Eric Martin Sobel

Eric Martin Sobel is an Adjunct Professor in both Human Genetics and Computational Medicine at the David Geffen School of Medicine, University of California Los Angeles (UCLA). His academic work bridges statistical genetics, computational biology, and epidemiological research with a strong focus on cancer genetics and health disparities.

Dr. Sobel's research primarily centers on statistical methods development for genetic analysis and their application to understanding the genetic basis of complex diseases, particularly cancer. His work demonstrates expertise in genome-wide association studies, Mendelian randomization, genotype imputation, and local ancestry inference. A significant portion of his research investigates gene-environment interactions, especially those involving insulin resistance, inflammation, and cancer risk among minority populations, with numerous publications focusing on African American and Hispanic/Latino women. His development of computational tools like MendelImpute.jl and contributions to the OpenMendel project highlight his technical contributions to the field of statistical genetics.

Analysis of Dr. Sobel's publication record reveals a consistent focus on methodological innovation applied to health disparities research. His work frequently employs advanced statistical techniques including random survival forest analysis to examine complex interactions between genetic variants and lifestyle factors. A notable trend is his emphasis on minority populations that are often underrepresented in genomic studies, particularly examining how genetic factors related to glucose metabolism interact with environmental exposures to influence cancer risk. His research bridges computational methodology with clinical applications, aiming to improve risk prediction models for medically underserved communities.

Dr. Sobel has been involved in numerous NIH-funded research projects, as evidenced by the multiple grant identifiers appearing in his publications, including several from the National Heart, Lung, and Blood Institute (NHLBI), National Human Genome Research Institute (NHGRI), and Women's Health Initiative (WHI). His collaborative work spans multiple institutions and regularly involves interdisciplinary teams combining expertise in genetics, epidemiology, biostatistics, and clinical medicine. While specific laboratory affiliations aren't detailed in the available information, his methodological work suggests strong connections to computational genetics groups and statistical genetics research centers.