Abstract
A haplotype is defined as a combination of alleles at adjacent loci belonging to the same chromosome that can be transmitted as a unit. In this study, we used both the Illumina BovineHD chip (HD chip) and imputed whole-genome sequence (WGS) data to explore haploblocks and assess haplotype effects, and the haploblocks were defined based on the different LD thresholds. The accuracies of genomic prediction (GP) for dressing percentage (DP), meat percentage (MP), and rib eye roll weight (RERW) based on haplotype were investigated and compared for both data sets in Chinese Simmental beef cattle. The accuracies of GP using the entire imputed WGS data were lower than those using the HD chip data in all cases. For DP and MP, the accuracy of GP using haploblock approaches outperformed the individual single nucleotide polymorphism (SNP) approach (GBLUP_In_Block) at specific LD levels. Hotelling’s test confirmed that GP using LD-based haplotypes from WGS data can significantly increase the accuracies of GP for RERW, compared with the individual SNP approach (∼1.4 and 1.9% for GHBLUP and GHBLUP+GBLUP, respectively). We found that the accuracies using haploblock approach varied with different LD thresholds. The LD thresholds (r2 ≥ 0.5) were optimal for most scenarios. Our results suggested that LD-based haploblock approach can improve accuracy of genomic prediction for carcass traits using both HD chip and imputed WGS data under the optimal LD thresholds in Chinese Simmental beef cattle.
Highlights
Genomic prediction (GP) has been widely used in the past decades (Meuwissen et al, 2001)
We found that the method based on haploblock reduced the number of variables for the whole-genome sequence (WGS) data
We found that genomic predictions using the HD chip were superior to the WGS data for all three traits (Figure 1); the accuracy of GBLUP_770K was 0.011, 0.01, and 0.015 higher than that of GBLUP_WGS for dressing percentage (DP), meat percentage (MP), and rib eye roll weight (RERW), respectively
Summary
Genomic prediction (GP) has been widely used in the past decades (Meuwissen et al, 2001). Many approaches, including GBLUP (VanRaden, 2008), Bayes alphabet (Habier et al, 2011; Gianola, 2013), and machine learning (Li et al, 2018; Yin et al, 2020), have been proposed to improve prediction accuracy. Most of these approaches were developed based on single nucleotide. Xu et al (2020) reported that the haplotypebased model using HD chip data can improve the accuracy by 5.4–9.8%, compared with the SNP-based approach for carcass and live weight traits
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