Understanding the genomic basis of problematic alcohol use through integrative analysis of multi-omics data

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This project seeks to further the understanding of the genomic etiology of alcohol use disorder (AUD). Genetic studies have identified loci associated with the disorder; however, the genetic architecture of AUD has not been fully explained. Currently, genome-wide variant data of the disorder are available for analysis, and functional genomic datasets are being generated from consortia. Sequencing data from biobanks would allow assessment of the contribution of coding and functional non-coding regions to AUD risk by using a proxy phenotype from the information of the Alcohol Use Disorder Identification Test (AUDIT) report. Prior work has shown that 1) AUD has a high genetic overlap with some neuropsychiatric disorders (NPDs), and 2) common-variant studies identify biological pathways associated with the disorder. Therefore, integrative analyses which incorporate multi-omics datasets and overlapping genetic information can help identify additional loci associated with the disorder. Thus, the candidate plans to use integrative methods to 1) increase power for genetic discovery, and 2) better understand the genomic architecture for AUD. AUD has a high genetic correlation with the AUDIT-based phenotype (AUDIT-P) which assesses the problematic consequences of drinking. AUD and AUDIT-P have similar genetic correlations with other NPDs. Multiple AUDIT-P datasets are publicly available, and a recent meta-analysis of AUD and AUDIT-P datasets shows increased statistical power for risk loci identification. For this reason, this proposal centers on identifying the genomic association for problematic alcohol use which includes AUD itself and/or AUDIT-P. To achieve this research goal, the candidate proposes the following aims. First, he plans to extend his previous methods to jointly analyze rare exonic variants and other omics datasets. Second, he will develop methods to integrate common variants, omics information and genetic overlap information to increase power for multi-trait analysis. Third, he proposes an integrative method to analyze rare variants from whole-genome sequencing data. Fourth, he will apply these methods to analyze large-scale variant and functional-genomic datasets. He plans to use systems biology approaches to elucidate analysis results from these methods. Also, he proposes using genetic model organisms (C. elegans) to better understand the results from computational approaches via RNA interference and CRISPR-Cas9 experiments. Dr. Nguyen has a background in mathematics, statistics and statistical genetics; however, he has not had prior experience in the field of genetics of AUD. The mentor team including leading experts in the field will help him to 1) gain expertise in the basic principles of psychopathology, alcohol related phenotypes, the nosology of NPDs and alcohol related phenotypes; 2) acquire understanding of genetic model organisms, RNAi and CRISPR-Cas9 experiments for validation of computational results; 3) gain expertise in additional types of omics data; and 4) develop needed independence in his career. These training goals will aid him in successfully conducting the proposed work, and building a NIH-funded translational research program focused on AUD and its comorbid conditions.