American Statistical Association
Selective genotyping is an economical strategy in detecting and mapping quantitative trait loci in the genetic dissection of complex disease. When the samples arise from different ethnic groups or an admixture population, all the existing selective genotyping methods may result in spurious association due to different ancestry distributions. The problem can be more serious when the sample size is large, a general requirement to obtain sufficient power to detect modest genetic effects for most complex traits. In this talk, we describe a useful strategy in selective genotyping while the population stratification is present. Our procedure uses a principal component based approach to eliminate any effect of population stratification. We evaluate the performance of our procedure using simulated data in a variety of population admixture models generated from empirical data.
Huann-Sheng Chen is an Associate Professor of the Department of Mathematical Sciences in Michigan Technological University. He recently took professional leave to join the National Cancer Institute of NIH. Dr. Chen earned his doctorate in Statistics from the University of Illinois at Urbana-Champaign. His research interests include statistical genetics, biostatistics and spatial statistics.
|Date:||Thursday, April 8, 2010|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York