Trans-omic Analysis of Alcohol Consumption and its Relation to Cardiovascular Disease

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PROJECT SUMMARY Alcohol consumption is an important modifiable lifestyle risk factor for cardiovascular disease (CVD). Advances in high-throughput technologies have made it possible to measure and analyze a variety of ‘omics’, that is, DNA methylation (methylome), gene expression (transcriptome), protein (proteome) and metabolite (metabolome), in relation to alcohol consumption and CVD in a cost-efficient manner. In this grant proposal, we will test the hypothesis that alcohol consumption alters multiple molecular processes across different “omics” dimensions and these molecular intermediates mediate alcohol’s effects on CVD. To test this hypothesis, we will assemble a multidisciplinary team to test five Specific Aims. In Aim 1, we will first identify alcohol-associated transcriptomic markers. We will then examine the associations of alcohol-associated transcriptomic markers with incident CVD (fatal and nonfatal coronary heart disease and ischemic stroke), and test whether these transcriptomic markers mediate the effect of alcohol intake on incident CVD. We will construct polygenic risk scores to examine the potential causal relationships between alcohol intake, transcriptomic markers, and incident CVD. We will also conduct a two-sample Mendelian randomization (MR) analysis to further test and quantify the causal relationships. In Aims 2 and 3, we will identify alcohol-associated proteomic (Aim 2) and metabolomic (Aim 3) markers and will test the relationship between alcohol, omics markers and CVD using similar methods outlined in Aim 1. Many alcohol-associated DNA methylation markers have been identified by our prior study in >13,000 participants. Therefore, in Aim 4, we will examine the relations of alcohol-associated DNA methylation markers with incident CVD using similar methods outlined in Aim 1. In Aim 5, we will integrate alcohol-associated multi-omics markers to identify key pathways and multi-omics modules associated with alcohol intake and CVD. We will perform Random Forest analysis to identify additional alcohol-associated multi- omics markers. We will also perform network analyses to identify modules of alcohol-associated multi-omics markers. We will examine the relationships between these identified multi-omics markers with incident CVD, and will functionally annotate these markers using bioinformatics tools (e.g., ENCODE, GTEx, Human Metabolome Database, and UniProt). With the completion of this project, we will highlight the novel molecular targets for both alcohol-associated CVD risk prevention and treatment. In addition, we will generate a multi-omics biomarker database of alcohol intake with tailored software to facilitate the future investigations of alcohol’s relations with other non-communicable diseases such as neurodegenerative diseases.