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Plants
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Advances in Genome-Wide Studies of Complex Agronomic Traits in Crops
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Advances in Genome-Wide Studies of Complex Agronomic Traits in Crops

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1069

Special Issue Editors

Dr. Hantao Wang

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Guest Editor
State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China
Interests: molecular mechanisms; regulatory network; QTL mapping; genome-wide association study
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Shen

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Guest Editor
College of Biological and Food Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
Interests: genomics; domestication; population genetics; QTL mapping; genome-wide association study; complex traits
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding the genetic basis of complex agronomic traits is essential for accelerating crop improvement in the face of climate change, food insecurity, and sustainability challenges. This Special Issue aims to highlight recent advances in genome-wide studies, including GWAS, QTL mapping, transcriptome-wide association studies (TWASs), and genomic prediction, that have unraveled the genetic architecture of yield, stress resistance, flowering time, nutrient use efficiency, and other key traits in crops. We welcome the submission of original research, reviews, and methodological advances that apply multi-omics integration, artificial intelligence (AI), machine learning (ML), or population genomics to decipher complex traits. Studies focusing on both major and underutilized crops, as well as comparative approaches across species, are encouraged. By compiling cutting-edge findings in this area, this issue aims to facilitate knowledge exchange and promote innovative strategies for crop genetic improvement.

Dr. Hantao Wang
Dr. Chao Shen
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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

  • genome-wide association study (GWAS)
  • quantitative trait loci (QTL)
  • complex traits
  • artificial intelligence (AI)
  • machine learning
  • crop genomics
  • multi-omics integration
  • genomic prediction
  • agronomic traits
  • population genomics

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Published Papers (2 papers)

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Research

17 pages, 1924 KB  
Article
Comparison of the Genetic Basis of Yield Traits Between Main and Ratoon Rice in an Eight-Way MAGIC Population
by Zhongmin Han, Ahmed Sherif, Mohammed Ayaad, Yongzhong Xing and Yuncai Lu
Plants 2025, 14(22), 3527; https://doi.org/10.3390/plants14223527 - 19 Nov 2025
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Abstract
Ratoon rice plays a crucial role in sustainable rice production due to its potential for additional harvests; however, the genetic basis of its yield remains to be explored. In this study, we aimed to precisely dissect the genetic basis of yield in ratoon [...] Read more.
Ratoon rice plays a crucial role in sustainable rice production due to its potential for additional harvests; however, the genetic basis of its yield remains to be explored. In this study, we aimed to precisely dissect the genetic basis of yield in ratoon rice by selecting 302 eight-way MAGIC lines that achieved synchronized heading within a 10-day period through staggered sowing. The eight parental lines exhibited distinct yield performances across both main and ratoon crops. Significant correlations were observed between the main and ratoon crops concerning panicle length (R = 0.67) and spikelets per panicle (R = 0.36). Genome-wide association studies (GWAS) revealed a total of 17 quantitative trait loci (QTLs) associated with five yield-related traits in both main and ratoon crops. Specifically, seven QTLs were detected for yield components in the main crop, while six QTLs were identified in the ratoon crop, in addition to five QTLs associated with ratooning ability. Notably, only one QTL, qPL1, was commonly detected in both crops, exhibiting opposite effects on tiller number across crop types. Among the QTLs specifically identified in the ratoon crop, qGY10 demonstrated the largest effect on ratoon grain yield without compromising the performance of the main crop. The known gene, Ghd7.1, exhibited pleiotropic effects on both ratooning ability and ratoon grain yield. Candidate gene analysis prioritized likely causal genes and defined key haplotypes within these QTL intervals by leveraging the genomic diversity of the eight founders. These findings underscore the distinct genetic determinants for yields in main and ratoon crops, providing a genetic basis for breeding high-yielding varieties in both crop types. Full article
(This article belongs to the Special Issue Advances in Genome-Wide Studies of Complex Agronomic Traits in Crops)
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21 pages, 2330 KB  
Article
Using Structural Equation Models to Interpret Genome-Wide Association Studies for Morphological and Productive Traits in Soybean [Glycine max (L.) Merr.]
by Matheus Massariol Suela, Camila Ferreira Azevedo, Ana Carolina Campana Nascimento, Gota Morota, Felipe Lopes da Silva, Gaspar Malone, Nizio Fernando Giasson and Moysés Nascimento
Plants 2025, 14(19), 3015; https://doi.org/10.3390/plants14193015 - 29 Sep 2025
Viewed by 577
Abstract
Understanding trait relationships is fundamental in soybean breeding because the goal is to maximize simultaneous gains. Standard multi-trait genome-wide association studies (MT-GWAS) identify variants linked to multiple traits but fail to capture phenotypic structures or interrelations. Structural Equation Models (SEM) account for covariances [...] Read more.
Understanding trait relationships is fundamental in soybean breeding because the goal is to maximize simultaneous gains. Standard multi-trait genome-wide association studies (MT-GWAS) identify variants linked to multiple traits but fail to capture phenotypic structures or interrelations. Structural Equation Models (SEM) account for covariances and recursion, enabling the decomposition of single nucleotide polymorphism (SNP) effects into direct or indirect components and identifying pleiotropic regions. We applied SEM to analyze morphology (pod thickness, PT) and yield traits (number of pods, NP; number of grains, NG; hundred-grain weight, HGW). The dataset comprised 96 soybean individuals genotyped with 4070 SNP markers. The phenotypic network was constructed using the hill-climbing algorithm, a class of score-based methods commonly applied to learn the structure of Bayesian networks, and structural coefficients were estimated with SEM. According to coefficient signs, we identified negative interrelationships between NG and HGW, and positive ones between NP and NG, and HGW and PT. NG, HGW, and PT showed indirect SNP effects. We also found loci jointly controlling traits. In total, 46 candidate genes were identified: 7 associated exclusively with NP and 4 associated with NG. An additional 15 genes were common to NP and NG, 3 were common to NP and HGW, 6 were common to NG and HGW, and 11 were common to NP, NG, and HGW. In summary, SEM-GWAS revealed novel relationships among soybean traits, including PT, supporting breeding programs. Full article
(This article belongs to the Special Issue Advances in Genome-Wide Studies of Complex Agronomic Traits in Crops)
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