Many comorbidities of the aging process, including cardiovascular disease, cognitive decline, and metabolic dysregulation, are thought to be significantly influenced by genetic factors. Type 2 diabetes (T2D), a disease which affects more than 25% of adults over age 65 in the United States, is also thought to have a significant genetic component, and individuals with T2D are at high risk for age-related comorbidities. Common and uncommon coding genetic variants may play a significant role in these age-related comorbidities, and this project aims to elucidate coding variants which are significantly associated with a wide range of biomedical measures characteristic of aging and with mortality. Illumina(R) HumanExome BeadChips, which include over 240,000 coding variants, will allow rapid and comprehensive analysis of relevant coding variants in the Diabetes Heart Study cohort, an extensively phenotyped family-based cohort enriched for patients with T2D. Our initial focus will be on the C1q and tumor necrosis factor (TNF) superfamily of genes. Uncommon coding variants in a member of this family, adiponectin, which lead to a dramatic reduction in plasma levels of this protein have recently been discovered, and we hypothesize that coding variants in other C1q/TNF superfamily members, which have diverse roles in metabolism, inflammation, and other processes, may contribute to age-related phenotypes. Genes related to inflammation, such as cytokines and their receptors, will also be of particular interest, as inflammatory processes are key to the pathogenesis of many age-related diseases, including T2D. Interesting findings from the Exome Chip data from the Diabetes Heart Study will be replicated in other cohorts relevant to aging. The proposed research will further my development as an independent investigator and give me valuable experience in the meaningful application of human genetics tools to aging research.
The goal of this project is to better understand how coding genetic variants may contribute to complex age-related phenotypes, with a particular emphasis on patients with Type 2 Diabetes, a disease which accelerates many aspects of the aging process. Knowledge of which variants are associated with age-related phenotypes, such as vascular calcification, cognitive declines, and bone mineral density, could improve risk prediction and allow for more personalized care, as well as potentially leading to new drugs and therapies through the elucidation of which genes and pathways are important for a particular age-related phenotype.
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