ArticleInternational journal of molecular sciences2022
Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 65 citations in OpenAlex.
- Identification of Genomic Regions for Partial Resistance to Soybean Rust Under Field Conditions Using FarmCPU and Machine Learning Approaches.Plants (Basel, Switzerland) · 2026Article
- Machine Learning-Based Genome-Wide Association Study Reveals Genetic Loci Associated with Body Measurement Traits in Yili Horses.Animals : an open access journal from MDPI · 2026Article
- Genome-wide association analysis of salinity tolerance-related loci in bread wheat (Triticum aestivum L.) at the germination stage.BMC plant biology · 2026Article
- Machine learning-driven discovery of host genetic factors for paratuberculosis in goats within the one health framework.BMC veterinary research · 2026Article
- Trait Association for Flowering Time in Lentil from Global Multi-Environment Data Using GWAS and Machine Learning.Plants (Basel, Switzerland) · 2026Article
- Genome-wide association study bridging genomics-phenomics gap in natural plant populations.Journal of applied genetics · 2026Review
- Candidate genes for anthracnose resistance in Senegalese sorghum: a machine learning-based exploration.Functional & integrative genomics · 2025Article
- Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP): Computational tool for interpreting ensembles of genomic prediction models.The plant genome · 2025Article
- Disentangling soybean GxE effects in an integrated genomic prediction and machine learning-GWAS workflow.Plant methods · 2025Article
- Genome-wide association study (GWAS) uncovers candidate genes linked to the germination performance of bread wheat (Triticum aestivum L.) under salt stress.BMC genomics · 2025Article
- Beyond the genome: the role of functional markers in contemporary plant breeding.Frontiers in plant science · 2025Review
- Applying traditional and machine learning-based GWAS approaches for marker-trait identification in wheat.Frontiers in plant science · 2025Article
- Exploiting light energy utilization strategies in Populus simonii through multitrait-GWAS: insights from stochastic differential models.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Omics-Driven Strategies for Developing Saline-Smart Lentils: A Comprehensive Review.International journal of molecular sciences · 2024Review
- Review
- Machine Learning-Based Identification of Mating Type and Metalaxyl Response inMicroorganisms · 2024Article
- Article
- Harnessing the power of machine learning for crop improvement and sustainable production.Frontiers in plant science · 2024Review
- Prediction and optimization of indirect shoot regeneration of Passiflora caerulea using machine learning and optimization algorithms.BMC biotechnology · 2023Article
- Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits.Plants (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.