Genotyping of Haliotis discus hannai and machine learning models to predict the heat resistant phenotype based on genotype

Noh, Eun Soo; Subramaniyam, Sathiyamoorty; Cho, Sunghyun; Kim, Young‐Ok; Park, Choul-Ji; Lee, Jehee; Nam, Bo‐Hye; Shin, Younhee

doi:10.3389/fgene.2023.1151427

Front. Genet.

2023

DOI: 10.3389/fgene.2023.1151427

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Genotyping of Haliotis discus hannai and machine learning models to predict the heat resistant phenotype based on genotype

Eun Soo Noh

Sathiyamoorty Subramaniyam²,

Sunghyun Cho³

et al.

Abstract: Haliotis discus hannai and machine learning models to predict the heat resistant phenotype based on genotype.

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2024

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Cited by 2 publications

References 23 publications

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Genome Resources for Identifying SNPs Associated With Eight Horticultural Traits in Commercial Korean Elite Radish (Raphanus sativus) Lines

Park,

Jung,

Lim

et al. 2024

Plant Breeding

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Radish (Raphanus sativus), which belongs to the family Brassicaceae, has relatively limited genomic resources, especially for elite lines used in commercial breeding and other agricultural applications. Thus, this study aimed to provide a comprehensive catalogue of genome sequences for 100 elite radish lines used in the Korean industry for commercial breeding purposes. These lines were sequenced and mapped to the elite Bakdal genome. A total of 33,919 high‐quality single nucleotide polymorphisms (SNPs) were identified and were found to be associated with eight distinct phenotypic traits. Five diverse machine learning (ML) models revealed that a subset of 198 SNPs had high predictive potential for the eight horticultural traits. Furthermore, the 100 elite lines were grouped into four clusters based on the eight traits, and their predictive potential was evaluated using the ML models trained using both individual and pooled SNPs. The accuracy ranged from 0.83 to 0.96 for the individually trained models and from 0.84 to 0.95 for the pooled models. This study provides a substantial basis for the advancement of digital/precision radish breeding.

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Genome Resources for Identifying SNPs Associated With Eight Horticultural Traits in Commercial Korean Elite Radish (Raphanus sativus) Lines

Park,

Jung,

Lim

et al. 2024

Plant Breeding

View full text Add to dashboard Cite

show abstract

Genotype-Driven Phenotype Prediction in Onion Breeding: Machine Learning Models for Enhanced Bulb Weight Selection

Choi,

Cho,

Choi

et al. 2024

Agriculture

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Onions (Allium cepa L.) are a globally significant horticultural crop, ranking second only to tomatoes in terms of cultivation and consumption. However, due to the crop’s complex genome structure, lengthy growth cycle, self-incompatibility, and susceptibility to disease, onion breeding is challenging. To address these issues, we implemented digital breeding techniques utilizing genomic data from 98 elite onion lines. We identified 51,499 high-quality variants and employed these data to construct a genomic estimated breeding value (GEBV) model and apply machine learning methods for bulb weight prediction. Validation with 260 new individuals revealed that the machine learning model achieved an accuracy of 83.2% and required only thirty-nine SNPs. Subsequent in silico crossbreeding simulations indicated that offspring from the top 5% of elite lines exhibited the highest bulb weights, aligning with traditional phenotypic selection methods. This approach demonstrates that early-stage selection based on genotypic information followed by crossbreeding can achieve economically viable breeding results. This methodology is not restricted to bulb weight and can be applied to various horticultural traits, significantly improving the efficiency of onion breeding through advanced digital technologies. The integration of genomic data, machine learning, and computer simulations provides a powerful framework for data-driven breeding strategies, accelerating the development of superior onion varieties to meet global demand.

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Genotyping of Haliotis discus hannai and machine learning models to predict the heat resistant phenotype based on genotype

Abstract: Haliotis discus hannai and machine learning models to predict the heat resistant phenotype based on genotype.

Cited by 2 publications

References 23 publications

Genome Resources for Identifying SNPs Associated With Eight Horticultural Traits in Commercial Korean Elite Radish (Raphanus sativus) Lines

Genome Resources for Identifying SNPs Associated With Eight Horticultural Traits in Commercial Korean Elite Radish (Raphanus sativus) Lines

Genotype-Driven Phenotype Prediction in Onion Breeding: Machine Learning Models for Enhanced Bulb Weight Selection

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