Genes identified in rodent studies of alcohol intake are enriched for heritability of human substance use

Abstract

Background: Rodent paradigms and human genome-wide association studies (GWAS) on drug use have the potential to provide biological insight into the pathophysiology of addiction. Methods:Using GeneWeaver, we created rodent alcohol and nicotine gene-sets derived from 19 gene expression studies on alcohol and nicotine outcomes. We partitioned the SNP heritability of these gene-sets using four large human GWAS: (1) alcoholic drinks per week, (2) problematic alcohol use, (3) cigarettes per day, and (4) smoking cessation. We benchmarked our findings with curated human alcohol and nicotine addiction gene-sets and performed specificity analyses using other rodent gene-sets (e.g., locomotor behavior) and other human GWAS (e.g., height). Results:The rodent alcohol gene-set was enriched for heritability of drinks per week, cigarettes per day, and smoking cessation, but not problematic alcohol use. However, the rodent nicotine gene-set was not significantly associated with any of these traits.Both rodent gene-sets showed enrichment for several non-substance-use GWAS, and the extent of this relationship tended to increase as a function of trait heritability. In general, larger gene-sets demonstrated more significant enrichment. Finally, when evaluating human traits with similar heritabilities, both rodent gene-sets showed greater enrichment for substance use traits. Conclusion:Our results suggest that rodent gene expression studies can help to identify genes that contribute to the heritability of some substance use traits in humans, yet there was less specificity than expected. We outline various limitations, interpretations, and considerations for future research.

Multi‐omics cannot replace sample size in genome‐wide association studies

“…Analyses focused on trait associations at the gene level, rather than individual SNPs 18 . This enabled the additional integration of multi‐omics data that did not use GWAS information, including results from post mortem studies of gene expression in patients and rodent models, and a gene‐set for AUD/PAU that integrates information from a variety of rodent data sources 15 . These methods were uniformly the least informative across all analyses.…”

“…Results from 7 tissues were integrated using an aggregated Cauchy association test, 46 which is also used by the FUSION TWAS method to integrate across tissues 31 . An additional list of genes from rodent studies enriched for the heritability of alcohol use disorder was included as an additional method 15 . MDD/DEP associated genes came from a study examining differentially expressed genes in two brain tissues across three mouse chronic stress models 44 .…”

“…15,16 As greater evidence arises for genome-wide significant signals to be enriched in regulatory regions, these multi-omics data have proved to be valuable in gene prioritization and interpretation. 15,16 Some have speculated that multi-omics could serve as a substitute for additional sample size, and "recover" true signal from smaller GWAS by increasing support for signals that do not meet criteria for genome-wide significance. [17][18][19] This is an appealing proposition, because it would reduce the need (and expense) for collection and phenotyping of larger samples.…”

“…In parallel to GWAS, there has been an explosion in multi‐omics data (e.g., gene expression, chromatin structure) which are now available to interrogate GWAS findings to reveal underlying biology and potential therapeutics 13,14 . For some traits, expression data can also be drawn from model organisms in experimentally controlled exposure and behavioral paradigms 15,16 . As greater evidence arises for genome‐wide significant signals to be enriched in regulatory regions, these multi‐omics data have proved to be valuable in gene prioritization and interpretation 15,16 …”

“… 13 , 14 For some traits, expression data can also be drawn from model organisms in experimentally controlled exposure and behavioral paradigms. 15 , 16 As greater evidence arises for genome‐wide significant signals to be enriched in regulatory regions, these multi‐omics data have proved to be valuable in gene prioritization and interpretation. 15 , 16 …”