Event Description
The department of Epidemiology and Biostatistics Seminar Series presents
Stella Aslibekyan, PhD,
Genetic Epidemiologist,
23andMe.
The promise of precision medicine is predicated on identifying both subgroups of individuals and subphenotypes of disease that are more likely to respond to tailored treatment strategies. These efforts are enabled by the ever-growing number and scale of 'big data' sets such as the UK Biobank, industry cohorts, and various disease consortia.
Increasingly, polygenic risk scores (PRS) trained and validated in such data sets are used for risk prediction. Because genetic factors commonly account for a fairly small fraction of the variance of most complex diseases, it is logical to explore augmenting genetic risk estimates with environmental contributions. However, non-genetic exposures pose a number of inferential challenges: measurement error, confounding, reverse causation, among others. Using examples from the 23andMe research data set, this seminar will explore current approaches to maximize the utility of non-genetic data for risk prediction and personalized medicine.
About the Speaker:
Aslibekyan leads the genetic epidemiology team at 23andMe, Inc. She earned her undergraduate honors degree in Human Biology from Stanford University, followed by graduate training in epidemiology at the Harvard School of Public Health and Brown University. She then spent 7 years as a faculty member at the University of Alabama at Birmingham.
Throughout her career, Aslibekyan has focused on the "nature and nurture" question-- i.e. the interplay of genetic and environmental factors in human health and disease. As a principal investigator on several extramural awards, she explored this question in diverse cohorts using complementary epidemiological approaches. Now she is focused on both "nature and nurture" in the largest genetic cohort in history: the 23andMe research participants. Aslibekyan also continues to teach courses in epidemiologic methods and enjoys mentoring early career scientists.
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