Dr. Chatterjee is the Chief and a Senior Investigator of the Biostatistics Branch of the Division of Cancer Epidemiology and Genetics (DECG), National Cancer Institute (NCI). He received his PhD in Statistics from the University of Washington, Seattle in 1999. His research focuses on statistical methods for modern genetic and molecular epidemiologic studies. His statistical areas of research, which cut across the different scientific disciplines, include regression analysis under complex sampling designs (e.g case-control and two-phase sampling), missing data, multivariate survival analysis and semiparametric inferences. He also actively collaborates in design and analysis of a variety of major cancer epidemiologic studies at NCI. His recent honors include NIH Merit Award (2003) for developing novel designs and methods for epidemiologic studies, DCEG Outstanding Mentoring award (2005) and election as a Fellow of the American Statistical Association (2008). He is also one of six recipients of a special R01 grant from Gene-Environment Initiative (GEI) program of NIH for developing statistical methods for gene-environment interaction studies.
Abstract
Detecting gene-gene interactions using genome-wide association studies in presence of population stratification
Some existing approaches to measure gene-gene interaction such as the case-only approach make a crucial assumption of gene-gene independence for physically distant genes. While this strategy is known to increase power considerably, it is prone to significant bias when the independence assumption is violated. To solve this problem, we use the idea of “genetic matching”, which has been proposed recently to control for stratification in a different context. Cases and controls are matched based on ancestry inferred from genome-wide null markers. Matched sets are then analyzed using extensions of conditional logistic regression that derive additional power from the gene-gene independence assumption. We compare our approach to some of the existing methods in terms of bias and efficiency, both using a GWAS data on prostate cancer from the CGEMS study and simulations under varying degrees of stratification.