Kernel methods for phenotyping complex plant architecture
Kernel methods for phenotyping complex plant architecture
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DOI：
10.1016/j.jtbi.2013.10.016
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发表时间：
2014-02-07
影响因子：
2
通讯作者：
Loustau, Sebastien
中科院分区：
文献类型：
--
作者：
Kawamura, Koji;Hibrand-Saint Oyant, Laurence;Loustau, Sebastien
植物构型的数量性状基因座（QTL）定位是理解植物构型遗传决定因素的关键步骤。以往的研究多采用简单的测量指标，如株高、茎粗和分枝强度等来定位植株构型的QTL。这些数量性状中的许多性状通常是相互关联的，这给QTL的检测带来了统计学上的问题。我们的目的是测试核方法的表型花序结构和QTL定位的适用性。我们首先测试核主成分分析（KPCA）和支持向量机（SVM）在人工数据集的模拟花序与不同类型的花分布，这是编码为一个序列的花数每节点沿着拍摄。说明了支持向量机和核主成分分析对不同花序类型的区分能力。然后，我们将KPCA表示的真实的数据集的玫瑰花序芽（n=1460）从98 F-1杂交映射人口。我们发现了具有高遗传力（> 0.7）的籽粒主成分，QTL分析确定了一个新的QTL，这是通过简单结构测量的逐性状分析所检测不到的。本文开发的主要工具可用于解决复杂（序列、三维结构、图形）表型性状QTL定位的一般问题。(C)2013爱思唯尔有限公司保留所有权利。
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 F-1 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. (C) 2013 Elsevier Ltd. All rights reserved.