Vincent P. Diego, PhD

Research Focus

Dr. Vincent P. Diego is a biologist with broad training in biostatistics, genetic epidemiology, and statistical genetics. His research interests are in the statistical genetics of physiological aging with an emphasis on the cardiometabolic syndrome diseases associated with aging. He has focused on developing genotype × environment interaction (GEI) approaches to study the role of the environment in the etiology of cardiometabolic syndrome.

Dr. Diego has combined the GEI model with the best linear unbiased prediction (BLUP) approach to clarify environmental exposures by accounting for their predicted genetic values (Manusov et al., 2022).  In this work, his team was able to show that the genetic effects underlying non-alcoholic fatty liver disease were dependent on the environmental component of depression, where the latter was computed using BLUP. In a current submission that has received a favorable peer review (but is still in the review process), Dr. Diego applied this approach to clarify the environmental signal elicited by the education environment. His team found that the genetic effects underlying statistical and physiological measures of heart disease risk varied as a function of the BLUP-clarified education environment (Diego et al., 2023). Dr. Diego has also extended the GEI model to jointly account for continuous and discrete environments (Diego et al., 2024a).

Dr. Diego is also investigating FVIII immunogenicity and inhibitor development, and has developed statistical approaches to studying this problem. He has developed a generalized linear mixed model (GLMM) to analyze dendritic cell protein processing and presentation assay (DC-PPPA) data from the FVIII Epitope Determination Study (Diego et al., 2020; Submitted).  For the Personalized Alternative Therapies for Hemophilia (PATH) Study, he has developed another type of GLMM for the analysis of determinants of inhibitor development in related individuals (Diego et al., 2018). Both GLMMS, for the FED Study and the PATH Study, accounted for important sources of non-independence and heterogeneity, which clarified and increased our power for risk factor estimation and prediction.