Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package ‘CPBayes’ implementing the proposed method. Genome-wide association studies (GWAS) have detected shared genetic susceptibility to various human diseases (pleiotropy). We propose a Bayesian meta-analysis method CPBayes that simultaneously evaluates the evidence of overall pleiotropy while determining which traits are pleiotropic. This approach investigates pleiotropy using GWAS summary statistics and allows for overlapping subjects across traits. It performs a fully Bayesian analysis and offers a flexible inference. CPBayes also provides additional information about a pleiotropic signal, such as the trait-specific posterior probability of association and the credible interval of unknown true genetic effects. Using computer simulations and an application to a large GWAS cohort, we demonstrate that CPBayes can offer improved accuracy compared to the existing subset-based meta-analysis approach ASSET. We provide a user-friendly R-package ‘CPBayes’ for general use of this approach. Genome-wide association studies (GWAS) have detected loci associated with multiple different traits and diseases  . For exaRSPO3, FTO, VEGFA, PEPD were associated with HDL and triglycerides, but not with LDL or total cholesterol. Hence, in addition to evaluating the evidence of overall pleiotropic association, it is crucial to determine which traits are associated with the risk locus to better interpret the pleiotropic signal. Another important consideration is the availability of individual level data from multiple GWAS of different phenotypes. When accessing individual level data is difficult, one can use recently developed methods to investigate pleiotropy using more readily available genome-wide (GW) summary statistics . CPBayes identified six independent pleiotropic loci associated with at least two phenotypes including a locus at chromosomal region 1q24.2 that exhibits an association with five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. ASSET identified a larger number of independent pleiotropic loci associated with more than one trait, but selected many phenotypes with very weak genetic effects. We provide an R-package ‘CPBayes’ implementing the proposed method for a general use.H0 denote the global null hypothesis of no association with any trait and H1 denote the global alternative hypothesis of overall association with at least one of the traits. Then locFDR is defined as the posterior probability of H0 being true conditioned on the summary statistics and BF is the ratio of the likelihood of summary statistics under H1 versus H0. A small value of locFDR  or a large value of Bayes factor  indicates an evidence for the overall pleiotropic association. We estimate the locFDR and BF based on the MCMC posterior sample of model parameters obtained by Gibbs sampling. The subset of traits which is selected as the set of non-null traits most often in the MCMC posterior sample is defined as the maximum a posteriori (MAP) estimate of the optimal subset of associated traits.Given the summary statistics for a SNP across multiple traits, CPBayes estimates two different measures evaluating overall pleiotropic association and an optimal subset of associated traits underlying a pleiotropic signal. The evidence for aggregate-level pleiotropic association is given by the local false discovery rate (locFDR) and the Bayes factor (BF). Let CPBayes explicitly accounts for possible correlation between the effect estimates across traits. To estimate the correlation structure of the effect estimates, we use two different approaches—one based on the number of overlapping cases and controls between studies and another based on genome-wide summary statistics across traits. The correlation formulae based on the number of shared cases and controls  is accurj), direction of associations, credible interval of true genetic effects, etc. Detailed description of CPBayes and a brief outline of ASSET, BH0.01 and GPA are provided in the “Material and methods” section and supporting information.CPBayes also provides some more insights into a pleiotropic signal, e.g., marginal trait-specific posterior probability of association . We assume that the SNP is in Hardy-Weinberg equilibrium (HWE). Let A (minor) and a be the two alleles at the SNP and p = P(A). Under HWE, the genotype probabilities are: P(AA) = p2, P(Aa) = 2p(1 − p), P(aa) = (1 − p)2. Given the log odds ratio (β) and disease prevalence, we compute the probability of observing each genotype conditioned on the case/control status using Bayes theorem. Based on these conditional probabilities, we simulate the genotypes in cases and controls.For non-overlapping case-control studies, we consider a separate group of 7000 cases and 10000 controls in each study. For overlapping case-control studies, we consider a distinct set of 7000 cases in each study, and a common set of 10000 controls shared across all the studies. For each disease, we assume an overall disease prevalence of 10% in the whole population. While simulating the genotype data for multiple case-control studies, we assume the standard logistic model of disease probability conditioning on the genotype: For the cohort study, we consider 15000 individuals. First, we generate the genotype data at a quantitative trait locus (QTL) and continuous multivariate phenotype data using the simulation model in Majumdar et al.  that wasWe emphasize that the simulation models used here are general in nature and independent of the modeling assumptions underlying CPBayes which are also very general and only require that the sample sizes of the participating GWAS should be sufficiently large to satisfy the standard asymptotic properties . These sFor overlapping case-control studies and a cohort study when the summary statistics are expected to be correlated, a combined strategy of CPBayes is implemented . If the K1 denote the number of associated phenotypes among a total of K phenotypes. We consider K = 5, 10, 15; K1 = 0, 1, 2, 3 when K = 5, K1 = 0, 2, 4, 6 when K = 10, K1 = 0, 3, 6, 9 when K = 15.First we evaluate the methods in the context of detecting overall pleiotropic association. Consider 1000 SNPs of which 2% (20) are risk SNPs and 98% are null SNPs (not associated with any trait). The minor allele frequency (MAF) at all SNPs are randomly simulated from Uniform distribution. One or more traits are associated with a risk SNP. Let K = 5, out of 20 risk SNPs, 8, 6 and 6 SNPs are associated with 1, 2 and 3 traits, respectively. For K = 10; 8, 6 and 6 risk SNPs are associated with 2, 4 and 6 traits, respectively. Similarly for K = 15; 8, 6 and 6 risk SNPs are associated with 3, 6 and 9 traits, respectively. For K = 5, first, K1 is set as 1 for 8 risk SNPs randomly selected from 20 risk SNPs without replacement; next, K1 is set as 2 for 6 risk SNPs randomly sampled from the remaining 12 risk SNPs. The same strategy is employed for K = 10 and 15. Corresponding to a risk SNP, each non-null trait is considered to be either positively or negatively associated with equal probability. The odds ratio (OR) for a non-null trait is randomly simulated from Uniform ) if the trait is positively (negatively) associated.For Note that the locFDR and Bayes factor are not comparable to the p-value. Hence we compared the number of risk SNPs identified as being associated (true discoveries) while adjusting the threshold of CPBayes locFDR and ASSET p-value such that both methods detect the same number of null SNPs as being associated . This can be viewed as a partial receiver operating characteristic (ROC) curve. The method with a greater area under the curve (AUC) is preferred. Coram et al.  considerIn 0.01  which is substantially higher than that of ASSET. For example, in K = 10  than 0.01 while selecting the replications showing evidence of pleiotropic association. We observed that for more stringent thresholds of locFDR, CPBayes provides a higher sensitivity compared to a larger threshold of locFDR while maintaining a good level of specificity. This is because a risk SNP will have a smaller locFDR if the odds ratios for the individual associated traits become stronger and this also leads to better sensitivity of the selected subset of associated traits for CPBayes while preserving a good level of specificity.We also considered more stringent threshold of locFDR  cohort obtained from dbGaP [dbGaP Study Accession: phs000674.v1.p1]. We also analyzed this dataset using ASSET for an empirical comparison of the methods. We restricted our analysis to 62,318 European-American individuals, who constitute more than 75% of the dbGaP data. We tested 657,184 SNPs genotyped across 22 autosomal chromosomes for their potential pleiotropic effects on 22 case-control phenotypes in the GERA cohort . Note th−5. This leaves 601,175 SNPs that were tested for pleiotropic association by CPBayes and ASSET. We adjusted the analysis for the following covariates: age, gender, smoking status, BMI category and 10 principal components of ancestry (PCs). We tested the single-trait association for each of 22 phenotypes by a logistic regression of the case-control status on the genotype incorporating the same set of adjusting covariates. We used SNP-trait effect estimates (log odds ratios) and their standard errors in CPBayes and ASSET. As the summary statistics are correlated here, we used the combined strategy of CPBayes and the correlated version of ASSET.Before our analysis, we undertook the following QC steps. First, we removed individuals with: over 3% of genotypes missing; any missing information on covariates (described below); genotype heterozygosity outside six standard deviations; first degree relatives; or discordant sex information. This left us with 53,809 individuals. Next, we removed SNPs with: MAF < 0.01; 10% or more missingness; or deviation from HWE at a level of significance 10r2 < 0.01 (r: the correlation between the genotypes at a pair of SNPs). Finally, we computed the correlation matrix of the effect estimates as the sample correlation matrix of Since we have environmental covariates in the GERA study, we estimated the correlation matrix of the effect estimates under the null using the GW summary statistics data . First, p counts  may be b−8 for ASSET. For CPBayes, a pre-fixed significance threshold of locFDR needs to be considered here. While concluding that a locFDR threshold of 5% indicates good evidence of association, selecting the most promising pleiotropic variants may require a more stringent threshold . Liley and Wallace . This probability can be estimated as the proportion of posterior samples in which z1 = 1 and z2 = 0.Let PPAβ. The posterior probability that Yj is positively associated is estimated as the proportion of positive βj among the posterior sample of βj. Yj is classified as being positively associated if this estimated proportion is greater than half. The posterior mean, median, and the 95% credible interval  of the true genetic effect on each phenotype can be computed based on the posterior sample of β.The direction of association between each non-null trait and a genetic variant can be estimated based on the posterior sample of τ2 (variance of null or very weak genetic effects across traits) to a fixed value 10−4. If β (log odds ratio) follows N, then P(0.98 < eβ < 1.02) = 0.954. It implies that under the spike (no association), the odds ratio for association between a variant and single trait will vary between 0.98 and 1.02 with a prior probability of 95.4%. In the MCMC, we updated the slab variance β ∼ N, then P(eβ < 0.98 or eβ > 1.02) = 0.99, which implies that under the slab (association) with variance 0.8, the odds ratio is smaller than 0.98 (a negative association) or larger than 1.02 (a positive association) with a prior probability of 99%. We note that a majority of the odds ratios reported for the GW significant associations with various complex traits are modest in size [After extensive experimentation with simulated data, we set the variance of the spike distribution  in size . Hence, τ2 = 10−3, 10−2, and τ2 = 10−4, We also explored other choices for these parameters by simulations, such as, accuracy . A smallq ∼ Beta, c1 > 0, c2 > 0; c1 = c2 = 1, c2 as 1. Choosing a smaller value of c1 than c2 induces a small value of E(q) which is appropriate for null SNPs but can severely penalize the power of CPBayes to detect the risk SNPs. Hence we consider a simple Empirical Bayes approach here. First, apply BH0.01 to the univariate association p-values for a SNP across traits and let 0.01. If c1 fixing c2 = 1. As we restrict c2 = 1, c1 varies from 0.01 provides good specificity and sensitivity while selecting non-null traits underlying a pleiotropic signal, it may not always be accurate. Hence in the prior, we do not allow c1 > 1 c1 = c2 = 1 corresponds to the Uniform distribution. On the other hand, allowing c1 which can cause computational issues in the MCMC due to generating too small value of q, and it can also severely affect CPBayes’ power of detecting risk variants and the sensitivity of selecting the non-null traits underlying a pleiotropic signal. We have observed in our simulation study that CPBayes performs well overall by using this Empirical Bayes approach to choose the hyperparameters in the prior of q.In the continuous spike and slab prior, r et al.  demonstrk, l ∈ {1, …, K} and k ≠ l,The summary statistics across traits can be correlated due to overlap or close genetic relatedness among subjects across different studies. For case-control studies, Zaykin and Kozbur  and Lin nk (or nl) denote the number of cases, controls, and total sample size for the study of Yk (or Yl); Yk and Yl. Let Yk but controls for Yl; similarly, let Yk but cases for Yl. Here, the above formula can be generalized to:Here Y1, …, YK is associated with the SNP and environmental covariates. An alternative strategy [This formula is accurate when none of the phenotypes strategy , 34 is bA denote the selected subset of non-null traits that contains K1 traits. Let B denote the subset of K1 traits that have the smallest univariate association p-values. If A and B match, we accept the results; otherwise, we implement CPBayes assuming that A is empty, we accept the results provided by the correlated version of CPBayes. In this combined strategy, we induce a frequentist sense of selection. Because, majority of the multiple testing procedures reject the null hypotheses for which smallest univariate p-values are obtained. However, we note that in the analysis of the GERA cohort, the combined strategy used the uncorrelated version very few times. The reason is that the non-sparse scenario may not occur frequently in real data.For strongly correlated summary statistics, when a majority of the traits are associated with the risk locus (non-sparse scenario), the Gibbs sampler can sometimes be trapped in a local mode rather than the global mode of the posterior distribution due to possible multi-modality of the posterior distribution of model parameters. We observed this pattern in our simulation study. It may result in an incorrect selection of associated traits, reducing the robustness of CPBayes. We noticed that, in such a scenario, if the summary statistics are assumed to be uncorrelated, the MCMC does not get trapped in a local mode and moves around the global mode. But ignoring the correlation can give a lower (larger) Bayes factor (locFDR) and sensitivity of the selected traits. Hence, for correlated summary statistics, we combine the correlated and the uncorrelated versions of CPBayes as follows. First, we execute CPBayes considering the correlation among τ = 0 and Z from the model. We note that, the latent association status (Z) could only be used in the model for the continuous spike. For the Dirac spike, the inclusion of Z in the model makes the corresponding MCMC reducible, and hence non-convergent to its stationary distribution (details not provided). Also, for the continuous spike, the full conditional posterior distributions of z1, …, zK are independent which leads to an explicit estimation of the locFDR/Bayes factor based on the MCMC sample. But, for the Dirac spike, the explicit estimation of the locFDR/Bayes factor appears to be very difficult in the correlated case, because the full conditional posterior distributions of β1, …, βK are not independent for correlated summary statistics.It is straightforward to observe that the Dirac spike can be obtained from the continuous spike by first setting kth phenotype Yk on genotype G, let k = 1, …, K. Adopting the framework of a fixed-effects meta analysis, for a subset of traits Z statistic as: Yk belonging to K separate GWAS for Y1, …, YK with the kth study having a sample size nk, one can consider Zmax. For more details, see Bhattacharjee et al. [Bhattacharjee et al.  introduce et al. .0.01.Benjamini and Hochberg  introduc0.01 on the univariate association p-values of K traits and assign zj = 1 if Yj is found to be significantly associated, otherwise set zj = 0; j = 1, …, K. We also choose an initial value of q as the proportion of non-null traits detected by BH0.01 .Here we state the Gibbs sampling algorithm for the continuous spike described in τj = τ if zj = 0; and zj = 1; j = 1, …, K. So 1 = S. Also, let βj− = , and Zj− = .Define Algorithm 1 Gibbs sampling for continuous spike in correlated caseStart:1: Z and q as described above.2: Assign the initial values of loop:3: β from its full conditional posterior distribution: 4: Simulate j = 1, …, K, update zj using the full conditional posterior probability: 5: For k0 = K − k1. Update q using 6: Let e1 = e2 = 1. Update d from its full conditional posterior distribution in a fixed range so that the slab variance v0, v1); let the corresponding range of d be given by: d0 < d < d1. If y follows a truncated k1 = 0, d is updated from the truncated (d0 < d < d1) Beta distribution.7: We assume that gotoloop until all the MCMC iterations are finished.8: d can be updated using the truncated central χ2 distribution as long as the second shape parameter of its Beta prior (e2) is 1.We note that, j = 1, …, K, update βj by sampling from its full conditional posterior distribution: If the summary statistics are uncorrelated, step 4 of Algorithm 1 is modified as: for S1 Text(PDF)Click here for additional data file.S1 FigIn this diagram, the spike variance is chosen as 0.1.(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 FigK and m denotes the minor allele frequency at the risk SNP. The total number of phenotypes is denoted by (PDF)Click here for additional data file.S4 FigK and m denotes the minor allele frequency at the risk SNP. The total number of studies is denoted by (PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 FigHere 1% of 1000 SNPs are risk SNPs and associated only with the second trait, and 99% SNPs are null.(PDF)Click here for additional data file.S7 FigHere 1% of 1000 SNPs are risk SNPs and associated only with the second trait, and 99% SNPs are null.(PDF)Click here for additional data file.S8 FigHere 1% of 1000 SNPs are risk SNPs and associated with both the traits, and 99% SNPs are null.(PDF)Click here for additional data file.S9 FigHere 2% of 1000 SNPs are risk SNPs and associated only with the second trait, and 98% SNPs are null.(PDF)Click here for additional data file.S10 Fig(PDF)Click here for additional data file.S11 Fig(PDF)Click here for additional data file.S12 Fig(PDF)Click here for additional data file.S13 Fig(PDF)Click here for additional data file.S14 Fig(PDF)Click here for additional data file.S15 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 TableHere 2 and 4 among 10 traits are associated.(PDF)Click here for additional data file.S5 TableHere 6 and 8 among 10 traits are associated.(PDF)Click here for additional data file.S6 TableSummary of measures for the evidence of the overall pleiotropic association for 50 non-overlapping case-control studies. Here 0, 5, and 10 among 50 traits are associated.(PDF)Click here for additional data file.S7 Table0.01 for 50 case-control studies.Accuracy in selection of associated traits by CPBayes and BH(PDF)Click here for additional data file.S8 Table(PDF)Click here for additional data file.S9 TableAt the beginning of the table, the number of independent null SNPs obtained by using different SNP filtering thresholds are listed. At the bottom of the table, we also provide the distance between the correlation matrix estimated by (PDF)Click here for additional data file.S10 Table(PDF)Click here for additional data file.S11 Table(PDF)Click here for additional data file.


Imputation is a computational method based on the principle of haplotype sharing allowing enrichment of genome-wide association study datasets. It depends on the haplotype structure of the population and density of the genotype data. The 1000 Genomes Project led to the generation of imputation reference panels which have been used globally. However, recent studies have shown that population-specific panels provide better enrichment of genome-wide variants. We compared the imputation accuracy using 1000 Genomes phase 3 reference panel and a panel generated from genome-wide data on 407 individuals from Western India (WIP). The concordance of imputed variants was cross-checked with next-generation re-sequencing data on a subset of genomic regions. Further, using the genome-wide data from 1880 individuals, we demonstrate that WIP works better than the 1000 Genomes phase 3 panel and when merged with it, significantly improves the imputation accuracy throughout the minor allele frequency range. We also show that imputation using only South Asian component of the 1000 Genomes phase 3 panel works as good as the merged panel, making it computationally less intensive job. Thus, our study stresses that imputation accuracy using 1000 Genomes phase 3 panel can be further improved by including population-specific reference panels from South Asia. This has led to the identification of hundreds of risk loci for certain diseases like type 2 diabetes (T2D); a majority of these loci have also been replicated in populations other than the one where they were identified in the first place2. Many genomic regions have also been fine-mapped to uncover the potential causal variant from among a pool of variants which are in linkage disequilibrium (LD) with one another in a haplotype block. The single nucleotide polymorphism (SNP) density of the genotyping arrays varies and so does the availability of genotype data across various association studies. Imputation is a useful and cost-effective computational strategy based on the pattern of LD structure and sharing of haplotype stretches among individuals. It allows analysing a larger number of variants without genotyping them directly. Thus, it helps to perform deeper genetic investigation using limited resources3 and increases the power of GWAS, meta-analyses and fine mapping studies. Researchers across the world have been using The 1000 Genomes phase 1 reference panel (1KGP1) for imputation with reasonable accuracy, however, the absence of genomic information on various populations raises concerns about its global suitability4. Recently, several novel loci have been reported to be associated with disease phenotypes in specific populations7, which brings GWASs to a phase where population-specific loci expand the understanding of the mechanisms and pathways linked to a disease. It is thus important to have appropriate reference panel for accurate imputation since features such as haplotype structure, presence of population-specific variants and altered frequency of variants influence the imputation quality and genomic coverage of the imputed variants3. Two independent studies using Japanese population reference panel (1KJPN) from 1070 Japanese individuals and Genome of Netherlands (GoNL) panel from 769 Dutch individuals have been shown to add to the imputation accuracy while using the 1KGP1 panel9. Recently, a reference panel of 64,976 haplotypes of predominantly European ancestry has been made available which enables accurate genotype imputation of variants with minor allele frequency (MAF) as low as 0.1% for European datasets10. India has a population size of more than 1.2 billion and comprises several thousand endogamous sub-populations14. Using high-throughput data, genetic evidence has provided evidence of the different origins of various populations and demonstrated the existence of various founder populations13. Thus, the Indian population structure makes it well-suited to explore population-specific genetic risk loci but such efforts have hardly been undertaken. One of the major concerns is false positive disease associations due to population stratification among individuals recruited from the same region since genetically different populations may live in close geographical proximity due to social and cultural traditions while maintaining their genetic isolation. The second and equally important factor is the absence of adequate representation of Indian population in the reference panels used for imputation analysis. The 1KGP1 has no representation while The 1000 Genomes phase 3 reference panel (referred as 1KGP3-ALL to denote the full panel hereafter) includes 5 populations related to the Indian subcontinent (though none from India) and hence is expected to provide better imputation accuracy than 1KGP115. We evaluated the imputation accuracy of GWAS data generated on individuals of Indo-European ethnicity from Western India, using 1KGP1 and 1KGP3-ALL panels. We further generated an Indian reference panel combining the Affy 6.0 data and Illumina CoreExomeBeadchip data and queried whether this panel in isolation or in combination with 1KGP3-ALL offers greater imputation accuracy in 3 different GWAS datasets  using high-density genotyping arrays have facilitated the understanding of genetic basis of many complex diseases and related intermediate traits15. The 1KGP1 included 1092 individuals from 14 populations and the 1KGP3-ALL has data from 2504 individuals from 27 world populations4. We investigated the utility of a newly generated Western-Indian reference panel (WIP) by comparing the imputation accuracy with 1KGP3-ALL and observed that the WIP confers a significant improvement in imputation accuracy across the minor allele frequency when merged with the 1KGP3-ALL panel (p < 0.05). We further demonstrate that using only the South Asian component of 1000 Genomes phase 3 dataset provides imputed data of similar quality as 1KGP3-ALL and may be used for imputation analysis using lesser computational resources. Based on our results, we make several recommendations for imputation/association analysis of GWAS data in South Asians, especially in Indians.The 1000 Genomes Project was launched to provide deeper characterization of the human genome sequence variations16. First, we compared 1KGP1 with 1KGP3-ALL at khap = 1000 and observed better imputation accuracy using the 1KGP3-ALL panel as measured by mean r-square metric  on 1880 subjects, were utilized to perform imputation by masked/imputed genotype approach using IMPUTE28416 SNPs19. The combined SNP dataset includes 931,371 high quality autosomal SNPs. The WIP was further merged with 1KGP3-ALL using IMPUTE2 to generate the WIP+1KGP3-ALL panel21 (see Methods). SNPs on chromosome 20 in Affy6.0 data on remaining 1880 individuals were imputed using the above reference panels. Comparison of the r-square values obtained from IMPUTE2 in leave-one-out scenario averaged for each MAF bin for SNPs common between the imputed datasets (18266 SNPs) shows that the WIP confers marginal enhancement in imputation performance than the 1KGP3-ALL panel 25. The imputed calls are assigned hard call genotypes based on probability values obtained for the three possible genotypes  and those not passing the probability threshold are ascribed as missing genotypes. Both the error rate in imputation calling between imputed genotype calls and observed NGS calls (percentage discordance), and the percentage of missing genotypes (missingness) were considered, which gives an idea about the imputation error rate . These panels were further combined with the WIP to get merged panels (WIP+1KGP3-SAS and WIP+1KGP3-EAS) respectively. Indeed, the imputation accuracy for 1KGP3-SAS reference panel was marginally better than the 1KGP3-ALL panel (p < 0.05 for 48.7% MAF bins)  improvement in the mean r-square values over few MAF bins for the WIP+1KGP3-ALL as compared to the 1KGP3-ALL panel  improvement in the average r-square value  on 48 Pathan and Sindhi subjects (as available from the HGDP-CEPH project)nel Fig. . Similarlue Fig.  in sever32 and unique population structure due to socio-cultural and historical peculiarities.In summary, we show that use of population-specific reference panel leads to better imputation which can improve results from association studies. We also find that despite the inclusion of 5 populations related to the Indian subcontinent in the 1000 Genomes phase 3 reference panel, there is still scope for the improvement of the imputation accuracy as demonstrated using a reference panel made using limited genotype data from a cohort from Western India. Owing to second highest genetic diversity in India after African populations, including more Indian samples from different ethnic backgrounds and sub-populations may help to enhance the accuracy of imputation. We also demonstrate that a cosmopolitan reference panel (1KGP3-ALL) is dispensable if we combine South Asian populations to make a reference panel (WIP+1KGP3-SAS) for imputation of samples from South Asia and more populations from South India or East India may further enrich the reference panel. This will further help to improve and broaden the scope of genetic association studies in South Asian populations and especially Indian diaspora which has been proposed to have an excess of genetic diseases19 and patients in the Wellgen study33; Illumina HumanCoreExome data on 407 individuals from the PMNS, Illumina Human660W-Quad BeadChip data on 590 individuals from North India27 and the Human Genome Diversity Panel (HGDP) data34 on Pathan and Sindhi populations (n = 48) were utilised for this study. Targeted NGS data on 3.57 Mb region was used for cross-validation of the imputation performance. All the subjects gave written informed consent and the study was approved by the Institutional Ethics Committee of Centre for Cellular and Molecular Biology, Hyderabad and all experiments were performed following guidelines for human research by Indian Council of Medical Research, Government of India, New Delhi.Genome-wide SNP data generated using Affy 6.0 array chip  on 2410 individuals comprising parents of the children in the Pune Maternal Nutrition Study−6 were removed. Reported gender was verified using two methods, X chromosome heterozygosity and average log ratio of chromosome X and Y intensity and 27 mismatch samples were excluded. Cryptic relatedness and duplicate samples were identified using identity by descent methods implemented in PLINK v1.07 (http://pngu.mgh.harvard.edu/purcell/plink/)35, and 104 individuals were excluded using PI_HAT ≥0.45. Overall, 123 samples were excluded for multiple overlapping reasons leaving 2287 individuals with 709687 autosomal SNPs for further analysis.Data from Affy 6.0 chips was collated and SNPs with call rate <97%, MAF <0.5%, or those with Hardy-Weinberg equilibrium (HWE) p < 1 × 10−6 were removed. Markers with bad clustering, heterozygote excess, poor separation of theta axis and with bad cluster separation were manually curated and bad calls were filtered out as recommended by Illumina. The data on 407 subjects was used for further quality control filtering and the SNP data was converted into the plus strand, hg19/build37 format. Monomorphic and non-autosomal SNPs were also excluded, leaving 303925 available SNPs on 407 samples for further analysis.Intensity files from Illumina HumanCoreExome-24v1.0 on 638 subjects from the PMNS were analysed using Genome Studio-v2011.1 (Genotyping module-v1.9.4). The samples with a call rate <98% were removed. In brief, markers with p10GC <0.38, low intensity (AB R mean ≤0.25), call frequency <0.95, HWE P < 1 × 10Based on an earlier GWAS, a 3.57 Mb target genomic region encompassing 37 genes on chromosomes 3, 5 and 10 was sequenced for 101 bp paired-end reads on 1055 samples from PMNS and Wellgen using HiSeq2000. The majority of samples (95%) had >30X depth of coverage and >90% bases were covered at least 4X. We mapped individual sequence reads to the human reference genome  with Burrows-Wheeler Aligner. After recalibration and realignment, the consensus genotypes were called using UnifiedGenotyper in GATKv2.3–9 and annotation of the variants was performed using ANNOVAR (May 2012 release). A total of 45068 SNPs passed the QC analysis and the data for 823 samples common to Affy6.0 individuals were used for further analysis.https://mathgen.stats.ox.ac.uk/impute/data_download_1000G_phase1_integrated_SHAPEIT2_16-06-14.html) and The 1000 Genomes phase 3  were downloaded. From the 1KGP3-ALL panel, South Asian and East Asian population-specific reference panels (1KGP3-SAS and 1KGP3-EAS respectively) were generated by subsetting the phased haplotypes from the 1KGP3-ALL panel using IMPUTE2 . The common SNPs from 1000 Genomes ITU population were extracted from the open access data and imputed using panels where ITU samples were removed from the reference panels.Publicly available reference panel datasets for The 1000 Genomes phase 1 . All of them produce similar assessments for common variants but IQS is especially recommended for rare and low frequency variants since it appropriately adjusts for chance agreements23. Hence, we used r-square for all comparisons and cross-checked the results of rare and low frequency variants using IQS. The r-square is the information metric calculated as the squared correlation between input and masked/imputed genotypes at a SNP and is shown as the “r2_type0” column in the _info file. It takes values between 0 and 1, where values near 1 indicate that a SNP has been imputed with high certainty3. Using R- packages, the r-square metric from the _info file averaged for each MAF bin (bin-size 0.1% MAF) was plotted for each imputed dataset16. The statistical significance of the differences in mean r-square values was analysed using two-tailed paired-end t-test taking all the r-square values at a given MAF bin for SNPs imputed using two different reference panels. IQS is based on Cohen’s kappa statistic and assesses the agreement between two methods while adjusting for chance agreement particularly for uncommon SNPs (MAF <5%)36. Concordance is ascertained based on best-guess genotype rather than imputed probabilities and we used concordance rate to further validate the imputed genotypes by comparing them with NGS genotypes on a subset of regions from chromosomes 3, 5 and 10 over a range of info thresholds (0.3 to 1.0). The discordant genotypes were compared using “–diff-site-discordance” utility in vcftools16 and the percentage discordance and missingness calculated from absolute number of SNPs at probability thresholds (0.33 to 1.00) were plotted.The study sample datasets were split chromosome-wise, pre-phased by SHAPEITv2 and imputed by IMPUTEv2.3.2Supplementary Information


Transcript co-expression is regulated by a combination of shared genetic and environmental factors. Here, we estimate the proportion of co-expression that is due to shared genetic variance. To do so, we estimated the genetic correlations between each pairwise combination of 2469 transcripts that are highly heritable and expressed in whole blood in 1748 unrelated individuals of European ancestry. We identify 556 pairs with a significant genetic correlation of which 77% are located on different chromosomes, and report 934 expression quantitative trait loci, identified in an independent cohort, with significant effects on both transcripts in a genetically correlated pair. We show significant enrichment for transcription factor control and physical proximity through chromatin interactions as possible mechanisms of shared genetic control. Finally, we construct networks of interconnected transcripts and identify their underlying biological functions. Using genetic correlations to investigate transcriptional co-regulation provides valuable insight into the nature of the underlying genetic architecture of gene regulation. Covariance of gene expression pairs is due to a combination of shared genetic and environmental factors. Here the authors estimate the genetic correlation between highly heritable pairs and identify transcription factor control and chromatin interactions as possible mechanisms of correlation. The degree of genetic variance that is shared between a pair of transcripts is the genetic correlation, a quantity that includes the effects of all variants, including those with small effects that are not identifiable through current expression quantitative trait loci (eQTL) studies. Genetic correlations are not the same as heritability, as they represent the proportion of overlapping genetic effects, and not their absolute magnitude; the expression levels of two transcripts could both be highly heritable but not be genetically correlated, or have low heritability and be completely correlated. Genetic and environmental correlations have traditionally been identified using twin models2, although more recently, approaches using genotype data from unrelated individuals have been developed. Mixed-model methods such as bivariate genome-wide relatedness restricted maximum likelihood (GREML)3, can be used to estimate genetic and environmental correlations from population-level data, by providing an estimate of the genetic co-variance for two traits, such as the expression levels of a pair of transcripts. Other methods such as cross-trait linkage disequilibrium (LD) Score Regression4 are useful alternatives when only summary statistics on genetic effects are available, although this is less powerful than using individual-level data5.The co-expression of a pair of transcripts, which can be represented by their phenotypic correlation, is due to the combined influence of shared genetic and environmental factors8. These studies have revealed some interesting results relating to epidemiological observations of shared co-morbidity. For example, they have confirmed and added resolution to previously known relationships, such as shared genetic control among metabolic diseases7, and identified novel relationships such as positive genetic correlations between anorexia nervosa and schizophrenia4. Importantly, such experiments can reveal pairs of diseases exhibiting genetic correlations close to zero, indicating that any observed co-morbidity is likely due to shared environmental factors.In humans, methods utilising genome-wide genotype data from unrelated individuals have been used to estimate the total shared genetic overlap between human diseases and complex traits (i.e. pleiotropy)9 are typically identified using traditional univariate mapping strategies11, although multivariate approaches have also been used13. Most eQTL studies have mapped cis-acting loci only-that is loci that are located in close proximity to the transcript(s), and many incidences of cis-effects shared between transcripts have been identified15. Recently Westra et al.16 used a combination of cis-mapping and trans-mapping to identify 103 independent eQTL with effects on two or more transcripts. Some of these eQTLs affect multiple transcripts in trans, and support previous work showing that a cis-acting eQTL for the transcription factor KLF14 gene acts as a master trans regulator of adipose gene expression17.Currently little is known about the total degree of shared genetic effects for expression levels of transcripts. However, there has been considerable effort to identify specific loci, which often have effects on two or more transcripts. These loci (eQTL)By establishing the total degree of shared genetic co-regulation between transcripts, as opposed to specific loci, we are able to: (i) identify networks consisting of transcripts located throughout the genome, whose expression levels are influenced by the same genetic variants, (ii) determine the proportion of variants on the same, or different, chromosomes and (iii) estimate the contribution of shared environmental factors to the co-variance of transcript expression.To provide an illustrative example of how molecular processes could underlie an observed genetic correlation for pairs of transcripts, consider the following simplified scenario: a gene encodes a transcription factor which has a large number of target genes. Suppose that the variation in the transcription factor’s expression levels is entirely due to a single-genetic variant. Under such a scenario, we would expect that the expression levels of the target genes would also be affected by the genetic variant. Thus, we should observe non-zero estimates of the genetic correlation between the expression levels of the transcription factor gene, and each of its target genes. A positive genetic correlation between two transcripts implies that, on average, shared genetic variants have an allelic effect in the same direction. Conversely, a negative genetic correlation implies that, on average, the allelic effect of the genetic variants is in the opposite direction.p < 1.81×10−8), of which 77% were located on different chromosomes. Using eQTL data from an independent cohort (n = 2104), we identified 934 incidences of eQTL with significant  effects on both transcripts in a genetically correlated pair. We then investigated the possible mechanisms that may lead to shared genetic control between a pair of transcripts. Our findings reveal significant enrichment for both of transcription factor control  and physical proximity through chromatin interactions . Finally, we used estimates of genetic correlations to construct graph networks of interconnected transcripts and identified the biological functions underlying many of the networks. Our findings demonstrate the utility of using genetic correlations to investigate transcriptional co-regulation and to gain valuable insight into the nature of the underlying genetic architecture of gene regulation. All results are made publicly available at http://computationalgenomics.com.au/shiny/rg/.Here, we have performed a systematic investigation of the degree of shared genetic control between pairs of expressed and highly heritable transcripts in whole blood. We have sought to understand the molecular processes that may give rise to the statistical observations of genetic correlations. We have estimated the genetic correlations between each pairwise combination of 2469 transcripts that are both highly heritable and expressed in whole blood in a cohort of 1748 unrelated individuals of European ancestry. We identified 556 pairs with a significant genetic correlation at a Bonferroni study-wide threshold  for the expression levels of pairs of transcripts. Expression levels were assayed using Illumina HT12-v4.0 expression arrays. We restricted our analysis to 2469 transcripts whose expression levels are highly heritable (rG for each pairwise combination.We used a bivariate GREML modelrG1. We identified 556 pairs of probes significant at a study-wide Bonferroni threshold of p < 1.81×10−8 (0.05/2755498); Fig. 19. For the 428 significant interchromosomal pairs, 36% (153) have a negative http://computationalgenomics.com.au/shiny/rg/.We obtained converged results for 2 755 498 probe pairs, representing 90.5% of the 3 045 512 we tested. Of the 290 014 non-converging pairs, 96% were from probe pairs with a phenotypic correlation  also had a significant association with the paired transcript in each rG pair. A schematic outlining our approach is given in Supplementary Fig. rG transcript pairs. We identified 934 incidences of eSNPs associated with both transcripts at a Bonferroni significance threshold of 0.05/1198525 = 4.1×10−8  and 58 with > 10 probes (2.9%).Estimates of LYZ on chromosome 12 and DUSP19 on chromosome 2 is −0.92 . In the LIFE-Heart data, the top eSNP for LYZ is cis-acting rs10784774[A/G] , with each copy of the A allele associated with a decrease in LYZ expression by 0.45 SD. We identified a shared effect of rs10784774 on DUSP19 , with each copy of the A allele associated in an increase in DUSP19 expression by 0.12 SD. This cis/trans relationship between rs10784774, LYZ and DUSP19 has been previously identified21, verifying the use of genetic correlations in identifying shared genetic control between genes. B: We identified instances where the top eSNP for a transcript was in trans and had a significant effect on the paired transcript, also in trans, which we term trans/trans eSNPs. For example, CD79A and OSBPL10 are located on chromosomes 19 and 3, respectively, and have an CD79A is rs7980008[A/G] , located at 30946602 bp on chromosome 12 (hg38), with each copy of the A allele associated with a 0.15 SD increase in expression of CD79A. rs7980008 was identified as having a significant effect on OSBPL10 , with each copy of the A allele increasing expression by 0.26 SD. C: SNP rs1354034[C/T], located at 56815721 bp on chromosome 3 (hg38) was identified as the top eSNP for 26 genes, 25 of which are not located on chromosome 3. At the FDR threshold of 0.05, we identify 183 significant genetic correlations between these 26 genes, with a mean |t-test, p < 4.1×10−8) within pairs, and 92% concordance between allelic direction and direction of the trans-master regulator, and is associated with blood cell phenotypes and cardiovascular diseases22, the genetic correlation analysis applied here has enabled the identification of a large, interconnected network of genetic co-regulated genes.To illustrate the relationship between transcript entclass1pt{minimaχ2, p = .9×10−8. 20, the direction of the rG estimate can be taken as an indicator of the direction of the allelic effect (β) for an eSNP associated with both transcripts; such that a positive β direction and negative β direction. In the first instance, we asked whether the distribution of β for probe pairs matching (i) +χ2 test   . We identified a significant enrichment for all window sizes , but a much greater enrichment for the p < 0.001) if we restrict our analysis to just the 428 and 14 020 interchromosomal pairs significant at the Bonferroni and FDR threshold respectively  http://computationalgenomics.com.au/shiny/rg/.Chromatin interactions do not occur randomly throughout the genome, instead showing enrichment for certain functional genomic regionsrG pairs, as well as identify novel relationships between genes.Biological networks can reveal interesting and potentially unexpected relationships between interacting genes. Genetic regulation often occurs at a network level, and genetic regulatory networks (GRNs) are useful for identifying master regulator genes that alter the expression of a set of molecular targets, and highlighting functional pathways. We hypothesised that genetically correlated transcripts would form the basis of GRNs and reveal the connectivity  between themselves and their 0: rG = 0, we estimated the empirical rG coefficient threshold corresponding to the 2nd, 3rd and 4th standard deviation (σ) from the mean. To identify whether subsets of Under a null model of H0 : rG = 0. Figure σ. Using the most connected gene (ZNF536) as an example, Fig. ZNF536, while Fig. rG transcripts located only on the same chromosome as ZNF536. Furthermore, we investigated the functional enrichment of each of the top 50 connected transcripts using a pathway analysis approach. Our results reveal significant  functional enrichment for 50 identified networks , regulation of immune response (p = 0.002), positive regulation of interferon-alpha production (p = 0.002), B cell proliferation involved in immune response (p = 0.003) and interleukin-1 beta secretion (p = 0.003).To understand the connectivity of genetically correlated transcripts, we constructed network graphs based on transcripts where we observed an enrichment in high ions 94.9% were inZNF536, the connections for each transcript isoform, both independent and shared, can be distinguished. The resulting graph network depicted in Supplementary Fig. ZNF536 transcripts (tagged by probes ILMN 1772155 and ILMN 2150586 on the Illumina HT12 array). Notably, each transcript has an associated set of connected transcripts that are predominantly distinct from one another. However, there are several transcripts whose regulation of expression appears to be shared by both ZNF536 transcripts. For example, the rG sign direction for RAB21 was in opposite directions for the two ZNF536 isoforms. This suggests that the loci that have shared effects on RAB21 and ZNF536 have on average opposite allelic effects for the alternative ZNF536 isoforms. We have produced a web application to visualise and download networks for each gene or transcript .By graphing interactions between transcript pairs, we were able to assess differences in genetic control for alternative isoform networks. In cases such as σ and compared them to our observed data. Under the null hypothesis (H0: rG = 0), the expected number of σ per transcript is 113 and 7, respectively. In our data, we observed 2317/2468 unique transcripts , and for unique genes above 3σ with > 7 |p = 3.62×10−2), revealing a strong enrichment for transcription factor-related connections between genetically correlated probes whose |Genes that exhibit a large number of significant genetic correlations with other genes (hub genes) may perform an important role in the regulation of their paired genes, with one possibility being hub genes encode transcription factors. An enrichment of transcription factors in σ) to visualise their connectivity and potential to form discrete transcriptional clusters. These transcript pairs were enriched for highly connected transcription factors, reflecting their widespread regulation of the expression of large numbers of genes. We traced the regulatory connectivity of ZNF536, a ubiquitously expressed member of the Krüppel C2H2 zinc finger transcription factor family28. ZNF536 is expressed in whole blood as two isoforms available on the Illumina HT12 array, both of which are highly correlated with many transcripts. We constructed a network graph for ZNF536 and demonstrated that each transcript isoform is genetically correlated with discrete groups of transcripts. Where both isoforms were correlated with the same transcript, such as RAB21, we observed opposite sign directions of ZNF536 isoforms exert different regulatory effects on certain targets. In general, little is known about C2H2 zinc finger transcription factors and their regulatory partners29, including ZNF536; however, our analyses demonstrate that using genetic correlations to uncover novel transcriptional networks can greatly improve our understanding of the strength and direction of genetically co-regulated transcripts.To better understand the relationship between the co-expression and genetic co-regulation of transcripts, we examined the phenotypic and genetic correlations between expressed and highly heritable transcripts in whole blood. We used the genotype and expression data of 1748 unrelated individuals of European ancestry to determine the average effect of all common loci on shared genetic mechanisms controlling gene expression. Our results show that the mean levels of shared genetic control between transcripts is close to zero, although there were a large number of significant transcript pairs with both positive and negative 3 estimate rG by capturing the effect of all common SNPs rather than each SNP individually, we can only infer the averaged effects of SNP-mediated regulation on a given pair of transcripts. Thus, to provide further verification of our rG estimates, we linked transcript pairs with high 15. By demonstrating the overlap of known peripheral blood mononuclear cell (PBMC) eQTLs with pairs of transcripts identified using an orthogonal method to capture the total shared genetic effects, we are able to identify many novel instances of trans-eQTL that would otherwise have been missed by traditional eQTL mapping strategies. These results support the evidence that the majority of genetic variance for gene expression is located on trans chromosomes19. Future work aiming to determine the effect of a given SNP on transcript expression would benefit from considering the use of nuclear run-on techniques such as GRO-Seq30 or NET-Seq31, which, coupled with chromatin immunoprecipitation data, provide higher resolution of transcriptional activity over time.Because mixed model methods such as bivariate GREMLtrans-eQTL, can be confidently determined. In the scenario presented in Fig. cis-eSNP alters the expression of a proximally located transcript (LYZ) and also drives the expression of another gene, DUSP19, in trans, termed a cis/trans shared eSNP. Our data uncovered the genetic co-regulatory relationship between LYZ and DUSP19 as a highly negative estimate of rG, indicating opposite directions of allelic effect by a genetic variant. Using genetic correlations alone, we observed strong co-regulation of expression between LYZ and DUSP19, the exact nature of which was even more precisely determined when combined with independent eQTL and Hi-C data. By overlapping our findings with eQTL data, we identified the eSNP (rs10784774) with the strongest association for at least one of the two transcripts, with SNP allelic effects consistent with the trans/trans eSNP  0.01 imputed to the 1000 Genome (phase 1v3) reference panel. Gene expression data has been normalised and corrected for known and hidden batch effects, and blood cell type composition. Transcripts tagged by the Illumina probes were annotated using custom mappings from the illuminaHumanv4.db package. We selected unrelated individuals from CAGE based on an identity-by-state genetic relatedness threshold of 0.05, and European ancestry through the projection of the first two principal components from CAGE genotypes against HapMap 3 ancestry cohorts. As the ability to accurately estimate rG is partly a function of the heritability of the transcripts, we restricted our analysis to those probes with Gene expression levels, measured from whole blood, and genotype data were available in 1748 unrelated individuals of European descent. These samples are a subset from the Consortium for the Architecture of Gene Expression (CAGE) cohort, which includes both multi-ethnic and related individuals. Full details of CAGE are given in Lloyd-Jones et al.rG) for each pairwise combination of the 2469 transcripts using a mixed-model bivariate GREML algorithm. GREML is described in detail in Lee et al.3, but briefly, for a pair of transcripts i and j, the linear mixed-effects model can be written as, yi = gi + ei and yj = gj + ej, where yi and yj are n*1 vectors of normalised gene expression levels for transcripts i and j respectively; gi and gj are n*1 vectors of random polygenic effects with A the genetic relationship matrix (GRM) estimated from 8 242 192 SNPs; and ei and ejnx1 vectors of residuals with I as the incidence matrix. The variance–covariance matrix for yi, yj is defined as,i and j, respectively. The genetic variance and covariance components were estimated using GREML, implemented in GCTA software, with We estimated the genetic correlation  used to estimate p-value below a threshold of 1×10−6) for each transcript. These SNPs were termed eSNPi and eSNPj respectively. We did not restrict our analysis to eSNPs within a certain distance from the transcript, meaning that the eSNP for a given transcript could potentially be located on a different chromosome. We then extracted the summary statistics for the association between eSNPi and transcript j and eSNPj and transcript i. This study design is summarised in Supplementary Fig. Using eQTL data generated from PBMC gene expression in an independent cohort, we investigated the relationship between shared effects of SNPs on both transcripts in 5, i = j = 0.375, N is the sample size =1748, and var(Aij) is the variance of the off-diagonals of the GRM =4.0×10−5. Assuming a Gaussian distribution, with mean of 0, under a null hypothesis, we are able to calculate the expected number of σ from the mean as m×(1−0.9973), where m is the number of H0: rG = 0, we would expect to observe σ from 0. We reasoned that a transcript exhibiting a large number of high igraph package for R. Transcripts were annotated using Illumina probe IDs.Network graphs are a powerful way to visualise the connectivity between genes or transcripts, and are useful for identifying novel connections and clusters of functionally related mRNAs. We hypothesised that transcripts that displayed strong genetic correlations with large numbers of other transcripts would form discrete clusters, or networks, illustrating the regulatory direction of effect on expression at the isoform level. Since, for a given transcript, we have estimated the σ) or 0.72 (3σ). A background list of 2330 unique genes, and 234 expressed and heritable TFs present in our data set were used in the analysis.Transcription factors are major drivers of gene expression and have been identified as the mechanisms responsible for observable gene co-expression networks. This led us to test the hypothesis that genes tagged by transcripts that form pairs with high cis-effects. LCL data generated by Lieberman-Aiden et al.23 was obtained from NCBI GEO (GSE18199) and K562 chromatin contact data was obtained from the study of Lan et al.25. Hi-C QC and filtering was performed using the Homer Hi-C34 analysis software with the default parameters. Based on methods detailed by Lan et al., the threshold of self-ligation events in the LCL data was set to 20 kb, and those interaction loci within < 20 kb of each other were removed. We performed simulations to test if our observed overlaps were greater than those expected by chance. We designed the simulations to ensure that the null distribution was of chromosome interactions between loci that matched the genomic regions from which the transcripts were located. To do this we randomly shuffled transcript i with respect to transcript j for the list of transcript pairs, creating permuted pairs for each of 128, 971 and 14 020 genomic positions. We then tested for overlap using windows of 100 kb, 250, 500 kb and 1 Mb between the permuted list and the map of chromosome interactions and then repeated the random shuffling process. We performed 1000 such permutations to generate a null distribution of chromosome interaction overlaps.In this study, we refer to the co-localisation of two chromosome regions as a chromosome interaction. We cross-referenced our transcript positional locations from the http://computationalgenomics.com.au/shiny/rg/. The genotype and gene expression data can be made available to researchers through the CAGE consortium. Contact J.E.P. for details.The genetic correlations data that support the findings of this study are available from Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Peer Review File


Rice breeding programs based on pedigree schemes can use a genomic model trained with data from their working collection to predict performances of progenies produced through rapid generation advancement.5–F7 lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high (r2 = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41–0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed.So far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 FThe online version of this article (10.1007/s00122-017-3011-4) contains supplementary material, which is available to authorized users. Genomic selection (GS) arose from the conjunction of new high-throughput marker technologies and new statistical methods  is the world’s most important staple food and will continue to be so in the coming decades. Genetic improvement is one of the major pillars of sustainable adaptation of rice production to ongoing global changes  the size and the degree of relatedness of the reference panel that maximize the accuracy of prediction of phenotype of progeny lines from several biparental crosses; (2) the effects of marker selection and statistical methods on prediction accuracy across generations, for three traits with different heritability; and (3) the accuracy of genomic prediction among the progeny of individual biparental crosses.5–F7) inbred lines. The diversity panel represents the working collection of the rice breeding program of Research Centre for Cereal and Industrial Crops (CREA), Vercelli, Italy. It is composed of 139 accessions of Italian origin and 145 accessions of diverse geographic origin (Supplementary Table 1), all belonging to the japonica subspecies of O. sativa and all adapted to cultivation in the irrigated rice ecosystem of temperate Mediterranean Europe , in an irrigated cropping system with standard crop management. The RP was phenotyped during the 2012 and 2013 rice cropping seasons under a complete randomization experimental design with three replicates per accession. The size of the individual plot was 1.70 m × 0.40 m, each plot contained three rows of 60 seeds. The PP was phenotyped during the 2014 and 2015 rice cropping seasons under randomized complete block design with three replicates. The size of the individual plot was 1.20 m × 0.80 m, each plot contained six rows of 40 seeds.The target traits for both RP and PP were days to flowering (FL), panicle weight (PW), and the nitrogen balance index (NI). FL was recorded as the number of days after sowing, when 50% of the plants in the plot were in flower. In the experiments related to RP, PW (g) was recorded by weighing a random sample of 50 panicles in the plot, and, in the experiments related to PP, by weighing 100 representative panicles. The measurements were harmonized to represent the weight of 100 panicles. NI, an indicator of the plant nitrogen status . The mixed model used for the RP was:The mixed model used for the PP was:A model-based diagnostic analysis was run for each field trial and each trait within the mixed model framework above, to detect potential outliers among the individual data points (plot level). The restricted likelihood distance (RLD) output of the diagnostic procedure was used to identify outliers. RLD is a global measure of the influence of the observations jointly on all parameters. If In addition to the above-mentioned diagnostic analysis, spatial homogeneity of the experimental fields, where RP phenotyping took place under a complete randomization design, was surveyed by visual analysis of the heat-map of the residuals . A slight discrepancy in the random distribution of the residuals was observed for NI only.H2, was calculated for each trait in each population using the formula of Holland et al.  were extracted for each trait to be used as phenotypes in the genomic prediction models.Broad sense heritability of accession means, d et al.  as folloTassel GBS pipeline v3.0 , and for SNP calling. The procedure yielded 246,554 SNPs with a call rate ≥ 80%. Filtering of the matrix for missing data with a threshold of 20% led to 70,530 SNPs with an average rate of 9.2% missing data. Missing SNP genotypes were then imputed using the FILLIN  algorithm in the Tassel GBS pipeline v3.0, with default settings. Filtering of this matrix for the rate of heterozygoty (threshold of 5%) and for a minor allele frequency  among the RP accessions and PP lines, considered together, led to a final working set of 43,686 SNP loci. The genotypic data are available at http://tropgenedb.cirad.fr/tropgene/JSP/interface.jsp?module=RICE, (Choose Tab Studies) as GS-Ruse_CREA_GBSgenotype_RP&PP.The genotyping procedure is detailed in Biscarini et al. . BrieflyThe genetic structure of the two populations was analyzed jointly using a distance-based method. First, a matrix of 4824 SNPs was extracted from the working genotypic dataset of 43,686 SNPs, by discarding loci that had imputed data and by imposing a minimum distance of 10 kb between two adjacent loci. Then an unweighted neighbor-joining tree based on a simple matching matrix was constructed using DarWin v6  described by Jacquin et al. , reproducing kernel Hilbert spaces regressions (RKHS) and BayesB . The choice of the three MAF thresholds  was intended to represent its distribution  within RP and PP.Twenty-one incidence matrices were constructed to investigate the effect of LD (7 threshold levels) and MAF (3 threshold levels), on the accuracy of genomic predictions within the RP. The seven LD thresholds, N = 289 entries and P = 43,686 SNPs), markers were selected based on the three MAF thresholds ; (2) for each of the three resulting matrices, the pairwise LD between markers was calculated for each chromosome; (3) for each marker and for each LD threshold, redundancy information was computed as the number of times the pairwise LD with other markers was above the LD threshold; (4) markers with a redundancy level above an empirical threshold of 30 were discarded. This empirical threshold of redundancy represented a good compromise between our two objectives, one to reduce redundancy, which varied widely between SNPs, the other to dispose of an ample range of marker density among the 21 incidence matrices. As a result, marker density ranged from 8.7 to 83.5 SNP per Mb for the seven LD thresholds under MAF ≥ 5%, from 5.0 to 69.9 under MAF ≥ 10% and from 3.1 to 52.4 under MAF ≥ 20% (Table The incidence matrices were constructed as follows: (1) using the genotypic dataset  of the 284 accessions of the RP as the training set and the remaining 95 (1/3) accessions as the validation set. Each cross validation experiment was repeated 100 times using 100 independent partitioning of the accessions into the training set and validation set. For each independent partitioning, the correlation between the predicted and the observed phenotype was calculated, so as to obtain 100 correlations for each cross validation experiment. The accuracy of each cross validation experiment was computed as the mean value of the 100 correlations.A total of 189 cross validation experiments were undertaken combining the above-described seven LD threshold levels, three MAF threshold levels, three prediction methods, and the three phenotypic traits. The same 100 independent partitioning of the training and validation sets was used for all 189 cross validation experiments.Six scenarios, representing different degrees of relatedness between the training set and the progeny set and different sizes of the training set, were considered Table . To thisThe six scenarios were implemented with seven incidence matrices corresponding to the seven thresholds of LD used in the cross validation experiments, a unique MAF threshold of ≥ 5%, and three prediction methods . The accuracy observed under scenarios S1, S2 and S3 was also compared with the accuracy obtained by three training sets of equivalent sizes selected using the dedicated CDmean optimization method  were also computed separately.r) of all prediction experiments were transformed into a Z-statistic using the equation: r variable. For each trait, a separate ANOVA was performed for the correlations of all the PPA and of the average PPA in the progeny prediction experiments. In each case, ANOVA was performed to partition the variance of accuracy into different sources, with all effects declared as fixed, and following two models. The first model compared the effects of LD, MAF and prediction method in the cross validation experiments, and the effect of LD, scenario and prediction method in the progeny prediction experiments, with no interaction. The second model accounted for all the principal effects as well as for all possible first-order interactions.The accuracy data , in the RP. For the PP, H2 was high for all traits (H2 ≥ 0.8). The precision of the H2 estimates was reasonably high, as the standard errors ranged between 0.007 and 0.052.The 43,686 SNP markers were unevenly distributed along the chromosomes. While the average marker density was 1 SNP per 8.8 kb, it ranged from one SNP every 5.1 kb on chromosome 11 to one SNP every 12.6 kb on chromosome 3 . The distance between a pair of adjacent SNPs ranged from 0.001 to 644 kb, with a median of 1.20 kb. The distance was < 20 kb in almost 90% of the pairs of adjacent markers and < 100 kb in 98.8%. The distance was > 100 kb in 500 pairs of adjacent SNPs, > 200 kb in 114 pairs and > 500 kb in only one pair.Even though the markers whose MAF was below 2.5% had been discarded, the distribution of MAF was still skewed toward low frequencies. The proportion of loci with a MAF < 10% was 38.5% in the RP and 30% in the PP. The differences in allele frequency between the two populations were mainly quantitative, not qualitative: for 93% of loci, the minor allele in RP was also the minor allele in PP, and the average MAF for these loci was 24.6 in RP and 22.7 in PP. Conversely, for 7% of loci, the minor allele in RP (average frequency of 42.7%) become a major allele in PP with an average frequency of 56.4%, suggesting the accentuation of allele frequency disequilibrium observed in RP. This tendency was confirmed by the allele frequencies in loci with the lowest MAF (< 10%) in RP. In these loci, which represented 16% of the total, only one minor allele in RP became a major allele in PP, while 951 minor alleles in RP were lost in PP and the corresponding loci became monomorphic with the major allele of RP. The MAF differences between RP and PP were even more pronounced for the smallest incidence matrix of 3322 SNPs, with a MAF < 5% for 25.7% of the loci and 9.2% of monomorphic loci.r2 value reached 0.62 and 0.66 in the RP and in the PP, respectively. In the RP, the r2 value dropped to half its initial level at around 350 kb and reached 0.2 at 800 kb and 0.1 at 2.9 Mb. As expected, the decay of LD was slower in the PP, reaching an r2 of 0.2 at 1.1 Mb and 0.1 at 3.9 Mb. Some differences in the speed of LD decay were observed between chromosomes, with the highest speed in chromosome 11 (r2 = 0.21) reached between 200 and 225 kb in the RP and an r2 of 0.20 reached between 300 and 350 kb, and the lowest in chromosome 5, with an r2 of 0.2 at 1–1.5 Mb in both populations.The decay of LD along physical distance is presented in Fig. japonica (217 accessions) and tropical japonica (67 accessions) sub-groups  among the three traits was r2 ≤ 0.25 and r2 ≤ 0.36. NI was the trait most affected by variations in the LD threshold, with a gain in APA of 0.12 (21.6%) between LD levels (r2 = 0.25 and r2 = 0.64) giving the lowest, and the highest APA, respectively. The MAF threshold leading to the overall highest APA (0.58) was MAF ≥ 5%. The higher MAF thresholds tested led to the same lower APA (0.57). Finally, the performances of the BayesB and RKHS methods were the same (0.58) and that of GBLUP was 0.56.The 189 cross validation experiments involving seven levels of LD, three levels of MAF, three prediction methods and the three phenotypic traits yielded average prediction accuracies (APA) ranging from 0.42 to 0.65 .All first rank interactions between the three factors affecting APA were significant except MAF × Method for NI and PW traits Table . Among tGiven these results, we decided to consider only one MAF threshold (≥ 5%) in the following steps of the study (progeny prediction) and to focus on the analysis of the effect of LD and prediction method.The 360 non-replicated experiments of genomic prediction of the progenies’ phenotype, involving the first five scenarios S1 – S5) of the relationship between the training set and the progeny set, seven LD thresholds, and three prediction methods led to progeny prediction accuracies (PPA) ranging from 0.23 to 0.51 (mean PPA 0.35) for the FL trait, 0.09 to 0.52 (mean PPA 0.33) for NI and 0.17 to 0.54 (mean PPA 0.38) for PW. The 72 replicated prediction experiments in scenario S6 led to an average PPA ranging from 0.05 to 0.22 (mean PPA 0.15) for the FL trait, 0.12 to 0.26 (mean PPA 0.21) for NI and 0.21 to 0.36 (mean PPA 0.30) for PW. The following comparisons of factors affecting PPA, especially the scenario factor, are based on PPA data for the non-replicated prediction experiments and on the average PPA for the replicated experiments of S6  were selected using the CDmean method, and used to predict progeny. The results  showed almost no gain in accuracy for FL and PW with the CDmean method, and an almost systematic gain in accuracy of about 0.1 for NI.To further explore the effect of relatedness between the training and the candidate set, we used the CDmean method .The magnitude of variation in PPA in relation with LD was much narrower. The highest mean PPA (0.36) was achieved with LD thresholds of As expected, the accuracy of progeny prediction within two individual crosses showed much larger variation (− 0.310 to 0.731) depending on the trait, the scenario and the size of the incidence matrix . Given the small number of progenies for each of the two crosses used for this analysis (20 and 9), drawing any conclusion regarding the effect of one individual factor  would be risky. However, the fact that accuracy above 0.7 was obtained in some conditions strongly suggests the feasibility of intra-cross progeny prediction using a diversity panel.The main objectives of this work were to assess the performance of genomic prediction among the progeny of biparental crosses, using a reference panel to train the model in rice, and to investigate the effect of the size of the reference panel and of the degree of relatedness with the progeny population on the accuracy of predictions, as well as the effect of LD and the training model. To set a base line for prediction accuracy and to reduce the number of possible options to be tested regarding LD and other characteristics of the incidence matrix, we started our study by evaluating the accuracy of genomic prediction within the reference population using a cross validation approach.japonica adapted to the irrigated lowland ecosystem of Europe, has probably contributed to the relatively high accuracy of genomic prediction for complex traits such as NI and PW.The average genomic prediction accuracies within the reference population ranged from 0.51 for NI to 0.63 for FL, in line with their degree of broad sense heritability. The highest accuracies were 0.65 for FL, 0.57 for NI, and 0.62 for PW. Beyond the high heritability, the rather narrow genetic diversity of our RP assembling temperate and tropical The accuracy of genomic prediction was affected in a complex way by interactions between LD, MAF and phenotypic traits, but did not question the well-established rule of balance between the number and the distribution of markers along the chromosome, and the LD within the population  of the early generations, and where the phenotypic evaluation starts with the advanced F5 or F6 generation. RGA consists in the fixation of F2 progenies through 2–3 generations of single seed descent per year in the greenhouse, until F5 or F6. However, our experiments diverged from this scheme by the very pronounced imbalance in the number of progeny per cross, which varied from 1 to 20.Our genomic prediction experiments on the line value of FThe accuracy of our progeny predictions among the 97 advanced lines of PP derived from 36 biparental crosses, involving 31 accessions of RP, varied greatly for each trait, depending on the composition of the training set and the LD. However, for each trait, the highest degree of accuracy achieved was only slightly below the highest accuracy achieved in the cross validation experiments in the RP: 0.51 versus 0.65 for FL, 0.52 versus 0.57 for NI and 0.54 versus 0.62 for PW. Similar results have been obtained in sugar beet  was much higher than that reported in the literature for the japonica group , the effective size of population Ne, the length of the genetic map, and the architecture of the target trait  progeny of a large set of biparental crosses. Accuracies of over 0.5 were obtained, even for complex traits such as NI and PW, when the parameters that affect the accuracy were optimized. Thus, breeders can use this prediction approach in the framework of a pedigree breeding scheme associated with RGA of early generations (in off-season nurseries or controlled environments), a practice aimed at reducing the length of the breeding cycle and hence accelerating genetic gain per unit of time  an average marker density above one per 22 kb (8324 SNPs) did not improve the accuracy of prediction in either cross validation within the RP or in progeny prediction and (2) relatively high accuracy could be achieved using only a rather small share of the RP, most related to PP, as the training set. Given the very uneven distribution of marker density along the chromosomes in our RP and PP, one would expect similar levels of prediction accuracy with a much smaller number of markers chosen based on LD distribution along the chromosomes, as already predicted in simulation studies Supplementary material 2 (PDF 221 kb)Supplementary material 3 (PDF 620 kb)Supplementary material 4 (PDF 614 kb)Supplementary material 5 (PDF 612 kb)Supplementary material 6 (PDF 223 kb)Supplementary material 7 (JPEG 124 kb)Supplementary material 8 (JPEG 692 kb)Supplementary material 9 (JPEG 85 kb)Supplementary material 10 (JPEG 98 kb)Supplementary material 11 (JPEG 156 kb)Supplementary material 12 (JPEG 138 kb)Supplementary material 13 (JPEG 141 kb)Supplementary material 14 (JPEG 116 kb)Below is the link to the electronic supplementary material.


Individuals with lower socio-economic status (SES) are at increased risk of physical and mental illnesses and tend to die at an earlier age , 2, 3. E •Common SNPs explain 21% of social deprivation and 11% of household income•Two loci attained genome-wide significance for household income•Genes in these loci have been linked to synaptic plasticity•Genetic correlations were found between both measures of SES and many other traits Individuals with lower socio-economic status (SES) are at increased risk of physical and mental illnesses. Hill et al. find extensive genetic correlations between SES and health, psychiatric, and cognitive traits. This suggests that the link between SES and health is driven, in part, by a shared genetic association. We found no genome-wide significant findings associated with social deprivation (see −8): rs187848990 on chromosome 2, and rs7252896, rs7255223, and rs8100891 on chromosome 19 . This region contains five genes: AFF3, CHST10, LONRF2, NMS, and PDCL3. The AFF3 gene has previously been associated with intellectual disability [CHST10 is involved with synaptic plasticity [−8. This region contains the gene ZNF507, which has been implicated in neurodevelopmental disorders including schizophrenia [The “clump” function in PLINK  was usedsability , and CHSophrenia , a disorophrenia . It is pophrenia .http://www.broadinstitute.org/gtex/), cis-eQTL associations were identified for the four household income genome-wide significant SNPs (Using the GTEx database (ant SNPs . For thiant SNPs .−4) and rs8100891  on chromosome 19. rs7252896 was not included in the education data and thus could not be replicated. We then sought to use a SNP that was in high linkage disequilibrium (LD) with rs7252896 to use as a proxy SNP for replication; however, there were no SNPs in the education dataset that were in LD with rs7252896 (r2 of greater than 0.5), excluding rs7255223 and rs8100891.We next sought to replicate the four genome-wide significant SNPs in a sample of ∼200,000 individuals who were assessed on the number of years of schooling completed, as this variable is often used as a measure of SES. Summary statistics were made available from the Social Science Genetic Association Consortium’s GWAS of educational attainment , with da−24) was found between social deprivation and years of education, and there was a genetic correlation of 0.903  between income and years of education. These substantial genetic correlations indicate that the two measures of SES, as measured in UK Biobank, have a very similar genetic architecture with a third SES variable—education—measured in an independent sample.We also used this education summary GWAS dataset to derive genetic correlations between both the social deprivation and household income variables in UK Biobank. A genetic correlation of 0.548 . Similarly, a polygenic score derived using household income in UK Biobank predicted a significant proportion of phenotypic variance for household income in GS:SFHS at each of the p value thresholds used, with polygenic scores derived using a p value threshold of 0.5 being the most predictive . The results of the polygenic profile scores illustrate that the molecular genetic architecture of these SES variables, as measured in the UK Biobank datasets, overlaps with that of GS:SFHS, indicating that the same genetic variants are associated with phenotypic variation in SES in each of these samples. The betas found using the polygenic profile score method predict only a small proportion of the phenotypic variance, which is in line with other phenotypes [Next, we used polygenic profile scores, derived using the social deprivation and household income variables’ GWASs in UK Biobank, to predict social deprivation  and household income in an independent sample, Generation Scotland: Scottish Family Health Study (GS:SFHS) , 16. Polenotypes .−6), gene-based association tests identified one gene associated with social deprivation: ACCSL on chromosome 11 (p = 3.48 × 10−7). For household income, 12 genes showed a significant association: KANSL1 (p = 8.20 × 10−8), MST1 (p = 1.10 × 10−7), RNF123 (p = 1.19 × 10−7), MAPT (p = 1.23 × 10−7), APEH (p = 2.64 × 10−7), BSN (p = 1.03 × 10−6), PLEKHM1 (p = 1.16 × 10−6), SGCD (p = 1.30 × 10−6), DAG1 (p = 1.55 × 10−6), CRHR1 (p = 2.39 × 10−6), AMT (p = 2.39 × 10−6), and ZDHHC11 (p = 2.54 × 10−6)  . The MAP23 p = 1. × 10−7, Partitioned heritability analysis was then conducted on both SES phenotypes in UK Biobank . The goaPartitioned heritability analysis was also used to conduct a cell-specific analysis of ten broad tissue types see . Figure We next derived genetic correlations, using linkage disequilibrium score (LDS) regression , betweenrg = 0.500; income, rg = 0.667), with participants’ verbal-numerical reasoning scores in the UK Biobank clinic visit , and also with longevity . The direction of effect in each instance indicates that more affluent SES is associated with longer life and higher intelligence. The average age of the participants in the GWAS for childhood intelligence was 11 years, whereas the measurements of SES from UK Biobank were taken at a mean age of 57 years. The finding of a genetic correlation between these two traits may indicate that a set of genetic variants contributes to higher intelligence, which in turn contributes to a higher SES in mid-life. Significant genetic correlations were found between household income and intracranial volume and infant head circumference .Following false discovery rate (FDR) correction for multiple comparisons, 16 of the 34 genetic correlations were statistically significant for the Townsend social deprivation measure see , and 24 A noteworthy feature of our findings is that the pattern of genetic correlations between our two measures of SES—one area-based and one individual-based—was very similar, and the genetic correlation between the two measures of SES was high, at 0.871 (SE = 0.064). The Townsend social deprivation score is widely used as a proxy indicator of adult socioeconomic status, usually in the absence of an individual measure. It has been shown to be predictive of cancer incidence, all-cause mortality, and other health outcomes . Such arThere are at least two explanations for the genetic SES-health correlations found using the LDS regression method. The first is that the genetic correlations might have been found as a result of the same genetic variants being directly involved in two phenotypes. The second is the notion of mediated pleiotropy, which describes situations in which a phenotype is causally related to another, perhaps via other variables; therefore, if a genetic variant is associated with the first phenotype, it will be indirectly associated with the second . Should The genetic correlations derived in the current paper cannot distinguish the direction of the effect of this shared genetic association between SES and health and cognitive variables. Whereas it is possible that a greater level of cognitive ability will facilitate an individual’s ability to move to a higher SES, it could also be the case that those in higher SES environments are exposed to environmental stimuli that facilitate their intellectual development. As both SES and cognitive ability are partly heritable, each of these possibilities would result in a genetic correlation between SES and cognitive ability. Mendelian randomization (MR) is a technique that sits above genetic correlations and polygenic profile scores in the so-called hierarchy of evidence  for asceThe associations between rs187848990 and rs8100891 with household income, along with the heritability estimates for both measures of SES, are, we think, the result of mediation through other phenotypes. Because genetic differences will not directly result in differences in SES, they may be contributing to differences in variables such as intelligence, personality, resistance to diseases, and other factors, which, in turn, can contribute to differences in SES.The effect sizes found for each individual SNP were small; however, as has been found for other polygenic traits, it is the combined effect of multiple SNPs that contributes to some of the observable phenotypic variance. Polygenic profile scores were created for 28 health-related phenotypes using published GWAS in all participants with genome-wide SNP data. When predicting into social deprivation based on the Townsend score, polygenic profile scores derived from the summary statistics of 11 GWASs of health-related traits predicted a significant proportion of phenotypic variance . When pr−8) and rs7467480   . The resThe results here show that 21% of people’s differences in area-level social deprivation and 11% of household income can be explained by additive common genetic factors. Four genome-wide significant SNPs were found for household income, leading to the identification of two independent genomic regions containing genes with known associations with intellectual disabilities, synaptic plasticity, and schizophrenia. Extensive genetic correlations were found between both measures of SES and health-related traits, indicating a highly diffuse genetic architecture. These genetic correlations might provide a partial explanation for the phenotypic association between SES and health—the majority of which, we think, is due to environmental factors.http://www.ukbiobank.ac.uk) [We examined two measures of SES that were available in UK Biobank (k.ac.uk) . The firk.ac.uk) —a measurhttps://census.ukdataservice.ac.uk/media/215850/Townsend2001.csv) and compared the distribution of Townsend scores from all of England and Wales to those in UK Biobank that had been genotyped. The data from the UK census exclude wards of less than 100 households, which only altered wards in the City of London and the Scilly Isles. The score was first reversed so that a greater Townsend score corresponds to a higher SES. As can be seen in We used data from the UK Census of 2001 (We conducted separate analyses for the Townsend deprivation score and household income. All phenotypes were adjusted for age, gender, assessment center, genotyping batch, genotyping array, and ten principal components in order to correct for population stratification prior to all analyses. See Conceptualization, W.D.H., C.R.G., I.J.D.; Software, D.C.M.L., G.D.; Formal Analysis, W.D.H., S.P.H., R.E.M., G.D.; Data Curation, W.D.H., S.P.H., S.E.H.; Writing – Original Draft, W.D.H., as discussed with I.J.D.; Writing – Review and Editing, W.D.H., S.P.H., R.E.M., S.E.H., D.C.M.L., G.D., A.O., A.M.M., C.R.G., I.J.D.