Large-Scale Identification of Common Trait and Disease Variants Affecting Gene Expression

Abstract

Genome-wide association studies (GWASs) have identified a multitude of genetic loci involved with traits and diseases. However, it is often unclear which genes are affected in such loci and whether the associated genetic variants lead to increased or decreased gene function. To mitigate this, we integrated associations of common genetic variants in 57 GWASs with 24 studies of expression quantitative trait loci (eQTLs) from a broad range of tissues by using a Mendelian randomization approach. We discovered a total of 3,484 instances of gene-trait-associated changes in expression at a false-discovery rate < 0.05. These genes were often not closest to the genetic variant and were primarily identified in eQTLs derived from pathophysiologically relevant tissues. For instance, genes with expression changes associated with lipid traits were mostly identified in the liver, and those associated with cardiovascular disease were identified in arterial tissue. The affected genes additionally point to biological processes implicated in the interrogated traits, such as the interleukin-27 pathway in rheumatoid arthritis. Further, comparing trait-associated gene expression changes across traits suggests that pleiotropy is a widespread phenomenon and points to specific instances of both agonistic and antagonistic pleiotropy. For instance, expression of SNX19 and ABCB9 is positively correlated with both the risk of schizophrenia and educational attainment. To facilitate interpretation, we provide this lexicon of how common trait-associated genetic variants alter gene expression in various tissues as the online database GWAS2Genes.

Keywords: GWASs; Mendelian randomization; antagonistic pleiotropy; common genetic variation; complex diseases; complex traits; cross phenotype; eQTLs; expression quantitative trait loci; gene expression; gene-set enrichment analysis; genome-wide association studies.

Figure 1

The Number of Genes Located between the Associated Gene and the Genetic Variant Affecting Expression Only genes that had an eQTL in our analysis were considered.

Figure 2

Overview of the Contribution of Different Studies and Tissues to the Identification of Trait-Associated Genes (A) Contribution of eQTL data from GTEx, STARNET, and CMC. (B) Contribution of different tissues for the various traits. Numbers indicate how many trait-associated genes were identified. The coloring indicates enrichment or depletion of associations for a given trait in the given tissue in standard deviations from the mean after correction for the number of eQTLs tested in the given tissue and adjustment of enrichments in traits with few associated genes (Material and Methods). Only traits with at least one associated gene are shown. Abbreviations: DLPFC, dorsolateral prefrontal cortex; ITA, internal thoracic artery; and AS, atherosclerosis. (C) Contributions of blood and non-blood samples to identifications of trait-associated genes.

Figure 3

Networks of Genes Associated across Traits (A) Associated genes shared within and across trait categories. Line thickness indicates the number of shared genes. Only traits with at least ten associated genes are shown. Furthermore, to reduce redundancy, we removed inflammatory bowel disease and retained only height and BMI from the anthropometric traits. (B) Genes associated with multiple autoimmune disorders. Line thickness indicates the number of traits with which the gene is associated. For both plots, the size of the circles indicates the number of genes whose expression was associated with multiple traits.

Figure 4

Traits Sharing Associated Genes with Educational Attainment and the Correlation in the Change of Expression Only traits sharing at least ten genes are shown. The size of the circles indicates the number of genes whose expression was associated with both of the connected traits. The numbers in parentheses indicate the counts of correlated and anticorrelated genes, respectively.

Figure 5

Expression Changes Indicative of Antagonistic Pleiotropy between Schizophrenia and Other Traits Line thickness indicates number of tissues in which an eQTL was identified. Myocardial infarction and coronary artery disease are grouped because the involved genes are identical. Red indicates upregulated genes, and blue indicates downregulated genes.