title: Irreducibility and efficiency of ESIP to sample marker genotypes in large pedigrees with loops.
abstract: Markov chain Monte Carlo (MCMC) methods have been proposed to overcome computational problems in linkage and segregation analyses. This approach involves sampling genotypes at the marker and trait loci. Among MCMC methods, scalar-Gibbs is the easiest to implement, and it is used in genetics. However, the Markov chain that corresponds to scalar-Gibbs may not be irreducible when the marker locus has more than two alleles, and even when the chain is irreducible, mixing has been observed to be slow. Joint sampling of genotypes has been proposed as a strategy to overcome these problems. An algorithm that combines the Elston-Stewart algorithm and iterative peeling (ESIP sampler) to sample genotypes jointly from the entire pedigree is used in this study. Here, it is shown that the ESIP sampler yields an irreducible Markov chain, regardless of the number of alleles at a locus. Further, results obtained by ESIP sampler are compared with other methods in the literature. Of the methods that are guaranteed to be irreducible, ESIP was the most efficient.
authors.affiliation: Department of Statistics, 317 Cockins Hall, Ohio State University, Columbus, OH 43210, USA.
authors.forename: Soledad A
authors.initials: SA
authors.lastname: Fernández
authors.forename: Rohan L
authors.initials: RL
authors.lastname: Fernando
authors.forename: Bernt
authors.initials: B
authors.lastname: Guldbrandtsen
authors.forename: Christian
authors.initials: C
authors.lastname: Stricker
authors.forename: Matthias
authors.initials: M
authors.lastname: Schelling
authors.forename: Alicia L
authors.initials: AL
authors.lastname: Carriquiry
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000235:genetics; D005838:Genotype; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D010375:Pedigree; D040641:Quantitative Trait Loci