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| Registros recuperados : 194 | |
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Biblioteca(s): |
Embrapa Pecuária Sul. |
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Data corrente: |
06/01/2020 |
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Data da última atualização: |
06/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
AGUILAR, I.; LEGARRA, A.; CARDOSO, F. F.; MASUDA, Y.; LOURENCO, D.; MISZTAL, I. |
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Afiliação: |
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. |
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Título: |
Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Genetics Selection Evolution, v. 51, n. 28, 20 June 2019. |
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DOI: |
doi.org/10.1186/s12711-019-0469-3 |
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Idioma: |
Inglês |
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Conteúdo: |
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. MenosBackground: 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 pedigr... Mostrar Tudo |
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Palavras-Chave: |
Gado Angus; Predição Genômica. |
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Thesagro: |
Bovino; Marcador Genético; Melhoramento Genético Animal. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://www.alice.cnptia.embrapa.br/alice/bitstream/doc/1118159/1/AguilaretalGSE5128.pdf
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Marc: |
LEADER 02328naa a2200253 a 4500
001 2118159
005 2020-01-06
008 2019 bl uuuu u00u1 u #d
024 7 $adoi.org/10.1186/s12711-019-0469-3$2DOI
100 1 $aAGUILAR, I.
245 $aFrequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle.$h[electronic resource]
260 $c2019
520 $aBackground: 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.
650 $aBovino
650 $aMarcador Genético
650 $aMelhoramento Genético Animal
653 $aGado Angus
653 $aPredição Genômica
700 1 $aLEGARRA, A.
700 1 $aCARDOSO, F. F.
700 1 $aMASUDA, Y.
700 1 $aLOURENCO, D.
700 1 $aMISZTAL, I.
773 $tGenetics Selection Evolution$gv. 51, n. 28, 20 June 2019.
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Registro original: |
Embrapa Pecuária Sul (CPPSUL) |
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