Abstract
Various methods have been proposed for genomic prediction (GP) in livestock. These methods have mainly focused on statistical considerations and did not include genome annotation information. In this study, to improve the predictive performance of carcass traits in Chinese Simmental beef cattle, we incorporated the genome annotation information into GP. Single nucleotide polymorphisms (SNPs) were annotated to five genomic classes: intergenic, gene, exon, protein coding sequences, and 3′/5′ untranslated region. Haploblocks were constructed for all markers and these five genomic classes by defining a biologically functional unit, and haplotype effects were modeled in both numerical dosage and categorical coding strategies. The first-order epistatic effects among SNPs and haplotypes were modeled using a categorical epistasis model. For all makers, the extension from the SNP-based model to a haplotype-based model improved the accuracy by 5.4–9.8% for carcass weight (CW), live weight (LW), and striploin (SI). For the five genomic classes using the haplotype-based prediction model, the incorporation of gene class information into the model improved the accuracies by an average of 1.4, 2.1, and 1.3% for CW, LW, and SI, respectively, compared with their corresponding results for all markers. Including the first-order epistatic effects into the prediction models improved the accuracies in some traits and genomic classes. Therefore, for traits with moderate-to-high heritability, incorporating genome annotation information of gene class into haplotype-based prediction models could be considered as a promising tool for GP in Chinese Simmental beef cattle, and modeling epistasis in prediction can further increase the accuracy to some degree.
Highlights
IntroductionGenomic prediction (GP), which uses whole-genome markers to predict genomic breeding value, has been widely used in breeding programs of plants (Heffner et al, 2009; Riedelsheimer et al, 2012; de los Campos et al, 2013; Hayes et al, 2013) and domestic animals (Sonesson and Meuwissen, 2009; Hayes et al, 2010; Erbe et al, 2012; de los Campos et al, 2013), disease risk prediction for Incorporating Genome Annotation Into genomic prediction (GP) humans (Vazquez et al, 2012; Akey et al, 2014; Abraham et al, 2016), and phenotype prediction of model organisms (Ober et al, 2012; Kooke et al, 2016)
Single nucleotide polymorphisms (SNPs) were divided into different genomic classes based on the genome annotation information, and genomic prediction (GP) was conducted for genomic classes using two strategies
Genome annotation information was incorporated into the haplotype-based prediction model for GP of three carcass traits in Chinese Simmental beef cattle
Summary
Genomic prediction (GP), which uses whole-genome markers to predict genomic breeding value, has been widely used in breeding programs of plants (Heffner et al, 2009; Riedelsheimer et al, 2012; de los Campos et al, 2013; Hayes et al, 2013) and domestic animals (Sonesson and Meuwissen, 2009; Hayes et al, 2010; Erbe et al, 2012; de los Campos et al, 2013), disease risk prediction for Incorporating Genome Annotation Into GP humans (Vazquez et al, 2012; Akey et al, 2014; Abraham et al, 2016), and phenotype prediction of model organisms (Ober et al, 2012; Kooke et al, 2016). Accompanied by the fast development of genotyping and sequencing technologies, various methods with different underlying statistical assumptions have been proposed for GP, including penalized and Bayesian regression methods (Whittaker et al, 2000; Meuwissen et al, 2001; Gianola et al, 2006; VanRaden, 2008; Bennewitz et al, 2009; Habier et al, 2011; Gianola, 2013; Morota and Gianola, 2014) These methods have been applied in cattle populations to improve the prediction accuracy of direct genomic estimated breeding values (DGVs) to some degree (Luan et al, 2009; Hayes et al, 2010; Bolormaa et al, 2013; Fernandes Júnior et al, 2016; Mehrban et al, 2017; Toghiani et al, 2017). The marker density of genomic classes declined after the partitioning, which caused fewer biallelic SNPs in LD with a QTL
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
