Abstract: Complex phenotypes vary in their distributions between worldwide populations due to both genetic and environmental factors. Partitioning the relative contributions of genetics and environmental exposures to population differences can guide clinical care inform epidemiological studies of disease. Within populations, disease states are assigned on the basis on easily measured biomarkers or surveys. However, mutations from independent genetic pathways can manifest with similar phenotypes, resulting in disease heterogeneity. This reduces the power of genetic studies and prevents precise treatment of disease. We consider statistical methods identify disease heterogeneity when genotypes and multiple phenotypes are jointly measured.
Dr. Zaitlen develops statistical and computational tools to understand the genetic basis of phenotypes. He is especially interested in human disease, variation in drug/treatment response, and outcomes. Ongoing projects primarily focus on incorporating environmental context into medical genetics. These include developing novel techniques to partition the proportion of phenotype driven by genetic and environmental factors in world-wide populations (Nature versus Nurture), and improving the power to identify disease causing mutations by leveraging gene-expression, meta-genomic, and clinical data (e.g., smoking status, BMI, and age). His work aims to improve the understanding of disease and contribute to human health.
Zaitlen Lab Website: