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首页> 外文会议>International conference on medical imaging computing and computer-assisted intervention >Large-Scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions
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Large-Scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions

机译：跨多个机构的阿尔茨海默氏病风险遗传因素的大规模协同成像遗传学研究

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摘要

Genome-wide association studies (GWAS) offer new opportunities to identify genetic risk factors for Alzheimer's disease (AD). Recently, collaborative efforts across different institutions emerged that enhance the power of many existing techniques on individual institution data. However, a major barrier to collaborative studies of GWAS is that many institutions need to preserve individual data privacy. To address this challenge, we propose a novel distributed framework, termed Local Query Model (LQM) to detect risk SNPs for AD across multiple research institutions. To accelerate the learning process, we propose a Distributed Enhanced Dual Polytope Projection (D-EDPP) screening rule to identify irrelevant features and remove them from the optimization. To the best of our knowledge, this is the first successful run of the computationally intensive model selection procedure to learn a consistent model across different institutions without compromising their privacy while ranking the SNPs that may collectively affect AD. Empirical studies are conducted on 809 subjects with 5.9 million SNP features which are distributed across three individual institutions. D-EDPP achieved a 66-fold speed-up by effectively identifying irrelevant features.
机译：全基因组关联研究（GWAS）为识别阿尔茨海默氏病（AD）的遗传危险因素提供了新的机会。最近，出现了跨不同机构的协作工作，这些工作增强了许多现有技术对单个机构数据的功能。但是，进行GWAS协作研究的主要障碍是许多机构需要维护个人数据的隐私。为了应对这一挑战，我们提出了一种新颖的分布式框架，称为本地查询模型（LQM），以跨多个研究机构检测AD的风险SNP。为了加快学习过程，我们提出了一种分布式增强双多面体投影（D-EDPP）筛选规则，以识别不相关的特征并将其从优化中删除。据我们所知，这是计算密集型模型选择程序的首次成功运行，可以在不同机构中学习一致的模型而又不损害其隐私，同时对可能共同影响AD的SNP进行排名。对具有590万SNP特征的809个主题进行了实证研究，这些特征分布在三个独立的机构中。 D-EDPP通过有效识别无关特征实现了66倍的加速。

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