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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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Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived
Bohong Guo1, Ziqian Xie2, Wei He2
1Department of Biostatistics & Data Science, School of Public Health, University of Texas Health Science Center, Houston, Texas 77030, USA.
Medrxiv : the Preprint Server for Health Sciences
|December 16, 2024
Summary
Joint Analysis of multi-phenotype GWAS (JAGWAS) significantly enhances genetic discovery for brain imaging phenotypes. This new method identified 6 times more genomic loci than traditional single-phenotype approaches, revealing novel insights into neurobiology.
Area of Science:
- Neuroimaging Genetics
- Computational Neuroscience
- Statistical Genomics
Background:
- Brain imaging provides rich data on brain structure and pathology.
- Previous genetic studies focused on individual image-derived phenotypes (IDPs), identifying some genetic loci.
- Unsupervised Deep learning derived Imaging Phenotypes (UDIPs) offer a high-dimensional approach, but single-phenotype analysis may miss complex genetic associations.
Purpose of the Study:
- To develop and validate a novel tool, Joint Analysis of multi-phenotype GWAS (JAGWAS), for efficient multivariate association statistics.
- To identify a greater number of genetic loci associated with brain imaging phenotypes compared to single-phenotype methods.
- To explore the neurobiological functions of newly identified genetic loci.
Main Methods:
- Developed JAGWAS, a tool for calculating multivariate association statistics from single-phenotype summary statistics.
- Applied JAGWAS to Unsupervised Deep learning derived Imaging Phenotypes (UDIPs) from T1 and T2 brain MRI data in UK Biobank cohorts.
- Performed independent replication and mapped identified loci to genes, assessing overlap with brain tissue expression quantitative trait loci (eQTLs).
Main Results:
- JAGWAS identified 195/168 independently replicated genomic loci for T1/T2 brain imaging phenotypes, a sixfold increase over single-phenotype GWAS.
- Replicated loci were mapped to 555/494 genes, with significant overlap (217/188 genes) with brain tissue eQTLs.
- Gene enrichment analysis revealed strong associations with neurobiological functions.
Conclusions:
- Multi-phenotype GWAS using JAGWAS is a powerful strategy for genetic discovery in high-dimensional brain imaging data.
- This approach significantly increases the yield of genetic loci associated with brain structure and pathology.
- The identified genes provide new targets for understanding brain function and disease.
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