Socioeconomic inequalities in cardiovascular disease: a causal perspective.

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

pdf/mendelian-randomization-causal-inference-leveraging-genetic-5cxkjachw6.pdf

Mendelian randomization: causal inference leveraging genetic data.

The importance of family-based sampling for biobanks

Background: The genetic and environmental underpinnings of simultaneous changes in weight and alcohol consumption are poorly known. Objective: We sought to quantify the environmental and genetic components underlying parallel changes in weight and alcohol consumption, and to investigate potential covariations between them. Methods: The analysis comprised 4461 adult participants (58% women) from the Finnish Twin Cohort. Four measures of alcohol consumption and body mass index (BMI) were available 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 MZ pairs, 293 DZ pairs) and female (316 MZ pairs, 487 DZ pairs) same-sex complete twin pairs in multivariate twin modeling. The variance and covariance of growth factors were then decomposed into genetic and environmental components. Results: The baseline heritabilities were similar in men (BMI: h2=79%; alcohol consumption: h2=49%) and women (h2=77%; h2=45%). Heritabilities of BMI change were similar in men (h2=52%) and women (h2=57%), but higher in men for change in alcohol consumption (h2=45%) than in women (h2=31%). Significant genetic correlations between BMI at baseline and change in alcohol consumption were observed in both men (r =-0.17(95% Confidence Interval: -0.29,-0.04)) and women (r=-0.18(-0.31,-0.06)). The genetic components of baseline and longitudinal change were correlated for both BMI and alcohol consumption with sex differences. Non-shared environmental factors affecting changes in alcohol consumption and BMI were correlated in men (r=0.18(0.06,0.30)). Among women, non-shared environmental factors affecting baseline alcohol consumption and the change in BMI were correlated (r=-0.11(-0.20,-0.01)). Conclusions: We provide evidence of genetic correlations between BMI and change in alcohol consumption. Independent of genetic effects, change in BMI and change in alcohol consumption covary. 

Genetic and environmental factors underlying parallel changes in body mass index and alcohol consumption: a 36-year longitudinal study of adult twins

Abstract Aims Many studies indicated use of diabetes medications can influence the electrocardiogram (ECG), which remains the simplest and fastest tool for assessing cardiac functions. However, few studies have explored the role of genetic factors in determining the relationship between the use of diabetes medications and ECG trace characteristics (ETC). Methods Genome-wide association studies (GWAS) were performed for 168 ETCs extracted from the 12-lead ECGs of 42,340 Europeans in the UK Biobank. The genetic correlations, causal relationships, and phenotypic relationships of these ETCs with medication usage, as well as the risk of cardiovascular diseases (CVDs), were estimated by linkage disequilibrium score regression (LDSC), Mendelian randomization (MR), and regression model, respectively. Results The GWAS identified 124 independent single nucleotide polymorphisms (SNPs) that were study-wise and genome-wide significantly associated with at least one ETC. Regression model and LDSC identified significant phenotypic and genetic correlations of T-wave area in lead aVR (aVR_T-area) with usage of diabetes medications (ATC code: A10 drugs, and metformin), and the risks of ischemic heart disease (IHD) and coronary atherosclerosis (CA). MR analyses support a putative causal effect of the use of diabetes medications on decreasing aVR_T-area, and on increasing risk of IHD and CA. Conclusion Patients taking diabetes medications are prone to have decreased aVR_T-area and an increased risk of IHD and CA. The aVR_T-area is therefore a potential ECG marker for pre-clinical prediction of IHD and CA in patients taking diabetes medications. 

Genetic evidence for T-wave area from 12-lead electrocardiograms to monitor cardiovascular diseases in patients taking diabetes medications

Previous Mendelian randomization (MR) studies using population samples (population-MR) have provided evidence for beneficial effects of educational attainment on health outcomes in adulthood. However, estimates from these studies may have been susceptible to bias from population stratification, assortative mating and indirect genetic effects due to unadjusted parental genotypes. Mendelian randomization using genetic association estimates derived from within-sibship models (within-sibship MR) can avoid these potential biases because genetic differences between siblings are due to random segregation at meiosis. Applying both population and within-sibship MR, we estimated the effects of genetic liability to educational attainment on body mass index (BMI), cigarette smoking, systolic blood pressure (SBP) and all-cause mortality. MR analyses used individual-level data on 72,932 siblings from UK Biobank and the Norwegian HUNT study and summary-level data from a within-sibship Genome-wide Association Study including over 140,000 individuals. Both population and within-sibship MR estimates provided evidence that educational attainment influences BMI, cigarette smoking and SBP. Genetic variant-outcome associations attenuated in the within-sibship model, but genetic variant-educational attainment associations also attenuated to a similar extent. Thus, within-sibship and population MR estimates were largely consistent. The within-sibship MR estimate of education on mortality was imprecise but consistent with a putative effect. These results provide evidence of beneficial individual-level effects of education (or liability to education) on adulthood health, independent of potential demographic and family-level confounders.

Educational attainment, health outcomes and mortality: a within-sibship Mendelian randomization study

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Educational attainment, health outcomes and mortality: a within-sibship Mendelian randomization study