A Hidden Markov Model Approach for Admixture Mapping of Complex Diseases

• Activity Number: 337
• Type: Contributed
• Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Chun Zhang*+ and Hongzhe Li
• Companies: University of California, Davis and University of California, Davis
• Keywords: logistic regression ; admixture mapping ; hidden Markov model ; likelihood ratio test ; linkage disequilibrium ; haplotype

Abstract

Admixture mapping is a potentially powerful method for mapping genes for complex human diseases, where the disease frequencies due to a particular disease locus are different between the two founding populations. The method tests for genetic linkage by detecting association of the allele ancestries and the disease. Since the ethnic-difference markers are not fully informative for the ancestry indicators, direct test of such association is not possible. We develop a hidden Markov model (HMM) framework for estimating the unobserved ethnicity haplotypes across a chromosomal region based on the marker haplotypes. In this modeling framework, we consider two different approaches for detecting genetic linkage based on case-control design. One approch tests the difference of the ethnicity origin estimated by the Vertibi algorithm between cases and controls. Another approach is based on a likelihood ratio test. We evaluate by simulations how several factors affect the power of the admixture mapping, including sample sizes, ethincity relative risks, marker densities and the number of markers that the candidate region should cover.