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Manjunatha PB, Aski MS, Mishra GP, Gupta S, Devate NB, Singh A, Bansal R, Kumar S, Nair RM, Dikshit HK. Genome-wide association studies for phenological and agronomic traits in mungbean ( Vigna radiata L. Wilczek). FRONTIERS IN PLANT SCIENCE 2023; 14:1209288. [PMID: 37810385 PMCID: PMC10558178 DOI: 10.3389/fpls.2023.1209288] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/25/2023] [Indexed: 10/10/2023]
Abstract
Mungbean (Vigna radiata L. Wilczek) is one of the important warm-season food legumes, contributing substantially to nutritional security and environmental sustainability. The genetic complexity of yield-associated agronomic traits in mungbean is not well understood. To dissect the genetic basis of phenological and agronomic traits, we evaluated 153 diverse mungbean genotypes for two phenological (days to heading and days to maturity) and eight agronomic traits (leaf nitrogen status using SPAD, plant height, number of primary branches, pod length, number of pods per plant, seeds per pod, 100-seed weight, and yield per plant) under two environmental conditions. A wide array of phenotypic variability was apparent among the studied genotypes for all the studied traits. The broad sense of heritability of traits ranged from 0.31 to 0.95 and 0.21 to 0.94 at the Delhi and Ludhiana locations, respectively. A total of 55,634 genome-wide single nucleotide polymorphisms (SNPs) were obtained by the genotyping-by-sequencing method, of which 15,926 SNPs were retained for genome-wide association studies (GWAS). GWAS with Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK) model identified 50 SNPs significantly associated with phenological and agronomic traits. In total, 12 SNPs were found to be significantly associated with phenological traits across environments, explaining 7%-18.5% of phenotypic variability, and 38 SNPs were significantly associated with agronomic traits, explaining 4.7%-27.6% of the phenotypic variability. The maximum number of SNPs (15) were located on chromosome 1, followed by seven SNPs each on chromosomes 2 and 8. The BLAST search identified 19 putative candidate genes that were involved in light signaling, nitrogen responses, phosphorus (P) transport and remobilization, photosynthesis, respiration, metabolic pathways, and regulating growth and development. Digital expression analysis of 19 genes revealed significantly higher expression of 12 genes, viz. VRADI01G08170, VRADI11G09170, VRADI02G00450, VRADI01G00700, VRADI07G14240, VRADI03G06030, VRADI02G14230, VRADI08G01540, VRADI09G02590, VRADI08G00110, VRADI02G14240, and VRADI02G00430 in the roots, cotyledons, seeds, leaves, shoot apical meristems, and flowers. The identified SNPs and putative candidate genes provide valuable genetic information for fostering genomic studies and marker-assisted breeding programs that improve yield and agronomic traits in mungbean.
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Affiliation(s)
- P. B. Manjunatha
- Division of Genetics, ICAR- Indian Agricultural Research Institute, New Delhi, India
| | - Muraleedhar S. Aski
- Division of Genetics, ICAR- Indian Agricultural Research Institute, New Delhi, India
| | - Gyan Prakash Mishra
- Division of Genetics, ICAR- Indian Agricultural Research Institute, New Delhi, India
| | - Soma Gupta
- Division of Genetics, ICAR- Indian Agricultural Research Institute, New Delhi, India
| | - Narayana Bhat Devate
- Division of Genetics, ICAR- Indian Agricultural Research Institute, New Delhi, India
| | - Akanksha Singh
- Amity Institute of Organic Agriculture, Amity University, Noida, India
| | - Ruchi Bansal
- Division of Plant Physiology, ICAR- Indian Agricultural Research Institute, New Delhi, India
| | - Shiv Kumar
- International Centre for Agricultural Research in the Dry Areas (ICARDA), New Delhi, India
| | | | - Harsh Kumar Dikshit
- Division of Genetics, ICAR- Indian Agricultural Research Institute, New Delhi, India
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Wang Z, Dhakal S, Cerit M, Wang S, Rauf Y, Yu S, Maulana F, Huang W, Anderson JD, Ma XF, Rudd JC, Ibrahim AMH, Xue Q, Hays DB, Bernardo A, St. Amand P, Bai G, Baker J, Baker S, Liu S. QTL mapping of yield components and kernel traits in wheat cultivars TAM 112 and Duster. FRONTIERS IN PLANT SCIENCE 2022; 13:1057701. [PMID: 36570880 PMCID: PMC9768232 DOI: 10.3389/fpls.2022.1057701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 11/04/2022] [Indexed: 06/17/2023]
Abstract
In the Southern Great Plains, wheat cultivars have been selected for a combination of outstanding yield and drought tolerance as a long-term breeding goal. To understand the underlying genetic mechanisms, this study aimed to dissect the quantitative trait loci (QTL) associated with yield components and kernel traits in two wheat cultivars `TAM 112' and `Duster' under both irrigated and dryland environments. A set of 182 recombined inbred lines (RIL) derived from the cross of TAM 112/Duster were planted in 13 diverse environments for evaluation of 18 yield and kernel related traits. High-density genetic linkage map was constructed using 5,081 single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing (GBS). QTL mapping analysis detected 134 QTL regions on all 21 wheat chromosomes, including 30 pleiotropic QTL regions and 21 consistent QTL regions, with 10 QTL regions in common. Three major pleiotropic QTL on the short arms of chromosomes 2B (57.5 - 61.6 Mbps), 2D (37.1 - 38.7 Mbps), and 7D (66.0 - 69.2 Mbps) colocalized with genes Ppd-B1, Ppd-D1, and FT-D1, respectively. And four consistent QTL associated with kernel length (KLEN), thousand kernel weight (TKW), plot grain yield (YLD), and kernel spike-1 (KPS) (Qklen.tamu.1A.325, Qtkw.tamu.2B.137, Qyld.tamu.2D.3, and Qkps.tamu.6A.113) explained more than 5% of the phenotypic variation. QTL Qklen.tamu.1A.325 is a novel QTL with consistent effects under all tested environments. Marker haplotype analysis indicated the QTL combinations significantly increased yield and kernel traits. QTL and the linked markers identified in this study will facilitate future marker-assisted selection (MAS) for pyramiding the favorable alleles and QTL map-based cloning.
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Affiliation(s)
- Zhen Wang
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Smit Dhakal
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Mustafa Cerit
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Shichen Wang
- Genomics and Bioinformatics Service Center, Texas A&M AgriLife Research, College Station, TX, United States
| | - Yahya Rauf
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Shuhao Yu
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Frank Maulana
- Noble Research Institute, Ardmore, OK, United States
| | - Wangqi Huang
- Noble Research Institute, Ardmore, OK, United States
| | | | - Xue-Feng Ma
- Noble Research Institute, Ardmore, OK, United States
| | - Jackie C. Rudd
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Amir M. H. Ibrahim
- Department of Soil and Crop Science, Texas A&M University, College Station, TX, United States
| | - Qingwu Xue
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Dirk B. Hays
- Department of Soil and Crop Science, Texas A&M University, College Station, TX, United States
| | - Amy Bernardo
- Central Small Grain Genotyping Lab and Hard Winter Wheat Genetics Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Manhattan, KS, United States
| | - Paul St. Amand
- Central Small Grain Genotyping Lab and Hard Winter Wheat Genetics Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Manhattan, KS, United States
| | - Guihua Bai
- Central Small Grain Genotyping Lab and Hard Winter Wheat Genetics Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Manhattan, KS, United States
| | - Jason Baker
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Shannon Baker
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
| | - Shuyu Liu
- Texas A&M AgriLife Research and Extension Center, Amarillo, TX, United States
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Gill HS, Halder J, Zhang J, Rana A, Kleinjan J, Amand PS, Bernardo A, Bai G, Sehgal SK. Whole-genome analysis of hard winter wheat germplasm identifies genomic regions associated with spike and kernel traits. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2953-2967. [PMID: 35939073 DOI: 10.1007/s00122-022-04160-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Genetic dissection of yield component traits including spike and kernel characteristics is essential for the continuous improvement in wheat yield. Genome-wide association studies (GWAS) have been frequently used to identify genetic determinants for spike and kernel-related traits in wheat, though none have been employed in hard winter wheat (HWW) which represents a major class in US wheat acreage. Further, most of these studies relied on assembled diversity panels instead of adapted breeding lines, limiting the transferability of results to practical wheat breeding. Here we assembled a population of advanced/elite breeding lines and well-adapted cultivars and evaluated over four environments for phenotypic analysis of spike and kernel traits. GWAS identified 17 significant multi-environment marker-trait associations (MTAs) for various traits, representing 12 putative quantitative trait loci (QTLs), with five QTLs affecting multiple traits. Four of these QTLs mapped on three chromosomes 1A, 5B, and 7A for spike length, number of spikelets per spike (NSPS), and kernel length are likely novel. Further, a highly significant QTL was detected on chromosome 7AS that has not been previously associated with NSPS and putative candidate genes were identified in this region. The allelic frequencies of important quantitative trait nucleotides (QTNs) were deduced in a larger set of 1,124 accessions which revealed the importance of identified MTAs in the US HWW breeding programs. The results from this study could be directly used by the breeders to select the lines with favorable alleles for making crosses, and reported markers will facilitate marker-assisted selection of stable QTLs for yield components in wheat breeding.
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Affiliation(s)
- Harsimardeep S Gill
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Jyotirmoy Halder
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Jinfeng Zhang
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Anshul Rana
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Jonathan Kleinjan
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Paul St Amand
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66506, USA
| | - Amy Bernardo
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66506, USA
| | - Guihua Bai
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS, 66506, USA
| | - Sunish K Sehgal
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA.
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Chu C, Wang S, Rudd JC, Ibrahim AMH, Xue Q, Devkota RN, Baker JA, Baker S, Simoneaux B, Opena G, Dong H, Liu X, Jessup KE, Chen MS, Hui K, Metz R, Johnson CD, Zhang ZS, Liu S. A new strategy for using historical imbalanced yield data to conduct genome-wide association studies and develop genomic prediction models for wheat breeding. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:18. [PMID: 37309459 PMCID: PMC10248704 DOI: 10.1007/s11032-022-01287-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 03/02/2022] [Indexed: 06/14/2023]
Abstract
Using imbalanced historical yield data to predict performance and select new lines is an arduous breeding task. Genome-wide association studies (GWAS) and high throughput genotyping based on sequencing techniques can increase prediction accuracy. An association mapping panel of 227 Texas elite (TXE) wheat breeding lines was used for GWAS and a training population to develop prediction models for grain yield selection. An imbalanced set of yield data collected from 102 environments (year-by-location) over 10 years, through testing yield in 40-66 lines each year at 6-14 locations with 38-41 lines repeated in the test in any two consecutive years, was used. Based on correlations among data from different environments within two adjacent years and heritability estimated in each environment, yield data from 87 environments were selected and assigned to two correlation-based groups. The yield best linear unbiased estimation (BLUE) from each group, along with reaction to greenbug and Hessian fly in each line, was used for GWAS to reveal genomic regions associated with yield and insect resistance. A total of 74 genomic regions were associated with grain yield and two of them were commonly detected in both correlation-based groups. Greenbug resistance in TXE lines was mainly controlled by Gb3 on chromosome 7DL in addition to two novel regions on 3DL and 6DS, and Hessian fly resistance was conferred by the region on 1AS. Genomic prediction models developed in two correlation-based groups were validated using a set of 105 new advanced breeding lines and the model from correlation-based group G2 was more reliable for prediction. This research not only identified genomic regions associated with yield and insect resistance but also established the method of using historical imbalanced breeding data to develop a genomic prediction model for crop improvement. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01287-8.
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Affiliation(s)
- Chenggen Chu
- Texas A&M AgriLife Research Center, Amarillo, TX 79106 USA
- Sugarbeet & Potato Research Unit, Edward T. Schafer Agricultural Research Center, USDA-ARS, Fargo, ND 58102 USA
| | - Shichen Wang
- Genomics and Bioinformatics Service Center, Texas A&M AgriLife Research, College Station, TX 77843 USA
| | - Jackie C. Rudd
- Texas A&M AgriLife Research Center, Amarillo, TX 79106 USA
| | - Amir M. H. Ibrahim
- Soil and Crop Sciences Department, Texas A&M University, College Station, TX 77843 USA
| | - Qingwu Xue
- Texas A&M AgriLife Research Center, Amarillo, TX 79106 USA
| | | | - Jason A. Baker
- Texas A&M AgriLife Research Center, Amarillo, TX 79106 USA
| | - Shannon Baker
- Texas A&M AgriLife Research Center, Amarillo, TX 79106 USA
| | - Bryan Simoneaux
- Soil and Crop Sciences Department, Texas A&M University, College Station, TX 77843 USA
| | - Geraldine Opena
- Soil and Crop Sciences Department, Texas A&M University, College Station, TX 77843 USA
| | - Haixiao Dong
- Soil and Crop Sciences Department, Washington State University, Pullman, WA 99164 USA
| | - Xiaoxiao Liu
- Texas A&M AgriLife Research Center, Amarillo, TX 79106 USA
| | - Kirk E. Jessup
- Texas A&M AgriLife Research Center, Amarillo, TX 79106 USA
| | - Ming-Shun Chen
- Hard Winter Wheat Genetics Research Unit, USDA-ARS, Manhattan, KS 66506 USA
| | - Kele Hui
- Texas A&M AgriLife Research Center, Amarillo, TX 79106 USA
| | - Richard Metz
- Genomics and Bioinformatics Service Center, Texas A&M AgriLife Research, College Station, TX 77843 USA
| | - Charles D. Johnson
- Genomics and Bioinformatics Service Center, Texas A&M AgriLife Research, College Station, TX 77843 USA
| | - Zhiwu S. Zhang
- Soil and Crop Sciences Department, Washington State University, Pullman, WA 99164 USA
| | - Shuyu Liu
- Texas A&M AgriLife Research Center, Amarillo, TX 79106 USA
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