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Co-expression-based models improve eQTL predictions for transcriptome-wide association studies and highlight new

Fabiana Rossi1,2, Leonardo Sportelli1,2, Gianluca C Kikidis1,2

  • 1Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy.

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Summary

This study reveals that including distant genetic effects (trans-regulation) significantly improves gene expression prediction and identifies new schizophrenia-associated genes, highlighting network interactions in disease risk.

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Area of Science:

  • Genetics
  • Neuroscience
  • Bioinformatics

Background:

  • Complex heritability phenotypes are often linked to non-coding genetic variants.
  • Current transcriptome-wide association studies mainly focus on local (cis) genetic effects, leaving distal (trans) regulatory influences largely unexplored.
  • Understanding gene regulation is crucial for explaining disease risk.

Purpose of the Study:

  • To develop and validate models that incorporate distal (trans) regulatory effects for improved gene expression prediction.
  • To identify novel genes associated with schizophrenia by integrating cis and trans genetic effects.
  • To elucidate the role of gene network interactions in complex disease etiology.

Main Methods:

  • Developed INGENE and MODULE models to capture trans-acting variant influences within gene coexpression networks using RNA sequencing data from six human brain regions.
  • Integrated cis-based predictors with the developed trans-regulatory models.
  • Applied the integrated framework to Psychiatric Genomics Consortium wave 3 genotypes.

Main Results:

  • Improved gene expression imputation for 18,744 genes across brain regions by incorporating trans-regulatory effects.
  • Identified 766 genes associated with schizophrenia (PFDR < 0.01) using the integrated framework.
  • Discovered 641 novel schizophrenia-associated genes not previously identified by transcriptome-wide analyses.

Conclusions:

  • Distal regulatory mechanisms and gene network interactions play a significant role in schizophrenia risk.
  • The developed framework enhances the identification of disease-associated genes by considering both cis and trans genetic effects.
  • This approach offers a more comprehensive understanding of the genetic architecture underlying complex phenotypes.