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Updated: Mar 30, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Two-dimensional Enrichment Analysis for Mining High-level Imaging Genetic Associations.

Xiaohui Yao¹, Jingwen Yan², Sungeun Kim³

¹Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA.

Brain Informatics and Health : 8Th International Conference, BIH 2015, London, UK, August 30-September 2, 2015 : Proceedings. BIH (Conference) (8Th : 2015 : London, England)

|November 17, 2015

Summary

Imaging Genetic Enrichment Analysis (IGEA) links gene sets with brain circuits to uncover genetic influences on brain imaging traits. This approach reveals novel biological insights into complex diseases.

Keywords:

Imaging genetics enrichment analysis genome wide association study quantitative trait

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Area of Science:

Neuroscience
Genetics
Bioinformatics

Background:

Enrichment analysis is crucial in genome-wide association studies (GWAS) for interpreting biological pathways.
Brain imaging genetics, studying genetic influences on brain structure and function, presents high-dimensional data challenges.

Purpose of the Study:

To introduce Imaging Genetic Enrichment Analysis (IGEA), a novel paradigm for brain imaging genetics.
To jointly analyze gene sets (GS) and brain circuits (BC) for enriched gene-QT associations.

Main Methods:

Developed the IGEA framework integrating gene expression data (Allen Human Brain Atlas) and imaging genetics data (Alzheimer's Disease Neuroimaging Initiative).
Examined GS-BC pairs for enrichment within gene-neuroimaging quantitative trait (QT) findings.

Main Results:

Identified 12 significant two-dimensional imaging genetics modules.
Several identified modules showed relevance to neurobiological pathways and neurodegenerative diseases.

Conclusions:

The IGEA framework offers a promising approach for dissecting the genetic underpinnings of complex brain disorders.
This method enhances biological interpretation in brain imaging genetics research.