A sampling algorithm for segregation analysis

Genetics Selection Evolution

John Henshall¹  Bruce Tier¹ 

[1] Animal Genetics and Breeding Unit (A joint institute of NSW Agriculture and the University of New England), University of New England, Armidale NSW 2351, Australia

关键词: Metropolis-Hastings;    quantitative trait loci;    Monte Carlo Markov chain;    descent graphs;   

Others  :  1094628
DOI  :  10.1186/1297-9686-33-6-587

 received in 2000-06-27, accepted in 2001-06-12,  发布年份 2001

【 摘 要 】

Methods for detecting Quantitative Trait Loci (QTL) without markers have generally used iterative peeling algorithms for determining genotype probabilities. These algorithms have considerable shortcomings in complex pedigrees. A Monte Carlo Markov chain (MCMC) method which samples the pedigree of the whole population jointly is described. Simultaneous sampling of the pedigree was achieved by sampling descent graphs using the Metropolis-Hastings algorithm. A descent graph describes the inheritance state of each allele and provides pedigrees guaranteed to be consistent with Mendelian sampling. Sampling descent graphs overcomes most, if not all, of the limitations incurred by iterative peeling algorithms. The algorithm was able to find the QTL in most of the simulated populations. However, when the QTL was not modeled or found then its effect was ascribed to the polygenic component. No QTL were detected when they were not simulated.

【 授权许可】

   
2001 INRA, EDP Sciences