Technical Note: Determining Peeling Order Using Sparse Matrix Algorithms
To study the effect of individual genes by segregation or linkage analyses, the likelihood of the model needs to be evaluated. The likelihood can be computed efficiently using the Elston-Stewart algorithm. This algorithm involves summing over the unobserved genotypes in the pedigree, which is called...
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| Vydáno v: | Journal of dairy science Ročník 85; číslo 6; s. 1623 - 1629 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Savoy, IL
Am Dairy Sci Assoc
01.06.2002
American Dairy Science Association |
| Témata: | |
| ISSN: | 0022-0302, 1525-3198 |
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| Abstract | To study the effect of individual genes by segregation or linkage analyses, the likelihood of the model needs to be evaluated. The likelihood can be computed efficiently using the Elston-Stewart algorithm. This algorithm involves summing over the unobserved genotypes in the pedigree, which is called peeling. An important aspect of this algorithm is to determine the order of peeling to maximize efficiency. This paper shows how determining peeling order is related to a problem in solving systems of symmetric sparse linear equations. It also shows how algorithms developed to efficiently solve those systems, can be used to determine the optimal order of peeling in the Elston-Stewart algorithm. |
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| AbstractList | To study the effect of individual genes by segregation or linkage analyses, the likelihood of the model needs to be evaluated. The likelihood can be computed efficiently using the Elston-Stewart algorithm. This algorithm involves summing over the unobserved genotypes in the pedigree, which is called peeling. An important aspect of this algorithm is to determine the order of peeling to maximize efficiency. This paper shows how determining peeling order is related to a problem in solving systems of symmetric sparse linear equations. It also shows how algorithms developed to efficiently solve those systems, can be used to determine the optimal order of peeling in the Elston-Stewart algorithm.To study the effect of individual genes by segregation or linkage analyses, the likelihood of the model needs to be evaluated. The likelihood can be computed efficiently using the Elston-Stewart algorithm. This algorithm involves summing over the unobserved genotypes in the pedigree, which is called peeling. An important aspect of this algorithm is to determine the order of peeling to maximize efficiency. This paper shows how determining peeling order is related to a problem in solving systems of symmetric sparse linear equations. It also shows how algorithms developed to efficiently solve those systems, can be used to determine the optimal order of peeling in the Elston-Stewart algorithm. To study the effect of individual genes by segregation or linkage analyses, the likelihood of the model needs to be evaluated. The likelihood can be computed efficiently using the Elston-Stewart algorithm. This algorithm involves summing over the unobserved genotypes in the pedigree, which is called peeling. An important aspect of this algorithm is to determine the order of peeling to maximize efficiency. This paper shows how determining peeling order is related to a problem in solving systems of symmetric sparse linear equations. It also shows how algorithms developed to efficiently solve those systems, can be used to determine the optimal order of peeling in the Elston-Stewart algorithm. |
| Author | Fernando, R. L Fernandez, S. A |
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| Keywords | Performance evaluation Linkage Monogeny Genetic improvement Sparse matrix Model study Simulation model Milk production Likelihood function |
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| SubjectTerms | Algorithms Animal productions Animals Biological and medical sciences Breeding Cattle Cattle - genetics dairy cows Female Fundamental and applied biological sciences. Psychology Genetic Linkage genetics Genotype Likelihood Functions linear models Male Mathematical Computing Pedigree recessive genes Terrestrial animal productions Vertebrates |
| Title | Technical Note: Determining Peeling Order Using Sparse Matrix Algorithms |
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