Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Genetics and Molecular Biology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572010000100033 |
Resumo: | The aim of this study was to compare iterative and direct solvers for estimation of marker effects in genomic selection. One iterative and two direct methods were used: Gauss-Seidel with Residual Update, Cholesky Decomposition and Gentleman-Givens rotations. For resembling different scenarios with respect to number of markers and of genotyped animals, a simulated data set divided into 25 subsets was used. Number of markers ranged from 1,200 to 5,925 and number of animals ranged from 1,200 to 5,865. Methods were also applied to real data comprising 3081 individuals genotyped for 45181 SNPs. Results from simulated data showed that the iterative solver was substantially faster than direct methods for larger numbers of markers. Use of a direct solver may allow for computing (co)variances of SNP effects. When applied to real data, performance of the iterative method varied substantially, depending on the level of ill-conditioning of the coefficient matrix. From results with real data, Gentleman-Givens rotations would be the method of choice in this particular application as it provided an exact solution within a fairly reasonable time frame (less than two hours). It would indeed be the preferred method whenever computer resources allow its use. |
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Genetics and Molecular Biology |
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Use of direct and iterative solvers for estimation of SNP effects in genome-wide selectionbreeding valuegenomic selectionmixed model equationsnumerical methodThe aim of this study was to compare iterative and direct solvers for estimation of marker effects in genomic selection. One iterative and two direct methods were used: Gauss-Seidel with Residual Update, Cholesky Decomposition and Gentleman-Givens rotations. For resembling different scenarios with respect to number of markers and of genotyped animals, a simulated data set divided into 25 subsets was used. Number of markers ranged from 1,200 to 5,925 and number of animals ranged from 1,200 to 5,865. Methods were also applied to real data comprising 3081 individuals genotyped for 45181 SNPs. Results from simulated data showed that the iterative solver was substantially faster than direct methods for larger numbers of markers. Use of a direct solver may allow for computing (co)variances of SNP effects. When applied to real data, performance of the iterative method varied substantially, depending on the level of ill-conditioning of the coefficient matrix. From results with real data, Gentleman-Givens rotations would be the method of choice in this particular application as it provided an exact solution within a fairly reasonable time frame (less than two hours). It would indeed be the preferred method whenever computer resources allow its use.Sociedade Brasileira de Genética2010-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572010000100033Genetics and Molecular Biology v.33 n.1 2010reponame:Genetics and Molecular Biologyinstname:Sociedade Brasileira de Genética (SBG)instacron:SBG10.1590/S1415-47572010005000014info:eu-repo/semantics/openAccessPimentel,Eduardo da Cruz GouveiaSargolzaei,MehdiSimianer,HennerSchenkel,Flávio SchrammLiu,ZengtingFries,Luiz AlbertoQueiroz,Sandra Aidar deeng2010-02-12T00:00:00Zoai:scielo:S1415-47572010000100033Revistahttp://www.gmb.org.br/ONGhttps://old.scielo.br/oai/scielo-oai.php||editor@gmb.org.br1678-46851415-4757opendoar:2010-02-12T00:00Genetics and Molecular Biology - Sociedade Brasileira de Genética (SBG)false |
dc.title.none.fl_str_mv |
Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection |
title |
Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection |
spellingShingle |
Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection Pimentel,Eduardo da Cruz Gouveia breeding value genomic selection mixed model equations numerical method |
title_short |
Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection |
title_full |
Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection |
title_fullStr |
Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection |
title_full_unstemmed |
Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection |
title_sort |
Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection |
author |
Pimentel,Eduardo da Cruz Gouveia |
author_facet |
Pimentel,Eduardo da Cruz Gouveia Sargolzaei,Mehdi Simianer,Henner Schenkel,Flávio Schramm Liu,Zengting Fries,Luiz Alberto Queiroz,Sandra Aidar de |
author_role |
author |
author2 |
Sargolzaei,Mehdi Simianer,Henner Schenkel,Flávio Schramm Liu,Zengting Fries,Luiz Alberto Queiroz,Sandra Aidar de |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Pimentel,Eduardo da Cruz Gouveia Sargolzaei,Mehdi Simianer,Henner Schenkel,Flávio Schramm Liu,Zengting Fries,Luiz Alberto Queiroz,Sandra Aidar de |
dc.subject.por.fl_str_mv |
breeding value genomic selection mixed model equations numerical method |
topic |
breeding value genomic selection mixed model equations numerical method |
description |
The aim of this study was to compare iterative and direct solvers for estimation of marker effects in genomic selection. One iterative and two direct methods were used: Gauss-Seidel with Residual Update, Cholesky Decomposition and Gentleman-Givens rotations. For resembling different scenarios with respect to number of markers and of genotyped animals, a simulated data set divided into 25 subsets was used. Number of markers ranged from 1,200 to 5,925 and number of animals ranged from 1,200 to 5,865. Methods were also applied to real data comprising 3081 individuals genotyped for 45181 SNPs. Results from simulated data showed that the iterative solver was substantially faster than direct methods for larger numbers of markers. Use of a direct solver may allow for computing (co)variances of SNP effects. When applied to real data, performance of the iterative method varied substantially, depending on the level of ill-conditioning of the coefficient matrix. From results with real data, Gentleman-Givens rotations would be the method of choice in this particular application as it provided an exact solution within a fairly reasonable time frame (less than two hours). It would indeed be the preferred method whenever computer resources allow its use. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572010000100033 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572010000100033 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S1415-47572010005000014 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Genética |
publisher.none.fl_str_mv |
Sociedade Brasileira de Genética |
dc.source.none.fl_str_mv |
Genetics and Molecular Biology v.33 n.1 2010 reponame:Genetics and Molecular Biology instname:Sociedade Brasileira de Genética (SBG) instacron:SBG |
instname_str |
Sociedade Brasileira de Genética (SBG) |
instacron_str |
SBG |
institution |
SBG |
reponame_str |
Genetics and Molecular Biology |
collection |
Genetics and Molecular Biology |
repository.name.fl_str_mv |
Genetics and Molecular Biology - Sociedade Brasileira de Genética (SBG) |
repository.mail.fl_str_mv |
||editor@gmb.org.br |
_version_ |
1752122383213264896 |