<html><head><style type='text/css'>p { margin: 0; }</style></head><body><div style='font-family: arial,helvetica,sans-serif; font-size: 12pt; color: #000000'><h1 class="page__title title" id="page-title">Statistical models to study how a genetic variant impacts an organism</h1><span class="event-speaker">
<a href="http://www.genome.duke.edu/directory/faculty/engelhardt/">Barbara Engelhardt</a> </span>
<span class="event-speaker-from">
(<a href="http://duke.edu/">Duke University</a>)
<br>Monday, March 3, 4:30pm<br>Computer Science 105<br><br></span><br>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.<div><br><span name="x"></span><br></div><br></div></body></html>