Literature DB >> 29573806

Differing genetic trend estimates from traditional and genomic evaluations of genotyped animals as evidence of preselection bias in US Holsteins.

Y Masuda1, P M VanRaden2, I Misztal3, T J Lawlor4.   

Abstract

The objective of this study was to compare genetic trends from single-step genomic BLUP (ssGBLUP) and traditional BLUP models for milk production traits of US Holsteins. Phenotypes were 305-d milk, fat, and protein yields from 21,527,040 cows recorded between January 1990 and August 2015. The pedigree file included 29,651,623 animals and was limited to 3 generations back from recorded or genotyped animals. Genotypes for 764,029 animals were used, and analyses were by a 3-trait repeatability model as used in the US official genetic evaluation. Unknown-parent groups were incorporated into the inverse of a relationship matrix (H-1 in ssGBLUP and A-1 in BLUP) with the QP transformation. For ssGBLUP, 18,359 genotyped animals were randomly chosen as core animals to calculate the inverse of the genomic relationship matrix with the APY algorithm. Computations took 6.5 h and 1.4 GB of memory for BLUP, and 13 h and 115 GB of memory for ssGBLUP. For genotyped sires with at least 10 daughters, the average genetic levels for predicted transmitting ability (PTA) and genomic PTA were similar up to 2008, with a higher level for ssGBLUP later (approximately by 36 kg for milk, 2.1 kg for fat, and 1.1 kg for protein for bulls born in 2010). For genotyped cows, the average genetic levels were similar up to 2006, with a higher level for ssGBLUP (approximately by 91 kg for milk, 3.6 kg for fat, and 2.7 kg for protein for cows born in 2012). For all cows, the average levels were slightly higher for ssGBLUP, with much smaller differences than for genotyped cows. Trends for BLUP indicate bias due to genomic preselection for genotyped sires and cows. For official evaluations released in December 2016, traditional PTA had the same trend as multiple-step genomic PTA for both genotyped bulls and cows except for the youngest bulls, who had traditional PTA slightly lower than genomic PTA. For genotyped bulls born in recent years, genetic gain for official traditional and genomic evaluations was similar in contrast to ssGBLUP and BLUP differences. Official PTA for cows were adjusted so that the Mendelian sampling variance was comparable with that for bulls, and those adjustments likely removed bias due to genomic preselection from traditional PTA, especially for genotyped cows. The ssGBLUP method seems to account partially for that bias and is computationally suitable for national evaluations. The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Entities:  

Keywords:  bias; genomic evaluation; predicted transmitting ability; single-step method

Mesh:

Year:  2018        PMID: 29573806     DOI: 10.3168/jds.2017-13310

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


  9 in total

1.  The impact of selective genotyping on the response to selection using single-step genomic best linear unbiased prediction.

Authors:  Jeremy T Howard; Tom A Rathje; Caitlyn E Bruns; Danielle F Wilson-Wells; Stephen D Kachman; Matthew L Spangler
Journal:  J Anim Sci       Date:  2018-11-21       Impact factor: 3.159

2.  Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP using real data from pigs.

Authors:  Ibrahim Jibrila; Jeremie Vandenplas; Jan Ten Napel; Rob Bergsma; Roel F Veerkamp; Mario P L Calus
Journal:  Genet Sel Evol       Date:  2022-06-28       Impact factor: 5.100

3.  Improvement of Genomic Predictions in Small Breeds by Construction of Genomic Relationship Matrix Through Variable Selection.

Authors:  Enrico Mancin; Lucio Flavio Macedo Mota; Beniamino Tuliozi; Rina Verdiglione; Roberto Mantovani; Cristina Sartori
Journal:  Front Genet       Date:  2022-05-18       Impact factor: 4.772

4.  Comparison of models for missing pedigree in single-step genomic prediction.

Authors:  Yutaka Masuda; Shogo Tsuruta; Matias Bermann; Heather L Bradford; Ignacy Misztal
Journal:  J Anim Sci       Date:  2021-02-01       Impact factor: 3.159

5.  Emerging issues in genomic selection.

Authors:  Ignacy Misztal; Ignacio Aguilar; Daniela Lourenco; Li Ma; Juan Pedro Steibel; Miguel Toro
Journal:  J Anim Sci       Date:  2021-06-01       Impact factor: 3.159

6.  Investigating the impact of preselection on subsequent single-step genomic BLUP evaluation of preselected animals.

Authors:  Ibrahim Jibrila; Jan Ten Napel; Jeremie Vandenplas; Roel F Veerkamp; Mario P L Calus
Journal:  Genet Sel Evol       Date:  2020-07-29       Impact factor: 4.297

7.  Changes in genetic parameters for fitness and growth traits in pigs under genomic selection.

Authors:  Jorge Hidalgo; Shogo Tsuruta; Daniela Lourenco; Yutaka Masuda; Yijian Huang; Kent A Gray; Ignacy Misztal
Journal:  J Anim Sci       Date:  2020-02-01       Impact factor: 3.159

Review 8.  Single-Step Genomic Evaluations from Theory to Practice: Using SNP Chips and Sequence Data in BLUPF90.

Authors:  Daniela Lourenco; Andres Legarra; Shogo Tsuruta; Yutaka Masuda; Ignacio Aguilar; Ignacy Misztal
Journal:  Genes (Basel)       Date:  2020-07-14       Impact factor: 4.096

9.  Variance estimates are similar using pedigree or genomic relationships with or without the use of metafounders or the algorithm for proven and young animals1.

Authors:  Michael N Aldridge; Jérémie Vandenplas; Rob Bergsma; Mario P L Calus
Journal:  J Anim Sci       Date:  2020-03-01       Impact factor: 3.159

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.