Literature DB >> 25074450

Estimation of genomic breeding values for residual feed intake in a multibreed cattle population.

M Khansefid1, J E Pryce2, S Bolormaa3, S P Miller4, Z Wang5, C Li6, M E Goddard7.   

Abstract

Residual feed intake (RFI) is a measure of the efficiency of animals in feed utilization. The accuracies of GEBV for RFI could be improved by increasing the size of the reference population. Combining RFI records of different breeds is a way to do that. The aims of this study were to 1) develop a method for calculating GEBV in a multibreed population and 2) improve the accuracies of GEBV by using SNP associated with RFI. An alternative method for calculating accuracies of GEBV using genomic BLUP (GBLUP) equations is also described and compared to cross-validation tests. The dataset included RFI records and 606,096 SNP genotypes for 5,614 Bos taurus animals including 842 Holstein heifers and 2,009 Australian and 2,763 Canadian beef cattle. A range of models were tested for combining genotype and phenotype information from different breeds and the best model included an overall effect of each SNP, an effect of each SNP specific to a breed, and a small residual polygenic effect defined by the pedigree. In this model, the Holsteins and some Angus cattle were combined into 1 "breed class" because they were the only cattle measured for RFI at an early age (6-9 mo of age) and were fed a similar diet. The average empirical accuracy (0.31), estimated by calculating the correlation between GEBV and actual phenotypes divided by the square root of estimated heritability in 5-fold cross-validation tests, was near to that expected using the GBLUP equations (0.34). The average empirical and expected accuracies were 0.30 and 0.31, respectively, when the GEBV were estimated for each breed separately. Therefore, the across-breed reference population increased the accuracy of GEBV slightly, although the gain was greater for breeds with smaller number of individuals in the reference population (0.08 in Murray Grey and 0.11 in Hereford for empirical accuracy). In a second approach, SNP that were significantly (P < 0.001) associated with RFI in the beef cattle genomewide association studies were used to create an auxiliary genomic relationship matrix for estimating GEBV in Holstein heifers. The empirical (and expected) accuracy of GEBV within Holsteins increased from 0.33 (0.35) to 0.39 (0.36) and improved even more to 0.43 (0.50) when using a multibreed reference population. Therefore, a multibreed reference population is a useful resource to find SNP with a greater than average association with RFI in 1 breed and use them to estimate GEBV in another breed.

Entities:  

Keywords:  genomewide association study; genomic selection; multibreed; residual feed intake

Mesh:

Year:  2014        PMID: 25074450     DOI: 10.2527/jas.2014-7375

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  15 in total

1.  Gene expression analysis of blood, liver, and muscle in cattle divergently selected for high and low residual feed intake.

Authors:  M Khansefid; C A Millen; Y Chen; J E Pryce; A J Chamberlain; C J Vander Jagt; C Gondro; M E Goddard
Journal:  J Anim Sci       Date:  2017-11       Impact factor: 3.159

2.  Genomic predictions in purebreds with a multibreed genomic relationship matrix1.

Authors:  Yvette Steyn; Daniela A L Lourenco; Ignacy Misztal
Journal:  J Anim Sci       Date:  2019-11-04       Impact factor: 3.159

3.  Combining NDVI and Bacterial Blight Score to Predict Grain Yield in Field Pea.

Authors:  Huanhuan Zhao; Babu R Pandey; Majid Khansefid; Hossein V Khahrood; Shimna Sudheesh; Sameer Joshi; Surya Kant; Sukhjiwan Kaur; Garry M Rosewarne
Journal:  Front Plant Sci       Date:  2022-06-28       Impact factor: 6.627

4.  Two-Variance-Component Model Improves Genetic Prediction in Family Datasets.

Authors:  George Tucker; Po-Ru Loh; Iona M MacLeod; Ben J Hayes; Michael E Goddard; Bonnie Berger; Alkes L Price
Journal:  Am J Hum Genet       Date:  2015-11-05       Impact factor: 11.025

5.  Impact of QTL properties on the accuracy of multi-breed genomic prediction.

Authors:  Yvonne C J Wientjes; Mario P L Calus; Michael E Goddard; Ben J Hayes
Journal:  Genet Sel Evol       Date:  2015-05-08       Impact factor: 4.297

6.  Sequence variants selected from a multi-breed GWAS can improve the reliability of genomic predictions in dairy cattle.

Authors:  Irene van den Berg; Didier Boichard; Mogens S Lund
Journal:  Genet Sel Evol       Date:  2016-11-04       Impact factor: 4.297

7.  Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture.

Authors:  Hossein Mehrban; Deuk Hwan Lee; Mohammad Hossein Moradi; Chung IlCho; Masoumeh Naserkheil; Noelia Ibáñez-Escriche
Journal:  Genet Sel Evol       Date:  2017-01-04       Impact factor: 4.297

8.  Comparing allele specific expression and local expression quantitative trait loci and the influence of gene expression on complex trait variation in cattle.

Authors:  Majid Khansefid; Jennie E Pryce; Sunduimijid Bolormaa; Yizhou Chen; Catriona A Millen; Amanda J Chamberlain; Christy J Vander Jagt; Michael E Goddard
Journal:  BMC Genomics       Date:  2018-11-03       Impact factor: 3.969

9.  Genomic correlation: harnessing the benefit of combining two unrelated populations for genomic selection.

Authors:  Laercio R Porto-Neto; William Barendse; John M Henshall; Sean M McWilliam; Sigrid A Lehnert; Antonio Reverter
Journal:  Genet Sel Evol       Date:  2015-11-02       Impact factor: 4.297

10.  Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits.

Authors:  Hassan Aliloo; Jennie E Pryce; Oscar González-Recio; Benjamin G Cocks; Ben J Hayes
Journal:  Genet Sel Evol       Date:  2016-02-01       Impact factor: 4.297

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