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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Updated: Apr 18, 2026

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FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies.

Travis Canida1,2, Zhenyao Ye3,4, Shao-Hsuan Wang5

  • 1Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America.

Biorxiv : the Preprint Server for Biology
|April 17, 2026
PubMed
Summary
This summary is machine-generated.

FM-GPT accurately identifies causal genes in phenome-wide studies by analyzing multiple traits. This method helps uncover shared biological mechanisms across diverse human conditions, advancing translational and comorbidity research.

Keywords:
Bayesian fine mappingelectronic health recordsneuroimagingphenome-wide studiestranscriptome-wide association studies

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Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Transcriptome-wide association studies (TWAS) integrate genetic and gene expression data to link genes with traits.
  • Spurious TWAS signals arise from linkage disequilibrium and correlated gene expression, necessitating fine-mapping methods.
  • Large-scale phenomic resources enable multi-trait analyses, moving beyond single-trait genetic studies.

Purpose of the Study:

  • Introduce FM-GPT, a Bayesian fine-mapping method for prioritizing causal genes across multiple correlated phenotypes in phenome-wide TWAS.
  • Enable analysis of mixed outcome types (binary, count, continuous) within phenome-wide association studies.
  • Reveal pleiotropic or phenotype-specific gene effects and disentangle complex gene-phenotype relationships.

Main Methods:

  • Developed FM-GPT, a novel Bayesian fine-mapping approach for phenome-wide TWAS.
  • Implemented gene-guided dimension reduction for phenotypes.
  • Validated through simulations and applied to UK Biobank data for brain imaging and electronic health records.

Main Results:

  • FM-GPT demonstrated superior accuracy in identifying true causal genes and controlling false positives in simulations.
  • Applied to UK Biobank data, FM-GPT significantly reduced the number of candidate causal genes.
  • Identified genes with pleiotropic effects on cortical thickness and genes influencing multiple medical conditions, revealing immune-metabolic trade-offs.

Conclusions:

  • FM-GPT effectively prioritizes causal genes in large-scale phenome-wide studies with complex, multi-trait phenotypes.
  • The method uncovers shared biological mechanisms underlying diverse human traits and diseases.
  • FM-GPT advances translational research and the study of disease comorbidities.