구.농수식품
- 조회726
Sara de las Heras‑Saldana1†, Bryan Irvine Lopez2†, Nasir Moghaddar1, Woncheoul Park2, Jong‑eun Park2, Ki Y. Chung3, Dajeong Lim2*, Seung H. Lee4, Donghyun Shin5 and Julius H. J. van der Werf1*
1 School of Environmental and Rural Science, University of New England, Armidale NSW 2351, Australia.
2 Animal Genomics and Bioinformatics Division, National Institute of Animal Science, Rural Development Administration, Wanju 55365, Republic of Korea.
3 Department of Beef Science, Korea National College of Agriculture and Fisheries, Jeonju, Republic of Korea.
4 Division of Animal and Dairy Science, Chungnam National University, Deajeon 34148, Republic of Korea.
5 The Animal Molecular Genetics and Breeding Centre, Jeonbuk National University, Jeonju 54896, Republic of Korea.
*Correspondence
†Sara de las Heras-Saldana, Bryan I Lopez joint frst authors
Abstract
Background
In this study, we assessed the accuracy of genomic prediction for carcass weight (CWT), marbling score (MS), eye muscle area (EMA) and back fat thickness (BFT) in Hanwoo cattle when using genomic best linear unbiased prediction (GBLUP), weighted GBLUP (wGBLUP), and a BayesR model. For these models, we investigated the potential gain from using pre-selected single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) on imputed sequence data and from gene expression information. We used data on 13,717 animals with carcass phenotypes and imputed sequence genotypes that were split in an independent GWAS discovery set of varying size and a remaining set for validation of prediction. Expression data were used from a Hanwoo gene expression experiment based on 45 animals.
Results
Using a larger number of animals in the reference set increased the accuracy of genomic prediction whereas a larger independent GWAS discovery dataset improved identification of predictive SNPs. Using pre-selected SNPs from GWAS in GBLUP improved accuracy of prediction by 0.02 for EMA and up to 0.05 for BFT, CWT, and MS, compared to a 50 k standard SNP array that gave accuracies of 0.50, 0.47, 0.58, and 0.47, respectively. Accuracy of prediction of BFT and CWT increased when BayesR was applied with the 50 k SNP array (0.02 and 0.03, respectively) and was further improved by combining the 50 k array with the top-SNPs (0.06 and 0.04, respectively). By contrast, using BayesR resulted in limited improvement for EMA and MS. wGBLUP did not improve accuracy but increased prediction bias. Based on the RNA-seq experiment, we identified informative expression quantitative trait loci, which, when used in GBLUP, improved the accuracy of prediction slightly, i.e. between 0.01 and 0.02. SNPs that were located in genes, the expression of which was associated with differences in trait phenotype, did not contribute to a higher prediction accuracy.
Conclusions
Our results show that, in Hanwoo beef cattle, when SNPs are pre-selected from GWAS on imputed sequence data, the accuracy of prediction improves only slightly whereas the contribution of SNPs that are selected based on gene expression is not significant. The benefit of statistical models to prioritize selected SNPs for estimating genomic breeding values is trait-specific and depends on the genetic architecture of each trait.
논문정보
- Research article
- 2020년 09월 (BRIC 등록일 2021-04-13)
- 국내(교신)+국외 연구진
- 생명과학 > 동물자원학
- 구・농수식품
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