scholarly journals Extending causality tests with genetic instruments: an integration of Mendelian Randomization and the Classical Twin Design

2017 ◽  
Author(s):  
Camelia C. Minică ◽  
Conor V. Dolan ◽  
Dorret I. Boomsma ◽  
Eco de Geus ◽  
Michael C. Neale
Keyword(s):  
Statistical Power ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Twin Design ◽  
Twin Model ◽  
Polygenic Scores ◽  
Exposure Variable ◽  
Causality Tests ◽  
Instrument Bias ◽  
Genetic Instruments

ABSTRACTMendelian Randomization (MR) is an important approach to modelling causality in non-experimental settings. MR uses genetic instruments to test causal relationships between exposures and outcomes of interest. Individual genetic variants have small effects, and so, when used as instruments, render MR liable to weak instrument bias. Polygenic scores have the advantage of larger effects, but may be characterized by direct pleiotropy, which violates a central assumption of MR.We developed the MR-DoC twin model by integrating MR with the Direction of Causation twin model. This model allows us to test pleiotropy directly. We considered the issue of parameter identification, and given identification, we conducted extensive power calculations. MR-DoC allows one to test causal hypotheses and to obtain unbiased estimates of the causal effect given pleiotropic instruments (polygenic scores), while controlling for genetic and environmental influences common to the outcome and exposure. Furthermore, MR-DoC in twins has appreciably greater statistical power than a standard MR analysis applied to singletons, if the unshared environmental effects on the exposure and the outcome are uncorrelated. Generally, power increases with: 1) decreasing residual exposure-outcome correlation, and 2) decreasing heritability of the exposure variable.MR-DoC allows one to employ strong instrumental variables (polygenic scores, possibly pleiotropic), guarding against weak instrument bias and increasing the power to detect causal effects. Our approach will enhance and extend MR’s range of applications, and increase the value of the large cohorts collected at twin registries as they correctly detect causation and estimate effect sizes even in the presence of pleiotropy.

scholarly journals Extending Causality Tests with Genetic Instruments: An Integration of Mendelian Randomization with the Classical Twin Design

2018 ◽  
Vol 48 (4) ◽  
pp. 337-349 ◽  
Author(s):  
Camelia C. Minică ◽  
Conor V. Dolan ◽  
Dorret I. Boomsma ◽  
Eco de Geus ◽  
Michael C. Neale
Keyword(s):  
Mendelian Randomization ◽  
Twin Design ◽  
Causality Tests ◽  
Classical Twin Design ◽  
Genetic Instruments

Powerful three-sample genome-wide design and robust statistical inference in summary-data Mendelian randomization

2019 ◽  
Vol 48 (5) ◽  
pp. 1478-1492 ◽  
Author(s):  
Qingyuan Zhao ◽  
Yang Chen ◽  
Jingshu Wang ◽  
Dylan S Small
Keyword(s):  
Odds Ratio ◽  
Statistical Power ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Density Lipoprotein ◽  
P Value ◽  
Genome Wide ◽  
A Genome ◽  
Summary Data ◽  
Genetic Instruments

Abstract Background Summary-data Mendelian randomization (MR) has become a popular research design to estimate the causal effect of risk exposures. With the sample size of GWAS continuing to increase, it is now possible to use genetic instruments that are only weakly associated with the exposure. Development We propose a three-sample genome-wide design where typically 1000 independent genetic instruments across the whole genome are used. We develop an empirical partially Bayes statistical analysis approach where instruments are weighted according to their strength; thus weak instruments bring less variation to the estimator. The estimator is highly efficient with many weak genetic instruments and is robust to balanced and/or sparse pleiotropy. Application We apply our method to estimate the causal effect of body mass index (BMI) and major blood lipids on cardiovascular disease outcomes, and obtain substantially shorter confidence intervals (CIs). In particular, the estimated causal odds ratio of BMI on ischaemic stroke is 1.19 (95% CI: 1.07–1.32, P-value <0.001); the estimated causal odds ratio of high-density lipoprotein cholesterol (HDL-C) on coronary artery disease (CAD) is 0.78 (95% CI: 0.73–0.84, P-value <0.001). However, the estimated effect of HDL-C attenuates and become statistically non-significant when we only use strong instruments. Conclusions A genome-wide design can greatly improve the statistical power of MR studies. Robust statistical methods may alleviate but not solve the problem of horizontal pleiotropy. Our empirical results suggest that the relationship between HDL-C and CAD is heterogeneous, and it may be too soon to completely dismiss the HDL hypothesis.

scholarly journals Application of Mendelian Randomization to Investigate the Association of Body Mass Index with Health Care Costs

2020 ◽  
Vol 40 (2) ◽  
pp. 156-169 ◽  
Author(s):  
Christoph F. Kurz ◽  
Michael Laxy
Keyword(s):  
Health Care ◽  
Body Mass Index ◽  
Body Mass ◽  
Health Care Costs ◽  
Instrumental Variables ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Omitted Variables ◽  
Care Costs ◽  
Genetic Instruments

Causal effect estimates for the association of obesity with health care costs can be biased by reversed causation and omitted variables. In this study, we use genetic variants as instrumental variables to overcome these limitations, a method that is often called Mendelian randomization (MR). We describe the assumptions, available methods, and potential pitfalls of using genetic information and how to address them. We estimate the effect of body mass index (BMI) on total health care costs using data from a German observational study and from published large-scale data. In a meta-analysis of several MR approaches, we find that models using genetic instruments identify additional annual costs of €280 for a 1-unit increase in BMI. This is more than 3 times higher than estimates from linear regression without instrumental variables (€75). We found little evidence of a nonlinear relationship between BMI and health care costs. Our results suggest that the use of genetic instruments can be a powerful tool for estimating causal effects in health economic evaluation that might be superior to other types of instruments where there is a strong association with a modifiable risk factor.

scholarly journals Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

2020 ◽  
Author(s):  
Jingshu Wang ◽  
Qingyuan Zhao ◽  
Jack Bowden ◽  
Gilbran Hemani ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Risk Factors ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Genetic Instruments

Over a decade of genome-wide association studies have led to the finding that significant genetic associations tend to spread across the genome for complex traits. The extreme polygenicity where "all genes affect every complex trait" complicates Mendelian Randomization studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing Mendelian Randomization methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE (Genome-wide mR Analysis under Pervasive PLEiotropy) to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using summary statistics from genome-wide association studies, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, adjust for confounding risk factors, and determine the causal direction. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and the potential pleiotropic pathways.

Maternal polycystic ovary syndrome and offspring birth weight: a Mendelian randomization study

2021 ◽  
Author(s):  
Yuexin Gan ◽  
Donghao Lu ◽  
Chonghuai Yan ◽  
Jun Zhang ◽  
Jian Zhao
Keyword(s):  
Birth Weight ◽  
Polycystic Ovary Syndrome ◽  
Causal Relationship ◽  
Mendelian Randomization ◽  
Polycystic Ovary ◽  
Causal Effect ◽  
Sensitivity Analyses ◽  
European Ancestry ◽  
Ovary Syndrome ◽  
Genetic Instruments

Abstract Background Observational associations between maternal polycystic ovary syndrome (PCOS) and offspring birth weight (BW) have been inconsistent and the causal relationship is still uncertain. Objective We conducted a two-sample Mendelian randomization (MR) study to estimate the causal effect of maternal PCOS on offspring BW. Methods We constructed genetic instruments for PCOS with 14 single nucleotide polymorphisms (SNPs) which were identified in the genome-wide association study (GWAS) meta-analysis including 10,074 PCOS cases and 103,164 controls of European ancestry from seven cohorts. The genetic associations of these SNPs with the offspring BW were extracted from summary statistics estimated by the Early Growth Genetics (EGG) consortium (n = 406,063 European-ancestry individuals) using the weighted linear model (WLM), an approximation method of structural equation model (SEM), which separated maternal genetic effects from fetal genetic effects. We used a two-sample MR design to examine the causal relationship between maternal PCOS and offspring BW. Sensitivity analyses were conducted to assess the robustness of the MR results. Results We found little evidence for a causal effect of maternal PCOS on offspring BW (-6.1 g, 95% confidence interval [CI]: -16.8 g, 4.6 g). Broadly consistent results were found in the sensitivity analyses. Conclusion Despite the large scale of this study, our results suggested little causal effect of maternal PCOS on offspring BW. MR studies with a larger sample size of women with PCOS or more genetic instruments that would increase the variation of PCOS explained are needed in the future.

scholarly journals Mendelian randomization analysis of C-reactive protein on colorectal cancer risk

2018 ◽  
Vol 48 (3) ◽  
pp. 767-780 ◽  
Author(s):  
Xiaoliang Wang ◽  
James Y Dai ◽  
Demetrius Albanes ◽  
Volker Arndt ◽  
Sonja I Berndt ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Instrumental Variables ◽  
Statistical Power ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
European Ancestry ◽  
Reverse Causality ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Increased Risk

Abstract Background Chronic inflammation is a risk factor for colorectal cancer (CRC). Circulating C-reactive protein (CRP) is also moderately associated with CRC risk. However, observational studies are susceptible to unmeasured confounding or reverse causality. Using genetic risk variants as instrumental variables, we investigated the causal relationship between genetically elevated CRP concentration and CRC risk, using a Mendelian randomization approach. Methods Individual-level data from 30 480 CRC cases and 22 844 controls from 33 participating studies in three international consortia were used: the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), the Colorectal Transdisciplinary Study (CORECT) and the Colon Cancer Family Registry (CCFR). As instrumental variables, we included 19 single nucleotide polymorphisms (SNPs) previously associated with CRP concentration. The SNP-CRC associations were estimated using a logistic regression model adjusted for age, sex, principal components and genotyping phases. An inverse-variance weighted method was applied to estimate the causal effect of CRP on CRC risk. Results Among the 19 CRP-associated SNPs, rs1260326 and rs6734238 were significantly associated with CRC risk (P = 7.5 × 10–4, and P = 0.003, respectively). A genetically predicted one-unit increase in the log-transformed CRP concentrations (mg/l) was not associated with increased risk of CRC [odds ratio (OR) = 1.04; 95% confidence interval (CI): 0.97, 1.12; P = 0.256). No evidence of association was observed in subgroup analyses stratified by other risk factors. Conclusions In spite of adequate statistical power to detect moderate association, we found genetically elevated CRP concentration was not associated with increased risk of CRC among individuals of European ancestry. Our findings suggested that circulating CRP is unlikely to be a causal factor in CRC development.

scholarly journals Differential Sleep Traits Have No Causal Effect on Inflammatory Bowel Diseases: A Mendelian Randomization Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Min Chen ◽  
Wen-Yan Peng ◽  
Tai-Chun Tang ◽  
Hui Zheng
Keyword(s):  
Ulcerative Colitis ◽  
Crohn’S Disease ◽  
Circadian Rhythms ◽  
Sleep Duration ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Sensitivity Analyses ◽  
Inflammatory Bowel ◽  
Genetic Instruments ◽  
Mr Study

Background: Previous studies suggested an association of sleep disorders with inflammatory bowel disease (IBD) and indicated that using pharmacological treatments for the modulation of circadian rhythms might prevent IBD pathogenesis or aggravation, but whether the effect of sleep traits on IBD was causal is inconclusive and, therefore, prevents drug repurposing based on the previous studies. We aimed to examine the causal effect of different sleep traits on the pathogenesis of IBD.Methods: Genetic instruments for sleep traits were selected from the largest GWAS studies available in the UK Biobank (n = 449,734) and the 23andMe Research (n = 541,333). A two-sample Mendelian randomization (MR) study was conducted to examine the association of the genetic instruments with IBD (12,882 cases and 21,770 controls), ulcerative colitis (6,968 cases, 20,464 controls), and Crohn’s disease (5,956 cases and 14,927 controls). We applied the inverse-variance weighted (IVW) method to estimate causal effects, and we used the weighted median and MR-Egger method for sensitivity analyses.Results: We found that sleep duration (OR, 1.00, 95% CI 1.00–1.01), short sleep duration (OR, 1.07, 95% CI 0.41–2.83), morningness (OR, 1.05, 95% CI 0.87–1.27), daytime napping (OR, 1.64, 95% CI 0.62–4.4), frequent insomnia (OR, 1.17, 95% CI 0.8–1.72), any insomnia (OR, 1.17, 95% CI 0.69–1.97), and snoring (OR, 0.31, 95% CI 0.06–1.54) had no causal effect on IBD, and these sleep traits had no causal effect on ulcerative colitis and Crohn’s disease either. Most of the sensitivity analyses showed consistent results with those of the IVW method.Conclusion: Our MR study did not support the causal effect of sleep traits on IBD. Pharmacological modulation of circadian rhythms for the prevention of IBD pathogenesis was unwarranted.

scholarly journals Mendelian Randomization With Refined Instrumental Variables From Genetic Score Improves Accuracy and Reduces Bias

2021 ◽  
Vol 12 ◽  
Author(s):  
Lijuan Lin ◽  
Ruyang Zhang ◽  
Hui Huang ◽  
Ying Zhu ◽  
Yi Li ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Statistical Power ◽  
Complex Disease ◽  
Type I Error ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Type I ◽  
Individual Data ◽  
Genetic Score ◽  
Mediation Effects

Mendelian randomization (MR) can estimate the causal effect for a risk factor on a complex disease using genetic variants as instrument variables (IVs). A variety of generalized MR methods have been proposed to integrate results arising from multiple IVs in order to increase power. One of the methods constructs the genetic score (GS) by a linear combination of the multiple IVs using the multiple regression model, which was applied in medical researches broadly. However, GS-based MR requires individual-level data, which greatly limit its application in clinical research. We propose an alternative method called Mendelian Randomization with Refined Instrumental Variable from Genetic Score (MR-RIVER) to construct a genetic IV by integrating multiple genetic variants based on summarized results, rather than individual data. Compared with inverse-variance weighted (IVW) and generalized summary-data-based Mendelian randomization (GSMR), MR-RIVER maintained the type I error, while possessing more statistical power than the competing methods. MR-RIVER also presented smaller biases and mean squared errors, compared to the IVW and GSMR. We further applied the proposed method to estimate the effects of blood metabolites on educational attainment, by integrating results from several publicly available resources. MR-RIVER provided robust results under different LD prune criteria and identified three metabolites associated with years of schooling and additional 15 metabolites with indirect mediation effects through butyrylcarnitine. MR-RIVER, which extends score-based MR to summarized results in lieu of individual data and incorporates multiple correlated IVs, provided a more accurate and powerful means for the discovery of novel risk factors.

scholarly journals 399The causal effect of BMI on neurodevelopment: a within family Mendelian randomization study using MoBa

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Amanda Hughes ◽  
Tim Morris ◽  
Ziada Ayorech ◽  
Martin Tesli ◽  
Helga Ask ◽  
...  
Keyword(s):  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Population Level ◽  
Parental Genotype ◽  
Adhd Symptoms ◽  
Autism Symptoms ◽  
Polygenic Scores ◽  
Neurodevelopmental Problems ◽  
Maternal Bmi

Abstract Background Higher BMI in childhood predicts subsequent neurodevelopmental and emotional problems, but it is unclear if associations are causal. Observational studies are vulnerable to reverse causation and confounding. Mendelian randomization (MR) studies with unrelated individuals can also suffer from familial biases, such as dynastic effects (“genetic nurture”). Methods We apply within-family MR (WFMR) to overcome these biases. We used genetic information from 26,370 family trios in the Norwegian Mother, Father and Child Cohort Study (MoBa) to construct BMI polygenic scores in children and both parents. By using all three polygenic scores to instrument BMI, we avoided familial biases affecting previous studies. Results Multivariable-adjusted and conventional MR models implied an impact of children’s BMI on depressive, ADHD, and autism symptoms. In conventional MR models, a 5kg/m2 increase in BMI corresponded to depressive symptoms 0.49 SD higher (95%CI: 0.24-0.73), and ADHD symptoms 0.49 SD higher (95%CI: 0.28-0.70). WFMR estimates were less precise but gave little evidence of causal impacts of children’s BMI. Maternal BMI was positively associated with children’s depressive (0.16 SD per 5kg/m2, 95%CI: 0.04-0.28) and autism symptoms, and paternal BMI with children’s ADHD symptoms. Conclusions Compared to conventional MR models, MR models accounting for parental genotype found less evidence of causal effects of children’s own BMI on emotional and neurodevelopmental symptoms. The discrepancy may suggest an influence of family or population-level effects. Key messages The influence of children’s own BMI on emotional and neurodevelopmental problems may have been overstated. Parental BMI, familial or population level effects may influence these outcomes.

scholarly journals Incorporating Polygenic Risk Scores in the ACE Twin Model to Estimate A–C Covariance

2021 ◽  
Author(s):  
Conor V. Dolan ◽  
Roel C. A. Huijskens ◽  
Camelia C. Minică ◽  
Michael C. Neale ◽  
Dorret I. Boomsma
Keyword(s):  
Parameter Identification ◽  
Environmental Variables ◽  
Statistical Power ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Twin Model ◽  
Polygenic Scores ◽  
Ace Model ◽  
Biasing Effects

AbstractThe assumption in the twin model that genotypic and environmental variables are uncorrelated is primarily made to ensure parameter identification, not because researchers necessarily think that these variables are uncorrelated. Although the biasing effects of such correlations are well understood, a method to estimate these parameters in the twin model would be useful. Here we explore the possibility of relaxing this assumption by adding polygenic scores to the (univariate) twin model. We demonstrate that this extension renders the additive genetic (A)—common environmental (C) covariance (σAC) identified. We study the statistical power to reject σAC = 0 in the ACE model and present the results of simulations.

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