Maximizing the Reliability of Genomic Selection by Optimizing the Calibration Set of Reference Individuals: Comparison of Methods in Two Diverse Groups of Maize Inbreds (Zea maysL.)

Abstract

Genomic selection refers to the use of genotypic information for predicting breeding values of selection candidates. A prediction formula is calibrated with the genotypes and phenotypes of reference individuals constituting the calibration set. The size and the composition of this set are essential parameters affecting the prediction reliabilities. The objective of this study was to maximize reliabilities by optimizing the calibration set. Different criteria based on the diversity or on the prediction error variance (PEV) derived from the realized additive relationship matrix-best linear unbiased predictions model (RA-BLUP) were used to select the reference individuals. For the latter, we considered the mean of the PEV of the contrasts between each selection candidate and the mean of the population (PEVmean) and the mean of the expected reliabilities of the same contrasts (CDmean). These criteria were tested with phenotypic data collected on two diversity panels of maize (Zea mays L.) genotyped with a 50k SNPs array. In the two panels, samples chosen based on CDmean gave higher reliabilities than random samples for various calibration set sizes. CDmean also appeared superior to PEVmean, which can be explained by the fact that it takes into account the reduction of variance due to the relatedness between individuals. Selected samples were close to optimality for a wide range of trait heritabilities, which suggests that the strategy presented here can efficiently sample subsets in panels of inbred lines. A script to optimize reference samples based on CDmean is available on request.A MONG the different methods that use molecular markers for selection, genomic selection (GS) has received considerable attention in the last decade. The objective of this approach is to predict the breeding values of candidates based on their molecular marker genotypes. A prediction formula is developed using the genotypes and phenotypes of reference individuals forming a calibration set (Meuwissen

Updating the reference population to achieve constant genomic prediction reliability across generations

“…The slopes in Reg1 were expected to be positive, as higher relationships between RP and EVA lead a higher reliability (Habier et al, 2010;Clark et al, 2012;Pszczola et al, 2012a;Rincent et al, 2012;Isidro et al, 2015;Wu et al, 2015). This was confirmed by the results of the current study as shown in Table 1.…”

“…This was confirmed by the results of the current study as shown in Table 1. In addition, low relationships in RP are desired (Pszczola et al, 2012a;Rincent et al, 2012;Isidro et al, 2015;Wu et al, 2015). Therefore, the slope in Reg2, which uses the off-diagonal elements of the inverted G, is expected to be positive (as confirmed by the results in Table 2).…”

“…Such procedure would, however, require a complex optimization algorithm which on one hand would maximize the relationships between RP and EVA but on the other hand would minimize the relationship of the update with already existing RP. Other studies showed that random sampling of the RP may be suboptimal and attempted to derive algorithms based on statistical properties of the genomic prediction model (Rincent et al, 2012;Isidro et al, 2015). Although these approaches may be theoretically more optimal, the advantage of our approach based on relationships is its' intuitiveness leading to easier application in practice.…”

“…Another important aspect is the RP design. An optimally designed RP is expected to maximize the reliability for a given population (Pszczola et al, 2012a;Rincent et al, 2012;Isidro et al, 2015). Several studies have shown that across scenarios the highest accuracies were obtained when the RP consisted of animals that are distantly related to each other (Clark et al, 2012;Pszczola et al, 2012a;Rincent et al, 2012;Isidro et al, 2015;Wu et al, 2015) and closely related to the evaluated animals (EVA) (Habier et al, 2010;Pszczola et al, 2012a;Wu et al, 2015).…”

Does encoding matter? A novel view on the quantitative genetic trait prediction problem

“…Whole genome prediction of complex phenotypic traits using high-density genotyping arrays has attracted a lot of attention, as it is relevant to the fields of plant and animal breeding and genetic epidemiology [1][2][3][4][5][6][7][8]. Given a set of biallelic molecular markers, such as SNPs, with genotype values typically encoded as {0, 1, 2} on a collection of plant, animal or human samples, the goal is to predict the quantitative trait values by simultaneously modeling all marker effects.…”