title: Maximum likelihood haplotyping for general pedigrees.
abstract: Haplotype data is valuable in mapping disease-susceptibility genes in the study of Mendelian and complex diseases. We present algorithms for inferring a most likely haplotype configuration for general pedigrees, implemented in the newest version of the genetic linkage analysis system SUPERLINK. In SUPERLINK, genetic linkage analysis problems are represented internally using Bayesian networks. The use of Bayesian networks enables efficient maximum likelihood haplotyping for more complex pedigrees than was previously possible. Furthermore, to support efficient haplotyping for larger pedigrees, we have also incorporated a novel algorithm for determining a better elimination order for the variables of the Bayesian network. The presented optimization algorithm also improves likelihood computations. We present experimental results for the new algorithms on a variety of real and semiartificial data sets, and use our software to evaluate MCMC approximations for haplotyping.
authors.affiliation: Computer Science Department, Technion, Haifa, Israel.
authors.forename: Ma'ayan
authors.initials: M
authors.lastname: Fishelson
authors.forename: Nickolay
authors.initials: N
authors.lastname: Dovgolevsky
authors.forename: Dan
authors.initials: D
authors.lastname: Geiger
mesh_terms: D000465:Algorithms; D001259:Ataxia / Q000235:genetics; D001499:Bayes Theorem; D002874:Chromosome Mapping / Q000379:methods; D003198:Computer Simulation; D005787:Gene Frequency / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D007965:Leukodystrophy, Globoid Cell / Q000235:genetics; D016013:Likelihood Functions; D010375:Pedigree; D012984:Software