ReviewHuman genetics2012
Causation and causal inference for genetic effects.
Review in Human genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- A practical guide for nephrologist peer reviewers: understanding and appraising Mendelian randomization studies.Renal failure · 2025Article
- Description, prediction and causation: Methodological challenges of studying child and adolescent development.Developmental cognitive neuroscience · 2020Review
- Commentary: The formal approach to quantitative causal inference in epidemiology: misguided or misrepresented?International journal of epidemiology · 2016Article
- Practical guidance for conducting mediation analysis with multiple mediators using inverse odds ratio weighting.American journal of epidemiology · 2015Article
- Causal Inference in the Age of Decision Medicine.Journal of data mining in genomics & proteomics · 2015Article
- An epigenetic signature in peripheral blood associated with the haplotype on 17q21.31, a risk factor for neurodegenerative tauopathy.PLoS genetics · 2014Article
- Combining genetic and nongenetic biomarkers to realize the promise of pharmacogenomics for inflammatory diseases.Pharmacogenomics · 2014Review
- Testing for direct genetic effects using a screening step in family-based association studies.Frontiers in genetics · 2013Article
- Study designs and methods post genome-wide association studies.Human genetics · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past three decades, substantial developments have been made on how to infer the causal effect of an exposure on an outcome, using data from observational studies, with the randomized experiment as the golden standard. These developments have reshaped the paradigm of how to build statistical models, how to adjust for confounding, how to assess direct effects, mediated effects and interactions, and even how to analyze data from randomized experiments. The congruence of random transmission of alleles during meiosis and the randomization in controlled experiments/trials, suggests that genetic studies may lend themselves naturally to a causal analysis. In this contribution, we will reflect on this and motivate, through illustrative examples, where insights from the causal inference literature may help to understand and correct for typical biases in genetic effect estimates.
Indexed as
Identifiers
22864952What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.