scholarly journals Welch-weighted Egger regression reduces false positives due to correlated pleiotropy in Mendelian randomization

2021 ◽  
Author(s):  
Brielin C Brown ◽  
David A Knowles
Keyword(s):  
Exploratory Data Analysis ◽  
Mendelian Randomization ◽  
False Positives ◽  
Causal Effects ◽  
Genetic Associations ◽  
Exploratory Data ◽  
Flexible Framework ◽  
The Uk ◽  
Modern Population ◽  
The Relationship

Modern population-scale biobanks contain simultaneous measurements of many phenotypes, providing unprecedented opportunity to study the relationship between biomarkers and disease. However, inferring causal effects from observational data is notoriously challenging. Mendelian randomization (MR) has recently received increased attention as a class of methods for estimating causal effects using genetic associations. However, standard methods result in pervasive false positives when two traits share a heritable, unobserved common cause. This is the problem of correlated pleiotropy. Here, we introduce a flexible framework for simulating traits with a common genetic confounder that generalizes recently proposed models, as well as simple approach we call Welch-weighted Egger regression (WWER) for estimating causal effects. We show in comprehensive simulations that our method substantially reduces false positives due to correlated pleiotropy while being fast enough to apply to hundreds of phenotypes. We first apply our method to a subset of the UK Biobank consisting of blood traits in inflammatorydisease, and then a broader set of 411 heritable phenotypes. We detect many effects with strong literaturesupport, as well as numerous behavioral effects that appear to stem from physician advice given to peopleat high risk for disease. We conclude that WWER is a powerful tool for exploratory data analysis inever-growing databases of genotypes and phenotypes

scholarly journals Causal Associations Between Blood Lipids and COVID-19 Risk: A Two-Sample Mendelian Randomization Study

2021 ◽  
Author(s):  
Kun Zhang ◽  
Shan-Shan Dong ◽  
Yan Guo ◽  
Shi-Hao Tang ◽  
Hao Wu ◽  
...  
Keyword(s):  
Total Cholesterol ◽  
Blood Lipids ◽  
Mendelian Randomization ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Outcome Data ◽  
Causal Effects ◽  
Lipoprotein Cholesterol ◽  
Global Pandemic ◽  
The Uk

Objective: Coronavirus disease 2019 (COVID-19) is a global pandemic caused by the severe acute respiratory syndrome coronavirus 2. It has been reported that dyslipidemia is correlated with COVID-19, and blood lipids levels, including total cholesterol, HDL-C (high-density lipoprotein cholesterol), and LDL-C (low-density lipoprotein cholesterol) levels, were significantly associated with disease severity. However, the causalities of blood lipids on COVID-19 are not clear. Approach and Results: We performed 2-sample Mendelian randomization (MR) analyses to explore the causal effects of blood lipids on COVID-19 susceptibility and severity. Using the outcome data from the UK Biobank (1221 cases and 4117 controls), we observed potential positive causal effects of dyslipidemia (odds ratio [OR], 1.27 [95% CI, 1.08–1.49], P =3.18×10 −3 ), total cholesterol (OR, 1.19 [95% CI, 1.07–1.32], P =8.54×10 −4 ), and ApoB (apolipoprotein B; OR, 1.18 [95% CI, 1.07–1.29], P =1.01×10 −3 ) on COVID-19 susceptibility after Bonferroni correction. In addition, the effects of total cholesterol (OR, 1.01 [95% CI, 1.00–1.02], P =2.29×10 −2 ) and ApoB (OR, 1.01 [95% CI, 1.00–1.02], P =2.22×10 −2 ) on COVID-19 susceptibility were also identified using outcome data from the host genetics initiative (14 134 cases and 1 284 876 controls). Conclusions: In conclusion, we found that higher total cholesterol and ApoB levels might increase the risk of COVID-19 infection.

scholarly journals Novel insights into the consequences of obesity: a phenotype-wide Mendelian randomization study

2022 ◽  
Author(s):  
Chang He ◽  
Miaoran Zhang ◽  
Jiuling Li ◽  
Yiqing Wang ◽  
Lanlan Chen ◽  
...  
Keyword(s):  
Instrumental Variables ◽  
Fixed Effects ◽  
Mendelian Randomization ◽  
Portion Size ◽  
Causal Effects ◽  
Calorie Intake ◽  
Smoking Behaviors ◽  
Prostate Cancers ◽  
The Uk ◽  
Consequences Of Obesity

AbstractObesity is thought to significantly impact the quality of life. In this study, we sought to evaluate the health consequences of obesity on the risk of a broad spectrum of human diseases. The causal effects of exposing to obesity on health outcomes were inferred using Mendelian randomization (MR) analyses using a fixed effects inverse-variance weighted model. The instrumental variables were SNPs associated with obesity as measured by body mass index (BMI) reported by GIANT consortium. The spectrum of outcome consisted of the phenotypes from published GWAS and the UK Biobank. The MR-Egger intercept test was applied to estimate horizontal pleiotropic effects, along with Cochran’s Q test to assess heterogeneity among the causal effects of instrumental variables. Our MR results confirmed many putative disease risks due to obesity, such as diabetes, dyslipidemia, sleep disorder, gout, smoking behaviors, arthritis, myocardial infarction, and diabetes-related eye disease. The novel findings indicated that elevated red blood cell count was inferred as a mediator of BMI-induced type 2 diabetes in our bidirectional MR analysis. Intriguingly, the effects that higher BMI could decrease the risk of both skin and prostate cancers, reduce calorie intake, and increase the portion size warrant further studies. Our results shed light on a novel mechanism of the disease-causing roles of obesity.

scholarly journals Genetically Determined Levels of Serum Metabolites and Risk of Neuroticism: A Mendelian Randomization Study

2020 ◽  
Author(s):  
Li Qian ◽  
Yajuan Fan ◽  
Fengjie Gao ◽  
Binbin Zhao ◽  
Bin Yan ◽  
...  
Keyword(s):  
Uric Acid ◽  
Lower Risk ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Strong Predictor ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Nucleotide Polymorphisms ◽  
Genetic Associations ◽  
Inverse Variance

Abstract Background Neuroticism is a strong predictor for a variety of social and behavioral outcomes, but the etiology is still unknown. Our study aims to provide a comprehensive investigation of causal effects of serum metabolome phenotypes on risk of neuroticism using Mendelian randomization (MR) approaches. Methods Genetic associations with 486 metabolic traits were utilized as exposures, and data from a large genome-wide association study of neuroticism were selected as outcome. For MR analysis, we used the standard inverse-variance weighted (IVW) method for primary MR analysis and 3 additional MR methods (MR-Egger, weighted median, and MR pleiotropy residual sum and outlier) for sensitivity analyses. Results Our study identified 31 metabolites that might have causal effects on neuroticism. Of the 31 metabolites, uric acid and paraxanthine showed robustly significant association with neuroticism in all MR methods. Using single nucleotide polymorphisms as instrumental variables, a 1-SD increase in uric acid was associated with approximately 30% lower risk of neuroticism (OR: 0.77; 95% CI: 0.62–0.95; PIVW = 0.0145), whereas a 1-SD increase in paraxanthine was associated with a 7% higher risk of neuroticism (OR: 1.07; 95% CI: 1.01–1.12; PIVW = .0145). Discussion Our study suggested an increased level of uric acid was associated with lower risk of neuroticism, whereas paraxanthine showed the contrary effect. Our study provided novel insight by combining metabolomics with genomics to help understand the pathogenesis of neuroticism.

scholarly journals Quantifying the Measurement Error on England and Wales EPC Ratings

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3523 ◽  
Author(s):  
Jenny Crawley ◽  
Phillip Biddulph ◽  
Paul J. Northrop ◽  
Jez Wingfield ◽  
Tadj Oreszczyn ◽  
...  
Keyword(s):  
Measurement Error ◽  
Exploratory Data Analysis ◽  
Energy Performance ◽  
Repeated Measurements ◽  
Stock Level ◽  
England And Wales ◽  
Deviation Measurement ◽  
Exploratory Data ◽  
The One ◽  
The Uk

Domestic Energy Performance Certificates (EPCs) are used in the UK to provide energy efficiency ratings for use in policy and investment decisions on individual dwellings and at a stock level. There is evidence that the process of creating an EPC introduces measurement error such that repeat assessments of the same property give different ratings, compromising their reliability. This study presents a novel error analysis to estimate the size of this effect, using repeated EPC assessments of 1.6 million existing dwellings in England and Wales. A statistical model of how measurement error contributes to variation between repeated measurements is set out, and exploratory data analysis is used to decide how to apply this model to the available data. The results predict that the one standard deviation measurement error decreases with EPC rating, from around ± 8.0 EPC points on a rating of 35 to ±2.4 on a rating of 85. This predicted error is higher than the limit recommended in UK guidance except in very efficient buildings; it can also result in dwellings being rated in the wrong EPC band, for example it was estimated that 24% of band D homes are rated as band C.

The relationship between service intensity and the quality of health care: an exploratory data analysis

2005 ◽  
Vol 18 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Daniel Friesner ◽  
Robert Rosenman
Keyword(s):  
Health Care ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Important Determinant ◽  
Washington State ◽  
General Definition ◽  
Service Intensity ◽  
Exploratory Data ◽  
The Relationship

This paper provides an empirical check of some assumptions used to define the quality of care in the health services literature. Specifically, we test (i) whether service intensity is the only important determinant of a provider's quality and (ii) whether higher service intensity always causes higher quality. Using a panel of hospitals from Washington State, we find evidence that rejects both of these assumptions. As a result, further work is needed to postulate a more general definition that does not rely on these assumptions.

scholarly journals The effect of education on adult mortality, health, and income: triangulating across genetic and policy reforms

2018 ◽  
Author(s):  
Neil M Davies ◽  
Matt Dickson ◽  
George Davey Smith ◽  
Frank Windmeijer ◽  
Gerard J van den Berg
Keyword(s):  
Genetic Variation ◽  
Life Expectancy ◽  
Mendelian Randomization ◽  
Policy Reform ◽  
Adult Mortality ◽  
Causal Effects ◽  
Natural Experiments ◽  
Policy Reforms ◽  
Sources Of Variation ◽  
The Uk

1AbstractOn average, educated people are healthier, wealthier and have higher life expectancy than those with less education. Numerous studies have attempted to determine whether these differences are caused by education, or are merely correlated with it and are ultimately caused by another factor. Previous studies have used a range of natural experiments to provide causal evidence. Here we exploit two natural experiments, perturbation of germline genetic variation associated with education which occurs at conception, known as Mendelian randomization, and a policy reform, the raising of the school leaving age in the UK in 1972. Previous studies have suggested that the differences in outcomes associated with education may be due to confounding. However, the two independent sources of variation we exploit largely imply consistent causal effects of education on outcomes much later in life.

scholarly journals RNA-Seq workflow: gene-level exploratory analysis and differential expression

F1000Research ◽  
2016 ◽  
Vol 4 ◽  
pp. 1070 ◽  
Author(s):  
Michael I. Love ◽  
Simon Anders ◽  
Vladislav Kim ◽  
Wolfgang Huber
Keyword(s):  
Differential Expression ◽  
Gene Expression Analysis ◽  
Exploratory Data Analysis ◽  
Reference Genome ◽  
Rna Seq ◽  
Differential Gene Expression Analysis ◽  
Gene Level ◽  
Exploratory Data ◽  
Differential Gene ◽  
The Relationship

Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample.We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.

scholarly journals Mendelian randomization analysis of Circulating adiponectin levels on prostate cancer

2021 ◽  
Author(s):  
Liangliang Ren ◽  
Chenhao Yu ◽  
Zhenwei Zhou ◽  
Gonghui Li
Keyword(s):  
Prostate Cancer ◽  
Protective Effect ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Meta Analysis ◽  
Causal Effects ◽  
Nucleotide Polymorphisms ◽  
Genetic Associations ◽  
Mendelian Randomization Analysis ◽  
Randomization Analysis

Abstract Background: Previous observational studies showed a conflict with the correlation between circulating adiponectin levels and prostate cancer. Methods: In this study, we employed Mendelian randomization analysis to identify the causal effects between them. 14 single nucleotide polymorphisms were screened from the largest-scale genome-wide association study meta-analysis of adiponectin in a multi-ethnic population. The SNP outcome effects were obtained from Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome and Japanese Encyclopedia of Genetic Associations by Riken. Inverse variance weighted model with random-effects was the main effect estimation in our study, alongside weighted median, MR-Egger, and weighted mode models.Results: The results showed no significant causal estimate but a potential protective effect of adiponectin on prostate cancer. In addition, two other research of adiponectin repeated the analysis to avoid the bias of human species showing the similar results. Conclusion: Our study did not provide significant evidence to support the causal effects of circulating adiponectin levels on prostate cancer, but most of our results showed a potential protective effect requiring larger-scale MR analysis to confirm.

scholarly journals Phenome-scale causal network discovery with bidirectional mediated Mendelian randomization

2020 ◽  
Author(s):  
Brielin C. Brown ◽  
David A. Knowles
Keyword(s):  
Biological Networks ◽  
Complex Traits ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Network ◽  
Network Discovery ◽  
Difficult Challenge ◽  
The Uk ◽  
Modern Population ◽  
Parsimonious Network

AbstractInference of directed biological networks from observational genomics datasets is a crucial but notoriously difficult challenge. Modern population-scale biobanks, containing simultaneous measurements of traits, biomarkers, and genetic variation, offer an unprecedented opportunity to study biological networks. Mendelian randomization (MR) has received attention as a class of methods for inferring causal effects in observational data that uses genetic variants as instrumental variables, but MR methods rely on assumptions that limit their application to complex traits at the biobank-scale. Moreover, MR estimates the total effect of one trait on another, which may be mediated by other factors. Biobanks include measurements of many potential mediators, in principle enabling the conversion of MR estimates into direct effects representing a causal network. Here, we show that this can be accomplished by a flexible two stage procedure we call bidirectional mediated Mendelian randomization (bimmer). First, bimmer estimates the effect of every trait on every other. Next, bimmer finds a parsimonious network that explains these effects using direct and mediated causal paths. We introduce novel methods for both steps and show via extensive simulations that bimmer is able to learn causal network structures even in the presence of non-causal genetic correlation. We apply bimmer to 405 phenotypes from the UK biobank and demonstrate that learning the network structure is invaluable for interpreting the results of phenome-wide MR, while lending causal support to several recent observational studies.

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