Mapping morphological shape as a high-dimensional functional curve

Guifang Fu, Mian Huang, Wenhao Bo, Han Hao, Rongling Wu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Detecting how genes regulate biological shape has become a multidisciplinary research interest because of its wide application in many disciplines. Despite its fundamental importance, the challenges of accurately extracting information from an image, statistically modeling the high-dimensional shape and meticulously locating shape quantitative trait loci (QTL) affect the progress of this research. In this article, we propose a novel integrated framework that incorporates shape analysis, statistical curve modeling and genetic mapping to detect significant QTLs regulating variation of biological shape traits. After quantifying morphological shape via a radius centroid contour approach, each shape, as a phenotype, was characterized as a high-dimensional curve, varying as angle h runs clockwise with the first point starting from angle zero. We then modeled the dynamic trajectories of three mean curves and variation patterns as functions of h. Our framework led to the detection of a few significant QTLs regulating the variation of leaf shape collected from a natural population of poplar, Populus szechuanica var tibetica. This population, distributed at altitudes 2000-4500mabove sea level, is an evolutionarily important plant species. This is the first work in the quantitative genetic shape mapping area that emphasizes a sense of 'function' instead of decomposing the shape into a few discrete principal components, as the majority of shape studies do.

Original languageEnglish (US)
Pages (from-to)461-471
Number of pages11
JournalBriefings in bioinformatics
Volume19
Issue number3
DOIs
StatePublished - May 1 2018

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Molecular Biology

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