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Updated: Sep 15, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Outlier Detection in Mendelian Randomization

Maximilian M Mandl1,2, Anne-Laure Boulesteix1,2, Stephen Burgess3,4

  • 1Institute for Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-Universität, München, Germany.

Statistics in Medicine
|July 14, 2025
PubMed

Insights

Mendelian randomization (MR) methods can over-identify genetic outliers due to pleiotropy. This study introduces a novel method to correct for overdispersion in heterogeneity statistics, improving the accuracy of causal inference from genetic data.

Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Mendelian randomization (MR) infers causal relationships using genetic variants as instrumental variables.
  • A core MR assumption is instrumental variable independence from outcomes, except via the exposure.
  • Pleiotropy, where variants affect outcomes through other pathways, violates this assumption and is common.

Purpose of the Study:

  • To address the overdispersion issue in heterogeneity statistics used to detect outlying genetic instruments in MR.
  • To develop a method for accurately identifying and removing pleiotropic instruments in Mendelian randomization analyses.

Main Methods:

  • Proposed a novel statistical method to correct for overdispersion in heterogeneity statistics.
  • Utilized an estimated inflation factor to identify and remove outlying genetic variants.
  • The method is applicable to both univariable and multivariable Mendelian randomization.

Main Results:

  • The new method effectively corrects for overdispersion in heterogeneity statistics.
  • Accurate removal of outlying instruments due to pleiotropy was achieved.
  • Improved reliability of causal effect estimates in Mendelian randomization.

Conclusions:

  • The developed method enhances the robustness of Mendelian randomization by accurately accounting for pleiotropic effects.
  • This approach improves the identification of valid genetic instruments, leading to more reliable causal inference.
  • The method is suitable for use with readily available summary-level genetic data.
Keywords:
Mendelian randomizationinstrumental variablesinvalid instrumentsoutlier detectionpleiotropy

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