Short communication: Genomic prediction using different single-step methods in the Finnish red dairy cattle population

Abstract

Single-step genomic prediction models utilizing both genotyped and nongenotyped animals are likely to become the prevailing tool in genetic evaluations of livestock. Various single-step prediction models have been proposed, based either on estimation of individual marker effects or on direct prediction via a genomic relationship matrix. In this study, a classical pedigree-based animal model, a regular single-step genomic BLUP (ssGBLUP) model, algorithm for proven and young (APY) with 2 strategies for choosing core animals, and a single-step Bayesian regression (ssBR) model were compared for 305-d production traits (milk, fat, protein) in the Finnish red dairy cattle population. A residual polygenic effect with 10% of total genetic variance was included in the single-step models to reduce inflation of genomic predictions. Validation reliability was calculated as the squared Pearson correlation coefficient between genomically enhanced breeding value (GEBV) and yield deviation for masked records for 2,056 validation cows from the last year in the data set investigated. The results showed that gains of 0.02 to 0.04 on validation reliability were achieved by using single-step methods compared with the classical animal model. The regular ssGBLUP model and ssBR model with an extra polygenic effect yielded the same results. The APY methods yielded similar reliabilities as the regular ssGBLUP and ssBR. Exact prediction error variance of GEBV could be obtained by ssBR to avoid any approximation methods used for ssGBLUP when inversion left-hand side of mixed model equations is computationally infeasible for large data sets.

Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments

“…validation animals and definition of bias. In our study, when the model was changed from PBLUP to ssGBLUP, dispersion or the regression slope of on for genotyped birds was improved for traits measured in B, but not for traits measured in C, and bias or difference in means between and for genotyped birds was improved for traits measured in B and C. In the literature, an increase in accuracy of EBV for non-genotyped individuals has also been reported when changing from PBLUP to ssGBLUP [18, 20–22], but the bias of prediction of these birds increased [18, 20, 21]. The cross-validation in these studies was based on corrected phenotypes and EBV.…”

Multi-trait single-step genomic prediction accounting for heterogeneous (co)variances over the genome

“…Analysis using the model of Fernando et al (2014) starts with an explicit imputation of markers for non-genotyped individuals, using pedigree information and genotypes of genotyped relatives. Then, marker effects and imputation residuals (ϵ) accounting for the part of breeding values, which cannot be modeled by imputed markers, are estimated (Gao et al 2018). Imputation residual is added to marker-based breeding value (sum of individual SNP effects) of non-genotyped individuals to obtain their total breeding values (Fernando et al 2014).…”

Optimized grouping to increase accuracy of prediction of breeding values based on group records in genomic selection breeding programs

“…With a numerator genetic relationship matrix, full-sibs in the same group have equal EBV whereas with a genomic relationship matrix, full-sibs in the same group can have different EBV. The benefit of using genomic information over pedigree information, in terms of accuracy of prediction, has been well documented in simulation studies [15–17] and empirical studies of chicken [18–20], cattle [21, 22] and pig [17, 23, 24] breeding schemes for individual records. The increase in accuracy of GBLUP prediction obtained by using individual records is due to an improved measurement of the relationships between animals and a better prediction of the Mendelian sampling terms [16].…”