Numerous human conditions are associated with the microbiome, yet studies are inconsistent as to the magnitude of the associations and the bacteria involved, likely reflecting insufficiently employed sample sizes. Here, we collected diverse phenotypes and gut microbiota from 34,057 individuals from Israel and the U.S.. Analyzing these data using a much-expanded microbial genomes set, we derive an atlas of robust and numerous unreported associations between bacteria and physiological human traits, which we show to replicate in cohorts from both continents. Using machine learning models trained on microbiome data, we show prediction accuracy of human traits across two continents. Subsampling our cohort to smaller cohort sizes yielded highly variable models and thus sensitivity to the selected cohort, underscoring the utility of large cohorts and possibly explaining the source of discrepancies across studies. Finally, many of our prediction models saturate at these numbers of individuals, suggesting that similar analyses on larger cohorts may not further improve these predictions. The human gut microbiota is linked to metabolic disorders such as diabetes and obesity but these links are based on relatively small cohorts of several dozens or hundreds of individuals –8. Althoreference-frames , with best practice being the ability to replicate results on independent datasets.Apart from cohort size, there are other challenges in finding robust signals from the microbiome. One such challenge stems from the large number of genes that are shared between different bacteria through mechanisms such as horizontal gene transfer , 27. Suc2 = 0.31). Notably, these associations replicate across continents, and models derived from the Israeli cohort generalize well to the U.S. cohort, so they are not specific to a certain environment.To address the above issues and with the aim of deriving robust microbiome associations, we used metagenomic sequencing to profile the gut microbiome of 34,057 individuals from both Israel and the U.S., for which we also obtained a rich set of self-reported phenotypes. We devised a novel algorithm for assessing bacterial RAs based on unique genetic elements, and applied it to the recent and much expanded SGB dataset of Pasolli et al. . Using tBy subsampling our cohort to typical cohort sizes used in other studies, we show that associations and predictions derived from smaller cohorts are highly variable and thus sensitive to the selected cohort, underscoring the need for larger cohorts in the microbiome field.S1 Table in S2 File). When comparing reported phenotypes, phenotypes show varying correlation with one another , with some phenotypes being highly correlated with one another, for example HbA1C% and blood glucose levels (Spearman correlation R = 0.65).We obtained gut metagenomic profiles from 30,083 and 3,974 individuals from Israel and the U.S., respectively, who submitted their sample to a consumer microbiome company and signed an appropriate consent form . Participants also answered questionnaires and provided self-reported phenotypic data and blood tests (S2 File). These test sets were only used once to evaluate the performance of the models developed on the discovery cohort.We randomly selected 90% of the samples from the Israeli cohort  to be our discovery cohort on which we trained predictive models using cross-validation and set aside as independent test sets the remaining 10% of the Israeli cohort  and the entire U.S. cohort  (Methods). We developed a method, Unique Relative Abundances (URA), for estimating the RA of each SGB in every sample (Methods), which can be applied to any set of species, and provides better predictive power than Metaphlan . Our method is based on examining only reads that map uniquely to a single SGB, since when using unique mappings, we expect uniform coverage across SGB genome bins having the same number of unique mappable regions. This property allows robust estimation of RAs, by calculating the coverage across unique-part-genomic bins for every SGB (Methods). The mean RAs of the different species are the same in the two Israeli cohorts but are somewhat different than in the US cohort (S2 File).To compute bacterial RA, we used a diverse set of 3,127 human microbiome species representatives from the greatly expanded species-level genome bins (SGBs) classification of Pasolli et al.  . When comparing the top decile and the bottom decile of alpha diversity, we found that HbA1C%, BMI, fasting glucose and fasting triglycerides are significantly higher in the bottom decile while age and HDL cholesterol are significantly lower in the bottom decile , including a trend across deciles (S3-S8 Tables in S2 File). Similarly, examining alpha diversity as a function of these traits, we found significant correlations between alpha diversity and each of these traits . Notably, these associations were consistent in both the Israeli and U.S. cohorts , even though the Israeli cohort has significantly higher alpha diversity values . The higher diversity of the Israeli cohort persisted even when subsampling the Israeli cohort to match the U.S. cohort on age, gender and BMI .We first examined the association of microbiome diversity and human phenotypes. To this end, we computed alpha diversity using the species level Shannon index and ranked individuals by deciles of alpha diversity (microbiome-association-index (b2) . Our previous estimates were based on a cohort of 715 individuals and therefore had wide 95% confidence intervals. We revisited these estimates for our two new and larger cohorts. We estimated explained-variance based on alpha-diversity alone , and based on the full species RAs . We find that alpha diversity shows significant correlations, yet overall small explained variance, to different traits. We believe that literature inconsistencies between phenotype-alpha-diversity associations are a result of lack of power detecting these associations. Notably, our new estimates agreed well with the findings in our previous study (S9 Table in S2 File), but the current much larger cohort of 30,083 Israeli individuals gives substantially narrower 95% confidence intervals (S10 Table in S2 File). We found that microbiome composition strongly associates with self-reported diabetes , age (b2 = 28%), HbA1C% , fasting blood glucose , BMI (b2 = 10%), fasting triglyceride (b2 = 9%), HDL cholesterol (b2 = 5%) and current smoking status (b2 = 17%). In contrast, the blood levels of thyroid-stimulating hormone (TSH), albumin and clotting  were not significantly associated with the microbiome in our cohort. Notably, b2 estimates from our U.S. cohort of 3,974 individuals were consistent with those derived from the Israeli cohort .We previously employed linear mixed models to estimate the fraction of phenotypic variance that can be inferred from microbiome composition, termed Methods), with only species RAs as input features to the model.We next asked whether various traits can be accurately predicted based only on microbiome composition. For this we used gradient boosted decision trees (GBDT)  such as age , gender (AUC = 0.78), HbA1C% (R2 = 0.25) and BMI (R2 = 0.15). Notably, skin microbiome was recently shown to best predict age , normalize = True. The rest of the parameters were the default. The input to the Ridge linear regression was log transformed SGB abundance.For binary phenotypes SGD classifier from the scikit-learn package was used, with default parameters .When using microbiome features for the prediction, only the top 1345 occurring SGBs were used, i.e., the SGBs that were found in at least 5% of the samples, to avoid overfitting on rare SGBs.For cohort size n  we repeated the following process 10 times: we randomly selected a subset of n samples, built a predictive model for the phenotype from the subset of samples, and tested its predictions on the independent test sets (test1-IL and test2-US test-sets described in main text), and calculated the accuracy (R square) of the prediction. By repeating the procedure 10 times we received the mean and standard deviation of the prediction accuracy estimate, for each IL trained subset size.For cohort size n  we repeated the following process 10 times: we randomly selected a subset of n samples from each cohort, calculated the Spearman correlations of each species with the phenotype in each cohort, and correlate the Spearman correlations of the two cohorts, over all species . We plot the mean and standard deviation of this correlation, as calculated over the 10 repeats.Gene prediction, and annotations, of the representative genomes of the SGBs were performed in advance by Pasolli et al., including assigning Kegg Orthologies (KOs) to identified genes. Similar to PICRUSt2  method, In order to gain insights that were not captured by the single species’ abundance, we looked only at KOs which appeared at least 5 times in the representatives. We calculate a pseudo-abundance as the sum of the abundances of all species it appears in. These pseudo-abundances sum the abundances of more than one species and were not derived from a single bacterial species’ abundance, and thus gave new correlations to high level modules and pathways.S18–S23 Tables in S2 File), and then ranked, from the most significantly negatively correlated to the most significantly positively correlated with the phenotype. This process was performed for each cohort separately, and shows significant concordance between the IL and the US cohorts .The vector of the gene’s pseudo-abundance  was correlated versus different phenotypes , with connection with the phenotype. We present all KOs and modules which were significantly correlated with at least 3 of the phenotypes tested .After ranking the KOs against a phenotype, ranksum analysis was used in order to search for pathways and modules which are significantly enriched or significantly diminished Click here for additional data file.S2 Fig2) of GBDT prediction of different phenotypes are obtained in a 10-fold cross validation scheme on the training set. (b) Same as (a), but shown is the area under the curve (AUC) for predicting binary phenotypes. (c) Comparison of the predictive power of GBDT model (in blue) versus Ridge regression model (in green). Coefficient of determination (R2) of prediction of different phenotypes based only on species level gut microbiome abundance. Results are obtained in a 10-fold cross validation scheme on the training set. (d) Same as (c), but shown is the area under the curve (AUC) for predicting binary phenotypes.(a) Comparison of the predictive power of three different sets of species level abundance estimations of gut microbiome. In blue, predictions are performed using the baseline Metaphlan species abundances, in green predictions are performed on abundances calculated using the URA algorithm on the sub-set of SGBs that were known prior to the work of Pasolli et al. , and in (TIFF)Click here for additional data file.S3 Fig(a)-(n) Scatter plots of 10-fold cross-validation predicted values of quantitative phenotypes when training on the Israeli train cohort using GBDT. R2 of prediction is reported. Black line represents x = y. (o)-(r) ROC curve plots of 10-fold cross-validation predicted values of binary phenotypes when training on the Israeli train cohort using GBDT. AUC of prediction is reported. Black line represents x = y.(TIFF)Click here for additional data file.S4 Fig2 of prediction is reported. Black line represents x = y. (o)-(r) ROC curve plots of 10-fold cross-validation predicted values of binary phenotypes when training on the Israeli train cohort using Ridge regression. AUC of prediction is reported. Black line represents x = y.(a)-(n) Scatter plots of 10-fold cross-validation predicted values of quantitative phenotypes when training on the Israeli train cohort using Ridge regression. R(TIFF)Click here for additional data file.S5 Fig2) and standard deviation error bars of predictions of age (a) and BMI (b) obtained using a GBDT (purple) or Ridge regression (green) models trained on sub-samples of the cohort train IL, of different sizes, and tested of the whole test US cohort. For each cohort size k, 10 random sub-samples of k individuals were obtained and the mean and standard deviation of their predictions are shown.(a)—(b) Coefficient of determination (R(TIFF)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S1 Dataset(DOCX)Click here for additional data file.


Local genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions. However, accurate estimation of local genetic correlation remains challenging, due to linkage disequilibrium in local genomic regions and sample overlap across studies. We introduce SUPERGNOVA, a statistical framework to estimate local genetic correlations using summary statistics from genome-wide association studies. We demonstrate that SUPERGNOVA outperforms existing methods through simulations and analyses of 30 complex traits. In particular, we show that the positive yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically distinct genetic signatures with bidirectional local genetic correlations.The online version contains supplementary material available at 10.1186/s13059-021-02478-w. Genome-wide association study (GWAS) has achieved remarkable success in the past 15 years and has identified numerous single-nucleotide polymorphisms (SNPs) associated with complex human traits and diseases .With these advancements, multi-trait modeling has undergone rapid developments, leading to the emergence of numerous methods that study the shared genetic basis across multiple phenotypes –8. AmongLocal genetic correlation analysis is another important approach to tackling the underlying etiological mechanisms shared by multiple complex traits , 26. InsHere, we introduce a novel statistical framework named SUPERGNOVA for local genetic correlation estimation. Based on the GNOVA approach which was designed for partitioning genetic correlation by functional annotation , SUPERGNz scores z1 and z2), genetic covariance ρ between two traits can be estimated by minimizing the “distance” between the empirical covariance of z scores, i.e., Genetic covariance (correlation) is defined as the covariance (correlation) of genetic effects on two traits. It is commonly used as an informative metric to quantify the shared genetic basis between traits. Given the marginal association statistics from two GWASs  and the covariance of non-genetic effects  on the two traits among shared individuals. Derivation of the theoretical covariance and other statistical details are reported in the Additional file where z scores from each GWAS can be highly correlated due to the extensive LD in local regions. Hence, most methods developed for global genetic correlation cannot be directly applied to estimate local correlations. In addition, ubiquitous sample overlap across GWASs introduces additional correlations among association statistics from different studies, which further complicates the estimation of genetic correlation. SUPERGNOVA resolves these statistical challenges by decorrelating local z scores with eigenvectors of the local LD matrix  can be defined in a similar way by focusing only on SNPs in a pre-specified genomic region the “” sectionrix Fig. . In pracWe performed simulations to assess the performance of SUPERGNOVA for both global and local genetic correlation analyses. We compared SUPERGNOVA with multiple state-of-the-art methods in six different simulation settings and repeated each setting 100 times. We used real genotype data from the Wellcome Trust Case Control Consortium (WTCCC) to simulate quantitative traits. After quality control, 15,918 samples and 287,539 SNPs remained in the dataset. We equally divided 15,918 samples into two subsets which we denote as set 1 and set 2. To assess the robustness of our approach to sample overlap between GWASs, we generated another dataset by combining 3979 samples from set 1 and 3980 samples from set 2. We refer to it as set 3. This results in a 50% sample overlap between set 1 and set 3. Detailed simulation settings and quality control procedures are described in the “We compared the performance of LDSC, GNOVA, and SUPERGNOVA on global genetic covariance estimation. We set the heritability to be fixed at 0.5 and the genetic covariance to range from 0 to 0.25. The covariance of non-genetic effects was 0.2 for the overlapped samples. The effect sizes of SNPs were generated from a multivariate normal distribution. Both SUPERGNOVA and GNOVA showed superior statistical power compared to LDSC in all settings  were used as the “background SNPs” in the analysis. We set the covariance in the small local region to be from 0 to 0.005. Outside of this region on chromosome 2, covariance was fixed as 0. The total heritability was set to be 0.5 and was equally distributed among all SNPs on chromosome 2 . Both SUPERGNOVA and ρ-HESS assume the SNPs in different regions to be independent. However, in practice, there can be weak LD between nearby regions which could bias the estimates towards the average genetic covariance of adjacent regions. When there is no overlapping sample between two studies, SUPERGNOVA estimates showed lower bias, well-controlled type I error, and good statistical power. On the other hand, ρ-HESS consistently underestimated local genetic covariance and had lower statistical power  and further evaluated the robustness of SUPERGNOVA under a set of mis-specified models, including models with sparse genetic architecture and effects dependent on minor allele frequencies (MAF) and LD. We also assessed how the size of local genomic regions affects the performance of SUPERGNOVA and the type-I inflation of LDSC on local genetic covariance estimation. These additional simulations showed highly consistent results. We describe the settings and results of these simulations in the Additional file p < 0.05/435 = 1.1e−4; Additional file p < 0.05/1,006,072 = 5.0e−8; Fig. p < 1e−4 in both GWASs.We applied SUPERGNOVA to estimate local and global genetic correlations among 30 phenotypes , but local genetic covariance revealed diverse architecture of genetic sharing locally. We estimated the proportion of correlated regions for each pair of traits using ashr [p = 6.1e−115), the highest among all trait pairs. Seventy-nine percent of regions showed correlated effects between body mass index (BMI) and high-density lipoprotein (HDL) cholesterol , the highest among negatively correlated traits. These results suggest extensive and “omnigenic” genetic sharing between these traits, which is also reflected in the substantial shift in the distribution of local genetic covariances  is known to control the development of neurons and synapses and is vital to learning, memory, and vulnerability to stress [NCAM1 at this locus is involved in development and maintenance of the nervous system and is associated with SCZ and comorbid alcohol and drug dependence [Our analyses also implicated several genomic regions showing correlated genetic effects on more than two traits. The o stress –36. We ipendence –39. Thespendence .p = 1.2e−6), OCD (p = 3.0e−6), CP (p = 3.4e−6), DrnkWk (p = 2.0e−5), MDD (p = 2.7e−5), and AN (p = 4.2e−4), at the SPI1 locus . We replicated the local correlations with DrnkWk (p = 7.5e−2) and NSM (p = 1.4e−2) using an independent GWAS of AD family history  is not genetically correlated with neuropsychiatric traits except education and cognition . We iden OCD p = .0e−6, CPAD GWASs . A recenid cells . However OCD p = .0e−6, CP OCD p = .0e−6, CPp = 3.2e−8) and EA . Cognitive phenotypes in these GWASs have been previously described in detail [p = 2.9e−29 with CP; correlation = − 0.41 and p = 2.0e−59 with EA), but the genetic correlation between ASD and ADHD was positive .We further demonstrate the power of SUPERGNOVA through an in-depth case study of the shared genetics between ASD and cognitive ability  cutoff of 0.1  and ADHD (p = 4.4e−2) in GWAS  showed significant and negative correlations between CP and both neurodevelopmental disorders  is a key TF in the central nervous system and a master regulator of gene expression changes in BD and SCZ [POU3F2 locus on chromosome 6  showed significant positive correlations between ASD and CP   in the 102 risk genes identified in the latest exome sequencing study of ASD [p = 0.034, the “Regions showing opposite correlation directions between ASD and CP were enriched for distinct mechanistic pathways  and geny of ASD ) showed p < 0.05/664 = 7.5e−5), including extremely high intelligence , household income , and loneliness  , BD , and NSM   in regions positively correlated between ASD and CP  . We did not identify a significant difference in the expression rate between genes in the ASD-CP positively correlated regions and genes in the ASD-CP negatively correlated regions . The average expression of both gene sets was significantly higher than background genes across prenatal and postnatal stages   while the association between PRS- and ASD status was not significant  in the 75% percentile to 101.7 and 84.0  in the 99% percentile  of ASD based on independent SNPs from genomic regions with positive and negative local correlations between ASD and CP, respectively, for 5469 ASD probands and 2132 healthy siblings in the Simons Foundation Powering Autism Research for Knowledge (SPARK) cohort the “” sectionp = 0.03; two-sample t test) . The repetitive behaviors scale-revised (RBS-R) questionnaire was used to quantify repetitive behaviors, including self-injuries, restricted behavior, compulsive behavior, stereotyped behavior, ritualistic behavior, and sameness behavior [p = 0.016; Additional file p = 0.4; Additional file p = 0.082) in probands with PRS+ above 99% percentile  scores the “” sectionbehavior . We obseOwing to increasingly accessible GWAS summary statistics and advances in statistical methods to directly model summary-level data, genetic correlation estimation, especially at the genome-wide scale, has become a routine procedure in post-GWAS analyses. These correlation estimates effectively summarize the complex etiologic sharing of multiple traits into concise, robust, and interpretable values, which provided novel insights into the shared genetic architecture of a spectrum of phenotypes. However, genome-wide genetic correlations only reflect the average concordance of genetic effects across the genome and often fail to reveal the local, heterogeneous pleiotropic effects, especially when the underlying genetic basis involves multiple etiologic pathways. To this end, methods that partition genetic covariance by functional annotation or local genetic region have achieved some success , 12. TheIn this paper, we have introduced SUPERGNOVA, a unified framework for both genome-wide and stratified genetic correlation analysis. Improved upon our previous work , SUPERGNApplied to 30 complex traits, SUPERGNOVA identified 150 trait pairs with significant local genetic covariance, including 86 pairs without a significant global correlation. We identified various patterns in the shared genetic architecture between traits, with some traits  showing ubiquitous genetic covariance in a large fraction of the genome and other traits  showing relatively sparse genetic sharing with strong pleiotropic effects. Our analyses also implicated hub regions in the genome that are significantly correlated across numerous neuropsychiatric phenotypes. These results can guide future modeling efforts on these traits as well as functional genomic studies that interrogate key regions with pervasive regulatory roles across many phenotypes.FMRP target genes contribute to the comorbidity of ASD and intellectual disability [ASD and cognitive ability showed significant, bidirectional local genetic correlations in our analysis. We performed in-depth analyses to further dissect the shared genetics of ASD and cognition. For many years, GWASs of ASD have failed to identify robust associations that can be consistently replicated, most likely due to “omnigenicity” , weak efsability , which sIn contrast, successful GWASs for ASD have just begun to emerge . It was These findings seemed paradoxical—whole exome sequencing (WES) reveals that shared genetic components contribute to ASD and intellectual disability while GWAS suggests that shared genetics contribute to ASD and higher cognitive ability. It raised two important questions. Why are ASD genes affected by common SNPs different from genes harboring rare protein-altering variants? Why do common and rare variants suggest opposite genetic relationships between ASD and cognition?KMT2E locus) all showed consistent local correlations between both ASD and ADHD with CP. Of note, the set of regions negatively correlated between ASD and CP had a 3.2-fold enrichment for chromatin modifier genes. Thus, a genetic signature with consistent results between common and rare variants was hidden in plain sight. These genes, affected by both rare protein-altering variants and common (possibly regulatory) SNPs, may contribute to ASD with comorbid intellectual impairment in part through dysregulating chromatin modification in the developing brain. The positive global correlation between ASD and cognition was explained by a second genetic signature driven by a different set of regions that showed positive local correlations and were significantly enriched for PSD genes. When calculating the total genetic covariance between ASD and CP in the genome, negatively correlated regions were overwhelmed by the positive covariance in regions involved in the second signature, thus showing a positive global covariance. PRS based on these two signatures (PRS+ and PRS−) showed distinct associations with ASD phenotypes in the SPARK cohort. Compared to PRS−, PRS+ could better distinguish ASD cases from healthy controls. Both PRS+ and PRS− were associated with IQ in ASD probands but with opposite directions. In addition, PRS− significantly predicted overall ASD symptom severity while PRS+ significantly predicted repetitive behaviors. We also observed an enrichment of Asperger’s disorder in probands with high PRS+ (and a slight depletion in probands with high PRS−), but these results only showed moderate statistical evidence and remain to be validated using larger samples in the future.We aimed to address these questions head-on using local genetic correlations. We identified significant positive correlations of ASD and CP in 18 genomic regions but also 6 regions showing significant negative correlations. Locally, we did not observe the paradoxical correlation pattern seen in the global analysis, i.e., two positively correlated neurodevelopmental disorders ASD and ADHD showing opposite correlations with cognitive measures. Regions that were significantly correlated in all three trait pairs . The non-genetic effects of the shared samples for the two traits are correlated:where ϕ1 and ϕ2 are standardized, we have i1 ≤ n1 and 1 ≤ i2 ≤ n2.Since trait values z scores of SNP j for trait 1 and trait 2 by z1 and z2 to denote the vectors for all SNPs’ z scores and use V to denote the LD matrix. Under a random design model, In GWAS summary data, we can approximate ρt is defined as the sum of genetic covariance and non-genetic effects covariance, i.e., ρt = ρ + ρe. We provide detailed derivations of (2) in the Additional file where ρ is the liability scale genetic covariance, ϕ is the standard normal density, τ1 and τ2, P1 and P2, and K1 and K2 are the liability threshold, sample prevalence, and population prevalence of study 1 and study 2, respectively. Details are provided in the Supplemental Note. Since the only distinction between observed and liability scale of genetic covariance is a positive constant, the observed and liability scale of genetic covariance is equivalent in terms of statistical significance and covariance direction between two traits.Results similar to (2) can be derived when one or both studies are case-control studies, where genetic covariance is on the observed scale. The observed scale genetic covariance is z score covariance matrix on that of the theoretical covariance matrix. GNOVA [Most existing genetic covariance methods are based on the idea of minimizing the “distance” between the empirical covariance matrix le, LDSC  regressementclasspt{minimaϕ1 and ϕ2 follow additive linear models:The statistical framework we introduced above can be easily generalized to local genetic covariance. We assume Xi and Yi are the genotypes and βi and γi are the effect sizes of SNPs in region i. In practice, I genomic regions can be mutually independent LD blocks defined by LDetect [zi1 and zi2 iswhere  LDetect . FollowiVi, ρi and mi are LD matrix, heritability, genetic covariance and number of SNPs for region i, respectively. ρt here is defined as the sum of local genetic covariance and non-genetic effects covariance, i.e., where mentclass2pt{minimzi1 and zi2  isFollowing (3), the covariance of Vi is Assume eigen decomposition of i) and Ui is the corresponding orthogonal matrix of eigenvectors. Denote j = 1, 2, …, mi, the expected value and variance of jth eigenvalue wij arewhere andKi eigenvalues to estimate ρi. The procedure to adaptively determine Ki is described in the following section. In practice, the LD matrices are estimated from an external reference panel  which is approximated by i, the theoretical variance of where  moments . Derivatmentclass2pt{minim Project ) and theHere, we denote Derivations of (5) and (6) are in the Additional file The derivation of (7) is in the Additional file Ki ′ s) to include in the local analysis.Our statistical framework leverages the complete LD matrix to reduce information loss in estimation and at the same time denoises the empirical LD by properly selecting the optimal number of eigenvalues  and empirical variance in (6) should be close. We know from (5) that theoretical variance decreases with the increase of Ki. However, the value of empirical variance rapidly increases when the cutoff for the eigenvalues approaches towards zero , this proportion is an estimation of type I error. When the true genetic covariance is nonzero , this proportion is an estimation of statistical power at significance level of 0.05.SUPERGNOVA and GWAS summary statistics of 29 complex traits included in our analyses are publicly available. We obtained the summary statistics of a recent lung cancer GWAS directly from the authors . DetailsWe accessed samples from the SPARK study through the Simons Foundation Autism Research Initiative (SFARI). Samples in the SPARK study were genotyped by the Illumina Infinium Global Screening Array. Details on these samples have been previously reported and are available on the SFARI website . Followiashr [We estimated the proportion of correlated regions with an R package called ashr  after thSPI1 locus, we defined a new genomic region centered at SPI1 with a 1-Mb span. We estimated the local genetic covariance between AD  and theIGAP2019 , 80.SPI1, blood monocyte and macrophage datasets [We obtained PU.1 binding sites as ChIP-seq peaks from the ReMap datasets  (GEO: GSdatasets ). Followdatasets , we expadatasets  to estimdatasets , 84 usindatasets  and PCs datasets  imputed datasets . We downdatasets  to map PKMT2E region (chr7: 104158491–105425027), we implemented cross-tissue transcriptome-wide association analysis using UTMOST [To identify genes associated with ADHD, ASD, CP and SCZ in brain tissues in the g UTMOST . We usedg UTMOST –92 . We only included protein-coding genes in our analysis, resulting in 317 positively correlated genes and 179 negatively correlated genes. We applied Enrichr [FMRP target genes, genes encoding PSD proteins, gene preferentially expressed in human embryonic brains, essential genes, chromatin modifier genes, genes with probability of loss-of-function intolerance (pLI) > 0.9, and SFARI evidence score based on previous literature [p value as the proportion of permutations with shuffled test statistics greater than the test statistic observed in real data.We used R package  Enrichr , 96 to i Enrichr  . We followed the definition of developmental stages in Li et al. [t test to determine the differential expression among ASD-CP positively correlated genes, negatively correlated genes, and background genes.We downloaded developmental bulk RNA-seq data from BrainSpan. Gene-level RPKMs were used across 524 samples from 42 individuals in 26 brain regions . We kepti et al. . We perf2 fold change> 0.1 (or < − 0.1) and q value< 0.05 resulting in 5562 prenatally biased genes and 5361 postnatally biased genes. We followed the definition of ASD-CP positively and negatively correlated genes from gene set enrichment analysis. Chi-squared test was performed to test if the distributions of prenatally and postnatally biased genes in ASD-CP positively and negatively correlated regions were significantly different from background genes.To study the relative prenatal and postnatal bias, we performed linear regression for the transformed RKPM of each gene on a binary “prenatal” stage variable. Sex was included as an adjustment variable. Genes were defined as prenatally  biased if logt test for quantitative phenotypes between probands with extreme PRS (top 1%) and other probands. We performed hypergeometric test to test enrichment of subtypes in the extreme PRS group.We used the 18 positively correlated regions and 6 negatively correlated regions (FDR < 0.1) between ASD and CP to construct PRS+ and PRS− of ASD. We clumped the SNPs by PLINK . We set Additional file 1. Supplementary note, containing further derivations of the statistical models, information about additional simulations, and discussion of interpretability of local genetic covariance.Additional file 2. Supplementary figures 1-40.Additional file 3. Supplementary tables 1-26.Additional file 4. Review history.


Polygenic risk scores (PRS) are valuable to translate the results of genome-wide association studies (GWAS) into clinical practice. To date, most GWAS have been based on individuals of European-ancestry leading to poor performance in populations of non-European ancestry.We introduce the polygenic transcriptome risk score (PTRS), which is based on predicted transcript levels (rather than SNPs), and explore the portability of PTRS across populations using UK Biobank data.p=0.013) in the African-descent samples where the loss of performance is most acute with better performance than PRS when used in combination.We show that PTRS has a significantly higher portability . Polygenic risk scores (PRS) for a variety of traits are promising to become accurate enough to be useful for clinical practice and realize the longstanding goal of personalized medicine. PRS for coronary artery disease (CAD) have been shown to provide prediction that has been compared to monogenic mutations of hypercholesterolemia . In pracHowever, these efforts are time consuming, enormously expensive and will have to be repeated at scale, for numerous traits, across numerous ancestry groups. Therefore, methods that can use GWAS studies from one population for prediction in other ancestry groups are highly desirable. Analysis of GWAS conducted in different populations suggested that a considerable fraction of causal SNPs are shared across populations . Hence, It is now widely understood that many association signals are driven by their effects on the transcriptome. PrediXcan  and otheTherefore, we propose the polygenic transcriptomic risk score (PTRS) as a gene-based complement to the PRS that can help improve portability across human ancestry groups. We recognize that PTRS alone does not outperform the state of the art PRS . HoweverIn this paper, we explore the properties of PTRS using the UK Biobank (UKB), which provides genotype and phenotype data in half a million individuals . Althougtraining for the calculation of weights for predicting gene expression levels using genotype data. The training of transcriptome (gene expression levels) prediction weights had been performed previously, and we simply downloaded them from predictdb.org. When we estimate optimal weights for PRS and PTRS, we will use the terms building or constructing. We performed the building of PRS and PTRS using the discovery set. The testing of the risk scores, PRS and PTRS, were performed in what we call the target sets. For the remainder of the paper, we will refer to individuals by their ancestry and drop the -descent suffix. Unless otherwise clarified, we will use the term transcriptome to mean the set of predicted expression levels of genes. GTEx EUR transcriptome should be interpreted as the set of predicted gene expression levels using weights trained in European-descent samples from GTEx. Similarly, MESA EUR transcriptome, will refer to the predicted transcriptome using weights trained with the MESA European-descent samples. MESA AFHI transcriptome will refer to the predicted transcriptome using weights trained with a combination of African American-descent and Hispanic individuals from the MESA study.Before describing the results, we define and clarify some terminology. In this paper, there are two types of prediction: (1) gene expression level prediction from genotype data and (2) complex trait prediction using PRS or PTRS. PRS uses genotype data directly and PTRS uses linear combinations of genotypes representing predicted gene expression levels. To simplify exposition, we will only use the term Experimental setupAn overview of the experimental setup describing the discovery, training, and target sets used in the paper is shown in Fig. For predicting the transcriptome, we downloaded prediction weights from multiple ancestries collected in predictdb.org. The first set of models had been trained in European individuals from the GTEx v8 release  in wholeFor our tests, we focused on the 17 anthropomorphic and blood phenotypes used by Martin et al. . We sampPredicted transcriptome captures a higher portion of chip heritability than expectedBefore building PTRS, we started by determining whether prediction of traits was possible using just predicted transcriptomes. If feasible, we also wanted to know how much of the trait variation can be captured by the predicted transcriptome. For this purpose, we calculated the proportion of variance explained (PVE) by the predicted transcriptome assuming random effects of gene expression levels. The approach is analogous to standard SNP-heritability estimation . For herIn this section, we calculated the predicted transcriptome using the GTEx EUR weights using the European target set genotype data. Using these predicted expression levels, we calculated the “predicted expression relatedness matrix” (instead of the genetic relatedness matrix) and applied the standard restricted maximum likelihood estimation to calculate the proportion of variance explained by the predicted transcriptome.Since the PVE for each trait will depend on the heritability of the trait, we calculated the proportion of PVE divided by the heritability of the trait. This quantity represents the proportion of heritability explained by the predicted transcriptome. We also estimated the SNP-heritability using the restricted maximum likelihood approach in the same cohort.Figure Aggregating predicted transcriptomes in multiple tissues increases the PVETo explore ways to increase the proportion of variance explained (PVE), we calculated the proportion explained collectively by the transcriptome predicted in 10 tissues selected among the ones with largest sample sizes in GTEx, including muscle, adipose, tibial artery, breast, lung, fibroblast, lung, tibial nerve, and skin, with sample sizes ranging from 337 to 602  proportion of variance explained than when using the MESA EUR transcriptome. For the European target set, the difference between using the MESA AFHI or the EUR transcriptomes was close to 0 . This lack of significance could be attributed to the limitations of the array-based MESA transcriptome data prompting the need to generate improved non-European transcriptome predictors.We found that Fig. b in the Building PRS and PTRSAfter having determined that it is possible to capture a significant portion of trait variability using predicted transcriptome, we proceeded to build the PRS and PTRS in our discovery set (356K Europeans from the UK Biobank).p-value thresholding approach  in the validation set and calculated the We quantified the prediction accuracy in each target set using the partial PTRS’ prediction accuracy approaches the approximate upper bound estimated by their PVEt-test p = 0.03) as shown for the 17 traits in Fig. The prediction accuracy of PTRS (GTEx EUR based) was on average lower than the accuracy of PRS (paired r for distinction) and the per-feature heritability/PVE: We found that PTRS was much closer to the optimal performance upper bound (estimated as the PVE) than PRS was to its upper bound (heritability), as shown in Fig. features . The higMatching training and target ancestries may improve prediction accuracyWe have suggestive evidence that matching training and target ancestries can improve the PVE in the African population. To test whether matching the training and target ancestries would improve the PTRS prediction accuracy, we examined the difference between using the African transcriptome (MESA AFHI) vs the European transcriptome (MESA EUR).For the European target set, the European transcriptome based PTRS had better accuracy than the AFHI transcriptome based one, with an gain of 0.74% (s.e.=0.096%) when using European vs AFHI, as hypothesized. For the African target set, however, the difference was not significant indicating that improvements in prediction across ancestries are probably needed to detect the potential gain.To avoid differences due to having different number of predicted genes, PTRS were built using only the genes that were present in both training sets, EUR and AFHI (see details in the “PTRS improves portability into the African-descent populationportability” as the predictive accuracy in each population relative to the European reference target set (EUR ref.). This is calculated as the ratio of the To test our hypothesis that PTRS can generalize more robustly across populations than the standard PRS, we defined “p=0.013). These results provide strong proof of principle that integrating predicted transcriptome as done with PTRS has the potential to improve portability of risk scores across populations despite the limitations of currently available AFR training data. In the European test set, we observed quite a bit of variability in the portability, ranging from 0.47 to 2.28, despite the fact that both European target sets were randomly sampled from the same European UK Biobank participant set. As expected, the median portability in the second EUR target set is centered around 1.Consistent with reports by , the porWe verified that the increase in portability of PTRS in the African target set was not driven by the use of elastic net instead of clumping and thresholding for building the PTRS as shown in Additional file Furthermore, we examined whether the population-matched transcriptome models could improve portability using the MESA models. However, we did not observe significant improvement relative to PRS .  to 35.5% (with a broader sets of tissues) of the SNP heritability, i.e., the total variation that can be explained using common SNPs. Promisingly, the actual predictors built on predicted transcriptomes had performances that were more than double the expected 22.9% of the PRS performance. We found that the portability of PTRS was significantly higher than the portability of PRS in the African target set, which is important since the African populations are the most affected by the Eurocentric bias in GWAS studies [In this paper, we have explored the use of PTRS to address the limited portability of PRS across ancestry groups. One intriguing application of our PTRS method is to transfer the polygenic knowledge derived in humans to other model systems. Currently, most attempts to follow up on human GWAS findings in model organisms focus on individual genes, despite the overwhelming evidence that individual genes seldom contribute even 1% of trait variability. As we improve our PTRS, it would be possible to build models based on human discovery sets and then use them to predict those traits in model organisms based on either measured or predicted transcript levels. Whether this approach could successfully predict traits that can be measured in other species  is currently unknown. The success of PTRS for cross-species translation is theoretically dependent on the extent to which gene expression differences have similar impacts across species. However, if we could build a transferable PTRS, we could envision to run experiments on model organisms, measure their transcriptomes (in the right context and tissue), and predict complex traits  that are hard or impossible to measure. This platform could be used to test various interventions and obtain effects on phenotypes that are more relevant to humans, opening up completely novel lines of investigation.p-value thresholding). Fourth, higher quality prediction models of the transcriptome in non-European ancestries are limited. Here, we used predictors trained in monocyte samples assayed with older array technology. Multiple ancestry models are currently being generated by us and other groups. For example, the MESA TOPMED project has assayed RNAseq, protein, methylation, and metabolomics data in African Americans, Hispanics, and Asian ancestry individuals which will allow the development of improved prediction models. Currently, our methods are based on transcriptomic data from bulk tissues; however, just as the choice of tissue is important, the choice of tissue and cell type has the potential to provide even better performance; however, the availability of such data, as well as the methods for implementing it, are currently limited. Furthermore, prediction models are currently based on variants in the vicinity of the gene. Improved predictors with genome-wide variants should be investigated. Finally, other approaches such as prioritizing functional annotations for variant selection [Our study points to promising strategies to improve risk prediction in general but it also has several limitations. First, PTRS are based on prediction models of gene expression traits which we estimated to account for less than a third of the total variability in the complex traits considered here. We expect this limitation to be mitigated as additional transcriptome reference sets in different contexts as well as other omics data covering mediating mechanisms missed in current models. The increase in the proportion of variance explained from a fifth when using whole blood predictors alone compared to over 35.5% when using 10 tissues indicates that much can be gained by increasing the breadth of reference omic data. Second, we used single tissue prediction models for most of the analysis in this paper, which captured a fifth of the variation in the complex traits here. This can be improved by developing approaches to integrate multiple tissue models. Third, weights for PRS were calculated using GWAS summary results (thresholding and pruning method) whereas PTRS weights were calculated using individual level data due to computational considerations. Individual-level based PRS would likely perform better, although whether they would be more portable is not obvious. For reassurance, we have ruled out the possibility that the portability improvement in the African target set was due to this different treatment of PTRS and PRS by re-calculating the portability of PTRS using the same approach as PRS  and the total variance explained by the predicted transcriptome. The portability, defined as the performance relative to the disease risk prediction in the European-descent target set, was higher for PTRS than PRS in the African-descent target set, where the loss of PRS performance is most pronounced. We hypothesize that these results reflect the conserved role of genes in disease even when the SNPs that predict disease differ between ancestry groups and suggest that integrating PTRS can be a useful tool to improve the transfer of PRS across populations. Corroborating this idea, we found that the combined PRS and PTRS had higher performance and portability in the African ancestry target set. The effect was not significant in other ancestry groups highlighting the need to continue improving the transcriptome prediction models as well as PTRS training.A critical advantage of PTRS is the clear biological interpretation—it provides information about how genes are related to a trait. Unlike PRS, PTRS is grounded in biology and can serve as a building block for future extensions. Also, tantalizing is the possibility of using PTRS to port polygenic signals not just over ancestry- but also species-boundaries. Finally, our results suggest that collecting a more diverse transcriptomic and other omic reference data could be a cost effective way to improve portability across populations.http://biobank.ctsu.ox.ac.uk/crystal/coding.cgi?id=1001). Throughout the paper, we labeled ‘British’ individuals as EUR, “Indian,” “Bangladeshi,” and “Pakistani” individuals as S.ASN, “Chinese” individuals as E.ASN, and “African” and “Caribbean” individuals as AFR. The measurements of the 17 quantitative phenotypes , sex (Data-Field 31), and the first 20 genetic PCs. The ancestry information of individuals was obtained from Data-Field 21000, and we kept individuals labeled as “British,” “Indian,” “Chinese,” or “African” , we collapsed multiple arrays by taking the average and we aggregated measurements across multiple instances by taking the first non-missing value. Individuals with missing phenotype in any of the 17 quantitative phenotypes or covariates were excluded.k among the 4 populations , we treated the distribution of the individuals, in the space of the first 10 PCs, as multivariate normal. And we calculated the observed population mean i in population k, we evaluated the “similarity” Sik to the population k as Sik will be much larger than others. So, we filtered out individuals with Sik≤−50 in the assigned population k. This cutoff was picked such that k′ has To ensure the quality of ancestry label, we removed individuals who deviate substantially from the population that they were assigned to. Specifically, for population The number of individuals remained after data retrieval and ancestry quality control is listed Additional file 2,sex×age, and sex×age2. In the GWAS run, we excluded variants with minor allele frequency < 0.001 and variants that significantly deviate from Hardy-Weinberg equilibrium . And the phenotype in their original scales were used.We built PRS using the genotypes and phenotypes of the individuals in the discovery data set . We performed GWAS (linear regression) using linear_regression_rows in hail v0.2 where we included covariates: first 20 genetic PCs, age, sex, ageR2>0.1 to or 250 kb within any variants that have already been included. The PRS was constructed on the basis of the set of variants obtained from the LD clumping along with the marginal effect size estimated in GWAS run. Specifically, we calculated PRSs at a series of GWAS p-value thresholds: 5×10−8,10−7,10−6,10−5,10−4,10−3, 0.01, 0.05, 0.1, 0.5, and 1. In other words, at threshold t, the PRS for individual i was calculated as To obtain relatively independent associations for PRS construction, we ran LD clumping using plink1.9 with option –clump –clump-p1 1 –clump-r2 0.1 –clump-kb 250. This command extracted genetic variants in the order of their GWAS significances and excluded all variants having Xij is the effect allele dosage of variant j in individual i and j from GWAS run.where At the testing stage, given the genotype of an individual, we calculated the PRS of the individual using Eq. We computed predicted gene expression for all individuals passing filtering steps and quality control. We utilized two sets of prediction models: 1) CTIMP models (proposed in ) trained CTIMP moi). To assess the potential predictive power of predicted transcriptome on the phenotypes of interest, we estimated the proportion of phenotypic variation that could be explained by the predicted transcriptome in aggregate. Specifically, we assume the following mixed effect model  was calculated using models from GTEx  and MESAg across N individuals. Similarly, let lth standardized covariate. We fit the following elastic net model. For each of the 17 quantitative phenotypes, we trained elastic net model to predict the phenotype of interest using the predicted transcriptome (in a single tissue) as features. The same set of covariates as the ones for PRS were used. Let β0 is the intercept, M is the number of genes, L is the number of covariates, l2 norm, and ∥β∥1 is the l1 norm of the effect size vector. Here, α controls the relative contribution of the l1 penalization term and λ controls the overall strength of regularization.where https://github.com/liangyy/ptrs-tf [α=0.1 (α = 0.5 and 0.9 show similar performance). And fixing the α value, as suggested in [λ’s starting from the highest. The maximum λ value, λmax, was determined as the smallest λ such that Eq. The model fitting procedure was implemented using tensorflow v2 with mini-batch proximal gradient method and the code is available at /ptrs-tf . We traiested in , we traiwhere the gradient is evaluated at λ=λmax, Eq. λ’s. We estimated λmax using the first 1000 individuals of the data. And the sequence of λ was set to be 20 equally spaced points in log scale with the maximum value being 1.5λmax and the minimum value being λmax/104. Among these PTRS models generated at different λ values, we only kept the first 11 non-degenerate PTRS models so that we have the same number of candidate models for both PRS and PTRS.So, at p-values. Second, we traversed the prioritized gene list from the most significant ones to the least ones, in which we included a gene into the returned gene list if the squared correlation (in terms of the predicted expression) of this gene and any other genes that have been included in the current returned gene list is smaller than 0.1.To implement clumping and thresholding based PTRS, we first obtained the gene/phenotype associations (PrediXcan association ) by assop-value thresholding. Specifically, at p-value thresholds 10−6,5×10−6,10−5,5×10−5,10−4,5×10−4,10−3,5×10−3, 0.01, 0.05, 0.1, 0.5, 1, we built a PTRS model by keeping the genes with p-values smaller than the cutoff. For these genes being included in the final PTRS model, we used the effect sizes estimated in the PrediXcan association as the PTRS weights.Among these genes being selected in LD clumping, we built PTRS models by At the testing stage, given the standardized (within the population) predicted transcriptome of an individual, we calculated the PTRS of the individual using the following: βλ denotes the βEN obtained at hyperparamter equal to λ. For the PTRS built upon from GTEx EUR predicted transcriptome, the target PTRS was calculated with the GTEx EUR transcriptome (transcriptome predicted with GTEx EUR gene expression prediction models). To examine the utility of population-matched prediction model, the PTRS on the target set were calculated with of both MESA EUR and MESA AFHI transcriptomes.where R2 of PRS/PTRS against the observed phenotype accounting for the set of covariates listed in Methods. Specifically, for individual i, let yi denote the observed phenotype. Partial R2 (denoted as y∼1+covariates (null model); and 2) i.e.To measure the predictive performance of PRS and PTRS, we calculated the partial H is the projection matrix of the null model, i.e.Cl being the lth covariate.where p-value cutoff in the clumping and thresholding based approach or λ value in elastic net), for each target set, we further split the target set into two equal-size parts, a validation set and a test set. First, we calculated As stated in the results section, PTRS weights were computed in the discovery set (UKB EUR) and tested in the 5 target sets. To determine the hyperparameter , we used the following procedure to obtain the prediction accuracy in each of the ancestry groups. Similar to the prediction accuracy quantification of PTRS and PRS alone, we first split the target set into validation and test sets. We used the validation set to the following: (i) determine the coefficients (c1 and c2) for each PTRS’ and PRS’ hyperparameter combination and (ii) select the best combined score among all PTRS and PRS combinations. And the test set was used to obtain an unbiased measure of the prediction accuracy of the selected score in the corresponding ancestry. To achieve the two goals above using the validation set, we further split the validation set into two equal-size parts. Using the first half of the validation set, we determined the coefficients (c1 and c2) for each of the PTRS and PRS combinations by minimizing the square difference between predicted and observed. Next, using the other half of the validation set, we calculated the prediction accuracy of each PTRS and PRS combination with the coefficients obtained above. We note that this accuracy measure is unbiased since neither PTRS and PRS weights nor the coefficients for combining PTRS and PRS were obtained using this data. Based on these prediction accuracy measures, for each ancestry, we selected the best-performing PRS and PTRS combination along with the corresponding coefficients. We report the prediction performance calculated in the test set using the optimal combined score selected in the validation set.We combined PTRS and PRS as a weighted sum, i.e., combined score=Portability was defined as the ratio of the prediction accuracy in each target set divided by the prediction accuracy in the European reference set. Therefore, by definition, portability in the EUR ref. set was 1.When calculating the portability of PTRS using MESA AFHI transcriptome, we used the MESA EUR model https://github.com/liangyy/ptrs-ukb) [https://github.com/liangyy/ptrs-tf) [https://predictdb.org [Code for data analysis is on Zenodo with the access code DOI: (10.5281/zenodo.5709387)  and GitHtrs-ukb) . Code foptrs-tf) . Predictctdb.org . The chiAdditional file 1 Tables S1-S3, legends for Tables S4-S7, and Figures S1-S7.Additional file 2 Table S4.Additional file 3 Table S5.Additional file 4 Table S6.Additional file 5 Table S7.Additional file 6 Review history.


High yield is the primary objective of maize breeding. Genomic dissection of grain yield and yield-related traits contribute to understanding the yield formation and improving the yield of maize. In this study, two genome-wide association study (GWAS) methods and genomic prediction were made on an association panel of 309 inbred lines. GWAS analyses revealed 22 significant trait–marker associations for grain yield per plant (GYP) and yield-related traits. Genomic prediction analyses showed that reproducing kernel Hilbert space (RKHS) outperformed the other four models based on GWAS-derived markers for GYP, ear weight, kernel number per ear and row, ear length, and ear diameter, whereas genomic best linear unbiased prediction (GBLUP) showed a slight superiority over other modes in most subsets of the trait-associated marker (TAM) for thousand kernel weight and kernel row number. The prediction accuracy could be improved when significant single-nucleotide polymorphisms were fitted as the fixed effects. Integrating information on population structure into the fixed model did not improve the prediction performance. For GYP, the prediction accuracy of TAMs derived from fixed and random model Circulating Probability Unification (FarmCPU) was comparable to that of the compressed mixed linear model (CMLM). For yield-related traits, CMLM-derived markers provided better accuracies than FarmCPU-derived markers in most scenarios. Compared with all markers, TAMs could effectively improve the prediction accuracies for GYP and yield-related traits. For eight traits, moderate- and high-prediction accuracies were achieved using TAMs. Taken together, genomic prediction incorporating prior information detected by GWAS could be a promising strategy to improve the grain yield of maize. Maize serves as an important cereal and forage crop and plays an important role in sustaining global food security. Improvement of grain yield is a major and longstanding breeding goal for maize. Kernel number per ear (KNE) and thousand kernel weight (HKW) are the major components of grain yield per plant (GYP). Kernel number per row (KNR) and kernel row number (KRN) are the important components of the KNE. Ear length (EL) and ear diameter (ED) affect GYP in different degrees. In general, compared to GYP, yield components and related traits are less affected by environments and have higher heritability, and therefore, can be directly used to facilitate the final yield of maize  influencing KRN, HKW, and kernel shapes was identified. Using an intermated B73 × Mo17 Syn10 doubled haploid population and a natural population, Zhang et al. (Using linkage mapping and genome-wide association study (GWAS), a large number of quantitative trait loci (QTLs) or single-nucleotide polymorphisms (SNPs) have been identified among different populations. For instance, under drought and heat environments, Millet et al.  detectedg et al.  detectedTraditional marker-assisted selection (MAS) and marker-assisted recurrent selection (MARS) use only a few large-effect QTLs or markers, where efficient selections are made in maize breeding programs. Genomic selection (GS) uses whole genome-wide molecular markers to predict the breeding values of individuals. Therefore, it can capture both major and minor effect markers and is efficient for complex traits, especially for grain yield. GS has been shown to outperform MAS for grain yield and physiological traits in maize doubled haploid populations  is predicted based on the marker effects estimated from the training population in a test population with genotypic data and no phenotypic data  were estimated using the Bayesian Markov chain Monte Carlo method in Structure v2.3.4  was used to control the population structure as previously described  of FarmCPU, whereas the PVE of CMLM was calculated using GAPIT. Candidate genes were scanned from 50 kb upstream to downstream of each significant locus using ANNOVAR  calculated by FarmCPU and CMLM based on BLUE values. The prediction accuracy of seven subsets was compared to that of all markers . For the eight traits, TAMs were all treated as the random effects (random model) in all GS models. For traits where significant SNPs (p <1.72E−05) were detected, the significant SNPs were treated as the fixed effects and other remaining markers were treated as the random effects (fixed model). In the fixed model, one Q matrix (Q1) calculated using Structure was added into GBLUP and RKHS models as the fixed effects to evaluate the impact of population structure on the prediction accuracy. In addition, significant SNPs were all fitted as the random effects in RKHS to evaluate their potential application.The prediction was done using GBLUP, Bayes A, Bayes B, Bayes C, and RKHS. Kernel averaging was used in the RKHS, and bandwidth parameters were set at 1/5M, 1/M, and 5/M, where M is the median squared Euclidean distance. Seven subset sizes of TAMs, that is, 100, 500, 1,000, 5,000, 10,000, 20,000, and 40,000 were selected according to the ranks of –logRandomized imputation was adopted for missing makers, according to the known genotype frequency. For each marker, individuals were coded as 2 , 0 , and 1 (heterozygous). Recoding and imputation were carried out using the R software. Five GS models, TAMs, fixed model, random model, and fixed effects of Q matrix were performed using the R package, BGLR  for all traits (Descriptive statistics revealed that extensive phenotypic variations were observed in GYP and seven yield-related traits in the panel under different environments . The herl traits . This shp-value threshold of 1.72E−05, and the average PVE of all significant signals was 4.20% (In total, 58,129 high-quality SNPs were used to perform GWAS for eight traits using BLUE values. FarmCPU and CMLM were used to control false associations for all traits. A total of 22 significant SNPs were identified with a as 4.20% . FarmCPUas 4.20% . One sigas 4.20% .Five GS models were evaluated using seven subsets of TAMs derived from FarmCPU and CMLM. The prediction accuracies ranged from 0.10 to 0.84 and differed among prediction models and traits. Regardless of the marker effects, the prediction accuracy of RKHS using TAMs was the highest, followed by GBLUP, and Bayes B was the least for GYP, EW, and KNE . The preThe prediction accuracies of using significant SNPs and population structure as the fixed effects were evaluated in traits where significant SNPs were detected. In most of the TAM subsets, using 4–8 significant SNPs as the fixed effects improved the prediction accuracy by 1.43–40% and 1.37–22.41% for KRN and EL, respectively, when compared with the random model in all five models . For GYPTo evaluate the effect of population structure on prediction accuracies, the Q matrix calculated using Structure was incorporated into the fixed model in GBLUP and RKHS. For GYP, EW, and KRN, the accuracy did not change when the Q matrix was included as a fixed effect in most cases of RKHS and GBLUP , 3, 5. FFor GYP, the prediction accuracies of TAMs derived from CMLM and FarmCPU were compared in the five models , regardlGenomic selection is a promising breeding method with the aim of accelerating the speed and efficiency of breeding processes. In contrast, GWAS is used to identify QTLs or genes that underlie important traits for breeding. They seek to model the different aspects of the genetic architecture of traits and have complementary advantages (Bian and Holland, Arabidopsis thaliana (González-Camacho et al., Prediction models are the major factors that affect the prediction accuracy of different traits. In this study, GBLUP, Bayes A, Bayes B, Bayes C, and RKHS were adopted to compare the prediction accuracies of eight traits based on GWAS-derived markers. The advantage of RKHS over the other four models was demonstrated using GYP, EW, KNE, KNR, EL, and ED in most TAM subsets, which was in line with many studies on maize, wheat, barley, and p values, identifying newly associated SNPs, and overlapping with the reported loci (Liu et al., Except for GYP, the prediction accuracy of TAMs produced by CMLM was consistently higher than that by FarmCPU. In multiple species, FarmCPU outperformed CMLM and other methods by controlling the inflation of Fusarium ear rot resistance. The study by Lozada et al. (Genome-wide association study is a rapid and effective method for identifying genetic variations in important germplasms. Based on the prior knowledge of the underlying genetic architecture detected by GWAS, the advantage of integrating GWAS with GS was identified in our association panel. Our results showed that subsets of TAMs that treated significant SNP as the fixed effects or random effects could improve the prediction accuracies of GYP and yield-related traits compared with all markers. This was similar to the results of the studies by Liu et al.  and Yuana et al.  proved ta et al. . In mosta et al. . The deca et al. . Higher a et al. . The mula et al. , which ma et al. .In this study, treating one or several significant SNPs as the fixed effects in GS models resulted in higher accuracies in most cases, compared with those with only the random effects, which was in accordance with the trends in accuracy improvement shown in maize, wheat, and rice (Arruda et al., a priori indicators, prediction strategies, allele effects, allele frequencies between groups, the features of traits, and populations (Guo et al., Integrating information on population structure into fixed models did not improve prediction performance and, in some cases, slightly decreased the accuracies. Similar results were found in the study by Rio et al. ; when tap values or marker effects might produce an improper marker set with low accuracies if the GWAS was incorrect (Jeong et al., The major limitation of incorporating TAMs into GS models depended on the accuracy of GWAS results. Marker selection strategies based on Despite these limitations, the combination of GWAS and GS offers an effective means for germplasm screening of traits with low heritability where, for instance, a 1% increase in prediction accuracy could improve genetic gains (Rice and Lipka, The original contributions presented in the study are included in the article/JM collected phenotypic data, performed GWAS and GS analyses, and wrote the manuscript. YC provided help for the phenotypic measurement. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


P-values to be shared with the scientific community.A joint analysis of location and scale can be a powerful tool in genome-wide association studies to uncover previously overlooked markers that influence a quantitative trait through both mean and variance, as well as to prioritize candidates for gene–environment interactions. This approach has recently been generalized to handle related samples, dosage data, and the analytically challenging X-chromosome. We disseminate the latest advances in methodology through a user-friendly R software package with added functionalities to support genome-wide analysis on individual-level or summary-level data. The implemented R package can be called from PLINK or directly in a scripting environment, to enable a streamlined genome-wide analysis for biobank-scale data. Application results on individual-level and summary-level data highlight the advantage of the joint test to discover more genome-wide signals as compared to a location or scale test alone. We hope the availability of gJLS2 software package will encourage more scale and/or joint analyses in large-scale datasets, and promote the standardized reporting of their  G) and a quantitative phenotype (denoted by Y), by testing the mean differences in Y according to the genotypes, otherwise known as a location test. More recently, several reports have investigated association with phenotypic variance of complex quantitative traits  or gene–environment (G×E) interactions; both referred to as G×E hereinafter. Unlike a direct test of G×E interaction, a scale test can be used to indirectly infer G×E without knowledge of the interacting covariates, thus alleviates the multiple hypothesis burden of testing all possible pairwise interactions, and the assumption that all interacting environmental variables could be (accurately) measured.Genetic association studies examine the relationship between the genotypes of a single-nucleotide polymorphism . Since methods for genome-wide association studies (GWAS) of location are well-established, the main focus was on improving scale tests tailored for genetic data. https://github.com/dsoave/gJLS; accessed 2022 February 13). Following the X-inclusive trend to genome-wide analyses, robust and powerful location . We hope the availability of this unifying software package will encourage more X-inclusive, genome-wide, gJLS2 association analysis for complex continuous traits, particularly for those believed to be enriched for genetic interactions.In this study, we describe a generalized joint location and scale analysis tool (gJLS2) as an update to the JLS and gJLS methodology for autosomes, with added functionalities that (1) support X-chromosome mean and variance association analyses, (2) handle imputed data as genotypic probabilities or in dosage format, (3) allow the incorporation of summary statistics for location and/or scale tests, (4) implement a flexible framework that can accommodate additional covariates in both the location and scale association models, and (5) improve the computational time required for large-scale genetic data such as the UK biobank or github for the most recent version:#install.packages(“devtools”)devtools::install_github(“WeiAkaneDeng/gJLS2”)https://weiakanedeng.github.io/gJLS2/. Any feedbacks/bugs can be reported under github’s issues tab (https://github.com/WeiAkaneDeng/gJLS2/issues).via the “devtools” package , rs986810 (C/T), rs180495 (G/A), rs5911042 (T/C), and rs4119090 (G/A) that are outside of the pseudo-autosomal region, to cover observed minor allele frequency  of 0.1, 0.2, 0.3, 0.4, and 0.45, respectively. See Supplementary Material Section 2 for more details on the simulated dataset. The summary statistics data comprised of association n > 5,000), it is recommended to run the location association analysis using the state-of-art linear mixed models, such as LMM-BOLT  is flexible to accommodate additional covariates and the default option. To account for related samples, the generalized least square (GLS) method is used by specifying a covariance structure for error terms in smaller samples. Users can either provide the covariance matrix or specify a structure for the covariance matrix according to predefined subgroups. However, for large population studies head(chrXdat) > locReg;CHR SNP gL1 X rs5983012_A 0.98776742 X rs4119090_G 0.92015693 X rs5911042_T 0.38980294 X rs986810_C 0.46191655 X rs180495_G 0.8767590Although the resulting 3-df test is robust to the choice of baseline allele and status of XCI, a recent report CHR SNP gS1 X rs5983012_A 0.13919092 X rs4119090_G 0.98284303 X rs5911042_T 0.14870174 X rs986810_C 0.95633905 X rs180495_G 0.3476929> scaleRegCHR SNP gS1 X rs5983012_A 0.17390622 X rs4119090_G 0.99999993 X rs5911042_T 0.11630234 X rs986810_C 0.95815895 X rs180495_G 0.3619056G additively (2 df) without the dominance term, or by replacing the genotype indicators for each observation, with the corresponding group probabilities (3 df). Similar to the location association, sample relatedness is dealt with using GLS for autosomal markers at the second stage of linear regression via the correlation matrix. Additional models, such as a sex-stratified variance test, can also be specified for X-chromosome. In this case, the scale test result is given by the Fisher’s method that combines female and male-specific variance test results.The imputed data are analyzed either by computing the dosage value and used in place of > scaleRegCHR SNP gS LevFemale LevMale Fisher Flagged1 X rs5983012_A 0.1391909 0.5296098 0.08223648 0.1800391 12 X rs4119090_G 0.9828430 0.9143432 0.97557661 0.9939473 03 X rs5911042_T 0.1487017 0.1625983 0.40384172 0.2444805 04 X rs986810_C 0.9563390 0.5349616 0.78109151 0.7824831 15 X rs180495_G 0.3476929 0.9717918 0.14364539 0.4144558 1Note that a “Flagged” column is appended for these results, indicating the minimum genotype count in either females/males is less than 30  or not (indicated by 0). This is based on the quality control that SNPs with a minimum count below 30 should be removed to avoid inflated type I errors CHR SNP gL gS gJLS1 X rs5983012_A 0.9943538 0.1198837 0.37274722 X rs4119090_G 0.8881506 0.9794193 0.99114013 X rs5911042_T 0.3488576 0.1514217 0.20817014 X rs986810_C 0.4898597 0.9244773 0.81160645 X rs180495_G 0.8702304 0.3619588 0.67886814P-values from location and scale tests are allowed and can be combined via Fisher’s method to give the corresponding test statistic for the joint analysis:P-value and P-value.Alternatively, summary statistics, i.e. P-values for X-chromosome markers.An important assumption underlying this simple method to combine evidence is the normality of the quantitative trait, which leads to ibutions , we stroThe main contribution of the software is the ability to handle X-chromosome  analysis and it is not our aim to compete with existing autosomal association tools that are geared toward whole-genome computations. As a result, for both location and scale association, data splitting remain our primary strategy to deal with biobank scale data. We also implemented the summary statistics option to encourage the inclusion of genome-wide results (autosome) computed using existing software as inputs for the location portion. For X-chromosome associations, we recommend using gJLS2 in non-GUI settings and provide 2 practical options for biobank-scale data.Rserve package to establish communication between R and PLINK 1.9. The following script demonstrates the joint analyses for X-chromosome SNPs that included additional covariates.R CMD Rserve --RS-port 8221 plink --bfile ./input/chrX_5_snp \--R run_gJLS2PLINK_ Xchr.R \--pheno ./input/Pheno.txt \--pheno-name pheno1 \--R-port 8221 \--covar ./input/Pheno.txt \--covar-name SEX, covar1, covar2, covar3 \--out ./output/testRunFor larger datasets, it is more convenient to run the joint analyses using the PLINK R plug-in following the a typical GWAS pipeline. The R plug-in relies on the https://github.com/WeiAkaneDeng/gJLS2/tree/main/inst/extdata).Rscript run_gJLS2.R --bfile ./input/chrX_5_snp \--pfile ./input/Pheno.txt \--pheno pheno1 \--Xchr TRUE \--write 10 \--nTasks 2 \--covar SEX, covar1, covar2, covar3 \--out ./output/testRun.results.txtAnother option is to use the Rscript provided that allows additional arguments to change how frequently the results are written to the output (–write) and to increase the number of cores used (–nTasks). A core is an independent processing unit on a central processing unit (CPU). Though a modern computer, usually containing 4–8 cores, is capable of handling parallel computing, we recommend the “split-apply-combine” strategy and employing high-performance computing for large-scale analyses. Scripts for both options are available from github  to achieve sample sizes of: 1,000, 5,000, 10,000, 50,000, 100,000, 200,000, 300,000, 400,000, and repeated the joint-location-scale analysis using a single core with 10GB memory via (1) PLINK R plug-in and (2) Rscript. The reason for keeping the 100 SNPs to estimate the performance metric is because the analysis can be easily divided to chunks and combined after. n = 488,377, m = 15,179), the gJLS analysis took ∼16 h for PLINK (using 3.5GB memory) and ∼ 21 h (using 1.2GB) for Rscript. The memory efficiency of Rscript is expected as the “BEDmatrix” only maps the required portion of genotype files into memory. However, the Rscript can be parallelized, when using 4 cores with 20GB allocated memory, the wall clock run time was reduced to ∼13 h (using 14.0 GB).For an X-chromosome wide analyses on UKB  an X-chromosome gJLS analysis on UK Biobank (UKB) data  on 4 complex traits previously studied in The gJLS2. We restricted analyses to white British samples , and for BMI, we further excluded those with diagnosed type 2 diabetes . We included only bi-allelic SNPs and filtered based on MAF < 0.01, HWE P-value <1E-5. A further check on sex-stratified MAFs  and Rscript (run_gJLS2.R) are provided in the inst/extdata folder of the R package along with the input files. It is worth noting the main advantages of the Rscript option beyond its flexibility: (1) the gJLS2 R package supports multi-core computing via the parallel base package and the argument “nTasks” can be used to specify the number of cores; (2) another useful feature is the “write” option that specifies the chunk size for the results to be written while the analysis is running and thus minimizes loss in case an interruption occurred.The base scripts for PLINK , to complete the analysis for 276,694 unrelated European samples and 15,179 X-chromosome variants per trait. The gL, gS, and gJLS2 istogram  for eachRscript run_gJLS2.R --sumfile ./input/ GIANT_BMI_chr16_gJLS_summary.txt \--out ./output/GIANT_BMI_Sum.chr16_results.txtRscript run_gJLS2.R --sumfile ./input/ GIANT_Height_chr16_gJLS_summary.txt \--out ./output/ GIANT_Height_Sum.chr16_results.txtThe Rscript is more flexible than the PLINK plug-in solution as it can also handle analysis of summary statistics. The input file should contain at least 3 columns with headers “SNP,” “gL,” and “gS,” while the output file has an additional column “gJLS.” We re-formatted the subset of chromosome 16 summary statistics of location and scale obtained from the GIANT consortium for BMI and height as inputs.P-values are presented using a Manhattan plot, quantile-quantile plot, and a histogram , which can be freely accessed from the online data portal. The genotype and phenotype data used to demonstrate the X-chromosome analysis are available from UK Biobank (https://www.ukbiobank.ac.uk/) release in March 2018 and under project identification number 64875. Data access can be requested with information provided here: http://www.ukbiobank.ac.uk/using-the-resource/. The genome-wide summary statistics are made available from the GIANT data portal .The gJLS2 package and the example datasets are available from G3 online.jkac049_Supplementary_DataClick here for additional data file.


Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs).N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort . Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms.A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program.Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods. Posttraumatic stress disorder (PTSD) may develop after exposure to traumatic life events. PTSD can severely impact the mental and physical health of affected individuals and impair their interpersonal relationships . While tGenome-wide association studies (GWASs) facilitate biological understanding of PTSD ,9, but arg) of PTSD and LTE was responsible for this result. To perform this investigation, we performed a GWAS of LTE in the most powered and unbiased  GWAS of total PTSD symptoms (MVPTOT). We contextualized genomic findings through functional annotation, tissue expression analyses, and phenome-wide association study (PheWAS).The Psychiatric Genomics Consortium (PGC)–PTSD is a global collaborative effort to study the genetic basis of PTSD through meta-analysis of diverse cohorts . Subsequunbiased  subsamplunbiased , then evet al. , and heterozygosity (within ± 0.2). Datasets were phased using SHAPEIT , and imputation methods for the included studies have been described in detail . In brie SHAPEIT  and impu SHAPEIT  with the SHAPEIT . For the SHAPEIT  and GWASn = 36 cohorts); PTSD case-control status was used if symptom scores were not available, using logistic regression (n = 15 cohorts). In both cases, 5 PCs were included as covariates to account for population stratification and genotyping artifacts. The UKBB PTSD GWAS included an additional PC as well as batch and assessment center covariates. Studies providing summary data used similar analytic strategies, as previously described (Only unrelated (π < 0.2) participants were retained for analysis. Principal components (PCs) were calculated within each cohort using EIGENSOFT v6.3.4 . The PTSescribed . For eacz scores by dividing effect sizes by standard errors , rg, and sample PTSD prevalence. Meta-analysis was conducted on the reweighted z scores. Only SNPs available in >90% of all samples  were included in analyses. Regional annotation plots of genome-wide significant loci were produced using Locus-Zoom  − 1]} was used to determine the proportion of inflation of test statistics owing to residual population stratification .Trait gression . The linfication . Heritabwas used . To idenFUMA v1.3.6a  was usedcis-expression QTL (eQTL) and cis-splicing QTL mapping was performed using FastQTL (The effects of GWAS loci on transcriptomic regulation of surrounding genes (locus within ± 1 Mb of the gene transcription starting site) were tested for 49 tissues in GTEx v8 with genome-wide false discovery rate correction applied. Using the same criteria, GTEx v8 data were also used to investigate the effects of GWAS loci on the regulation of alternative splicing isoforms. A detailed description regarding GTEx v8 quantitative trait locus (QTL) mapping data by the GTEx Consortium is available . Briefly FastQTL  includin FastQTL , sequencTOT (dbGaP study accession phs001672.v4.p1) was used to replicate GWAS results. MVPTOT included 186,689 European ancestry participants who completed the PTSD Checklist–Civilian Version and passed QC. Details of MVPTOT have been published .Summary data from MVPublished . SNPs wep values. MTAG was used to perform multivariate analysis with PTSD and LTE GWASs. The maxFDR statistic was used to test for MTAG model assumptions   performsumptions .https://atlas.ctglab.nl/) (p < 1.05 × 10−5).To understand further how functional changes of significant loci are associated with human traits and diseases, we conducted a PheWAS of leading SNPs from PTSD and LTE loci using data from the GWAS Atlas (available at lab.nl/) . BonferrN = 134,586 participants). Across the cohorts, PTSD was assessed using a variety of different methods (n = 19 methods); the most common methods were versions of the Clinician-Administered PTSD Scale (n = 18 studies) and PTSD Checklist (n = 14 studies). The majority of participants  were analyzed based on PTSD symptom scores; the remaining participants  did not have symptom scores available and were analyzed based on PTSD case-control status.The PTSD GWAS meta-analysis included 182,199 participants of European ancestry from 51 cohorts . The larh2SNP of meta-analysis of cohorts analyzed by symptom scores was 0.0547  . The rg between the symptom score and case-control analyses was very high . Thus, symptom score and case-control GWASs were meta-analyzed. We identified 5 genome-wide significant loci  (The × 10−39) . The h2Sant loci . Leadingnd PHF2) .h2SNP was 0.0389 , 31% lower than the PTSD GWAS without LTE covariate adjustment (p = 8.6 × 10−20). There was a genome-wide significant locus in an uncharacterized region, CTC-340A15.2, on chromosome 5 that was not identified in the PTSD GWAS . Six loci showed genome-wide significance , an intergenic region in chromosome 14 near MDGA, and upstream of CCDC8 on chromosome 19. Gene-based analysis identified SGCD  and C20orf112 . GWAS of LTE count weighted by trauma-specific PTSD prevalences yielded highly similar results, being highly genetically correlated to the unweighted count .We performed GWAS of LTE count in the UKBB subset of the PGC-PTSD GWAS data . Of participants, 30.9% reported 1 LTE, 14.8% reported 2 LTEs, 6.3% reported 3 LTEs, and 3.3% reported 4 or more LTEs . The h2Sificance , Table 2rg between PTSD and LTE was high . To explore this genetic overlap, we contrasted patterns of rg of PTSD and LTE to other traits. Testing 772 human traits and diseases, we observed 269 and 217 rgs that survived Bonferroni multiple testing correction (p < 6.47 × 10−5) for PTSD and LTE, respectively  . Conversely, risk taking showed a significantly higher genetic correlation with LTE  than with PTSD .The ectively . There w × 10−5) . FifteenSGCD on chromosome 5, an intergenic region on chromosome 16 near ZKSCAN2 and AQP8, and the intron of STAU1 on chromosome 20. In gene-based analysis, there were 8 significant genes, including 5 genes not identified from the original GWAS gene-based analysis   .TOT (p < .006) .Of the 9 loci identified across the PTSD GWASs (5 from the PGC GWAS and 4 from the MTAG), 4 replicated significantly in MVP < .006) . Of the 06)  . We obsep = 2.9 × 10−5, frontal cortex Brodmann area (BA) 9 p = 3.53 × 10−5, cerebellum p = 1.09 × 10−4, anterior cingulate cortex BA 24 p = 1.29 × 10−4, cerebellar hemisphere p = 1.43 × 10−3, nucleus accumbens/basal ganglia p = 3.6 × 10−4). There was no significant enrichment detected in any sets from the list of curated gene sets and Gene Ontology terms (We found enrichment of genes involved in brain transcriptomic regulation in PTSD . All bragy terms .p = 1.27 × 10−11), rs10821140 , rs146918648 .We identified 200 phenome-wide significant associations , with morg = 0.92–1.14) , we performed a GWAS of LTE and contrasted it to GWAS results for PTSD. We found that LTE has h2SNP comparable to PTSD and high rg compared with PTSD. We leveraged the rg to significantly enhance PTSD discovery power using a multivariate approach  , we founapproach .h2SNP of PTSD adjusted for LTE being lower than the unadjusted estimate is that it may have removed genetic effects on PTSD mediated by trauma exposure  effects and thus would be smaller.One explanation for exposure ,13. Giverg to quantify the genetic overlap between LTE and PTSD, finding similar magnitude to findings from twin studies  is implicated in PTSD response to trauma-, fear-, and threat-related stimuli ,56. BA 9Stress-related disorders are phenotypically complex and heterogeneous , which lNovel replicable risk loci for PTSD identified by incorporating quantitative symptom data and trauma exposure information into GWASs offer us new insights into the genetic architecture of PTSD. Beyond the nature of LTE as an environmental exposure, there is a heritable component to LTE that overlaps highly with PTSD to impart an enhanced understanding of PTSD genetics. In future investigations, the genetic architectures of PTSD and LTE could be further delineated using causal mediation analysis , which cmmc2mmc3