ArticleScientific reports2016
HASE: Framework for efficient high-dimensional association analyses.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- MR-link-2: pleiotropy robust cis Mendelian randomization validated in three independent reference datasets of causality.Nature communications · 2025Article
- The blood metabolome of cognitive function and brain health in middle-aged adults - influences of genes, gut microbiome, and exposome.medRxiv : the preprint server for health sciences · 2024Article
- A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data.Genome biology · 2024Article
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- Evidence for involvement of the alcohol consumption WDPCP gene in lipid metabolism, and liver cirrhosis.Scientific reports · 2023Article
- BOSO: A novel feature selection algorithm for linear regression with high-dimensional data.PLoS computational biology · 2022Article
- GenNet framework: interpretable deep learning for predicting phenotypes from genetic data.Communications biology · 2021Article
- The single-cell eQTLGen consortium.eLife · 2020Article
- Full exploitation of high dimensionality in brain imaging: The JPND working group statement and findings.Alzheimer's & dementia (Amsterdam, Netherlands) · 2019Article
- Heritability and genome-wide associations studies of cerebral blood flow in the general population.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2018Article
- Decentralized Analysis of Brain Imaging Data: Voxel-Based Morphometry and Dynamic Functional Network Connectivity.Frontiers in neuroinformatics · 2018Article
- Gray matter heritability in family-based and population-based studies using voxel-based morphometry.Human brain mapping · 2017Article
- Maximizing the Potential of Longitudinal Cohorts for Research in Neurodegenerative Diseases: A Community Perspective.Frontiers in neuroscience · 2017Article
- Heritability of the shape of subcortical brain structures in the general population.Nature communications · 2016Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.