Linkage disequilibrium assessment via log-linear modeling of SNP haplotype frequencies.
Analyses of high-density single-nucleotide polymorphism (SNP) data, such as genetic mapping and linkage disequilibrium (LD) studies, require phase-known haplotypes to allow for the correlation between tightly linked loci. However, current SNP genotyping technology cannot determine phase, which must be inferred statistically. In this paper, we present a new Bayesian Markov chain Monte Carlo (MCMC) algorithm for population haplotype frequency estimation, particularly in the context of LD assessment. The novel feature of the method is the incorporation of a log-linear prior model for population haplotype frequencies. We present simulations to suggest that 1) the log-linear prior model is more appropriate than the standard coalescent process in the presence of recombination (>0.02 cM between adjacent loci), and 2) there is substantial inflation in measures of LD obtained by a "two-stage" approach to the analysis by treating the "best" haplotype configuration as correct, without regard to uncertainty in the recombination process.
Morris
Andrew
A
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. amorris@well.ox.ac.uk
Pedder
Alan
A
Ayres
Karen
K
D000465:Algorithms; D001499:Bayes Theorem; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D016014:Linear Models; D015810:Linkage Disequilibrium / Q000235:genetics*; D016015:Logistic Models; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*