Abstract #301333

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JSM 2003 Abstract #301333
Activity Number: 335
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #301333
Title: Finding Starting Points for MCMC Analysis of Genetic Data from Large and Complex Pedigrees
Author(s): Yuqun Luo*+ and Shili Lin
Companies: The Ohio State University and The Ohio State University
Address: 672 Ashtabula Court, Columbus, OH, 43210,
Keywords: START ; heated Gibbs sampler ; heated relaxed penetrance ; genotypic configuration ; inheritance vector ; Hutterite pedigree
Abstract:

Genetic data from founder populations are advantageous for studies of complex traits. However, the desire to analyze data on many polymorphic loci observed on large and complex pedigrees from such populations poses challenges to current standard approaches. A viable alternative to solving such problems is via MCMC procedures. However, finding starting points for the Markov chains is a difficult problem when the pedigree is not single-locus peelable. We propose a generalization of the heated Gibbs sampler with relaxed penetrances (HGRP) [Lin et al. 1993] to search for starting points. HGRP guarantees that a starting point will be found if there is no error in the data, but the chain usually needs to be run for a long time if the pedigree is extremely large and complex. By introducing a forcing step, the current algorithm substantially reduces the state space, hence effectively speeds up the process of finding a starting point. Our algorithm also has a built-in preprocessing procedure for Mendelian error detection. The algorithm has been applied to both simulated and real data on two large and complex Hutterite pedigrees under many settings, and good results are obtained.

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