Literature DB >> 33719333

Identification of the QTL-allele System Underlying Two High-Throughput Physiological Traits in the Chinese Soybean Germplasm Population.

Lei Wang1, Fangdong Liu1, Xiaoshuai Hao1, Wubin Wang1,2,3,4, Guangnan Xing1,2,3,4, Jingjing Luo5, Guodong Zhou5, Jianbo He1,2,3,4, Junyi Gai1,2,3,4,6.   

Abstract

The QTL-allele system underlying two spectral reflectance physiological traits, NDVI (normalized difference vegetation index) and CHL (chlorophyll index), related to plant growth and yield was studied in the Chinese soybean germplasm population (CSGP), which consisted of 341 wild accessions (WA), farmer landraces (LR), and released cultivars (RC). Samples were evaluated in the Photosynthetic System II imaging platform at Nanjing Agricultural University. The NDVI and CHL data were obtained from hyperspectral reflectance images in a randomized incomplete block design experiment with two replicates. The NDVI and CHL ranged from 0.05-0.18 and 1.20-4.78, had averages of 0.11 and 3.57, and had heritabilities of 78.3% and 69.2%, respectively; the values of NDVI and CHL were both significantly higher in LR and RC than in WA. Using the RTM-GWAS (restricted two-stage multi-locus genome-wide association study) method, 38 and 32 QTLs with 89 and 82 alleles and 2-4 and 2-6 alleles per locus were identified for NDVI and CHL, respectively, which explained 48.36% and 51.35% of the phenotypic variation for NDVI and CHL, respectively. The QTL-allele matrices were established and separated into WA, LR, and RC submatrices. From WA to LR + RC, 4 alleles and 2 new loci emerged, and 1 allele was excluded for NDVI, whereas 6 alleles emerged, and no alleles were excluded, in LR + RC for CHL. Recombination was the major motivation of evolutionary differences. For NDVI and CHL, 39 and 32 candidate genes were annotated and assigned to GO groups, respectively, indicating a complex gene network. The NDVI and CHL were upstream traits that were relatively conservative in their genetic changes compared with those of downstream agronomic traits. High-throughput phenotyping integrated with RTM-GWAS provides an efficient procedure for studying the population genetics of traits.
Copyright © 2021 Wang, Liu, Hao, Wang, Xing, Luo, Zhou, He and Gai.

Entities:  

Keywords:  QTL-allele matrix; annual wild soybean (G. soja Sieb. & Zucc.); chlorophyll index (CHL); cultivated soybean (G. max (L.) Merr.); high-throughput phenotyping; normalized difference vegetation index (NDVI); restricted two-stage multi-locus genome-wide association study (RTM-GWAS); spectral reflectance image

Year:  2021        PMID: 33719333      PMCID: PMC7947801          DOI: 10.3389/fgene.2021.600444

Source DB:  PubMed          Journal:  Front Genet        ISSN: 1664-8021            Impact factor:   4.599


  19 in total

1.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves.

Authors:  Anatoly A Gitelson; Yuri Gritz; Mark N Merzlyak
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Review 2.  Review: High-throughput phenotyping to enhance the use of crop genetic resources.

Authors:  G J Rebetzke; J Jimenez-Berni; R A Fischer; D M Deery; D J Smith
Journal:  Plant Sci       Date:  2018-06-21       Impact factor: 4.729

3.  A High-Throughput, Field-Based Phenotyping Technology for Tall Biomass Crops.

Authors:  Maria G Salas Fernandez; Yin Bao; Lie Tang; Patrick S Schnable
Journal:  Plant Physiol       Date:  2017-06-15       Impact factor: 8.340

Review 4.  10 Years of GWAS Discovery: Biology, Function, and Translation.

Authors:  Peter M Visscher; Naomi R Wray; Qian Zhang; Pamela Sklar; Mark I McCarthy; Matthew A Brown; Jian Yang
Journal:  Am J Hum Genet       Date:  2017-07-06       Impact factor: 11.025

5.  An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding.

Authors:  Jianbo He; Shan Meng; Tuanjie Zhao; Guangnan Xing; Shouping Yang; Yan Li; Rongzhan Guan; Jiangjie Lu; Yufeng Wang; Qiuju Xia; Bing Yang; Junyi Gai
Journal:  Theor Appl Genet       Date:  2017-08-21       Impact factor: 5.699

6.  Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat.

Authors:  Jessica Rutkoski; Jesse Poland; Suchismita Mondal; Enrique Autrique; Lorena González Pérez; José Crossa; Matthew Reynolds; Ravi Singh
Journal:  G3 (Bethesda)       Date:  2016-09-08       Impact factor: 3.154

7.  Bi-Phenotypic Trait May Be Conferred by Multiple Alleles in a Germplasm Population.

Authors:  Fangdong Liu; Jianbo He; Wubin Wang; Guangnan Xing; Junyi Gai
Journal:  Front Genet       Date:  2020-06-03       Impact factor: 4.599

8.  Comparative Aerial and Ground Based High Throughput Phenotyping for the Genetic Dissection of NDVI as a Proxy for Drought Adaptive Traits in Durum Wheat.

Authors:  Giuseppe E Condorelli; Marco Maccaferri; Maria Newcomb; Pedro Andrade-Sanchez; Jeffrey W White; Andrew N French; Giuseppe Sciara; Rick Ward; Roberto Tuberosa
Journal:  Front Plant Sci       Date:  2018-06-26       Impact factor: 5.753

9.  Comprehensive Identification of Drought Tolerance QTL-Allele and Candidate Gene Systems in Chinese Cultivated Soybean Population.

Authors:  Wubin Wang; Bin Zhou; Jianbo He; Jinming Zhao; Cheng Liu; Xianlian Chen; Guangnan Xing; Shouyi Chen; Han Xing; Junyi Gai
Journal:  Int J Mol Sci       Date:  2020-07-08       Impact factor: 5.923

Review 10.  Phenotyping Plant Responses to Biotic Stress by Chlorophyll Fluorescence Imaging.

Authors:  María Luisa Pérez-Bueno; Mónica Pineda; Matilde Barón
Journal:  Front Plant Sci       Date:  2019-09-18       Impact factor: 5.753

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  1 in total

Review 1.  Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review.

Authors:  Qinlin Xiao; Xiulin Bai; Chu Zhang; Yong He
Journal:  J Adv Res       Date:  2021-05-12       Impact factor: 10.479

  1 in total

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