| dc.contributor.author | Gamazon, Eric R. | |
| dc.date.accessioned | 2020-10-07T16:47:00Z | |
| dc.date.available | 2020-10-07T16:47:00Z | |
| dc.date.issued | 2020-01 | |
| dc.identifier.issn | 0376-8716 | |
| dc.identifier.uri | http://hdl.handle.net/1803/16189 | |
| dc.description | Only Vanderbilt University affiliated authors are listed on VUIR. For a full list of authors, access the version of record at https://www.sciencedirect.com/science/article/pii/S0376871619304806?via%3Dihub | en_US |
| dc.description.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. | en_US |
| dc.description.sponsorship | ATM and EMD are supported by the Foundation Volksbond Rotterdam, ATM is supported by the Netherlands Organization of Scientific Research (NWO Vidi grant 016.Vidi.185.044, PI T.J. Galama). ERG is supported by the National Human Genome Research Institute of the National Institutes of Health under Award Number R35HG010718. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. FV is supported by the Investissement d'Avenir program managed by the ANR under reference ANR-11-IDEX-0004-02. KJHV is supported in part by a 2014 NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation. ERG benefited from a Clare Hall Fellowship at the University of Cambridge. The funding sources had no involvement in study design; in the collection, analysis and interpretation of the data; in the writing of the report or the decision to submit for publication. | en_US |
| dc.language.iso | en_US | en_US |
| dc.publisher | Drug and Alcohol Dependence | en_US |
| dc.rights | This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed. | |
| dc.source.uri | https://www.sciencedirect.com/science/article/pii/S0376871619304806?via%3Dihub | |
| dc.subject | Addiction | en_US |
| dc.subject | eQTLs | en_US |
| dc.subject | Functional annotation | en_US |
| dc.subject | GTEx | en_US |
| dc.subject | Substance use | en_US |
| dc.subject | S-PrediXcan | en_US |
| dc.title | Post-GWAS analysis of six substance use traits improves the identification and functional interpretation of genetic risk loci | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | 10.1016/j.drugalcdep.2019.107703 | |
