[Ml-stat-talks] Colloquium Speaker Barbara Engelhardt Monday, March 3, 4:30pm

David Blei blei at CS.Princeton.EDU
Fri Feb 28 12:18:24 EST 2014


ml-stat-talks:

ml-stat-talks

it has been a wonderful semester of statistics & machine learning
talks. monday's CS colloquium continues the tradition.  barbara does
top-tier statistical machine learning and computational biology.

best
dave


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Statistical models to study how a genetic variant impacts an organism

Barbara Engelhardt (Duke University)
Monday, March 3, 4:30pm
Computer Science 105


Consider sequencing the genome of a newborn, and selecting targeted
therapeutics early in life to reduce her lifetime risk of addiction,
obesity, type II diabetes, or pancreatic cancer. While genome-wide
association studies (GWAS) have unquestionably been successful in
identifying reproducible genomic risk factors for complex human
diseases, the promise of developing therapeutics to reduce the
heritable portion of disease risk is far from fulfillment. The
essential technological developments to fulfill this promise, however,
are mainly in statistics and computation rather than in genomic
experimental methods. I describe three genomic studies from my recent
work. First, I identified a genetic variant that behaves differently
depending on whether or not an individual takes cholesterol-reducing
drugs. Second, I found that genetic variants that are associated with
different cell traits are co-localized with a large variety of
different regulatory mechanisms more often than expected. Third, I
developed a model to uncover genetic variants that affect many traits
simultaneously, where the trait measurements have substantial
technical noise. Throughout, I emphasize statistical and computational
challenges, and innovations necessary to fulfill this promise of
genomic studies.




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