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

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 heritabilit... Full description

Main Author: Dekkers Jack CM
Contributors: Fernando Rohan L | Author
Totir Liviu R | Author
Fernández Soledad A | Author
Guldbrandtsen Bernt | Author
Contained in: Genetics Selection Evolution (01.11.2003)
Journal Title: Genetics Selection Evolution 01.11.2003
Fulltext access: Fulltext access (direct link - free access) 10.1186/1297-9686-35-7-585
Links: Additional Link (dx.doi.org)
Additional Link (doaj.org)
Additional Link (www.gsejournal.org)
Fulltext access (doaj.org)
Fulltext access (doaj.org)
ISSN: 0999-193X
DOI: 10.1186/1297-9686-35-7-585
Language: German
English
French
Physical Description: Online-Ressource
ID (e.g. DOI, URN): 10.1186/1297-9686-35-7-585
PPN (Catalogue-ID): DOAJ001375415
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520 |a 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. 
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