Genomic BLUP Decoded: A Look into the Black Box of Genomic Prediction

Abstract

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. To understand better how genomic relationships capture relationships at QTL, we propose to apply concepts of pedigree analyses that define founders in a recent past generation. Based on these concepts, we show that coefficients of the genomic relationship matrix do not explain genetic covariances between individuals at QTL unless either there is linkage disequilibrium (LD) between QTL and SNPs measured in founders or selection candidates are related by pedigree to the training individuals. The latter results in cosegregation of alleles at QTL and SNPs that are

Accuracy of genomic selection models in a large population of open-pollinated families in white spruce

“…However, because the paternal contribution to within-provenance relatedness was not accounted for, markers may have been capturing this cryptic/unknown relatedness. Indeed, markers can capture additive genetic relationships between individuals (Fernando, 1998) that affect the estimates of accuracy of GEBVs even in the absence of LD between markers and QTLs (Habier et al, 2007(Habier et al, , 2013. This observation supports the need to carefully design CV schemes in order to better identify the origin of the accuracies obtained, and CV should account for family structure in the data to allow for long-lasting genomics-based breeding plans (Habier et al, 2010).…”

A comparison of genomic selection models across time in interior spruce (Picea engelmannii × glauca) using unordered SNP imputation methods

“…measurements (Figure 2, Table 4). This scenario is of interest because the PA is expected to decline after each breeding cycle owing to the decay of SNP-QTL LD as a result of recombination in the offspring (Habier et al, 2013). Thus, the TPA of GS methods is an important consideration for retraining said models as it offers potential to further accelerate the breeding cycle if target phenotypes can be selected earlier.…”

“…The theoretical increase in genetic gain produced by GS hinges on the capacity of the prediction models to remain relevant in the next generation. For this to occur, PA must ideally be based on SNP-QTL LD rather than kinship (Habier et al, 2013). Recently, the source of the relationship between marker and QTL described by GS models has been decomposed by Habier et al (2013) into factors involving both kinship and pure marker-QTL LD, generating questions about the validity of such models in subsequent generations.…”

“…Sub-optimally, PA was derived through kinship information between individuals in the training and VPs. GS PA can be composed of a combination of the two factors, leading to inflated estimates of PA to occur without proper validation because the kinship component is anticipated to decay more rapidly than marker-QTL LD in the progeny generations (Habier et al, 2007(Habier et al, , 2010(Habier et al, , 2013. In a study of a large population of white spruce open-pollinated families, Beaulieu et al (2014a) observed that PA decreased significantly when kinship between the training and VPs was restricted.…”

Review: How to improve genomic predictions in small dairy cattle populations

“…This may be a more efficient use of LD between QTL and markers and results in a more constant LD between QTL and prediction markers over generations. Accordingly the superiority of Bayesian models over GBLUP, is larger when the relationship between test and reference animals is weak (Gao et al, 2012;Habier et al, 2013). This indicates that Bayesian variable selection models have the potential to utilize information across distantly related breeds and improve multi-breed evaluations.…”