Modeling heterogeneous (co)variances from adjacent-SNP groups improves genomic prediction for milk protein composition traits

Abstract

Background: Accurate genomic prediction requires a large reference population, which is problematic for traits that are expensive to measure. Traits related to milk protein composition are not routinely recorded due to costly procedures and are considered to be controlled by a few quantitative trait loci of large effect. The amount of variation explained may vary between regions leading to heterogeneous (co)variance patterns across the genome. Genomic prediction models that can efficiently take such heterogeneity of (co)variances into account can result in improved prediction reliability. In this study, we developed and implemented novel univariate and bivariate Bayesian prediction models, based on estimates of heterogeneous (co)variances for genome segments (BayesAS). Available data consisted of milk protein composition traits measured on cows and de-regressed proofs of total protein yield derived for bulls. Single-nucleotide polymorphisms (SNPs), from 50K SNP arrays, were grouped into non-overlapping genome segments. A segment was defined as one SNP, or a group of 50, 100, or 200 adjacent SNPs, or one chromosome, or the whole genome. Traditional univariate and bivariate genomic best linear unbiased prediction (GBLUP) models were also run for comparison. Reliabilities were calculated through a resampling strategy and using deterministic formula.

Results: BayesAS models improved prediction reliability for most of the traits compared to GBLUP models and this gain depended on segment size and genetic architecture of the traits. The gain in prediction reliability was especially marked for the protein composition traits β-CN, κ-CN and β-LG, for which prediction reliabilities were improved by 49 percentage points on average using the MT-BayesAS model with a 100-SNP segment size compared to the bivariate GBLUP. Prediction reliabilities were highest with the BayesAS model that uses a 100-SNP segment size. The bivariate versions of our BayesAS models resulted in extra gains of up to 6% in prediction reliability compared to the univariate versions.

Conclusions: Substantial improvement in prediction reliability was possible for most of the traits related to milk protein composition using our novel BayesAS models. Grouping adjacent SNPs into segments provided enhanced information to estimate parameters and allowing the segments to have different (co)variances helped disentangle heterogeneous (co)variances across the genome.

Fig. 1

Proportion of genomic variance explained by each chromosome. Proportion of the genomic variance in the milk protein composition traits explained by each chromosome from the ST-BayesAS model taking chromosomes as segments

Fig. 2

Covariance between each protein composition trait with total protein yield explained by 100-SNP genomic segments

Fig. 3

Prediction reliability across MT-BayesAS models. Reliability of models according to segment sizes of 1, 50, 100, and 200 SNPs, chromosome, and whole genome. G-$\mathrm{\kappa }$-CN = glycosylated-$\mathrm{\kappa }$-CN; $\mathrm{\alpha }$ _S1-CN-8P = $\mathrm{\alpha }$ _S1-CN with eight phosphorylated serine groups

Fig. 4

Reliability of prediction using various proportions of genomic segments. Predictions were based on post-Gibbs analyses of samples from the MT-100-BayesA model. Segments were ranked based on explained covariance separately for each training set