Use of gene expression and whole-genome sequence information to improve the accuracy of genomic prediction for carcass t
- PDF / 2,026,763 Bytes
- 16 Pages / 595.276 x 790.866 pts Page_size
- 97 Downloads / 168 Views
EARCH ARTICLE
Ge n e t i c s Se l e c t i o n Ev o l u t i o n
Open Access
Use of gene expression and whole‑genome sequence information to improve the accuracy of genomic prediction for carcass traits in Hanwoo cattle 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*
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 expres‑ sion 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, com‑ pared to a 50 k standard SNP array that gave accuracies of 0.50, 0.47, 0.58, and 0.47, respectively. Accuracy of predic‑ tion 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.
*Correspondence: [email protected]; [email protected] † Sara de las Heras-Saldana, Bryan I Lopez joint first authors 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 Full list of author information is available at the end of the article © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appr
Data Loading...
