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

Marees AT, Gamazon ER, Gerring Z, Vorspan F, Fingal J, van den Brink W, Smit DJA, Verweij KJH, Kranzler HR, Sherva R, Farrer L, International Cannabis Consortium, Gelernter J, Derks EM. Post-GWAS analysis of six substance use traits improves the identification and functional interpretation of genetic risk loci. Drug and Alcohol Dependence. 2020 Jan 1; 206:107703.

Related HSR&D Project(s)

• SDR 16-348 – PRIME Care (PRecision medicine In MEntal health Care)

Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects.

If you have VA-Intranet access, click here for more information vaww.hsrd.research.va.gov/dimensions/

VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address.    Search Dimensions for VA for this citation
* Don't have VA-internal network access or a VA email address? Try searching the free-to-the-public version of Dimensions

Search for Abstract from PubMed

---

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.