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Updated: Nov 26, 2025
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
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GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations
Hung-Hsin Chen1, Lauren E Petty1, William Bush2
1Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
Current Genetic Medicine Reports
|December 14, 2020
Summary
Identifying causal genes for neurodegenerative diseases and neuropsychiatric disorders is challenging. Integrating multi-omics data with genetic variants helps understand their biological roles and develop new treatments.
Area of Science:
- Neurogenetics
- Genomics
- Systems Biology
Background:
- Neurodegenerative diseases and neuropsychiatric disorders have complex, heritable etiologies.
- Genome-Wide Association Studies (GWAS) identify genetic variants but interpreting their biological roles is challenging.
Purpose of the Study:
- To review 'omics' approaches that bridge the gap between associated genetic variants and phenotypes.
- To define the functional roles of GWAS loci in neuropsychiatric disorders and traits.
Main Methods:
- Review of common 'omics' approaches including nearest-gene mapping, trans-ethnic fine mapping, and annotation enrichment analysis.
- Examination of genetically-regulated gene expression (GReX), transcriptomics, proteomics, metabolomics, and microbiomics.
Main Results:
- Various 'omics' approaches offer insights into biological mechanisms with varying strengths and limitations.
- Genetically-regulated gene expression (GReX) aggregates genetic variant effects at the gene level.
- Proteomic, metabolomic, and microbiomic studies enhance understanding of neuropsychiatric traits.
Conclusions:
- Despite a decade of GWAS, the biological function of identified variants in neuropsychiatric traits remains limited.
- Technological advancements enable integration of transcriptomics, metabolomics, proteomics, pharmacology, and toxicology.
- Electronic health records and multi-omics data complement genomic analyses for deeper insights.
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