Genomic predictions combining SNP markers and copy number variations in Nellore cattle

El Hamidi A Hay,Yuri T Utsunomiya,George E Liu,Li Ma,Derek M Bickhart,Yang Zhou,Haroldo H R Neves,Jose Fernando Garcia,Lingyang Xu,Roberto Carvalheiro

doi:10.1186/s12864-018-4787-6

Abstract

BackgroundDue to the advancement in high throughput technology, single nucleotide polymorphism (SNP) is routinely being incorporated along with phenotypic information into genetic evaluation. However, this approach often cannot achieve high accuracy for some complex traits. It is possible that SNP markers are not sufficient to predict these traits due to the missing heritability caused by other genetic variations such as microsatellite and copy number variation (CNV), which have been shown to affect disease and complex traits in humans and other species.ResultsIn this study, CNVs were included in a SNP based genomic selection framework. A Nellore cattle dataset consisting of 2230 animals genotyped on BovineHD SNP array was used, and 9 weight and carcass traits were analyzed. A total of six models were implemented and compared based on their prediction accuracy. For comparison, three models including only SNPs were implemented: 1) BayesA model, 2) Bayesian mixture model (BayesB), and 3) a GBLUP model without polygenic effects. The other three models incorporating both SNP and CNV included 4) a Bayesian model similar to BayesA (BayesA+CNV), 5) a Bayesian mixture model (BayesB+CNV), and 6) GBLUP with CNVs modeled as a covariable (GBLUP+CNV). Prediction accuracies were assessed based on Pearson’s correlation between de-regressed EBVs (dEBVs) and direct genomic values (DGVs) in the validation dataset. For BayesA, BayesB and GBLUP, accuracy ranged from 0.12 to 0.62 across the nine traits. A minimal increase in prediction accuracy for some traits was noticed when including CNVs in the model (BayesA+CNV, BayesB+CNV, GBLUP+CNV).ConclusionsThis study presents the first genomic prediction study integrating CNVs and SNPs in livestock. Combining CNV and SNP marker information proved to be beneficial for genomic prediction of some traits in Nellore cattle.

Highlights

Due to the advancement in high throughput technology, single nucleotide polymorphism (SNP) is routinely being incorporated along with phenotypic information into genetic evaluation
Combining copy number variation (CNV) and SNP marker information proved to be beneficial for genomic prediction of some traits in Nellore cattle
A similar observation of two alleles was found for biallelic duplications, the event was either with no duplication or with duplication. These results indicated that deletions and biallelic duplications could be accurately genotyped with defined genomic coordinates and mainly 2 states, which were similar to the behaviors of common SNPs

Summary

Introduction

Due to the advancement in high throughput technology, single nucleotide polymorphism (SNP) is routinely being incorporated along with phenotypic information into genetic evaluation. This approach often cannot achieve high accuracy for some complex traits. Studies on the loss of copy number of MHC class II genes within other species have revealed increased susceptibility of that species to pathogens and cancers, such as the Tasmanian devil facial tumor epidemic [20] This serves as a warning to all animal breeders, as a loss of diversity at this locus due to improperly managed selective breeding or imposed population bottlenecks could increase the susceptibility of their herds to epidemics [21]. WC1 genes are unique to the cattle, sheep, and pig genomes, and encode pattern recognition receptors expressed on γδ-T cells [22]

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