The impact of rare variation on gene expression across tissues

GTEx Consortium

Research output: Contribution to journalArticle

43 Citations (Scopus)

Abstract

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk1-4. 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 tissues8-11, 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.

Original languageEnglish (US)
Pages (from-to)239-243
Number of pages5
JournalNature
Volume550
Issue number7675
DOIs
StatePublished - Oct 11 2017
Externally publishedYes

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Gene Expression
Genome
RNA Sequence Analysis
Genetic Code
Genetic Association Studies
Statistical Models
Genotype
RNA
Genes
Proteins

ASJC Scopus subject areas

  • General

Cite this

The impact of rare variation on gene expression across tissues. / GTEx Consortium.

In: Nature, Vol. 550, No. 7675, 11.10.2017, p. 239-243.

Research output: Contribution to journalArticle

GTEx Consortium. / The impact of rare variation on gene expression across tissues. In: Nature. 2017 ; Vol. 550, No. 7675. pp. 239-243.
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abstract = "Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk1-4. 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 tissues8-11, 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.",
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