Harnessing Artificial Intelligence and Machine Learning for Identifying Quantitative Trait Loci (QTL) Associated with Seed Quality Traits in Crops

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Detalles Bibliográficos
Publicado en:	Plants vol. 14, no. 11 (2025), p. 1727
Autor principal:	Kassem My Abdelmajid
Publicado:	MDPI AG
Materias:	Physiology Accuracy Artificial intelligence Datasets Deep learning Genome-wide association studies Genetic markers Quantitative trait loci Genomics Metabolomics Artificial neural networks Accumulation Corn Polygenic inheritance Mapping Feature selection Nutritive value Machine learning Transcriptomics Genotypes Gene mapping Gene loci Genotype & phenotype Explainable artificial intelligence Learning algorithms Proteins Physical characteristics Nutrient content Crop production Crops Chromosomes Nutrients Phenotyping Crop resilience Graph neural networks Linkage analysis Marketability Genomic analysis Algorithms Soybeans Neural networks Climate change
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	Seed quality traits, such as seed size, oil and protein content, mineral accumulation, and morphological characteristics, are crucial for enhancing crop productivity, nutritional value, and marketability. Traditional quantitative trait loci (QTL) mapping methods, such as linkage analysis and genome-wide association studies (GWAS), have played fundamental role in identifying loci associated with these complex traits. However, these approaches often struggle with high-dimensional genomic data, polygenic inheritance, and genotype-by-environment (GXE) interactions. Recent advances in artificial intelligence (AI) and machine learning (ML) provide powerful alternatives that enable more accurate trait prediction, robust marker-trait associations, and efficient feature selection. This review presents an integrated overview of AI/ML applications in QTL mapping and seed trait prediction, highlighting key methodologies such as LASSO regression, Random Forest, Gradient Boosting, ElasticNet, and deep learning techniques including convolutional neural networks (CNNs) and graph neural networks (GNNs). A case study on soybean seed mineral nutrients accumulation illustrates the effectiveness of ML models in identifying significant SNPs on chromosomes 8, 9, and 14. LASSO and ElasticNet consistently achieved superior predictive accuracy compared to tree-based models. Beyond soybean, AI/ML methods have enhanced QTL detection in wheat, lettuce, rice, and cotton, supporting trait dissection across diverse crop species. I also explored AI-driven integration of multi-omics data—genomics, transcriptomics, metabolomics, and phenomics—to improve resolution in QTL mapping. While challenges remain in terms of model interpretability, biological validation, and computational scalability, ongoing developments in explainable AI, multi-view learning, and high-throughput phenotyping offer promising avenues. This review underscores the transformative potential of AI in accelerating genomic-assisted breeding and developing high-quality, climate-resilient crop varieties.
ISSN:	2223-7747
DOI:	10.3390/plants14111727
Fuente:	Agriculture Science Database