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Likelihood-based Mendelian randomization analysis with automated instrument selection and horizontal pleiotropic

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Mendelian randomization with automated instrument determination (MRAID) improves causal inference by automatically selecting genetic instruments and modeling pleiotropy. This method enhances accuracy and power for identifying disease risk factors.

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Area of Science:

  • Genetics
  • Epidemiology
  • Statistical genetics

Background:

  • Mendelian randomization (MR) is crucial for identifying causal disease risk factors.
  • Existing MR methods face challenges with instrument selection and horizontal pleiotropy.

Purpose of the Study:

  • To introduce Mendelian randomization with automated instrument determination (MRAID), a novel method for robust causal inference.
  • To enhance the accuracy and power of MR analyses by automating instrument selection and accounting for complex pleiotropic effects.

Main Methods:

  • MRAID models candidate single-nucleotide polymorphisms in linkage disequilibrium to automatically select instruments.
  • It employs a joint likelihood framework to model uncorrelated and correlated horizontal pleiotropy.
  • A scalable sampling-based algorithm computes calibrated P-values for statistical significance.

Main Results:

  • Simulations demonstrate MRAID provides calibrated type I error control and reduces false positives.
  • MRAID shows increased statistical power compared to existing MR approaches.
  • Application to U.K. Biobank data identified lifestyle causal risk factors for cardiovascular diseases.

Conclusions:

  • MRAID offers an effective and robust approach for Mendelian randomization analysis.
  • The method improves the reliability of causal inference in complex trait genetics.
  • MRAID facilitates the discovery of novel disease risk factors through large-scale genetic data analysis.