Meta-regression of genome-wide association studies to estimate age-varying genetic effects.

Fixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age (or other characteristics), and not allowing for this in a GWAS might lead to bias. Meta-regression models between study heterogeneity and allows effect modification of the genetic effects to be explored. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes. With simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of the FTO SNP rs9939609 on Body Mass Index (BMI). Fixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both main genetic effects and age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. Our findings challenge the assumption that genetic effects are consistent across all ages and provide a method for exploring this. GWAS consortia should be encouraged to use meta-regression to explore age-varying genetic effects.

Methodological approaches, challenges, and opportunities in the application of Mendelian randomisation to lifecourse epidemiology: A systematic literature review.

Diseases diagnosed in adulthood may have antecedents throughout (including prenatal) life. Gaining a better understanding of how exposures at different stages in the lifecourse influence health outcomes is key to elucidating the potential benefits of disease prevention strategies. Mendelian randomisation (MR) is increasingly used to estimate causal effects of exposures across the lifecourse on later life outcomes. This systematic literature review explores MR methods used to perform lifecourse investigations and reviews previous work that has utilised MR to elucidate the effects of factors acting at different stages of the lifecourse. We conducted searches in PubMed, Embase, Medline and MedRXiv databases. Thirteen methodological studies were identified. Four studies focused on the impact of time-varying exposures in the interpretation of "standard" MR techniques, five presented methods for repeat measures of the same exposure, and four described methodological approaches to handling multigenerational exposures. A further 127 studies presented the results of an applied research question. Over half of these estimated effects in a single generation and were largely confined to the exploration of questions regarding body composition. The remaining mostly estimated maternal effects. There is a growing body of research focused on the development and application of MR methods to address lifecourse research questions. The underlying assumptions require careful consideration and the interpretation of results rely on select conditions. Whilst we do not advocate for a particular strategy, we encourage practitioners to make informed decisions on how to approach a research question in this field with a solid understanding of the limitations present and how these may be affected by the research question, modelling approach, instrument selection, and data availability.

Exploring the causal effects of genetic liability to ADHD and Autism on Alzheimer's disease.

Few studies suggest possible links between attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and Alzheimer's disease but they have been limited by small sample sizes, diagnostic and recall bias. We used two-sample Mendelian randomization (MR) to estimate the bidirectional causal association between genetic liability to ADHD and ASD on Alzheimer's disease. In addition, we estimated the causal effects independently of educational attainment and IQ, through multivariable Mendelian randomization (MVMR). We employed genetic variants associated with ADHD (20,183 cases/35,191 controls), ASD (18,381 cases/27,969 controls), Alzheimer's disease (71,880 cases/383,378 controls), educational attainment (n = 766,345) and IQ (n = 269,867) using the largest GWAS of European ancestry. There was limited evidence to suggest a causal effect of genetic liability to ADHD (odds ratio [OR] = 1.00, 95% CI: 0.98-1.02, P = 0.39) or ASD (OR = 0.99, 95% CI: 0.97-1.01, P = 0.70) on Alzheimer's disease. Similar causal effect estimates were identified as direct effects, independent of educational attainment (ADHD: OR = 1.00, 95% CI: 0.99-1.01, P = 0.76; ASD: OR = 0.99, 95% CI: 0.98-1.00, P = 0.28) and IQ (ADHD: OR = 1.00, 95% CI: 0.99-1.02. P = 0.29; ASD: OR = 0.99, 95% CI: 0.98-1.01, P = 0.99). Genetic liability to Alzheimer's disease was not found to have a causal effect on risk of ADHD or ASD (ADHD: OR = 1.12, 95% CI: 0.86-1.44, P = 0.37; ASD: OR = 1.19, 95% CI: 0.94-1.51, P = 0.14). We found limited evidence to suggest a causal effect of genetic liability to ADHD or ASD on Alzheimer's disease; and vice versa.

Parental inflammatory bowel disease and autism in children.

Evidence linking parental inflammatory bowel disease (IBD) with autism in children is inconclusive. We conducted four complementary studies to investigate associations between parental IBD and autism in children, and elucidated their underlying etiology. Conducting a nationwide population-based cohort study using Swedish registers, we found evidence of associations between parental diagnoses of IBD and autism in children. Polygenic risk score analyses of the Avon Longitudinal Study of Parents and Children suggested associations between maternal genetic liability to IBD and autistic traits in children. Two-sample Mendelian randomization analyses provided evidence of a potential causal effect of genetic liability to IBD, especially ulcerative colitis, on autism. Linkage disequilibrium score regression did not indicate a genetic correlation between IBD and autism. Triangulating evidence from these four complementary approaches, we found evidence of a potential causal link between parental, particularly maternal, IBD and autism in children. Perinatal immune dysregulation, micronutrient malabsorption and anemia may be implicated.

Causal effects of circulating cytokine concentrations on risk of Alzheimer's disease and cognitive function.

BACKGROUND: There is considerable evidence suggesting a role of neuroinflammation in the pathogenesis of Alzheimer's disease. Establishing causality is challenging due to bias from reverse causation and residual confounding. METHODS: We used two-sample MR to explore causal effects of circulating cytokine concentrations on Alzheimer's disease risk and cognitive function. We employed genetic variants from the largest publicly available genome-wide association studies (GWASs) of cytokine concentrations (N = 8,293), Alzheimer's disease (71,880 cases/383,378 controls), prospective memory (N = 152,605 to 462,302), reaction time (N = 454,157 to 459,523) and fluid intelligence (N = 149,051). RESULTS: Evidence suggest that 1 standard deviation (SD) increase in levels of CTACK (CCL27) (OR = 1.09 95%CI: 1.01 to 1.19, p = 0.03) increased risk of Alzheimer's disease. There was weak evidence of a causal effect of MIP-1b (CCL4) (OR = 1.04 95% CI: 0.99 to 1.09, p = 0.08), Eotaxin (OR = 1.08 95% CI: 0.99 to 1.17, p = 0.10), GROa (CXCL1) (OR = 1.04 95% CI: 0.99 to 1.10, p = 0.15), MIG (CXCL9) (OR = 1.17 95% CI: 0.97 to 1.41, p = 0.10), IL-8 (Wald ratio: OR = 1.21 95% CI: 0.97 to 1.51, p = 0.09) and IL-2 (Wald Ratio: OR = 1.21 95% CI: 0.94 to 1.56, p = 0.14) on Alzheimer's disease risk. A 1 SD increase in concentration of Eotaxin (IVW: OR = 1.05 95% CI: 0.98 to 1.13, p = 0.14), IL-8 (OR = 1.21 95% CI: 1.07 to 1.37, p = 0.003) and MCP1 (OR = 1.07 95% CI: 1.03 to 1.13, p = 0.003) were associated with lower fluid intelligence, and IL-4 (OR = 0.86 95%CI: 0.79 to 0.98, p = 0.02) with higher. CONCLUSIONS: Our findings suggest a causal role of cytokines in the pathogenesis of Alzheimer's disease and fluid intelligence.

Weight change in middle adulthood and risk of cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.

Obesity is a risk factor for several major cancers. Associations of weight change in middle adulthood with cancer risk, however, are less clear. We examined the association of change in weight and body mass index (BMI) category during middle adulthood with 42 cancers, using multivariable Cox proportional hazards models in the European Prospective Investigation into Cancer and Nutrition cohort. Of 241 323 participants (31% men), 20% lost and 32% gained weight (>0.4 to 5.0 kg/year) during 6.9 years (average). During 8.0 years of follow-up after the second weight assessment, 20 960 incident cancers were ascertained. Independent of baseline BMI, weight gain (per one kg/year increment) was positively associated with cancer of the corpus uteri (hazard ratio [HR] = 1.14; 95% confidence interval: 1.05-1.23). Compared to stable weight (±0.4 kg/year), weight gain (>0.4 to 5.0 kg/year) was positively associated with cancers of the gallbladder and bile ducts (HR = 1.41; 1.01-1.96), postmenopausal breast (HR = 1.08; 1.00-1.16) and thyroid (HR = 1.40; 1.04-1.90). Compared to maintaining normal weight, maintaining overweight or obese BMI (World Health Organisation categories) was positively associated with most obesity-related cancers. Compared to maintaining the baseline BMI category, weight gain to a higher BMI category was positively associated with cancers of the postmenopausal breast (HR = 1.19; 1.06-1.33), ovary (HR = 1.40; 1.04-1.91), corpus uteri (HR = 1.42; 1.06-1.91), kidney (HR = 1.80; 1.20-2.68) and pancreas in men (HR = 1.81; 1.11-2.95). Losing weight to a lower BMI category, however, was inversely associated with cancers of the corpus uteri (HR = 0.40; 0.23-0.69) and colon (HR = 0.69; 0.52-0.92). Our findings support avoiding weight gain and encouraging weight loss in middle adulthood.

Using Mendelian randomisation to assess causality in observational studies.

OBJECTIVE: Mendelian randomisation (MR) is a technique that aims to assess causal effects of exposures on disease outcomes. The paper aims to present the main assumptions that underlie MR, the statistical methods used to estimate causal effects and how to account for potential violations of the key assumptions. METHODS: We discuss the key assumptions that should be satisfied in an MR setting. We list the statistical methodologies used in two-sample MR when summary data are available to estimate causal effects (ie, Wald ratio estimator, inverse-variance weighted and maximum likelihood method) and identify/adjust for potential violations of MR assumptions (ie, MR-Egger regression and weighted Median approach). We also present statistical methods and graphical tools used to evaluate the presence of heterogeneity. RESULTS: We use as an illustrative example of a published two-sample MR study, investigating the causal association of body mass index with three psychiatric disorders (ie, bipolar disorder, schizophrenia and major depressive disorder). We highlight the importance of assessing the results of all available methods rather than each method alone. We also demonstrate the impact of heterogeneity in the estimation of the causal effects. CONCLUSIONS: MR is a useful tool to assess causality of risk factors in medical research. Assessment of the key assumptions underlying MR is crucial for a valid interpretation of the results.

An infectious way to teach students about outbreaks.

The study of infectious disease outbreaks is required to train today's epidemiologists. A typical way to introduce and explain key epidemiological concepts is through the analysis of a historical outbreak. There are, however, few training options that explicitly utilise real-time simulated stochastic outbreaks where the participants themselves comprise the dataset they subsequently analyse. In this paper, we present a teaching exercise in which an infectious disease outbreak is simulated over a five-day period and subsequently analysed. We iteratively developed the teaching exercise to offer additional insight into analysing an outbreak. An R package for visualisation, analysis and simulation of the outbreak data was developed to accompany the practical to reinforce learning outcomes. Computer simulations of the outbreak revealed deviations from observed dynamics, highlighting how simplifying assumptions conventionally made in mathematical models often differ from reality. Here we provide a pedagogical tool for others to use and adapt in their own settings.