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  • Review Article
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Using genetic data to strengthen causal inference in observational research

Nature Reviews Genetics volume 19, pages 566–580 (2018)Cite this article

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Abstract

Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology — including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining — has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.

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Fig. 1: Including multiple instruments in Mendelian randomization.
Fig. 2: Causal mapping.

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Acknowledgements

The authors thank S. Gage and J. M. Vink for cannabis initiation summary statistics and syntax and J. Rees for multivariable Mendelian randomization (MR) syntax. J.-B.P. is a fellow of MQ: Transforming Mental Health (MQ16IP16) and affiliated with the Centre for Research in Epidemiology and Population Health (CESP), French National Institute for Health and Medical Research (INSERM), Université de Paris-Sud, Université de Versailles-Saint Quentin, and Université Paris-Saclay, Paris, France. P.F.O. receives funding from the UK Medical Research Council (MR/N015746/1) and the Wellcome Trust (109863/Z/15/Z). This report represents independent research (partly) funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service (NHS) Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health.

Reviewer information

Nature Reviews Genetics thanks G. Hemani and M. C. Neale for their contribution to the peer review of this work.

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Authors and Affiliations

  1. Department of Clinical, Educational and Health Psychology, University College London, London, UK

    Jean-Baptiste Pingault & Tabea Schoeler

  2. Social, Genetic, and Developmental Psychiatry, King’s College London, De Crespigny Park, London, UK

    Jean-Baptiste Pingault, Paul F. O’Reilly & Frühling Rijsdijk

  3. Centre for Longitudinal Studies, Department of Social Science, UCL Institute of Education, University College London, London, UK

    George B. Ploubidis

  4. Department of Health Sciences, University of Leicester, Leicester, UK

    Frank Dudbridge

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  1. Jean-Baptiste Pingault
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  2. Paul F. O’Reilly
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  3. Tabea Schoeler
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Contributions

J.-B.P. and T.S. researched data for the article. J.-B.P. and F.D. wrote the manuscript. All authors contributed substantially to discussion of content and reviewed and/or edited the manuscript before submission.

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Correspondence to Jean-Baptiste Pingault.

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Related links

Twin Registries: http://www.twinstudies.org/information/twinregisters/

DAGitty: http://www.dagitty.net/

LD Hub: http://ldsc.broadinstitute.org/

MR-base: http://www.mrbase.org/

Supplementary information

41576_2018_20_MOESM1_ESM.pdf

Supplementary information | A file of supplementary material providing further details on the data analysis for Figures 1 and 2.

Glossary

Causal risk and protective factors

Factors whose different values predict different risks of the outcome (either an elevated risk or a protective effect), with all other factors being held constant.

Phenotypes

Measurable individual characteristics.

Confounding

A phenomenon whereby a variable (the confounder) has a causal effect on both the risk factor and the outcome, generating a spurious association between the two.

Genetic confounding

Confounding created by genetic factors influencing both the risk factor and the outcome.

Causal inference methods

Methods that aim to clarify the causal status of a risk factor, either by providing a direct estimate of the causal effect or by ruling out possible sources of confounding (for example, removing the possibility of genetic confounding).

Genetically informed methods

Methods that use genetic information, such as known genetic relationships (for example, twins) or genetic variation data.

Instrumental variable

A variable that is used as a proxy for an exposure X to estimate the causal effect of X on an outcome. This variable must be robustly associated with X, independent of all confounders of the effect of X on an outcome Y, and its effect on Y must be entirely mediated by X.

Mendelian randomization

A method that uses single nucleotide polymorphisms (SNPs) associated with an exposure as instruments to probe the causal nature of the relationship between this exposure and an outcome of interest.

Counterfactual

Also known as potential outcomes. The counterfactual is a treatment (or value of a risk factor) that an individual is not exposed to. The potential outcome is the outcome that would be obtained under this counterfactual treatment.

Exchangeability

Verified when the expected outcome in the non-treated group would have been the same as the outcome in the treated group if subjects in the non-treated group had received the treatment. Conditional exchangeability occurs when exchangeability is verified in each stratum of a confounder after conditioning (adjusting) for the confounder.

Genetic relatedness

Occurs when two individuals share a proportion of their genome identical by descent, as a result of inheritance from a recent common ancestor.

d-Separated

An exposure X and an outcome Y are d-separated through the process of d-separation, in which all backdoor paths between X and Y are blocked, to estimate the unconfounded effect of X on Y.

Relevance

A core assumption of instrumental variable estimation, whereby the instrument used must be robustly associated with the exposure of interest.

Exclusion restriction

A core assumption of instrumental variable estimation whereby the effect of the instrument on the outcome must act entirely through its effect on the exposure (that is, not directly and not via confounders or other mediators).

Backdoor paths

Also known as unblocked paths. A path between an exposure X and an outcome Y through a confounder, which biases the estimation of the causal effect of X on Y.

Structural equation modelling

Multivariate statistical technique combining factor analysis and regression analysis to estimate networks of relationships between latent and observed variables.

Sensitivity analysis

An analysis conducted to assess how robust an association of interest is to potential unobserved confounding or other sources of bias.

Heritability

The proportion of variance in a phenotype that can be attributed to genetic differences among individuals in a given population. Narrow-sense heritability estimates additive genetic effects. Broad-sense heritability includes both additive and dominance effects.

Environmental influences

Influences that contribute to make two individuals (for example, twins) similar (shared environmental influences) or dissimilar (non-shared environmental influences) to each other.

Single nucleotide polymorphisms

(SNPs). DNA sequence variation arising from differences in a single nucleotide: adenine (A), thymine (T), cytosine (C) or guanine (G).

Polygenic

Influenced by variants in many genes.

Pleiotropy

Occurs when a genetic locus (for example, a single nucleotide polymorphism (SNP)) affects more than one trait.

Genome-wide association studies

(GWAS). Studies in which hundreds of thousands to millions of genetic variants are tested for an association with a phenotype.

Heterogeneity

When several estimates of the same effect do not converge towards the same value, whether in meta-analyses or in Mendelian randomization analyses using many genetic instruments.

Collider bias

When a variable (the collider) is independently caused by the exposure and outcome of interest; controlling for it creates an association between exposure and outcome.

Allelic scores

Computed as a polygenic score but summarizes genetic information derived from a few to a few hundred single nucleotide polymorphisms (SNPs) as opposed to polygenic scores, which rely on thousands up to all SNPs in the genome.

Polygenic scores

Individual-level scores that summarize genetic risk (or protection) for a given phenotype. For each single nucleotide polymorphism (SNP), a score is computed by counting effect alleles in an individual and weighting them by the effect size of this SNP. A polygenic score is computed by summing scores from a large number, potentially all, of the SNPs in the genome.

Dynastic effects

Occur when genetic variants in parents are transmitted to the offspring but also contribute to parental phenotype and in turn to the environment experienced by the child. This induces a correlation between offspring genotypes and the offspring’s environment.

Summary association statistics

Effect sizes and standard errors derived from a genome-wide association study for each single nucleotide polymorphism (SNP). They may include other summary statistics (for example, allele frequency or imputation accuracy).

Genetic correlations

The correlation between causal effect sizes for two phenotypes across single nucleotide polymorphisms (SNPs). Typically reported as the correlation across the whole genome and will differ when restricted to pleiotropic SNPs only.

Phenome-wide association studies

(PheWAS). These studies estimate the association of one or a few genetic variants of particular interest against many phenotypes, that is, a selection of all possible phenotypes or phenome.

Colocalization methods

When a genetic region contains variants associated with more than one phenotype, colocalization methods aim to determine whether this is due to shared or distinct causal variants.

Linkage disequilibrium

(LD). Nonrandom associations between alleles at different loci.

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Pingault, JB., O’Reilly, P.F., Schoeler, T. et al. Using genetic data to strengthen causal inference in observational research. Nat Rev Genet 19, 566–580 (2018). https://doi.org/10.1038/s41576-018-0020-3

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