Title: Efficient and Powerful Methods for Genome and Epigenome-Wide Association Studies
Abstract:
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.
Bio:
Jennifer Listgarten is a Researcher in the eScience group at Microsoft Research in Los Angeles. Her work focuses on the development and application of novel statistical and machine learning methods for the analysis of high-throughput, biologically-based data, with particular focuses, past and present, including microarray expression, mass spectrometry-based proteomics, HIV vaccine research, transplantation, and genetics. Prior to joining Microsoft, Jennifer completed a Ph.D. in Computer Science in the machine learning group at the University of Toronto, her home town.
Papers related to the talk:
FaST-LMM-Select for addressing confounding from spatial structure and rare variants
Jennifer Listgarten, Christoph Lippert, David Heckerman
Nature Genetics, 45, 470-471 (2013) doi:10.1038/ng.2620 (journal link)
A powerful and efficient set test for genetic markers that handles confounders
Jennifer Listgarten, Christoph Lippert, Eun Yong Kang, Jing Xiang, Carl M. Kadie, David Heckerman
Bioinformatics 2013, doi: 10.1093/bioinformatics/btt177 (open access)
Patterns of methylation heritability in a genome-wide analysis of four brain regions
Gerald Quon, Christoph Lippert, David Heckerman, Jennifer Listgarten
Nucleic Acids Research, 2013, doi: 10.1093/nar/gks1449
Improved linear mixed models for genome-wide association studies
Jennifer Listgarten, Christoph Lippert, Carl M. Kadie, Robert I. Davidson, Eleazar Eskin andDavid Heckerman
Nature Methods, 2012, doi:10.1038/nmeth.2037
FaST Linear Mixed Models for Genome-Wide Association Studies
Christoph Lippert, Jennifer Listgarten, Ying Liu, Carl M. Kadie, Robert I. Davidson and David Heckerman
Nature Methods, Aug. 2011, doi:10.1038/nmeth.1681 ,
Correction for Hidden Confounders in the Genetic Analysis of Gene Expression
Jennifer Listgarten, Carl Kadie, Eric Schadt, David Heckerman
Proceedings of the National Academy of Sciences, September 1, 2010, doi: 10.1073/pnas.1002425107