Large-scale cis-and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression

Structural variants (SVs) are an important source of human genetic diversity but their contribution to traits, disease, and gene regulation remains unclear. We mapped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single nucleotide (SNV), and short insertion/deletion (indel) variants from deep whole genome sequencing (WGS). We estimate that SVs are causal at 3.5–6.8% of eQTLs – a substantially higher fraction than prior estimates – and that expression-altering SVs have larger effect sizes than SNVs and indels. We identified 789 putative causal SVs predicted to directly alter gene expression: most (88.3%) are noncoding variants enriched at enhancers and other regulatory elements, and 52 are linked to genome-wide association study loci. We observe a notable abundance of rare, high impact SVs associated with aberrant expression of nearby genes. These results suggest that comprehensive WGS-based SV analyses will increase the power of common and rare variant association studies.

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk1,2,3,4. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants1,5. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles1,6,7, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues8,9,10,11, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release12. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs, indicating their potential involvement in regulation of gene expression. Here, we propose a transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) that uses multiple SNPs as instruments and multiple gene expression traits as exposures, simultaneously. Applied to 43 human phenotypes, it uncovers 3,913 putatively causal gene–trait associations, 36% of which have no genome-wide significant SNP nearby in previous GWAS. Using independent association summary statistics, we find that the majority of these loci were missed by GWAS due to power issues. Noteworthy among these links is educational attainment-associated BSCL2 , known to carry mutations leading to a Mendelian form of encephalopathy. We also find pleiotropic causal effects suggestive of mechanistic connections. TWMR better accounts for pleiotropy and has the potential to identify biological mechanisms underlying complex traits.