Bayesian logistic regression using a perfect phylogeny.

Journal article

Haplotype data capture the genetic variation among individuals in a population and among populations. An understanding of this variation and the ancestral history of haplotypes is important in genetic association studies of complex disease. We introduce a method for detecting associations between disease and haplotypes in a candidate gene region or candidate block with little or no recombination. A perfect phylogeny demonstrates the evolutionary relationship between single-nucleotide polymorphisms (SNPs) in the haplotype blocks. Our approach extends the logic regression technique of Ruczinski and others (2003) to a Bayesian framework, and constrains the model space to that of a perfect phylogeny. Environmental factors, as well as their interactions with SNPs, may be incorporated into the regression framework. We demonstrate our method on simulated data from a coalescent model, as well as data from a candidate gene study of sarcoidosis.

Authors: Clark, T; De Iorio, M; Griffiths, R

• Publication status: Published
• Journal: Biostatistics (Oxford, England)
• Volume: 8
• Issue: 1
• Pages: 32-52
• Publication date: 2007-01-01
• DOI: 10.1093/biostatistics/kxj030
• EISSN: 1468-4357
• ISSN: 1465-4644
• Language: English
• Keywords: Phylogeny; Logistic Models; Haplotypes; Humans; HLA-DQ Antigens; Male; Female; Bayes Theorem; Tumor Necrosis Factor-alpha; Computer Simulation; Receptors, CCR5; Sarcoidosis; HLA-DQ beta-Chains; Receptors, CCR2; Models, Genetic; Polymorphism, Single Nucleotide; Receptors, Chemokine