Genetic effects on gene expression across human tissues

GTEx Consortium, et al

Illustration of the 44 tissues and cell lines included in the GTEx v6p project plus 3 data graphs — a, Illustration of the 44 tissues and cell lines included in the GTEx v6p project with the associated number of cis- (left) and trans-eGenes (right) and sample sizes. Each tissue has a unique color code (defined in Supplementary Fig. 5). b, Fraction of genes that are eGenes across all tissues by transcript class. The three tissues highlighted are: testis, which has the highest proportion of trans-eGenes; skeletal muscle, which has the largest sample size; and fibroblasts, which have the highest proportion of cis-eGenes. Dark bars depict the fraction of all curated human genes in GENCODE v19. Light bars depict the fraction of genes expressed in one or more tissues. c, Proportion of expressed genes that are cis-eGenes (y-axis) as a function of tissue sample size (x-axis). Colors represent tissues, as in a. d, Number of trans-eQTLs (x-axis) per tissue (y-axis), with sample size indicated by point size.

Nature 550, 204–213 (12 October 2017)

Abstract

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.