Target chromosome-segment substitution: A way to breeding by design in rice

Progress in plant breeding depends on the development of genetic resources,genetic knowledge,and breeding techniques.The core of plant breeding is the use of naturally occurring variation.At the beginning of the post-genomic era,a new concept of"breeding by design"was proposed,which aims to control all allelic variation for all genes of agronomic importance.In the past two decades,we have applied a three-step strategy for research on rice breeding by design.In the first step,we constructed a singlesegment substitution line(SSSL)library using Huajingxian 74(HJX74),an elite xian(indica)rice cultivar,as the recipient in which to assemble genes from the rice AA genome.In the second step,we identified a series of desirable genes in the SSSL library.In the third step,we designed new rice lines,and achieved the breeding goals by pyramiding target genes in the HJX74-SSSL library.This review introduces the background,concept,and strategy of breeding by design,as well as our achievements in rice breeding by design using the HJX74-SSSL platform.Our practice shows that target chromosome-segment substitution is a way to breeding by design.

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 × 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 improvement approach is suggested. PLSRGA is powerful for hybrid QTL-allele detection and cross-SPH improvement.

Computational tools for plant genomics and breeding

Plant genomics and crop breeding are at the intersection of biotechnology and information technology.Driven by a combination of highthroughput sequencing,molecular biology and data science,great advances have been made in omics technologies at every step along the central dogma,especially in genome assembling,genome annotation,epigenomic profiling,and transcriptome profiling.These advances further revolutionized three directions of development.One is genetic dissection of complex traits in crops,along with genomic prediction and selection.The second is comparative genomics and evolution,which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery.The third direction is the development of deep learning approaches for the rational design of biological sequences,especially proteins,for synthetic biology.All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design.