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

International Cannabis Consortium

doi:10.1016/j.drugalcdep.2019.107703

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

International Cannabis Consortium

Research output: Contribution to Journal › Article › Academic › peer-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 language	English
Article number	107703
Pages (from-to)	107703
Journal	Drug and Alcohol Dependence
Volume	206
DOIs	https://doi.org/10.1016/j.drugalcdep.2019.107703
Publication status	Published - 1 Jan 2020

Bibliographical note

Funding

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.

Funders	Funder number
ANR-11-IDEX-0004-02
Foundation Volksbond Rotterdam
Netherlands Organization of Scientific Research
National Institutes of Health
National Human Genome Research Institute	R35HG010718
Brain and Behavior Research Foundation
National Alliance for Research on Schizophrenia and Depression
University of Cambridge
Agence Nationale de la Recherche
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	016

Keywords

Addiction
Functional annotation
GTEx
S-PrediXcan
Substance use
eQTLs

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.drugalcdep.2019.107703

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

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",

author = "Marees, {Andries T} and Gamazon, {Eric R} and Zachary Gerring and Florence Vorspan and Josh Fingal and {van den Brink}, Wim and Smit, {Dirk J A} and Verweij, {Karin J H} and Kranzler, {Henry R} and Richard Sherva and Lindsay Farrer and Joel Gelernter and Derks, {Eske M} and {International Cannabis Consortium}",

year = "2020",

month = jan,

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doi = "10.1016/j.drugalcdep.2019.107703",

language = "English",

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journal = "Drug and Alcohol Dependence",

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T1 - Post-GWAS analysis of six substance use traits improves the identification and functional interpretation of genetic risk loci

AU - Marees, Andries T

AU - Gamazon, Eric R

AU - Gerring, Zachary

AU - Vorspan, Florence

AU - Fingal, Josh

AU - van den Brink, Wim

AU - Smit, Dirk J A

AU - Verweij, Karin J H

AU - Kranzler, Henry R

AU - Sherva, Richard

AU - Farrer, Lindsay

AU - Gelernter, Joel

AU - Derks, Eske M

AU - International Cannabis Consortium

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Addiction

KW - Functional annotation

KW - GTEx

KW - S-PrediXcan

KW - Substance use

KW - eQTLs

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U2 - 10.1016/j.drugalcdep.2019.107703

DO - 10.1016/j.drugalcdep.2019.107703

M3 - Article

C2 - 31785998

SN - 0376-8716

VL - 206

SP - 107703

JO - Drug and Alcohol Dependence

JF - Drug and Alcohol Dependence

M1 - 107703

ER -

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

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

Persistent URL (handle)

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