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Meta-analysis models with group structure for pleiotropy detection at gene and variant level using summary statistics

Pierre-Emmanuel Sugier1,2,3, Yazdan Asgari2, Mohammed Sedki4

  • 1Laboratoire de Mathématiques et de leurs Applications de Pau, Université de Pau et des Pays de l'Adour, UMR CNRS 5142, E2S-UPPA, France.

Biostatistics (Oxford, England)
|November 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces MPSG, a novel method for analyzing pleiotropy (one gene affecting multiple traits) across diseases. MPSG enhances the discovery of shared genetic risk factors and biological pathways by considering all genetic data simultaneously.

Keywords:
group structuremeta-analysispleiotropysparsity

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Pleiotropy, where a single gene influences multiple traits, is crucial for understanding complex human diseases.
  • Genome-wide association studies (GWASs) reveal shared genetic risk factors, but existing methods analyze pleiotropic associations individually.
  • Leveraging diverse GWAS summary statistics can uncover novel pleiotropic associations and biological pathways.

Purpose of the Study:

  • To develop a new method, MPSG (Meta-analysis model adapted for Pleiotropy Selection with Group structure), for simultaneous analysis of pleiotropic associations.
  • To overcome the limitations of existing methods that examine pleiotropic associations one by one.
  • To identify potential pleiotropic genes and pathways by integrating comprehensive genetic information.

Main Methods:

  • MPSG employs a penalized multivariate meta-analysis approach tailored for pleiotropy.
  • The method incorporates group structure information within the data for variant and gene selection.
  • An alternating direction method of multipliers algorithm was implemented for MPSG.
  • Performance was benchmarked against established methods like GCPBayes, PLACO, and ASSET using various summary statistics.

Main Results:

  • MPSG demonstrated effective selection of relevant variants and genes (or pathways) by considering all genetic information concurrently.
  • The method was applied to identify potential pleiotropic genes between breast and thyroid cancers.
  • Comparative analysis confirmed MPSG's performance against other meta-analysis approaches.

Conclusions:

  • MPSG offers a powerful new approach for dissecting pleiotropy and understanding shared genetic architectures across diseases.
  • This method enhances the discovery of novel pleiotropic genes and biological pathways by integrating multi-phenotype GWAS data.
  • The application to breast and thyroid cancers highlights MPSG's utility in identifying cross-disease genetic links.