Mapping morphological shape as a high-dimensional functional curve.

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 θ 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 θ. 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 2000-4500 m above 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.

Plant phenotyping relevance

葉形状を画像から定量化し、形状表現と統計的遺伝解析を統合する新規フレームワークが研究の中心であり、植物表現型の取得・抽出手法に該当する。

Code and data availability

The supplied blocks contain no data availability, code availability, or repository statements. The paper describes leaf shape data (106 Populus szechuanica var. tibetica trees, 29 SSR markers, RCC curves) and an FDA/QTL mapping method, but no public dataset, images, code, or model deposit is mentioned, and no authors'-

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