Accuracy of genomic prediction using mixed low-density marker panels

文献类型： 外文期刊

第一作者： Hou, Lianjie

作者： Hou, Lianjie;Liang, Wenshuai;Xu, Guli;Huang, Bo;Zhang, Xiquan;Wang, Chong;Hu, Ching Yuan

作者机构：

关键词： genomic selection; SNP imputation; low-density polymorphism panel; mixed low-density panel

期刊名称：ANIMAL PRODUCTION SCIENCE （ 影响因子：1.533； 五年影响因子：1.779 ）

ISSN： 1836-0939

年卷期： 2020 年 60 卷 8 期

页码：

收录情况： SCI

摘要： 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.

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