Technical Note : Computing strategies in genome-wide selection
| العنوان: | Technical Note : Computing strategies in genome-wide selection |
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| المؤلفون: | Andres Legarra, Ignacy Misztal |
| المساهمون: | Station d'Amélioration Génétique des Animaux (SAGA), Institut National de la Recherche Agronomique (INRA), University of Georgia [USA] |
| المصدر: | Journal of Dairy Science Journal of Dairy Science, American Dairy Science Association, 2008, 91 (1), pp.360-366 |
| بيانات النشر: | HAL CCSD, 2008. |
| سنة النشر: | 2008 |
| مصطلحات موضوعية: | Male, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Computation, Type (model theory), Polymorphism, Single Nucleotide, 03 medical and health sciences, Mice, Covariate, Genetics, Animals, Selection (genetic algorithm), ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Mathematics, 0303 health sciences, Markov chain, Models, Genetic, Body Weight, 0402 animal and dairy science, Computational Biology, Technical note, 04 agricultural and veterinary sciences, Genomics, Computer Science::Numerical Analysis, 040201 dairy & animal science, Data set, Genome-wide selection, genomic selection, genetic evaluation, marker-assisted selection, Animal Science and Zoology, Female, Algorithm, Algorithms, Food Science, Cholesky decomposition |
| الوصف: | Genome-wide genetic evaluation might involve the computation of BLUP-like estimations, potentially including thousands of covariates (i.e., single-nucleotide polymorphism markers) for each record. This implies dense Henderson's mixed-model equations and considerable computing resources in time and storage, even for a few thousand records. Possible computing options include the type of storage and the solving algorithm. This work evaluated several computing options, including half-stored Cholesky decomposition, Gauss-Seidel, and 3 matrix-free strategies: Gauss-Seidel, Gauss-Seidel with residuals update, and preconditioned conjugate gradients. Matrix-free Gauss-Seidel with residuals update adjusts the residuals after computing the solution for each effect. This avoids adjusting the left-hand side of the equations by all other effects at every step of the algorithm and saves considerable computing time. Any Gauss-Seidel algorithm can easily be extended for variance component estimation by Markov chain-Monte Carlo. Let m and n be the number of records and markers, respectively. Computing time for Cholesky decomposition is proportional to n3. Computing times per round are proportional to mn2 in matrix-free Gauss-Seidel, to n2 for half-stored Gauss-Seidel, and to n and m for the rest of the algorithms. Algorithms were tested on a real mouse data set, which included 1,928 records and 10,946 single-nucleotide polymorphism markers. Computing times were in the order of a few minutes for Gauss-Seidel with residuals update and preconditioned conjugate gradients, more than 1 h for half-stored Gauss-Seidel, 2 h for Cholesky decomposition, and 4 d for matrix-free Gauss-Seidel. Preconditioned conjugate gradients was the fastest. Gauss-Seidel with residuals update would be the method of choice for variance component estimation as well as solving. |
| اللغة: | English |
| تدمد: | 0022-0302 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd98a56a7653f07e9f6c9b2047048704Test https://hal.inrae.fr/hal-02658143Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....dd98a56a7653f07e9f6c9b2047048704 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 00220302 |
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