Journal Article

Fixed-length haplotypes can improve genomic prediction accuracy in an admixed dairy cattle population

Druet, Tom, Hess, Andrew, Hess, Melanie, Garrick, Dorian

2017

Overview

Background Fitting covariates representing the number of haplotype alleles rather than single nucleotide polymorphism (SNP) alleles may increase genomic prediction accuracy if linkage disequilibrium between quantitative trait loci and SNPs is inadequate. The objectives of this study were to evaluate the accuracy, bias and computation time of Bayesian genomic prediction methods that fit fixed-length haplotypes or SNPs. Genotypes at 37,740 SNPs that were common to Illumina BovineSNP50 and high-density panels were phased for ~58,000 New Zealand dairy cattle. Females born before 1 June 2008 were used for training, and genomic predictions for milk fat yield (n = 24,823), liveweight (n = 13,283) and somatic cell score (n = 24,864) were validated within breed (predominantly Holstein–Friesian, predominantly Jersey, or admixed KiwiCross) in later-born females. Covariates for haplotype alleles of five lengths (125, 250, 500 kb, 1 or 2 Mb) were generated and rare haplotypes were removed at four thresholds (1, 2, 5 or 10%), resulting in 20 scenarios tested. Genomic predictions fitting covariates for either SNPs or haplotypes were calculated by using BayesA, BayesB or BayesN. This is the first study to quantify the accuracy of genomic prediction using haplotypes across the whole genome in an admixed population. Results A correlation of 0.349 ± 0.016 between yield deviation and genomic breeding values was obtained for milk fat yield in Holstein–Friesians using BayesA fitting covariates. Genomic predictions were more accurate with short haplotypes than with SNPs but less accurate with longer haplotypes than with SNPs. Fitting only the most frequent haplotype alleles reduced computation time with little decrease in prediction accuracy for short haplotypes. Trends were similar for all traits and breeds and there was little difference between Bayesian methods. Conclusions Fitting covariates for haplotype alleles rather than SNPs can increase prediction accuracy, although it decreased drastically for long (>500 kb) haplotypes. In this population, fitting 250 kb haplotypes with a 1% frequency threshold resulted in the highest genomic prediction accuracy and fitting 125 kb haplotypes with a 10% frequency threshold improved genomic prediction accuracy with comparable computation time to fitting SNPs. This increased accuracy is likely to increase genetic gain by changing the ranking of selection candidates.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,EDP Sciences
• Subject: Accuracy / Agriculture / Alleles / Analysis / Animal fat / Animal Genetics and Genomics / Animal husbandry / Animal populations / Animal production & animal husbandry / Animals / Bayes Theorem / Bayesian analysis / Biomedical and Life Sciences / body weight / Breeding / breeding value / Cattle / Cattle - genetics / Computing time / Dairy cattle / Dairying / Datasets / Evolutionary Biology / Female / Females / Gene loci / Gene mapping / Gene polymorphism / Genetic aspects / Genetic improvement / Genetics & genetic processes / Genome / Genomes / Genomics / Genotype / Genotype & phenotype / Genotypes / Génétique & processus génétiques / Haplotypes / Holstein / Jersey / Lactation - genetics / Life Sciences / Linkage Disequilibrium / Livestock breeding / Markov analysis / Methods / Milk / Milk - chemistry / milk fat yield / New Zealand / Nucleotides / Oils & fats / Panels / Phenotype / Polymorphism / Polymorphism, Single Nucleotide / Population / Population genetics / prediction / Predictions / Productions animales & zootechnie / Quantitative genetics / Quantitative trait loci / Ranking / Research Article / Sciences du vivant / Single nucleotide polymorphisms / Single-nucleotide polymorphism / somatic cells / Thresholds