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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
04/07/2019 |
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Actualizado : |
04/07/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
AGUILAR, I.; LEGARRA, A.; CARDOSO, F.; MASUDA, Y.; LOURENCO, D.; MISZTAL, I. |
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Afiliación : |
IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRÉS LEGARRA, INRA (Institut National de la Recherche Agronomique); FERNANDO CARDOSO, Department of Animal Science, Federal University of Pelotas, Brazil; Embrapa Pecuária Sul, Brazil; YUTAKA MASUDA, Department of Animal and Dairy Science, University of Georgia, United States; DANIELA LOURENCO, Department of Animal and Dairy Science, University of Georgia, United States; IGNACY MISZTAL, Department of Animal and Dairy Science, University of Georgia, United States. |
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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. (Short Communication) |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
Genetics Selection Evolution, 20 June 2019, v. 51, Issue 1, Article number 28. OPEN ACCESS. |
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ISSN : |
0999-193X |
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DOI : |
10.1186/s12711-019-0469-3 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 3 January 2019 // Accepted: 27 May 2019 // Published Online: 20 June 2019.
Funding text: This study was partially funded by the American Angus Association (St. Joseph, MO) and by Agriculture and Food Research Initiative Competitive Grants No. 2015-67015-22936 from the US Department of Agriculture?s National Institute of Food and Agriculture.
Availability of data and materials: The data that support the fndings of this study were provided from the American Angus Association but restrictions apply to the availability of these data, which were used under license for the current study, and thus are not publicly available. The methods described here are included using ?OPTION snp_p_value? in the parameter fle in software blupf90 (factorization of the mixed model equations and solving of the SSGBLUP equations) and postGSf90
(backsolving of snp efects and computation of p-values), available at http://nce.ads.uga.edu/software/. |
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Contenido : |
ABSTRACT.
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 single-marker 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.
© 2019 The Author(s). MenosABSTRACT.
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 single-marker 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... Presentar Todo |
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Palabras claves : |
ANIMALIA. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12994/1/s12711-019-0469-3.pdf
https://gsejournal.biomedcentral.com/track/pdf/10.1186/s12711-019-0469-3
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Marc : |
LEADER 03268naa a2200229 a 4500
001 1059927
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008 2019 bl uuuu u00u1 u #d
022 $a0999-193X
024 7 $a10.1186/s12711-019-0469-3$2DOI
100 1 $aAGUILAR, I.
245 $aFrequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. (Short Communication)$h[electronic resource]
260 $c2019
500 $aArticle history: Received: 3 January 2019 // Accepted: 27 May 2019 // Published Online: 20 June 2019. Funding text: This study was partially funded by the American Angus Association (St. Joseph, MO) and by Agriculture and Food Research Initiative Competitive Grants No. 2015-67015-22936 from the US Department of Agriculture?s National Institute of Food and Agriculture. Availability of data and materials: The data that support the fndings of this study were provided from the American Angus Association but restrictions apply to the availability of these data, which were used under license for the current study, and thus are not publicly available. The methods described here are included using ?OPTION snp_p_value? in the parameter fle in software blupf90 (factorization of the mixed model equations and solving of the SSGBLUP equations) and postGSf90 (backsolving of snp efects and computation of p-values), available at http://nce.ads.uga.edu/software/.
520 $aABSTRACT. 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 single-marker 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. © 2019 The Author(s).
653 $aANIMALIA
700 1 $aLEGARRA, A.
700 1 $aCARDOSO, F.
700 1 $aMASUDA, Y.
700 1 $aLOURENCO, D.
700 1 $aMISZTAL, I.
773 $tGenetics Selection Evolution, 20 June 2019$gv. 51, Issue 1, Article number 28. OPEN ACCESS.
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