Data from: Genomic BLUP decoded: a look into the black box of genomic prediction

Genomic best linear unbiased prediction (BLUP) is a statistical method
that uses relationships between individuals calculated from
single-nucleotide polymorphisms (SNPs) to capture relationships at
quantitative trait loci (QTL). We show that genomic BLUP exploits not only
linkage disequilibrium (LD) and additive-genetic relationships, but also
cosegregation to capture relationships at QTL. Simulations were used to
study the contributions of those types of information to accuracy of
genomic estimated breeding values (GEBVs), their persistence over
generations without retraining, and their effect on the correlation of
GEBVs within families. We show that accuracy of GEBVs based on
additive-genetic relationships can decline with increasing training data
size and speculate that modeling polygenic effects via pedigree
relationships jointly with genomic breeding values using Bayesian methods
may prevent that decline. Cosegregation information from half sibs
contributes little to accuracy of GEBVs in current dairy cattle breeding
schemes but from full sibs it contributes considerably to accuracy within
family in corn breeding. Cosegregation information also declines with
increasing training data size, and its persistence over generations is
lower than that of LD, suggesting the need to model LD and cosegregation
explicitly. The correlation between GEBVs within families depends largely
on additive-genetic relationship information, which is determined by the
effective number of SNPs and training data size. As genomic BLUP cannot
capture short-range LD information well, we recommend Bayesian methods
with t-distributed priors.