Identification of Genes Affecting Liability to Complex Disease by the Analysis of Haploytpes

• Activity Number: 217
• Type: Contributed
• Date/Time: Tuesday, August 5, 2003 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Jung-Ying Tzeng*+ and Bernie Devlin and Larry A. Wasserman and Kathryn M. Roeder
• Companies: Carnegie Mellon University and University of Pittsburgh and Carnegie Mellon University and Carnegie Mellon University
• Keywords: association analysis ; haplotype sharing ; genomic control ; case-control study

Abstract

Liability genes (LG) for complex disease refer to those genes that contain variants affecting the risk of developing disease. One approach to detecting LG involves population-based association analysis, i.e., case-control studies. To detect the association between genetic variants and disease, we use the fact that LG can be identified by the excess of haplotype sharing (HS) among cases. We introduce a class of 1-df statistics to measure HS, and present a testing procedure based on this class of statistics for initial genome scan. Major issues involved in constructing this approach are confounding and phase-missing problem. We tackle the problem of confounding by generalizing the Genomic Control principle to our class of statistics. We show that this procedure can be robust to phase-unknown problem. We also perform a power comparison between this test and the Pearson's chi-squared test, another commonly used association test. Our results lend insight concerning the relative power of the two approaches in the rare and common variant settings.