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Genome-Wide Association Study Accelerates Deciphering of Crop Complex Agronomic Traits
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Genome-Wide Association Study Accelerates Deciphering of Crop Complex Agronomic Traits

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Molecular Biology".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2230

Special Issue Editors

Dr. Qing Li

E-Mail Website
Guest Editor
State Key Laboratory of Rice Biology, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 311401, China
Interests: plant genomics and genetics; molecular biology; crop molecular breeding; plant nutrient utilization
Dr. Liping Dai

E-Mail Website
Guest Editor
The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Lin’an, Hangzhou, China
Interests: GWAS; molecular mechanisms of plant drought resistance; crop molecular breeding
Dr. Deyong Ren

E-Mail Website
Guest Editor
State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 311401, China
Interests: rice genomics and genetics; molecular biology; rice molecular breeding
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Genome-wide association study (GWAS) uses statistical methods to find associations between sequence polymorphisms and phenotypic variation among different accessions. GWAS has two significant advantages over conventional QTL mapping using bi-parental populations in crops. First, the genetic materials used for GWAS populations contain more natural variation than the two parental lines used for segregation populations. Second, most GWAS can achieve relatively high mapping resolution due to diverse historical recombination events. In the past decade, GWAS has proved to be a powerful method for dissecting complex agronomic traits and has been used to identify causative loci or genes underlying these traits in crops. A series of critical genes were successfully identified from rice, maize, wheat, soybean, and other crops and were further confirmed in the subsequent functional experiments.

Here, we propose a Special Issue “Genome-Wide Association Study Accelerates Deciphering of Crop Complex Agronomic Traits” in Plants. This Special Issue aims to provide a platform for researchers to share the latest findings and advancements in using GWAS to unravel the mysteries of intricate crop agronomic traits. We welcome original research articles, critical review papers, and opinions that delve into various aspects of this field. Additionally, we encourage contributions to optimize GWAS data processing, analysis, and visualization techniques.

Dr. Qing Li
Dr. Liping Dai
Dr. Deyong Ren
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 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

  • genome wide association study (GWAS)
  • crops
  • agronomic traits
  • QTL
  • molecular mechanism

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Published Papers (2 papers)

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Research

18 pages, 22746 KiB  
Article
Genome-Wide Association for Morphological and Agronomic Traits in Phaseolus vulgaris L. Accessions
by Stephanie Mariel Alves, Giselly Figueiredo Lacanallo, Maria Celeste Gonçalves-Vidigal, Mariana Vaz Bisneta, Andressa Gonçalves Vidigal Rosenberg and Pedro Soares Vidigal Filho
Plants 2024, 13(18), 2638; https://doi.org/10.3390/plants13182638 - 21 Sep 2024
Viewed by 1026
Abstract
Exploring genetic resources through genomic analyses has emerged as a powerful strategy to develop common bean (Phaseolus vulgaris L.) cultivars that are both productive and well-adapted to various environments. This study aimed to identify genomic regions linked to morpho-agronomic traits in Mesoamerican [...] Read more.
Exploring genetic resources through genomic analyses has emerged as a powerful strategy to develop common bean (Phaseolus vulgaris L.) cultivars that are both productive and well-adapted to various environments. This study aimed to identify genomic regions linked to morpho-agronomic traits in Mesoamerican and Andean common bean accessions and to elucidate the proteins potentially involved in these traits. We evaluated 109 common bean accessions over three agricultural years, focusing on traits including the grain yield (YDSD), 100-seed weight (SW), number of seeds per pod (SDPD), number of pods per plant (PDPL), first pod insertion height (FPIH), plant height (PLHT), days to flowering (DF), and days to maturity (DPM). Using multilocus methods such as mrMLM, FASTmrMLM, FASTmrEMMA, ISIS EM-BLASSO, and pLARmEB, we identified 36 significant SNPs across all chromosomes (Pv01 to Pv11). Validating these SNPs and candidate genes in segregating populations is crucial for developing more productive common bean cultivars through marker-assisted selection. Full article
(This article belongs to the Special Issue Genome-Wide Association Study Accelerates Deciphering of Crop Complex Agronomic Traits)
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Figure 1

Figure 1
<p>Precipitation (mm) and minimum and maximum temperature (degrees Celsius) in the experiments conducted at the Technical Irrigation Center (CTI) in Maringá, PR, Brazil, in 2019, 2020, and 2021.</p> Full article ">Figure 2
<p>Manhattan and QQ plots obtained in GWAS for the yield and its components and plant architecture: (<b>a</b>) YDSD, grain yield (kg ha<sup>−1</sup>); (<b>b</b>) SW, 100-seed weight (g); (<b>c</b>) PDPL, number of pods per plant; (<b>d</b>) FPIH, first pod insertion height (cm); (<b>e</b>) PLHT, plant height (cm). The gray threshold line was considered for LOD score ≥ 3. The pink dots indicate the SNPs significantly associated with traits.</p> Full article ">Figure 3
<p>Manhattan and QQ plots obtained in GWAS for phenology: (<b>a</b>) DF, number of days for flowering; (<b>b</b>) DPM, number of days for maturity. The gray threshold line was considered for LOD score ≥ 3. The pink dots indicate the SNPs significantly associated with traits.</p> Full article ">Figure 4
<p>Accessions of common beans from the Nupagri Germplasm Bank (photos taken by UEM/ASC).</p> Full article ">
20 pages, 1812 KiB  
Article
An Extended Application of the Fast Multi-Locus Ridge Regression Algorithm in Genome-Wide Association Studies of Categorical Phenotypes
by Jin Zhang, Bolin Shen, Ziyang Zhou, Mingzhi Cai, Xinyi Wu, Le Han and Yangjun Wen
Plants 2024, 13(17), 2520; https://doi.org/10.3390/plants13172520 - 7 Sep 2024
Viewed by 937
Abstract
Categorical (either binary or ordinal) quantitative traits are widely observed to measure count and resistance in plants. Unlike continuous traits, categorical traits often provide less detailed insights into genetic variation and possess a more complex underlying genetic architecture, which presents additional challenges for [...] Read more.
Categorical (either binary or ordinal) quantitative traits are widely observed to measure count and resistance in plants. Unlike continuous traits, categorical traits often provide less detailed insights into genetic variation and possess a more complex underlying genetic architecture, which presents additional challenges for their genome-wide association studies. Meanwhile, methods designed for binary or continuous phenotypes are commonly used to inappropriately analyze ordinal traits, which leads to the loss of original phenotype information and the detection power of quantitative trait nucleotides (QTN). To address these issues, fast multi-locus ridge regression (FastRR), which was originally designed for continuous traits, is used to directly analyze binary or ordinal traits in this study. FastRR includes three stages of continuous transformation, variable reduction, and parameter estimation, and it can computationally handle categorical phenotype data instead of link functions introduced or methods inappropriately used. A series of simulation studies demonstrate that, compared with four other continuous or binary or ordinal approaches, including logistic regression, FarmCPU, FaST-LMM, and POLMM, the FastRR method outperforms in the detection of small-effect QTN, accuracy of estimated effect, and computation speed. We applied FastRR to 14 binary or ordinal phenotypes in the Arabidopsis real dataset and identified 479 significant loci and 76 known genes, at least seven times as many as detected by other algorithms. These findings underscore the potential of FastRR as a very useful tool for genome-wide association studies and novel gene mining of binary and ordinal traits. Full article
(This article belongs to the Special Issue Genome-Wide Association Study Accelerates Deciphering of Crop Complex Agronomic Traits)
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Figure 1

Figure 1
<p>A flow chart of the FastRR method.</p> Full article ">Figure 2
<p>The phenotypic distribution of fourteen binary or ordinal traits in the <span class="html-italic">Arabidopsis</span> real dataset. (<b>A</b>–<b>J</b>) for ten binary traits (avrPphB, avrRpm1, avrRpt2, avrB, Anthocyanin 10, 16, and 22, Leaf roll 10, 16, and 22); (<b>K</b>–<b>N</b>) for four ordinal traits (Leaf serr 10, 16, 22, and Silique 22).</p> Full article ">Figure 3
<p>The statistical power for QTN detected by five methods in the first simulation experiment under (<b>A</b>) a normal distribution with 5 hierarchical levels, (<b>B</b>) a uniform distribution with 5 hierarchical levels, and (<b>C</b>) a binomial distribution with 2 hierarchical levels.</p> Full article ">Figure 4
<p>ROC curves for the five methods of the first simulation experiment. From top to bottom, each row represents 2 (<b>A</b>–<b>C</b>), 5 (<b>D</b>–<b>F</b>), and 10 (<b>G</b>–<b>I</b>) times the polygenic background, respectively.</p> Full article ">Figure 5
<p>The average computing time using five methods in three simulation experiments. From top to bottom, each row represents the first (<b>A</b>–<b>C</b>), second (<b>D</b>–<b>F</b>), and third (<b>G</b>–<b>I</b>) simulation experiment, respectively.</p> Full article ">Figure 6
<p>A heatmap of known genes identified by five methods for fourteen binary or ordinal traits in the <span class="html-italic">Arabidopsis</span> real dataset.</p> Full article ">
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