in LD with it, complicating even the single-locus interpretation . More geFurther complications will arise if there is selection bias on the participants in the population-based or family-based GWAS , as the some signal of a genetic causal effect. While this use is valid, one might argue that, given that it is well established that almost any trait is partially heritable and that genetic correlations are widespread, it should not be surprising that there are non-zero genetic effects on a trait (within families (To date, one use of family studies has been to provide rigorous evidence that the PGS contains  a trait . The focfamilies . Since,


Previous studies have found that migraine patients are associated with white matter lesions (WMLs), but the causal relationship between the two remains unclear. We intend to explore the bidirectional causal relationship between migraine and WMLs using a two-sample mendelian randomization (MR) method. We employed summary-level data from a recent large-scale genome-wide association study (GWAS) that characterized three white matter (WM) phenotypes: white matter hyperintensities , fractional anisotropy , and mean diffusivity , as well as migraine . The inverse variance-weighted (IVW) method was used as the main approach for analyzing causality. Weighted median analysis, simple median analysis, and MR-Egger regression served as complementary methods. The bidirectional MR study affords no support for causality between WMLs and migraine. In all MR methods, there was no obvious causal evidence between them. In our bidirectional MR study, we didn't reach this conclusion that WMLs can cause migraine, migraine wouldn’t increase the risk of WMLs, either. Migraine is a chronic disease manifested as recurrent moderate to severe headaches3, migraine attack is often accompanied by nausea, vomiting, photophobia and fear of sound, and its symptoms generally last for 4–72 h4, which deteriorated patients’ quality of life. The routine examination of migraine patients often has no positive findings, but with the popularization of magnetic resonance imaging (MRI), more and more migraineurs have found WMLs, These WMHs can be seen on T2 weighted or fluid attenuated inversion recovery (FLAIR) sequences and are usually distributed in the deep or periventricular white matter, showing multiple, small, punctate lesions. Adult migraineurs have two to four times the risk of concurrent white matter lesions compared to those without migraine5. In some cases, WMLs can be diagnosed a potential disease, and migraine may be just a symptom6. Although WMLs are often found in patients with migraine, population-based studies show that migraine may be a potential risk factor for WMLs8, but not all migraineurs present with WMLs on MRI, the potential mechanisms remain unclear9. Despite the prevalence of migraine-associated white matter lesions, their pathophysiology remains poorly understood10, The relationship between the two has always troubled us and is highly worthy of further research. The evidence is primarily based on observational research13, but the direct clues to the causal relationship between them are still uncertain. There is a need to employ a suitable method to explore whether migraine plays a key role in white matter lesions, and vice versa.Migraine is one of the most common complaints in the outpatient department. The World Health Organization ranks migraine as the third most common and second most disabling neurological disorder in the world14. It relies on the natural, random assignment of genetic variants during meiosis, producing a random distribution of genetic variants in the population. Individuals inherit genetic variants affecting or not affecting risk factors at birth and are then recorded as having the disease or not, and since these genetic variants are usually not associated with confounding factors, differences in outcomes between carried and uncarried variants can be attributed to differences in risk factors15. The principle of MR is based on Mendelian genetic law and instrumental variables (IVs) estimation method. The mendelian randomization method uses genetic variation as IVs to derive the causal relationship between outcomes and exposures, which can effectively avoid the confounding bias of traditional epidemiological studies16. Thus, we used a two-sample MR design to assess whether migraine increases white matter lesions and to explore whether WMLs increase the risk of migraine.MR is an approach of using genetic variation to estimate the causal association between exposure and outcome17. They are all of European ancestry, recruited at the age of 40–69 years and the time span is from 2016 to October 201818. The image data covers approximately 20,000 individuals, the summary-level GWAS data was publicly available and accurate individual image information could be applied to the organization. This GWAS consisted of the next three MRI markers: white matter hyperintensities data acquired in 18,381 Europeans, fractional anisotropy data acquired in 17,673 Europeans and mean diffusivity data obtained from 17,467 Europeans. WMH traits were log-transformed and normalized to brain volume, the reduced white matter integrity may not be observed by conventional MRI, but can be imaged with alternative imaging techniques, such as diffusion tensor imaging (DTI)19. FA mainly responds to the integrity of fiber bundles, and MD mainly represents damage to axons and myelin sheaths. A decrease in FA values and an increase in MD values indicate that the microstructure of white matter is disrupted20. To obtain a single measurement of FA and MD for the entire white matter from DTI images, FactoMineR was used to perform principal component analysis (PCA) of FA and MD measurements for different brain bundles analyzed17. Among these data, people with central nervous system (CNS) disorders that specifically cause white matter lesions were excluded from the downstream association analyses, such as stoke, Parkinson’s disease, multiple sclerosis, dementia, or any other degenerative disease of CNS were excluded. We can obtain summary-level GWAS data for white matter MRI characteristics from the Cerebrovascular Disease Knowledge Portal .Summary-level GWAS data of MRI characteristics of white matter were acquired from the UK Biobank (UKB)21. This is the largest GWAS data of migraine so far. Two methods were used to identify migraine cases in the entire cohort, including self-report and compliance with ICHD-II criteria. Self-reported migraine was categorized based on responses to the initial 1998 questionnaire, “Has a doctor ever said that you suffer or have suffered from ……” (Yes/No)22.We acquired GWAS summary-level data for migraine from the International Headache Genetics Consortium (IHGC), which included 48,975 cases and 540,381 controls of European ancestry, except for 23andMep < 5 × 10−8) were selected from the exposed GWAS, and to avoid bias caused by relevant instrumental variables, we removed SNPs with linkage disequilibrium based on the r2 < 0.001 linkage disequilibrium threshold within 1000 kb as instrumental variables for the final MR study. We used the F statistic of each SNP (F = beta2/se2) to assess the power for SNPs, and SNPs with lower statistical power were removed23. SNPs that were not available for outcome data were replaced by proxies (r2 > 0.9), we can get the proxies by searching the website SNIPA (https://snipa.helmholtzmuenchen.de/snipa3/), each trait that we selected of IVs was shown in supplemental Tables Single nucleotide polymorphisms (SNPs) that reached genome-wide significant levels p < 5 × 0−8 were 16  to assess horizontal pleiotropic outliers26. The Cochrane’s Q value was used to evaluate the heterogeneity. All of our analyses were conducted by the ‘‘Two sample MR’’ and ‘‘MR-PRESSO’’ packages in version R 4.0.3. Results are expressed as odds ratios (ORs) per an approximate 1 standard deviation (SD) raise of exposure. P < 0.05 was considered as statistical significance. The MR should be carried out under three basic assumptions: 1) The genetic variation is strongly associated with exposure; (2) the genetic variation is independent of outcome except by means of exposure; and (3) genetic variation is independent of any potential confounding factors The geneP = 0.21), MD , or WMH , , migraine on MD , or migraine versus WMH  , which may be an intermediate mediator that leads to white matter lesions in the brain34. Further studies on the pathophysiology of migraine and white matter lesions are warranted.In this study, we assessed the causal association between migraine and white matter lesions using MR method. We find no causal relationship between them, which can be explained in several ways. First, white matter lesions and migraine may be comorbid, and both share common risk factors, such as smoking. Second, White matter changes appeared to be benign and nonprogressive in individuals with migraine. A longitudinal MRI study over 8–12 years showed no association between migraine and WMH progression over timeOne of the advantages of our research is the use of a Mendelian randomization design, by using randomly assigned genetic variations as IVs, our research has greatly reduced confusion or reversed causal bias, so it produced convincing results. Furthermore, because the analysis was limited to people of European ancestry, biases brought by demographic structure are impossible to influence our results. Other advantages include the consistency of causal estimates using multiple analytical methods, and the robust estimation effect of each IVs . In addition, The GWAS data we use has a large sample size, the study was reported according to the statement of STROBE-MR.Our study has several limitations. First, self-reported migraine is not as reliable as the objective scale or physician's recent diagnosis and may have some bias, including patients with a larger sample size who meet the diagnostic criteria for migraine can reduce bias. Second, because the subject in this study was limited to Europe, this conclusion may not apply to non-European people. Studies with larger sample sizes of multi-ethnic GWAS data are needed for further extrapolation to humans. Finally, the three key assumptions of MR study are relatively strict and difficult to meet in practice, both migraine and white matter lesions both have complex etiologies. It may not be possible to measure all exposure-related confounders and to completely eliminate biased estimates of causal inference.In conclusion, this study does not find a causal relationship between migraine and white matter lesions. Further efforts are still needed to investigate the pathophysiology between migraine and white matter lesions.Supplementary Information 1.Supplementary Information 2.


Ovum pick up and in vitro embryo production (OPU-IVEP) is a critical technique in the dairy industry that is attracting a lot of attention, especially in China’s dairy industry. The aim of the technique is to produce as many embryos with excellent genetic material as possible. However, there is a large variation in the ability of donor cows to produce embryos, and this ability is heritable. In this case, genetic selection and animal breeding will be beneficial to improving efficiency. Considering the large differences among different populations, the genetic parameters regarding the Chinese population are currently essential for genetic selection in the future. In this study, we analyzed the phenotypic and genetic characteristics of in vitro embryo production traits in Chinese Holstein cattle. The results of this study may help to understand the breeding potential of in vitro embryo production traits.Ovum pick up and in vitro embryo production (OPU-IVEP) is an essential technique in the dairy industry. The production efficiency of OPU-IVEP is significantly influenced by various factors, and phenotypic and genetic characteristics are highly variable in different populations. The objectives of this study were (1) to reveal the phenotypic characteristics, including population distribution, and impacts of donor age and month on in vitro embryo production and (2) to estimate genetic parameters for five in vitro embryo production traits in Chinese Holstein cattle. A total of 7311 OPU-IVEP records of 867 Holstein heifers from August 2021 to March 2023 were collected in this study. Five in vitro embryo production traits were defined, including the number of cumulus–oocyte complexes (NCOC), the number of cleaved embryos (NCLV), the number of grade I embryos (NGE), and the proportion of NCLV to NCOC (PCLV) and NGE to NCOC (PGE). A univariate repeatability animal model was employed to estimate heritability and repeatability, and a bivariate repeatability animal model was employed to estimate the genetic correlations among five in vitro embryo production traits. It was found that the in vitro embryo production traits were significantly influenced by season, as the NGE and PGE were significantly decreased from June to August. In addition, the production efficiency of OPU-IVEP was also influenced by donor age. On the observed scale, the estimates of heritability were 0.33 for NCOC, 0.24 for NCLV, 0.16 for NGE, 0.06 for PCLV, and 0.10 for PGE, respectively. On the log-transformed scale, the estimates of heritability of NCOC, NCLV, and NGE were 0.34, 0.18, and 0.13. The genetic correlations among NCOC, NCLV, and NGE ranged from 0.61 (NCLV and NGE) to 0.95 (NCOC and NCLV), considering both scales. However, there were low genetic correlations between NCOC and proportion traits (PCLV and PGE) on both the observed scale and the log-transformed scale. In the end, the variation in Chinese Holstein cattle was found to be considerable. The EBV value and average NCOC, NGE, and PGE for the top 10% donors presented extreme differences to those for the bottom 10% donors for NCOC (24.02 versus 2.60), NGE (3.42 versus 0.36), and PGE (30.54% versus 3.46%). Overall, the results of this study reveal that in vitro embryo production traits are heritable with low to high heritability, and the count traits  and proportion traits (PCLV and PGE) reflect different aspects of in vitro embryo production and should be incorporated into genetic selection for improving the embryo production efficiency of dairy cattle. However, there was high variability in the embryo production efficiency of cows, and large differences were found for the number of in vitro embryos produced by each donor cow. To increase efficiency and improve the benefit of in vitro embryo production, identifying and selecting proper donors prior to the ovum pick up (OPU) operation is the next step [In recent decades, assisted reproductive technologies have been critical tools in domestic industry . In vitrThe technique firstly relies on the ultrasound-guided transvaginal ovum pick up operation to collect cumulus–oocyte complexes (COC). The number of COC (NCOC) are similar to the number of viable oocytes, which is also a commonly used trait. The NCOC is the first trait used to evaluate donor ability . After 2The effects of some environmental and physiological factors on in vitro embryo production in Holstein cattle and water buffalo have been reported in previous studies. For example, Zeron et al.  found thExcept for the non-genetic factors previously mentioned, differences in genetic background are further reasons for the variation in IVEP ,15,16. IThe development of IVEP technology has attracted increasing interest, and investigating the genetic architecture of IVEP traits is important for improving embryo production efficiency. IVEP technology has also developed rapidly in the Chinese dairy industry, and IVEP traits are useful for donor selection and expected to be introduced into breeding projects in the future. However, to our knowledge, there is a lack of studies focusing on the genetic parameters of IVEP traits in Chinese dairy cattle. The objectives of the current study were to reveal the population characteristics of IVEP traits in Chinese Holstein cattle, including phenotypic and genetic parameters. The present study contributes to the genetic basis of cow reproductive performance and provides useful information for achieving breeding objectives in cattle.In this study, a total of 7311 OPU-IVEP records were collected. These records were from 867 Holstein heifers, daughters of 70 sires and 387 dams, and OPU operations were performed from August 2021 to March 2023 in the Austasia herd . The age of the donors ranged from 8 to 37 months, and the majority of donors were between 9 and 19 months of age (98.9%), with good milk production performance. The average frequency of OPU operation per donor cow was 8.4 times.The pedigree was provided by the Dairy Association of China . Animals with OPU records were traced for as many generations as possible. Finally, the pedigree file used in this study included 2919 females and 535 bulls born between 1945 and 2022.Oocytes were collected via ultrasound-guided transvaginal ovarian puncture, which was carried out as described by Seneda . The ovu2 in air. After in vitro maturation (IVM), the oocytes were washed in fertilization medium and then incubated for 8 h in a Petri dish containing 100 µL microdrops of Tyrode’s albumin lactate pyruvate (TALP) medium supplemented with 10 μg/mL heparin and 160 µL penicillamine–hypotaurine–epinephrine. In vitro culturing (IVC) was performed for 8–10 h at 38.5 °C in an atmosphere of 5% CO2 in air. The culture medium was renewed for each microdrop after 2 days, and at 7 days after in vitro fertilization, the embryos were evaluated before freezing or transferred using the standard developed by Lonergan et al. [The oocytes used for IVEP were incubated for 24 h at 38.5 °C under an atmosphere of 5% COn et al. . The serFive IVEP traits were analyzed in this study, including NCOC, NCLV, NGE, PCLV, and PGE. The NCOC, NCLV, and NGE were defined as the number of all oocytes flushed in OPU operations, embryos cleaved after IVF, and grade I embryos evaluated at the end of IVC, respectively. The PCLV and PGE were defined as the proportion of NCLV to NCOC (PCLV) and NGE to NCOC (PGE), respectively.To achieve normal distribution, the NCOC, NCLV, and NGE were transformed as follows:Variance and co-variance components for five IVEP traits were estimated using the average information restricted maximum likelihood algorithm implemented in the DMU software . A univaI is the identity matrix; and It was assumed that To estimate the genetic correlations between NCOC and other traits, a bivariate repeatability animal model was constructed, and this model is as follows:To estimate the genetic correlations among other traits, another bivariate repeatability animal model was constructed, and this model is as follows:The fixed and random effects included in this model were the same as in model 2. It was assumed that 2), repeatability (r2), and genetic correlations  and low-frequency group (OPU frequency of less than 9 times) according to the frequency of OPU-IVEP. Donors in the high-frequency group usually had consistent records at every month and age, whereas donors in the low-frequency group were unable to achieve the same level of consistency. Considering the possibility of different trends appearing during aging, it was necessary to analyze the effect of age in the two groups separately. Interestingly, the current study demonstrated that artificial selection had positive effects on the outcome of OPU-IVEP. Furthermore, in both groups, the elder donors always had higher PGE than the younger donors, which is similar to previous findings in buffalo  and HolsThis study firstly estimated the genetic parameters of IVEP traits in Chinese Holstein cattle. In general, low to moderate heritability 0.007–0.323) and repeatability (0.015–0.627) were found for the count IVEP traits  in Holstein, dairy Gir, and Guzerá populations 3 and rep,17,18,19Moderate to high genetic correlations were observed among NCOC, NCLV, and NGE, similar to several previous reports ,17,19. PHuge variance within the population was found in this study. The best donors produced, on average, 24.02 oocytes and 3.42 grade I embryos, and the worst donors only produced 2.60 oocytes and 0.36 grade I embryos, and this trend was shown for the percentage of NGE to NCOC. A similar result was presented in another study on Canadian Holstein cattle, wherein it was found that the number of embryos (NE) and viable embryos (VE) via superovulation also had large variance. The NE and VE values for the best 10% donors were 15.61 and 13.40, respectively, while the numbers were less than 5 for the worst 10% donors  also repThis is the first study of its kind to investigate the population characteristics of in vitro embryo production traits in Chinese Holstein cattle. In this study, in vitro embryo production (IVEP) traits were found to be significantly influenced by season and donor age. Donors producing in vitro embryos should be protected from heat stress in order to attain ideal outcomes. Elder donors are more likely to yield high efficiency in embryo production and produce good embryos. In vitro embryo production traits have low to high heritability, and genetic selection would improve the economic merit of in vitro embryo production. The count IVEP traits  and percentage IVEP traits  reflect different aspects of in vitro embryo production, including donor performance in oocyte recruitment, oocyte quality, and so on. When embryo production needs to be carried out on a large scale, these traits should be considered in genetic selection for dairy cattle.


Cucurbita maxima), maize (Zea mays), and wheat (Triticum aestivum L.), were reanalyzed to illustrate the use of EHPGS.Hybrid breeding employs heterosis, which could potentially improve the yield and quality of a crop. Genomic selection (GS) is a promising approach for the selection of quantitative traits in plant breeding. The main objectives of this study are to (i) propose a GS-based approach to identify potential parental lines and superior hybrid combinations from a breeding population, which is composed of hybrids produced by a half diallel mating design; (ii) develop a software package for users to carry out the proposed approach. An R package, designated EHPGS, was generated to facilitate the employment of the genomic best linear unbiased model considering additive plus dominance marker effects for the hybrid performance evaluation. The R package contains a Bayesian statistical algorithm for calculating genomic estimated breeding value (GEBVs), GEBV-based specific combining ability, general combining ability, mid-parent heterosis, and better-parent heterosis. Three datasets that have been published in literature, including pumpkin ( Several field, vegetable, and flower crops use hybrids, including maize, sorghum, and sunflower. Interestingly, hybrid rice has been adopted and hybrid wheat research is drawing new attention2. Therefore, it is important and challenging to develop a highly efficient approach for identifying potential parental lines and superior hybrid combinations from many possible candidates. To create such an approach, we constructed a prediction model to screen out the desired individuals based on genomic selection (GS)3. To facilitate practical applications, we also generated a software package to implement our proposed GS-based approach.Hybrid plant breeding can potentially be used as a method that employs heterosis to boost yield stability, allow the combination of dominant major genes, and offer a built-in plant variety protection system4. However, the number of crossing combinations can be prohibitively high for extensive testing in a field experiment.Diallel mating designs have been traditionally used to evaluate the combining ability of parental lines in hybrids and to predict hybrid performance on quantitative traits of interest. To analyze diallel crosses, the total genetic variability is often separated into the general combining ability (GCA) for parental lines, and the specific combining ability (SCA) for hybrid combinations. The GCA is a measure of additive gene activity that relates to the average performance of a particular inbred line in hybrid combinations. The SCA is a measure of combining ability that links to the non-additive effects, including dominance and epistatic effects. In addition, mid-parent heterosis (MPH) is defined as the difference between a hybrid’s performance and the average performance of its parental lines, while better-parent heterosis (BPH) is defined as hybrid performance superior to the higher or better parental line6. The conceptual basis of GS is the utilization of a training population with known phenotype and genotype data to build a prediction model that uses individuals with known genotype data only to predict genomic estimated breeding values (GEBVs)7. This GS-based approach has been applied to predict hybrid performance for several crops, such as barley8, maize10, rice12, wheat14, and pumpkin15. More recently, hybrid rice performance based on parental characteristics was evaluated using artificial neural networks, adaptive neuro-fuzzy inference system, and support vector machine16.Due to the availability of high-density single nucleotide polymorphism (SNP) markers across an entire genome, GS becomes a promising approach to reduce cost and accelerate breeding cycles for plant breedingCucurbita maxima), maize (Zea mays), and wheat (Triticum aestivum L.), were reanalyzed to illustrate the application of our proposed approach.In this study, we obtained the required estimates for hybrid performance evaluation based on a GBLUP model, which took both additive and dominance marker effects into account. The GBLUP model was built based on a training population with known phenotype and genotype data. Here, we proposed a Bayesian statistical algorithm for the parameter estimation. Three datasets that have been published in literature, including pumpkin ; The GBLUP model considering additive plus dominance effects can be described as follows:17:Let 18, In this study, the breeding population was composed of all possible hybrid combinations in a half diallel mating design. Let The genomic estimated genotypic values for the individuals in the breeding population were then predicted by:Pi ⨂ Pj. Moreover, let Pi ⨂ Pj of additive and dominance effects, respectively. From the article19,Let From Eq. , the BLUi, and LetFrom Eq. , the BLU15, the GEBV-based MPH and BPH for Pi ⨂ Pj can be estimated by:From the article20, was used to estimate the required parameters. The algorithm can be described as follows.Step 1: Set initial values for the parameters in the model.For a given training population with known phenotype and genotype data, a Bayesian Gibbs sampling (BGS) algorithm, modified from an algorithm presented in the articleThe default values are given by:Step 2: Rewrite Eq. . A referenced manual and a tutorial including a demonstration example are provided in the package.An R package called as EHPGS generated for executing the proposed approach is available from GitHub  method was performed for estimating the variance components by using another R package sommer24. A comparison of the results obtained from the two-stage approach and ours was discussed in the next section.The pumpkin dataset was analyzed using a two-stage approach in the articleThe Bayesian reproducing kernel Hilbert space (RKHS) method in BGLR is another Bayesian algorithm that has been commonly used to perform GEBV prediction for the GBLUP model in Eq. . To compTo further examine whether the proposed BGS algorithm can more accurately estimate known variance components compared to established methods, such as the REML method in sommer, and the Bayesian RKHS method in BGLR, a simulation study was conducted as follows. The estimated values for the model parameters obtained from the training data  (kg) was analyzed for evaluation of hybrid performance15. The phenotype data were historical data collected from 1988 to 2016. All the trials were conducted at a single location experiment in southern area of Taiwan. Every hybrid had six to ten observations at each time point, and the average of them was used as the phenotypic observation for the hybrid of the year. Because the phenotypic values of every hybrid were observed for more than one year, the different year effects were therefore removed based on the assumption that they were random effects following a normal distribution.A pumpkin dataset which contained 119 intra-crossing hybrid combinations of C. maxima with 142 inbred lines, C. pepo with 60 inbred lines and C. moschata with 118 inbred lines. After SNP calling, 76,815 SNPs were extracted from the parental lines, and only 4,521 SNPs remaining for C. maxima after the filtering by missing rate ≥ 0.05, minor allele frequency (MAF) < 0.05, and a series of operations for determining linkage disequilibrium (LD) blocks. The 142 inbred lines produced C. maxima hybrids were used in the current study to build a GBLUP model for evaluating the performance of the 10,011 hybrid combinations.The germplasm collection of the pumpkin set consisted of 320 parental lines, which were classified into three clusters: 2. The 24 diverse parents were classified into two groups according to the germplasm origin and a principal component analysis. The two groups were (i) the temperate and mixed (TM) group, consisting of 11 inbred lines ; and (ii) the tropical and sub-tropical (TS) group consisting of the remaining 13 inbred lines . There were A maize dataset was analyzed to study the optimal designs for GS in hybrid crops, which consisted of 276 hybrids derived from 24 parental lines in a half diallel mating design25 at www.panzea.org, which consisted of 10,296,310 SNP markers. The SNP markers were first filtered by missing rate ≥ 0.05 and MAF ≤ 0.1, resulting in 134,726 SNPs remaining. Missing genotypes were then imputed with the homozygote of the major allele. To screen out reliable SNPs for building a GBLUP model, the retained SNPs were further filtered by LD blocks. The LD parameter 26 with a sliding window = 10. A smooth function between loess.smooth with a second-degree locally weighted polynomial regression. The LD decay of ten chromosomes is displayed in Fig. Genotype data for the 24 inbred lines were extracted from the Maize HapMap V227. A set of 1604 wheat hybrids produced from crosses among the 15 male lines and 120 female lines were then evaluated for grain yield (YLD) (Mg/ha) in 11 environments. Grain yield data for all A genome-based establishment of a high-yielding heterotic pattern for hybrid wheat breeding was investigated, and the study was based on 135 advanced elite winter wheat lines2. An optimal training population with 600 individuals, determined by the r-score criterion28, was used in the current study to build the GBLUP model for the performance evaluation on the 2556 hybrid combinations.To study optimal designs for GS, 2556 hybrid combinations, produced by the half diallel mating design on 72 lines selected from the original 135 elite wheat lines, were analyzed in the articleBy the half diallel mating design, the 142 parental lines produced . From the table, the estimates of the A-VC, D-VC, and genomic heritability are given by C. maxima.The estimates for the variance components and genomic heritability are shown in Table 3mentclass2pt{minimThere were For illustration purposes, we reported the top 15 superior hybrids with the largest GEBVs, together with their SCAs, MPHs, and BPHs in Table The estimates for the variance components and genomic heritability are also displayed in Table By the half diallel mating design, the 72 parental lines produced 29. However, only a few of the top hybrids had a positive but too small BPH value in the case of wheat, indicating that the heterosis existing in this dataset may not be adequate for practical utility. A wheat hybrid has a small positive or negative BPH value because one of its parents is inferior30.In summary, the BPH values were consistently positive for the top hybrids in both the pumpkin and maize datasets, implying that there exists a strong and useful heterosis in the two crops. The valuable result can also be found in literatureScatter plots of all available phenotypic values  and their GEBVs in each dataset are displayed in Figs. 15, together with those identified by our proposed approach are displayed in Table The top 25 superior hybrids identified by the two-stage approachOverall, the results of the identified top parental lines and hybrid combinations between the Bayesian RKHS method in BGLR and our BGS algorithm were highly consistent with each other. Pearson’s correlations between GEBVs and phenotypic values for the datasets are displayed in Table Side-by-side box-plots for the estimates of the variance components over the 3000 repetitions in the simulation study are displayed in Fig. The mean and the standard deviation over the 50 resulting values in the cross-validation analysis are displayed in Table In this study, a software package called EHPGS was generated for identifying potential parental lines and superior hybrid combinations from a breeding population, which is composed of all possible hybrids produced by a half diallel mating design. A training population with known phenotype and genotype data is required to build the GBLUP model, and then a set of parental lines with known genotype data is also required to perform GEBV prediction for its derived hybrid combinations. Any dataset with such training population and parental line set can fit the package. For an input dataset, EHPGS generates GEBVs, SCAs, GCAs, MPHs, and BPHs for all potential candidates to achieve the task.Supplementary Information.