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Genetics, Vol. 168, 503-511, September 2004, Copyright © 2004
doi:10.1534/genetics.104.029603

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Sequencing Complex Diseases With HapMap

Tian Liu*, Julie A. Johnson{dagger}, George Casella* and Rongling Wu*,1

* Department of Statistics, University of Florida, Gainesville, Florida 32611
{dagger} Department of Pharmacy Practice, University of Florida, Gainesville, Florida 32611

1 Corresponding author: Department of Statistics, 533 McCarty Hall C, University of Florida, Gainesville, FL 32611.
E-mail: rwu{at}stat.ufl.edu

Determining the patterns of DNA sequence variation in the human genome is a useful first step toward identifying the genetic basis of a common disease. A haplotype map (HapMap), aimed at describing these variation patterns across the entire genome, has been recently developed by the International HapMap Consortium. In this article, we present a novel statistical model for directly characterizing specific sequence variants that are responsible for disease risk based on the haplotype structure provided by HapMap. Our model is developed in the maximum-likelihood context, implemented with the EM algorithm. We perform simulation studies to investigate the statistical properties of this disease-sequencing model. A worked example from a human obesity study with 155 patients was used to validate this model. In this example, we found that patients carrying a haplotype constituted by allele Gly16 at codon 16 and allele Gln27 at codon 27 genotyped within the ß2AR candidate gene display significantly lower body mass index than patients carrying the other haplotypes. The implications and extensions of our model are discussed.




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