An integrative systems‐based analysis of substance use: eQTL‐informed gene‐based tests, gene networks, and biological mechanisms

Abstract

Genome-wide association studies have identified multiple genetic risk factors underlying susceptibility to substance use, however, the functional genes and biological mechanisms remain poorly understood. The discovery and characterization of risk genes can be facilitated by the integration of genome-wide association data and gene expression data across biologically relevant tissues and/or cell types to identify genes whose expression is altered by DNA sequence variation (expression quantitative trait loci; eQTLs). The integration of gene expression data can be extended to the study of genetic co-expression, under the biologically valid assumption that genes form coexpression networks to influence the manifestation of a disease or trait. Here, we integrate genome-wide association data with gene expression data from 13 brain tissues to identify candidate risk genes for 8 substance use phenotypes. We then test for the enrichment of candidate risk genes within tissue-specific gene co-expression networks to identify modules (or groups) of functionally related genes whose dysregulation is associated with variation in substance use. We identified eight gene modules in brain that were enriched with gene-based association signals for substance use phenotypes. For example, a single module of 40 co-expressed genes was enriched with gene-based associations for drinks per week and biological pathways involved in GABA synthesis, release, reuptake and degradation. Our study demonstrates the utility of eQTL and gene co-expression analysis to uncover novel biological mechanisms for substance use traits.

Functional 3’-UTR Variants Identify Regulatory Mechanisms Impacting Alcohol Use Disorder and Related Traits

“…Other MGExA-identified genes, such as Signal Transducer And Activator Of Transcription 6 34 ( STAT6 ) and TAOK2 35 have been found to be involved in the response to ethanol in animal models. MGExA also identified genes associated with addiction to other substances, including nicotine (KAT8 Regulatory NSL Complex Subunit 1 36 ; KANSL1 ) and opioids ( DRD2 37 ). MGExA genes were also associated with other disorders of the brain such as Alzheimer’s disease (Protein Tyrosine Kinase 2 Beta 38 ; PTK2B ), and schizophrenia ( SREBF2 39 ).…”

Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

“…eQTL mapping is used to identify the association of a genetic variant with gene expression level based on transcriptome analysis. The term eQTL either means the presence of such association between a variant (eVariant) and the expression level(s) of gene/genes (eGene/eGenes) [ 6 , 123 , 124 ] or refers to the variant itself that displays such association [ 28 , 125 , 126 , 127 , 128 ]. When searching for an eQTL, differential gene expression in the transcriptomes of the subjects with different genotypes is determined for each SNP.…”

From classical mendelian randomization to causal networks for systematic integration of multi-omics

“…The key feature of causal networks is being discovery-based, and suitable for handling large-scale data, where we have limited knowledge about the underlying interconnectivity. There are different applications of systematic analysis of omics including causal networks ( Zhu et al, 2012 ; Franzén et al, 2016 ; Broumand and Dadaneh, 2018 ; Ahangaran et al, 2019 ; Ahangaran et al, 2020 ; Yazdani et al, 2020 ; Gerring et al, 2021 ). For instance one of the early applications is the integration of genetic variants, metabolites, gene expressions, and proteins on yeast data to identify the underlying molecular networks ( Zhu et al, 2012 ).…”