Meta-analyses to investigate gene-environment interactions in neuroepidemiology

Interplay of ADHD Polygenic Liability With Birth-Related, Somatic, and Psychosocial Factors in ADHD: A Nationwide Study

OBJECTIVE

Attention deficit hyperactivity disorder (ADHD) is a multifactorial neurodevelopmental disorder, yet the interplay between ADHD polygenic risk scores (PRSs) and other risk factors remains relatively unexplored. The authors investigated associations, confounding, and interactions of ADHD PRS with birth-related, somatic, and psychosocial factors previously associated with ADHD.

METHODS

Participants included a random general population sample (N=21,578) and individuals diagnosed with ADHD (N=13,697) from the genotyped Danish iPSYCH2012 case cohort, born between 1981 and 2005. The authors derived ADHD PRSs and identified 24 factors previously associated with ADHD using national registers. Logistic regression was used to estimate associations of ADHD PRS with each risk factor in the general population. Cox models were used to evaluate confounding of risk factor associations with ADHD diagnosis by ADHD PRS and parental psychiatric history, and interactions between ADHD PRS and each risk factor.

RESULTS

ADHD PRS was associated with 12 of 24 risk factors (odds ratio range, 1.03-1.30), namely, small gestational age, infections, traumatic brain injury, and most psychosocial risk factors. Nineteen risk factors were associated with ADHD diagnosis (odds ratio range, 1.20-3.68), and adjusting for ADHD PRS and parental psychiatric history led to only minor attenuations. Only the interaction between ADHD PRS and maternal autoimmune disease survived correction for multiple testing.

CONCLUSIONS

Higher ADHD PRS in the general population is associated with small increases in risk for certain birth-related and somatic ADHD risk factors, and broadly to psychosocial adversity. Evidence of gene-environment interaction was limited, as was confounding by ADHD PRS and family psychiatric history on ADHD risk factor associations. This suggests that the majority of the investigated ADHD risk factors act largely independently of current ADHD PRS to increase risk of ADHD.

Prevention of MS Requires Intervention on the Causes of the Disease: Reconciling Genes, Epigenetics, and Epstein Barr Virus

Prevention of multiple sclerosis requires intervention on modifiable causes of the condition making it necessary to establish what those causes are. MS is often stated to be a polygenic disease, with causal contributions from environmental factors and gene-environment interactions, implying an additive and independent relationship of these factors. Mechanistically there are no independent contributions of genes or environmental factors to traits. This model is unrealistic but still useful and underlies the concept of heritability, a foundational parameter in population genetics. However, it perpetuates a debate on an irreconcilable dichotomy about whether MS is primarily genetic or environmental. In particular, epidemiological evidence now exists for a causal, possibly even necessary, role for Epstein Barr Virus in MS. The additive model makes it unintuitive to reconcile MS as a genetic disease but also independently a viral illness. In this perspective it is argued that starting from a realistic interaction only model, based on broadly accepted biological premises, and working forward to explain why the classical additive model gives useful results, there is actually no paradox. An integrated approach using population genetic studies, immunology and molecular virology offers a particularly promising route to establish the elusive role of EBV in MS pathology, as EBV is a large and complex virus and its latency, dysregulated in most EBV-related pathologies, is hard to study in vivo. This approach may offer a route to prevention of MS altogether.

How do age and major risk factors for mortality interact over the life-course? Implications for health disparities research and public health policy

A critical question in life-course research is whether the relationship between a risk factor and mortality strengthens, weakens, or remains constant with age. The objective of this paper is to shed light on the importance of measurement scale in examining this question. Many studies address this question solely on the multiplicative (relative) scale and report that the hazard ratio of dying associated with a risk factor declines with age. A wide set of risk factors have been shown to conform to this pattern including those that are socioeconomic, behavioral, and physiological in nature. Drawing from well-known principles on interpreting statistical interactions, we show that evaluations on the additive (absolute) scale often lead to a different set of conclusions about how the association between a risk factor and mortality changes with age than interpretations on the multiplicative scale. We show that on the additive scale the excess death risks posed by key socio-demographic and behavioral risk factors increase with age. Studies have not generally recognized the additive interpretation, but it has relevancy for testing life-course theories and informing public health interventions. We discuss these implications and provide general guidance on choosing a scale. Data from the U.S. National Health Interview Survey are used to provide empirical support.

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Studies often conclude that the effect of demographic and behavioral risk factors on mortality weakens with age.

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We show that this conclusion is premature as studies often fail to interpret their findings on the additive scale.

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We show empirically that on the additive scale the excess death risks posed by key risk factors strengthens with age.

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The general pattern of increasing susceptibility by age on the additive scale has not been previously recognized.

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We argue that the pattern has critical implications for sociological theory and public health policy.

The role of epidemiology in MS research: Past successes, current challenges and future potential

Multiple sclerosis (MS) is a multifaceted condition, with a range of environmental, behavioural and genetic factors implicated in its aetiology and clinical course. Successes in advancing our appreciation of the roles of Epstein-Barr virus, vitamin D/UV and the HLA-DRB1 locus; and our greater understanding of these and related factors’ modes of action in MS and other conditions, can be attributed in no small part to the work of generations of epidemiologists. Hardly content to rest on our laurels, however, there are yet a range of unsolved conundrums in MS, including some changes in epidemiological characteristics (e.g. increasing incidence and sex ratio), to say nothing of the unresolved parts regarding what underlies MS risk and its clinical course. There is evidence that epidemiology will continue to play a crucial role in unravelling the architecture of MS causation and clinical course. While classic epidemiological methods are ongoing, novel avenues for research include gene-environment interaction studies, the world of ‘-omic’ research, and the utilisation of mobile and social media tools to both access and track study populations, which means that the epidemiological discoveries of the past century may be but a glimpse of our understanding in the next few decades.

Genetic epidemiology and insights into interactive genetic and environmental effects in autism spectrum disorders

Understanding the pathogenesis of neurodevelopmental disorders has proven to be challenging. Using autism spectrum disorder (ASD) as a paradigmatic neurodevelopmental disorder, this article reviews the existing literature on the etiological substrates of ASD and explores how genetic epidemiology approaches including gene-environment interactions (G×E) can play a role in identifying factors associated with ASD etiology. New genetic and bioinformatics strategies have yielded important clues to ASD genetic substrates. The next steps for understanding ASD pathogenesis require significant effort to focus on how genes and environment interact with one another in typical development and its perturbations. Along with larger sample sizes, future study designs should include sample ascertainment that is epidemiologic and population-based to capture the entire ASD spectrum with both categorical and dimensional phenotypic characterization; environmental measurements with accuracy, validity, and biomarkers; statistical methods to address population stratification, multiple comparisons, and G×E of rare variants; animal models to test hypotheses; and new methods to broaden the capacity to search for G×E, including genome-wide and environment-wide association studies, precise estimation of heritability using dense genetic markers, and consideration of G×E both as the disease cause and a disease course modifier. Although examination of G×E appears to be a daunting task, tremendous recent progress in gene discovery has opened new horizons for advancing our understanding of the role of G×E in the pathogenesis of ASD and ultimately identifying the causes, treatments, and even preventive measures for ASD and other neurodevelopmental disorders.