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A comparison of alternative methods to compute conditional genotype probabilities for genetic evaluation with finite locus models.
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MedLine Citation:
PMID:  14604509     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Liviu R Totir; Rohan L Fernando; Jack C M Dekkers; Soledad A Fernández; Bernt Guldbrandtsen
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Genetics, selection, evolution : GSE     Volume:  35     ISSN:  0999-193X     ISO Abbreviation:  Genet. Sel. Evol.     Publication Date:    2003 Nov-Dec
Date Detail:
Created Date:  2003-11-07     Completed Date:  2004-07-13     Revised Date:  2009-06-19    
Medline Journal Info:
Nlm Unique ID:  9114088     Medline TA:  Genet Sel Evol     Country:  France    
Other Details:
Languages:  eng     Pagination:  585-604     Citation Subset:  IM    
Affiliation:
Department of Animal Science, Iowa State University, Ames, IA 50011-3150, USA. ltotir@iastate.edu
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MeSH Terms
Descriptor/Qualifier:
Genotype*
Models, Genetic*
Probability*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Full Text
Journal Information
Journal ID (nlm-ta): Genet Sel Evol
ISSN: 0999-193X
ISSN: 1297-9686
Publisher: BioMed Central
Article Information
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Copyright ? 2003 INRA, EDP Sciences
Received Day: 27 Month: 2 Year: 2002
Accepted Day: 5 Month: 5 Year: 2003
collection publication date: Year: 2003
Electronic publication date: Day: 15 Month: 11 Year: 2003
Volume: 35 Issue: 7
First Page: 585 Last Page: 604
ID: 2698000
Publisher Id: 1297-9686-35-7-585
PubMed Id: 14604509
DOI: 10.1186/1297-9686-35-7-585

A comparison of alternative methods to compute conditional genotype probabilities for genetic evaluation with finite locus models
Liviu R Totir1 Email: ltotir@iastate.edu
Rohan L Fernando12
Jack CM Dekkers12
Soledad A Fern?ndez3
Bernt Guldbrandtsen4
1Department of Animal Science, Iowa State University, Ames, IA 50011-3150, USA
2Lawrence H. Baker Center for Bio-informatics and Biological Statistics, Iowa State University, Ames, IA 50011-3150, USA
3Department of Statistics, The Ohio State University, Columbus, OH 43210, USA
4Danish Institute of Animal Science, Foulum, Denmark

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Article Categories:
  • Research

Keywords: genotype probabilities, finite locus models, Markov chain Monte Carlo.

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