Ten simple rules for conducting a mendelian randomization study
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Bibliographic record
Abstract
Mendelian randomization (MR) is an epidemiological technique for estimating causal relationships using observational data, which has become very popular in recent years following publication of a seminal article by Smith and Ebrahim in 2003 [1]. MR is a specific form of “instrumental variables” (IV) analysis (the latter being first invented by Phillip and Sewall Wright in the 1920s [2]) that uses genetic variants to proxy a modifiable variable (which we term the “exposure” variable here) in order to estimate the causal relationship between the exposure and an outcome of interest. To understand how this causal inference technique works, it is useful to think of MR as similar to a “natural” randomized controlled trial [3] where individuals are randomly assigned to groups based on the alleles that they inherit from their parents (Fig 1). MR takes advantage of Mendel’s laws of segregation and independent assortment, which state that offspring inherit alleles randomly from their parents and randomly with respect to other genes in the genome (with certain exceptions [1]). Therefore, genetic variants that are related to an exposure of interest can be used to proxy the part of the exposure variable that is independent of possible confounding influences from the environment and other traits. Providing several assumptions are satisfied (see below), and the principle of gene–environment equivalence (i.e., perturbing the exposure genetically has the same effect as perturbing the exposure by other means), statistical association between the genetic variant and the outcome is indicative of a causal relationship between the exposure and the outcome and can be used to estimate the magnitude of the causal relationship using IV methods. Although originally developed as a way to estimate causal relationships between modifiable environmental exposures and medically relevant outcomes, in recent years, MR has been utilized in many other situations including studies of molecular biomarkers, in pharmacogenetics, in the social sciences, and in other discplines that use observational frameworks [4,5]. Open in a separate window Fig 1 Similarities between the MR study design and a randomized controlled trial. MR, mendelian randomization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it