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 statistical 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 is a Senior Researcher at Microsoft Research New England. She took a long and winding road to find her current area of interest in computational biology, starting off with an undergraduate degree in Physics, followed by a Master’s in Computer Vision before completing a Ph.D. in Machine Learning at the University of Toronto. Her current focus is in machine learning and applied statistics with application to problems in biology. She works on both methods development and applications enabling new insights into basic biology and medicine. Particular areas of focus have included statistical genetics (genetic and epigenetic association studies in the presence of unknown confounders); immunoinformatics, liquid-chromatography proteomics, and microarray analysis.