Methods For Genome And Epigenome-Wide Association Studies
Understanding the genetic underpinnings of disease is important for screening, treatment, drug development, and basic biological insight. Genome-wide associations, wherein individual or sets of genetic markers are systematically scanned for association with disease are one window into disease processes. Naively, these associations can be found by use of a simple statistical test. However, a wide variety of confounders lie hidden in the data, leading to both spurious associations and missed associations if not properly addressed. These confounders include population structure, family relatedness, cell type heterogeneity, and environmental confounders. I will discuss the state-of-the art approaches (based on linear mixed models) for conducting these analyses, in which the confounders are automatically deduced, and then corrected for, by the data and model.
Jennifer Listgarten took a long and winding road to find her current area of interest in computational biology. She started off with a Physics degree, followed by a Masterís in Computer Vision before completing a Ph.D. in Machine Learning at the University of Toronto with advisors Sam Roweis and Radford Neal. Within computational biology, Jennifer is interested in methods development, especially using insights from machine learning along with more traditional applied statistics. She also has an interest in application of these methods to discover new biological/medical insights. Jennifer has worked in a broad set of domain areas including gene expression studies, LC-MS proteomics, immunoinformatics, statistical genetics, and epigenetics. She is currently also starting to explore topics related to cancer and wearables.