title: Kernel methods for phenotyping complex plant architecture.
abstract: The Quantitative Trait Loci (QTL) mapping of plant architecture is a critical step for understanding the genetic determinism of plant architecture. Previous studies adopted simple measurements, such as plant-height, stem-diameter and branching-intensity for QTL mapping of plant architecture. Many of these quantitative traits were generally correlated to each other, which give rise to statistical problem in the detection of QTL. We aim to test the applicability of kernel methods to phenotyping inflorescence architecture and its QTL mapping. We first test Kernel Principal Component Analysis (KPCA) and Support Vector Machines (SVM) over an artificial dataset of simulated inflorescences with different types of flower distribution, which is coded as a sequence of flower-number per node along a shoot. The ability of discriminating the different inflorescence types by SVM and KPCA is illustrated. We then apply the KPCA representation to the real dataset of rose inflorescence shoots (n=1460) obtained from a 98 F1 hybrid mapping population. We find kernel principal components with high heritability (>0.7), and the QTL analysis identifies a new QTL, which was not detected by a trait-by-trait analysis of simple architectural measurements. The main tools developed in this paper could be use to tackle the general problem of QTL mapping of complex (sequences, 3D structure, graphs) phenotypic traits.
authors.affiliation: INRA, Institut de Recherche en Horticulture et Semences (INRA, Agrocampus-Ouest, Université d'Angers), SFR 149 QUASAV, 49071 Beaucouzé, France; Department of Environmental Engineering, Osaka Institute of Technology, 5-16-1 Ohmiya, Asahi-ku, Osaka, 535-8585, Japan. Electronic address: kawamura@env.oit.ac.jp.
authors.forename: Koji
authors.initials: K
authors.lastname: Kawamura
authors.affiliation: INRA, Institut de Recherche en Horticulture et Semences (INRA, Agrocampus-Ouest, Université d'Angers), SFR 149 QUASAV, 49071 Beaucouzé, France.
authors.forename: Laurence
authors.initials: L
authors.lastname: Hibrand-Saint Oyant
authors.affiliation: INRA, Institut de Recherche en Horticulture et Semences (INRA, Agrocampus-Ouest, Université d'Angers), SFR 149 QUASAV, 49071 Beaucouzé, France.
authors.forename: Fabrice
authors.initials: F
authors.lastname: Foucher
authors.affiliation: INRA, Institut de Recherche en Horticulture et Semences (INRA, Agrocampus-Ouest, Université d'Angers), SFR 149 QUASAV, 49071 Beaucouzé, France.
authors.forename: Tatiana
authors.initials: T
authors.lastname: Thouroude
authors.affiliation: LAMERA, UMR CNRS 6093, Université d'Angers, 2 Bvd Lavoisier 49045 Angers Cedex, France. Electronic address: sebastien.loustau@univ-angers.fr.
authors.forename: Sébastien
authors.initials: S
authors.lastname: Loustau
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping; D003198:Computer Simulation; D003433:Crosses, Genetic; D019992:Databases as Topic; D056626:Inflorescence / Q000033:anatomy & histology; D040582:Inheritance Patterns / Q000235:genetics; D010641:Phenotype; D025341:Principal Component Analysis; D040641:Quantitative Trait Loci / Q000235:genetics; D027826:Rosa / Q000033:anatomy & histology*; D018709:Statistics, Nonparametric; D060388:Support Vector Machine
keywords: Inflorescence; Kernel principal component analysis; Machine learning; QTL mapping; Support vector machines