Genome-wide regression models considering general and specific combining ability predict hybrid performance in oilseed rape with similar accuracy regardless of trait architecture

Genomic Prediction of Pumpkin Hybrid Performance

Genomic prediction has become an increasingly popular tool for hybrid performance evaluation in plant breeding mainly because that it can reduce cost and accelerate a breeding program. In this study, we propose a systematic procedure to predict hybrid performance using a genomic selection (GS) model that takes both additive and dominance marker effects into account. We first demonstrate the advantage of the additive-dominance effects model over the only additive effects model through a simulation study. Based on the additive-dominance model, we predict genomic estimated breeding values (GEBVs) for individual hybrid combinations and their parental lines. The GEBV-based specific combining ability (SCA) for each hybrid and general combining ability (GCA) for its parental lines are then derived to quantify the degree of midparent heterosis (MPH) or better-parent heterosis (BPH) of the hybrid. Finally, we estimate the variance components resulting from additive and dominance gene action effects and heritability using a genomic best linear unbiased predictor (g-BLUP) model. These estimates are used to justify the results of the genomic prediction study. A pumpkin ( spp.) data set is given to illustrate the provided procedure. The data set consists of 320 parental lines with 61,179 collected single nucleotide polymorphism (SNP) markers; 119, 120, and 120 phenotypic values of hybrids on three quantitative traits within maxima Duchesne; and 89, 111, and 90 phenotypic values of hybrids on the same three quantitative traits within Dechesne.

Effective Genomic Selection in a Narrow-Genepool Crop with Low-Density Markers: Asian Rapeseed as an Example

Genomic selection (GS) has revolutionized breeding for quantitative traits in plants, offering potential to optimize resource allocation in breeding programs and increase genetic gain per unit of time. Modern high-density single nucleotide polymorphism (SNP) arrays comprising up to several hundred thousand markers provide a user-friendly technology to characterize the genetic constitution of whole populations and for implementing GS in breeding programs. However, GS does not build upon detailed genotype profiling facilitated by maximum marker density. With extensive genome-wide linkage disequilibrium (LD) being a common characteristic of breeding pools, fewer representative markers from available high-density genotyping platforms could be sufficient to capture the association between a genomic region and a phenotypic trait. To examine the effects of reduced marker density on genomic prediction accuracy, we collected data on three traits across 2 yr in a panel of 203 homozygous Chinese semiwinter rapeseed ( L.) inbred lines, broadly encompassing allelic variability in the Asian genepool. We investigated two approaches to selecting subsets of markers: a trait-dependent strategy based on genome-wide association study (GWAS) significance thresholds and a trait-independent method to detect representative tag SNPs. Prediction accuracies were evaluated using cross-validation with ridge-regression best linear unbiased predictions (rrBLUP). With semiwinter rapeseed as a model species, we demonstrate that low-density marker sets comprising a few hundred to a few thousand markers enable high prediction accuracies in breeding populations with strong LD comparable to those achieved with high-density arrays. Our results are valuable for facilitating routine application of cost-efficient GS in breeding programs.