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

Genetics Selection Evolution. 2003;35(7):585-604 DOI 10.1186/1297-9686-35-7-585

 

Journal Homepage

Journal Title: Genetics Selection Evolution

ISSN: 0999-193X (Print); 1297-9686 (Online)

Publisher: BMC

Society/Institution: French National Institute for Agricultural Research

LCC Subject Category: Agriculture: Animal culture | Science: Biology (General): Genetics

Country of publisher: United Kingdom

Language of fulltext: German, French, English

Full-text formats available: PDF, HTML

 

AUTHORS

Dekkers Jack CM
Fernando Rohan L
Totir Liviu R
Fernández Soledad A
Guldbrandtsen Bernt

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 26 weeks

 

Abstract | Full Text

<p>Abstract</p> <p>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.</p>