Print Email Facebook Twitter HASE Title HASE: Framework for efficient high-dimensional association analyses Author Roshchupkin, G. V. (Erasmus MC) Adams, H. H H (Erasmus MC) Vernooij, M. W. (Erasmus MC) Hofman, A. (Erasmus MC) Van Duijn, C. M. (Erasmus MC) Ikram, M. A. (Erasmus MC) Niessen, W.J. (TU Delft ImPhys/Quantitative Imaging; Erasmus MC) Date 2016-10-26 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. Subject Genome-wide association studiesSoftware To reference this document use: http://resolver.tudelft.nl/uuid:14bdab7a-6b74-4f8c-b51e-57da140b151f DOI https://doi.org/10.1038/srep36076 ISSN 2045-2322 Source Scientific Reports, 6 Part of collection Institutional Repository Document type journal article Rights © 2016 G. V. Roshchupkin, H. H H Adams, M. W. Vernooij, A. Hofman, C. M. Van Duijn, M. A. Ikram, W.J. Niessen Files PDF srep36076.pdf 909.36 KB Close viewer /islandora/object/uuid:14bdab7a-6b74-4f8c-b51e-57da140b151f/datastream/OBJ/view