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

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dc.contributor.authorHay, El Hamidi A.
dc.contributor.authorUtsunomiya, Yuri T.
dc.contributor.authorXu, Lingyang
dc.contributor.authorZhou, Yang
dc.contributor.authorNeves, Haroldo H. R.
dc.contributor.authorCarvalheiro, Roberto
dc.contributor.authorBickhart, Derek M.
dc.contributor.authorMa, Li
dc.contributor.authorGarcia, Jose Fernando
dc.contributor.authorLiu, George E.
dc.date.accessioned2021-07-08T15:23:00Z
dc.date.available2021-07-08T15:23:00Z
dc.date.issued2018-06-05
dc.description.abstractDue 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. In 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). This 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.en_US
dc.description.urihttps://doi.org/10.1186/s12864-018-4787-6
dc.identifierhttps://doi.org/10.13016/oxrx-3u8e
dc.identifier.citationHay, E.H.A., Utsunomiya, Y.T., Xu, L. et al. Genomic predictions combining SNP markers and copy number variations in Nellore cattle. BMC Genomics 19, 441 (2018).en_US
dc.identifier.urihttp://hdl.handle.net/1903/27322
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.relation.isAvailableAtCollege of Agriculture & Natural Resourcesen_us
dc.relation.isAvailableAtAnimal & Avian Sciencesen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectGenomic selectionen_US
dc.subjectComplex traiten_US
dc.subjectCNVen_US
dc.subjectSNPen_US
dc.subjectNellore cattleen_US
dc.titleGenomic predictions combining SNP markers and copy number variations in Nellore cattleen_US
dc.typeArticleen_US

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