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Susmitha P, Kumar P, Yadav P, Sahoo S, Kaur G, Pandey MK, Singh V, Tseng TM, Gangurde SS. Genome-wide association study as a powerful tool for dissecting competitive traits in legumes. FRONTIERS IN PLANT SCIENCE 2023; 14:1123631. [PMID: 37645459 PMCID: PMC10461012 DOI: 10.3389/fpls.2023.1123631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/08/2023] [Indexed: 08/31/2023]
Abstract
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.
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Affiliation(s)
- Pusarla Susmitha
- Regional Agricultural Research Station, Acharya N.G. Ranga Agricultural University, Andhra Pradesh, India
| | - Pawan Kumar
- Department of Genetics and Plant Breeding, College of Agriculture, Chaudhary Charan Singh (CCS) Haryana Agricultural University, Hisar, India
| | - Pankaj Yadav
- Department of Bioscience and Bioengineering, Indian Institute of Technology, Rajasthan, India
| | - Smrutishree Sahoo
- Department of Genetics and Plant Breeding, School of Agriculture, Gandhi Institute of Engineering and Technology (GIET) University, Odisha, India
| | - Gurleen Kaur
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Manish K. Pandey
- Department of Genomics, Prebreeding and Bioinformatics, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India
| | - Varsha Singh
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Te Ming Tseng
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Sunil S. Gangurde
- Department of Plant Pathology, University of Georgia, Tifton, GA, United States
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Gelaw YM, Eleblu JSY, Ofori K, Fenta BA, Mukankusi C, Emam EA, Offei S. High-density DArTSeq SNP markers revealed wide genetic diversity and structured population in common bean (Phaseolus vulgaris L.) germplasm in Ethiopia. Mol Biol Rep 2023; 50:6739-6751. [PMID: 37389701 PMCID: PMC10374692 DOI: 10.1007/s11033-023-08498-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 04/28/2023] [Indexed: 07/01/2023]
Abstract
INTRODUCTION Common bean is one of the widely consumed food security crop in Africa, Asia, and South America. Understanding genetic diversity and population structure is crucial for designing breeding strategies. MATERIALS Two hundred and eighty-nine germplasm were recently collected from different regions of Ethiopia and introduced from CIAT to estimate genetic diversity and population structure using 11,480 DArTSeq SNP markers. RESULTS The overall mean genetic diversity and polymorphic information content (PIC) were 0.38 and 0.30, respectively, suggested the presence of adequate genetic diversity among the genotypes. Among the geographical regions, landraces collected from Oromia showed the highest diversity (0.39) and PIC (0.30). The highest genetic distance was observed between genotypes collected from SNNPR and CIAT (0.49). In addition, genotypes from CIAT were genetically more related to improved varieties than the landraces which could be due to sharing of parents in the improvement process. The analysis of molecular variance revealed that the largest proportion of variation was due to within the population both in geographical region (63.67%) and breeding status (61.3%) based classification. Model-based structure analysis delineated the 289 common bean genotypes into six hypothetical ancestoral populations. CONCLUSIONS The genotypes were not clustered based on geographical regions and they were not the main drivers for the differentiation. This indicated that selection of the parental lines should be based on systematic assessment of the diversity rather than geographical distance. This article provides new insights into the genetic diversity and population structure of common bean for association studies, designing effective collection and conservation for efficient utilization for the improvement of the crop.
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Affiliation(s)
- Yonas Moges Gelaw
- Haramaya University, Dire Dawa, Ethiopia.
- West Africa Centre for Crop Improvement, University of Ghana, Legon, Accra, Ghana.
| | - John S Y Eleblu
- West Africa Centre for Crop Improvement, University of Ghana, Legon, Accra, Ghana
| | - Kwadwo Ofori
- West Africa Centre for Crop Improvement, University of Ghana, Legon, Accra, Ghana
| | - Berhanu Amsalu Fenta
- Melkassa Agricultural Research Center, Ethiopian Institute of Agricultural Research (EIAR), Adama, Ethiopia
| | - Clare Mukankusi
- International Centre for Tropical Agriculture (CIAT), Kawanda, Kampala, Uganda
| | | | - Samuel Offei
- West Africa Centre for Crop Improvement, University of Ghana, Legon, Accra, Ghana
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Izquierdo P, Kelly JD, Beebe SE, Cichy K. Combination of meta-analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean. THE PLANT GENOME 2023:e20328. [PMID: 37082832 DOI: 10.1002/tpg2.20328] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 02/08/2023] [Accepted: 03/01/2023] [Indexed: 05/03/2023]
Abstract
Increasing seed yield in common bean could help to improve food security and reduce malnutrition globally due to the high nutritional quality of this crop. However, the complex genetic architecture and prevalent genotype by environment interactions for seed yield makes increasing genetic gains challenging. The aim of this study was to identify the most consistent genomic regions related with seed yield components and phenology reported in the last 20 years in common bean. A meta-analysis of quantitative trait locus (QTL) for seed yield components and phenology (MQTL-YC) was performed for 394 QTL reported in 21 independent studies under sufficient water and drought conditions. In total, 58 MQTL-YC over different genetic backgrounds and environments were identified, reducing threefold on average the confidence interval (CI) compared with the CI for the initial QTL. Furthermore, 40 MQTL-YC identified were co-located with 210 SNP peak positions reported via genome-wide association (GWAS), guiding the identification of candidate genes. Comparative genomics among these MQTL-YC with MQTL-YC reported in soybean and pea allowed the identification of 14 orthologous MQTL-YC shared across species. The integration of MQTL-YC, GWAS, and comparative genomics used in this study is useful to uncover and refine the most consistent genomic regions related with seed yield components for their use in plant breeding.
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Affiliation(s)
- Paulo Izquierdo
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, MI, USA
| | - James D Kelly
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, MI, USA
| | - Stephen E Beebe
- Bean Program, Crops for Health and Nutrition Area, Alliance Bioversity International-CIAT, Cali, Colombia
| | - Karen Cichy
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, MI, USA
- USDA-ARS, Sugarbeet and Bean Research Unit, East Lansing, MI, USA
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Salgotra RK, Stewart CN. Genetic Augmentation of Legume Crops Using Genomic Resources and Genotyping Platforms for Nutritional Food Security. PLANTS 2022; 11:plants11141866. [PMID: 35890499 PMCID: PMC9325189 DOI: 10.3390/plants11141866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 07/11/2022] [Accepted: 07/12/2022] [Indexed: 11/24/2022]
Abstract
Recent advances in next generation sequencing (NGS) technologies have led the surge of genomic resources for the improvement legume crops. Advances in high throughput genotyping (HTG) and high throughput phenotyping (HTP) enable legume breeders to improve legume crops more precisely and efficiently. Now, the legume breeder can reshuffle the natural gene combinations of their choice to enhance the genetic potential of crops. These genomic resources are efficiently deployed through molecular breeding approaches for genetic augmentation of important legume crops, such as chickpea, cowpea, pigeonpea, groundnut, common bean, lentil, pea, as well as other underutilized legume crops. In the future, advances in NGS, HTG, and HTP technologies will help in the identification and assembly of superior haplotypes to tailor the legume crop varieties through haplotype-based breeding. This review article focuses on the recent development of genomic resource databases and their deployment in legume molecular breeding programmes to secure global food security.
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Affiliation(s)
- Romesh K. Salgotra
- School of Biotechnology, Sher-e-Kashmir University of Agricultural Sciences & Technology of Jammu, Chatha, Jammu 190008, India
- Correspondence: (R.K.S.); (C.N.S.J.)
| | - Charles Neal Stewart
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA
- Correspondence: (R.K.S.); (C.N.S.J.)
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Genomics Associated Interventions for Heat Stress Tolerance in Cool Season Adapted Grain Legumes. Int J Mol Sci 2021; 23:ijms23010399. [PMID: 35008831 PMCID: PMC8745526 DOI: 10.3390/ijms23010399] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 12/22/2021] [Accepted: 12/27/2021] [Indexed: 11/16/2022] Open
Abstract
Cool season grain legumes occupy an important place among the agricultural crops and essentially provide multiple benefits including food supply, nutrition security, soil fertility improvement and revenue for farmers all over the world. However, owing to climate change, the average temperature is steadily rising, which negatively affects crop performance and limits their yield. Terminal heat stress that mainly occurred during grain development phases severely harms grain quality and weight in legumes adapted to the cool season, such as lentils, faba beans, chickpeas, field peas, etc. Although, traditional breeding approaches with advanced screening procedures have been employed to identify heat tolerant legume cultivars. Unfortunately, traditional breeding pipelines alone are no longer enough to meet global demands. Genomics-assisted interventions including new-generation sequencing technologies and genotyping platforms have facilitated the development of high-resolution molecular maps, QTL/gene discovery and marker-assisted introgression, thereby improving the efficiency in legumes breeding to develop stress-resilient varieties. Based on the current scenario, we attempted to review the intervention of genomics to decipher different components of tolerance to heat stress and future possibilities of using newly developed genomics-based interventions in cool season adapted grain legumes.
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