Mapping morphological shape as a high-dimensional functional curve. (6th January 2017)

• Record Type: Journal Article
• Authors: Fu, Guifang; Huang, Mian; Bo, Wenhao; Hao, Han; Wu, Rongling
• Is Part Of: Briefings in bioinformatics. Volume 19:Number 3(2018:May)
• Page Start: 461
• Page End: 471
• Publication Date: 2017-01-06
• Subjects: morphological shape -- quantitative shape genetic mapping -- functional data analysis -- Populus szechuanica var tibetica -- leaf shape; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbw111
• Languages: English
• ISSNs: 1467-5463

Abstract

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.