ArticleProceedings of the National Academy of Sciences of the United States of America2018
Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Overcoming attenuation bias in regressions using polygenic indices.Nature communications · 2023Pooled it
- Using Mendelian randomization analysis to better understand the relationship between mental health and substance use: a systematic review.Psychological medicine · 2021Pooled it
- Observational epidemiological studies can mitigate genetic confounding with a genetic relatedness matrix.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The Economics and Econometrics of Gene-Environment Interplay.The Review of economic studies · 2026Article
- An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference.Research square · 2025Article
- An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference.bioRxiv : the preprint server for biology · 2025Article
- Alcohol consumption and allergic diseases: Mendelian randomization evidence from China.Global health action · 2024Article
- Candidate-gene studies related to drug pharmacokinetics, pharmacodynamics, safety, and efficacy in the era of extensive technological developments: could we empower them by more efficient implementation of established epidemiological concepts?Croatian medical journal · 2024Article
- Controlling for polygenic genetic confounding in epidemiologic association studies.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Implications of the genomic revolution for education research and policy.British educational research journal · 2024Article
- Controlling for polygenic genetic confounding in epidemiologic association studies.bioRxiv : the preprint server for biology · 2024Article
- The identification of mediating effects using genome-based restricted maximum likelihood estimation.PLoS genetics · 2023Article
- Cohort profile: Genetic data in the German Socio-Economic Panel Innovation Sample (SOEP-G).PloS one · 2023Article
- Review
- Assessing the contribution of genetic nurture to refractive error.European journal of human genetics : EJHG · 2022Article
- The association of serum magnesium and chronic kidney disease: a two-sample mendelian randomization study of European descent.European journal of clinical nutrition · 2022Article
- Alcohol Consumption and Mild Cognitive Impairment: A Mendelian Randomization Study from Rural China.Nutrients · 2022Article
- Social and genetic associations with educational performance in a Scandinavian welfare state.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Human brain anatomy reflects separable genetic and environmental components of socioeconomic status.Science advances · 2022Article
- Incarceration, polygenic risk, and depressive symptoms among males in late adulthood.Social science research · 2022Article
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Abstract
Identifying causal effects in nonexperimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables [i.e., Mendelian randomization (MR)]. However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in nonexperimental data that would also undermine the ability of MR to correct for endogeneity bias from nongenetic sources. Here, we propose an alternative approach, genetic instrumental variable (GIV) regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGSs) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into nonoverlapping subsamples, we obtain multiple indicators of the outcome PGSs that can be used as instruments for each other and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA.
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