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

Widely used genomic prediction models may not properly account for
heterogeneous (co)variance structure across the genome. Models such
as BayesA and BayesB assume locus-specific variance, which are highly influenced by the prior
for (co)variance of single nucleotide polymorphism (SNP) effect, regardless
of the size of data. Models such as BayesC or GBLUP assume a common (co)variance for a proportion (BayesC) or all (GBLUP) of the SNP effects. In this study, we propose a multi-trait Bayesian whole genome regression method
(BayesN0), which is based on grouping a number of predefined SNPs to account
for heterogeneous (co)variance structure across the genome.
This model was also implemented in single-step Bayesian regression
(ssBayesN0). For practical implementation, we considered multi-trait
single-step SNPBLUP models, using (co)variance estimates from
BayesN0 or ssBayesN0. Genotype data were simulated using haplotypes
on first five chromosomes of 2,200 Danish Holstein cattle,
and phenotypes were simulated for two traits with heritabilities 0.1
or 0.4, assuming 200 QTL. We compared prediction accuracy from different
prediction models and different region sizes (one SNP, 100 SNPs,
one chromosome or whole genome). In general, highest accuracies were
obtained when 100 adjacent SNPs were grouped together. The ssBayesN0 improved accuracies over BayesN0, and using
(co)variance estimates from ssBayesN0 generally yielded higher accuracies than
using (co)variance estimates from BayesN0, for
the 100 SNPs region size. Our results suggest that it could be a good strategy
to estimate (co)variance components from ssBayesN0, and then to use
those estimates in genomic prediction using multi-trait single-step
SNPBLUP, in routine genomic evaluations.