The impact of rare variation on gene expression across tissues.
Li X., Kim Y., Tsang EK., Davis JR., Damani FN., Chiang C., Hess GT., Zappala Z., Strober BJ., Scott AJ., Li A., Ganna A., Bassik MC., Merker JD., GTEx Consortium None., Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group None., Statistical Methods groups—Analysis Working Group None., Enhancing GTEx (eGTEx) groups None., NIH Common Fund None., NIH/NCI None., NIH/NHGRI None., NIH/NIMH None., NIH/NIDA None., Biospecimen Collection Source Site—NDRI None., Biospecimen Collection Source Site—RPCI None., Biospecimen Core Resource—VARI None., Brain Bank Repository—University of Miami Brain Endowment Bank None., Leidos Biomedical—Project Management None., ELSI Study None., Genome Browser Data Integration &Visualization—EBI None., Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz None., Hall IM., Battle A., Montgomery SB.
Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, 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 tissues, 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 release. 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.