Accuracy of genomic prediction using mixed low-density marker panels
Lianjie Hou
A , Wenshuai Liang A , Guli Xu A , Bo Huang A , Xiquan Zhang A , Ching Yuan Hu B and Chong Wang A C
A National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, No. 483, Wushan Road, Tianhe District, Guangzhou 510642, PR China.
B Department of Human Nutrition, Food and Animal Sciences College of Tropical Agriculture and Human Resources, University of Hawaii at Manoa, 1955 East-west Road, AgSci, 415J Honolulu, HI 96822, USA.
C Corresponding author. Email: betty@scau.edu.cn
Animal Production Science 60(8) 999-1007 https://doi.org/10.1071/AN18503
Submitted: 14 August 2018 Accepted: 25 September 2019 Published: 17 April 2020
Abstract
Low-density single-nucleotide polymorphism (LD-SNP) panel is one effective way to reduce the cost of genomic selection in animal breeding. The present study proposes a new type of LD-SNP panel called mixed low-density (MLD) panel, which considers SNPs with a substantial effect estimated by Bayes method B (BayesB) from many traits and evenly spaced distribution simultaneously. Simulated and real data were used to compare the imputation accuracy and genomic-selection accuracy of two types of LD-SNP panels. The result of genotyping imputation for simulated data showed that the number of quantitative trait loci (QTL) had limited influence on the imputation accuracy only for MLD panels. Evenly spaced (ELD) panel was not affected by QTL. For real data, ELD performed slightly better than did MLD when panel contained 500 and 1000 SNP. However, this advantage vanished quickly as the density increased. The result of genomic selection for simulated data using BayesB showed that MLD performed much better than did ELD when QTL was 100. For real data, MLD also outperformed ELD in growth and carcass traits when using BayesB. In conclusion, the MLD strategy is superior to ELD in genomic selection under most situations.
Additional keywords: genomic selection, SNP imputation, low-density polymorphism panel, mixed low-density panel.
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