Abstract
BackgroundCurrently, genomic prediction in cattle is largely based on panels of about 54k single nucleotide polymorphisms (SNPs). However with the decreasing costs of and current advances in next-generation sequencing technologies, whole-genome sequence (WGS) data on large numbers of individuals is within reach. Availability of such data provides new opportunities for genomic selection, which need to be explored.MethodsThis simulation study investigated how much predictive ability is gained by using WGS data under scenarios with QTL (quantitative trait loci) densities ranging from 45 to 132 QTL/Morgan and heritabilities ranging from 0.07 to 0.30, compared to different SNP densities, with emphasis on divergent dairy cattle breeds with small populations. The relative performances of best linear unbiased prediction (SNP-BLUP) and of a variable selection method with a mixture of two normal distributions (MixP) were also evaluated. Genomic predictions were based on within-population, across-population, and multi-breed reference populations.ResultsThe use of WGS data for within-population predictions resulted in small to large increases in accuracy for low to moderately heritable traits. Depending on heritability of the trait, and on SNP and QTL densities, accuracy increased by up to 31 %. The advantage of WGS data was more pronounced (7 to 92 % increase in accuracy depending on trait heritability, SNP and QTL densities, and time of divergence between populations) with a combined reference population and when using MixP. While MixP outperformed SNP-BLUP at 45 QTL/Morgan, SNP-BLUP was as good as MixP when QTL density increased to 132 QTL/Morgan.ConclusionsOur results show that, genomic predictions in numerically small cattle populations would benefit from a combination of WGS data, a multi-breed reference population, and a variable selection method.
Highlights
Genomic prediction in cattle is largely based on panels of about 54k single nucleotide polymorphisms (SNPs)
According to [12, 13], the accuracy of genomic prediction depends on the parameter λ = Th2/ML, where T is the number of individuals with genotypes and phenotype in the training data, h2 is the heritability of the trait, M is the effective number of loci per Morgan (~2Ne), and L is the genome size in Morgan
linkage disequilibrium (LD) ranged from ~0.36 to 0.40 at genomic distances of 0 to 50 kb, respectively, and this trend was similar in both populations
Summary
Genomic prediction in cattle is largely based on panels of about 54k single nucleotide polymorphisms (SNPs). Genomic selection (GS) is becoming the standard approach to generate genetic progress in livestock It was pioneered in the dairy cattle sector because of its potential to achieve high accuracy for non-phenotyped animals, thereby reducing generation intervals by reducing the need for progeny-testing. It has been implemented through the use of panels of SNPs (single-nucleotide polymorphisms) that are distributed over the whole genome, and various commercial bovine SNP chips are available, Iheshiulor et al Genet Sel Evol (2016) 48:15 compared the use of HD to 54k SNP chips and reported small or no increases in accuracy of GEBV or increases for some traits only. In the case of across-breed predictions, the use of WGS data could reduce or remove the need to rely on associations between SNPs and QTL which may not persist across the breeds being evaluated [8]
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