Bayes estimates of haplotype effects.
We describe a Markov chain Monte Carlo implementation of a Bayesian approach to estimating associations of a trait with a large set of haplotypes recently introduced by Clayton and Jones [Am J Hum Genet 65:1161-9, 2000]. The model uses the length of the longest segment in common between any two haplotypes to define the prior correlation structure for the set of haplotype effects, using an intrinsic autocorrelation model. When applied to the Genetic Analysis Workshop 12 data for trait Q1, we found highly significant variation between haplotypes, using either a structured or unstructured covariance matrix.
Thomas
D C
DC
Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, CHP-220, Los Angeles, CA 90089-9011, USA.
Morrison
J L
JL
Clayton
D G
DG
D001499:Bayes Theorem; D002874:Chromosome Mapping / Q000706:statistics & numerical data*; D020022:Genetic Predisposition to Disease / Q000235:genetics; D014644:Genetic Variation; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D019655:Quantitative Trait, Heritable