Genomic prediction of carcass traits using different haplotype block partitioning methods in beef cattle. (14th November 2022)

• Record Type: Journal Article
• Authors: Li, Hongwei; Wang, Zezhao; Xu, Lei; Li, Qian; Gao, Han; Ma, Haoran; Cai, Wentao; Chen, Yan; Gao, Xue; Zhang, Lupei; Gao, Huijiang; Zhu, Bo; Xu, Lingyang; Li, Junya
• Is Part Of: Evolutionary applications. Volume 15:Number 12(2022)
• Page Start: 2028
• Page End: 2042
• Publication Date: 2022-11-14
• Subjects: Bayesian models -- beef cattle -- GBLUP -- haplotype -- linkage disequilibrium; Evolution (Biology) -- Periodicals; Genetics -- Periodicals; Natural selection -- Periodicals; Ecology -- Periodicals; 576.8
• DOI: 10.1111/eva.13491
• Languages: English
• ISSNs: 1752-4571

Abstract

Abstract: Genomic prediction (GP) based on haplotype alleles can capture quantitative trait loci (QTL) effects and increase predictive ability because the haplotypes are expected to be in linkage disequilibrium (LD) with QTL. In this study, we constructed haploblocks using LD‐based and the fixed number of single nucleotide polymorphisms (fixed‐SNP) methods with Illumina BovineHD chip in beef cattle. To evaluate the performance of different haplotype block partitioning methods, we constructed haploblocks based on LD thresholds (from r 2 > 0.2 to r 2 > 0.8) and the number of fixed‐SNPs (5, 10, 20). The performance of predictive methods for three carcass traits including liveweight (LW), dressing percentage (DP), and longissimus dorsi muscle weight (LDMW) was evaluated using three approaches (GBLUP and BayesB model based on the SNP, GH BLUP, and BayesBH models based on the haploblock, and GH BLUP+GBLUP and BayesBH+BayesB models based on the combined haploblock and the nonblocked SNPs, which were located between blocks). In this study, we found the accuracies of LD‐based and fixed‐SNP haplotype Bayesian methods outperformed the Bayesian models (up to 8.54 ± 7.44% and 5.74 ± 2.95%, respectively). GH BLUP showed a high improvement (up to 11.29 ± 9.87%) compared with GBLUP. The Bayesian models have higher accuracies than BLUP models in most scenarios. The average computing time of the BayesBH+BayesB model can reduce by 29.3% compared with the BayesB model. The prediction accuracies using the LD‐based haplotype method showed higher improvements than the fixed‐SNP haplotype method. In addition, to avoid the influence of rare haplotypes generated from haplotype construction, we compared the performance of GP by filtering four types of minor haplotype allele frequency (MHAF) (0.01, 0.025, 0.05, and 0.1) under different conditions (LD levels were set at r 2 > 0.3, and the fixed number of SNPs was 5). We found the optimal MHAF threshold for LW was 0.01, and the optimal MHAF threshold for DP and LDMW was 0.025.