Genetic Associations of Circulating Cardiovascular Proteins With Gestational Hypertension and Preeclampsia

Importance

Hypertensive disorders of pregnancy (HDPs), including gestational hypertension and preeclampsia, are important contributors to maternal morbidity and mortality worldwide. In addition, women with HDPs face an elevated long-term risk of cardiovascular disease.

Objective

To identify proteins in the circulation associated with HDPs.

Design, Setting, and Participants

Two-sample mendelian randomization (MR) tested the associations of genetic instruments for cardiovascular disease-related proteins with gestational hypertension and preeclampsia. In downstream analyses, a systematic review of observational data was conducted to evaluate the identified proteins' dynamics across gestation in hypertensive vs normotensive pregnancies, and phenome-wide MR analyses were performed to identify potential non-HDP-related effects associated with the prioritized proteins. Genetic association data for cardiovascular disease-related proteins were obtained from the Systematic and Combined Analysis of Olink Proteins (SCALLOP) consortium. Genetic association data for the HDPs were obtained from recent European-ancestry genome-wide association study meta-analyses for gestational hypertension and preeclampsia. Study data were analyzed October 2022 to October 2023.

Exposures

Genetic instruments for 90 candidate proteins implicated in cardiovascular diseases, constructed using cis-protein quantitative trait loci (cis-pQTLs).

Main Outcomes and Measures

Gestational hypertension and preeclampsia.

Results

Genetic association data for cardiovascular disease-related proteins were obtained from 21 758 participants from the SCALLOP consortium. Genetic association data for the HDPs were obtained from 393 238 female individuals (8636 cases and 384 602 controls) for gestational hypertension and 606 903 female individuals (16 032 cases and 590 871 controls) for preeclampsia. Seventy-five of 90 proteins (83.3%) had at least 1 valid cis-pQTL. Of those, 10 proteins (13.3%) were significantly associated with HDPs. Four were robust to sensitivity analyses for gestational hypertension (cluster of differentiation 40, eosinophil cationic protein [ECP], galectin 3, N-terminal pro-brain natriuretic peptide [NT-proBNP]), and 2 were robust for preeclampsia (cystatin B, heat shock protein 27 [HSP27]). Consistent with the MR findings, observational data revealed that lower NT-proBNP (0.76- to 0.88-fold difference vs no HDPs) and higher HSP27 (2.40-fold difference vs no HDPs) levels during the first trimester of pregnancy were associated with increased risk of HDPs, as were higher levels of ECP (1.60-fold difference vs no HDPs). Phenome-wide MR analyses identified 37 unique non-HDP-related protein-disease associations, suggesting potential on-target effects associated with interventions lowering HDP risk through the identified proteins.

Conclusions and Relevance

Study findings suggest genetic associations of 4 cardiovascular disease-related proteins with gestational hypertension and 2 associated with preeclampsia. Future studies are required to test the efficacy of targeting the corresponding pathways to reduce HDP risk.

Birth weight influences cardiac structure, function, and disease risk: evidence of a causal association

BACKGROUND AND AIMS

Low birth weight is a common pregnancy complication, which has been associated with higher risk of cardiometabolic disease in later life. Prior Mendelian randomization (MR) studies exploring this question do not distinguish the mechanistic contributions of variants that directly influence birth weight through the foetal genome (direct foetal effects), vs. variants influencing birth weight indirectly by causing an adverse intrauterine environment (indirect maternal effects). In this study, MR was used to assess whether birth weight, independent of intrauterine influences, is associated with cardiovascular disease risk and measures of adverse cardiac structure and function.

METHODS

Uncorrelated (r2 < .001), genome-wide significant (P < 5 × 10-8) single nucleotide polymorphisms were extracted from genome-wide association studies summary statistics for birth weight overall, and after isolating direct foetal effects only. Inverse-variance weighted MR was utilized for analyses on outcomes of atrial fibrillation, coronary artery disease, heart failure, ischaemic stroke, and 16 measures of cardiac structure and function. Multiple comparisons were accounted for by Benjamini-Hochberg correction.

RESULTS

Lower genetically-predicted birth weight, isolating direct foetal effects only, was associated with an increased risk of coronary artery disease (odds ratio 1.21, 95% confidence interval 1.06-1.37; P = .031), smaller chamber volumes, and lower stroke volume, but higher contractility.

CONCLUSIONS

The results of this study support a causal role of low birth weight in cardiovascular disease, even after accounting for the influence of the intrauterine environment. This suggests that individuals with a low birth weight may benefit from early targeted cardiovascular disease prevention strategies, independent of whether this was linked to an adverse intrauterine environment during gestation.

Genetically proxied low-density lipoprotein cholesterol lowering via PCSK9-inhibitor drug targets and risk of congenital malformations

AIMS

Current guidelines advise against the use of lipid-lowering drugs during pregnancy. This is based only on previous observational evidence demonstrating an association between statin use and congenital malformations, which is increasingly controversial. In the absence of clinical trial data, we aimed to use drug-target Mendelian randomization to model the potential impact of fetal LDL-lowering, overall and through PCSK9 drug targets, on congenital malformations.

METHODS AND RESULTS

Instrumental variants influencing LDL levels overall and through PCSK9-inhibitor drug targets were extracted from genome-wide association study (GWAS) summary data for LDL on 1 320 016 individuals. Instrumental variants influencing circulating PCSK9 levels (pQTLs) and liver PCSK9 gene expression levels (eQTLs) were extracted, respectively, from a GWAS on 10 186 individuals and from the genotype-tissue expression project. Gene-outcome association data was extracted from the 7th release of GWAS summary data on the FinnGen cohort (n = 342 499) for eight categories of congenital malformations affecting multiple systems. Genetically proxied LDL-lowering through PCSK9 was associated with higher odds of malformations affecting multiple systems [OR 2.70, 95% confidence interval (CI) 1.30-5.63, P = 0.018], the skin (OR 2.23, 95% CI 1.33-3.75, P = 0.007), and the vertebral, anorectal, cardiovascular, tracheo-esophageal, renal, and limb association (VACTERL) (OR 1.51, 95% CI 1.16-1.96, P = 0.007). An association was also found with obstructive defects of the renal pelvis and ureter, but this association was suggestive of horizontal pleiotropy. Lower PCSK9 pQTLs were associated with the same congenital malformations.

CONCLUSION

These data provide genetic evidence supporting current manufacturer advice to avoid the use of PCSK9 inhibitors during pregnancy.

Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes

Our understanding of the genetics of the human cerebral cortex is limited both in terms of the diversity and the anatomical granularity of brain structural phenotypes. Here we conducted a genome-wide association meta-analysis of 13 structural and diffusion magnetic resonance imaging-derived cortical phenotypes, measured globally and at 180 bilaterally averaged regions in 36,663 individuals and identified 4,349 experiment-wide significant loci. These phenotypes include cortical thickness, surface area, gray matter volume, measures of folding, neurite density and water diffusion. We identified four genetic latent structures and causal relationships between surface area and some measures of cortical folding. These latent structures partly relate to different underlying gene expression trajectories during development and are enriched for different cell types. We also identified differential enrichment for neurodevelopmental and constrained genes and demonstrate that common genetic variants associated with cortical expansion are associated with cephalic disorders. Finally, we identified complex interphenotype and inter-regional genetic relationships among the 13 phenotypes, reflecting the developmental differences among them. Together, these analyses identify distinct genetic organizational principles of the cortex and their correlates with neurodevelopment.

Overcoming attenuation bias in regressions using polygenic indices

Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N < 1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI.

Sex-Specific Reproductive Factors Augment Cardiovascular Disease Risk in Women: A Mendelian Randomization Study

Background Observational studies suggest that reproductive factors are associated with cardiovascular disease, but these are liable to influence by residual confounding. This study explores the causal relevance of reproductive factors on cardiovascular disease in women using Mendelian randomization. Methods and Results Uncorrelated (r²<0.001), genome-wide significant (P<5×10^-8) single-nucleotide polymorphisms were extracted from sex-specific genome-wide association studies of age at first birth, number of live births, age at menarche, and age at menopause. Inverse-variance weighted Mendelian randomization was used for primary analyses on outcomes of atrial fibrillation, coronary artery disease, heart failure, ischemic stroke, and stroke. Earlier genetically predicted age at first birth increased risk of coronary artery disease (odds ratio [OR] per year, 1.49 [95% CI, 1.28-1.74], P=3.72×10^-7) heart failure (OR, 1.27 [95% CI, 1.06-1.53], P=0.009), and stroke (OR, 1.25 [95% CI, 1.00-1.56], P=0.048), with partial mediation through body mass index, type 2 diabetes, blood pressure, and cholesterol traits. Higher genetically predicted number of live births increased risk of atrial fibrillation (OR for <2, versus 2, versus >2 live births, 2.91 [95% CI, 1.16-7.29], P=0.023), heart failure (OR, 1.90 [95% CI, 1.28-2.82], P=0.001), ischemic stroke (OR, 1.86 [95% CI, 1.03-3.37], P=0.039), and stroke (OR, 2.07 [95% CI, 1.22-3.52], P=0.007). Earlier genetically predicted age at menarche increased risk of coronary artery disease (OR per year, 1.10 [95% CI, 1.06-1.14], P=1.68×10^-6) and heart failure (OR, 1.12 [95% CI, 1.07-1.17], P=5.06×10^-7); both associations were at least partly mediated by body mass index. Conclusions These results support a causal role of a number of reproductive factors on cardiovascular disease in women and identify multiple modifiable mediators amenable to clinical intervention.

Using genetic association data to guide drug discovery and development: Review of methods and applications

Evidence on the validity of drug targets from randomized trials is reliable but typically expensive and slow to obtain. In contrast, evidence from conventional observational epidemiological studies is less reliable because of the potential for bias from confounding and reverse causation. Mendelian randomization is a quasi-experimental approach analogous to a randomized trial that exploits naturally occurring randomization in the transmission of genetic variants. In Mendelian randomization, genetic variants that can be regarded as proxies for an intervention on the proposed drug target are leveraged as instrumental variables to investigate potential effects on biomarkers and disease outcomes in large-scale observational datasets. This approach can be implemented rapidly for a range of drug targets to provide evidence on their effects and thus inform on their priority for further investigation. In this review, we present statistical methods and their applications to showcase the diverse opportunities for applying Mendelian randomization in guiding clinical development efforts, thus enabling interventions to target the right mechanism in the right population group at the right time. These methods can inform investigators on the mechanisms underlying drug effects, their related biomarkers, implications for the timing of interventions, and the population subgroups that stand to gain the most benefit. Most methods can be implemented with publicly available data on summarized genetic associations with traits and diseases, meaning that the only major limitations to their usage are the availability of appropriately powered studies for the exposure and outcome and the existence of a suitable genetic proxy for the proposed intervention.

Association of Hypertensive Disorders of Pregnancy With Future Cardiovascular Disease

CONCLUSIONS AND RELEVANCE

The findings of this study provide genetic evidence supporting an association between HDPs and higher risk of coronary artery disease and stroke, which is only partially mediated by cardiometabolic factors. This supports classification of HDPs as risk factors for cardiovascular disease.

DESIGN, SETTING, AND PARTICIPANTS

A genome-wide genetic association study using mendelian randomization (MR) was performed from February 16 to March 4, 2022. Primary analysis was conducted using inverse-variance-weighted MR. Mediation analyses were performed using a multivariable MR framework. All studies included patients predominantly of European ancestry. Female-specific summary-level data from FinnGen (sixth release).

EXPOSURES

Uncorrelated (r2<0.001) single-nucleotide variants (SNVs) were selected as instrumental variants from the FinnGen consortium summary statistics for exposures of any HDP, gestational hypertension, and preeclampsia or eclampsia.

IMPORTANCE

Hypertensive disorders in pregnancy (HDPs) are major causes of maternal and fetal morbidity and are observationally associated with future maternal risk of cardiovascular disease. However, observational results may be subject to residual confounding and bias.

MAIN OUTCOMES AND MEASURES

Genetic association estimates for outcomes were extracted from genome-wide association studies of 122 733 cases for coronary artery disease, 34 217 cases for ischemic stroke, 47 309 cases for heart failure, and 60 620 cases for atrial fibrillation.

OBJECTIVE

To investigate the association of HDPs with multiple cardiovascular diseases.

RESULTS

Genetically predicted HDPs were associated with a higher risk of coronary artery disease (odds ratio [OR], 1.24; 95% CI, 1.08-1.43; P = .002); this association was evident for both gestational hypertension (OR, 1.08; 95% CI, 1.00-1.17; P = .04) and preeclampsia/eclampsia (OR, 1.06; 95% CI, 1.01-1.12; P = .03). Genetically predicted HDPs were also associated with a higher risk of ischemic stroke (OR, 1.27; 95% CI, 1.12-1.44; P = 2.87 × 10-4). Mediation analysis revealed a partial attenuation of the effect of HDPs on coronary artery disease after adjustment for systolic blood pressure (total effect OR, 1.24; direct effect OR, 1.10; 95% CI, 1.02-1.08; P = .02) and type 2 diabetes (total effect OR, 1.24; direct effect OR, 1.16; 95% CI, 1.04-1.29; P = .008). No associations were noted between genetically predicted HDPs and heart failure (OR, 0.97; 95% CI, 0.76-1.23; P = .79) or atrial fibrillation (OR, 1.11; 95% CI, 0.65-1.88; P = .71).

The identification of mediating effects using genome-based restricted maximum likelihood estimation

Mediation analysis is commonly used to identify mechanisms and intermediate factors between causes and outcomes. Studies drawing on polygenic scores (PGSs) can readily employ traditional regression-based procedures to assess whether trait M mediates the relationship between the genetic component of outcome Y and outcome Y itself. However, this approach suffers from attenuation bias, as PGSs capture only a (small) part of the genetic variance of a given trait. To overcome this limitation, we developed MA-GREML: a method for Mediation Analysis using Genome-based Restricted Maximum Likelihood (GREML) estimation. Using MA-GREML to assess mediation between genetic factors and traits comes with two main advantages. First, we circumvent the limited predictive accuracy of PGSs that regression-based mediation approaches suffer from. Second, compared to methods employing summary statistics from genome-wide association studies, the individual-level data approach of GREML allows to directly control for confounders of the association between M and Y. In addition to typical GREML parameters (e.g., the genetic correlation), MA-GREML estimates (i) the effect of M on Y, (ii) the direct effect (i.e., the genetic variance of Y that is not mediated by M), and (iii) the indirect effect (i.e., the genetic variance of Y that is mediated by M). MA-GREML also provides standard errors of these estimates and assesses the significance of the indirect effect. We use analytical derivations and simulations to show the validity of our approach under two main assumptions, viz., that M precedes Y and that environmental confounders of the association between M and Y are controlled for. We conclude that MA-GREML is an appropriate tool to assess the mediating role of trait M in the relationship between the genetic component of Y and outcome Y. Using data from the US Health and Retirement Study, we provide evidence that genetic effects on Body Mass Index (BMI), cognitive functioning and self-reported health in later life run partially through educational attainment. For mental health, we do not find significant evidence for an indirect effect through educational attainment. Further analyses show that the additive genetic factors of these four outcomes do partially (cognition and mental health) and fully (BMI and self-reported health) run through an earlier realization of these traits.

Investigating the potential impact of PCSK9-inhibitors on mood disorders using eQTL-based Mendelian randomization

Prescription of PCSK9-inhibitors has increased in recent years but not much is known about its off-target effects. PCSK9-expression is evident in non-hepatic tissues, notably the brain, and genetic variation in the PCSK9 locus has recently been shown to be associated with mood disorder-related traits. We investigated whether PCSK9 inhibition, proxied by a genetic reduction in expression of PCSK9 mRNA, might have a causal adverse effect on mood disorder-related traits. We used genetic variants in the PCSK9 locus associated with reduced PCSK9 expression (eQTLs) in the European population from GTEx v8 and examined the effect on PCSK9 protein levels and three mood disorder-related traits (major depressive disorder, mood instability, and neuroticism), using summary statistics from the largest European ancestry genome-wide association studies. We conducted summary-based Mendelian randomization analyses to estimate the causal effects, and attempted replication using data from eQTLGen, Brain-eMETA, and the CAGE consortium. We found that genetically reduced PCSK9 gene-expression levels were significantly associated with reduced PCSK9 protein levels but not with increased risk of mood disorder-related traits. Further investigation of nearby genes demonstrated that reduced USP24 gene-expression levels was significantly associated with increased risk of mood instability (p-value range = 5.2x10-5-0.03), and neuroticism score (p-value range = 2.9x10-5-0.02), but not with PCSK9 protein levels. Our results suggest that genetic variation in this region acts on mood disorders through a PCSK9-independent pathway, and therefore PCSK9-inhibitors are unlikely to have an adverse impact on mood disorder-related traits.

Safety of beta-blocker and calcium channel blocker antihypertensive drugs in pregnancy: a Mendelian randomization study

BACKGROUND

Beta-blocker (BB) and calcium channel blocker (CCB) antihypertensive drugs are commonly used in pregnancy. However, data on their relative impact on maternal and foetal outcomes are limited. We leveraged genetic variants mimicking BB and CCB antihypertensive drugs to investigate their effects on risk of pre-eclampsia, gestational diabetes and birthweight using the Mendelian randomization paradigm.

METHODS

Genetic association estimates for systolic blood pressure (SBP) were extracted from summary data of a genome-wide association study (GWAS) on 757,601 participants. Uncorrelated single-nucleotide polymorphisms (SNPs) associated with SBP (p < 5 × 10^-8) in BB and CCB drug target gene regions were selected as proxies for drug target perturbation. Genetic association estimates for the outcomes were extracted from GWASs on 4743 cases and 136,325 controls (women without a hypertensive disorder in pregnancy) for pre-eclampsia or eclampsia, 7676 cases and 130,424 controls (women without any pregnancy-related morbidity) for gestational diabetes, and 155,202 women (who have given birth at least once) for birthweight of the first child. All studies were in European ancestry populations. Mendelian randomization estimates were generated using the two-sample inverse-variance weighted model.

RESULTS

Although not reaching the conventional threshold for statistical significance, genetically-proxied BB was associated with reduced risk of pre-eclampsia (OR per 10 mmHg SBP reduction 0.27, 95%CI 0.06-1.19, p = 0.08) and increased risk of gestational diabetes (OR per 10 mmHg SBP reduction 2.01, 95%CI 0.91-4.42, p = 0.08), and significantly associated with lower birthweight of first child (beta per 10 mmHg SBP reduction - 0.27, 95%CI - 0.39 to - 0.15, p = 1.90 × 10^-5). Genetically-proxied CCB was associated with reduced risk of pre-eclampsia and eclampsia (OR 0.62, 95%CI 0.43-0.89, p = 9.33 × 10^-3), and was not associated with gestational diabetes (OR 1.05, 95% CI 0.76-1.45, p = 0.76) or changes in birthweight of first child (beta per 10 mmHg SBP reduction 0.02, 95%CI - 0.04-0.07, p = 0.54).

CONCLUSIONS

While BB and CCB antihypertensive drugs may both be efficacious for lowering blood pressure in pregnancy, this genetic evidence suggests that BB use may lower birthweight. Conversely, CCB use may reduce risk of pre-eclampsia and eclampsia without impacting gestational diabetes risk or birthweight. These data support further study on the effects of BBs on birthweight.

Multivariate estimation of factor structures of complex traits using SNP-based genomic relationships

Background

Heritability and genetic correlation can be estimated from genome-wide single-nucleotide polymorphism (SNP) data using various methods. We recently developed multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) for statistically and computationally efficient estimation of SNP-based heritability (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h^2_{\text{SNP}}$$\end{document}hSNP2) and genetic correlation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho _G$$\end{document}ρG) across many traits in large datasets. Here, we extend MGREML by allowing it to fit and perform tests on user-specified factor models, while preserving the low computational complexity.

Results

Using simulations, we show that MGREML yields consistent estimates and valid inferences for such factor models at low computational cost (e.g., for data on 50 traits and 20,000 individuals, a saturated model involving 50 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h^2_{\text{SNP}}$$\end{document}hSNP2’s, 1225 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho _G$$\end{document}ρG’s, and 50 fixed effects is estimated and compared to a restricted model in less than one hour on a single notebook with two 2.7 GHz cores and 16 GB of RAM). Using repeated measures of height and body mass index from the US Health and Retirement Study, we illustrate the ability of MGREML to estimate a factor model and test whether it fits the data better than a nested model. The MGREML tool, the simulation code, and an extensive tutorial are freely available at https://github.com/devlaming/mgreml/.

Conclusion

MGREML can now be used to estimate multivariate factor structures and perform inferences on such factor models at low computational cost. This new feature enables simple structural equation modeling using MGREML, allowing researchers to specify, estimate, and compare genetic factor models of their choosing using SNP data.

Combining evidence from Mendelian randomization and colocalization: Review and comparison of approaches

Mendelian randomization and colocalization are two statistical approaches that can be applied to summarized data from genome-wide association studies (GWASs) to understand relationships between traits and diseases. However, despite similarities in scope, they are different in their objectives, implementation, and interpretation, in part because they were developed to serve different scientific communities. Mendelian randomization assesses whether genetic predictors of an exposure are associated with the outcome and interprets an association as evidence that the exposure has a causal effect on the outcome, whereas colocalization assesses whether two traits are affected by the same or distinct causal variants. When considering genetic variants in a single genetic region, both approaches can be performed. While a positive colocalization finding typically implies a non-zero Mendelian randomization estimate, the reverse is not generally true: there are several scenarios which would lead to a non-zero Mendelian randomization estimate but lack evidence for colocalization. These include the existence of distinct but correlated causal variants for the exposure and outcome, which would violate the Mendelian randomization assumptions, and a lack of strong associations with the outcome. As colocalization was developed in the GWAS tradition, typically evidence for colocalization is concluded only when there is strong evidence for associations with both traits. In contrast, a non-zero estimate from Mendelian randomization can be obtained despite only nominally significant genetic associations with the outcome at the locus. In this review, we discuss how the two approaches can provide complementary information on potential therapeutic targets.

Sleep Disordered Breathing, Obesity and Atrial Fibrillation: A Mendelian Randomisation Study

It remains unclear whether the association between obstructive sleep apnoea (OSA), a form of sleep-disordered breathing (SDB), and atrial fibrillation (AF) is causal or mediated by shared co-morbidities such as obesity. Existing observational studies are conflicting and limited by confounding and reverse causality. We performed Mendelian randomisation (MR) to investigate the causal relationships between SDB, body mass index (BMI) and AF. Single-nucleotide polymorphisms associated with SDB (n = 29) and BMI (n = 453) were selected as instrumental variables to investigate the effects of SDB and BMI on AF, using genetic association data on 55,114 AF cases and 482,295 controls. Primary analysis was conducted using inverse-variance weighted MR. Higher genetically predicted SDB and BMI were associated with increased risk of AF (OR per log OR increase in snoring liability 2.09 (95% CI 1.10-3.98), p = 0.03; OR per 1-SD increase in BMI 1.33 (95% CI 1.24-1.42), p < 0.001). The association between SDB and AF was not observed in sensitivity analyses, whilst associations between BMI and AF remained consistent. Similarly, in multivariable MR, SDB was not associated with AF after adjusting for BMI (OR 0.68 (95% CI 0.42-1.10), p = 0.12). Higher BMI remained associated with increased risk of AF after adjusting for OSA (OR 1.40 (95% CI 1.30-1.51), p < 0.001). Elevated BMI appears causal for AF, independent of SDB. Our data suggest that the association between SDB, in general, and AF is attributable to mediation or confounding from obesity, though we cannot exclude that more severe SDB phenotypes (i.e., OSA) are causal for AF.

Genetic predispositions moderate the effectiveness of tobacco excise taxes

BACKGROUND

Tobacco consumption is one of the leading causes of preventable death. In this study, we analyze whether someone's genetic predisposition to smoking moderates the response to tobacco excise taxes.

METHODS

We interact polygenic scores for smoking behavior with state-level tobacco excise taxes in longitudinal data (1992-2016) from the US Health and Retirement Study (N = 12,058).

RESULTS

Someone's genetic propensity to smoking moderates the effect of tobacco excise taxes on smoking behavior along the extensive margin (smoking vs. not smoking) and the intensive margin (the amount of tobacco consumed). In our analysis sample, we do not find a significant gene-environment interaction effect on smoking cessation.

CONCLUSIONS

When tobacco excise taxes are relatively high, those with a high genetic predisposition to smoking are less likely (i) to smoke, and (ii) to smoke heavily. While tobacco excise taxes have been effective in reducing smoking, the gene-environment interaction effects we observe in our sample suggest that policy makers could benefit from taking into account the moderating role of genes in the design of future tobacco control policies.

Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior

Human variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data (N = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets.

A comparison of robust Mendelian randomization methods using summary data

The number of Mendelian randomization (MR) analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. Since it is unlikely that all genetic variants will be valid instrumental variables, several robust methods have been proposed. We compare nine robust methods for MR based on summary data that can be implemented using standard statistical software. Methods were compared in three ways: by reviewing their theoretical properties, in an extensive simulation study, and in an empirical example. In the simulation study, the best method, judged by mean squared error was the contamination mixture method. This method had well-controlled Type 1 error rates with up to 50% invalid instruments across a range of scenarios. Other methods performed well according to different metrics. Outlier-robust methods had the narrowest confidence intervals in the empirical example. With isolated exceptions, all methods performed badly when over 50% of the variants were invalid instruments. Our recommendation for investigators is to perform a variety of robust methods that operate in different ways and rely on different assumptions for valid inferences to assess the reliability of MR analyses.