TAILIEUCHUNG - Báo cáo khoa hoc:" Sampling genotypes in large pedigrees with loops"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: Sampling genotypes in large pedigrees with loops | Genet Sei. Evol. 33 2001 337-367 INRA EDP Sciences 2001 337 Original article Sampling genotypes in large pedigrees with loops Soledad a. Fernandeza b Rohan L. Fernandoa c Bernt Guldbrandtsend Liviu R. Totira Alicia L. Carriquiryb c a Department of Animal Science Iowa State UniversitY 225 Kildee Hall Ames IA 50011 USA b Department of Statistics Iowa State UniversitY 225 Kildee Hall Ames IA 50011 USA c Lawrence H. Baker Center for Bioinformatics and Biological Statistics Iowa State UniversitY Ames IA 50011 USA d Danish Institute of Animal Science Foulum Denmark Received 19 October 2000 accepted 23 FebruarY 2001 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. Scalar-Gibbs is easy to implement and it is widely 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. These problems do not arise if the genotypes are sampled jointly from the entire pedigree. This paper proposes amethod to jointly sample genotypes. The method combines the Elston-Stewart algorithm and iterative peeling and is called the ESIP sampler. For a hypothetical pedigree genotype probabilities are estimated from samples obtained using ESIP and also scalar-Gibbs. Approximate probabilities were also obtained by iterative peeling. Comparisons of these with exact genotypic probabilities obtained by the Elston-Stewart algorithm showed that ESIP and iterative peeling yielded genotypic probabilities that were very close to the exact values. Nevertheless estimated probabilities from scalar-Gibbs with a chain of length 235 000 including a burn-in of 200 000 steps were less accurate than probabilities estimated using ESIP with a chain of length 10 000 with a burn-in of 5 000 steps. The

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