Explore the vast range of titles available.

Abstract Background Observational studies have shown a link between elevated body mass index (BMI) and the risk of polycystic ovary syndrome (PCOS). While Mendelian randomization (MR) studies in Europeans have suggested a causal role of increased BMI in PCOS, whether the same role is suggested in Asians has yet to be investigated. We used MR studies to infer causal effects using genetic data from East Asian populations. Methods and Findings We performed a 2-sample bidirectional MR analysis using summary statistics from genome-wide association studies (GWAS) of BMI (with up to 173 430 individuals) and PCOS (4386 cases and 8017 controls) in East Asian populations. Seventy-eight single nucleotide polymorphisms (SNPs) correlated with BMI were selected as genetic instrumental variables to estimate the causal effect of BMI on PCOS using the inverse-variance weighted (IVW) method. To test the reliability of the results, further sensitivity analyses included MR–Egger regression, weighted median estimates, and leave-one-out analysis. The IVW analysis indicated a significant association between high BMI and the risk of PCOS (odds ratio per standard deviation higher BMI, 2.208; 95% confidence interval 1.537 to 3.168, P = 1.77 × 10–5). In contrast, the genetic risk of PCOS had no significant effect on BMI. Conclusions The results of our bidirectional MR study showed that an increase in BMI causes PCOS, while PCOS does not cause an increased BMI. This study provides further genetic support for a link between BMI and PCOS. Further research is needed to interpret the potential mechanisms of this association.

With the increasing availability of large-scale GWAS summary data on various traits, Mendelian randomization (MR) has become commonly used to infer causality between a pair of traits, an exposure and an outcome. It depends on using genetic variants, typically SNPs, as instrumental variables (IVs). The inverse-variance weighted (IVW) method (with a fixed-effect meta-analysis model) is most powerful when all IVs are valid; however, when horizontal pleiotropy is present, it may lead to biased inference. On the other hand, Egger regression is one of the most widely used methods robust to (uncorrelated) pleiotropy, but it suffers from loss of power. We propose a two-component mixture of regressions to combine and thus take advantage of both IVW and Egger regression; it is often both more efficient (i.e. higher powered) and more robust to pleiotropy (i.e. controlling type I error) than either IVW or Egger regression alone by accounting for both valid and invalid IVs respectively. We propose a model averaging approach and a novel data perturbation scheme to account for uncertainties in model/IV selection, leading to more robust statistical inference for finite samples. Through extensive simulations and applications to the GWAS summary data of 48 risk factor-disease pairs and 63 genetically uncorrelated trait pairs, we showcase that our proposed methods could often control type I error better while achieving much higher power than IVW and Egger regression (and sometimes than several other new/popular MR methods). We expect that our proposed methods will be a useful addition to the toolbox of Mendelian randomization for causal inference.

Abstract Individuals with schizophrenia have a reduced life-expectancy compared to the general population, largely due to an increased risk of cardiovascular disease (CVD). Clinical and epidemiological studies have been unable to unravel the nature of this relationship. We obtained summary-data of genome-wide-association studies of schizophrenia (N = 130 644), heart failure (N = 977 323), coronary artery disease (N = 332 477), systolic and diastolic blood pressure (N = 757 601), heart rate variability (N = 46 952), QT interval (N = 103 331), early repolarization and dilated cardiomyopathy ECG patterns (N = 63 700). We computed genetic correlations and conducted bi-directional Mendelian randomization (MR) to assess causality. With multivariable MR, we investigated whether causal effects were mediated by smoking, body mass index, physical activity, lipid levels, or type 2 diabetes. Genetic correlations between schizophrenia and CVD were close to zero (−0.02–0.04). There was evidence that liability to schizophrenia causally increases heart failure risk. This effect remained consistent with multivariable MR. There was also evidence that liability to schizophrenia increases early repolarization pattern, largely mediated by BMI and lipids. Finally, there was evidence that liability to schizophrenia increases heart rate variability, a direction of effect contrasting clinical studies. There was weak evidence that higher systolic blood pressure increases schizophrenia risk. Our finding that liability to schizophrenia increases heart failure is consistent with the notion that schizophrenia involves a systemic dysregulation of the body with detrimental effects on the heart. To decrease cardiovascular mortality among individuals with schizophrenia, priority should lie with optimal treatment in early stages of psychosis.

Human disease genetics is extended to the identification of individuals who remain healthy despite carrying highly penetrant disease-causing mutations. Genetic studies of human disease have traditionally focused on the detection of disease-causing mutations in afflicted individuals. Here we describe a complementary approach that seeks to identify healthy individuals resilient to highly penetrant forms of genetic childhood disorders. A comprehensive screen of 874 genes in 589,306 genomes led to the identification of 13 adults harboring mutations for 8 severe Mendelian conditions, with no reported clinical manifestation of the indicated disease. Our findings demonstrate the promise of broadening genetic studies to systematically search for well individuals who are buffering the effects of rare, highly penetrant, deleterious mutations. They also indicate that incomplete penetrance for Mendelian diseases is likely more common than previously believed. The identification of resilient individuals may provide a first step toward uncovering protective genetic variants that could help elucidate the mechanisms of Mendelian diseases and new therapeutic strategies.

Mendelian randomization (MR) has emerged as a major tool for the investigation of causal relationship among traits, utilizing results from large-scale genome-wide association studies. Bias due to horizontal pleiotropy, however, remains a major concern. We propose a novel approach for robust and efficient MR analysis using large number of genetic instruments, based on a novel spike-detection algorithm under a normal-mixture model for underlying effect-size distributions. Simulations show that the new method, MRMix, provides nearly unbiased or/and less biased estimates of causal effects compared to alternative methods and can achieve higher efficiency than comparably robust estimators. Application of MRMix to publicly available datasets leads to notable observations, including identification of causal effects of BMI and age-at-menarche on the risk of breast cancer; no causal effect of HDL and triglycerides on the risk of coronary artery disease; a strong detrimental effect of BMI on the risk of major depressive disorder. Mendelian randomization (MR) is a powerful and widely used method for causal inference leveraging genetic information. Here, the authors develop MRMix, an MR method using mixture models for more robust and efficient estimation of causal effects.

Integrating results from genome-wide association studies (GWASs) and gene expression studies through transcriptome-wide association study (TWAS) has the potential to shed light on the causal molecular mechanisms underlying disease etiology. Here, we present a probabilistic Mendelian randomization (MR) method, PMR-Egger, for TWAS applications. PMR-Egger relies on a MR likelihood framework that unifies many existing TWAS and MR methods, accommodates multiple correlated instruments, tests the causal effect of gene on trait in the presence of horizontal pleiotropy, and is scalable to hundreds of thousands of individuals. In simulations, PMR-Egger provides calibrated type I error control for causal effect testing in the presence of horizontal pleiotropic effects, is reasonably robust under various types of model misspecifications, is more powerful than existing TWAS/MR approaches, and can directly test for horizontal pleiotropy. We illustrate the benefits of PMR-Egger in applications to 39 diseases and complex traits obtained from three GWASs including the UK Biobank. Transcriptome-wide association studies integrate GWAS and transcriptome data to examine the molecular mechanisms underlying disease etiology. Here the authors present PMR-Egger, a powerful TWAS method based on probabilistic Mendelian Randomization.

Mendelian randomization (MR) is becoming more and more popular for inferring causal relationship between an exposure and a trait. Typically, instrument SNPs are selected from an exposure GWAS based on their summary statistics and the same summary statistics on the selected SNPs are used for subsequent analyses. However, this practice suffers from selection bias and can invalidate MR methods, as showcased via two popular methods: the summary data-based MR (SMR) method and the two-sample MR Steiger method. The SMR method is conservative while the MR Steiger method can be either conservative or liberal. A simple and yet more powerful alternative to SMR is proposed.

Background Observational studies have shown that moderate-to-vigorous physical activity (MVPA), vigorous physical activity (VPA), sedentary behaviours, and sleep duration were associated with cardiovascular diseases (CVDs) and lipid levels. However, whether such observations reflect causality remain largely unknown. We aimed to investigate the causal associations of physical activity, sedentary behaviours, and sleep duration with coronary artery disease (CAD), myocardial infarction (MI), stroke and lipid levels. Methods We conducted a Mendelian randomization (MR) study using genetic variants as instruments which are associated with physical activity, sedentary behaviours, and sleep duration to examine the causal effects on CVDs and lipid levels. This study included analyses of 4 potentially modifiable factors and 7 outcomes. Thus, the threshold of statistical significance is P  = 1.8 × 10 − 3 (0.05/4 × 7) after Bonferroni correction. Results In the present study, there was suggestive evidence for associations of genetically predicted VPA with CAD (odds ratio, 0.65; 95% confidence intervals, 0.47–0.90; P  = 0.009) and MI (0.74; 0.59–0.93; P  = 0.010). However, genetically predicted VPA, MVPA, sleep duration and sedentary behaviours did not show significant associations with stroke and any lipid levels. Conclusions Our findings from the MR approach provided suggestive evidence that vigorous exercise decreased risk of CAD and MI, but not stroke. However, there was no evidence to support causal associations of MVPA,sleep duration or sedentary behaviours with cardiovascular risk and lipid levels. Translational perspective The findings of this study did not point out specific recommendations on increasing physical activity required to deliver significant health benefits. Nevertheless, the findings allowed clinicians and public health practitioners to provide advice about increasing the total amount of excising time by demonstrating that such advice can be effective. Reliable assessment of the association of physical activity levels with different subtypes of CVDs is needed to provide the basis for a comprehensive clinical approach on CVDs prevention, which can be achieved through lifestyle interventions in addition to drug therapy.

Purpose Observational studies have suggested that polyunsaturated fatty acids (PUFAs) may decrease Alzheimer’s disease (AD) risk. In the present study, we examined this hypothesis using a Mendelian randomization analysis. Methods We used summary statistics data for single-nucleotide polymorphisms associated with plasma levels of n-6 PUFAs (linoleic acid, arachidonic acid) and n-3 PUFAs (alpha-linolenic acid, eicosapentaenoic acid, docosapentaenoic acid, docosahexaenoic acid), and the corresponding data for AD from a genome-wide association meta-analysis of 63,926 individuals (21,982 diagnosed AD cases, 41,944 controls). Results None of the genetically predicted PUFAs was significantly associated with AD risk; odds ratios (95% confidence interval) per 1 SD increase in PUFA levels were 0.98 (0.93, 1.03) for linoleic acid, 1.01 (0.98, 1.05) for arachidonic acid, 0.96 (0.88, 1.06) for alpha-linolenic acid, 1.03 (0.93, 1.13) for eicosapentaenoic acid, 1.03 (0.97, 1.09) for docosapentaenoic acid, and 1.01 (0.81, 1.25) for docosahexaenoic acid. Conclusions This study did not support the hypothesis that PUFAs decrease AD risk.