Advanced search
1 file | 3.11 MB Add to list

Understanding genetic control of root system architecture in soybean : insights into the genetic basis of lateral root number

(2019) PLANT CELL AND ENVIRONMENT. 42(1). p.212-229
Author
Organization
Abstract
Developing crops with better root systems is a promising strategy to ensure productivity in both optimum and stress environments. Root system architectural traits in 397 soybean accessions were characterized and a high-density single nucleotide polymorphisms (SNPs)-based genome-wide association study was performed to identify the underlying genes associated with root structure. SNPs associated with root architectural traits specific to landraces and elite germplasm pools were detected. Four loci were detected in landraces for lateral root number (LRN) and distribution of root thickness in diameter Class I with a major locus on chromosome 16. This major loci was detected in the coding region of unknown protein, and subsequent analyses demonstrated that root traits are affected with mutated haplotypes of the gene. In elite germplasm pool, 3 significant SNPs in alanine-glyoxalate aminotransferase, Leucine-Rich Repeat receptor/No apical meristem, and unknown functional genes were found to govern multiple traits including root surface area and volume. However, no major loci were detected for LRN in elite germplasm. Nucleotide diversity analysis found evidence of selective sweeps around the landraces LRN gene. Soybean accessions with minor and mutated allelic variants of LRN gene were found to perform better in both water-limited and optimal field conditions.
Keywords
GENOME-WIDE ASSOCIATION, CELL-WALL, PLASMA-MEMBRANE, TRAITS, ARABIDOPSIS, DROUGHT, TOLERANCE, LINKAGE, STRESS, GROWTH, development, drought, genetic variation, genome-wide association, root, architecture, yield under drought stress

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 3.11 MB

Citation

Please use this url to cite or link to this publication:

MLA
Prince, Silvas J et al. “Understanding Genetic Control of Root System Architecture in Soybean : Insights into the Genetic Basis of Lateral Root Number.” PLANT CELL AND ENVIRONMENT 42.1 (2019): 212–229. Print.
APA
Prince, S. J., Valliyodan, B., Ye, H., Yang, M., Tai, S., Hu, W., Murphy, M., et al. (2019). Understanding genetic control of root system architecture in soybean : insights into the genetic basis of lateral root number. PLANT CELL AND ENVIRONMENT, 42(1), 212–229.
Chicago author-date
Prince, Silvas J, Babu Valliyodan, Heng Ye, Ming Yang, Shuaishuai Tai, Wushu Hu, Mackensie Murphy, et al. 2019. “Understanding Genetic Control of Root System Architecture in Soybean : Insights into the Genetic Basis of Lateral Root Number.” Plant Cell and Environment 42 (1): 212–229.
Chicago author-date (all authors)
Prince, Silvas J, Babu Valliyodan, Heng Ye, Ming Yang, Shuaishuai Tai, Wushu Hu, Mackensie Murphy, Lorellin A Durnell, Li Song, Trupti Joshi, Yang Liu, Jan Van de Velde, Klaas Vandepoele, J Grover Shannon, and Henry T Nguyen. 2019. “Understanding Genetic Control of Root System Architecture in Soybean : Insights into the Genetic Basis of Lateral Root Number.” Plant Cell and Environment 42 (1): 212–229.
Vancouver
1.
Prince SJ, Valliyodan B, Ye H, Yang M, Tai S, Hu W, et al. Understanding genetic control of root system architecture in soybean : insights into the genetic basis of lateral root number. PLANT CELL AND ENVIRONMENT. 2019;42(1):212–29.
IEEE
[1]
S. J. Prince et al., “Understanding genetic control of root system architecture in soybean : insights into the genetic basis of lateral root number,” PLANT CELL AND ENVIRONMENT, vol. 42, no. 1, pp. 212–229, 2019.
@article{8587994,
  abstract     = {Developing crops with better root systems is a promising strategy to ensure productivity in both optimum and stress environments. Root system architectural traits in 397 soybean accessions were characterized and a high-density single nucleotide polymorphisms (SNPs)-based genome-wide association study was performed to identify the underlying genes associated with root structure. SNPs associated with root architectural traits specific to landraces and elite germplasm pools were detected. Four loci were detected in landraces for lateral root number (LRN) and distribution of root thickness in diameter Class I with a major locus on chromosome 16. This major loci was detected in the coding region of unknown protein, and subsequent analyses demonstrated that root traits are affected with mutated haplotypes of the gene. In elite germplasm pool, 3 significant SNPs in alanine-glyoxalate aminotransferase, Leucine-Rich Repeat receptor/No apical meristem, and unknown functional genes were found to govern multiple traits including root surface area and volume. However, no major loci were detected for LRN in elite germplasm. Nucleotide diversity analysis found evidence of selective sweeps around the landraces LRN gene. Soybean accessions with minor and mutated allelic variants of LRN gene were found to perform better in both water-limited and optimal field conditions.},
  author       = {Prince, Silvas J and Valliyodan, Babu and Ye, Heng and Yang, Ming and Tai, Shuaishuai and Hu, Wushu and Murphy, Mackensie and Durnell, Lorellin A and Song, Li and Joshi, Trupti and Liu, Yang and Van de Velde, Jan and Vandepoele, Klaas and Shannon, J Grover and Nguyen, Henry T},
  issn         = {0140-7791},
  journal      = {PLANT CELL AND ENVIRONMENT},
  keywords     = {GENOME-WIDE ASSOCIATION,CELL-WALL,PLASMA-MEMBRANE,TRAITS,ARABIDOPSIS,DROUGHT,TOLERANCE,LINKAGE,STRESS,GROWTH,development,drought,genetic variation,genome-wide association,root,architecture,yield under drought stress},
  language     = {eng},
  number       = {1},
  pages        = {212--229},
  title        = {Understanding genetic control of root system architecture in soybean : insights into the genetic basis of lateral root number},
  url          = {http://dx.doi.org/10.1111/pce.13333},
  volume       = {42},
  year         = {2019},
}

Altmetric
View in Altmetric
Web of Science
Times cited: