The Meaning of "Cause" in Genetics

The providential randomisation of genotypes

When building causal knowledge in behavioural genetics, the natural randomisation of genotypes at conception (approximately analogous to the artificial randomisation occurring in randomised controlled trials) facilitates the discovery of genetic causes. More importantly, the randomisation of genetic material within families also enables a better identification of (environmental) risk factors and aetiological pathways to diseases and behaviours.

Could interventions on physical activity mitigate genomic liability for obesity? Applying the health disparity framework in genetically informed studies

Polygenic scores (PGS) are now commonly available in longitudinal cohort studies, leading to their integration into epidemiological research. In this work, our aim is to explore how polygenic scores can be used as exposures in causal inference-based methods, specifically mediation analyses. We propose to estimate the extent to which the association of a polygenic score indexing genetic liability to an outcome could be mitigated by a potential intervention on a mediator. To do this this, we use the interventional disparity measure approach, which allows us to compare the adjusted total effect of an exposure on an outcome, with the association that would remain had we intervened on a potentially modifiable mediator. As an example, we analyse data from two UK cohorts, the Millennium Cohort Study (MCS, N = 2575) and the Avon Longitudinal Study of Parents and Children (ALSPAC, N = 3347). In both, the exposure is genetic liability for obesity (indicated by a PGS for BMI), the outcome is late childhood/early adolescent BMI, and the mediator and potential intervention target is physical activity, measured between exposure and outcome. Our results suggest that a potential intervention on child physical activity can mitigate some of the genetic liability for childhood obesity. We propose that including PGSs in a health disparity measure approach, and causal inference-based methods more broadly, is a valuable addition to the study of gene-environment interplay in complex health outcomes.

New historical and philosophical perspectives on quantitative genetics

The aim of this virtual special issue is to bring together philosophical and historical perspectives to address long-standing issues in the interpretation, utility, and impacts of quantitative genetics methods and findings. Methodological approaches and the underlying scientific understanding of genetics and heredity have transformed since the field's inception. These advances have brought with them new philosophical issues regarding the interpretation and understanding of quantitative genetic results. The contributions in this issue demonstrate that there is still work to be done integrating old and new methodological and conceptual frameworks. In some cases, new results are interpreted using assumptions based on old concepts and methodologies that need to be explicitly recognised and updated. In other cases, new philosophical tools can be employed to synthesise historical quantitative genetics work with modern methodologies and findings. This introductory article surveys three general themes that have dominated philosophical discussion of quantitative genetics throughout history: (1) how methodologies have changed and transformed our knowledge and interpretations; (2) whether or not quantitative genetics can offer explanations relating to causation and prediction; and (3) the importance of defining the phenotypes under study. We situate the contributions in this virtual special issue within a historical framework addressing these three themes.

Research Review: How to interpret associations between polygenic scores, environmental risks, and phenotypes

BACKGROUND

Genetic influences are ubiquitous as virtually all phenotypes and most exposures typically classified as environmental have been found to be heritable. A polygenic score summarises the associations between millions of genetic variants and an outcome in a single value for each individual. Ever lowering costs have enabled the genotyping of many samples relevant to child psychology and psychiatry research, including cohort studies, leading to the proliferation of polygenic score studies. It is tempting to assume that associations detected between polygenic scores and phenotypes in those studies only reflect genetic effects. However, such associations can reflect many pathways (e.g. via environmental mediation) and biases.

METHODS

Here, we provide a comprehensive overview of the many reasons why associations between polygenic scores, environmental exposures, and phenotypes exist. We include formal representations of common analyses in polygenic score studies using structural equation modelling. We derive biases, provide illustrative empirical examples and, when possible, mention steps that can be taken to alleviate those biases.

RESULTS

Structural equation models and derivations show the many complexities arising from jointly modelling polygenic scores with environmental exposures and phenotypes. Counter-intuitive examples include that: (a) associations between polygenic scores and phenotypes may exist even in the absence of direct genetic effects; (b) associations between child polygenic scores and environmental exposures can exist in the absence of evocative/active gene-environment correlations; and (c) adjusting an exposure-outcome association for a polygenic score can increase rather than decrease bias.

CONCLUSIONS

Strikingly, using polygenic scores may, in some cases, lead to more bias than not using them. Appropriately conducting and interpreting polygenic score studies thus requires researchers in child psychology and psychiatry and beyond to be versed in both epidemiological and genetic methods or build on interdisciplinary collaborations.

On the causal interpretation of heritability from a structural causal modeling perspective

Heritability estimated using the analysis of variance (ANOVA) for ascribing causal responsibility to genes for a phenotype has been criticized widely. First, there are problems associated with articulating the exact causal meaning of heritability in the standard model. Second, in conditions of gene-environment interaction or covariation that violate the assumptions made by the standard model, a causal interpretation of heritability is thought to be unwarranted. This paper aims to rethink these ideas and associated disputes from a structural causal modeling (SCM) perspective. Using SCM, we show that, in the standard model, heritability reflects the causal effect of eliminating genotypic differences on the change of phenotypic variance of a population. In the presence of interaction or covariation, heritability is estimated incorrectly using ANOVA. However, SCM can provide the causal effect of genotypes on the phenotypic variance regarding particular interventions. We also show that SCM can identify different types of causal effect and answer individual-level causal questions. We conclude that SCM has the resources to provide a systematic causal interpretation that can supplement traditional heritability estimates via ANOVA and offer a more substantial causal analysis of genetic causation.

Causal Inference with Genetic Data: Past, Present, and Future

The set of methods discussed in this collection has emerged from the convergence of two scientific fields-genetics and causal inference. In this introduction, we discuss relevant aspects of each field and show how their convergence arises from the natural experiments that genetics offer. We present introductory concepts useful to readers unfamiliar with genetically informed methods for causal inference. We conclude that existing applications and foreseeable developments should ensure that we rapidly reap the rewards of this relatively new field, not only in terms of our understanding of human disease and development, but also in terms of tangible translational applications.

Unifying heritability in evolutionary theory

Despite being widely used in both biology and psychology as if it were a single notion, heritability is not a unified concept. This is also true in evolutionary theory, in which the word 'heritability' has at least two technical definitions that only partly overlap. These yield two approaches to heritability: the 'variance approach' and the 'regression approach.' In this paper, I aim to unify these two approaches. After presenting them, I argue that a general notion of heritability ought to satisfy two desiderata-'general applicability' and 'separability of the causes of resemblance.' I argue that neither the variance nor the regression approach satisfies these two desiderata concomitantly. From there, I develop a general definition of heritability that relies on the distinction between intrinsic and extrinsic properties. I show that this general definition satisfies the two desiderata. I then illustrate the potential usefulness of this general definition in the context of microbiome research.

Half a century later and we're back where we started: How the problem of locality turned in to the problem of portability

In the 1970s, Lewontin sparked a debate about a problem of locality, by making the case that any given heritability estimate is local to the original population and environment studied, and could not be generalized to other populations and environments. Nearly 50 years later, a new problem of portability has emerged: the predictive accuracy of polygenic scores diminishes when applied to populations whose characteristics are different from the original population sample. This paper briefly reviews the nature of each problem and analyzes their similarities and differences in three areas: 1) conceptual underpinnings, 2) causal explanations, and 3) practical, social, and political implications. Although conceptually and methodologically different from the problem of locality in important respects, the problem of portability facing contemporary genomics today should come as no surprise, as it is an inevitable outcome of the kinds of problematic inferences detailed by Lewontin nearly half a century ago.

Causal Effects of Genetically Predicted Iron Status on Sepsis: A Two-Sample Bidirectional Mendelian Randomization Study

Background/Aim: Several observational studies showed a significant association between elevated iron status biomarkers levels and sepsis with the unclear direction of causality. A two-sample bidirectional mendelian randomization (MR) study was designed to identify the causal direction between seven iron status traits and sepsis. Methods: Seven iron status traits were studied, including serum iron, ferritin, transferrin saturation, transferrin, hemoglobin, erythrocyte count, and reticulocyte count. MR analysis was first performed to estimate the causal effect of iron status on the risk of sepsis and then performed in the opposite direction. The multiplicative random-effects and fixed-effects inverse-variance weighted, weighted median-based method and MR-Egger were applied. MR-Egger regression, MR pleiotropy residual sum and outlier (MR-PRESSO), and Cochran's Q statistic methods were used to assess heterogeneity and pleiotropy. Results: Genetically predicted high levels of serum iron (OR = 1.21, 95%CI = 1.13-1.29, p = 3.16 × 10^-4), ferritin (OR = 1.32, 95%CI = 1.07-1.62, p =0.009) and transferrin saturation (OR = 1.14, 95%CI = 1.06-1.23, p = 5.43 × 10^-4) were associated with an increased risk of sepsis. No significant causal relationships between sepsis and other four iron status biomarkers were observed. Conclusions: This present bidirectional MR analysis suggested the causal association of the high iron status with sepsis susceptibility, while the reverse causality hypothesis did not hold. The levels of transferrin, hemoglobin, erythrocytes, and reticulocytes were not significantly associated with sepsis. Further studies will be required to confirm the potential clinical value of such a prevention and treatment strategy.

Twins and Causal Inference: Leveraging Nature's Experiment

In this review, we discuss how samples comprising monozygotic and dizygotic twin pairs can be used for the purpose of strengthening causal inference by controlling for shared influences on exposure and outcome. We begin by briefly introducing how twin data can be used to inform the biometric decomposition of population variance into genetic, shared environmental, and nonshared environmental influences. We then discuss how extensions to this model can be used to explore whether associations between exposure and outcome survive correction for shared etiology (common causes). We review several analytical approaches that can be applied to twin data for this purpose. These include multivariate structural equation models, cotwin control methods, direction of causation models (cross-sectional and longitudinal), and extended family designs used to assess intergenerational associations. We conclude by highlighting some of the limitations and considerations that researchers should be aware of when using twin data for the purposes of interrogating causal hypotheses.