Integrated analysis of rare exonic variants provides additional insights into alcohol use disorder risk

Alcohol use disorder (AUD) is common and affects millions of people in the United States. Twin and family studies show that this disorder is heritable, and genome wide association studies (GWASs) report multiple loci associated with AUD. Post-GWAS analyses of GWAS signals yield enrichment in several gene-sets that show nominal enrichment in brain tissues. Unfortunately, only a handful of genes have been reported from rare-variant studies. However, recent studies strongly imply that for many complex disorders, common and rare variant findings, while not converging at gene level, converge at gene-set level. In addition, recent studies have showed that using phenotypic information predicted from biobanks can increase genetic discoveries. These suggest that using different approaches for rare variant analyses can improve detection power for genes associated with AUD. Here, we explore integrative approaches to prioritize genes associated with a proxy phenotype of AUD, the Alcohol Use Disorder Identification Test-Problems (AUDIT-P) phenotype. We analyzed the whole-exome-sequencing (WES) datasets of 500K people from the UK Biobank. First, we conducted WES analyses for individuals whose AUDIT scores are available. Second, we developed a pipeline to jointly model rare variants and gene-sets to improve statistical power. Finally, we used a machine learning approach developed by our group to predict AUDIT scores for all the 500K people, and re-analyzed their WES datasets. We first analyzed loss-of-function and missense variants from WES sequencing datasets of 133,914 people with AUDIT scores available. Three significant genes (ADH1C, FPR1, VPS29) were observed. The most significant signal was for ADH1C (adjusted p-value = 0.4 × 10-5). Next, to prioritize additional genes, we jointly analyzed gene-level statistics and 181 gene sets curated from previous studies. We prioritized several genes (max posterior probability > 0.8) including ADH1C. Finally, to see if the prediction of phenotypic information can help prioritize genes, we conducted WES analysis for all 414,508 samples of European descent with full predicted AUDIT scores. Statistical power for ADH1C was substantially improved (adjusted p-value = 3.3 × 10-21). Our results present top significant genes for AUD obtained by analyzing rare variants from a large-scale WES dataset. The results also include 1) the additional biological information into AUD, and 2) integrative approaches for incorporating functional genomics and health care record datasets to improve genetic discoveries. We are improving these integrate approaches to be able to increase statistical power for the prioritization of genes.