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

Umeda, Makoto; Sakaigaichi, Takeo; Tanaka, Minoru; Tarumoto, Yusuke; Adachi, Katsuki; Hattori, Tomokazu; Hayano, Michiko; Takahashi, Hiroyuki; Tamura, Yasuaki; Kimura, Tatsuro; Mori, Masaaki

doi:10.1270/jsbbs.20137

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…We cultivated plants in accordance with customary agricultural practices [30,35], that is, single-bud stalks were embedded in the field with the plant density generated using a 110 cm width of furrow and 15 cm plant distance for new planting. Chemical fertilizer was applied as a basal dressing (7.2 g N m −2 , 12.0 g P 2 O 5 m −2 , 6.0 g K 2 O m −2 ) and top dressing twice (4.5 g N m −2 , 4.5 g K 2 O m −2 ) according to the regional cultivation guideline.…”

Section: Plant Materials and Cultivation Methodsmentioning

confidence: 99%

A Data-Driven Approach to Sugarcane Breeding Programs with Agronomic Characteristics and Amino Acid Constituent Profiling

Ishikawa,

Date,

Umeda

et al. 2024

Metabolites

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Sugarcane (Saccharum spp. hybrids) and its processed products have supported local industries such as those in the Nansei Islands, Japan. To improve the sugarcane quality and productivity, breeders select better clones by evaluating agronomic characteristics, such as commercially recoverable sugar and cane yield. However, other constituents in sugarcane remain largely unutilized in sugarcane breeding programs. This study aims to establish a data-driven approach to analyze agronomic characteristics from breeding programs. This approach also determines a correlation between agronomic characteristics and free amino acid composition to make breeding programs more efficient. Sugarcane was sampled in clones in the later stage of breeding selection and cultivars from experimental fields on Tanegashima Island. Principal component analysis and hierarchical cluster analysis using agronomic characteristics revealed the diversity and variability of each sample, and the data-driven approach classified cultivars and clones into three groups based on yield type. A comparison of free amino acid constituents between these groups revealed significant differences in amino acids such as asparagine and glutamine. This approach dealing with a large volume of data on agronomic characteristics will be useful for assessing the characteristics of potential clones under selection and accelerating breeding programs.

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Section: Plant Materials and Cultivation Methodsmentioning

confidence: 99%

A Data-Driven Approach to Sugarcane Breeding Programs with Agronomic Characteristics and Amino Acid Constituent Profiling

Ishikawa,

Date,

Umeda

et al. 2024

Metabolites

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“…It is thought that the use of GRAS-Di would be promoted by effectively using dominant markers. In fact, in the F 1 pseudo-testcross method applied to heterozygous perennials, where the use of uniparental dominant markers is preferred over co-dominant markers, high-density male and female linkage maps using GRAS-Di markers have been facilitated in sugarcane (Umeda et al 2021) and apple (Moriya et al 2021).…”

Section: Bs Breeding Sciencementioning

confidence: 99%

“…This method can be ap-plied to thousands of samples by using primer sets containing different index sequences (Miki et al 2020). Use of GRAS-Di maps has been applied in several cultivars because of the high reproducibility of this method and the very low percentage of missing values (Fekih et al 2023, Miki et al 2020, Moriya et al 2021, Takeshima et al 2022, Umeda et al 2021. However, there are no reports of using this method for linkage mapping in cruciferous crops.…”

Section: Introductionmentioning

confidence: 99%

Quantitative trait locus (QTL) analysis and fine-mapping for <i>Fusarium oxysporum</i> disease resistance in <i>Raphanus sativus</i> using GRAS-Di technology

Ezeah,

Shimazu,

Kawanabe

et al. 2023

Breed. Sci.

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Fusarium wilt is a significant disease in radish, but the genetic mechanisms controlling yellows resistance (YR) are not well understood. This study aimed to identify YR-QTLs and to fine-map one of them using F 2:3 populations developed from resistant and susceptible radish parents. In this study, two high-density genetic maps each containing shared co-dominant markers and either female or male dominant markers that spanned 988.6 and 1127.5 cM with average marker densities of 1.40 and 1.53 cM, respectively, were generated using Genotyping by Random Amplicon Sequencing-Direct (GRAS-Di) technology. We identified two YR-QTLs on chromosome R2 and R7, and designated the latter as ForRs1 as the major QTL. Fine mapping narrowed down the ForRs1 locus to a 195 kb region. Among the 16 predicted genes in the delimited region, 4 genes including two receptor-like protein and -kinase genes (RLP/RLK) were identified as prime candidates for ForRs1 based on the nucleotide sequence comparisons between the parents and their predicted functions. This study is the first to use a GRAS-Di for genetic map construction of cruciferous crops and fine map the YR-QTL on the R7 chromosome of radish. These findings will provide groundbreaking insights into radish YR breeding and understanding the genetics of YR mechanism.

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Machine learning for genomic and pedigree prediction in sugarcane

Inamori,

Kimura,

Mori

et al. 2024

The Plant Genome

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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.

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

Cited by 7 publications

References 27 publications

A Data-Driven Approach to Sugarcane Breeding Programs with Agronomic Characteristics and Amino Acid Constituent Profiling

A Data-Driven Approach to Sugarcane Breeding Programs with Agronomic Characteristics and Amino Acid Constituent Profiling

Quantitative trait locus (QTL) analysis and fine-mapping for <i>Fusarium oxysporum</i> disease resistance in <i>Raphanus sativus</i> using GRAS-Di technology

Machine learning for genomic and pedigree prediction in sugarcane

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