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Drug Alcohol Depend. 2019 Nov 4:107703. doi: 10.1016/j.drugalcdep.2019.107703. [Epub ahead of print]

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

Author information

1
Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; QIMR Berghofer, Translational Neurogenomics group, Brisbane, Australia; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands. Electronic address: andriestm@hotmail.com.
2
Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States; Clare Hall, University of Cambridge, Cambridge, CB3 9AL, United Kingdom.
3
QIMR Berghofer, Translational Neurogenomics group, Brisbane, Australia.
4
Assistance Publique - Hôpitaux de Paris, Hôpital Fernand Widal, Département de Psychiatrie et de Médecine Addictologique, 200 rue du Faubourg Saint Denis, 75010 Paris, France; Inserm umr-s 1144, Université Paris Descartes, Université Paris Diderot, 4 avenue de l'Observatoire, 75006 Paris, France.
5
Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands.
6
Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Behavioural Science Institute, Radboud University, Montessorilaan 3, 6525 HR Nijmegen, the Netherlands.
7
Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine and Crescenz VAMC, Philadelphia, PA 19104, United States.
8
Section of Biomedical Genetics, Department of Medicine, Boston University School of Medicine, Boston, MA, United States.
9
Department of Psychiatry, Genetics, and Neuroscience, Yale University School of Medicine, New Haven, CT, United States.
10
Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; QIMR Berghofer, Translational Neurogenomics group, Brisbane, Australia.

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.

KEYWORDS:

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

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