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Support Interval for Two-Sample Summary Data-Based Mendelian Randomization.

Kai Wang1

  • 1Department of Biostatistics, University of Iowa, 145 N Riverside Dr., Iowa City, IA 52242, USA.

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Summary
This summary is machine-generated.

The summary-data-based Mendelian randomization (SMR) method can produce biased causal effect estimates due to instrument SNP selection. A new profile likelihood method corrects for this selection bias, offering robust coverage for causal inference.

Keywords:
SMRmendelian randomizationprofile likelihoodsummary statisticssupport

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Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • The summary-data-based Mendelian randomization (SMR) method is widely used for estimating causal effects.
  • Instrument single nucleotide polymorphism (SNP) selection from exposure genome-wide association studies (GWAS) without correction can introduce bias.
  • Existing methods may yield invalid inference due to unaddressed SNP selection bias.

Purpose of the Study:

  • To develop a robust statistical method for causal effect estimation in Mendelian randomization.
  • To address and correct for bias arising from instrument SNP selection in SMR analysis.
  • To provide a more reliable interval estimate for causal effects in the presence of selection.

Main Methods:

  • Utilized a likelihood approach derived from the sampling distribution of SNP effects in exposure and outcome GWAS.
  • Developed a profile likelihood support for interval estimation, accounting for instrument SNP selection.
  • Avoided asymptotic theory due to an effective sample size of 1.

Main Results:

  • The proposed profile likelihood support demonstrates robust coverage for causal effect interval estimation.
  • The standard confidence interval from the SMR method exhibits lower-than-nominal coverage.
  • The variance of the two-stage least squares estimate matches the SMR variance for one-sample data without selection.

Conclusions:

  • The new profile likelihood method provides a statistically sound approach to Mendelian randomization.
  • Correcting for instrument SNP selection is crucial for valid causal inference.
  • This method enhances the reliability of estimating causal effects in genetic association studies.