Research Associate (Associate Professor)
B.S. (Physics), University of California San Diego, 1989,
Ph.D. (Physics), University of Chicago, 1996
My research focus is on developing both new statistical methods and computational techniques to overcome the difficult problems in the analysis of genetic data, with the objective of the methods being to find genes that influence complex traits. A primary interest is on developing models and approximations that are highly computationally efficient while leveraging as much of the information in the data as possible. Ultimately, application of these methods help us to discover the regions of the genome that contribute to a trait's variability.
Some specific interests include:
Development of linear mixed model and generalized linear mixed model approaches to the analysis of genomic data. Applications include identifying genes for complex traits, particularly in the presence of relatedness or population structure, and estimating the genetic influence (i.e. heritability) of complex traits.
Identification of shared genomic regions between individuals who may be closely or distantly related, particularly when the individuals are from isolated populations with complex relatedness structures. We can use this knowledge to help find genes, predict phenotypes, estimate relatedness and understand past demography.
Application of our complex trait mapping methods to human and model organism (e.g. mouse) data sets. Model organisms have very uniform environments, which can removing some confounding effects, but may often have very complex, and unknown relatedness. We apply the methods we develop to correctly discover and model relatedness in order to do valid statistical inference.