HASE: Framework for efficient high-dimensional association analyses

G. V. Roshchupkin, H. H H Adams, M. W. Vernooij, A. Hofman, C. M. Van Duijn, M. A. Ikram, W. J. Niessen

    Research output: Contribution to journalArticleScientificpeer-review

    10 Citations (Scopus)
    24 Downloads (Pure)


    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.

    Original languageEnglish
    Article number36076
    JournalScientific Reports
    Publication statusPublished - 26 Oct 2016


    • Genome-wide association studies
    • Software


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