Within family Mendelian randomization studies
Open accessAbstract
Mendelian randomization (MR) is increasingly used to make causal inferences in a wide range of fields, from drug development to etiologic studies. Causal inference in MR is possible because of the process of genetic inheritance from parents to offspring. Specifically, at gamete formation and conception, meiosis ensures random allocation to the offspring of one allele from each parent at each locus, and these are unrelated to most of the other inherited genetic variants. To date, most MR studies have used data from unrelated individuals. These studies assume that genotypes are independent of the environment across a sample of unrelated individuals, conditional on covariates. Here we describe potential sources of bias, such as transmission ratio distortion, selection bias, population stratification, dynastic effects and assortative mating that can induce spurious or biased SNP–phenotype associations. We explain how studies of related individuals such as sibling pairs or parent–offspring trios can be used to overcome some of these sources of bias, to provide potentially more reliable evidence regarding causal processes. The increasing availability of data from related individuals in large cohort studies presents an opportunity to both overcome some of these biases and also to evaluate familial environmental effects.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
WS-MR is largely robust against demographic and indirect genetic effects that could distort estimates from nonfamily designs.
“WS-MR is largely robust against demographic and indirect genetic effects that could distort estimates from nonfamily designs7,25.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [25]
Within-family MR requires data from either pairs of siblings or mother-father-child trios and allows for estimation of causal effects using MR after family-level structure has been taken into account.
“Within-family MR requires data from either pairs of siblings or mother–father–child trios and allows for estimation of causal effects using MR after family-level structure has been taken into account.164,167”
From Mendelian randomization (2022) · cites this paper as [164]
Population stratification, dynastic effects and assortative mating all induce bias by creating an artefactual relation at the population level between the genetic variants and the outcome, violating the second IV condition.
“However, population stratification, dynastic effects and assortative mating all induce bias by creating an artefactual relation at the population level between the genetic variants and the outcome, violating the second IV condition.65,163–166”
From Mendelian randomization (2022) · cites this paper as [164]
Mendelian randomisation provides an opportunity to study the association between exposures and outcomes while reducing potential bias from confounding and reverse causation.
“MR thus provides an opportunity to study the association between exposures and outcomes while reducing potential bias from confounding and reverse causation.9”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [9]
The citation lists haven’t been fetched yet
References 70 · Cited by 158
- Authors
- Davies, N. M., Howe, L. J., Brumpton, B., Havdahl, A., Evans, D. M., & Davey Smith, G.
- Affiliations
- University of Bristol (GB)
- Norwegian University of Science and Technology (NO)
- Norwegian Institute of Public Health (NO)
- The University of Queensland (AU)
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, United Kingdom
- Corresponding author
- Davies, Neil M
- Venue
- Human Molecular Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-10-15
- Volume
- 28
- Issue
- R2
- Pages
- R170-R179
- ISSN
- 0964-6906 (ISSN-L)
- 1460-2083
- OpenAlex
- W2981338841
- Cited by
- 158
- References
- 70
- Citations by year
- 2026: 15 · 2025: 27 · 2024: 28 · 2023: 16 · 2022: 24 · 2021: 33 · 2020: 15
- FWCI
- 2.41
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Human Molecular Genetics (publishedVersion)
- PubMed (publishedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Bristol Research (University of Bristol) (submittedVersion)
- Funders
- Medical Research Council (MC_UU_12013/1, MC_UU_12013/9, MC_UU_00011/1)
- Economics and Social Research Council (ES/N000757/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- Mendelian randomization, sibling pairs, parent-offspring trios, causal inference, segregation distortion, selection bias, population stratification, assortative mating
- MeSH terms
- Family, Family Characteristics, Genotype, Humans, Population, Reproduction, Selection Bias, Polymorphism, Single Nucleotide, Sociobiology, Genetic Association Studies, Mendelian Randomization Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
