Hardoon David R, Ettinger Ulrich, Mourão-Miranda Janaina, Antonova Elena, Collier David, Kumari Veena, Williams Steven C R, Brammer Michael
Computational Statistics & Machine Learning Centre, Dept. of Computer Science, University College London, London WC1E 6BT, United Kingdom.
Neurosci Lett. 2009 Feb 6;450(3):281-6. doi: 10.1016/j.neulet.2008.11.035. Epub 2008 Nov 18.
Considerable research effort has focused on achieving a better understanding of the genetic correlates of individual differences in volumetric and morphological brain measures. The importance of these efforts is underlined by evidence suggesting that brain changes in a number of neuropsychiatric disorders are at least partly genetic in origin. The currently used methods to study these relationships are mostly based on single-genotype univariate analysis techniques. These methods are limited as multiple genes are likely to interact with each other in their influences on brain structure and function. In this paper we present a feasibility study where we show that by using kernel correlation analysis, with a new genotypes representation, it is possible to analyse the relative associations of several genetic polymorphisms with brain structure. The implementation of the method is demonstrated on genetic and structural magnetic resonance imaging (MRI) data acquired from a group of 16 healthy subjects by showing the multivariate genetic influence on grey and white matter.
大量的研究工作致力于更深入地理解个体在大脑体积和形态测量方面差异的遗传相关性。有证据表明，许多神经精神疾病中的大脑变化至少部分源于遗传，这凸显了这些研究工作的重要性。目前用于研究这些关系的方法大多基于单基因型单变量分析技术。由于多个基因在影响大脑结构和功能时可能相互作用，这些方法存在局限性。在本文中，我们进行了一项可行性研究，结果表明通过使用核相关分析以及一种新的基因型表示方法，可以分析多种基因多态性与大脑结构之间的相对关联。通过展示对一组16名健康受试者获取的遗传数据和结构磁共振成像（MRI）数据中多变量遗传对灰质和白质的影响，论证了该方法的实施。
