Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA.
Neuroimage. 2017 Oct 1;159:107-121. doi: 10.1016/j.neuroimage.2017.07.030. Epub 2017 Jul 20.
Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs.
功能表型（例如皮质下表面代表），在成像遗传学研究中经常出现，已被用于检测复杂遗传性神经精神和神经退行性疾病的潜在基因。然而，现有的统计方法在很大程度上忽略了功能特征（例如功能平滑度和相关性）。本文旨在开发一种功能全基因组关联分析（FGWAS）框架，以有效地对功能表型进行全基因组分析。FGWAS 由三个组件组成：多变量变系数模型、全局确定独立性筛选程序和检验程序。与标准多变量回归模型相比，多变量变系数模型通过整合平滑系数函数和功能主成分分析，明确地对功能表型的功能特征进行建模。在统计学上，与现有的全基因组关联研究（GWAS）方法相比，FGWAS 可以大大提高发现影响大脑结构和功能的重要遗传变异的检测能力。模拟研究表明，FGWAS 在控制总体错误率的情况下，在搜索极其大的搜索空间中的稀疏信号方面优于现有的 GWAS 方法。我们已成功将 FGWAS 应用于来自阿尔茨海默病神经影像学倡议的 708 名受试者、左右海马表面 30000 个顶点和 501584 个 SNP 的大规模分析。
