Post-GWAS analysis of six substance use traits improves the identification and functional interpretation of genetic risk loci

Andries T. Marees*, Eric R. Gamazon, Zachary Gerring, Florence Vorspan, Josh Fingal, Wim van den Brink, Dirk J.A. Smit, Karin J.H. Verweij, Henry R. Kranzler, Richard Sherva, Lindsay Farrer, Joel Gelernter, Eske M. Derks

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background: Little is known about the functional mechanisms through which genetic loci associated with substance use traits ascertain their effect. This study aims to identify and functionally annotate loci associated with substance use traits based on their role in genetic regulation of gene expression. Methods: We evaluated expression Quantitative Trait Loci (eQTLs) from 13 brain regions and whole blood of the Genotype-Tissue Expression (GTEx) database, and from whole blood of the Depression Genes and Networks (DGN) database. The role of single eQTLs was examined for six substance use traits: alcohol consumption (N = 537,349), cigarettes per day (CPD; N = 263,954), former vs. current smoker (N = 312,821), age of smoking initiation (N = 262,990), ever smoker (N = 632,802), and cocaine dependence (N = 4,769). Subsequently, we conducted a gene level analysis of gene expression on these substance use traits using S-PrediXcan. Results: Using an FDR-adjusted p-value <0.05 we found 2,976 novel candidate genetic loci for substance use traits, and identified genes and tissues through which these loci potentially exert their effects. Using S-PrediXcan, we identified significantly associated genes for all substance traits. Discussion: Annotating genes based on transcriptomic regulation improves the identification and functional characterization of candidate loci and genes for substance use traits.

Original languageEnglish
Article number107703
Pages (from-to)1-9
JournalDrug and Alcohol Dependence
Volume206
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • Addiction
  • eQTLs
  • Functional annotation
  • GTEx
  • S-PrediXcan
  • Substance use

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