A systems approach to the genetic study of alcohol dependence

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Alcohol dependence (AD), one of the leading causes of disability worldwide, is a chronic and recurrent psychiatric illness. Twin studies have established a significant genetic contribution to AD susceptibility. Variations in hundreds of genes likely contribute to the etiology of AD, with each genetic variant conferring only a small increase in risk. Although numerous genes may contribute to the etiology of complex diseases, they tend to fall into a smaller number of biological pathways. In addition, accumulating evidence suggests a large portion of the risk variants for complex diseases are located in regulatory DNA sequences in disease-related tissue or cell types. Studies leveraging already existing data may increase the power of gene discovery for these disorders, which include AD. This proposal aims to employ a systems biology-based approach to identify gene networks and regulatory variants underlying AD. To that end, we will perform integrated analysis of genome-wide association studies (GWAS) of AD with brain-specific differential gene co-expression networks (DCNs) and transcriptional regulatory networks (TRNs). Our approach to network construction will use brain region-specific data, on gene expression and regulatory function. Our specific aims are: 1) Identify gene subnetworks underlying AD through integrated analysis of GWAS with brain-specific DCNs; 2) Identify regulatory risk variant sets through integrated analysis of GWAS with brain-specific TRNs; and 3) Evaluate the function of identified gene subnetworks and regulatory variants using existing imaging genetics data. We have assembled an outstanding multidisciplinary team with expertise in AD genetics, genomics, computational biology, and neuroimaging. Our goal is to apply multidisciplinary and cutting-edge analytical strategies in the service of advancing the field of AD genetics. The identification and characterization of risk genes and regulatory variants would help improve our understanding of the biological mechanisms that underlie AD, moving us closer to designing effective prevention and treatment for the disorder.