HASE: Framework for efficient high-dimensional association analyses.

Area of Science:

• Genomics
• Proteomics
• Medical Imaging
• Computational Biology

Background:

• High-throughput technologies generate vast biological and environmental data.
• Integrating data from multiple high-throughput technologies is computationally intensive and impractical for collaboration.
• Existing methods require extensive computing resources and large data sharing, hindering multicenter studies.

Purpose of the Study:

• Introduce the HASE framework to overcome computational and data-sharing limitations in cross-technology investigations.
• Enable efficient, large-scale, multicenter association studies by reducing computational demands.
• Facilitate novel discoveries by making complex biological data analysis more accessible.

Main Methods:

• Developed the HASE (High-throughput Analysis of Scientific Experiments) framework.
• Implemented a novel meta-analytical method for powerful analysis without sharing individual participant data.
• Reduced computational time from years to hours and data exchange from terabytes to gigabytes.

Main Results:

• Demonstrated HASE efficiency by associating 9 million genetic variants with 1.5 million brain imaging voxels across three cohorts (N=4,034).
• Achieved computational time reduction to hours on standard infrastructure.
• Meta-analysis yielded identical statistical power to pooled analyses.

Conclusions:

• HASE framework significantly enhances the feasibility of high-dimensional association studies.
• Enables large multicenter studies, accelerating scientific discovery in genomics, proteomics, and medical imaging.
• Provides a practical solution for collaborative research involving large-scale biological datasets.