HpQTL: a geometric morphometric platform to compute the genetic architecture of heterophylly

Lidan Sun, Jing Wang, Xuli Zhu, Libo Jiang, Kirk Gosik, Mengmeng Sang, Fengsuo Sun, Tangren Cheng, Qixiang Zhang, Rongling Wu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Heterophylly, i.e. morphological changes in leaves along the axis of an individual plant, is regarded as a strategy used by plants to cope with environmental change. However, little is known of the extent to which heterophylly is controlled by genes and how each underlying gene exerts its effect on heterophyllous variation. We described a geometric morphometric model that can quantify heterophylly in plants and further constructed an R-based computing platform by integrating this model into a genetic mapping and association setting. The platform, named HpQTL, allows specific quantitative trait loci mediating heterophyllous variation to be mapped throughout the genome. The statistical properties of HpQTL were examined and validated via computer simulation. Its biological relevance was demonstrated by results from a real data analysis of heterophylly in a wood plant, mei (Prunus mume). HpQTL provides a powerful tool to analyze heterophylly and its underlying genetic architecture in a quantitative manner. It also contributes a new approach for genome-wide association studies aimed to dissect the programmed regulation of plant development and evolution.

Original languageEnglish (US)
Pages (from-to)603-612
Number of pages10
JournalBriefings in bioinformatics
Volume19
Issue number4
DOIs
StatePublished - Jul 20 2018

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Molecular Biology

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