Notably, two years of schooling had a larger effect on intelligence than the lifetime consequences, since birth, of SES or cogPGS-based inequalities. However, schooling showed no interaction with cogPGS or SES for the three intelligence domains tested. While schooling had strong main effects on intelligence, it did not lessen, nor widen the impact of these preexisting SES or genetic factors.Schooling, socioeconomic status (SES), and genetics all impact intelligence. However, it is unclear to what extent their contributions are unique and if they interact. Here we used a multi-trait polygenic score for cognition (cogPGS) with a quasi-experimental regression discontinuity design to isolate how months of schooling relate to intelligence in 6567 children (aged 9–11). We found large, independent effects of schooling ( There has been considerable controversy on the source of these differences. Decades of research have shown intelligence to be highly associated with genetic differences (heritability estimates around 0.6) but also impacted by experiences and context 12.Inter-individual differences in intelligence predict a variety of important life outcomes, such as life satisfaction, mortality, and educational achievement (EA)15. This has been shown in longitudinal studies controlling for prior intelligence16 and in studies evaluating the cognitive effects of policy changes regarding compulsory schooling17. A third method to evaluate the impact of schooling  exploits the fact children are put in a grade based on an arbitrary age cut-off, and allows us to separate the effect of chronological age from months of schooling18. Studies using this method also replicate the findings that schooling affects intelligence20. Thus, many lines of research provide converging evidence that schooling can change abilities often thought to be “fixed”, such as fluid intelligence and working memory, with estimates of one year of additional schooling benefitting cognitive abilities somewhere between 1 to 5 IQ points, or 0.07 to 0.3 SD21. What is less clear is how the impact of schooling interacts with the environments that children experience and with their genetic predispositions—in other words, do school settings amplify preexisting differences , or conversely, weaken existing differences between children .Schooling is an important environmental factor, having a large impact on intelligence in children22. While commonly implicated in initial differences in intelligence, SES has also been found to widen existing differences throughout development25. Recently, research on SES has been criticized for neglecting the role of genetics—as parents not only hand down environments but also genes27. For example, a genetically informed twin study found a strong genetic influence on SES and its association with intelligence28. While there is a large body of research looking at how preexisting SES differences interplay with years of schooling29, none of these previous studies have incorporated genetics.Much of a child’s environment is captured by socioeconomic status (SES), a summary measure usually comprised of household income, parental education, and neighborhood quality30. This and similar polygenic scores correlate moderately (r ~0.3) with SES32, but little is known about their unique contributions to different domains of intelligence. And even less known if, or how, they interplay with schooling.Fortunately, genome-wide association studies (GWAS) with extremely large sample sizes and the viability of polygenic scores, have made it possible to incorporate genetically informative measures into a study. A polygenic score is an index that combines thousands/millions of DNA regions (each with only a tiny effect on the trait of interest) and gives a value to each individual representing their genetic propensity. Relevant to us here, a multi-trait cognitive polygenic score (cogPGS) was recently shown to predict 7–10% of the variance in cognitive performance36. To date, there are only a few studies on GE interplay using polygenic scores. Of these studies, most have focused on SES as the environmental variable of interest and none have used the environmental variable of schooling, which is particularly relevant to intelligence. Unfortunately, GE-interplay results with SES have been inconsistent for educational achievement/attainment—some positive, negative, or null41. While there is a high correlation between educational achievement and intelligence, educational achievement is a broader concept thought to also include personality characteristics such as consciousness and openness to experience43. Furthermore, we are not aware of any research examining if schooling moderates’ genetic effects on intelligence.Gene-by-environment (GE) interplay is often proposed as an explanation of how intelligence can show high heritability alongside malleabilitycIQ), fluid intelligence (fIQ), and working memory (WM). We chose these domains of intelligence since they are particularly important for educational outcomes44. WM was included since WM has shown to be heavily impacted by schooling21, malleable45, and potentially relevant for GE-interplay46. Our main aim was to examine if GE-interplay is present for these domains of cognition. Specifically, we tested if the effect of (1) schooling or (2) SES is moderated by cogPGS, and (3) a three-way interaction between schooling, SES, and cogPGS. These interactions allowed us to test if schooling enhances or compensates for preexisting genetic and environmental inequity.Schooling, SES, and genetics thus represent three substantial contributors to intelligence, but it is unclear to what extent their contributions are (1) unique and (2) interact with one another. In a sample of 3rd to 5th grade children, we estimated the unique contributions of a year of schooling, SES, and cogPGS on crystallized intelligence . SES and cogPGS were positively correlated with each other and each cognitive domain  for each cognitive domain we examined . One year of schooling  contributed 0.22 SD, 0.14 SD, and 0.14 SD to the development of cIQ, fIQ, and WM, respectively  more than fIQ (ratio = 0.54). Interestingly, WM, a subtask of fIQ, had the highest ratio of 2.2, indicating for this age range, 1 year of schooling had double the effect of 1 year of chronological age. There were no significant differences between the schooling coefficients for cIQ, fIQ, or WM (|Z| < 1.96).We found that schooling had a significant effect  yet worse in cIQ  and WM . CogPGS and SES both showed independent significant effects  for cIQ, fIQ, and WM. The effect sizes of cogPGS (β = 0.16) and SES (β = 0.29) were the largest for cIQ. CogPGS showed similarly sized effects for fIQ (β = 0.09) and WM (β = 0.09), yet SES had a slightly higher effect for WM (β = 0.22) than fIQ (β = 0.18).Sex had a significant effect for each cognitive domain, with females performing better in cIQ, fIQ, and WM. Since research has shown that different subcomponents of SES could relate differently to intelligence50, we also performed an analysis of each subcomponent. All components were significant (pFDR < 0.001) for each cognitive domain. cIQ showed the largest effects for parental education (β = 0.26), family income (β = 0.22) and neighborhood quality (β = 0.11). In a similar fashion to the SES composite, WM had slightly larger effect sizes than fIQ for parental education , family income , and neighborhood quality .The analysis above showed that our combined measure of SES had effects on 52. Therefore, we conducted a post hoc sibling analysis  to estimate within (βw) and between (βB) family effects of cogPGS on cIQ, fIQ, and WM. Within-family effects (βw) are less confounded by the shared environment since the transmission of alleles is random, giving each sibling an equal probability of inheriting any given allele. Yet, there is no variance within families for SES and very little for schooling therefore, the sibling analysis is limited to genetics (cogPGS). We found cIQ’s within-family effect  to be roughly half of the between-family effect . For fIQ we found a significant within-family effect , yet the between-family effect was not significant . For WM, neither the within-family  nor the between-family  effects were significant.Gene-environment correlations can inflate the estimation of cogPGS on phenotypes53. We, therefore, carried out post hoc Bayesian null hypothesis testing using region of practical equivalence (ROPE) boundaries with 95% highest-density intervals (HDI) for the two-way interaction terms of interest  and WM (5%). However, most of the terms have a substantial part of their distribution overlapping with the ROPE boundaries of 0.02. This means we cannot confirm nor disprove the null hypothesis for a maximal effect size of interest of 0.02 SDs.Almost all interaction terms were entirely within a ROPE boundary of 0.05 Fig. . This me= 0.03 & CFI = 0.987; Supplementary Fig. g) and used them as the dependent variable. The standardized effect of schooling on g was 0.136 SD (pFDR < 0.001)—a similar magnitude as fIQ and WM’s effects. Mirroring the other cognitive domains, no two- or three-way interactions were significant following FDR correction . The results from that analysis stayed generally the same as the analysis of the full population (Supplementary Table = 0.005–0.020), yet this was followed by larger decreases from SES (β range = −0.046 to −0.080).CogPGS has less accuracy in non-European populations therefore, we conducted a post hoc reanalysis in the full model Eq.  for eachcIQ, fIQ, and WM. In line with previous research, we found the raw effect of schooling on cIQ to be larger than for fIQ, though this difference was not significant13. This was also true for the relative influence of schooling compared to the effect of chronological age, as that ratio for cIQ was almost double that of fIQ (1.1 vs 0.54), showing almost near equal influences per year of schooling and age. Surprisingly, WM had the highest ratio (2.2), with the effect of schooling being more than double that of age. While this is in agreement with a previous study in younger children21, it should be interpreted with caution as the WM measure comprised of only one task59.Schooling showed substantial and independent effects for each intelligence domain tested: cIQ, fIQ, and WM. In a follow-up analysis, we estimated the contribution of each SES component separately. Notably, all SES components were significant for each intelligence domain, with the effects from parental education and income being similar in size while neighborhood quality was roughly half.As expected, SES and cogPGS were highly correlated with each other, highlighting the need to isolate the independent effects of each. Both had large, independent effects on 31, which makes it difficult to support causal inferences of these factors as well as interpret the interaction between them 60. This gene-environment dependence can cause spurious gene-environment interactions61. Our sibling analysis sheds light on this issue. We found the within-family effect of cogPGS to be roughly half of the between-family effect for cIQ, in line with the literature52. This indicates the presence of passive genotype-environment correlations—whereby parents create family environments consistent with their genotypes, which in turn facilitate the development of their children’s intelligence. Since we only had data from 392 families, a lack of statistical power is most likely the reason for our null findings for fIQ and WM. While our cogPGS estimate in the full sample should be interpreted with caution, previous research has shown SES to be the major source of these between-family effects62.A child’s SES is not independent of their cogPGS34, the transactional model35 and the bioecological model36. In line with this, a meta-analysis on twin research found the heritability of intelligence to increase with higher SES in the United States63. But this effect is far from consistent. The same meta-analysis did not replicate this finding on data collected outside the United States. Furthermore, a large twin study found negative and null results for heritability by SES interaction regarding mathematics and reading in Florida64. Two studies using a similar polygenic marker to ours did not find evidence of GE-interplay for EA66, yet a study with 130,000 adults in the UK found a very small negative interaction of SES with neighborhood quality for fIQ and EA40. Crucially, the standardized effect size of this interaction was (β < 0.02 SD)—translating to less than a third IQ point throughout one’s entire life—in turn having no practical effect for the individual.Predominant theories of GE-interplay imply a positive sign—genetically endowed cognition influences one’s proximal environment, and that environment, in turn, influences one’s cognition in continuous, reciprocal interactions, such as the multiplier theory39. However, we see their result as more relevant to inform a cogPGS-SES effect rather than the gene-by-schooling interaction.We did not find any significant interaction between schooling and SES or cogPGS. One strength of our design is that schooling is, in principle, independent from cogPGS and SES. We are not aware of any other research looking at GE-interplay with schooling. A recent study did find low PGS children in high-SES schools to continue with mathematics much longer than genetically similar children in low-SES schools29. Conversely, there is some evidence that schooling might lessen socioeconomic disparities between children  for cognitive skills67.We expected our interaction terms to either compensate or accelerate preexisting differences. Schooling, for example, could increase  economic/genetic inequality or lessen these differences between children . The Coleman Report, a seminal study with more than half a million students and over 3000 schools in 1966, controversially concluded schools did not contribute to widening achievement gaps between children55. For example, an interaction with schooling as small as 0.02 SD could accumulate over five years to 0.1 SD or roughly 30% of the largest SES main effect . This is, of course, a simplistic scenario assuming no counteracting mechanisms, yet it illustrates how very small effect sizes can become consequential69. In contrast, cogPGS–SES’ interaction is a lifelong effect and does not have the potential to accumulate in the same way. However, our sample had a slightly lower SES (Cohen’s d = −0.23) than the average for the United States, therefore, we cannot rule out an interaction at the lower tail of the SES distribution.Our study indicates schooling to not be a major driving force for either increasing or decreasing differences due to SES or cogPGS. Yet, we emphasize caution in interpreting these null effects as our range of schooling (3rd–5th grade) was limited, and Bayesian analysis showed that an effect of less than 0.02 SD could not be ruled out  the relative sample size differences between the GWAS included and (2) the genetic correlation of these traits. In our case, there are sample size differences between educational attainment and cognitive ability, yet the very high (r ~ 0.75) genetic correlation between these traits most likely mitigates this issue73. Lastly, a strength of our study is that we controlled for ancestry-based genetic PCs in the full model, rather than just correcting cogPGS. While this means SES’s relationship with cognition is controlled for population stratification, it also brought the limitation that we had to exclude subjects without DNA, resulting in the average level of SES increasing.One limitation of this study is that the 1.1 million individuals used to estimate the cogPGS are heavily biased towards those of European descent and from higher SES areasWe found that schooling causes relatively large increases in children’s intelligence. The two years of schooling (3rd to 5th grade) caused a larger difference in intelligence than either SES or cogPGS. However, schooling did not change the rank order of individuals’ intelligence. This was shown by the lack of significant two-way interactions between Schooling, SES, and cogPGS, although our power to detect potentially meaningful small effects for schooling was limited. Intriguingly, we did not find any interaction between SES and cogPGS, this means that children’s genetic differences do not matter more, or less, for intelligence dependent upon their SES background.74. To correct this, we randomly selected one child per family. We also excluded children that reported ever repeating a grade, as this would bias our statistical analysis. This resulted in a total of 6567 children between grades three and five enrolled in regular private or public schooling.We used data from the Adolescent Brain Cognitive Development (ABCD) study, which was reviewed and approved by the central institutional review board at the University of California, San Diego. Written parental informed consent, along with child assent, was obtained for all participants. Part of the sample included siblings, along with non-representative amounts of monozygotic and dizygotic twinsSince recruitment was continuous throughout the year, this allowed us to measure schooling in months. We excluded summer vacation (15 June until 15 August) from our schooling variable by coding children recruited during summer as having the last month of schooling from the previous grade. This resulted in a variable with 10 months of schooling per year. Chronological age was also measured in months to preserve anonymity.fIQ), and crystallized intelligence (cIQ) from the NIH toolbox cognition battery75. These tasks have been shown to have good reliability and validity77. cIQ consisted of a picture vocabulary task and an oral reading recognition task. fIQ consisted of five tasks; pattern comparison processing speed test, a list-sorting WM test, a picture sequence memory test, a flanker task, and a dimensional change card sort task. One of the fluid tasks, list-sorting WM, was used as our WM measure. We used the sum scores provided by the NIH toolbox, rather than latent factors, to facilitate comparison with other studies. All outliers were brought to the fence (Tukey/Boxplot Method). Variables were then standardized to a mean of zero and a standard deviation of one.Our endogenous variables were working memory (WM), fluid  and neighborhood quality . Children missing more than one of the three measures were excluded (n = 45). Parental education was recorded to reflect middle school or less (1), some high school (2), high school graduate (3), some college/associates degree (4), bachelor’s degree (5), a master’s degree (6), or professional degree (7). Our measure of neighborhood quality was the area deprivation index calculated from the American Community Survey using the address of primary residency78. The SES composite and each subcomponent were standardized with a mean of zero and a standard deviation of one.SES was defined as the first principal component from a probabilistic PCA of total household income, highest parental education, and neighborhood quality. We used a probabilistic PCA because of the decent amounts of non-overlapping missing data in parental income , or had minor allele frequencies (MAF) <1%. Moreover, individuals with an absolute autosomal heterozygosity >0.2 or more than 2% missing genotypes were excluded. These filtering steps resulted in a cleaned dataset of 10,069 individuals and 430,622 variants. Subsequently, haplotypes were pre-phased with SHAPEIT2. Genetic markers were imputed using the IMPUTE4 software. As the reference population, we used the 1000 Genomes haplotypes—Phase 3 integrated variant set release in NCBI build 37 (hg19) coordinates. This is a mixed, multi-ethnic population dataset consisting of 2504 samples and 5008 haplotypes from populations of Europeans, Africans, East Asians, Southern Asians, and Americans (https://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3.html). One of the main advantages of this imputation approach is that it provides better concordance in diverse human populations . After imputation, genotypes with an INFO score <0.3 or a MAF <0.001% were excluded. The final number of SNPs after imputation was 40,637,119 in a total of 10,069 individuals.Quality control (QC), imputation, and genetic PCA were performed by the National Bioinformatics Infrastructure Sweden (NBIS), as a service contracted by us. Before imputation, SNPs were excluded if they had high levels of missing data , departed from Hardy–Weinberg equilibrium as calculated in the lfa R package (sHWE)  module as implemented in RICOPILI. First, SNPs were pruned to ensure that there is little linkage disequilibrium between SNPs . The LD pruning was repeated until 100 K SNPs were reached. Then, the resulting SNPs go into the PCA. We used the first 20 principal components (PCs) from that genetic PCA as covariates in our main analyses.82. This was calculated by the sum of effect sizes of thousands of SNPs  that were discovered by a large genome-wide association study on educational attainment, mathematical ability, and general cognitive ability30. That study has available all effects sizes and p values of their SNPs on the website of the Social Science Genetics Association Consortium (https://www.thessgac.org/data). We used the data available by the consortium from a multi-trait analysis of GWAS83, which, in our case, represents a joint polygenic score focused on a GWAS of cognitive performance and complemented by information from a GWAS on educational attainment, a GWAS on the highest-level math class completed, and a GWAS on self-reported math ability. This joint analysis is ideal because pairwise genetic correlations of these traits were high30. Furthermore, these GWAS had hundreds of thousands of individuals, and such a large sample size allows new studies to detect effects in samples of a few hundred individuals with 80% statistical power30.With the SNPs QCed and imputed, our group then created polygenic scores for cognitive performance  for each participant using PRSice-2P < 5e-5 from the GWAS as we wanted to use the full GWAS sample (limited SNP release due to anonymity concerns). After adopting a clumping sliding window of 250 kb, with the linkage disequilibrium clumping set to r2 > 0.25, this resulted in 5255 SNPs for our dataset. Finally, we normalized the polygenic scores .For the creation of cogPGS in our samples, we performed clumping and pruning to remove nearby SNPs that are correlated with one another. We used the SNPs below the original threshold of fIQ, cIQ, and WM); Y denotes these outcome variables. Every model included a random intercept per collection site (j) to account for the clustering of individuals (i). Models were fit using maximum likelihood estimation with the R package lme4 (v. 1.1–27.1)85. All results are reported in standard units , unless specified otherwise. P values on all models were derived using Satterthwaite’s degrees of freedom method with the lmerTest package (v. 3.1-3)86, with an alpha level of 0.05.We used random intercept mixed-effects models to predict the effect of schooling from our three cognitive variables of interest  is on the level of grade. We included post hoc sensitivity checks using grades 3–5 and comparing the two most frequent, grades 4 and 5, for Eq. z-test on the difference between the coefficients. This method sums the standard error from the coefficients as error variance, assuming no correlation between the coefficients, thereby being conservative89. We used an absolute z-value of 1.96, corresponding to an alpha level of 0.05.We broke the equation into four steps to more clearly convey the identification assumptions behind our estimandssign Eq.  was usedβ* represents 20 fixed effects for each principal component. This model allowed us to estimate the causal effect of one year of schooling, along with estimating the independent effects of chronological age, sex, cogPGS, and SES. Crucially, schooling is the only exogenous variable. It is important to highlight that by adding 20 genetic principal components to the model, we are controlling all variables for population stratification, most notably SES’s relationship with intelligence.We then add a fixed effect for cogPGS and SES, while controlling sex and 20 genetic principal components (Eq. β6) or SES . To properly isolate schooling’s interaction effects and avoid spurious results, we included interactions of no interest with age . We were also interested to see if SES interacted with cogPGS , yet we emphasize caution in interpreting this term as any interaction found could be spurious due to endogeneity between SES and cogPGS90. We refer to these interactions as GE-interplay, highlighting that they could be caused by gene-by-environment interaction (GxE) and/or gene-by-environment correlation (rGE). rGE refers to the fact that genotypes and environments are not randomly distributed, while GxE is an interaction in the classical sense whereby the environment (or genotype) interacts with the other91. Due to schooling’s exogenous nature, a significant interaction would be most likely due to GxE, yet this is not the case for an interaction between SES and cogPGS which could be the result of either GxE or rGE.In Eq. β11). To accomplish this, we added a three-way interaction term, which was then compared, using a likelihood-ratio test to Eq. Lastly, we were interested in the presence of a three-way interaction between schooling, SES, and cogPGS Eq. , β11. To50. We considered the entire subcomponent analysis a family, therefore, p values were corrected using a false discovery rate (FDR) for the number of terms tested.In cases of significant SES composite findings, we also reported the results for each SES subcomponent separately. This was done for two reasons: (1) research showing specific SES measures to relate to intelligence more than others, and (2) call in the literature to treat SES measures independentlycIQ, fIQ, and WM we conducted a sibling analysis using the procedure described by Selzam and colleagues 201951. This allowed us to estimate the within-family direct genetic effects (βw) and the between-family genetic effects (βB). For families to be included in the analysis they needed to have at least two non-identical siblings. We considered the entire sibling analysis a family; therefore, p values were corrected using false discovery rate (FDR) for the number of terms of interest tested. SES has been shown to largely explain between-family effects therefore cogPGS in our main analysis is, to some extent, controlled for these between-family effects62.To estimate the within and between-family effect cogPGS on 92. This included all the tasks from the NIH toolbox cognition battery along with the overall score on a WM nback task and the total raw score from matrix reasoning on the Wechsler Intelligence Scale for Children. A single factor solution fit the data well (RMSEA = 0.03 and CFI = 0.987) when correlated covariances were added between the two crystallized tasks, the two WM tasks, and a cluster of three tasks sharing similar attributes . We then extracted factor scores and fit them to Eqs. We fit nine cognitive tasks into a single-factor solution using confirmatory factor analysis with the Lavaan package in R , resulting in the exclusion of 43% of the sample58. The proportion is equal to the full ABCD sample, which is representative of the United States. Yet this subsample had significantly higher SES . All variables were (re)standardized to this population with a mean of 0 and an SD of 1. We then fit Eq. cIQ, fIQ, WM, and g and used FDR correction on the p values  in 15 chains. We set weakly informative, normally distributed priors for our terms of interest, keeping the default non-informative priors unless otherwise specified. For cIQ age (β1) and schooling (β2) had normally distributed priors with a mean of .20 and a standard deviation (SD) of 0.15. CogPGS (β4) had a prior with a mean of 0.15 and an SD of 0.1, while SES (β3) had a larger mean of 0.25 and an SD of 0.2. All two-way interaction terms (β6-10) had two-tailed priors with a mean of zero and an SD of 0.1. Gender and the 20 PCs had default flat priors. For fIQ and WM we set identical priors; these were a mean of 0.15 and an SD of 0.15 for age (β1) and schooling (β2). CogPGS (β4) had a prior with a mean of 0.1 and an SD of 0.1, while SES (β3) was double that with a mean of 0.2 and an SD of 0.2. All two-way interaction terms (β6-10) had identical priors to cIQ, along with flat priors for gender and each principal component. We also fit Eq. β11) had a mean of 0 and an SD of 0.1. Notably, our results for the interactions in Eqs. To have a better understanding of our results, we conducted Bayesian mixed-effects models of Eq.  CogPGS β had a pr CogPGS β had a prβ6, β7, β10, β11) for cIQ, fIQ, and WM. Crucially, we used two ROPE boundaries in standard units of 0.05 and 0.02. This was done for two reasons, (1) a lack of consensus in the field on a minimal effect size of interest, and (2) schooling representing a cumulative process. Since the schooling interaction terms  are cumulative, they could warrant a smaller minimal effect size of interest than the cogPGS-SES’ interaction56. We then calculated the percentage of the estimated 95% HDI lying within our ROPE boundaries, giving us the probability of the null hypothesis. If 95% of the estimates' posterior distribution lies within ROPE boundaries, the null hypothesis can be confirmed as any probable effect is considered too small to be meaningful.We then computed the 95% highest-density intervals (HDI) of the posterior distributions for our three interaction terms of interest (Further information on research design is available in the Supplementary MaterialReporting Summary


Sorghum bicolor (L.) Moench) accessions from the Sorghum Association Panel (SAP) at an average coverage of 38× (25–72×), enabling the development of a high‐density genomic marker set of 43 983 694 variants including single‐nucleotide polymorphisms (approximately 38 million), insertions/deletions (indels) (approximately 5 million), and copy number variants (CNVs) (approximately 170 000). We observe slightly more deletions among indels and a much higher prevalence of deletions among CNVs compared to insertions. This new marker set enabled the identification of several novel putative genomic associations for plant height and tannin content, which were not identified when using previous lower‐density marker sets. WGS identified and scored variants in 5‐kb bins where available genotyping‐by‐sequencing (GBS) data captured no variants, with half of all bins in the genome falling into this category. The predictive ability of genomic best unbiased linear predictor (GBLUP) models was increased by an average of 30% by using WGS markers rather than GBS markers. We identified 18 selection peaks across subpopulations that formed due to evolutionary divergence during domestication, and we found six Fst peaks resulting from comparisons between converted lines and breeding lines within the SAP that were distinct from the peaks associated with historic selection. This population has served and continues to serve as a significant public resource for sorghum research and demonstrates the value of improving upon existing genomic resources.Association mapping panels represent foundational resources for understanding the genetic basis of phenotypic diversity and serve to advance plant breeding by exploring genetic variation across diverse accessions. We report the whole‐genome sequencing (WGS) of 400 sorghum ( The whole‐genome sequencing of the sorghum association panel, which represents the most heavily utilized panel in sorghum breeding, will serve as a pivotal resource in the sorghum community to provide improved genotype‐to‐phenotype associations, genomic prediction, and pan‐genome graph construction as demonstrated here. These data will support ongoing and future research focused on gene discovery and trait introgression in the sorghum community and further provide insights into the historic selection and domestication of this multi‐purpose crop. Zea mays L.)    Moench)  , the first sorghum diversity panel, is composed of temperate‐adapted breeding lines, as well as converted (photoperiod‐insensitive) tropical accessions from the Sorghum Conversion Program (SCP)  for population and translational genomic analysis using WGS. The application of high‐throughput genotyping and robust variant discovery for the highly diverse SAP provides the genomic resources necessary for acceleration of gene discovery, genomics‐assisted breeding, and genetic engineering toward improved cultivar development and carbon‐negative agriculture. We demonstrate the value of the WGS genomic resource over GBS markers through comparative advantages in the identification of novel genomic associations and increased accuracy in genomic prediction for various traits.http://www.ars‐grin.gov) (File S1). Seedlings were grown by sowing three to five seeds from each accession in a plastic pot in the Biosystems Research Complex greenhouse at Clemson University, Clemson, SC. Tissue was collected from 2‐week‐old seedlings and lyophilized for three days in a LABCONCO FreeZone 4.5‐L −50°C benchtop freeze dryer prior to DNA extraction. Phenotypic data for all the traits used in genome‐wide association and prediction analyses were derived from previously published datasets   in the R programming language    > 5% for each subpopulation  and temperate breeding lines (N = 96) within the panel using the same parameters mentioned above (File S1). Tajima's D for the whole panel was calculated for 1‐Mb non‐overlapping windows using the vcftools function ‐‐TajD.Subpopulations identified using ADMIXTURE analysis (K = 6) were used to estimate Fmiss = 0.3 ‐‐maf = 0.05). All models were run using a MAF filter of 0.05 and linear mixed models (LMMs) of the following the form:y is a vector of phenotypic values for a single trait, X is a numeric genotype matrix generated from the SNPs, β represents an unknown vector of fixed effects and includes the effect size for each SNPs, Z is the design matrix for random effects, u is an unknown vector of random effects, and ε is the unknown vector of residuals. These models test the alternative hypothesis H1: β ≠ 0 against the null hypothesis H0: β = 0 for each SNP. Manhattan and Q‐Q plots were generated using R‐CMplot version 3.6.0 and ggplot2 version 3.3.5. GEMMA was also used to run Bayesian sparse LMMs (BSLMMs) to better identify causative variants, with a probit model used for binary phenotypic data. The BSLMM model assumes fixed effects are distributed according to the sparse prior β ∼ πN  was calculated on a condition‐by‐condition basis using ashr in R , where σ2g is additive genetic variance and A is the realized additive relationship matrix calculated from an n × m genotype matrix with n genotypes and m markers using the A.mat function from the rrBLUP package , where σ2e is the residual variance and I is an identity matrix.Previously published SAP phenotypic data , and P‐values were adjusted using Benjamini–Hochberg correction  were used to identify signatures of artificial selection due to temperate conversion and/or breeding.The development of sorghum racial types is thought to be an outcome of multiple domestication events and subsequent local adaptations leading to the distinct population structure observed  for subpopulations identified using population structure analysis . Another commonly selected region around 21–29 Mb of Chr3 had 68 genes that included several genes involved in biological regulation and molecular function including photosynthesis, but no QTL was previously mapped in the region , which is associated with tannin/polyphenolic content  that  encodes for yellow pericarp (seed color) in sorghum  of significant sweeps, followed by durra (seven), while kafir had the lowest 1)  and height (Dw2: Sobic.006G067700) genes that were introgressed for early maturity and short stature, respectively, during sorghum conversion  explaining a large majority of the phenotypic variation in our population. All three major dwarfing loci showed significant association for PH using all three variant types independently  also showed a significant association with PH for SNP as well as indel variants  and tannin content have been thoroughly examined due to their significant impacts on both historic selection , was not identified in GWAS using tannin content likely due to duplicate recessive epistatic interactions between the Tan1 and Tan2 loci  for all traits compared to GBLUP models using GBS marker data  for the whole genome but varied across chromosomes , which is consistent with previous results that found limited recombination on Chr6  sorghum gene, Sobic.002G169633, which is located approximately 130 kb upstream of the associated GWAS peak and encodes a protein kinase with NB‐ARC and LRR domains  that showed common FTan1 locus. The tannin loci have historically been subjected to bidirectional selection because of varied local herbivore threats and human taste sensitivity, resulting in natural variation around these loci across sorghum germplasm  and tannin content loci (Tan1) identified previously using GBS markers, we identified novel associations across two loci for PH and four loci for tannin content that were not identified with GBS markers. Previous studies have demonstrated that WGS improves both the mapping resolution and the ability to identify novel associations over marker data derived from GBS , we also detected a significant association for the putative Dw4 locus at approximately 6.6 Mb of Chr6 as previously reported  we have identified is consistent with previous association results in the SAP  using a genomic relatedness matrix derived from GBS or WGS markers to compare their predictive ability for agronomic, yield, and quality traits. On average, a 29% increase in predictive ability was observed across nine traits, which is a substantial increase for most of these traits as they are quantitative and complex traits. This improvement in predictive ability is due to the ability of WGS markers to capture additive genomic relationships better because of increased density and coverage of genetic markers, subsequently resulting in improvement in the total genetic variance explained by the model .Figure S9. Heatmap showing genomic relatedness between individual accessions within the sorghum association panel.Figure S10. Regions across the sorghum genome demonstrating selective sweeps for various subpopulations based on ADMIXTURE analysis.Figure S11. Measures of Tajima's D across the genome within the photoperiod converted lines (Conv), breeding lines (Bred), and the whole population.Figure S12. Genome‐wide association for presence of testa layer using a probit Bayesian sparse linear mixed model.Figure S13. Heatmap for linkage disequilibrium around association peak for tannin content in chromosome 3.Figure S14. Genome‐wide measures for pleiotropic effects of associated regions for 19 traits.Figure S15. Trait correlation across the sorghum genome for the 19 traits in the pleiotropy analysis.Figure S16. Genome‐wide associations for grain yield components (a) and grain composition (b) using linear mixed models.Figure S17. Variant graph of Dw1 locus at different aspects demonstrating macro‐ and micro‐variations in the locus structure.Click here for additional data file.


Vigna mungo L. Hepper) is an important tropical and sub-tropical short-duration legume that is rich in dietary protein and micronutrients. Producing high-yielding blackgram varieties is hampered by insufficient genetic variability, absence of suitable ideotypes, low harvest index and susceptibility to biotic-abiotic stresses. Seed yield, a complex trait resulting from the expression and interaction of multiple genes, necessitates the evaluation of diverse germplasm for the identification of novel yield contributing traits. Henceforth, a panel of 100 blackgram genotypes was evaluated at two locations (Ludhiana and Gurdaspur) across two seasons (Spring 2019 and Spring 2020) for 14 different yield related traits. A wide range of variability, high broad-sense heritability and a high correlation of grain yield were observed for 12 out of 14 traits studied among all environments. Investigation of population structure in the panel using a set of 4,623 filtered SNPs led to identification of four sub-populations based on ad-hoc delta K and Cross entropy value. Using Farm CPU model and Mixed Linear Model algorithms, a total of 49 significant SNP associations representing 42 QTLs were identified. Allelic effects were found to be statistically significant at 37 out of 42 QTLs and 50 known candidate genes were identified in 24 of QTLs.Blackgram ( Vigna mungo L. Hepper), a diploid (2n = 2X = 22), short duration legume crop of family Leguminaceae, was domesticated in Northern South Asia from progenitor Vigna mungo var. silvestris  involved in yield and yield related traits using GBS. This study provides a unique genomic resource for the genetic dissection of important traits aimed at blackgram improvement.V. mungo) genotypes was used for the present study. These included 54 genotypes procured from National Bureau of Plant Genetic Resources (NBPGR), New Delhi, India, while, the remaining genotypes were from germplasm collection of Punjab Agricultural University (PAU), Ludhiana, India (Spring 2019 and Spring 2020). The seeds were sown in a 2 m long row with 10 cm plant to plant spacing and 30 cm row to row spacing. Ludhiana  lies in a sub-tropical zone characterized by relatively high temperatures and low precipitation while Gurdaspur region  is characterized by lower temperature and high humidity coupled with abundant rainfall. The weekly mean temperature, relative humidity and rainfall for Ludhiana and Gurdaspur have been given in A panel consisting of 100 blackgram  is the effect of jth incomplete block within the ith replicate, Genk is the effect of the kth genotype and εijk is the error effect associated with the ith replication, jth incomplete block and kth genotype, assumed to be normally distributed with zero mean and variance σ2ε (l and Genl x Yeari are the effects of the lth year and Genotype x Year (G x Y) interactions represented by effect on the ith genotype in the lth year in the linear model for integrated analysis for multi-environment (across the years). The resulting analysis produced the adjusted trait phenotypic values as BLUPs (Best linear unbiased predictions) within and across environments. The genotypes are considered random effects in the BLUPs model, minimizing/eliminating the effect of the environment from phenotypic effects. The broad-sense heritability of trait at individual environment and across environments was calculated asData was collected in three replicates from five randomly selected plants of each genotype in each replicate for plant height at 90% pod maturity (PHM), branches per plant (BpP), nodes per plant (NpP), internodal length (IL = PHM/NpP), clusters per plant (CpP), pods per plant (PpP), pod length (PL), seeds per pod (SpP), biological yield per plant (BYpP), yield per plant (YpP), harvest index (HI) and hundred seed weight (HSW). Days to 50% flowering (DtF) and days to 90% pod maturity (DtM) were recorded based on the entire plot. For phenotypic analysis, environments were designated as E1 , E2 , E3 (Ludhiana combined for years 2019 & 2020), E4 , E5  and E6 (Gurdaspur combined for years 2019 & 2020). Due to the significant differences between two selected locations, combined analysis over two selected locations has not been done. Descriptive statistical analysis across all the environments was done using Meta-R v6.0 software . Statistance σ2ε . Yearl aWhere  effects . The LSD package  in R v4.phred score less than 34, were excluded from further analysis. High-quality paired end reads were aligned using Burrows-Wheeler Aligner (BWA) . A quality threshold score of 10 was applied to validate SNP loci  method . Genotyper (BWA)  to VignaSNP loci . Sorted SNP loci  with minSNP loci . After SSNP loci . The genBayesian-based approach in STRUCTURE v2.3.4 software  using a p-value of 0.001 or -log10p-value of 3.00 was used as a threshold to determine the significance of association   and Farmociation . The marociation . MTAs wee ≤ 0.05 .Vigna radiata reference genome (www.ncbi.nlm.nih.gov/assembly/GCF_000741045.1). A window of 200-kb region, upstream and downstream of the associated SNPs was searched to identify already reported candidate genes related to different traits studied  for all traits under all environments suggested strong genetic control (2 was observed for PHM (0.99 and 0.98), whereas lowest for DtF (0.61 and 0.91) both at Ludhiana and Gurdaspur. CpP , PpP , PL , SpP , BYpP , YpP , HSW  and HI  also recorded high H2.Higher broad sense heritability  were depicted by both the methods of second-order rate of change of the likelihood  as well 10 p-value ≥ 3, PVE >10%) with 12 of the 14 traits studied, across the three environments of the each locations  and YpP, HI (Q.YpP.8 and Q. HI.8.2), respectively , Q. NpP.6  and Q. CpP.10 both had three SNPs , whereas other QTLs were defined by a single SNP. Two QTLs for plant height at 90% pod maturity Q. PHM.4 and Q. PHM.8 showed consistent effect across all environments of both the locations. Among four QTLs identified for YpP, two (QTLs Q. YpP.4.1 and Q. YpP.8) were detected at Ludhiana, whereas Q. YpP.4.2 and Q. YpP.5 were detected at Gurdaspur. Three QTLs for HSW were detected, two for Ludhiana and one for Gurdaspur (Q.HSW.7) which was detected by both the FarmCPU  and MLM  algorithms. Out of seven QTLs for HI, two were detected for Ludhiana (Q. HI.1 and Q. HI.8.2) and five were detected for Gurdaspur . The phenotypic variation explained (PVE) by significant SNPs under FarmCPU method ranged from 10.751% (Q.YpP.8) to 22.292% (Q.NpP.6) at Ludhiana and 10.47% (Q. PHM.6.1) to 24.26% (Q. CpP.1) at Gurdaspur, whereas under MLM method, it ranged from 10.73% (Q.SpP.8.1) to 16.80% (Q.PHM.8) at Ludhiana and 10.08% (Q.PHM.4) to 14.69% (Q.CpP.4) at Gurdaspur.A few genomic regions harbored multiple significantly associated SNPs, as t-test statistic) for the respective traits  and significantly different mean values observed at Ludhiana (E3) and Gurdaspur (E6) and are presented in Q. SpP.8.2 was found to be superior at Ludhiana, whereas alternative allele was found to be superior at Gurdaspur. Allelic effects of YpP, HI and HSW are presented in Q. YpP.4.1; 1.38 g at Gurdaspur (E6) by Q. YpP.4.2; 1.26 g at Gurdaspur (E6) by Q. YpP.5 and 0.94 g at Ludhiana (E3) by Q. YpP.8; whereas significant phenotypic differences produced by superior and inferior alleles for HI were 3.91% at Ludhiana (E3) by Q. HI.1; 3.19% at Gurdaspur (E6) by Q. HI.5; 3.89% at Gurdaspur (E6) by Q. HI.6; 4.38% at Gurdaspur (E6) by Q. HI.7.1; 3.26% at Gurdaspur (E6) by Q. HI.8.1; 3.43% at Ludhiana (E3) by Q. HI.8.2 and 3.66% at Gurdaspur (E6) by Q. HI.10. Likewise, significant phenotypic differences produced by superior and inferior alleles for HSW were 0.32 g at Ludhiana (E3) by Q. HSW.6, 0.25 g at Gurdaspur (E6) by Q. HSW.7 and 0.24 g at Ludhiana (E3) by Q. HSW.10. A total of six genotypes  were selected carrying superior alleles for all the traits under study and respective QTLs presented in Out of 49 SNPs/MTAs, 44 SNPs representing 37 QTLs showed significant difference, whereas five SNPs representing five QTLs were found to be non-significant , LOC106757069 (-65.973 kb), LOC106757136 (-47.55 kb), LOC106756978 (-43.026 kb), LOC111241394 (-33.113 kb), LOC106757804 (-2.328 kb), LOC106757666 (50.731 kb), LOC106756983 (98.775 kb), LOC106756984 (118.512 kb) coding for already known enzyme E3 ubiquitin-enzyme ligase MARCH1, bZIP transcription factor 53, enzyme trichome birefringence-like 6, histone-lysine N-methyltransferase EZ2-like, DELLA enzyme RGL1-like, DEAD-box ATP-dependent RNA helicase 24, probable WRKY transcription factor 23, gibberellin 2-beta-dioxygenase two and transcription factor JAMYB-like for plant height were located near Q. PHM.3.1.The gene LOC106766854 (-33.322 kb) and LOC106765724 (-33.083 kb), were found close to the Q. IL.1.1 coding for pectate lyase-like and pectate lyase enzymes, respectively. Another gene LOC106762425 (-99.425 kb-), with enzyme cytokinin hydroxylase, was found in proximity with Q. IL.1.2. The QTL Q. IL.5 was near the genes LOC106760883 (-189.1 kb) and LOC106762422 (16.957 kb) coding for purine permease 1 and ethylene-responsive transcription factor RAP2-4 enzymes. For clusters per plant, three genes LOC106768944, LOC106760064 and LOC106760083 were in proximity of Q. CpP.1  coding SKP1-interacting partner 15, receptor-like protein 12 and polygalacturonase-like enzyme, respectively.For internodal length, two genes LOC106757271 (71.381 kb), LOC106757661 (95.044 kb) and LOC106756994 (100.043 kb) were found close to the Q. SpP.3 with enzyme galactinol synthase 2, Golgi apparatus membrane enzyme-like enzyme ECHIDNA and alkaline/neutral invertase A respectively. A gene LOC106765120 (64.962 kb) with enzyme dihydrofolate synthetase, was found in the vicinity of Q. SpP.6. Another ethylene-responsive transcription factor 1B-like enzyme coding gene LOC106770299 (147.684 kb) was found close to the Q. SpP.8.2. For yield per plant, a gene LOC106759105 (7.311kb) with MYB-related protein 305-like enzyme was close to the QTL: Q. YpP.4.1. Four genes LOC106761836 (-177.436 kb), LOC106760678 (-107.67 kb), LOC106762074 (-50.026 kb) and LOC106759995 (141.103 kb) coding CLAVATA3/ESR (CLE)-related protein 5-like, transcription factor PCF5, sodium/calcium exchanger NCL and basic leucine zipper 34 isoform X1 enzymes were in vicinity of Q. YpP.5.For seeds per pod, three genes LOC111242272 (116.438 kb) and LOC106771274 (129.392 kb) with alpha-mannosidase-like and putative 12-oxophytodienoate reductase 11 enzymes was close to Q. YpP.8. For harvest index, gene LOC106758323 (-174.544 kb) coding for UV-B-induced protein At3g17800, chloroplastic isoform X1 was related to Q. HI.1. The gene LOC106760579 (-189.197 kb) was found in the proximity of Q. HI.5 coding cytochrome P450 CYP72A219-like enzyme. The gene LOC106769438 with protein root UVB sensitive six enzyme function was lying 105.344 kb of Q. HI.7. Two genes LOC111242272 (116.438 kb) and LOC106771274 (129.392 kb), with enzymes alpha-mannosidase-like and putative 12-oxophytodienoate reductase 11 respectively, were close to the Q. HI.8.2. Two genes LOC106764301 (-15.52 kb) and LOC106765194 (12.233 kb) with enzymes putative pentatricopeptide repeat-containing protein At1g12700 and peroxidase four, respectively, were close to the Q. HSW.6. The gene LOC106776199 (-175.528 kb) encoding bromodomain-containing protein 4B enzyme was related to Q. HSW.10. The nodes per plant (NpP) had two QTLs, but no genes already known for nodes per plant around the vicinity of 200 kb of significant QTLs were found.Two genes LOC106756983), cytochrome P450 CYP72A219 (LOC106760579) are involved in Diterpenoid biosynthesis (ko00904) pathway (LOC106774489), and DEAD-box ATP-dependent RNA helicase 24 (LOC106757804) play role in Spliceosome (ko03040) process. Moreover, histone-lysine N-methyltransferase EZ2 found to be involved in Lysine degradation (Amino acid metabolism) ko0310. Besides that, pectate lyase-like (LOC106766854), pectate lyase (LOC106765724), and polygalacturonase-like (LOC106760083) have been found to play important role in Pentose and glucuronate interconversions (ko00040). LRR receptor-like serine/threonine-protein kinase RPK2 (LOC106766388), alkaline/neutral invertase A (LOC106756994) participates in Starch and sucrose metabolism (k000500). Galactinol synthase 2 (LOC106757271), alkaline/neutral invertase A (LOC106756994) have been shown to play role in Galactose metabolism (ko00052). Peroxidase 4 (LOC106765194) was found to play role in Phenylpropanoid biosynthesis (ko00940) (Some of the genomic regions significantly associated with the trait loci  such as  pathway . PHD finko00940) .Blackgram is one of the most popular pulses in Southeast Asia, with India contributing 54% of world production . InspiteEnvironment factors highly influence the complex traits; therefore, the material was replicated both in time and space. Previous studies have also reported a wide range of phenotypic variability for traits such as PHM, BpP, IL, CpP, PpP and HI . High heAd-hoc delta K and Cross entropy values suggested presence of four sub-populations in the blackgram germplasm which was further supported by phylogenetic analysis which in turn indicated significant diversity in the panel. High Fst values in 2, 3 and 4 sub-populations indicated them to be highly differentiated. A diverse germplasm panel can be a good source for a wide range of traits for breeding and research purpose . Modelli10p-value and PVE suggested presence of major QTLs i.e. Q. DtF.10, Q. PHM.3.2, Q. NpP.6, Q. IL.8, Q. CpP.10, Q. SpP.3, Q. BYpP.9, Q. YpP.8, and Q. HI.8.2 at Ludhiana, whereas Q. PHM.3.1, Q. PHM.6.1, Q. NpP.4, Q. IL.1.1, Q. CpP.4, Q. SpP.6, Q. BYpP.8, Q. YpP.4.2, and Q. HI.6 at Gurdaspur were found significant for trait regulation. QTLs Q. PHM.8, and Q. IL.8 were found to be located at same position on eighth chromosome controlling PHM and IL, respectively. The pleiotropy of increased PHM with increased IL has been earlier reported in faba bean , DtF (1), PHM (8), BpP (1), NpP (2), IL (4), CpP (5), PL (1), SpP (4), BYpP (2), HI (7) and HSW (3). High -logaba bean .Q. DtF.10 with superior allele TT resulted in earlier flowering with a mean of 41.42 days as compared to 43.05 with alternative allele. Q. PHM.3.1 with superior allele AA recorded higher mean plant height of 28.54 cm, and 43.23 cm as compared to 19.21 cm, and 27.21 cm with alternative allele GG at Ludhiana, and Gurdaspur respectively. Superior allele TT of QTL Q. IL.8 decreased internode length with mean value of 2.01 cm, whereas alternate allele observed higher internodal length with mean value of 2.83 cm. For clusters per plant, Q. CpP.1 with superior allele AA exhibited mean of 18.84 clusters, whereas alternate allele TT exhibited mean of 12.73 clusters with The SNP S6.1.29235837 of Q. SpP.6 having allele AA produced more average seeds per pod of 6.71 in contrast to 6.45 by alternate allele. Higher yield per plant with average of 5.47 g was observed by presence of AA allele of QTL Q. YpP.5 while 4.21 g yield was observed with alternate allele. Q. HI.1 with allele GG exhibited an increased mean value of harvest index (22.53%) as compared to a lower harvest index due to alternate allele (18.62%). Q. HI.8.2 with allele TT showed a higher harvest index (22.87%) as compared to alternate allele (19.44%). Allelic effects with superior alleles and alternative alleles have been reported for significantly associated markers for root, nutrient uptake and yield related traits in rice , DELLA enzyme RGL1-like in Arabidopsis , and pectate lyase (PL) in rice , cytokinnucifera , lysine-nucifera , pollen nucifera , LRR recnucifera , eukaryonucifera , MLO-liknucifera , and DDBnucifera  has beenFor seeds per pod, galactinol synthase two in Chickpea , Golgi aThe genes detected for HSW, pentatricopeptide repeat-containing protein At1g80550, have been earlier reported for kernel development in maize ; peroxidGossypium hirsutum L.), GhDET2 along with BRs are known to play a crucial role in the initiation and elongation of cotton fiber cells  are endogenous phytohormones that are involved in the regulation of the life cycle of plants. It has been identified that the locus encoding gibberellin 2-beta-dioxygenase/GA 2-oxidase present in vicinity of SNP S3.1.7993147 on chromosome 3 significantly associated with plant height at 90% pod maturity participates in Diterpenoid biosynthesis (ko00904) pathway. The role of this locus in the regulation of plant growth in rice has been demonstrated by er cells . Similarer cells . Additioer cells . Using KGWAS analysis led to identification of novel MTA and few putative candidate genes. Though candidate genes need to be examined further and detailed investigations would validate their roles in governing agronomically important traits but the MTA will really help in selecting the genotypes with positive associations. The information derived from this study can be used in the generation of SNP based molecular markers to select traits of interest and accelerate blackgram breeding programme with a better ideotype. Since the blackgram is neglected crop in term of number of genetic and genomic resources, the current study, first of its kind in this crop will definitely open new avenues for broadening its base.


Consistent with previous observations, we found significant phenotypic correlation between the various components of MetS and PTSD severity scores. To examine if this observed symptom correlations stem from a shared genetic background, we conducted genetic correlation analysis using summary statistics data from large-scale genetic studies. Our results show robust positive genetic correlation between PTSD and MetS , and obesity-related components of MetS . Prioritizing genomic regions with larger local genetic correlation implicate three significant loci. Overall, these findings show significant genetic overlap between PTSD and MetS, which may in part account for the markedly increased occurrence of MetS among PTSD patients.Post-traumatic stress disorder (PTSD) is a highly debilitating psychiatric disorder that can be triggered by exposure to extreme trauma. Even if PTSD is primarily a psychiatric condition, it is also characterized by adverse somatic comorbidities. One illness commonly co-occurring with PTSD is Metabolic syndrome (MetS), which is defined by a set of health risk/resilience factors including obesity, elevated blood pressure, lower high-density lipoprotein cholesterol, higher low-density lipoprotein cholesterol, higher triglycerides, higher fasting blood glucose and insulin resistance. Here, phenotypic association between PTSD and components of MetS are tested on a military veteran cohort comprising chronic PTSD presentation ( Post-traumatic stress disorder (PTSD) is a highly debilitating psychiatric disorder that can develop after experiencing extreme traumatic event ,2. BesidWhile PTSD is classified as a mental disorder, chronic PTSD is characterized by somatic comorbidities that affect the whole-body system besides the brain ,9. One oBesides the higher co-occurrence with PTSD, another intriguing fact is the significant phenotypic and genetic association of MetS with other stress-related psychiatric disorders including major depressive disorder (MDD). MDD is a mood disorder that shares a lot of symptomatology with PTSD. In addition to the substantial symptom overlaps, PTSD and MDD also have high genetic overlap ,14. VariMost studies pertaining to understanding the relationship between PTSD and MetS are limited to showing their epidemiologic co-occurrence. Fundamental questions as to the nature of this commonly observed comorbidity are yet to be addressed. What is the relationship between PTSD severity and level of metabolic dysregulation? How much of observed association between PTSD phenotypes and MetS as a whole and its components stem from genetic underpinning? What is the direction of causality, if there is any, between these associations? Owing to the high prevalence and the detrimental consequences of these disorders, answers to these questions have important practical implications. These include how to judiciously prioritize treatments targeting a particular illness from among a set of comorbidities so as to maximize benefit in terms of the overall health of an individual. For example, in the absence of causal influences between co-occurring disorders, targeting one condition will not have a direct impact on reducing risk for the other. Genetic studies of the psychiatric and metabolic traits have recently witnessed analysis with large-scale sample sizes ,19,20,21In this study, we aim to enhance our understanding of the link between PTSD and MetS as well as its components. First, we test the phenotypic association between PTSD phenotypes (PTSD status and symptom severity scores) and multiple metabolic traits using data from the PTSD Systems Biology Consortium (SBC) cohort, a well-characterized cohort comprising US veterans of the Iraq and Afghanistan wars. Then, we estimate genetic correlations between PTSD and metabolic traits using data from large-scale GWAS summary statistics. We also identified pleiotropic genetic regions contributing to the shared genetics between PTSD and MetS. Finally, in order to gain insight into the presence and direction of causal influences between Mets and psychiatric disorders, we conduct Mendelian randomization analysis. p = 1.7E-05 and CAPS . The co-occurrence between PTSD and MetS has been reported in multiple studies including in meta-analysis ,22. BesiOwing to the inclusion criteria of the study recruitment, the distribution of the PTSD symptom severity scores seems to have a bimodal distribution . To probe the effect of this distribution on the observed association, we performed additional tests using the non-parametric Spearman rank correlation test. Results from Spearman rank correlation test also showed significant association between PTSD severity scores and the various components of MetS . Analysin = 1134), we did not find a similar pattern of association between the same MetS components and PCL score . In order to determine whether these phenotypic associations stem from a shared genetic underpinning, we estimated pairwise genetic correlations between PTSD and MetS as well as the individual components of MetS. Based on GWAS summary statistics from recent large-scale studies on MetS-related traits with European-ancestry participants, SNP-array based heritability estimates were computed to ensure they are sufficiently powered and large enough proportion of the phenotype variance is explained. All summary statistics used for further analysis have SNP heritability estimates of more than 5%. Using estimates from LD score regression (LDSC), there is evidence for a statistically significant genetic correlation between PTSD diagnosis and MetS . Despite the strong phenotypic correlation between PTSD and sugar-related traits, the current study did not find corresponding statistically significant genetic correlation. This can be due to the fact that the GWAS summary statistics for these two traits are much less powered  compared to the other traits. Details of genetic correlation estimates between PTSD and various metabolic traits are listed in Obesity-related components (BMI and WHR) also show statistically significant genetic correlations. On the other hand, the remaining components of MetS in which we investigated genetic correlation estimates did not pass Bonferroni multiple-testing corrected Some previous studies reported significant genetic correlation between MetS and depression phenotypes ,23. To cg,local = 0.707, p= 3.30 E-05), (ii) chromosome 20:43.01–44.07 Mb , and (iii) chromosome 4:102.54–104.38 Mb . These regions contain genes previously implicated in genetic studies of relevant traits including SLC39A8 and HNF4A  chromosome 13:111.62–112.32 Mb (rnd HNF4A . Notablyp < 1E-08) and LD independent (pruning with r2 threshold of 0.1 and window of 10 Mb) SNP’s as genetic instruments. Testing the causal hypothesis that PTSD results in MetS using the inverse variance weighted (IVW) Mendelian randomization method did not result in a statistically significant estimate . Similarly, using 146 genome-wide significant and independent SNP’s in the MetS summary statistics, a test of the effect of MetS on PTSD also did not pass a significant threshold .Since the metabolic traits are correlated with chronic but not short-duration PTSD, we hypothesized the direction of causal influence might be from PTSD to metabolic dysregulation, but not the reverse. To test this hypothesis, we first selected 16 genome-wide significant (p = 1.3E-04) rather than from MetS to MDD . This comports with previous reports of the effect of depression phenotypes on metabolic dysregulation [Next, since we could not determine the direction of influence between PTSD and MetS despite the significant genetic correlation, we sought to assess the direction of influence with MDD, which is another stress-related psychiatric disorder with large-scale well-powered genetic study for Mendelian randomization analysis. A similar IVW analysis suggest the direction of influence is from MDD to MetS  and physical ailments  ,25. UndeSome clinical implications of the results of the present study are worth mentioning. First, it is important to acknowledge that the relationship between PTSD and MetS is not limited to increased preponderance in binary case–control comparison. Higher PTSD symptom severity scores are also associated with elevated level of metabolic dysfunction. Second, the epidemiologic link between PTSD and metabolic syndrome may arise from a shared genetic underpinning. Future risk stratification and management strategies can benefit from incorporating this insight. Third, the level of genetic overlap between metabolic traits with PTSD is comparable to that of major depression. Fourth, the direction of causal influence is likely from the psychiatric illnesses leading to metabolic dysfunction, but may not be the other way around. Therefore, interventions that target the psychiatric disorders may also help reduce metabolic-related disease risk, which are some of the leading causes of mortality in the general public. In fact, the high comorbidity with MetS and its components is thought to be a major factor responsible for the markedly high (2 to 3 times higher) mortality rate among PTSD patients ,27. In oMetS is a major risk factor for adverse cardiovascular events, diabetes and all-cause mortality ,29. Its Besides the overall MetS diagnosis, the MetS-related metabolic traits have previously been reported to be associated with PTSD phenotypes. PTSD is reported to be significantly associated with higher BMI, and particularly with central obesity ,34. MultThis study involved estimating the level of genome-wide correlations. Future studies that can identify specific polymorphisms, genes, pathways and metabolites involved in the common pathophysiology of PTSD and metabolic dysfunction are needed. In particular, abundance of many metabolites  is shown to be highly genetically influenced ,41, makiThe findings of the present study should be interpreted in light of its strengths and limitations. Most previous studies focused on comparing the presence and prevalence of MetS and associated metabolic traits between PTSD patients with healthy controls. Here, the phenotypic associations with PTSD were assessed using two quantitative scores of PTSD symptom severity. This approach can capture the effect of disease severity in addition to a binary disease onset. Another strength of the study is that the genetic correlation estimates are obtained from large-scale genetic analysis results. To ensure this summary statistics are well-powered, SNP heritability of the summary statistics data are computed and only those with sufficient minimum threshold are used, resulting in a more reliable genetic correlation estimate. The main limitations of the study are the following. First, to avoid population stratification effects, only genetic studies with European-ancestry participants are collected and analyzed. Therefore, the results with this summary data may not necessarily generalize on other ancestral groups. Second, genetic correlations between psychiatric conditions and metabolic traits are reported to be gender and age dependent . In the PTSD, MetS and its components are heritable traits with large-scale publicly available GWAS summary statistics data, enabling application of modern genetic analysis tools to interrogate the mechanism of influence among them ,46. In tFuture studies need to further investigate many aspects of the pathophysiology of the co-occurrence of PTSD and MetS. These include (i) replicating and refining the current findings with additional datasets as results from new larger cross-ancestry genetic studies become available, (ii) identifying metabolites and other small molecule biochemicals that mediate the effect of genetics on PTSD/MDD, (iii) uncovering specific genes and pathways underpinning the genetic correlations between PTSD/MDD and metabolic dysregulation, and (iv) identifying potential pleiotropic genetic variants that are shared between PTSD and metabolite genetics.The current study involves examining the relationship between PTSD and metabolic dysregulation at genetic and phenotypic levels. These two aspects are done in parallel on independent datasets .The SBC (Systems Biology Cohort) consists of participants from US military veterans who served in OEF (Operation Enduring Freedom) and/or OIF (Operation Iraqi Freedom) ,48. All n = 1134) of the cohort, only PCL is available to assess PTSD symptoms. Metabolic traits  are also measured in the same manner as the SBC dataset. Written informed consent was obtained from all participants in both cohorts and approvals were obtained from all appropriate Institutional Review Boards.The FCC (Fort Campbell Cohort) participants comprises active-duty service members in the US Army with recent PTSD symptom presentation from recent trauma exposure (within 90–180 days of return from deployment) . Given tSeveral large-scale GWAS summary statistics pertaining to MetS or its components as well as PTSD and depression are obtained from public repositories. The PTSD GWAS summary statistics is from the Psychiatric Genomic Consortium (PGC) freeze-2 study of the European-ancestry participants . The stuPreprocessing and quality-control step is done on each of the summary statistics individually as follows. Rare variants  are filtered out. If z-statistics is not given, it is computed from the effect size (regression coefficient or odds-ratios) of the univariate tests (Z = beta/SE or Z = log(OR)/SE). If rsID’s of genetic variants are not provided in the original summary statistics, its rsID is looked up from dbSNP database and inserted as an additional column. After computing SNP array heritability on the given summary statistics, if it does not explain a significant variance of the phenotype (g). Its estimate is obtained from GWAS summary statistics data using cross-trait LD score regression (LDSC) [The proportion of variance of the association between two genetically influenced traits that is attributable to genetic background is known as genetic correlation (rn (LDSC) . A Python (LDSC) .p < 1E-05) associations with PTSD are selected, resulting in 50 independent regions for further analysis. Univariate local heritability analysis on these 50 genomic regions was conducted for PTSD susceptibility. A total of 43 regions with Bonferroni corrected significance (p < 0.05/50 = 0.001) are selected for further bivariate local genetic correlations. Local genetic correlation estimates between PTSD and MetS are analyzed on these 43 genomic regions. Both univariate local heritability and bivariate local genetic correlation analysis are conducted with LAVA  [A predefined LD blocks that was defined using European European-ancestry 1000-genome data is obtained . The whociation) . Regionaciation) . All genp < 5E-08) are selected. To enforce independence among genetic instruments, LD clumping procedure with window distance of 10 Mb and r2 threshold of 0.1 is conducted. For each instrumental variable, the corresponding SNP-outcome association statistics is obtained from the three summary statistics. After ensuring the same effect allele is used in the exposure and outcome summary statistics, palindromic SNP pairs  are removed.Genetic instrumental variables are selected based on association statistics on the exposure phenotype summary statistics  Mendelian randomization analysis is performed using the inverse variance weighted (IVW) method . Briefly


Genomic selection (GS) has revolutionized animal and plant breeding after the first implementation via early selection before measuring phenotypes. Besides genome, transcriptome and metabolome information are increasingly considered new sources for GS. Difficulties in building the model with multi-omics data for GS and the limit of specimen availability have both delayed the progress of investigating multi-omics.G matrix and T matrix), combined with the best linear unbiased prediction (BLUP) for GS. Here, we defined five kernel-based prediction models: genomic BLUP (GBLUP), transcriptome-BLUP (TBLUP), multi-omics BLUP (MBLUP, We utilized the Cosine kernel to map genomic and transcriptomic data as We concluded that the inclusion of transcriptome data in GS had the potential to improve accuracy. Moreover, wmssBLUP is accepted to be a promising alternative for the present situation in which plenty of individuals are genotyped when fewer are transcribed.The online version contains supplementary material available at 10.1186/s40104-022-00756-6. A significant objective of genetics is to examine the connection between genotypes and phenotypes. Although genome-wide association studies (GWASs) have mapped thousands of common genetic variants for complex traits the causal variants and genes at these loci remain unknown . This isAlternatively, genomic prediction (GP) is an ensemble of methods to estimate the breeding values with higher reliability earlier in life by combining DNA variants jointly using existing identification, pedigree, and phenotype databases for individuals , 5. OverAfterward, one critical issue for phenotypic prediction is how to model non-additive effects (dominance or epistatic effects). Several research confirmed that using non-additive relationships that could improve the prediction of phenotypes , 19. IncG matrix. So, we tried the Cosine kernel to map genomic and transcriptomic data as G matrix and T matrix). In this study, we firstly defined three prediction models: GBLUP (genomic data), TBLUP (transcriptomic data), and MBLUP , Beijing, China .The Huaxi cattle population with an average age of 26 months and an average pre-slaughter weight of 700 kg were from Ulgai, Xilingol League, and Inner Mongolia of China. After weaning, all calves were moved to a fattening farm under uniform management and standardized feeding based on a total mixed ration (TMR) according to the eighth revised edition of nutrition requirement of beef cattle  . AnimalsWe collected four traits for GP: longissimus dorsi muscles , water holding capacity (WHC), shear force , and meat pH. The WHC was determined using TA-XT plus Texture Analyser 12,785  according to reference NY/T 1333–2007 . The SF P < 10–6). Besides, the individuals with genotype missing rate greater than 10% were removed from the analysis. After testing QC criteria, 1478 cattle with 607,198 SNPs on 29 autosomal chromosomes with an average distance of 3 kb were included in subsequent studies. Among the resource population, all individuals were slaughtered between 2010 and 2021 when they were 18 to 24 months old, and 122 individuals were sampled for RNA extraction and transcriptome sequencing.Genomic DNA was isolated from blood samples  using the TIANamp Blood DNA Kit . DNA quality was acceptable when the A260/A280 ratio was 1.8–2.0. All individuals were genotyped with an Illumina BovineHD BeadChip that contained 770,000 SNPs. Before quality control (QC), we removed the sex chromosome, the mitochondrial DNA, and unknown placement markers from the genotype. QC procedures were carried out using the PLINK v1.9 [LDM (12-13th ribs) was collected within 30 min after slaughter, and samples were immediately washed with phosphate-buffered saline (PBS) to avoid contamination. While slices of muscle tissues were saved into Eppendorf (EP) tubes and then frozen in liquid nitrogen. The total RNA was extracted by TRIzol reagent . Criteria of RNA concentration tested by Qubit® RNA Assay Kit , RNA purity tested by NanoPhotometer® spectrophotometer , and RNA integrity tested by RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system  were used to describe the total RNA quality. Presently, these samples (28S/18S > 1.8 and OD260/280 ratio > 1.9) were used to construct cDNA libraries and performed RNA sequencing when the RNA integrity number (RIN) > 7. The cDNA library construction was generated by IlluminaTruSeqTM RNA Kit , and the RNA sequencing was performed on an Illumina NovaSeq 6000 platform by paired-end strategy (read length 150 bp).To obtain clean reads, MD5 values reflected the integrity of the raw sequencing reads, and FastQC (v0.11.9) assessed the quality of the reads in terms of base composition and quality distribution . All seqBos taurus ARS-UCD1.2 [n = 3729) [HISAT2 (v2.2.1) was used to compare clean reads to reference the genome S-UCD1.2 . The catS-UCD1.2 , 34. FeaS-UCD1.2 . After t = 3729) . After tTo remove the fixed effects in prediction, we used adjusted phenotypic values of phenotypes in subsequent analysis:y is a vector of phenotypic, X is the design matrix of relevant observations, and where GBLUP assumes that all SNPs contribute to the genetic variance and follow the same normal distribution . GEBVs wZ is an incidence matrix for individual effects. G matrix was calculated as i-th marker [where h marker .G matrix, which had a well-matched performance for the G matrix, with time consumption reduced by 20 times [Based on our previous study, the kernel matrix transformed by Cosine kernel was analogous to the numerator relationship 20 times . In thisG matrix, i and j in an m-dimensional feature space, respectively, where m is the number of SNPs. For the T matrix, G matrix and T matrix were measured by the cosine of the angle between two vectors, and the regularization factor C was determined by gird search as 0.05 in our previous study.For the M matrix was used to replace the G matrix in traditional GBLUP equations. Here, the For experimental populations who have both genomic and transcriptomic data , we defined an MBLUP, where the Inspired by the single-step best linear unbiased prediction (ssBLUP) :\documenthe inverse of To address the issue that large numbers of individuals were genotyped but fewer are transcribed, here, we built multi-omics single-step best linear unbiased prediction (mssBLUP):G for transcribed animals. The G for non-transcribed animals. The G describing the relationships between transcribed and non-transcribed animals.where G matrix in ssBLUP, in which the For higher accuracy, several research studies weighted the  by SNPs –40. Aftewhere the The inverse of Here, the gradient of parameter For experimental populations with transcriptome data , because of the limited group size, we adopt Leave One Out (LOO) to assess the predictive performance of GBLUP, MBLUP, and TBLUP. LOO is simple cross-validation (CV), which generated lower generalization errors than cross-validation in the small-scale population . Each leFor the resource population , the predictive accuracies of GBLUP , mssBLUP, and wmssBLUP were quantified with Pearson’s correlation based on five replicates of fivefold CV. The predictive accuracy performance of each method was the average Pearson correlation of 5 replicates in the validation subset.M matrix. The predictive accuracies of MBLUP for four traits in the experiment population are shown in Fig. The weighting parameter ratio in MBLUP was set with a gradient of 0.01–0.99 for determining the optimal coefficient for the w was set as 0–1 to explore the optimum predictive accuracies for each trait. When For wmssBLUP, the gradient of weight parameter ak Fig. S. The detw, we also considered the influence of the proportion of transcribed data in the whole resource population on the statistical power of the wmssBLUP. Based on the weighted value w for each trait mentioned above, firstly, 600, 720, 840, 960, 1080, 1200, and 1320 individuals from the resource population (1358 cattle with genotype only) were randomly extracted, respectively. And then combined the selected groups with the experimental population (120 cattle with both genotyped and transcribed) to build the Additionally, except for selecting the optimum weighted parameter G matrix . Afterward, the proposition of integrating multiple omics is transformed into the conventional calculation problem of matrixes. Therefore, we evaluated three kernel-based investigating methods with the above foundation: MBLUP, mssBLUP, and wmssBLUP.Genomic selection (GS) is a promising method in modern molecular breeding practice because of its demonstrated effectiveness in predicting accuracies and shortening generation intervals. As a significant advance of next-generation sequencing, transcriptome and other omics data provide new information sources for phenotypic prediction. Meanwhile, the current progress of GS using genomic data alone has reached a bottleneck and integrating multi-omics as a novel predictive factor analysis may be a promising way to improve accuracies . Based oVanRaden ) with a For MBLUP in this study, we set a weighted parameter, valued between 0 and 1, aimed to explore an optimal ratio for each trait. The reported studies had proven that the resource population scale significantly determined the reliability of genomic predictions , 44. DueG and T matrix. In this study, compared with traditional GBLUP, wmssBLUP and mssBLUP had 4.18% and 3.37% average improvement of predictive accuracy. And the weighted coefficient w changed with the data structure. This is also in agreement with our earlier observations, which showed that predictive ability can be improved when combining transcripts with SNPs, but it depends on the traits [Before the genotyping technology matured a decade ago, GP was limited by the number of animals for which both genotypes and phenotypes were available. A single-step BLUP (ssBLUP) was proposed to predict GEBV using information from genotype and pedigree simultaneously . The wei matrix) , 50. Sime traits . For anoentclass1pt{minimae traits , 53. TheTherefore, future studies on the current topic are recommended: 1) One such decision concerns which suitable kernel to use. Studies found that taking the Gaussian kernel mapping transcriptome data for predicting phenotypes gained little effect . 2) The In the present study, we proposed three Cosine kernel-based methods to investigate multi-omics data: MBLUP, mssBLUP, and wmssBLUP. Our results showed MBLUP was far preferred to GBLUP (ratio = 1) in four traits. While, wmssBLUP and mssBLUP outperformed GBLUP, and the average improvement was 4.18% and 3.37%. We also found the transcriptome data has the potential to improve genomic predictions if they can be included on a larger scale. Our research proposes reliable and robust alternatives for the present situation in that large numbers of individuals were genotyped, but fewer were transcribed.Additional file 1:Fig. S1. The determination of the weight parameter w in wmssBLUP.Additional file 2:Fig. S2. The comparison of the accuracy of wmssBLUP and GBLUP in different population scales.Additional file 3:Table S1. The details of 25 Pearson’s correlation coefficients of GBLUP, mssBLUP, and wmssBLUP for LDM, HWC, SF, and pH.Additional file 4:Table S2. The summary of the accuracy of wmssBLUP and GBLUP for increasing population scale.