PROJECT SUMMARYPrecision Medicine refers to the customization of medical treatment to the individual characteristics of eachpatient. The Million Veteran Program (MVP) provides a unique opportunity to perform large-scale genome-wideassociation studies (GWAS) and further our understanding of Precision Medicine across multiple traits anddiseases. While well powered GWAS have identified multiple risk variants, there has been limited conclusivefindings on the genetic factors contributing to complex traits due to small effect sizes. In addition, the majorityof common risk variants are within non-coding regions of the genome and, as such, the functional relevance ofmost discovered loci remains unclear. Our group and others have shown that a large portion of phenotypicvariability in disease risk can be explained by regulatory variants, i.e. genetic variants that affect epigeneticmechanisms and the expression levels of genes. Studying gene expression and epigenome changes directly inMVP samples is not feasible as such data are not available. To overcome these limitations, we propose toapply a machine learning approach that leverages existing molecular data (unrelated to MVP) as a referencepanel and directly impute multi-tissue and genome-wide gene expression and epigenome profiles in MVPsamples using the existing MVP genotypes. As reference panel, we will use large-scale datasets withgenotyping and molecular profiling that our group and others have generated, including, but not limited to, theCommonMind consortium, psychENCODE, AD-AMP, STARNET and GTEx. Imputed MVP gene expressionand epigenome data provides a powerful cohort to “translate” genetic findings to dysregulation of specificmolecular pathways across multiple traits that will enhance drug discovery. We propose to study geneexpression and epigenome perturbations in neuropsychiatric -- including schizophrenia, bipolar disorder, post-traumatic stress disorder, alcohol abuse, recurrent depression and suicidal ideations -- and cardiometabolic --including type 2 diabetes, hypertension, hyperlipidemia, coronary heart disease, history of myocardial infarctionand bloodwork-quantified (glucose, Hb1Ac and lipid profile) -- traits. These disease-associated signatures canbe further explored in terms of enrichment with specific molecular networks. We propose to construct tissuespecific weighted gene-gene interaction and causal probabilistic networks and assess the enrichment withdisease-associated signatures to identify subnetworks, molecular processes and key drivers. Overall, the scaleof data generation and its integration into predictive models will provide a wealth of data for other diseasesbeyond the immediate goals of this proposal that have the potential to increase our understanding of PrecisionMedicine.
|Effective start/end date||1/10/18 → 30/09/20|
- U.S. Department of Veterans Affairs