Bayesian haplotype inference for multiple linked single-nucleotide polymorphisms.
Haplotypes have gained increasing attention in the mapping of complex-disease genes, because of the abundance of single-nucleotide polymorphisms (SNPs) and the limited power of conventional single-locus analyses. It has been shown that haplotype-inference methods such as Clark's algorithm, the expectation-maximization algorithm, and a coalescence-based iterative-sampling algorithm are fairly effective and economical alternatives to molecular-haplotyping methods. To contend with some weaknesses of the existing algorithms, we propose a new Monte Carlo approach. In particular, we first partition the whole haplotype into smaller segments. Then, we use the Gibbs sampler both to construct the partial haplotypes of each segment and to assemble all the segments together. Our algorithm can accurately and rapidly infer haplotypes for a large number of linked SNPs. By using a wide variety of real and simulated data sets, we demonstrate the advantages of our Bayesian algorithm, and we show that it is robust to the violation of Hardy-Weinberg equilibrium, to the presence of missing data, and to occurrences of recombination hotspots.
Niu
Tianhua
T
Program for Population Genetics, Harvard School of Public Health, Boston, MA, USA.
Qin
Zhaohui S
ZS
Xu
Xiping
X
Liu
Jun S
JS
D000465:Algorithms; D001499:Bayes Theorem; D002874:Chromosome Mapping / Q000379:methods*; D002895:Chromosomes, Human, Pair 5 / Q000235:genetics; D003198:Computer Simulation; D019005:Cystic Fibrosis Transmembrane Conductance Regulator / Q000235:genetics; D008040:Genetic Linkage / Q000235:genetics*; D005819:Genetic Markers; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D009010:Monte Carlo Method; D007703:Peptidyl-Dipeptidase A / Q000235:genetics; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D018343:Receptors, Adrenergic, beta-2 / Q000235:genetics; D011995:Recombination, Genetic / Q000235:genetics; D012107:Research Design; D012680:Sensitivity and Specificity; D012984:Software
C505032:CFTR protein, human; D005819:Genetic Markers; D018343:Receptors, Adrenergic, beta-2; D019005:Cystic Fibrosis Transmembrane Conductance Regulator; D007703:Peptidyl-Dipeptidase A