A novel procedure for identifying a hybrid QTL-allele system for hybrid-vigor improvement, with a case study in soybean (Glycine max) yield

"Breeding by design" for pure lines may be achieved by construction of an additive QTL-allele matrix in a germplasm panel or breeding population, but this option is not available for hybrids, where both additive and dominance QTL-allele matrices must be constructed. In this study, a hybrid-QTL identification approach, designated PLSRGA, using partial least squares regression (PLSR) for model fitting integrated with a genetic algorithm (GA) for variable selection based on a multi-locus, multi-allele model is described for additive and dominance QTL-allele detection in a diallel hybrid population (DHP). The PLSRGA was shown by simulation experiments to be superior to single-marker analysis and was then used for QTL-allele identification in a soybean DPH yield experiment with eight parents. Twenty-eight main-effect QTL with 138 alleles and nine QTL x environment QTL with 46 alleles were identified, with respective contributions of 61.8% and 23.5% of phenotypic variation. Main-effect additive and dominance QTL-allele matrices were established as a compact form of the DHP genetic structure. The mechanism of heterosis superior-to-parents (or superior-to-parents heterosis, SPH) was explored and might be explained by a complementary locus-set composed of OD+ (showing positive over-dominance, most often), PD+ (showing positive partial-to-complete dominance, less often) and HA+ (showing positive homozygous additivity, occasionally) loci, depending on the parental materials. Any locus-type, whether OD+, PD + and HA+, could be the best genotype of a locus. All hybrids showed various numbers of better or best genotypes at many but not necessarily all loci, indicating further SPH improvement. Based on the additive/dominance QTL-allele matrices, the best hybrid genotype was predicted, and a hybrid improve-ment approach is suggested. PLSRGA is powerful for hybrid QTL-allele detection and cross-SPH improvement.(c) 2022 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).