Machine learning for genomic and pedigree prediction in sugarcane.

Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non-additive genetic effects is essential in genome prediction (GP) models of crops with highly heterozygous polyploid genomes. This study incorporates non-additive genetic effects and pedigree information using machine learning methods to track sugarcane breeding lines and enhance the prediction by assessing the degree of association between genotypes. This study measured the stalk biomass and sugar content of 297 clones from 87 families within a breeding population used in the Japanese sugarcane breeding program. Subsequently, we conducted analyses based on the marker genotypes of 33,149 single-nucleotide polymorphisms. To validate the accuracy of GP in the population, we first predicted the prediction accuracy of the best linear unbiased prediction (BLUP) based on a genomic relationship matrix. Prediction accuracy was assessed using two different cross-validation methods: repeated 10-fold cross-validation and leave-one-family-out cross-validation. The accuracy of GP of the first and second methods ranged from 0.36 to 0.74 and 0.15 to 0.63, respectively. Next, we compared the prediction accuracy of BLUP and two machine learning methods: random forests and simulation annealing ensemble (SAE), a newly developed machine learning method that explicitly models the interaction between variables. Both pedigree and genomic information were utilized as input in these methods. Through repeated 10-fold cross-validation, we found that the accuracy of the machine learning methods consistently surpassed that of BLUP in most cases. In leave-one-family-out cross-validation, SAE demonstrated the highest accuracy among the methods. These results underscore the effectiveness of GP in Japanese sugarcane breeding and highlight the significant potential of machine learning methods.

Detection of a major QTL related to smut disease resistance inherited from a Japanese wild sugarcane using GRAS-Di technology.

Smut disease of sugarcane causes considerable yield losses and the use of resistant varieties is the best control practice. Our group identified a Japanese wild sugarcane with highly smut disease resistance named 'Iriomote8'. In this study, we conducted QTL analysis for smut disease resistance using a mapping population derived from a resistant variety 'Yaenoushie', in which resistance is inherited from 'Iriomote8'. We identified 4813 non-redundant markers using GRAS-Di technology and developed a linkage map of mapping parents. We evaluated smut disease resistance of the mapping population by the inoculation test. Consequently, a large number of clones did not show the disease symptoms and the distribution of smut disease incidence tended to be "L shaped". Composite interval mapping detected an identical QTL for indices of smut disease incidence with a markedly high LOD score (26.6~45.6) at the end of linkage group 8 of 'Yaenoushie'. This QTL explained approximately 50% of the cases of smut disease incidence. In the mapping population, there were no correlations between the indices of smut disease incidence and other agronomic traits. In conclusion, this QTL could be used for marker-assisted selection to significantly improve smut disease resistance without negative effects on other agronomic traits.

Potential of Genomic Selection in Mass Selection Breeding of an Allogamous Crop: An Empirical Study to Increase Yield of Common Buckwheat.

To evaluate the potential of genomic selection (GS), a selection experiment with GS and phenotypic selection (PS) was performed in an allogamous crop, common buckwheat (Fagopyrum esculentum Moench). To indirectly select for seed yield per unit area, which cannot be measured on a single-plant basis, a selection index was constructed from seven agro-morphological traits measurable on a single plant basis. Over 3 years, we performed two GS and one PS cycles per year for improvement in the selection index. In GS, a prediction model was updated every year on the basis of genotypes of 14,598-50,000 markers and phenotypes. Plants grown from seeds derived from a series of generations of GS and PS populations were evaluated for the traits in the selection index and other yield-related traits. GS resulted in a 20.9% increase and PS in a 15.0% increase in the selection index in comparison with the initial population. Although the level of linkage disequilibrium in the breeding population was low, the target trait was improved with GS. Traits with higher weights in the selection index were improved more than those with lower weights, especially when prediction accuracy was high. No trait changed in an unintended direction in either GS or PS. The accuracy of genomic prediction models built in the first cycle decreased in the later cycles because the genetic bottleneck through the selection cycles changed linkage disequilibrium patterns in the breeding population. The present study emphasizes the importance of updating models in GS and demonstrates the potential of GS in mass selection of allogamous crop species, and provided a pilot example of successful application of GS to plant breeding.

Three dominant awnless genes in common wheat: Fine mapping, interaction and contribution to diversity in awn shape and length.

The awn is a long needle-like structure formed at the tip of the lemma in the florets of some grass species. It plays a role in seed dispersal and protection against animals, and can contribute to the photosynthetic activity of spikes. Three main dominant inhibitors of awn development (Hd, B1 and B2) are known in hexaploid wheat, but the causal genes have not been cloned yet and a genetic association with awn length diversity has been found only for the B1 allele. To analyze the prevalence of these three awning inhibitors, we attempted to predict the genotypes of 189 hexaploid wheat varieties collected worldwide using markers tightly linked to these loci. Using recombinant inbred lines derived from two common wheat cultivars, Chinese Spring and Mironovskaya 808, both with short awns, and a high-density linkage map, we performed quantitative trait locus analysis to identify tightly linked markers. Because this linkage map was constructed with abundant array-based markers, we converted the linked markers to PCR-based markers and determined the genotypes of 189 hexaploids. A significant genotype-phenotype correlation was observed at the Hd and B1 regions. We also found that interaction among these three awning inhibitors is involved in development of a membranous outgrowth at the base of awn resembling the Hooded mutants of barley. For the hooded awn phenotype, presence of the Hd dominant allele was essential but not sufficient, so B2 and other factors appear to act epistatically to produce the ectopic tissue. On the other hand, the dominant B1 allele acted as a suppressor of the hooded phenotype. These three awning inhibitors largely contribute to the genetic variation in awn length and shape of common wheat.

A high-density genetic map with array-based markers facilitates structural and quantitative trait locus analyses of the common wheat genome.

The large genome and allohexaploidy of common wheat have complicated construction of a high-density genetic map. Although improvements in the throughput of next-generation sequencing (NGS) technologies have made it possible to obtain a large amount of genotyping data for an entire mapping population by direct sequencing, including hexaploid wheat, a significant number of missing data points are often apparent due to the low coverage of sequencing. In the present study, a microarray-based polymorphism detection system was developed using NGS data obtained from complexity-reduced genomic DNA of two common wheat cultivars, Chinese Spring (CS) and Mironovskaya 808. After design and selection of polymorphic probes, 13,056 new markers were added to the linkage map of a recombinant inbred mapping population between CS and Mironovskaya 808. On average, 2.49 missing data points per marker were observed in the 201 recombinant inbred lines, with a maximum of 42. Around 40% of the new markers were derived from genic regions and 11% from repetitive regions. The low number of retroelements indicated that the new polymorphic markers were mainly derived from the less repetitive region of the wheat genome. Around 25% of the mapped sequences were useful for alignment with the physical map of barley. Quantitative trait locus (QTL) analyses of 14 agronomically important traits related to flowering, spikes, and seeds demonstrated that the new high-density map showed improved QTL detection, resolution, and accuracy over the original simple sequence repeat map.

Rapid genotyping with DNA micro-arrays for high-density linkage mapping and QTL mapping in common buckwheat (Fagopyrum esculentum Moench).

For genetic studies and genomics-assisted breeding, particularly of minor crops, a genotyping system that does not require a priori genomic information is preferable. Here, we demonstrated the potential of a novel array-based genotyping system for the rapid construction of high-density linkage map and quantitative trait loci (QTL) mapping. By using the system, we successfully constructed an accurate, high-density linkage map for common buckwheat (Fagopyrum esculentum Moench); the map was composed of 756 loci and included 8,884 markers. The number of linkage groups converged to eight, which is the basic number of chromosomes in common buckwheat. The sizes of the linkage groups of the P1 and P2 maps were 773.8 and 800.4 cM, respectively. The average interval between adjacent loci was 2.13 cM. The linkage map constructed here will be useful for the analysis of other common buckwheat populations. We also performed QTL mapping for main stem length and detected four QTL. It took 37 days to process 178 samples from DNA extraction to genotyping, indicating the system enables genotyping of genome-wide markers for a few hundred buckwheat plants before the plants mature. The novel system will be useful for genomics-assisted breeding in minor crops without a priori genomic information.