Bayesian logistic regression using a perfect phylogeny.
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.
Clark
Taane G
TG
Department of Statistics, University of Oxford, Oxford, UK. taane.clark@imperial.ac.uk
De Iorio
Maria
M
Griffiths
Robert C
RC
D001499:Bayes Theorem; D003198:Computer Simulation; D005260:Female; D006683:HLA-DQ Antigens / Q000235:genetics; D059866:HLA-DQ beta-Chains; D006239:Haplotypes / Q000235:genetics; D006801:Humans; D016015:Logistic Models; D008297:Male; D008957:Models, Genetic; D010802:Phylogeny; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D054390:Receptors, CCR2; D019713:Receptors, CCR5 / Q000235:genetics; D019707:Receptors, Chemokine / Q000235:genetics; D012507:Sarcoidosis / Q000235:genetics; D014409:Tumor Necrosis Factor-alpha / Q000235:genetics
C515334:CCR2 protein, human; D006683:HLA-DQ Antigens; D059866:HLA-DQ beta-Chains; C069051:HLA-DQB1 antigen; D054390:Receptors, CCR2; D019713:Receptors, CCR5; D019707:Receptors, Chemokine; D014409:Tumor Necrosis Factor-alpha