Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle.

Detalhes bibliográficos
Autor(a) principal: AGUILAR, I.
Data de Publicação: 2019
Outros Autores: LEGARRA, A., CARDOSO, F. F., MASUDA, Y., LOURENCO, D., MISZTAL, I.
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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spelling 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
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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)
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instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
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