Integrating advancements in root phenotyping and genome‐wide association studies to open the root genetics gateway. Issue 6 (1st November 2022)

• Record Type: Journal Article
• Authors: Abbas, Mubashir; Abid, Muhammad Ali; Meng, Zhigang; Abbas, Manzar; Wang, Peilin; Lu, Chao; Askari, Muhammad; Akram, Umar; Ye, Yulu; Wei, Yunxiao; Wang, Yuan; Guo, Sandui; Liang, Chengzhen; Zhang, Rui
• Is Part Of: Physiologia plantarum. Volume 174:Issue 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-01
• Subjects: Plant physiology -- Periodicals; 571.2
• DOI: 10.1111/ppl.13787
• Languages: English
• ISSNs: 0031-9317

Abstract

Abstract: Plant adaptation to challenging environmental conditions around the world has made root growth and development an important research area for plant breeders and scientists. Targeted manipulation of root system architecture (RSA) to increase water and nutrient use efficiency can minimize the adverse effects of climate change on crop production. However, phenotyping of RSA is a major bottleneck since the roots are hidden in the soil. Recently the development of 2‐ and 3D root imaging techniques combined with the genome‐wide association studies (GWASs) have opened up new research tools to identify the genetic basis of RSA. These approaches provide a comprehensive understanding of the RSA, by accelerating the identification and characterization of genes involved in root growth and development. This review summarizes the latest developments in phenotyping techniques and GWAS for RSA, which are used to map important genes regulating various aspects of RSA under varying environmental conditions. Furthermore, we discussed about the state‐of‐the‐art image analysis tools integrated with various phenotyping platforms for investigating and quantifying root traits with the highest phenotypic plasticity in both artificial and natural environments which were used for large scale association mapping studies, leading to the identification of RSA phenotypes and their underlying genetics with the greatest potential for RSA improvement. In addition, challenges in root phenotyping and GWAS are also highlighted, along with future research directions employing machine learning and pan‐genomics approaches.