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LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
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Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models.

Guifang Fu1, Xiaotian Dai1, Jürgen Symanzik1

  • 1Department of Mathematics and Statistics, Utah State University, Logan, UT, 84321, USA.

The New Phytologist
|September 22, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel data-driven framework to analyze leaf shape variation, integrating genetic and environmental factors. It effectively identifies key drivers of leaf morphology, advancing our understanding of plant evolution.

Keywords:
gene-environmentgene-geneleaf shapequantitative genetic shape mappingradius-centroid-contourrandom forests

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Area of Science:

  • Evolutionary Biology
  • Plant Morphology
  • Quantitative Genetics

Background:

  • Leaf shape is a critical trait in various scientific disciplines.
  • Understanding the genetic and environmental interactions shaping leaf shape variation remains a complex challenge.

Purpose of the Study:

  • To develop and validate a data-driven framework for analyzing leaf shape variation.
  • To investigate the relative importance of genetic and environmental factors, including their interactions, in modulating leaf shape.

Main Methods:

  • A framework integrating shape feature extraction, dimension reduction, and tree-based statistical models was employed.
  • Input data included leaf shape images, genetic data, and environmental data.
  • The framework was validated using simulations and a dataset from Populus szechuanica var. tibetica.

Main Results:

  • The framework successfully identified novel leaf shape characteristics and confirmed previous findings.
  • It provided quantitative models for polygenic, plastic, epistatic, and gene-environment interactive effects.
  • The approach demonstrated its power in ranking the importance of different variables influencing leaf shape.

Conclusions:

  • The developed framework offers a sophisticated, data-driven methodology for leaf morphology research.
  • This approach advances the discernment of quantitative leaf shape characteristics by modeling complex genetic and environmental interactions.
  • The methods are adaptable for application to other leaf morphology datasets.