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

Nishio M, Satoh M - Genet. Sel. Evol. (2015)

Bottom Line: Genetic effects are often modeled as additively acting marker allele effects.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.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.

View Article: PubMed Central - PubMed

Affiliation: NARO Institute of Livestock and Grassland Science, 305-0901, Ikenodai 2, Tsukuba, Japan. mtnishio@affrc.go.jp.

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus

Genotypic values for the four genotypes (A1A1,A1A2,A2A1, andA2A2).
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Fig1: Genotypic values for the four genotypes (A1A1,A1A2,A2A1, andA2A2).

Mentions: In this model, the heterozygous genotypic values were + ε and −ε deviations from δ, i.e., d1 and d2 can be rewritten as δ + ε and δ − ε, respectively (Figure 1). With imprinting, reciprocal heterozygotes differ in their genotypes. For example, in the case of complete inactivation of the maternal allele (i.e., ε = a and δ = 0), the genotypic value of A2A1 is the same as that of A2A2, whereas the genotypic value of A1A2 is the same as that of A1A1. If the paternally-derived A1 allele randomly combines with maternally-derived alleles from a population, the frequencies of the genotypes produced will be 1−q for A1A1 and q for A1A2. The genotypic values of A1A1 and A1A2 are a and d1, respectively. Taking in account the proportions at which they occur, the mean value of genotypes produced from the paternally-derived A1 allele is (1−q)a + qd1. The mean genotypic value in the entire population (μ) is as follows:Figure 1


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

Nishio M, Satoh M - Genet. Sel. Evol. (2015)

Genotypic values for the four genotypes (A1A1,A1A2,A2A1, andA2A2).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4404063&req=5

Fig1: Genotypic values for the four genotypes (A1A1,A1A2,A2A1, andA2A2).
Mentions: In this model, the heterozygous genotypic values were + ε and −ε deviations from δ, i.e., d1 and d2 can be rewritten as δ + ε and δ − ε, respectively (Figure 1). With imprinting, reciprocal heterozygotes differ in their genotypes. For example, in the case of complete inactivation of the maternal allele (i.e., ε = a and δ = 0), the genotypic value of A2A1 is the same as that of A2A2, whereas the genotypic value of A1A2 is the same as that of A1A1. If the paternally-derived A1 allele randomly combines with maternally-derived alleles from a population, the frequencies of the genotypes produced will be 1−q for A1A1 and q for A1A2. The genotypic values of A1A1 and A1A2 are a and d1, respectively. Taking in account the proportions at which they occur, the mean value of genotypes produced from the paternally-derived A1 allele is (1−q)a + qd1. The mean genotypic value in the entire population (μ) is as follows:Figure 1

Bottom Line: Genetic effects are often modeled as additively acting marker allele effects.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.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.

View Article: PubMed Central - PubMed

Affiliation: NARO Institute of Livestock and Grassland Science, 305-0901, Ikenodai 2, Tsukuba, Japan. mtnishio@affrc.go.jp.

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus