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 10
Special Issue Editors
Interests: plant genomics and genetics; molecular biology; crop molecular breeding; plant nutrient utilization
Interests: GWAS; molecular mechanisms of plant drought resistance; crop molecular breeding
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
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Keywords
- genome wide association study (GWAS)
- crops
- agronomic traits
- QTL
- molecular mechanism
