Literature DB >> 26805987

Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals.

Y Masuda1, I Misztal2, S Tsuruta2, A Legarra3, I Aguilar4, D A L Lourenco2, B O Fragomeni2, T J Lawlor5.   

Abstract

The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix GAPY(-1) based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 randomly chosen core animals, 0.45 with the 9,406 bulls, and 7,422 cows as core animals, and 0.44 with the remaining sets. With phenotypes truncated in 2009 and the preconditioned conjugate gradient to solve mixed model equations, the number of rounds to convergence for core animals defined by bulls was 1,343; defined by bulls and cows, 2,066; and defined by 10,000 random animals, at most 1,629. With complete phenotype data, the number of rounds decreased to 858, 1,299, and at most 1,092, respectively. Setting up GAPY(-1) for 569,404 genotyped animals with 10,000 core animals took 1.3h and 57 GB of memory. The validation reliability with APY reaches a plateau when the number of core animals is at least 10,000. Predictions with APY have little differences in reliability among definitions of core animals. Single-step genomic BLUP with APY is applicable to millions of genotyped animals.
Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  final score; genomic evaluation; genomic relationship matrix

Mesh:

Year:  2016        PMID: 26805987     DOI: 10.3168/jds.2015-10540

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  25 in total

1.  Sparse single-step genomic BLUP in crossbreeding schemes.

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Journal:  J Anim Sci       Date:  2018-06-04       Impact factor: 3.159

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Authors:  Mohammad Ali Nilforooshan; Michael Lee
Journal:  J Anim Sci       Date:  2019-03-01       Impact factor: 3.159

3.  Efficient single-step genomic evaluation for a multibreed beef cattle population having many genotyped animals.

Authors:  E A Mäntysaari; R D Evans; I Strandén
Journal:  J Anim Sci       Date:  2017-11       Impact factor: 3.159

4.  Single-step SNP-BLUP with on-the-fly imputed genotypes and residual polygenic effects.

Authors:  Matti Taskinen; Esa A Mäntysaari; Ismo Strandén
Journal:  Genet Sel Evol       Date:  2017-03-30       Impact factor: 4.297

5.  Efficient genetic value prediction using incomplete omics data.

Authors:  Matthias Westhues; Claas Heuer; Georg Thaller; Rohan Fernando; Albrecht E Melchinger
Journal:  Theor Appl Genet       Date:  2019-01-17       Impact factor: 5.699

6.  Dimensionality of genomic information and performance of the Algorithm for Proven and Young for different livestock species.

Authors:  Ivan Pocrnic; Daniela A L Lourenco; Yutaka Masuda; Ignacy Misztal
Journal:  Genet Sel Evol       Date:  2016-10-31       Impact factor: 4.297

7.  Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals.

Authors:  Rohan L Fernando; Hao Cheng; Bruce L Golden; Dorian J Garrick
Journal:  Genet Sel Evol       Date:  2016-12-08       Impact factor: 4.297

8.  An efficient exact method to obtain GBLUP and single-step GBLUP when the genomic relationship matrix is singular.

Authors:  Rohan L Fernando; Hao Cheng; Dorian J Garrick
Journal:  Genet Sel Evol       Date:  2016-10-27       Impact factor: 4.297

9.  The Dimensionality of Genomic Information and Its Effect on Genomic Prediction.

Authors:  Ivan Pocrnic; Daniela A L Lourenco; Yutaka Masuda; Andres Legarra; Ignacy Misztal
Journal:  Genetics       Date:  2016-03-04       Impact factor: 4.562

10.  A fast indirect method to compute functions of genomic relationships concerning genotyped and ungenotyped individuals, for diversity management.

Authors:  Jean-Jacques Colleau; Isabelle Palhière; Silvia T Rodríguez-Ramilo; Andres Legarra
Journal:  Genet Sel Evol       Date:  2017-12-01       Impact factor: 4.297

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