The impact of rare variation on gene expression across tissues

Journal article

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.

Authors: Li, X; Kim, Y; Tsang, EK; Davis, JR; Damani, FN

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Publishing Group
• Journal: Nature
• Volume: 550
• Issue: 7675
• Pages: 239-243
• Publication date: 2017-10-11
• Acceptance date: 2017-09-13
• DOI: 10.1038/nature24267
• EISSN: 1476-4687
• ISSN: 0028-0836
• Pmid: 29022581
• Language: English
• Keywords: Biospecimen Collection Source Site—NDRI; NIH/NIDA; Genome Browser Data Integration &Visualization—EBI; Biospecimen Core Resource—VARI; Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group; Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz; organ specificity; genomics; Statistical Methods groups—Analysis Working Group; genotype; models, genetic; genetic variation; gene expression profiling; ELSI Study; GTEx Consortium; genome, human; NIH/NIMH; sequence analysis, RNA; Biospecimen Collection Source Site—RPCI; NIH/NCI; Brain Bank Repository—University of Miami Brain Endowment Bank; NIH/NHGRI; Bayes theorem; Leidos Biomedical—Project Management; Enhancing GTEx (eGTEx) groups; NIH Common Fund