Open Access

A comparison of alternative methods to compute conditional genotype probabilities for genetic evaluation with finite locus models

  • Liviu R Totir1Email author,
  • Rohan L Fernando1, 2,
  • Jack CM Dekkers1, 2,
  • Soledad A Fernández3 and
  • Bernt Guldbrandtsen4
Genetics Selection Evolution200335:585

https://doi.org/10.1186/1297-9686-35-7-585

Received: 27 February 2002

Accepted: 5 May 2003

Published: 15 November 2003

Abstract

An increased availability of genotypes at marker loci has prompted the development of models that include the effect of individual genes. Selection based on these models is known as marker-assisted selection (MAS). MAS is known to be efficient especially for traits that have low heritability and non-additive gene action. BLUP methodology under non-additive gene action is not feasible for large inbred or crossbred pedigrees. It is easy to incorporate non-additive gene action in a finite locus model. Under such a model, the unobservable genotypic values can be predicted using the conditional mean of the genotypic values given the data. To compute this conditional mean, conditional genotype probabilities must be computed. In this study these probabilities were computed using iterative peeling, and three Markov chain Monte Carlo (MCMC) methods – scalar Gibbs, blocking Gibbs, and a sampler that combines the Elston Stewart algorithm with iterative peeling (ESIP). The performance of these four methods was assessed using simulated data. For pedigrees with loops, iterative peeling fails to provide accurate genotype probability estimates for some pedigree members. Also, computing time is exponentially related to the number of loci in the model. For MCMC methods, a linear relationship can be maintained by sampling genotypes one locus at a time. Out of the three MCMC methods considered, ESIP, performed the best while scalar Gibbs performed the worst.

Keywords

genotype probabilities finite locus models Markov chain Monte Carlo

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Authors’ Affiliations

(1)
Department of Animal Science, Iowa State University
(2)
Lawrence H. Baker Center for Bio-informatics and Biological Statistics, Iowa State University
(3)
Department of Statistics, The Ohio State University
(4)
Danish Institute of Animal Science

Copyright

© INRA, EDP Sciences 2003

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