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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Related Experiment Video

Updated: Nov 9, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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A robust two-sample transcriptome-wide Mendelian randomization method integrating GWAS with multi-tissue eQTL summary

Kevin J Gleason1, Fan Yang2, Lin S Chen1

  • 1Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA.

Genetic Epidemiology
|April 9, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces MR-MtRobin, a new method for accurate transcriptome-wide Mendelian randomization (TWMR) analysis. It addresses invalid genetic instruments to improve the detection of gene expression

Keywords:
correlated horizontal pleiotropycross-tissue eQTL effectsmulti-tissue eQTLsummary statisticstwo-sample transcriptome-wide Mendelian randomizationuncorrelated horizontal pleiotropy

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Two-sample Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to identify genetically regulated risk exposures for complex diseases from summary statistics.
  • Transcriptome-wide MR (TWMR) applies MR to gene expression, but invalid IVs (eQTLs with pleiotropic effects or correlated variants) can bias results.
  • Limited numbers of expression quantitative trait loci (eQTLs) per gene challenge the detection of invalid IVs in TWMR.

Purpose of the Study:

  • To develop a robust method, MR-MtRobin, for accurate TWMR inference in the presence of invalid IVs.
  • To leverage multi-tissue eQTL data within a mixed model framework to distinguish pleiotropy from estimation errors.
  • To enhance power and precision by selecting cross-tissue eQTLs with consistent effects across eQTL and GWAS data.

Main Methods:

  • Proposed MR-MtRobin method utilizing multi-tissue eQTL data in a mixed model.
  • Distinguishing IV-specific random effects (pleiotropy) from eQTL summary statistic estimation errors.
  • Estimating fixed effects (dependence) between eQTL and GWAS effects, accounting for invalid IVs.

Main Results:

  • MR-MtRobin accurately infers gene expression-disease associations despite invalid IVs.
  • The method improves statistical power and precision by selecting consistent cross-tissue eQTLs.
  • Application to schizophrenia risk identified associated genes using integrated data from Psychiatric Genomics Consortium and GTEx.

Conclusions:

  • MR-MtRobin offers a reliable approach for TWMR analysis in the presence of pleiotropy and invalid IVs.
  • The method enhances the discovery of genetically regulated gene expression risk factors for complex diseases.
  • This work advances the understanding of genetic contributions to schizophrenia.