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Related Experiment Video

Updated: Sep 2, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Evaluating and implementing block jackknife resampling Mendelian randomization to mitigate bias induced by

Si Fang1,2, Gibran Hemani1,2, Tom G Richardson1,2,3

  • 1Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK.

Human Molecular Genetics
|August 6, 2022
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Participant overlap can bias Mendelian randomization (MR) and polygenic risk score (PRS) studies. A block jackknife resampling framework effectively mitigates this overfitting bias in PRS construction and MR analyses, ensuring more robust genetic insights.

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Area of Science:

  • Genetics and Bioinformatics
  • Statistical Genetics
  • Epidemiology

Background:

  • Participant overlap in genome-wide association studies (GWAS) can introduce overfitting bias into Mendelian randomization (MR) and polygenic risk score (PRS) analyses.
  • This bias can affect the accuracy of causal inference and genetic risk prediction.

Purpose of the Study:

  • To evaluate a block jackknife resampling framework for GWAS and PRS construction to mitigate overfitting bias in MR.
  • To implement and assess this framework in a causal inference setting using UK Biobank data.

Main Methods:

  • Simulated PRS and MR under three scenarios: external GWAS weights, overlapping GWAS sample weights, and block jackknife resampling.
  • Applied the block jackknife resampling MR framework to examine body mass index effects on circulating biomarkers.
  • Extended the framework to sex-stratified, multivariate, and bidirectional analyses.

Main Results:

  • Block-jackknifing PRS showed no overfitting bias (mean R2=0.034) compared to externally weighted PRS (mean R2=0.040) at a stringent P-value threshold (P < 5 × 10-8).
  • Overlapping sample PRS exhibited higher variance explained (mean R2=0.048).
  • Overfitting worsened with liberal P-value thresholds (P < 0.05), but jackknife estimates remained robust (mean R2=0.084) versus overlapping (mean R2=0.103) and external (mean R2=0.086).
  • Applied MR analysis yielded comparable estimates to external instruments, while overfitted scores produced narrower confidence intervals.

Conclusions:

  • The block jackknife resampling framework effectively mitigates overfitting bias in PRS construction and MR analyses.
  • This method provides robust and reliable estimates, particularly in the presence of sample overlap or when using more liberal P-value thresholds.
  • The framework's extension to complex settings enhances its utility for causal inference research.