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A sampling algorithm for segregation analysis

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Author(s): Tier Bruce | Henshall John

Journal: Genetics Selection Evolution
ISSN 0999-193X

Volume: 33;
Issue: 6;
Start page: 587;
Date: 2001;
Original page

Keywords: descent graphs | Monte Carlo Markov chain | quantitative trait loci | Metropolis-Hastings

ABSTRACT
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.

Tango Jona
Tangokurs Rapperswil-Jona

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