Statistical methods for cis-mendelian randomization

  • How does Mendelian randomisation work?

    NCI.
    Mendelian randomization is a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in observational studies..

  • What are the three assumptions of Mendelian randomization?

    Applied to Mendelian randomization, these assumptions are that (i) the genotype is associated with the exposure; (ii) the genotype is associated with the outcome through the studied exposure only (exclusion restriction assumption); and (iii) the genotype is independent of other factors which affect the outcome ( .

  • What is the IVW method?

    Combines Wald ratio (or Ratio estimates) together in fixed effect meta-analysis, where the weight of each ratio is the inverse of the variance of the SNP-outcome association..

  • What is the Mendelian randomization ratio method?

    Mendelian randomization is a form of instrumental variable analysis that uses SNP associations from genome-wide association studies as instruments to study and uncover causal relationships between complex traits..

  • What is the method of Mendelian randomization?

    Mendelian randomization is a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in observational studies..

  • Applied to Mendelian randomization, these assumptions are that (i) the genotype is associated with the exposure; (ii) the genotype is associated with the outcome through the studied exposure only (exclusion restriction assumption); and (iii) the genotype is independent of other factors which affect the outcome (
  • Combines Wald ratio (or Ratio estimates) together in fixed effect meta-analysis, where the weight of each ratio is the inverse of the variance of the SNP-outcome association.
  • Mendelian randomization is a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in observational studies.
3.
  • 3.1. Notation.
  • 3.2. Single‐variant MR.
  • 3.3. LD‐pruning.
  • 3.4. Conditional analysis.
  • 3.5. Principal component analysis.
  • 3.6. The JAM algorithm.
  • 3.7. Factor‐based methods.
  • 3.8. Separating variable selection from estimation.
We review methods for variable selection and estimation in cis-MR with summary-level data, ranging from stepwise pruning and conditional analysis to principal 

Do CIS-Mendelian randomization studies use protein expression as a risk factor?

Here, we focus on two-sample summary-data Mendelian randomization analyses with many correlated variants from a single gene region, and particularly on cis-Mendelian randomization studies which use protein expression as a risk factor.

,

How to replicate a Mendelian randomization analysis?

Aiming to replicate a two-sample Mendelian randomization analysis, we then bootstrapped the UK Biobank data and obtained two genetic matrices G1; G2 of sizes N1; N2 respectively.
Using the genetic matrix G1, we simulated risk factor measurements according to (8) and used them to obtain G X summary statistics.

,

What methods are used for variable selection and estimation in CIS -Mr?

We review methods for variable selection and estimation in cis -MR with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis, and Bayesian variable selection.

,

What methods are used for variable selection and estimation in CIS-Mendelian randomization?

We review methods for variable selection and estimation in cis-Mendelian randomization with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis and Bayesian variable selection.


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