Candidate. Amit V. Khera, MD MSc is a board-certified physician in internal medicine and cardiology at Massachusetts General Hospital (MGH), an Instructor in Medicine at Harvard Medical School (HMS), and an affiliated researcher at the Broad Institute of Harvard/MIT. He has a track record of scientific commitment and productivity at each phase of training and has 11 first-author original research articles. He seeks to expand upon previous training in clinical medicine and epidemiology to catalyze a career within genomic medicine. Mentorship, Training Activities, and Environment. Dr. Khera will perform the proposed work at MGH and the Broad under the primary mentorship of Dr. Sekar Kathiresan, a physician scientist and international leader in complex trait genetics with an outstanding track record for mentorship. Co-Mentor Dr. Mark Daly will provide complementary expertise in statistical genetics and mapping human disease loci. This mentorship team will be complemented by a highly committed and accomplished Advisory Committee of Drs. Benjamin Neale, Nilanjan Chatterjee, Heidi Rehm, and Daniel MacArthur. Formal coursework will enhance a multi-disciplinary experiential learning effort to gain requisite skills in clinical informatics, statistical genetics, computational biology, and responsible research conduct. Research. For any individual, inherited risk can be driven by a rare, large-effect mutation or the cumulative impact of many common, small-effect genetic variants (`polygenic risk'). This polygenic risk accounts for a significant proportion of heritability across a range of complex traits and diseases. However, the optimal approach to constructing such a score, the transferability across racial/ethnic groups, incremental value in risk prediction, and extent to which polygenic risk is modified by genetic or non-genetic factors remain uncertain. The PI will first implement several computational algorithms to derive polygenic scores for coronary artery disease, determine the best score in an independent testing dataset, and assess the predictive capacity in cohorts of >400,000 multiethnic individuals. Second, he will generalize this approach to at least 8 additional heritable diseases and determine the extent to which these scores enhance risk prediction beyond traditional risk factors and family history in >400,000 individuals with genotyping array or whole genome sequencing data available. Third, he will determine how rare, large effect mutations and environmental factors interact with polygenic risk for disease to influence disease penetrance. Successful completion of the proposed studies will lay the scientific foundation for the systematic assessment of polygenic risk for a range of common diseases and the ultimate disclosure of this risk to individuals and their health care providers to facilitate disease prevention. Furthermore, the proposal will provide key training of the PI in several domains (statistical genetics, computational biology, cloud computing, clinical informatics) and facilitate his transition to an independent physician investigator within genomic medicine.
A polygenic risk score can quantify the cumulative impact of many small-effect genetic variants on risk of disease. The applicant will derive and validate polygenic risk scores for at least 9 common diseases and determine their clinical utility in disease prediction. Successful completion will lay the scientific foundation for genetic risk assessment for a range of diseases and the ultimate disclosure of this risk to healthy individuals and their health care providers to facilitate disease prevention.
