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Transcriptome-wide association studies accounting for colocalization using Egger regression.

Richard Barfield1, Helian Feng2, Alexander Gusev3,4

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Summary
This summary is machine-generated.

This study introduces LD-aware MR-Egger regression to accurately identify disease susceptibility genes by accounting for linkage disequilibrium (LD) confounding in transcriptome-wide association studies (TWAS). The method improves causal gene detection power and controls statistical error rates.

Keywords:
Mendelian randomizationgene Expressiongenome-wide association studytransciptome-wide association study

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Area of Science:

  • Genetics
  • Statistical Genomics
  • Bioinformatics

Background:

  • Integrating genome-wide association (GWAS) and expression quantitative trait locus (eQTL) data in transcriptome-wide association studies (TWAS) enhances the detection of disease loci and causal genes.
  • Linkage disequilibrium (LD) and pleiotropy often confound TWAS, making it challenging to distinguish true causal genes from those that colocalize due to LD.

Purpose of the Study:

  • To develop and evaluate a method that can distinguish true causal genes from LD-confounded colocalization in TWAS.
  • To assess the performance of a novel LD-aware Mendelian randomization (MR) Egger regression method in controlling type I error and maintaining power for gene identification.

Main Methods:

  • Extension of the Mendelian randomization (MR) Egger regression method to incorporate LD information using only summary association data from GWAS and eQTL studies.
  • Derivation of the standard TWAS approach within the MR framework to compare its performance against the novel method.
  • Simulation studies to evaluate type I error control and power under various scenarios, including pleiotropic and LD-confounded eQTL effects.

Main Results:

  • Standard TWAS fails to control type I error for causal gene identification when eQTLs exhibit pleiotropic or LD-confounded effects.
  • LD-aware MR-Egger (LDA MR-Egger) regression effectively controls type I error in the presence of LD confounding, achieving comparable power to other valid methods.
  • All examined methods, including LDA MR-Egger, can exhibit inflated type I error when direct genetic effects on traits correlate with eQTL associations.

Conclusions:

  • LDA MR-Egger regression offers a robust approach for identifying causal genes in TWAS by addressing LD confounding.
  • The method provides valuable guidance for susceptibility gene identification, as demonstrated by its application to a large-scale breast cancer GWAS.
  • Careful consideration of the correlation between direct genetic effects and eQTL associations is crucial for accurate causal inference in gene-trait relationships.