JSM 2005 Online Program

Abstract #302815

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	84
Type:	Invited
Date/Time:	Monday, August 8, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Biopharmaceutical Section
Abstract - #302815
Title:	Likelihood-based Analysis for Mixture Models with Applications to Genetic Linkage and Association Studies
Author(s):	Yongzhao Shao*+
Companies:	New York University
Address:	Division of Biostatistics, School of Medicine, New York, NY, 10016, United States
Keywords:	association and linkage analysis ; genetic markers ; population admixture
Abstract:	Because many diseases have a genetic component, it is an important and challenging task in medicine and pharmacology to identify genes that underlie these diseases. Genetic linkage and association analyses are crucial steps toward identifying genetic markers associated with the disease and/or, more importantly, associated with desirable drug response (safety and/or efficacy). Detecting genetic linkage via likelihood analysis of mixture models has received enormous attention since Morton (1956) due to several reasons. One reason is identifying genes that underlie complex diseases must overcome many practical challenges, including genetic and non-genetic heterogeneities for which the most natural statistical tools are finite mixture models. Another reason is the likelihood ratio test (LRT or LOD) score has served as benchmark of classical linkage analysis; however, the LRT for mixture models has long been known as nonstandard and complicated. In this talk, we will present recent advances concerning likelihood-based linkage and association analysis.

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