A sampling algorithm for segregation analysis

Free access article

Issue		Genet. Sel. Evol. Volume 33, Number 6, November-December 2001


Page(s)		587 - 603
DOI		10.1051/gse:2001133

DOI: 10.1051/gse:2001133

Genet. Sel. Evol. 33 (2001) 587-603

A sampling algorithm for segregation analysis

Bruce Tier and John Henshall

Animal Genetics and Breeding Unit, University of New England, Armidale NSW 2351, Australia

(Received 27 June 2000; accepted 12 June 2001)

Abstract
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.

Key words: descent graphs / Monte Carlo Markov chain / quantitative trait loci / Metropolis-Hastings

Correspondence and reprints: Bruce Tier
e-mail: btier@pobox.une.edu.au

© INRA, EDP Sciences 2001

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