FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data.
FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data.
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FVGWAS：大规模成像遗传数据的快速体素基因组范围关联分析
DOI：
10.1016/j.neuroimage.2015.05.043
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发表时间：
2015-09
期刊：
影响因子：
5.7
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Huang M;Nichols T;Huang C;Yu Y;Lu Z;Knickmeyer RC;Feng Q;Zhu H;Alzheimer's Disease Neuroimaging Initiative
越来越多的大规模成像遗传学研究正在广泛进行，以收集丰富的成像，遗传和临床数据，以检测复杂遗传的神经精神和神经退行性疾病的推定基因。几个主要的大数据挑战来自于测试全基因组（NC > 1200万个已知变体）与来自数千名受试者（n ~ 103）的大脑中数百万个位置（NV ~ 106）的信号的关联。本文的目的是建立一个快速体素全基因组关联分析（FVGWAS）框架，以便对全脑数据进行全基因组分析。FVGWAS由三个部分组成，包括异方差线性模型、全局确定独立筛选（G-SIS）程序和基于野生自举方法的检测程序。具体地，对于标准线性关联，与FVGWAS的O（（NC + NV）n2）相比，体素全基因组关联分析（VGWAS）方法的计算复杂度为O（nNV NC）。仿真研究表明，FVGWAS是一种有效的方法，可以在极大的搜索空间中搜索稀疏信号，同时控制了整个家族的错误率。最后，我们成功地将FVGWAS应用于ADNI数据的大规模成像遗传数据分析，其中包括708名受试者，RAVENS地图中的193，275个体素和501，584个SNP，单个CPU的总处理时间为203，645秒。我们的FVG-WAS可能是大规模成像遗传分析的一个有价值的统计工具箱，因为该领域正在迅速发展超高分辨率成像和全基因组测序。
More and more large-scale imaging genetic studies are being widely conducted to collect a rich set of imaging, genetic, and clinical data to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. Several major big-data challenges arise from testing genome-wide (NC > 12 million known variants) associations with signals at millions of locations (NV ~ 106) in the brain from thousands of subjects (n ~ 103). The aim of this paper is to develop a Fast Voxelwise Genome Wide Association analysiS (FVGWAS) framework to e ciently carry out whole-genome analyses of whole-brain data. FVGWAS consists of three components including a heteroscedastic linear model, a global sure independence screening (G-SIS) procedure, and a detection procedure based on wild bootstrap methods. Specifically, for standard linear association, the computational complexity is O(nNV NC) for voxelwise genome wide association analysis (VGWAS) method compared with O((NC + NV)n2) for FVGWAS. Simulation studies show that FVGWAS is an effcient method of searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. Finally, we have successfully applied FVGWAS to a large-scale imaging genetic data analysis of ADNI data with 708 subjects, 193,275 voxels in RAVENS maps, and 501,584 SNPs, and the total processing time was 203,645 seconds for a single CPU. Our FVG-WAS may be a valuable statistical toolbox for large-scale imaging genetic analysis as the field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing.