首页|The impact of rare Single Nucleotide Polymorphism variants on the genomic evaluation of dairy cattle
The impact of rare Single Nucleotide Polymorphism variants on the genomic evaluation of dairy cattle
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NSTL
The experiment described in this study was designed to test the effect of rare variants on the genomic prediction in dairy cattle. Common polymorphisms are capable of explaining only a small proportion of the underlying genetic variation of complex phenotypes. Variants representing functional mutations with large effects on complex phenotypes are expected to be rare due to natural or artificial selection pressure. Therefore, it is important to check whether the use of rare variants could increase the accuracy of ranking of animals by providing a tool for more precise differentiation between bulls with a high additive genetic merit. The goal of our study was to vetify whether including rare variants in a genomic selection model provides a more accurate description of the additive genetic background of traits under selection in dairy cattle. The number of animals used in the analyses varies and depending on the trait it ranged from 77,578 individuals for type trait to 100,650 individuals for somatic cell score. We used the linear mixed model to cum pare estimates of SNP effects for Holstein-Friesian cattle of the two data sets - a set containing only single nucleotide polymorphisms defined by minor allele frequency greater than 1%, which is routinely used in the Polish genomic evaluation system (16,216 SNPs), and a set containing SNPs selected based only on the call rate (54,378 SNPs). Based on the SNP estimates we also calculated Direct Genomic Values (DGV) and Genomically Enhanced Breeding Values (GEBV) and compared them between both data sets. IAII the analyses were conducted for production, fertility, conformation and udder health traits. We also assessed the time required for the two most computationally demanding components of genomic selection, i.e. preparation of genotype data and estimation of SNP effects between those two data sets. The results of our study indicated that the analysis including rare variants resulted in changes in the individual ranking of the top 100 male and female candidates, whereas it had no effect on the outcome of the quality of EBV prediction as expressed by the Interbull validation test.
rare variantsgenome-wide association studyvalidation testSNP chipgenomic selectionGENOTYPE IMPUTATIONIDENTIFICATION
Suchocki, Tomasz、Jakimowicz, Michalina、Dziech, Arkadiusz、Szyda, Joanna、Zarnecki, Andrzej
NSTL
