SPEAKER: Timothy A. Thornton, Ph.D.
UC President's Postdoctoral Fellow
Department of Statistics
University of California, Berkeley

ABSTACT:

Genetic association testing has proven to be a valuable tool for the mapping of complex traits. Technological advances have made it feasible to perform case-control association studies on a genome-wide basis. Some of the characteristics of the data include missing information, and the need to analyze hundreds of thousands or millions of genetic markers in a single study, which puts a premium on computational speed of the methods. The observations in these studies can have several sources of dependence, including population structure and relatedness among the sampled individuals, where some of this structure may be unknown. Neglecting such structure in the data can lead to seriously spurious associations. We describe a new approach to this problem.