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OTTERS: a powerful TWAS framework leveraging summary-level reference data.

Qile Dai1,2, Geyu Zhou3, Hongyu Zhao3,4

  • 1Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA, 30322, USA.

Nature Communications
|March 7, 2023
PubMed
Summary
This summary is machine-generated.

A new tool called OTTERS (Omnibus Transcriptome Test using Expression Reference Summary data) allows transcriptome-wide association studies (TWAS) using only summary data. This method enhances the power and applicability of TWAS by leveraging larger reference datasets.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Traditional transcriptome-wide association studies (TWAS) methods often necessitate individual-level genotype and gene expression data for reference panels.
  • This requirement limits the application of TWAS to publicly available summary-level eQTL (expression quantitative trait loci) datasets, which are more common and can represent larger sample sizes.
  • Developing TWAS methods that utilize summary-level data is crucial for expanding the scope and statistical power of genetic association studies.

Purpose of the Study:

  • To develop a novel TWAS framework, named OTTERS (Omnibus Transcriptome Test using Expression Reference Summary data), capable of utilizing summary-level eQTL data.
  • To adapt existing polygenic risk score (PRS) methodologies for estimating eQTL weights from summary statistics.
  • To perform an omnibus TWAS that integrates information from multiple PRS methods for enhanced power and robustness.

Main Methods:

  • The OTTERS framework was developed by adapting multiple polygenic risk score (PRS) methods.
  • These adapted PRS methods are used to derive eQTL weights from summary-level eQTL reference data.
  • An omnibus test is then conducted within the OTTERS framework to combine evidence across different PRS-based predictions.

Main Results:

  • Simulations demonstrated that OTTERS is a practical and powerful tool for conducting TWAS.
  • Application studies confirmed the utility and effectiveness of the OTTERS framework in real-world genetic analyses.
  • The method successfully estimates eQTL weights and performs TWAS using only summary-level data.

Conclusions:

  • OTTERS provides a valuable solution for performing TWAS when only summary-level eQTL data is available.
  • The framework enhances the power of TWAS by enabling the use of larger, publicly available reference datasets.
  • OTTERS represents a significant advancement in genetic association analysis, broadening the applicability and statistical rigor of TWAS.