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Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization.

Lu Liu1, Ping Zeng2, Fuzhong Xue1

  • 1Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China.

American Journal of Human Genetics
|January 12, 2021
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Summary
This summary is machine-generated.

We developed moPMR-Egger, a new method for multiple outcome probabilistic Mendelian randomization with Egger assumption, to analyze multiple traits simultaneously in transcriptome-wide association studies (TWAS). This approach enhances the power of TWAS by jointly analyzing correlated traits and controlling for pleiotropy.

Keywords:
PRMUK Biobankblood pressuremultiple traitspleiotropyprobabilistic Mendelian randomizationtranscriptome-wide association studies

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Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Transcriptome-wide association studies (TWAS) investigate gene regulatory mechanisms by integrating genome-wide association studies (GWAS) and gene expression data.
  • Existing TWAS methods are predominantly univariate, analyzing one trait at a time, which limits their power for correlated complex traits.
  • Joint analysis of multiple correlated traits can enhance the statistical power of TWAS by leveraging shared genetic underpinnings.

Purpose of the Study:

  • To develop a novel multivariate method for TWAS that can jointly analyze multiple outcome traits.
  • To improve the power and accuracy of TWAS by accounting for correlated traits and horizontal pleiotropy.
  • To provide a robust framework for identifying gene-trait associations and understanding shared genetic regulation.

Main Methods:

  • Developed moPMR-Egger (multiple outcome probabilistic Mendelian randomization with Egger assumption), a multivariate TWAS method.
  • moPMR-Egger examines genes individually, using cis-single nucleotide polymorphisms (SNPs) as instrumental variables to test joint causal effects on multiple traits.
  • The method incorporates testing and control for horizontal pleiotropic effects to enhance reliability and minimize false positives.

Main Results:

  • In simulations, moPMR-Egger demonstrated calibrated type I error control for both causal and horizontal pleiotropic effects testing.
  • moPMR-Egger showed increased power compared to univariate TWAS approaches in detecting causal associations.
  • Application to UK Biobank data for 11 traits revealed 13.15% more gene associations than univariate methods and identified distinct regulatory mechanisms for blood pressures.

Conclusions:

  • moPMR-Egger offers a powerful and robust multivariate approach for TWAS, outperforming univariate methods.
  • The method effectively identifies gene-trait associations by jointly analyzing multiple correlated traits and controlling for pleiotropy.
  • This advancement facilitates a deeper understanding of complex trait genetics and gene regulatory networks.