High-frequency marker haplotypes in the genomic selection of dairy cattle

Abstract

The aim of this study was to predict the genomic breeding value (DGV) of production, selected conformation and reproductive traits, and somatic cell score of dairy cattle in Poland using high-frequency marker haplotypes. The dataset consisted of phenotypic, genotypic, and pedigree data of 1216 Polish Holstein-Friesian bulls. The genotypic data consisted of 54,000 single-nucleotide polymorphisms (SNPs). The data were divided into two subsets: a test dataset ( n = 1064) and a validation dataset ( n = 152). Genotypic data were selected using three criteria: the percentage of missing genotypes, minor allele frequency, and linkage disequilibrium. The purpose of the data selection was to identify blocks of SNPs that were then used for the construction of haplotypes. Only haplotypes with a frequency higher than 25% were selected. DGV was predicted using four variants of a linear model with random haplotype effects and deregressed breeding values as the response variables. The accuracy of genomic prediction was checked by comparing DGVs with estimated breeding values (EBVs) using two methods: Pearson’s correlations and the regression of EBV on DGV. The use of high-frequency haplotypes showed a tendency to underestimate DGVs. None of the models tested was clearly superior with regard to the traits studied. DGVs of production and conformation traits as well as somatic cell score (medium or high heritability traits) were more accurate than those estimated for fertility traits (low heritability traits).

A Comprehensive Comparison of Haplotype-Based Single-Step Genomic Predictions in Livestock Populations With Different Genetic Diversity Levels: A Simulation Study

“… Cuyabano et al (2015) also obtained gains in accuracy of up to 1.3% using pre-selected SNPs associated with the trait combined with the haplotypes as covariates in the models for production, fertility, and health traits. Mucha et al (2019) showed no differences in predictions with high-frequency haplotypes compared to SNPs when evaluating reproductive performance traits and somatic cell score in Polish dairy cattle. Additionally, Feitosa et al (2019) obtained nearly the same accuracy and bias for meat fatty acid (MFA) traits in Nellore cattle when fitting individual SNPs or haplotypes.…”

“…Previous studies based on simulated data have shown that fitting haplotypes can substantially improve the performance of genomic predictions compared to individual SNP-based methods ( Calus et al, 2008 ; Villumsen et al, 2009 ). However, none or only small increases in the predictive ability of GEBVs have been observed in practice (e.g., Cuyabano et al, 2014 , 2015 ; Hess et al, 2017 ; Karimi et al, 2018 ; Mucha et al, 2019 ; Won et al, 2020 ). The large majority of the studies evaluating haplotype-based models were done in dairy cattle populations (real or simulated datasets), which usually have high LD levels between SNP markers and lower genetic diversity (Ne lower than 100; Makanjuola et al, 2020 ).…”

Incorporating Genome Annotation Into Genomic Prediction for Carcass Traits in Chinese Simmental Beef Cattle

“…Haplotypes have been used widely in human genetics research (Curtis et al, 2001;Chapman et al, 2003;Curtis, 2007); in animal breeding studies, haplotypes have been used for the GP of breeding values with the use of high density SNP chips (Calus et al, 2008;Boichard et al, 2012;Cuyabano et al, 2014;Mucha et al, 2019). In this study, haplotype-based prediction models (G H BLUP and C H M) were applied to the whole genome-wide markers, and the result of this scenario was treated as a benchmark.…”

“…To efficiently use the genome properties to define haploblocks and reduce the number of variables for the GP models, several researchers used only haplotypes with a high frequency in the population (Mucha et al, 2019) or based on LD threshold to define haploblocks (Cuyabano et al, 2015). For instance, Cuyabano et al (2014) used an average LD threshold (≥0.45) to construct haploblocks and found that prediction accuracies increased for the three traits compared with the commonly-used individual SNP.…”

Genomic Evaluation of Primiparous High-Producing Dairy Cows: Inbreeding Effects on Genotypic and Phenotypic Production–Reproductive Traits

“…Health parameters, selection for organic production, reduced waste, and gas emission are also currently considered traits of interest [ 13 , 14 ]. Therefore, at present, it becomes necessary to add new parameters to genotype analyses to select animals with more accurate estimates of SNPs’ effects that directly or indirectly affect profitability [ 15 ]. Consequently, routine genotyping of primiparous cows and the incorporation of new indices such as inbreeding can significantly aid in making better management decisions in herds [ 4 , 16 ].…”