Abstract：
:High-throughput technology can now provide rich information on a person's biological makeup and environmental surroundings. Important discoveries have been made by relating these data to various health outcomes in fields such as genomics, proteomics, and medical imaging. However, cross-investigations between several high-throughput technologies remain impractical due to demanding computational requirements (hundreds of years of computing resources) and unsuitability for collaborative settings (terabytes of data to share). Here we introduce the HASE framework that overcomes both of these issues. Our approach dramatically reduces computational time from years to only hours and also requires several gigabytes to be exchanged between collaborators. We implemented a novel meta-analytical method that yields identical power as pooled analyses without the need of sharing individual participant data. The efficiency of the framework is illustrated by associating 9 million genetic variants with 1.5 million brain imaging voxels in three cohorts (total N = 4,034) followed by meta-analysis, on a standard computational infrastructure. These experiments indicate that HASE facilitates high-dimensional association studies enabling large multicenter association studies for future discoveries.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Roshchupkin GV,Adams HH,Vernooij MW,Hofman A,Van Duijn CM,Ikram MA,Niessen WJdoi
10.1038/srep36076subject
Has Abstractpub_date
2016-10-26 00:00:00pages
36076issn
2045-2322pii
srep36076journal_volume
6pub_type
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