Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle.
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1118159 |
Resumo: | Background: Single-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for genome-wide association studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for singlemarker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and procedures, and by applying these to a large beef cattle population. Methods: P-values were obtained based on the prediction error (co)variances for single nucleotide polymorphisms (SNPs), which were obtained from the prediction error (co)variances of genomic predictions based on the inverse of the coefficient matrix and formulas to estimate SNP effects. Results: Computation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation of statistics was observed. The SNPs that passed the Bonferroni threshold of 10-5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the same significance levels and effects, some of them explained less genetic variance due to lower allele frequency. Conclusions: The use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals are genotyped. |
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Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle.Gado AngusPredição GenômicaBovinoMarcador GenéticoMelhoramento Genético AnimalBackground: Single-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for genome-wide association studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for singlemarker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and procedures, and by applying these to a large beef cattle population. Methods: P-values were obtained based on the prediction error (co)variances for single nucleotide polymorphisms (SNPs), which were obtained from the prediction error (co)variances of genomic predictions based on the inverse of the coefficient matrix and formulas to estimate SNP effects. Results: Computation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation of statistics was observed. The SNPs that passed the Bonferroni threshold of 10-5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the same significance levels and effects, some of them explained less genetic variance due to lower allele frequency. Conclusions: The use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals are genotyped.Ignacio Aguilar, INIA; Andres Legarra, INRA; FERNANDO FLORES CARDOSO, CPPSUL; Yutaka Masuda, University of Georgia; Daniela Lourenco, University of Georgia; Ignacy Misztal, University of Georgia.AGUILAR, I.LEGARRA, A.CARDOSO, F. F.MASUDA, Y.LOURENCO, D.MISZTAL, I.2020-01-06T18:24:04Z2020-01-06T18:24:04Z2020-01-0620192020-01-06T18:24:04Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleGenetics Selection Evolution, v. 51, n. 28, 20 June 2019.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1118159doi.org/10.1186/s12711-019-0469-3enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2020-01-06T18:24:11Zoai:www.alice.cnptia.embrapa.br:doc/1118159Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542020-01-06T18:24:11falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542020-01-06T18:24:11Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. |
title |
Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. |
spellingShingle |
Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. AGUILAR, I. Gado Angus Predição Genômica Bovino Marcador Genético Melhoramento Genético Animal |
title_short |
Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. |
title_full |
Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. |
title_fullStr |
Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. |
title_full_unstemmed |
Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. |
title_sort |
Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. |
author |
AGUILAR, I. |
author_facet |
AGUILAR, I. LEGARRA, A. CARDOSO, F. F. MASUDA, Y. LOURENCO, D. MISZTAL, I. |
author_role |
author |
author2 |
LEGARRA, A. CARDOSO, F. F. MASUDA, Y. LOURENCO, D. MISZTAL, I. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Ignacio Aguilar, INIA; Andres Legarra, INRA; FERNANDO FLORES CARDOSO, CPPSUL; Yutaka Masuda, University of Georgia; Daniela Lourenco, University of Georgia; Ignacy Misztal, University of Georgia. |
dc.contributor.author.fl_str_mv |
AGUILAR, I. LEGARRA, A. CARDOSO, F. F. MASUDA, Y. LOURENCO, D. MISZTAL, I. |
dc.subject.por.fl_str_mv |
Gado Angus Predição Genômica Bovino Marcador Genético Melhoramento Genético Animal |
topic |
Gado Angus Predição Genômica Bovino Marcador Genético Melhoramento Genético Animal |
description |
Background: Single-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for genome-wide association studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for singlemarker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and procedures, and by applying these to a large beef cattle population. Methods: P-values were obtained based on the prediction error (co)variances for single nucleotide polymorphisms (SNPs), which were obtained from the prediction error (co)variances of genomic predictions based on the inverse of the coefficient matrix and formulas to estimate SNP effects. Results: Computation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation of statistics was observed. The SNPs that passed the Bonferroni threshold of 10-5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the same significance levels and effects, some of them explained less genetic variance due to lower allele frequency. Conclusions: The use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals are genotyped. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 2020-01-06T18:24:04Z 2020-01-06T18:24:04Z 2020-01-06 2020-01-06T18:24:04Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Genetics Selection Evolution, v. 51, n. 28, 20 June 2019. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1118159 doi.org/10.1186/s12711-019-0469-3 |
identifier_str_mv |
Genetics Selection Evolution, v. 51, n. 28, 20 June 2019. doi.org/10.1186/s12711-019-0469-3 |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1118159 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1794503487219826688 |