Twins and Causal Inference: Leveraging Nature's Experiment

Migraine heritability and beyond: A scoping review of twin studies

OBJECTIVE

This scoping review aimed to summarize current knowledge from twin studies on migraine. Migraine heritability, genetic correlations with migraine comorbid disorders, and the use of discordant twin pairs in migraine research are described. Further, the review considers the unused potential of twin studies in migraine research and reflects on future directions.

BACKGROUND

Twin studies can be used to understand how heritable and environmental factors influence human traits and disorders. The classical twin design compares the resemblance of a trait in monozygotic twins to that in dizygotic twins. The classical twin design can be extended to estimate the genetic correlation between disorders, model causality, and describe differences within discordant twin pairs.

METHODS

Studies focusing on migraine and using a twin study design were included. The search was performed on the PubMed-MEDLINE database using the search terms "migraine" AND "twin" OR "twins." It was done in May 2023, rerun in November 2023, and managed with the Covidence software.

RESULTS

The search identified 52 twin studies on migraine. In 24 papers, the heritability of migraine was estimated with a classical twin design. Heritability estimates ranged from 0.36 to 0.48 for studies with adults, both men and women, and unspecified migraine. Migraine heritability was predominantly estimated with twin cohorts of North European ancestry, and only two studies examined migraine subtypes. A multilevel classical twin design was used in 11 studies to examine the co-occurrence between migraine and comorbid disorders. The differences within migraine discordant twin pairs were examined in nine studies.

CONCLUSION

The heritability of migraine was estimated with a classical twin design in twin cohorts from seven different countries, with remarkably similar results across studies. Future studies should include migraine subtypes and twin cohorts of non-North European ancestry to better reflect the global population. Beyond heritability estimations, the twin method is a valuable tool for understanding causality and describing differences within discordant twin pairs. Despite more than 80 years of twin studies in migraine research, the twin design has a large unused potential to advance our understanding of migraine.

Calculating Within-Pair Difference Scores in the Co-twin Control Design. Effects of Alternative Strategies

Co-twin studies are an elegant and powerful design that allows controlling for the effect of confounding variables, including genetic and a range of environmental factors. There are several approaches to carry out this design. One of the methods commonly used, when contrasting continuous variables, is to calculate difference scores between members of a twin pair on two associated variables, in order to analyse the covariation of such differences. However, information regarding whether and how the different ways of estimating within-pair difference scores may impact the results is scant. This study aimed to compare the results obtained by different methods of data transformation when performing a co-twin study and test how the magnitude of the association changes using each of those approaches. Data was simulated using a direction of causation model and by fixing the effect size of causal path to low, medium, and high values. Within-pair difference scores were calculated as relative scores for diverse within-pair ordering conditions or absolute scores. Pearson's correlations using relative difference scores vary across the established scenarios (how twins were ordered within pairs) and these discrepancies become larger as the within-twin correlation increases. Absolute difference scores tended to produce the lowest correlation in every condition. Our results show that both using absolute difference scores or ordering twins within pairs, may produce an artificial decrease in the magnitude of the studied association, obscuring the ability to detect patterns compatible with causation, which could lead to discrepancies across studies and erroneous conclusions.

Familial effects account for association between chronic pain and past month smoking

BACKGROUND

Smoking is associated with chronic pain, but it is not established whether smoking causes pain or if the link is due to familial effects. One proposed mechanism is that smoking strengthens maladaptive cortico-striatal connectivity, which contributes to pain chronification. We leveraged a twin design to assess direct effects of smoking on pain controlling for familial confounds, and whether cortico-striatal connectivity mediates this association.

METHODS

In a population-based sample of 692 twins (age = 28.83 years), we assessed past-month smoking frequency (n = 132 used in the past month), presence and severity of a current pain episode (n = 179 yes), and resting-state functional connectivity of the nucleus accumbens and medial prefrontal cortex (NAc-mPFC).

RESULTS

Smoking was significantly associated with pain, but the association was not significantly mediated by NAc-mPFC connectivity. In a co-twin control model, smoking predicted which families had more pain but could not distinguish pain between family members. Pain risk was 43% due to additive genetic (A) and 57% due to non-shared environmental (E) influences. Past-month smoking frequency was 71% genetic and 29% non-shared environmental. Smoking and pain significantly correlated phenotypically (r = 0.21, p = 0.001) and genetically (rg = 0.51, p < 0.001), but not environmentally (re = -0.18, p = 0.339).

CONCLUSIONS

Pain and smoking are associated; however, the association appears to reflect shared familial risk factors, such as genetic risk, rather than being causal in nature. The connectivity strength of the reward pathway was not related to concurrent pain and smoking in this sample.

SIGNIFICANCE

Smoking does not appear to directly cause chronic pain; rather, there may be shared biopsychosocial risk factors, including genetic influences, that explain their association. These findings can be integrated into future research to identify shared biological pathways of both chronic pain and smoking behaviours as a way to conceptualize pain chronification.

On the detection of population heterogeneity in causation between two variables: Finite mixture modeling of data collected from twin pairs

Causal inference is inherently complex, often dependent on key assumptions that are sometimes overlooked. One such assumption is the potential for unidirectional or bidirectional causality, while another is population homogeneity, which suggests that the causal direction between two variables remains consistent across the study sample. Discerning these processes requires meticulous data collection through an appropriate research design and the use of suitable software to define and fit alternative models. In psychiatry, the co-occurrence of different disorders is common and can stem from various origins. A patient diagnosed with two disorders might have one recognized as primary and the other as secondary, suggesting the existence of two types of comorbidity within the population. For example, in some individuals, depression might lead to substance use, while in others, substance use could lead to depression. Identifying the primary disorder is crucial for developing effective treatment plans. This article explores the use of finite mixture models to depict within-sample heterogeneity. We begin with the Direction of Causation (DoC) model for twin data and extend it to a mixture distribution model. This extension allows for the calculation of the likelihood of each individual's data for the two alternate causal directions. Given twin data, there are four possible pairwise combinations of causal direction. Through simulations, we investigate the Direction of Causation Twin Mixture (mixCLPM) model's potential to detect and model heterogeneity due to varying causal directions.

Lifestyle and psychosocial associations with cognition at the cusp of midlife using twins and siblings

INTRODUCTION

This study investigates the relationship between cognitive functioning and 59 modifiable and intrinsic factors at the cusp of midlife.

METHODS

We analyzed data from 1221 participants in the Colorado Adoption/Twin Study of Lifespan behavioral development and cognitive aging (CATSLife; Mage = 33.20, %Female = 52.74). We assessed the impact of 59 factors on cognitive functioning using regularized regression and co-twin control models, controlling for earlier-life cognitive functioning and gray matter volume.

RESULTS

Eight robust factors were identified, including education attainment, cognitive complexity, purpose-in-life, and smoking status. Twins reporting higher levels of cognitive complexity and purpose-in-life showed better cognitive performance than their cotwin, while smoking was negatively associated. Using meta-analytically derived effect size threshold, we additionally identified that twins experiencing more financial difficulty tend to perform less well compared with their cotwin.

DISCUSSION

The findings highlight the early midlife link between cognitive functioning and lifestyle/psychological factors, beyond prior cognitive performance, brain status, genetic and familial confounders. Our results further highlight the potential of established adulthood as a crucial window for dementia prevention interventions targeting lifestyle and psychosocial factors.

Highlights

Cog complexity(+), purpose-in-life(+) were associated with cognition in early midlife.Smoking(-) was also associated with cognition in early midlife.Results were consistent controlling for genetic and environmental confounds.Association between EA and cognition might be mostly genetic and familial confounded.

Causal and common risk pathways linking childhood maltreatment to later intimate partner violence victimization

Childhood maltreatment and intimate partner violence (IPV) victimization are major psychiatric risk factors. Maltreatment substantially increases the likelihood of subsequent IPV victimization, but what drives this association is poorly understood. We analyzed retrospective self-reports of maltreatment and IPV in 12794 participants (58% women, 42% men) from the Twins Early Development Study at ages 21 and 26 using quantitative genetic methods. We estimated the etiological influences common to maltreatment and IPV, and the direct causal effect of maltreatment on IPV beyond such common influences. Participants exposed to maltreatment (~7% of the sample) were 3 times more likely to experience IPV victimization than their peers at age 21, 4 times more likely at 26. The association between maltreatment and IPV was mostly due to environmental influences shared by co-twins (42-43%) and genetic influences (30-33%). The association between maltreatment and IPV was similar for women and men, but its etiology partly differed by sex. Maltreatment had a moderate-to-large effect on IPV in phenotypic models (β = 0.23-0.34), decreasing to a small-to-moderate range in causal models accounting for their common etiology (β = 0.15-0.21). Risk factors common to maltreatment and IPV victimization are largely familial in origin, environmental and genetic. Even considering common risk factors, experiencing maltreatment is causally related to subsequent IPV victimization. Interventions promoting safe intimate relationships among young adults exposed to maltreatment are warranted and should address family-level environmental risk and individual-level risk shaped by genetics.

Comparing findings from the random-intercept cross-lagged panel model and the monozygotic twin difference cross-lagged panel model: Maladaptive parenting and offspring emotional and behavioural problems

Background

In this study we compare results obtained when applying the monozygotic twin difference cross-lagged panel model (MZD-CLPM) and a random intercept cross-lagged panel model (RI-CLPM) to the same data. Each of these models is designed to strengthen researchers' ability to draw causal inference from cross-lagged associations. We explore differences and similarities in how each model does this, and in the results each model produces. Specifically, we examine associations between maladaptive parenting and child emotional and behavioural problems in identical twins aged 9, 12 and 16.

Method

Child reports of 5698 identical twins from the Twins Early Development Study (TEDS) were analysed. We ran a regular CLPM to anchor our findings within the current literature, then applied the MZD-CLPM and the RI-CLPM.

Results

The RI-CLPM and MZD-CLPM each enable researchers to evaluate the direction of effects between correlated variables, after accounting for unmeasured sources of potential confounding. Our interpretation of these models therefore focusses primarily on the magnitude and significance of cross-lagged associations. In both the MZD-CLPM and the RI-CLPM behavioural problems at age 9 resulted in higher levels of maladaptive parenting at age 12. Other effects were not consistently significant across the two models, although the majority of estimates pointed in the same direction.

Conclusion

In light of the triangulated methods, differences in the results obtained using the MZD-CLPM and the RI-CLPM underline the importance of careful consideration of what sources of unmeasured confounding different models control for and that nuance is required when interpreting findings using such models. We provide an overview of what the CLPM, RI-CLPM and MZD-CLPM can and cannot control for in this respect and the conclusions that can be drawn from each model.

Concurrent and longitudinal associations of developmental language disorder with peer victimization in adolescence: evidence from a co-twin study

BACKGROUND

Children with developmental language disorder (DLD) experience higher levels of peer victimization than their peers. However, it is not known if such associations reflect genetic and environmental confounding. We used a co-twin control design to investigate the association of language difficulties (DLD and separately poor pragmatic language) with peer victimization and compare the developmental trajectories of peer victimization across adolescence for those with and without language difficulties.

METHODS

Participants were 3,400 pairs of twins in the Twins Early Development Study (TEDS), a UK-based population birth cohort. Language abilities were assessed via online tests at age 11 and peer victimization was self-reported at ages 11, 14 and 16. Language difficulties were defined as language abilities at least -1.25 SD below the mean of the TEDS sample. We performed linear regressions and latent growth curve modeling at a population level and within monozygotic and same-sex dizygotic twin pairs.

RESULTS

At population level, youth with DLD experienced higher levels of peer victimization at ages 11 (β = 0.27, 95% Confidence Interval (CI) 0.20-0.35), 14 (β = 0.15, 95% CI 0.03-0.27) and 16 (β = 0.17, 95% CI 0.03-0.32) and a sharper decline in peer victimization between ages 11 and 16 compared to their peers without DLD. The associations between DLD and peer victimization were reduced in strength and not statistically significant in within-twin models. Moreover, there was no difference in the rate of change in peer victimization between twin pairs discordant for DLD. Results were similar for the association of poor pragmatic language with peer victimization.

CONCLUSIONS

Associations between language difficulties (DLD and separately, poor pragmatic language) and peer victimization were confounded by genetic and shared environmental factors. Identifying specific factors underlying these associations is important for guiding future work to reduce peer victimization among adolescents with language difficulties.

Frequency of Consumption of Food Groups and Cardio-Metabolic Risk Factors: A Genetically Informative Twin Study in Sri Lanka

Low- and middle-income countries (LMICs) globally have undergone rapid urbanisation, and changes in demography and health behaviours. In Sri Lanka, cardio-vascular disease and diabetes are now leading causes of mortality. High prevalence of their risk factors, including hypertension, dysglycaemia and obesity have also been observed. Diet is a key modifiable risk factor for both cardio-vascular disease and diabetes as well as their risk factors. Although typically thought of as an environmental risk factor, dietary choice has been shown to be genetically influenced, and genes associated with this behaviour correlate with metabolic risk indicators. We used Structural Equation Model fitting to investigate the aetiology of dietary choices and cardio-metabolic phenotypes in COTASS, a population-based twin and singleton sample in Colombo, Sri Lanka. Participants completed a Food Frequency Questionnaire (N = 3934) which assessed frequency of intake of 14 food groups including meat, vegetables and dessert or sweet snacks. Anthropometric (N = 3675) and cardio-metabolic (N = 3477) phenotypes were also collected including weight, blood pressure, cholesterol, fasting plasma glucose and triglycerides. Frequency of consumption of most food items was found to be largely environmental in origin with both the shared and non-shared environmental influences indicated. Modest genetic influences were observed for some food groups (e.g. fruits and leafy greens). Cardio-metabolic phenotypes showed moderate genetic influences with some shared environmental influence for Body Mass Index, blood pressure and triglycerides. Overall, it seemed that shared environmental effects were more important for both dietary choices and cardio-metabolic phenotypes compared to populations in the Global North.

The relationship of school performance with self-control and grit is strongly genetic and weakly causal

The non-cognitive skills self-control and grit are often considered predictors of school performance, but whether this relationship is causal remains unclear. We investigated the causality of this association using a twin design. Specifically, we evaluated the direct impact of self-control and grit on school performance, while controlling for genetic or environmental influences common to all three traits (i.e., confounding). Teachers of 4891 Dutch 12-year-old twin pairs (of which 3837 were complete pairs) completed a survey about school performance (school grades), self-control (ASEBA self-control scale), and the perseverance aspect of grit. Our analysis aimed to determine the direct impact of self-control and grit on school performance, while simultaneously controlling for genetic or environmental confounding. Establishing the regression relationship corrected for confounding supports the interpretation of the regression relationship as causal. In all analyses, we corrected for sex, rater bias of the teachers, and parental socioeconomic status. Initially, in the standard regression, self-control, and grit explained 28.4% of the school performance variance. However, allowing for genetic confounding (due to genetic pleiotropy) revealed that most of this association could be attributed to genetic influences that the three traits share. In the presence of genetic pleiotropy, the phenotypic regression of school performance on self-control and grit accounted for only 4.4% (i.e., the effect size association with the causal hypothesis). In conclusion, self-control and grit predict school performance primarily due to genetic pleiotropy, with a much smaller causal effect (R2 = 4.4%). This suggests that interventions targeting self-control and grit alone may yield limited improvements in school performance.

Causal models and causal modelling in obesity: foundations, methods and evidence

Discussing causes in science, if we are to do so in a way that is sensible, begins at the root. All too often, we jump to discussing specific postulated causes but do not first consider what we mean by, for example, causes of obesity or how we discern whether something is a cause. In this paper, we address what we mean by a cause, discuss what might and might not constitute a reasonable causal model in the abstract, speculate about what the causal structure of obesity might be like overall and the types of things we should be looking for, and finally, delve into methods for evaluating postulated causes and estimating causal effects. We offer the view that different meanings of the concept of causal factors in obesity research are regularly being conflated, leading to confusion, unclear thinking and sometimes nonsense. We emphasize the idea of different kinds of studies for evaluating various aspects of causal effects and discuss experimental methods, assumptions and evaluations. We use analogies from other areas of research to express the plausibility that only inelegant solutions will be truly informative. Finally, we offer comments on some specific postulated causal factors. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

The Co-Twin Control Design: Implementation and Methodological Considerations

Establishing causal relationships in observational studies is an important step in research and policy decision making. The association between an exposure and an outcome can be confounded by multiple factors, often making it hard to draw causal conclusions. The co-twin control design (CTCD) is a powerful approach that allows for the investigation of causal effects while controlling for genetic and shared environmental confounding factors. This article introduces the CTCD and offers an overview of analysis methods for binary and continuous outcome and exposure variables. Tools for data simulation are provided, along with practical guidance and accompanying scripts for implementing the CTCD in R, SPSS, and Stata. While the CTCD offers valuable insights into causal inference, it depends on several assumptions that are important when interpreting CTCD results. By presenting a broad overview of the CTCD, this article aims to equip researchers with actionable recommendations and a comprehensive understanding of the design's strengths and limitations.

Stressful life events increase the risk of major depressive episodes: A population-based twin study

BACKGROUND

Previous studies have found that stressful life events (SLEs) are associated with an increased risk of adult depression. However, many studies are observational in nature and limited by methodological issues, such as potential confounding by genetic factors. Genetically informative research, such as the co-twin control design, can strengthen causal inference in observational studies. Discrete-time survival analysis has several benefits and multilevel survival analysis can incorporate frailty terms (random effects) to estimate the components of the biometric model. In the current study, we investigated associations between SLEs and depression risk in a population-based twin sample (N = 2299).

METHODS

A co-twin control design was used to investigate the influence of the occurrence of SLEs on depression risk. The co-twin control design involves comparing patterns of associations in the full sample and within dizygotic (DZ) and monozygotic twins (MZ). Associations were modelled using discrete-time survival analysis with biometric frailty terms. Data from two time points were used in the analyses. Mean age at Wave 1 was 28 years and mean age at Wave 2 was 38 years.

RESULTS

SLE occurrence was associated with increased depression risk. Co-twin control analyses indicated that this association was at least in part due to the causal influence of SLE exposure on depression risk for event occurrence across all SLEs and for violent SLEs. A minor proportion of the total genetic risk of depression reflected genetic effects related to SLEs.

CONCLUSIONS

The results support previous research in implicating SLEs as important risk factors with probable causal influence on depression risk.

Exercise and Experiments of Nature

In this article, we highlight the contributions of passive experiments that address important exercise-related questions in integrative physiology and medicine. Passive experiments differ from active experiments in that passive experiments involve limited or no active intervention to generate observations and test hypotheses. Experiments of nature and natural experiments are two types of passive experiments. Experiments of nature include research participants with rare genetic or acquired conditions that facilitate exploration of specific physiological mechanisms. In this way, experiments of nature are parallel to classical "knockout" animal models among human research participants. Natural experiments are gleaned from data sets that allow population-based questions to be addressed. An advantage of both types of passive experiments is that more extreme and/or prolonged exposures to physiological and behavioral stimuli are possible in humans. In this article, we discuss a number of key passive experiments that have generated foundational medical knowledge or mechanistic physiological insights related to exercise. Both natural experiments and experiments of nature will be essential to generate and test hypotheses about the limits of human adaptability to stressors like exercise. © 2023 American Physiological Society. Compr Physiol 13:4879-4907, 2023.

Associations Between Adolescent Pain and Psychopathology in the Adolescent Brain Cognitive Development (ABCD) Study

Pain and psychopathology co-occur in adolescence, but the directionality and etiology of these associations are unclear. Using the pain questionnaire and the Child Behavior Checklist from the Adolescent Brain Cognitive Development study (n = 10,414 children [770 twin pairs] aged 12-13), we estimated longitudinal, co-twin control, and twin models to evaluate the nature of these associations. In two-wave cross-lag panel models, there were small cross-lag effects that suggested bidirectional associations. However, the co-twin control models suggested that most associations were familial. Pain at age 12 and 13 was mostly environmental (A = 0-12%, C = 15-30%, E = 70-73%) and the twin models suggested that associations with psychopathology were primarily due to shared environmental correlations. The exception was externalizing, which had a phenotypic prospective effect on pain, a significant within-family component, and a non-shared environmental correlation at age 12. Environmental risk factors may play a role in pain-psychopathology co-occurrence. Future studies can examine risk factors such as stressful life events.

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.

Taking the problem of colliders seriously in the study of crime: A research note

Objectives

We provide a brief overview of collider bias and its implications for criminological research.

Methods

Owing to the nature of the topics studied, as well as the common data sources used to carry out much of this research, work in the field may often become vulnerable to a specific methodological problem known as collider bias. Collider bias occurs when exposure variables and outcomes independently cause a third variable, and this variable is included in statistical models. Colliders represent somewhat of a paradox in that there is scholarship discussing the issue, yet it has managed to remain a relatively cryptic threat compared to other sources of bias.

Results

We argue that, far from being an obscure concern, colliders almost certainly have pervasive impact in criminal justice and criminology.

Conclusion

We close by offering a general set of strategies for addressing the challenges posed by collider bias. While there is no panacea, there are better practices, many of which are underutilized in the disciplines that study crime and its attendant topics.

Genetic and Environmental Factors Underlying Parallel Changes in Body Mass Index and Alcohol Consumption: A 36-Year Longitudinal Study of Adult Twins

INTRODUCTION

While the genetic and environmental underpinnings of body weight and alcohol use are fairly well-known, determinants of simultaneous changes in these traits are still poorly known. We sought to quantify the environmental and genetic components underlying parallel changes in weight and alcohol consumption and to investigate potential covariation between them.

METHODS

The analysis comprised 4,461 adult participants (58% women) from the Finnish Twin Cohort with four measures of alcohol consumption and body mass index (BMI) over a 36-year follow-up. Trajectories of each trait were described by growth factors, defined as intercepts (i.e., baseline) and slopes (i.e., change over follow-up), using latent growth curve modeling. Growth values were used for male (190 monozygotic pairs, 293 dizygotic pairs) and female (316 monozygotic pairs, 487 dizygotic pairs) same-sex complete twin pairs in multivariate twin modeling. The variances and covariances of growth factors were then decomposed into genetic and environmental components.

RESULTS

The baseline heritabilities were similar in men (BMI: h2 = 79% [95% confidence interval: 74, 83]; alcohol consumption: h2 = 49% [32, 67]) and women (h2 = 77% [73, 81]; h2 = 45% [29, 61]). Heritabilities of BMI change were similar in men (h2 = 52% [42, 61]) and women (h2 = 57% [50, 63]), but the heritability of change in alcohol consumption was significantly higher (p = 0.03) in men (h2 = 45% [34, 54]) than in women (h2 = 31% [22, 38]). Significant additive genetic correlations between BMI at baseline and change in alcohol consumption were observed in both men (rA = -0.17 [-0.29, -0.04]) and women (rA = -0.18 [-0.31, -0.06]). Non-shared environmental factors affecting changes in alcohol consumption and BMI were correlated in men (rE = 0.18 [0.06, 0.30]). Among women, non-shared environmental factors affecting baseline alcohol consumption and the change in BMI were inversely correlated (rE = -0.11 [-0.20, -0.01]).

CONCLUSIONS

Based on genetic correlations, genetic variation underlying BMI may affect changes in alcohol consumption. Independent of genetic effects, change in BMI correlates with change in alcohol consumption in men, suggesting direct effects between them.

Behaviour genetics and sleep: A narrative review of the last decade of quantitative and molecular genetic research in humans

During the last decade quantitative and molecular genetic research on sleep has increased considerably. New behavioural genetics techniques have marked a new era for sleep research. This paper provides a summary of the most important findings from the last ten years, on the genetic and environmental influences on sleep and sleep disorders and their associations with health-related variables (including anxiety and depression) in humans. In this review we present a brief summary of the main methods in behaviour genetic research (such as twin and genome-wide association studies). We then discuss key research findings on: genetic and environmental influences on normal sleep and sleep disorders, as well as on the association between sleep and health variables (highlighting a substantial role for genes in individual differences in sleep and their associations with other variables). We end by discussing future lines of enquiry and drawing conclusions, including those focused on problems and misconceptions associated with research of this type. In this last decade our knowledge about genetic and environmental influences on sleep and its disorders has expanded. Both, twin and genome-wide association studies show that sleep and sleep disorders are substantially influenced by genetic factors and for the very first time multiple specific genetic variants have been associated with sleep traits and disorders.

Ten challenges for clinical translation in psychiatric genetics

Genome-wide association studies have identified hundreds of robust genetic associations underlying psychiatric disorders and provided important biological insights into disease onset and progression. There is optimism that genetic findings will pave the way to precision psychiatry by facilitating the development of more effective treatments and the identification of groups of patients that these treatments should be targeted toward. However, there are several challenges that must be addressed before genetic findings can be translated into the clinic. In this Perspective, we highlight ten challenges for the field of psychiatric genetics, focused on the robust and generalizable detection of genetic risk factors, improved definition and assessment of psychopathology and achieving better clinical indicators. We discuss recent advancements in the field that will improve the explanatory and predictive power of genetic data and ultimately contribute to improving the management and treatment of patients with a psychiatric disorder.

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.

The Meaning of "Cause" in Genetics

Causation has multiple distinct meanings in genetics. One reason for this is meaning slippage between two concepts of the gene: Mendelian and molecular. Another reason is that a variety of genetic methods address different kinds of causal relationships. Some genetic studies address causes of traits in individuals, which can only be assessed when single genes follow predictable inheritance patterns that reliably cause a trait. A second sense concerns the causes of trait differences within a population. Whereas some single genes can be said to cause population-level differences, most often these claims concern the effects of many genes. Polygenic traits can be understood using heritability estimates, which estimate the relative influences of genetic and environmental differences to trait differences within a population. Attempts to understand the molecular mechanisms underlying polygenic traits have been developed, although causal inference based on these results remains controversial. Genetic variation has also recently been leveraged as a randomizing factor to identify environmental causes of trait differences. This technique-Mendelian randomization-offers some solutions to traditional epidemiological challenges, although it is limited to the study of environments with known genetic influences.

Triangulating Evidence through the Inclusion of Genetically Informed Designs

Much research effort is invested in attempting to determine causal influences on disease onset and progression to inform prevention and treatment efforts. However, this is often dependent on observational data that are prone to well-known limitations, particularly residual confounding and reverse causality. Several statistical methods have been developed to support stronger causal inference. However, a complementary approach is to use design-based methods for causal inference, which acknowledge sources of bias and attempt to mitigate these through the design of the study rather than solely through statistical adjustment. Genetically informed methods provide a novel and potentially powerful extension to this approach, accounting by design for unobserved genetic and environmental confounding. No single approach will be absent from bias. Instead, we should seek and combine evidence from multiple methodologies that each bring different (and ideally uncorrelated) sources of bias. If the results of these different methodologies align-or triangulate-then we can be more confident in our causal inference. To be truly effective, this should ideally be done prospectively, with the sources of evidence specified in advance, to protect against one final source of bias-our own cognitions, expectations, and fondly held beliefs.

Family-Based Designs that Disentangle Inherited Factors from Pre- and Postnatal Environmental Exposures: In Vitro Fertilization, Discordant Sibling Pairs, Maternal versus Paternal Comparisons, and Adoption Designs

Identifying environmental risk and protective exposures that have causal effects on health is an important scientific goal. Many environmental exposures are nonrandomly allocated and influenced by dispositional factors including inherited ones. We review family-based designs that can separate the influence of environmental exposures from inherited influences shared between parent and offspring. We focus on prenatal exposures. We highlight that the family-based designs that can separate the prenatal environment from inherited confounds are different to those that are able to pull apart later-life environmental exposures from inherited confounds. We provide a brief review of the literature on maternal smoking during pregnancy and offspring attention-deficit/hyperactivity disorder (ADHD) and conduct problems; these inconsistencies in the literature make a review useful and this illustrates that results of family-based genetically informed studies are inconsistent with a causal interpretation for this exposure and these two offspring outcomes.