Genome-wide association study as a powerful tool for dissecting competitive traits in legumes

Frontiers in Plant Science

Plant Science

Pankaj Yadav¹  Pawan Kumar²  Smrutishree Sahoo³  Manish K. Pandey⁴  Sunil S. Gangurde⁵  Varsha Singh⁶  Te Ming Tseng⁶  Gurleen Kaur⁷  Pusarla Susmitha⁸ 

[1] Department of Bioscience and Bioengineering, Indian Institute of Technology, Rajasthan, India;Department of Genetics and Plant Breeding, College of Agriculture, Chaudhary Charan Singh (CCS) Haryana Agricultural University, Hisar, India;Department of Genetics and Plant Breeding, School of Agriculture, Gandhi Institute of Engineering and Technology (GIET) University, Odisha, India;Department of Genomics, Prebreeding and Bioinformatics, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India;Department of Plant Pathology, University of Georgia, Tifton, GA, United States;Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States;Horticultural Sciences Department, University of Florida, Gainesville, FL, United States;Regional Agricultural Research Station, Acharya N.G. Ranga Agricultural University, Andhra Pradesh, India;

关键词: breeding;    genomic selection;    linkage;    mapping;    phenotyping;    protein;   

DOI  :  10.3389/fpls.2023.1123631

 received in 2022-12-14, accepted in 2023-06-08,  发布年份 2023

来源: Frontiers

【 摘 要 】

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

【 授权许可】

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Copyright © 2023 Susmitha, Kumar, Yadav, Sahoo, Kaur, Pandey, Singh, Tseng and Gangurde