ReviewEuropean journal of epidemiology2019
Commonly used estimates of the genetic contribution to disease are subject to the same fallacies as bad luck estimates.
Review in European journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Gain of function NOTCH4 variants disrupt angiogenesis in systemic sclerosis.Annals of the rheumatic diseases · 2026Article
- The proportion of Alzheimer's disease attributable to apolipoprotein E.NPJ dementia · 2026Article
- Impact of numeracy preferences on information needs for genome sequencing results.Patient education and counseling · 2021Article
- Estimation of Primary Prevention of Gout in Men Through Modification of Obesity and Other Key Lifestyle Factors.JAMA network open · 2020Observational
- Objectives, design and main findings until 2020 from the Rotterdam Study.European journal of epidemiology · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The scientific debate following the initial formulation of the "bad luck" hypothesis in cancer development highlighted how measures based on analysis of variance are inappropriately used for risk communication. The notion of "explained" variance is not only used to quantify randomness, but also to quantify genetic and environmental contribution to disease in heritability coefficients. In this paper, we demonstrate why such quantifications are generally as problematic as bad luck estimates. We stress the differences in calculation and interpretation between the heritability coefficient and the population attributable fraction, the estimated fraction of all disease events that would not occur if an intervention could successfully prevent the excess genetic risk. We recommend using the population attributable fraction when communicating results regarding the genetic contribution to disease, as this measure is both more relevant from a public health perspective and easier to understand.
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