Benjamin Franklin Voight, Ph.D.

Our lab develops and utilizes statistical genetics, computational biology, and population genetics-based approaches to understand the biological underpinnings and evolutionary history of human traits and complex disease. Keywords Population Genetics, Statistical Genetics, Bioinformatics, Computational Biology, Statistics, Systems Biology, Metabolic Disorder, Type-2 Diabetes, Coronary Heart Disease, Metabolic-associated Steatotic Liver Disease, Obesity, Myocardial Infarction, Cholesterol Levels, Natural Selection, Demography, Genome-wide Association Study (GWAS), Next-Generation Sequencing, Mendelian randomization. Research Summary The central aim in my lab is to understand the genetic, biological, and evolutionary basis of metabolic and cardiovascular phenotypes in human populations. To build this understanding, the lab constructs computational and statistical tools grounded in principles of population biology and quantitative genetics. These tools are then applied to genetic data collected across thousands of whole human genomes. My research has answered population genetic questions about recent demographic and selective events in human populations, and work to develop new statistics to identify selective pressures is ongoing. Recent work in the lab has focused on statistical models which capture variability in the rate of mutation in the human genome. I have an active interest in mapping risk alleles for common diseases, particularly type-2 diabetes and coronary heart disease, but perhaps more importantly, to identify the causal variant, gene, and mechanism that influences risk to these diseases from existing non-coding associations identified by genome-wide association studies. I continue to utilize the framework of Mendelian Randomization, to perform causal inference studies between genetically-heritable biomarkers and complex diseases. Work in the lab is toward applications, but also development of novel methodologies. In the coming years, the lab activities will focus on several key areas of interest, which includes: - Developing statistical models to capture variability in the rate of mutation in human genomes, with application to identifying de novo mutations and rare variation related to human disease - Computational methods and functional characterization of the causal variants and genes related to non-coding associations for type 2 diabetes and heart disease - Developing informational and statistical tools which interrogate human genetic association data together with other sources of 'omics data to construct credibly actionable information on pathways responsible for disease susceptibility - Population genetic methods to identify loci in the human genome which are targets of natural selective pressures, and to further identify the causal variants and genes responsible - Performing large-scale genomic studies using data from the Million Veteran Program, a large (>1M) multi-ethnic cohort enriched for cardiometabolic disease.