Genomic best linear unbiased prediction method including imprinting effects for genomic evaluation

• Published in: Genetics selection evolution (Paris) Vol. 47; no. 1; p. 32
• Main Authors: Nishio, Motohide, Satoh, Masahiro
• Format: Journal Article
• Language: English
• Published: France BioMed Central Ltd 19-04-2015; BioMed Central
• Subjects: Alleles; Analysis; Animal husbandry; Animals; Beef cattle; Behavior; Breeding - methods; Cattle; Chromosomes; Computer Simulation; DNA methylation; Estimates; Female; Genes; Genetic aspects; Genetic diversity; Genetic effects; Genetic research; Genetic variance; Genomic Imprinting; Genomics - methods; Heritability; Life Sciences; Livestock; Male; Mathematical models; Methods; Models, Genetic; Models, Statistical; Nucleotides; Plant breeding; Population; Predictions; Quantitative genetics; Quantitative Trait Loci; Regression coefficients; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Statistical analysis; Statistical models; Variance
• ISSN: 1297-9686; 0999-193X; 1297-9686
• DOI: 10.1186/s12711-015-0091-y

Genomic best linear unbiased prediction (GBLUP) is a statistical method used to predict breeding values using single nucleotide polymorphisms for selection in animal and plant breeding. Genetic effects are often modeled as additively acting marker allele effects. However, the actual mode of biological action can differ from this assumption. Many livestock traits exhibit genomic imprinting, which may substantially contribute to the total genetic variation of quantitative traits. Here, we present two statistical models of GBLUP including imprinting effects (GBLUP-I) on the basis of genotypic values (GBLUP-I1) and gametic values (GBLUP-I2). The performance of these models for the estimation of variance components and prediction of genetic values across a range of genetic variations was evaluated in simulations.
Estimates of total genetic variances and residual variances with GBLUP-I1 and GBLUP-I2 were close to the true values and the regression coefficients of total genetic values on their estimates were close to 1. Accuracies of estimated total genetic values in both GBLUP-I methods increased with increasing degree of imprinting and broad-sense heritability. When the imprinting variances were equal to 1.4% to 6.0% of the phenotypic variances, the accuracies of estimated total genetic values with GBLUP-I1 exceeded those with GBLUP by 1.4% to 7.8%. In comparison with GBLUP-I1, the superiority of GBLUP-I2 over GBLUP depended strongly on degree of imprinting and difference in genetic values between paternal and maternal alleles. When paternal and maternal alleles were predicted (phasing accuracy was equal to 0.979), accuracies of the estimated total genetic values in GBLUP-I1 and GBLUP-I2 were 1.7% and 1.2% lower than when paternal and maternal alleles were known.
This simulation study shows that GBLUP-I1 and GBLUP-I2 can accurately estimate total genetic variance and perform well for the prediction of total genetic values. GBLUP-I1 is preferred for genomic evaluation, while GBLUP-I2 is preferred when the imprinting effects are large, and the genetic effects differ substantially between sexes.