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Genetic architecture of variation in Arabidopsis thaliana rosettes

Author

Listed:
  • Odín Morón-García
  • Gina A Garzón-Martínez
  • M J Pilar Martínez-Martín
  • Jason Brook
  • Fiona M K Corke
  • John H Doonan
  • Anyela V Camargo Rodríguez

Abstract

Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.

Suggested Citation

  • Odín Morón-García & Gina A Garzón-Martínez & M J Pilar Martínez-Martín & Jason Brook & Fiona M K Corke & John H Doonan & Anyela V Camargo Rodríguez, 2022. "Genetic architecture of variation in Arabidopsis thaliana rosettes," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-22, February.
    • Handle: RePEc:plo:pone00:0263985
    DOI: 10.1371/journal.pone.0263985
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    References listed on IDEAS

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    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    2. Anyela Camargo & Dimitra Papadopoulou & Zoi Spyropoulou & Konstantinos Vlachonasios & John H Doonan & Alan P Gay, 2014. "Objective Definition of Rosette Shape Variation Using a Combined Computer Vision and Data Mining Approach," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-11, May.
    3. Daniel E Runcie & Lorin Crawford, 2019. "Fast and flexible linear mixed models for genome-wide genetics," PLOS Genetics, Public Library of Science, vol. 15(2), pages 1-24, February.
    4. Marco Bürger & Björn C. Willige & Joanne Chory, 2017. "A hydrophobic anchor mechanism defines a deacetylase family that suppresses host response against YopJ effectors," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
    5. Yinglun Li & Weiliang Wen & Xinyu Guo & Zetao Yu & Shenghao Gu & Haipeng Yan & Chunjiang Zhao, 2021. "High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-19, January.
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