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QTL Mapping of Seed Quality Traits in Crops, 2nd Edition
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QTL Mapping of Seed Quality Traits in Crops, 2nd Edition

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Genetics, Genomics and Biotechnology".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2463

Special Issue Editor

Prof. Dr. Abdelmajid Kassem

E-Mail Website
Guest Editor
Plant Genomics and Biotechnology Lab, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA
Interests: plant genetics, genomics, and biotechnology; QTL mapping of important agronomic traits especially seed composition traits in soybean and other crops
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Seeds of major crops are rich in valuable compounds that are essential for the food and feed industries. The nutritional and health benefits of animal feed and human diets rely heavily on seed-derived proteins, amino acids, oils, fatty acids (such as palmitic, stearic, oleic, linoleic, linolenic acids), sugars (including glucose, galactose, sucrose, raffinose), isoflavones (daidzein, genistein, glycitein), vitamins, minerals, secondary metabolites, and other nutrients. Understanding the genetic basis of these beneficial compounds is critical. Over the past three decades, numerous studies have been conducted to identify and map the quantitative trait loci (QTL) associated with these traits. However, many of these QTL regions remain poorly characterized, with most candidate genes still unidentified.

This Special Issue of Plants aims to advance the field by focusing on the genetic and QTL mapping of seed quality traits in crops, utilizing both traditional mapping populations (such as recombinant inbred lines (RILs), F2, doubled haploid, etc.) and genome-wide association studies (GWAS). Submissions that identify candidate genes within the identified QTL regions are especially encouraged.

We also welcome papers that integrate artificial intelligence (AI) and machine learning (ML) techniques in the analysis of genetic and QTL data. AI and ML offer powerful tools for uncovering hidden patterns, predicting trait outcomes, and enhancing the precision of QTL mapping and candidate gene identification. Studies leveraging these advanced computational approaches to gain new insights into seed quality traits are highly welcomed. We look forward to receiving your valuable contributions to this Special Issue.

Prof. Dr. Abdelmajid Kassem
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • crops
  • QTL mapping
  • seed quality traits
  • GWAS
  • seed protein
  • oil
  • fatty acids
  • amino acids
  • sugars
  • isoflavones
  • AI
  • ML

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Published Papers (4 papers)

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Research

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18 pages, 2180 KiB  
Article
Identification of Quantitative Trait Loci for Grain Quality Traits in a Pamyati Azieva × Paragon Bread Wheat Mapping Population Grown in Kazakhstan
by Akerke Amalova, Simon Griffiths, Aigul Abugalieva, Saule Abugalieva and Yerlan Turuspekov
Plants 2025, 14(11), 1728; https://doi.org/10.3390/plants14111728 - 5 Jun 2025
Viewed by 364
Abstract
High grain quality is a key target in wheat breeding and is influenced by genetic and environmental factors. This study evaluated 94 recombinant inbred lines (RILs) from a Pamyati Azieva × Paragon (PA × P) mapping population grown in two regions in Kazakhstan [...] Read more.
High grain quality is a key target in wheat breeding and is influenced by genetic and environmental factors. This study evaluated 94 recombinant inbred lines (RILs) from a Pamyati Azieva × Paragon (PA × P) mapping population grown in two regions in Kazakhstan to assess the genetic basis of six grain quality traits: the test weight per liter (TWL, g/L), grain protein content (GPC, %), gluten content (GC, %), gluten deformation index in flour (GDI, unit), sedimentation value in a 2% acetic acid solution (SV, mL), and grain starch content (GSC, %). A correlation analysis revealed a trade-off between protein and starch accumulation and an inverse relationship between grain quality and yield components. Additionally, GPC exhibited a negative correlation with yield per square meter (YM2), underscoring the challenge of simultaneously improving grain quality and yield. With the use of the QTL Cartographer statistical package, 71 quantitative trait loci (QTLs) were identified for the six grain quality traits, including 20 QTLs showing stability across multiple environments. Notable stable QTLs were detected for GPC on chromosomes 4A, 5B, 6A, and 7B and for GC on chromosomes 1D and 6A, highlighting their potential for marker-assisted selection (MAS). A major QTL found on chromosome 1D (QGDI-PA × P.ipbb-1D.1, LOD 19.4) showed a strong association with gluten deformation index, emphasizing its importance in improving flour quality. A survey of published studies on QTL identification in common wheat suggested the likely novelty of 12 QTLs identified for GDI (five QTLs), TWL (three QTLs), SV, and GSC (two QTLs each). These findings underscore the need for balanced breeding strategies that optimize grain composition while maintaining high productivity. With the use of SNP markers associated with the identified QTLs for grain quality traits, the MAS approach can be implemented in wheat breeding programs. Full article
(This article belongs to the Special Issue QTL Mapping of Seed Quality Traits in Crops, 2nd Edition)
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13 pages, 2575 KiB  
Article
Mapping of a Quantitative Trait Locus for Stay-Green Trait in Common Wheat
by Xin Li, Xin Bai, Lijuan Wu, Congya Wang, Xinghui Liu, Qiqi Li, Xiaojun Zhang, Fang Chen, Chengda Lu, Wei Gao and Tianling Cheng
Plants 2025, 14(5), 727; https://doi.org/10.3390/plants14050727 - 27 Feb 2025
Viewed by 592
Abstract
The stay-green (SG) trait enhances photosynthetic activity during the late grain-filling period, benefiting grain yield under drought and heat stresses. CH7034 is a wheat breeding line with SG. To clarify the SG loci carried by CH7034 and obtain linked molecular markers, in this [...] Read more.
The stay-green (SG) trait enhances photosynthetic activity during the late grain-filling period, benefiting grain yield under drought and heat stresses. CH7034 is a wheat breeding line with SG. To clarify the SG loci carried by CH7034 and obtain linked molecular markers, in this study, a recombinant inbred line (RIL) population derived from the cross between CH7034 and non-SG SY95-71 was genotyped using the Wheat17K single-nucleotide polymorphism (SNP) array, and a high-density genetic map covering 21 chromosomes and consisting of 2159 SNP markers was constructed. Then, the chlorophyll content of flag leaf from each RIL was estimated for mapping, and one QTL for SG on chromosome 7D was identified, temporarily named QSg.sxau-7D, with the maximum phenotypic variance explained of 8.81~11.46%. A PCR-based diagnostic marker 7D-16 for QSg.sxau-7D was developed, and the CH7034 allele of 7D-16 corresponded to the higher flag leaf chlorophyll content, while the 7D-16 SY95-71 allele corresponded to the lower value, which confirmed the genetic effect on SG of QSg.sxau-7D. QSg.sxau-7D located in the 526.4~556.2 Mbp interval is different from all the known SG loci on chromosome 7D, and 69 high-confidence annotated genes within the interval expressed throughout the entire period of flag leaf senescence. Moreover, results of an association analysis based on the diagnostic marker showed that there is a positive correlation between QSg.sxau-7D and thousand-grain weight. Our results revealed a novel QTL QSg.sxau-7D whose CH7034 allele had a strong effect on SG, which can be applied in further wheat molecular breeding. Full article
(This article belongs to the Special Issue QTL Mapping of Seed Quality Traits in Crops, 2nd Edition)
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15 pages, 2927 KiB  
Article
Identification of a New Major Oil Content QTL Overlapped with FAD2B in Cultivated Peanut (Arachis hypogaea L.)
by Feifei Wang, Huarong Miao, Shengzhong Zhang, Xiaohui Hu, Chunjuan Li, Weiqiang Yang and Jing Chen
Plants 2025, 14(4), 615; https://doi.org/10.3390/plants14040615 - 18 Feb 2025
Cited by 1 | Viewed by 563
Abstract
High oil content in peanut seeds is a key breeding objective for peanut (Arachis hypogaea L.) quality improvement. In order to explore the genetic basis of oil content in peanuts, a recombinant inbred line (RIL) population consisting of 256 lines was phenotyped [...] Read more.
High oil content in peanut seeds is a key breeding objective for peanut (Arachis hypogaea L.) quality improvement. In order to explore the genetic basis of oil content in peanuts, a recombinant inbred line (RIL) population consisting of 256 lines was phenotyped across six environments. Continuous distribution and transgressive segregation for both oil content and oleic acid content were demonstrated across all environments. Quantitative trait locus (QTL) analysis yielded 15 additive QTLs explaining 4.34 to 23.10% of phenotypic variations. A novel stable and major QTL region conditioning oil content (qOCB09.1) was mapped to chromosome B09, spanning a 1.99 Mb genomic region with 153 putative genes, including the oleic acid gene FAD2B, which may influence the oil content. Candidate genes were identified and diagnostic markers for this region were developed for further investigation. Additionally, 18 pairs of epistatic interactions involving 35 loci were identified to affect the oil content, explaining 1.25 to 1.84% of phenotypic variations. These findings provide valuable insights for further map-based cloning of favorable alleles for oil content in peanuts. Full article
(This article belongs to the Special Issue QTL Mapping of Seed Quality Traits in Crops, 2nd Edition)
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Review

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17 pages, 563 KiB  
Review
Harnessing Artificial Intelligence and Machine Learning for Identifying Quantitative Trait Loci (QTL) Associated with Seed Quality Traits in Crops
by My Abdelmajid Kassem
Plants 2025, 14(11), 1727; https://doi.org/10.3390/plants14111727 - 5 Jun 2025
Viewed by 539
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue QTL Mapping of Seed Quality Traits in Crops, 2nd Edition)
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