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Shen M, Shi L, Xing M, Jiang H, Ma Y, Ma Y, Zhang L. Unravelling the Metabolic Underpinnings of Gestational Diabetes Mellitus: A Comprehensive Mendelian Randomisation Analysis Identifying Causal Metabolites and Biological Pathways. Diabetes Metab Res Rev 2024; 40:e3839. [PMID: 39216101 DOI: 10.1002/dmrr.3839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 06/16/2024] [Accepted: 07/24/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) has a strong genetic predisposition. Integrating metabolomics with Mendelian randomisation (MR) analysis offers a potent method to uncover the metabolic factors causally linked to GDM pathogenesis. OBJECTIVES This study aims to identify specific metabolites and metabolic pathways causally associated with GDM susceptibility through a comprehensive MR analysis. Additionally, it seeks to explore the potential of these identified metabolites as circulating biomarkers for early GDM detection and risk assessment. Furthermore, it aims to evaluate the implicated metabolic pathways as potential therapeutic targets for preventive or interventional strategies against GDM. METHODS A two-sample MR study was conducted using summary statistics from a metabolite genome-wide association study (GWAS) of 8299 individuals and a GDM GWAS comprising 13,039 cases and 197,831 controls. Rigorous criteria were applied to select robust genetic instruments for 850 metabolites. RESULTS MR analysis revealed 47 metabolites exhibiting putative causal associations with GDM risk. Among these, five metabolites demonstrated statistically significant associations after multiple-testing correction: Beta-citrylglutamate, Isobutyrylcarnitine (c4), 1,2-dilinoleoyl-GPC (18:2/18:2), Alliin and Cis-3,4-methyleneheptanoylcarnitine. Importantly, all these metabolites exhibited protective effects against GDM development. Additionally, metabolic pathway enrichment analysis implicated the methionine metabolism and spermidine and spermine biosynthesis pathways in the pathogenesis of GDM. CONCLUSION This comprehensive MR study has robustly identified specific metabolites and metabolic pathways with causal links to GDM susceptibility. These findings provide novel insights into the metabolic underpinnings of GDM aetiology and offer promising translational implications. The identified metabolites could serve as potential circulating biomarkers for early detection and risk stratification, while the implicated metabolic pathways may represent therapeutic targets for preventive or interventional strategies against GDM.
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Affiliation(s)
- Min Shen
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Lei Shi
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Mengzhen Xing
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Hehe Jiang
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yuning Ma
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yuxia Ma
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Linlin Zhang
- Shandong University of Traditional Chinese Medicine, Jinan, China
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Hu X, Cai M, Xiao J, Wan X, Wang Z, Zhao H, Yang C. Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics. Am J Hum Genet 2024; 111:1717-1735. [PMID: 39059387 PMCID: PMC11339627 DOI: 10.1016/j.ajhg.2024.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/26/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices. Our study focused on the following crucial aspects: type I error control in the presence of various confounding scenarios (e.g., population stratification, pleiotropy, and family-level confounders like assortative mating), the accuracy of causal effect estimates, replicability, and power. By comprehensively evaluating the performance of compared methods over one thousand exposure-outcome trait pairs, our study not only provides valuable insights into the performance and limitations of the compared methods but also offers practical guidance for researchers to choose appropriate MR methods for causal inference.
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Affiliation(s)
- Xianghong Hu
- School of Mathematical Sciences, Institute of Statistical Sciences, Shenzhen University, Shenzhen 518060, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China; Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China
| | - Mingxuan Cai
- Department of Biostatistics, City University of Hong Kong, Hong Kong, China
| | - Jiashun Xiao
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China
| | - Xiaomeng Wan
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China; Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China
| | - Zhiwei Wang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China; Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA.
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China; Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Big Data Bio-Intelligence Lab, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
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Gorfine M, Qu C, Peters U, Hsu L. Unveiling challenges in Mendelian randomization for gene-environment interaction. Genet Epidemiol 2024; 48:164-189. [PMID: 38420714 PMCID: PMC11197907 DOI: 10.1002/gepi.22552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/29/2023] [Accepted: 01/30/2024] [Indexed: 03/02/2024]
Abstract
Gene-environment (GxE) interactions play a crucial role in understanding the complex etiology of various traits, but assessing them using observational data can be challenging due to unmeasured confounders for lifestyle and environmental risk factors. Mendelian randomization (MR) has emerged as a valuable method for assessing causal relationships based on observational data. This approach utilizes genetic variants as instrumental variables (IVs) with the aim of providing a valid statistical test and estimation of causal effects in the presence of unmeasured confounders. MR has gained substantial popularity in recent years largely due to the success of genome-wide association studies. Many methods have been developed for MR; however, limited work has been done on evaluating GxE interaction. In this paper, we focus on two primary IV approaches: the two-stage predictor substitution and the two-stage residual inclusion, and extend them to accommodate GxE interaction under both the linear and logistic regression models for continuous and binary outcomes, respectively. Comprehensive simulation study and analytical derivations reveal that resolving the linear regression model is relatively straightforward. In contrast, the logistic regression model presents a considerably more intricate challenge, which demands additional effort.
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Affiliation(s)
- Malka Gorfine
- Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel
| | - Conghui Qu
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Li Hsu
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington, USA
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Bollen KA, Gates KM, Luo L. A Model Implied Instrumental Variable Approach to Exploratory Factor Analysis (MIIV-EFA). PSYCHOMETRIKA 2024; 89:687-716. [PMID: 38532229 DOI: 10.1007/s11336-024-09949-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 12/30/2023] [Indexed: 03/28/2024]
Abstract
Spearman (Am J Psychol 15(1):201-293, 1904. https://doi.org/10.2307/1412107 ) marks the birth of factor analysis. Many articles and books have extended his landmark paper in permitting multiple factors and determining the number of factors, developing ideas about simple structure and factor rotation, and distinguishing between confirmatory and exploratory factor analysis (CFA and EFA). We propose a new model implied instrumental variable (MIIV) approach to EFA that allows intercepts for the measurement equations, correlated common factors, correlated errors, standard errors of factor loadings and measurement intercepts, overidentification tests of equations, and a procedure for determining the number of factors. We also permit simpler structures by removing nonsignificant loadings. Simulations of factor analysis models with and without cross-loadings demonstrate the impressive performance of the MIIV-EFA procedure in recovering the correct number of factors and in recovering the primary and secondary loadings. For example, in nearly all replications MIIV-EFA finds the correct number of factors when N is 100 or more. Even the primary and secondary loadings of the most complex models were recovered when the sample sizes were at least 500. We discuss limitations and future research areas. Two appendices describe alternative MIIV-EFA algorithms and the sensitivity of the algorithm to cross-loadings.
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Affiliation(s)
- Kenneth A Bollen
- Thurstone Psychometric Laboratory, Department of Psychology and Neuroscience, Department of Sociology, University of North Carolina at Chapel Hill, 235 E. Cameron Avenue, Chapel Hill, NC, 27599-3270, USA.
- Department of Psychology and Neuroscience, Department of Sociology, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC, 27599-3270, USA.
| | - Kathleen M Gates
- Thurstone Psychometric Laboratory, Department of Psychology and Neuroscience, Department of Sociology, University of North Carolina at Chapel Hill, 235 E. Cameron Avenue, Chapel Hill, NC, 27599-3270, USA
| | - Lan Luo
- Thurstone Psychometric Laboratory, Department of Psychology and Neuroscience, Department of Sociology, University of North Carolina at Chapel Hill, 235 E. Cameron Avenue, Chapel Hill, NC, 27599-3270, USA
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Shen M, Zhang L, Chen C, Wei X, Ma Y, Ma Y. Investigating the causal relationship between immune cell and Alzheimer's disease: a mendelian randomization analysis. BMC Neurol 2024; 24:98. [PMID: 38500057 PMCID: PMC10946133 DOI: 10.1186/s12883-024-03599-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/12/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND Complex interactions between the immune system and the brain may affect neural development, survival, and function, with etiological and therapeutic implications for neurodegenerative diseases. However, previous studies investigating the association between immune inflammation and Alzheimer's disease (AD) have yielded inconsistent results. METHODS We applied Mendelian randomization (MR) to examine the causal relationship between immune cell traits and AD risk using genetic variants as instrumental variables. MR is an epidemiological study design based on genetic information that reduces the effects of confounding and reverse causation. We analyzed the causal associations between 731 immune cell traits and AD risk based on publicly available genetic data. RESULTS We observed that 5 immune cell traits conferred protection against AD, while 7 immune cell traits increased the risk of AD. These immune cell traits mainly involved T cell regulation, monocyte activation and B cell differentiation. Our findings suggest that immune regulation may influence the development of AD and provide new insights into potential targets for AD prevention and treatment. We also conducted various sensitivity analyses to test the validity and robustness of our results, which revealed no evidence of pleiotropy or heterogeneity. CONCLUSION Our research shows that immune regulation is important for AD and provides new information on potential targets for AD prevention and treatment. However, this study has limitations, including the possibility of reverse causality, lack of validation in independent cohorts, and potential confounding by population stratification. Further research is needed to validate and amplify these results and to elucidate the potential mechanisms of the immune cell-AD association.
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Affiliation(s)
- Min Shen
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China
| | - Linlin Zhang
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China
| | - Chen Chen
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China
| | - Xiaocen Wei
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China
| | - Yuning Ma
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China.
| | - Yuxia Ma
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China.
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Zou J, Talluri R, Shete S. Approaches to estimate bidirectional causal effects using Mendelian randomization with application to body mass index and fasting glucose. PLoS One 2024; 19:e0293510. [PMID: 38457457 PMCID: PMC10923437 DOI: 10.1371/journal.pone.0293510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 10/16/2023] [Indexed: 03/10/2024] Open
Abstract
Mendelian randomization (MR) is an epidemiological framework using genetic variants as instrumental variables (IVs) to examine the causal effect of exposures on outcomes. Statistical methods based on unidirectional MR (UMR) are widely used to estimate the causal effects of exposures on outcomes in observational studies. To estimate the bidirectional causal effects between two phenotypes, investigators have naively applied UMR methods separately in each direction. However, bidirectional causal effects between two phenotypes create a feedback loop that biases the estimation when UMR methods are naively applied. To overcome this limitation, we proposed two novel approaches to estimate bidirectional causal effects using MR: BiRatio and BiLIML, which are extensions of the standard ratio, and limited information maximum likelihood (LIML) methods, respectively. We compared the performance of the two proposed methods with the naive application of UMR methods through extensive simulations of several scenarios involving varying numbers of strong and weak IVs. Our simulation results showed that when multiple strong IVs are used, the proposed methods provided accurate bidirectional causal effect estimation in terms of median absolute bias and relative median absolute bias. Furthermore, compared to the BiRatio method, the BiLIML method provided a more accurate estimation of causal effects when weak IVs were used. Therefore, based on our simulations, we concluded that the BiLIML should be used for bidirectional causal effect estimation. We applied the proposed methods to investigate the potential bidirectional relationship between obesity and diabetes using the data from the Multi-Ethnic Study of Atherosclerosis cohort. We used body mass index (BMI) and fasting glucose (FG) as measures of obesity and type 2 diabetes, respectively. Our results from the BiLIML method revealed the bidirectional causal relationship between BMI and FG in across all racial populations. Specifically, in the White/Caucasian population, a 1 kg/m2 increase in BMI increased FG by 0.70 mg/dL (95% confidence interval [CI]: 0.3517-1.0489; p = 8.43×10-5), and 1 mg/dL increase in FG increased BMI by 0.10 kg/m2 (95% CI: 0.0441-0.1640; p = 6.79×10-4). Our study provides novel findings and quantifies the effect sizes of the bidirectional causal relationship between BMI and FG. However, further studies are needed to understand the biological and functional mechanisms underlying the bidirectional pathway.
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Affiliation(s)
- Jinhao Zou
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Rajesh Talluri
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Data Science, The University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Sanjay Shete
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center Houston, Texas, United States of America
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Xia Z, Liu Y, Liu C, Dai Z, Liang X, Zhang N, Wu W, Wen J, Zhang H. The causal effect of air pollution on the risk of essential hypertension: a Mendelian randomization study. Front Public Health 2024; 12:1247149. [PMID: 38425468 PMCID: PMC10903282 DOI: 10.3389/fpubh.2024.1247149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 01/22/2024] [Indexed: 03/02/2024] Open
Abstract
Background Air pollution poses a major threat to human health by causing various illnesses, such as cardiovascular diseases. While plenty of research indicates a correlation between air pollution and hypertension, a definitive answer has yet to be found. Methods Our analyses were performed using the Genome-wide association study (GWAS) of exposure to air pollutants from UKB (PM2.5, PM10, NO2, and NOX; n = 423,796 to 456,380), essential hypertension from FinnGen (42,857 cases and 162,837 controls) and from UKB (54,358 cases and 408,652 controls) as a validated cohort. Univariable and multivariable Mendelian randomization (MR) were conducted to investigate the causal relationship between air pollutants and essential hypertension. Body mass index (BMI), alcohol intake frequency, and the number of cigarettes previously smoked daily were included in multivariable MRs (MVMRs) as potential mediators/confounders. Results Our findings suggested that higher levels of both PM2.5 (OR [95%CI] per 1 SD increase in predicted exposure = 1.24 [1.02-1.53], p = 3.46E-02 from Finn; OR [95%CI] = 1.04 [1.02-1.06], p = 7.58E-05 from UKB) and PM10 (OR [95%CI] = 1.24 [1.02-1.53], p = 3.46E-02 from Finn; OR [95%CI] = 1.04 [1.02-1.06], p = 7.58E-05 from UKB) were linked to an increased risk for essential hypertension. Even though we used MVMR to adjust for the impacts of smoking and drinking on the relationship between PM2.5 exposure and essential hypertension risks, our findings suggested that although there was a direct positive connection between them, it is not present after adjusting BMI (OR [95%CI] = 1.05 [0.87-1.27], p = 6.17E-01). Based on the study, higher exposure to PM2.5 and PM10 increases the chances of developing essential hypertension, and this influence could occur through mediation by BMI. Conclusion Exposure to both PM2.5 and PM10 is thought to have a causal relationship with essential hypertension. Those impacted by substantial levels of air pollution require more significant consideration for their cardiovascular health.
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Affiliation(s)
- Zhiwei Xia
- Department of Neurology, Hunan Aerospace Hospital, Changsha, Hunan Province, China
| | - Yinjiang Liu
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Chao Liu
- Department of Neurosurgery, Central Hospital of Zhuzhou, Zhuzhou, Hunan Province, China
| | - Ziyu Dai
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan Province, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan Province, China
| | - Xisong Liang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan Province, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan Province, China
| | - Nan Zhang
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wantao Wu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan Province, China
| | - Jie Wen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan Province, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan Province, China
| | - Hao Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
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Min S, Xing M, Jiang H, Zhang L, Chen C, Ma Y, Ma Y. Exploring causal correlations between inflammatory cytokines and varicose veins: A Mendelian randomization analysis. Int Wound J 2024; 21:e14714. [PMID: 38353374 PMCID: PMC10865274 DOI: 10.1111/iwj.14714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 01/08/2024] [Accepted: 01/11/2024] [Indexed: 02/16/2024] Open
Abstract
This study aimed to investigate the causal relationship between inflammatory cytokines and the risk of varicose veins. The data were sourced from genome-wide association studies (GWAS) of European individuals. Multiple Mendelian randomization (MR) methods were used to evaluate the association between inflammatory cytokines and varicose veins. The study found significant associations between elevated levels of certain inflammatory biomarkers (e.g., CASP-8, Vascular endothelial growth factor A levels (VEGF_A)) and an increased risk of varicose veins, while others (e.g., 4EBP1, MMP-10) showed a protective effect. The MR-Egger Intercept and heterogeneity tests indicated no significant pleiotropy or heterogeneity. This comprehensive MR analysis identifies several cytokines as potential contributors to the pathogenesis of varicose veins, offering insights into novel therapeutic targets. Our findings underscore the importance of inflammation in varicose veins and suggest that targeting specific cytokines could be a promising strategy for the treatment and prevention of varicose veins.
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Affiliation(s)
- Shen Min
- Department of Acupuncture and Massage CollegeShandong University of Traditional Chinese MedicineJinanChina
| | - Mengzhen Xing
- Department of Acupuncture and Massage CollegeShandong University of Traditional Chinese MedicineJinanChina
| | - Hehe Jiang
- Department of Acupuncture and Massage CollegeShandong University of Traditional Chinese MedicineJinanChina
| | - Linlin Zhang
- Department of Acupuncture and Massage CollegeShandong University of Traditional Chinese MedicineJinanChina
| | - Chen Chen
- Department of Acupuncture and Massage CollegeShandong University of Traditional Chinese MedicineJinanChina
| | - Yuning Ma
- Department of Acupuncture and Massage CollegeShandong University of Traditional Chinese MedicineJinanChina
| | - Yuxia Ma
- Department of Acupuncture and Massage CollegeShandong University of Traditional Chinese MedicineJinanChina
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Doretti M, Genbäck M, Stanghellini E. Mediation analysis with case-control sampling: Identification and estimation in the presence of a binary mediator. Biom J 2024; 66:e2300089. [PMID: 38285401 DOI: 10.1002/bimj.202300089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 10/08/2023] [Accepted: 11/11/2023] [Indexed: 01/30/2024]
Abstract
With reference to a stratified case-control (CC) procedure based on a binary variable of primary interest, we derive the expression of the distortion induced by the sampling design on the parameters of the logistic model of a secondary variable. This is particularly relevant when performing mediation analysis (possibly in a causal framework) with stratified case-control (SCC) data in settings where both the outcome and the mediator are binary. Despite being designed for parametric identification, our strategy is general and can be used also in a nonparametric context. With reference to parametric estimation, we derive the maximum likelihood (ML) estimator and the M-estimator of the joint outcome-mediator parameter vector. We then conduct a simulation study focusing on the main causal mediation quantities (i.e., natural effects) and comparing M- and ML estimation to existing methods, based on weighting. As an illustrative example, we reanalyze a German CC data set in order to investigate whether the effect of reduced immunocompetency on listeriosis onset is mediated by the intake of gastric acid suppressors.
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Affiliation(s)
- Marco Doretti
- Department of Statistics, Computer Science, and Applications, University of Florence, Florence, Italy
| | - Minna Genbäck
- Department of Statistics, USBE, Umeå University, Umeå, Sweden
| | - Elena Stanghellini
- Department of Statistics, USBE, Umeå University, Umeå, Sweden
- Department of Economics, University of Perugia, Perugia, Italy
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Gao D, Hu J, Bradley CJ, Yang F. Instrumental variable analysis for cost outcome: Application to the effect of primary care visit on medical cost among low-income adults. Stat Med 2023; 42:4349-4376. [PMID: 37828812 PMCID: PMC10644894 DOI: 10.1002/sim.9865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 06/12/2023] [Accepted: 07/20/2023] [Indexed: 10/14/2023]
Abstract
Medical cost data often consist of zero values as well as extremely right-skewed positive values. A two-part model is a popular choice for analyzing medical cost data, where the first part models the probability of a positive cost using logistic regression and the second part models the positive cost using a lognormal or Gamma distribution. To address the unmeasured confounding in studies on cost outcome under two-part models, two instrumental variable (IV) methods, two-stage residual inclusion (2SRI) and two-stage prediction substitution (2SPS) are widely applied. However, previous literature demonstrated that both the 2SRI and the 2SPS could fail to consistently estimate the causal effect among compliers under standard IV assumptions for binary and survival outcomes. Our simulation studies confirmed that it continued to be the case for a two-part model, which is another nonlinear model. In this article, we develop a model-based IV approach, Instrumental Variable with Two-Part model (IV2P), to obtain a consistent estimate of the causal effect among compliers for cost outcome under standard IV assumptions. In addition, we develop sensitivity analysis approaches to allow the evaluation of the sensitivity of the causal conclusions to potential quantified violations of the exclusion restriction assumption and the randomization of IV assumption. We apply our method to a randomized cash incentive study to evaluate the effect of a primary care visit on medical cost among low-income adults newly covered by a primary care program.
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Affiliation(s)
- Dexiang Gao
- University of Colorado Cancer Center Biostatistics Core, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Junxiao Hu
- University of Colorado Cancer Center Biostatistics Core, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Cathy J. Bradley
- Department of Health Systems, Management & Policy, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Fan Yang
- Yau Mathematical Sciences Center, Tsinghua University, Beijing, China
- Yanqi Lake Beijing Institute of Mathmatical Sciences and Applications, Beijing, China
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11
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Vancak V, Sjölander A. Sensitivity analysis of G-estimators to invalid instrumental variables. Stat Med 2023; 42:4257-4281. [PMID: 37497859 DOI: 10.1002/sim.9859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 06/05/2023] [Accepted: 07/14/2023] [Indexed: 07/28/2023]
Abstract
Instrumental variables regression is a tool that is commonly used in the analysis of observational data. The instrumental variables are used to make causal inference about the effect of a certain exposure in the presence of unmeasured confounders. A valid instrumental variable is a variable that is associated with the exposure, affects the outcome only through the exposure (exclusion), and is not confounded with the outcome (exogeneity). Unlike the first assumption, the other two are generally untestable and rely on subject-matter knowledge. Therefore, a sensitivity analysis is desirable to assess the impact of assumptions' violation on the estimated parameters. In this paper, we propose and demonstrate a new method of sensitivity analysis for G-estimators in causal linear and non-linear models. We introduce two novel aspects of sensitivity analysis in instrumental variables studies. The first is a single sensitivity parameter that captures violations of exclusion and exogeneity assumptions. The second is an application of the method to non-linear models. The introduced framework is theoretically justified and is illustrated via a simulation study. Finally, we illustrate the method by application to real-world data and provide guidelines on conducting sensitivity analysis.
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Affiliation(s)
- Valentin Vancak
- Dept. of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arvid Sjölander
- Dept. of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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12
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Adeleke MO, O'Keeffe AG, Baio G. Approaches to risk ratio estimation in a regression discontinuity design: Application to the prescription of statins for cholesterol reduction in UK primary care. Stat Methods Med Res 2023; 32:1994-2015. [PMID: 37590094 DOI: 10.1177/09622802231192958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
In recent years regression discontinuity designs have been used increasingly for the estimation of treatment effects in observational medical data where a rule-based decision to apply treatment is taken using a continuous assignment variable. Most regression discontinuity design applications have focused on effect estimation where the outcome of interest is continuous, with scenarios with binary outcomes receiving less attention, despite their ubiquity in medical studies. In this work, we develop an approach to estimation of the risk ratio in a fuzzy regression discontinuity design (where treatment is not always strictly applied according to the decision rule), derived using common regression discontinuity design assumptions. This method compares favourably to other risk ratio estimation approaches: the established Wald estimator and a risk ratio estimate from a multiplicative structural mean model, with promising results from extensive simulation studies. A demonstration and further comparison are made using a real example to evaluate the effect of statins (where a statin prescription is made based on a patient's 10-year cardiovascular disease risk score) on low-density lipoprotein cholesterol reduction in UK Primary Care.
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Affiliation(s)
- Mariam O Adeleke
- Department of Statistical Science, University College London, London, UK
| | - Aidan G O'Keeffe
- School of Mathematical Sciences, University of Nottingham, Nottingham, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Gianluca Baio
- Department of Statistical Science, University College London, London, UK
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res 2023; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.3] [Citation(s) in RCA: 180] [Impact Index Per Article: 180.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2023] [Indexed: 08/08/2023] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, WC1E 6BT, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Fernando P. Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Michael V. Holmes
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jean V. Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Caroline L. Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
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14
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Feng Y, Liu X, Tan H. Causal association of peripheral immune cell counts and atrial fibrillation: A Mendelian randomization study. Front Cardiovasc Med 2023; 9:1042938. [PMID: 36684582 PMCID: PMC9853293 DOI: 10.3389/fcvm.2022.1042938] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 12/14/2022] [Indexed: 01/09/2023] Open
Abstract
Background Atrial fibrillation (AF) is the most common and persistent form of arrhythmia. Recently, increasing evidence has shown a link between immune responses and atrial fibrillation. However, whether the immune response is a cause or consequence of AF remains unknown. We aimed to determine whether genetically predicted peripheral immunity might have a causal effect on AF. Methods First, we performed Mendelian randomization (MR) analyses using genetic variants strongly associated with neutrophil, eosinophil, basophil, lymphocyte, and monocyte cell counts as instrumental variables (IVs). Lymphocyte counts were then subjected to further subgroup analysis. The effect of immune cell counts on AF risk was measured using summary statistics from genome-wide association studies (GWAS). Results Two-sample MR analysis revealed that a higher neutrophil count, basophil count and lymphocyte count had a causal effect on AF [Odds ratio (OR), 1.06, 95% confidence interval (CI), 1.01-1.10, P = 0.0070; OR, 1.10; 95% CI, 1.04-1.17; P = 0.0015; OR, 0.96; 95% CI, 0.93-0.99; P = 0.0359]. In addition, in our further analysis, genetically predicted increases in CD4 + T-cell counts were also associated with an increased risk of AF (OR, 1.04; 95% CI, 1.0-.09; P = 0.0493). Conclusion Our MR analysis provided evidence of a genetically predicted causal relationship between higher peripheral immune cell counts and AF. Subgroup analysis revealed the key role of peripheral lymphocytes in AF, especially the causal relationship between CD4 + T cell count and AF. These findings are beneficial for future exploration of the mechanism of AF.
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15
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Ricciardi F, Liverani S, Baio G. Dirichlet process mixture models for regression discontinuity designs. Stat Methods Med Res 2023; 32:55-70. [PMID: 36366738 DOI: 10.1177/09622802221129044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
The regression discontinuity design is a quasi-experimental design that estimates the causal effect of a treatment when its assignment is defined by a threshold for a continuous variable. The regression discontinuity design assumes that subjects with measurements within a bandwidth around the threshold belong to a common population, so that the threshold can be seen as a randomising device assigning treatment to those falling just above the threshold and withholding it from those who fall below. Bandwidth selection represents a compelling decision for the regression discontinuity design analysis as results may be highly sensitive to its choice. A few methods to select the optimal bandwidth, mainly from the econometric literature, have been proposed. However, their use in practice is limited. We propose a methodology that, tackling the problem from an applied point of view, considers units' exchangeability, that is, their similarity with respect to measured covariates, as the main criteria to select subjects for the analysis, irrespectively of their distance from the threshold. We cluster the sample using a Dirichlet process mixture model to identify balanced and homogeneous clusters. Our proposal exploits the posterior similarity matrix, which contains the pairwise probabilities that two observations are allocated to the same cluster in the Markov chain Monte Carlo sample. Thus we include in the regression discontinuity design analysis only those clusters for which we have stronger evidence of exchangeability. We illustrate the validity of our methodology with both a simulated experiment and a motivating example on the effect of statins on cholesterol levels.
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Affiliation(s)
| | - Silvia Liverani
- School of Mathematical Sciences, Queen Mary University of London, London, UK.,The Alan Turing Institute, London, UK
| | - Gianluca Baio
- Department of Statistical Sciences, University College London, London, UK
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16
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Wang T, Cheng J, Wang Y. Genetic support of a causal relationship between iron status and atrial fibrillation: a Mendelian randomization study. GENES & NUTRITION 2022; 17:8. [PMID: 35637428 PMCID: PMC9153204 DOI: 10.1186/s12263-022-00708-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 03/31/2022] [Indexed: 11/10/2022]
Abstract
Abstract
Background
Atrial fibrillation is the most common arrhythmia disease. Animal and observational studies have found a link between iron status and atrial fibrillation. However, the causal relationship between iron status and AF remains unclear. The purpose of this investigation was to use Mendelian randomization (MR) analysis, which has been widely applied to estimate the causal effect, to reveal whether systemic iron status was causally related to atrial fibrillation.
Methods
Single nucleotide polymorphisms (SNPs) strongly associated (P < 5 × 10−8) with four biomarkers of systemic iron status were obtained from a genome-wide association study involving 48,972 subjects conducted by the Genetics of Iron Status consortium. Summary-level data for the genetic associations with atrial fibrillation were acquired from the AFGen (Atrial Fibrillation Genetics) consortium study (including 65,446 atrial fibrillation cases and 522,744 controls). We used a two-sample MR analysis to obtain a causal estimate and further verified credibility through sensitivity analysis.
Results
Genetically instrumented serum iron [OR 1.09; 95% confidence interval (CI) 1.02–1.16; p = 0.01], ferritin [OR 1.16; 95% CI 1.02–1.33; p = 0.02], and transferrin saturation [OR 1.05; 95% CI 1.01–1.11; p = 0.01] had positive effects on atrial fibrillation. Genetically instrumented transferrin levels [OR 0.90; 95% CI 0.86–0.97; p = 0.006] were inversely correlated with atrial fibrillation.
Conclusion
In conclusion, our results strongly elucidated a causal link between genetically determined higher iron status and increased risk of atrial fibrillation. This provided new ideas for the clinical prevention and treatment of atrial fibrillation.
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17
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Doretti M, Raggi M, Stanghellini E. Exact parametric causal mediation analysis for a binary outcome with a binary mediator. STAT METHOD APPL-GER 2022. [DOI: 10.1007/s10260-021-00562-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
AbstractWith reference to causal mediation analysis, a parametric expression for natural direct and indirect effects is derived for the setting of a binary outcome with a binary mediator, both modelled via a logistic regression. The proposed effect decomposition operates on the odds ratio scale and does not require the outcome to be rare. It generalizes the existing ones, allowing for interactions between both the exposure and the mediator and the confounding covariates. The derived parametric formulae are flexible, in that they readily adapt to the two different natural effect decompositions defined in the mediation literature. In parallel with results derived under the rare outcome assumption, they also outline the relationship between the causal effects and the correspondent pathway-specific logistic regression parameters, isolating the controlled direct effect in the natural direct effect expressions. Formulae for standard errors, obtained via the delta method, are also given. An empirical application to data coming from a microfinance experiment performed in Bosnia and Herzegovina is illustrated.
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18
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Wang L, Zhang Y, Richardson TS, Robins JM. Estimation of local treatment effects under the binary instrumental variable model. Biometrika 2021. [DOI: 10.1093/biomet/asab003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Summary
Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper we consider estimation of the local average treatment effect under the binary instrumental variable model. We discuss the challenges of causal estimation with a binary outcome and show that, surprisingly, it can be more difficult than in the case with a continuous outcome. We propose novel modelling and estimation procedures that improve upon existing proposals in terms of model congeniality, interpretability, robustness and efficiency. Our approach is illustrated via simulation studies and a real data analysis.
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Affiliation(s)
- Linbo Wang
- Department of Statistics, University of Illinois at Urbana-Champaign, 725 South Wright Street, Champaign, Illinois 61820, U.S.A
| | - Yuexia Zhang
- Department of Computer and Mathematical Sciences, University of Toronto, Toronto, Ontario M1C 1A4, Canada
| | - Thomas S Richardson
- Department of Statistics, University of Washington, Box 354322, Seattle, Washington 98195, U.S.A
| | - James M Robins
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, Massachusetts 02115, U.S.A.
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Wang X, Fang X, Zheng W, Zhou J, Song Z, Xu M, Min J, Wang F. Genetic Support of A Causal Relationship Between Iron Status and Type 2 Diabetes: A Mendelian Randomization Study. J Clin Endocrinol Metab 2021; 106:e4641-e4651. [PMID: 34147035 PMCID: PMC8530720 DOI: 10.1210/clinem/dgab454] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Indexed: 12/15/2022]
Abstract
CONTEXT Iron overload is a known risk factor for type 2 diabetes (T2D); however, iron overload and iron deficiency have both been associated with metabolic disorders in observational studies. OBJECTIVE Using mendelian randomization (MR), we assessed how genetically predicted systemic iron status affected T2D risk. METHODS A 2-sample MR analysis was used to obtain a causal estimate. We selected genetic variants strongly associated (P < 5 × 10-8) with 4 biomarkers of systemic iron status from a study involving 48 972 individuals performed by the Genetics of Iron Status consortium and applied these biomarkers to the T2D case-control study (74 124 cases and 824 006 controls) performed by the Diabetes Genetics Replication and Meta-analysis consortium. The simple median, weighted median, MR-Egger, MR analysis using mixture-model, weighted allele scores, and MR based on a Bayesian model averaging approaches were used for the sensitivity analysis. RESULTS Genetically instrumented serum iron (odds ratio [OR]: 1.07; 95% CI, 1.02-1.12), ferritin (OR: 1.19; 95% CI, 1.08-1.32), and transferrin saturation (OR: 1.06; 95% CI, 1.02-1.09) were positively associated with T2D. In contrast, genetically instrumented transferrin, a marker of reduced iron status, was inversely associated with T2D (OR: 0.91; 95% CI, 0.87-0.96). CONCLUSION Genetic evidence supports a causal link between increased systemic iron status and increased T2D risk. Further studies involving various ethnic backgrounds based on individual-level data and studies regarding the underlying mechanism are warranted for reducing the risk of T2D.
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Affiliation(s)
- Xinhui Wang
- The Fourth Affiliated Hospital, School of Public Health, Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Xuexian Fang
- The Fourth Affiliated Hospital, School of Public Health, Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Wanru Zheng
- The Fourth Affiliated Hospital, School of Public Health, Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Jiahui Zhou
- The Fourth Affiliated Hospital, School of Public Health, Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Zijun Song
- The Fourth Affiliated Hospital, School of Public Health, Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Mingqing Xu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Junxia Min
- The Fourth Affiliated Hospital, School of Public Health, Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Fudi Wang
- The Fourth Affiliated Hospital, School of Public Health, Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China
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20
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Adam Y, Samtal C, Brandenburg JT, Falola O, Adebiyi E. Performing post-genome-wide association study analysis: overview, challenges and recommendations. F1000Res 2021; 10:1002. [PMID: 35222990 PMCID: PMC8847724 DOI: 10.12688/f1000research.53962.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/22/2021] [Indexed: 12/17/2022] Open
Abstract
Genome-wide association studies (GWAS) provide huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis. Finally, we include a custom pGWAS pipeline to guide new users when performing their research.
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Affiliation(s)
- Yagoub Adam
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun, 112233, Nigeria
| | - Chaimae Samtal
- Laboratory of Biotechnology, Environment, Agri-food and Health, Sidi Mohammed Ben Abdellah University, Fez, Fez-Meknes, 30000, Morocco
| | - Jean-tristan Brandenburg
- Sydney Brenner Institute for Molecular Bioscience (SBIMB), University of the Witwatersrand, Johannesburg, South Africa
| | - Oluwadamilare Falola
- Laboratory of Biotechnology, Environment, Agri-food and Health, Sidi Mohammed Ben Abdellah University, Fez, Fez-Meknes, 30000, Morocco
| | - Ezekiel Adebiyi
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun, 112233, Nigeria
- Computer & Information Sciences, Covenant University, Ota, Ogun, 112233, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence, Covenant University, Ota, Ogun, 112233, Nigeria
- Applied Bioinformatics Division, German Cancer Center DKFZ - Heidelberg University, Heidelberg, Baden-Württemberg, 69120, Germany
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21
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Giannini HM, Meyer NJ. Genetics of Acute Respiratory Distress Syndrome: Pathways to Precision. Crit Care Clin 2021; 37:817-834. [PMID: 34548135 DOI: 10.1016/j.ccc.2021.05.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Clinical risk factors alone fail to fully explain acute respiratory distress syndrome (ARDS) risk or ARDS death, suggesting that individual risk factors contribute. The goals of genomic ARDS studies include better mechanistic understanding, identifying dysregulated pathways that may be amenable to pharmacologic targeting, using genomic causal inference techniques to find measurable traits with meaning, and deconvoluting ARDS heterogeneity by proving reproducible subpopulations that may share a unique biology. This article discusses the latest advances in ARDS genomics, provides historical perspective, and highlights some of the ways that the coronavirus disease 2019 (COVID-19) pandemic is accelerating genomic ARDS research.
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Affiliation(s)
- Heather M Giannini
- University of Pennsylvania Perelman School of Medicine, 3400 Spruce Street, 5038 Gates Building, Philadelphia, PA 19104, USA
| | - Nuala J Meyer
- University of Pennsylvania Perelman School of Medicine, 3400 Spruce Street, 5038 Gates Building, Philadelphia, PA 19104, USA.
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22
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Zhao P, Wang J, Tang X, Yang W. Double penalized regularization estimation for partially linear instrumental variable models with ultrahigh dimensional instrumental variables. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.1965166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Peixin Zhao
- College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, China
- Chongqing Key Laboratory of Social Economic and Applied Statistics, Chongqing, China
| | - Junqi Wang
- College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, China
| | - Xinrong Tang
- Department of Assets Management, Chongqing Technology and Business University, Chongqing, China
| | - Weiming Yang
- College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, China
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23
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Tchetgen Tchetgen E, Sun B, Walter S. The GENIUS Approach to Robust Mendelian Randomization Inference. Stat Sci 2021. [DOI: 10.1214/20-sts802] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Eric Tchetgen Tchetgen
- Eric Tchetgen Tchetgen is Professor, Department of Statistics, The Wharton School of the University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - BaoLuo Sun
- BaoLuo Sun is Assistant Professor, Department of Statistics and Applied Probability, National University of Singapore, Singapore, Republic of Singapore
| | - Stefan Walter
- Stefan Walter is Principal Investigator, Department of Medicine and Public Health, Rey Juan Carlos University, Madrid, Spain
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Foley CN, Mason AM, Kirk PDW, Burgess S. MR-Clust: clustering of genetic variants in Mendelian randomization with similar causal estimates. Bioinformatics 2021; 37:531-541. [PMID: 32915962 PMCID: PMC8088327 DOI: 10.1093/bioinformatics/btaa778] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 08/06/2020] [Accepted: 09/01/2020] [Indexed: 01/22/2023] Open
Abstract
Motivation Mendelian randomization is an epidemiological technique that uses genetic variants as instrumental variables to estimate the causal effect of a risk factor on an outcome. We consider a scenario in which causal estimates based on each variant in turn differ more strongly than expected by chance alone, but the variants can be divided into distinct clusters, such that all variants in the cluster have similar causal estimates. This scenario is likely to occur when there are several distinct causal mechanisms by which a risk factor influences an outcome with different magnitudes of causal effect. We have developed an algorithm MR-Clust that finds such clusters of variants, and so can identify variants that reflect distinct causal mechanisms. Two features of our clustering algorithm are that it accounts for differential uncertainty in the causal estimates, and it includes ‘null’ and ‘junk’ clusters, to provide protection against the detection of spurious clusters. Results Our algorithm correctly detected the number of clusters in a simulation analysis, outperforming methods that either do not account for uncertainty or do not include null and junk clusters. In an applied example considering the effect of blood pressure on coronary artery disease risk, the method detected four clusters of genetic variants. A post hoc hypothesis-generating search suggested that variants in the cluster with a negative effect of blood pressure on coronary artery disease risk were more strongly related to trunk fat percentage and other adiposity measures than variants not in this cluster. Availability and implementation MR-Clust can be downloaded from https://github.com/cnfoley/mrclust. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Christopher N Foley
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SR, UK
| | - Amy M Mason
- Department of Public Health and Primary Care, Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge CB1 8RN, UK
| | - Paul D W Kirk
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SR, UK.,Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge CB2 0AW, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SR, UK.,Department of Public Health and Primary Care, Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge CB1 8RN, UK
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25
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Chen L, Jiang C. Does Chronic Intestinal Inflammation Promote Atrial Fibrillation: A Mendelian Randomization Study With Populations of European Ancestry. Front Cardiovasc Med 2021; 8:641291. [PMID: 34041279 PMCID: PMC8141578 DOI: 10.3389/fcvm.2021.641291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 03/23/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Inflammatory bowel disease (IBD), comprising ulcerative colitis (UC), and Crohn's disease (CD), has been reported to be associated with an increased risk of atrial fibrillation (AF). However, the causal role of the chronic intestinal inflammation (CII) in the development of AF remains controversial. We use Mendelian randomization (MR) analysis to explore the causal inference of CII on AF. Methods: A two-sample MR analysis was performed to estimate the potential causal effect of CII on AF. Statistical summaries for the associations between single nucleotide polymorphisms (SNPs) and phenotypes of CII were obtained from genome-wide association studies (GWAS) with cohorts of CD (n = 51,874), UC (n = 47,745), and IBD (n = 65,642) of European descent. The GWAS of 1,030,836 people of European ancestry, including 60,620 AF cases and 970,216 controls was collected to identify genetic variants underlying AF. The causal inference was estimated using the multiplicative random effects inverse-variance weighted method (IVW). The methods of MR-Egger, simple median, and weighted median were also employed to avoid the bias of pleiotropy effects. Results: Using three sets of SNPs (75 SNPs of CD, 60 SNPs of UC, and 95 SNPs of IBD), multiplicative random-effect IVW model estimated a universal null effect of CII on AF (CD: OR = 1.0059, 95% CI: 0.9900, 1.0220, p = 0.47; UC: OR = 1.0087, 95% CI: 0.9896, 1.0281, p = 0.38; IBD: OR = 1.0080, 95% CI: 0.9908, 1.0255, p = 0.37). Similar results were observed using the MR-Egger, simple median, weighted median methods. Conclusion: As opposing to the traditional observational studies, our two-sample MR analysis did not find enough evidence to support a causal role of either CD or UC in the development of AF.
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Affiliation(s)
- LaiTe Chen
- Department of Cardiology of Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - ChenYang Jiang
- Department of Cardiology of Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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26
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Chen L, Fu G, Jiang C. Mendelian randomization as an approach to assess causal effects of inflammatory bowel disease on atrial fibrillation. Aging (Albany NY) 2021; 13:12016-12030. [PMID: 33824227 PMCID: PMC8109086 DOI: 10.18632/aging.202906] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 03/23/2021] [Indexed: 02/07/2023]
Abstract
Background: Despite growing evidence indicating that patients with inflammatory bowel disease (IBD) have an increased risk of atrial fibrillation (AF), owing to the potential biases of confounding effects and reverse causation, the specific relationship between IBD and AF remains controversial. The aim of this study is to determine whether there is a causal effect of IBD on AF. Methods: A two-sample Mendelian randomization (MR) study was performed to evaluate the causal effect of IBD on AF. Statistical summaries for the associations between single nucleotide polymorphisms (SNPs) and traits of interest were obtained from independent consortia with European populations. The dataset of IBD was acquired from genome-wide association studies (GWAS), including more than 75,000 cases and controls. A GWAS with 60,620 AF cases and 970,216 controls was used to identify genetic variation underlying AF. The causal effect was estimated using the multiplicative random effects inverse-variance weighted method (IVW), followed by sensitivity analysis. Results: Using 81 SNPs, there was no evidence to suggest an association between genetically predicted IBD and risk of AF with multiplicative random-effects IVW MR analysis (odds ratio = 1.0000, 95% confidence interval: 0.9994 1.0005, p = 0.88). Conclusion: As opposed to current assumptions, no substantial evidence was found to support a causal role of IBD in the development of AF.
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Affiliation(s)
- LaiTe Chen
- Department of Cardiology of Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, HangZhou, China
| | - GuoSheng Fu
- Department of Cardiology of Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, HangZhou, China
| | - ChenYang Jiang
- Department of Cardiology of Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, HangZhou, China
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Zhou W, Liu G, Hung RJ, Haycock PC, Aldrich MC, Andrew AS, Arnold SM, Bickeböller H, Bojesen SE, Brennan P, Brunnström H, Melander O, Caporaso NE, Landi MT, Chen C, Goodman GE, Christiani DC, Cox A, Field JK, Johansson M, Kiemeney LA, Lam S, Lazarus P, Marchand LL, Rennert G, Risch A, Schabath MB, Shete SS, Tardón A, Zienolddiny S, Shen H, Amos CI. Causal relationships between body mass index, smoking and lung cancer: Univariable and multivariable Mendelian randomization. Int J Cancer 2021; 148:1077-1086. [PMID: 32914876 PMCID: PMC7845289 DOI: 10.1002/ijc.33292] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 07/24/2020] [Accepted: 07/29/2020] [Indexed: 12/19/2022]
Abstract
At the time of cancer diagnosis, body mass index (BMI) is inversely correlated with lung cancer risk, which may reflect reverse causality and confounding due to smoking behavior. We used two-sample univariable and multivariable Mendelian randomization (MR) to estimate causal relationships of BMI and smoking behaviors on lung cancer and histological subtypes based on an aggregated genome-wide association studies (GWASs) analysis of lung cancer in 29 266 cases and 56 450 controls. We observed a positive causal effect for high BMI on occurrence of small-cell lung cancer (odds ratio (OR) = 1.60, 95% confidence interval (CI) = 1.24-2.06, P = 2.70 × 10-4 ). After adjustment of smoking behaviors using multivariable Mendelian randomization (MVMR), a direct causal effect on small cell lung cancer (ORMVMR = 1.28, 95% CI = 1.06-1.55, PMVMR = .011), and an inverse effect on lung adenocarcinoma (ORMVMR = 0.86, 95% CI = 0.77-0.96, PMVMR = .008) were observed. A weak increased risk of lung squamous cell carcinoma was observed for higher BMI in univariable Mendelian randomization (UVMR) analysis (ORUVMR = 1.19, 95% CI = 1.01-1.40, PUVMR = .036), but this effect disappeared after adjustment of smoking (ORMVMR = 1.02, 95% CI = 0.90-1.16, PMVMR = .746). These results highlight the histology-specific impact of BMI on lung carcinogenesis and imply mediator role of smoking behaviors in the association between BMI and lung cancer.
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Affiliation(s)
- Wen Zhou
- Department of Epidemiology, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
| | - Geoffrey Liu
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Rayjean J. Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Epidemiology Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Philip C. Haycock
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Melinda C. Aldrich
- Department of Thoracic Surgery and Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Angeline S. Andrew
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire
| | | | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-Universität Göttingen, Göttingen, Germany
| | - Stig E. Bojesen
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Paul Brennan
- Genetic Epidemology Group, International Agency for Research on Cancer, Lyon, France
| | | | | | - Neil E. Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Chu Chen
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center and Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA
| | - Gary E. Goodman
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center and Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA
| | - David C. Christiani
- Departments of Environmental Health and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Angela Cox
- Academic Unit of Clinical Oncology, University of Sheffield, Sheffield, UK
| | - John K. Field
- Department of Molecular and Clinical Cancer Medicine, Roy Castle Lung Cancer Research Programme, The University of Liverpool Cancer Research Centre, Liverpool, UK
| | | | - Lambertus A. Kiemeney
- Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Stephen Lam
- Department of Integrative Oncology, British Columbia Cancer Agency, Vancouver, British Columbia, Canada
| | - Philip Lazarus
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Carmel Medical Center and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology and Clalit National Cancer Control Center, Haifa, Israel
| | - Angela Risch
- Department of Biosciences, Allergy-Cancer-BioNano Research Centre, University of Salzburg, Salzburg, Austria
- Cancer Cluster Salzburg, University of Salzburg, Salzburg, Austria
- Division of Cancer Epigenomics, DKFZ – German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Matthew B. Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Sanjay S. Shete
- Department of Biostatistics, Division of Basic Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Adonina Tardón
- Faculty of Medicine, University of Oviedo and ISPA and CIBERESP, Oviedo, Spain
| | | | - Hongbing Shen
- Department of Epidemiology, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Christopher I. Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
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28
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Culliford R, Cornish AJ, Law PJ, Farrington SM, Palin K, Jenkins MA, Casey G, Hoffmeister M, Brenner H, Chang-Claude J, Kirac I, Maughan T, Brezina S, Gsur A, Cheadle JP, Aaltonen LA, Dunlop MG, Houlston RS. Lack of an association between gallstone disease and bilirubin levels with risk of colorectal cancer: a Mendelian randomisation analysis. Br J Cancer 2021; 124:1169-1174. [PMID: 33414539 PMCID: PMC7961009 DOI: 10.1038/s41416-020-01211-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 10/09/2020] [Accepted: 11/25/2020] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Epidemiological studies of the relationship between gallstone disease and circulating levels of bilirubin with risk of developing colorectal cancer (CRC) have been inconsistent. To address possible confounding and reverse causation, we examine the relationship between these potential risk factors and CRC using Mendelian randomisation (MR). METHODS We used two-sample MR to examine the relationship between genetic liability to gallstone disease and circulating levels of bilirubin with CRC in 26,397 patients and 41,481 controls. We calculated the odds ratio per genetically predicted SD unit increase in log bilirubin levels (ORSD) for CRC and tested for a non-zero causal effect of gallstones on CRC. Sensitivity analysis was applied to identify violations of estimator assumptions. RESULTS No association between either gallstone disease (P value = 0.60) or circulating levels of bilirubin (ORSD = 1.00, 95% confidence interval (CI) = 0.96-1.03, P value = 0.90) with CRC was shown. CONCLUSIONS Despite the large scale of this study, we found no evidence for a causal relationship between either circulating levels of bilirubin or gallstone disease with risk of developing CRC. While the magnitude of effect suggested by some observational studies can confidently be excluded, we cannot exclude the possibility of smaller effect sizes and non-linear relationships.
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Affiliation(s)
- Richard Culliford
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
| | - Alex J Cornish
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Philip J Law
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Susan M Farrington
- Cancer Research UK Edinburgh Centre and Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Kimmo Palin
- Medicum and Genome-Scale Biology Research Program, Research Programs Units, Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, VIC, Australia
| | - Graham Casey
- Centre for Public Health Genomics, University of Virginia, Virginia, VA, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center, Heidelberg, Germany
- German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Jenny Chang-Claude
- Unit of Genetic Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Cancer Epidemiology Group, University Medical Center Hamburg-Eppendorf, University Cancer Center Hamburg, Hamburg, Germany
| | - Iva Kirac
- Department of Surgical Oncology, University Hospital for Tumours, Sestre milosrdnice University Hospital Centre, Zagreb, Croatia
| | - Tim Maughan
- Department of Oncology, University of Oxford, Oxford, UK
| | - Stefanie Brezina
- Institute of Cancer Research, Department of Medicine I, Medical University of Vienna, Vienna, Austria
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University of Vienna, Vienna, Austria
| | - Jeremy P Cheadle
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | - Lauri A Aaltonen
- Medicum and Genome-Scale Biology Research Program, Research Programs Units, Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
| | - Malcom G Dunlop
- Cancer Research UK Edinburgh Centre and Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
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29
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Abstract
Mendelian randomization (MR) is the use of genetic variants associated with an exposure to estimate the causal effect of that exposure on an outcome. Mediation analysis is the method of decomposing the effects of an exposure on an outcome, which act directly, and those that act via mediating variables. These effects are decomposed through the use of multivariable analysis to estimate the causal effects between three types of variables: exposures, mediators, and an outcome. Multivariable MR (MVMR) is a recent extension to MR that uses genetic variants associated with multiple, potentially related exposures to estimate the effect of each exposure on a single outcome. MVMR allows for equivalent analysis to mediation within the MR framework and therefore can also be used to estimate mediation effects. This approach retains the benefits of using genetic instruments for causal inference, such as avoiding bias due to confounding, while allowing for estimation of the different effects required for mediation analysis. This review explains MVMR, what is estimated when one exposure is a mediator of another in an MVMR estimation, and how MR and MVMR can therefore be used to estimate mediated effects. This review then goes on to consider the advantages and limitations of using MR and MVMR to conduct mediation analysis.
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Affiliation(s)
- Eleanor Sanderson
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Clifton BS8 2BN, United Kingdom
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30
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Rothenhäusler D, Meinshausen N, Bühlmann P, Peters J. Anchor regression: Heterogeneous data meet causality. J R Stat Soc Series B Stat Methodol 2021. [DOI: 10.1111/rssb.12398] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | | | | | - Jonas Peters
- Department of Mathematical Sciences University of Copenhagen Copenhagen Denmark
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31
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Saunders CN, Cornish AJ, Kinnersley B, Law PJ, Houlston RS. Searching for causal relationships of glioma: a phenome-wide Mendelian randomisation study. Br J Cancer 2021; 124:447-454. [PMID: 33020596 PMCID: PMC7852872 DOI: 10.1038/s41416-020-01083-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 07/27/2020] [Accepted: 09/04/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The aetiology of glioma is poorly understood. Summary data from genome-wide association studies (GWAS) can be used in a Mendelian randomisation (MR) phenome-wide association study (PheWAS) to search for glioma risk factors. METHODS We performed an MR-PheWAS analysing 316 phenotypes, proxied by 8387 genetic variants, and summary genetic data from a GWAS of 12,488 glioma cases and 18,169 controls. Causal effects were estimated under a random-effects inverse-variance-weighted (IVW-RE) model, with robust adjusted profile score (MR-RAPS), weighted median and mode-based estimates computed to assess the robustness of findings. Odds ratios per one standard deviation increase in each phenotype were calculated for all glioma, glioblastoma (GBM) and non-GBM tumours. RESULTS No significant associations (P < 1.58 × 10-4) were observed between phenotypes and glioma under the IVW-RE model. Suggestive associations (1.58 × 10-4 < P < 0.05) were observed between leukocyte telomere length (LTL) with all glioma (ORSD = 3.91, P = 9.24 × 10-3) and GBM (ORSD = 4.86, P = 3.23 × 10-2), but the association was primarily driven by the TERT variant rs2736100. Serum low-density lipoprotein cholesterol and plasma HbA1C showed suggestive associations with glioma (ORSD = 1.11, P = 1.39 × 10-2 and ORSD = 1.28, P = 1.73 × 10-2, respectively), both associations being reliant on single genetic variants. CONCLUSIONS Our study provides further insight into the aetiological basis of glioma for which published data have been mixed.
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Affiliation(s)
- Charlie N Saunders
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK.
| | - Alex J Cornish
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK
| | - Ben Kinnersley
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK
| | - Philip J Law
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK
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32
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Diemer EW, Labrecque JA, Neumann A, Tiemeier H, Swanson SA. Mendelian randomisation approaches to the study of prenatal exposures: A systematic review. Paediatr Perinat Epidemiol 2021; 35:130-142. [PMID: 32779786 PMCID: PMC7891574 DOI: 10.1111/ppe.12691] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 04/30/2020] [Accepted: 05/05/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND Mendelian randomisation (MR) designs apply instrumental variable techniques using genetic variants to study causal effects. MR is increasingly used to evaluate the role of maternal exposures during pregnancy on offspring health. OBJECTIVES We review the application of MR to prenatal exposures and describe reporting of methodologic challenges in this area. DATA SOURCES We searched PubMed, EMBASE, Medline Ovid, Cochrane Central, Web of Science, and Google Scholar. STUDY SELECTION AND DATA EXTRACTION Eligible studies met the following criteria: (a) a maternal pregnancy exposure; (b) an outcome assessed in offspring of the pregnancy; and (c) a genetic variant or score proposed as an instrument or proxy for an exposure. SYNTHESIS We quantified the frequency of reporting of MR conditions stated, techniques used to examine assumption plausibility, and reported limitations. RESULTS Forty-three eligible studies were identified. When discussing challenges or limitations, the most common issues described were known potential biases in the broader MR literature, including population stratification (n = 29), weak instrument bias (n = 18), and certain types of pleiotropy (n = 30). Of 22 studies presenting point estimates for the effect of exposure, four defined their causal estimand. Twenty-four studies discussed issues unique to prenatal MR, including selection on pregnancy (n = 1) and pleiotropy via postnatal exposure (n = 10) or offspring genotype (n = 20). CONCLUSIONS Prenatal MR studies frequently discuss issues that affect all MR studies, but rarely discuss problems specific to the prenatal context, including selection on pregnancy and effects of postnatal exposure. Future prenatal MR studies should report and attempt to falsify their assumptions, with particular attention to issues specific to prenatal MR. Further research is needed to evaluate the impacts of biases unique to prenatal MR in practice.
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Affiliation(s)
- Elizabeth W. Diemer
- Department of Child and Adolescent PsychiatryErasmus MCRotterdamThe Netherlands
| | | | - Alexander Neumann
- Department of Child and Adolescent PsychiatryErasmus MCRotterdamThe Netherlands,Lady Davis Institute for Medical ResearchJewish General HospitalMontrealQCCanada
| | - Henning Tiemeier
- Department of Child and Adolescent PsychiatryErasmus MCRotterdamThe Netherlands,Department of Social and Behavioral ScienceHarvard T.H. Chan School of Public HealthBostonMAUSA
| | - Sonja A. Swanson
- Department of EpidemiologyErasmus MCRotterdamThe Netherlands,Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonMAUSA
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33
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Goetghebeur E, le Cessie S, De Stavola B, Moodie EEM, Waernbaum I. Formulating causal questions and principled statistical answers. Stat Med 2020; 39:4922-4948. [PMID: 32964526 PMCID: PMC7756489 DOI: 10.1002/sim.8741] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Revised: 05/10/2020] [Accepted: 08/05/2020] [Indexed: 12/13/2022]
Abstract
Although review papers on causal inference methods are now available, there is a lack of introductory overviews on what they can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline ("point exposure") and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score-based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a "simulation learner," that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on www.ofcaus.org, where SAS and Stata code for analysis is also provided.
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Affiliation(s)
- Els Goetghebeur
- Department of Applied Mathematics, Computer Science and StatisticsGhent UniversityGhentBelgium
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
| | - Saskia le Cessie
- Department of Clinical Epidemiology/Biomedical Data SciencesLeiden University Medical CenterLeidenThe Netherlands
| | - Bianca De Stavola
- Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
| | - Erica EM Moodie
- Division of BiostatisticsMcGill UniversityMontrealQuebecCanada
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Hines O, Diaz-Ordaz K, Vansteelandt S, Jamshidi Y. Causal graphs for the analysis of genetic cohort data. Physiol Genomics 2020; 52:369-378. [PMID: 32687429 DOI: 10.1152/physiolgenomics.00115.2019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The increasing availability of genetic cohort data has led to many genome-wide association studies (GWAS) successfully identifying genetic associations with an ever-expanding list of phenotypic traits. Association, however, does not imply causation, and therefore methods have been developed to study the issue of causality. Under additional assumptions, Mendelian randomization (MR) studies have proved popular in identifying causal effects between two phenotypes, often using GWAS summary statistics. Given the widespread use of these methods, it is more important than ever to understand, and communicate, the causal assumptions upon which they are based, so that methods are transparent, and findings are clinically relevant. Causal graphs can be used to represent causal assumptions graphically and provide insights into the limitations associated with different analysis methods. Here we review GWAS and MR from a causal perspective, to build up intuition for causal diagrams in genetic problems. We also examine issues of confounding by ancestry and comment on approaches for dealing with such confounding, as well as discussing approaches for dealing with selection biases arising from study design.
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Affiliation(s)
- Oliver Hines
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom.,Molecular and Clinical Sciences Institute, St. George's, University of London, London, United Kingdom
| | - Karla Diaz-Ordaz
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Stijn Vansteelandt
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom.,Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Yalda Jamshidi
- Molecular and Clinical Sciences Institute, St. George's, University of London, London, United Kingdom
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35
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res 2020; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.2] [Citation(s) in RCA: 365] [Impact Index Per Article: 91.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2020] [Indexed: 01/01/2023] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, WC1E 6BT, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Fernando P. Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Michael V. Holmes
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jean V. Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Caroline L. Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
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36
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Liu C, Zhao P, Yang Y. Regularization statistical inferences for partially linear models with high dimensional endogenous covariates. J Korean Stat Soc 2020. [DOI: 10.1007/s42952-020-00067-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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37
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res 2019; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.1] [Citation(s) in RCA: 560] [Impact Index Per Article: 112.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/18/2019] [Indexed: 12/20/2022] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, WC1E 6BT, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Fernando P. Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Michael V. Holmes
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jean V. Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Caroline L. Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
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John ER, Abrams KR, Brightling CE, Sheehan NA. Assessing causal treatment effect estimation when using large observational datasets. BMC Med Res Methodol 2019; 19:207. [PMID: 31726969 PMCID: PMC6854791 DOI: 10.1186/s12874-019-0858-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 10/23/2019] [Indexed: 11/10/2022] Open
Abstract
Background Recently, there has been a heightened interest in developing and evaluating different methods for analysing observational data. This has been driven by the increased availability of large data resources such as Electronic Health Record (EHR) data alongside known limitations and changing characteristics of randomised controlled trials (RCTs). A wide range of methods are available for analysing observational data. However, various, sometimes strict, and often unverifiable assumptions must be made in order for the resulting effect estimates to have a causal interpretation. In this paper we will compare some common approaches to estimating treatment effects from observational data in order to highlight the importance of considering, and justifying, the relevant assumptions prior to conducting an observational analysis. Methods A simulation study was conducted based upon a small cohort of patients with chronic obstructive pulmonary disease. Two-stage least squares instrumental variables, propensity score, and linear regression models were compared under a range of different scenarios including different strengths of instrumental variable and unmeasured confounding. The effects of violating the assumptions of the instrumental variables analysis were also assessed. Sample sizes of up to 200,000 patients were considered. Results Two-stage least squares instrumental variable methods can yield unbiased treatment effect estimates in the presence of unmeasured confounding provided the sample size is sufficiently large. Adjusting for measured covariates in the analysis reduces the variability in the two-stage least squares estimates. In the simulation study, propensity score methods produced very similar results to linear regression for all scenarios. A weak instrument or strong unmeasured confounding led to an increase in uncertainty in the two-stage least squares instrumental variable effect estimates. A violation of the instrumental variable assumptions led to bias in the two-stage least squares effect estimates. Indeed, these were sometimes even more biased than those from a naïve linear regression model. Conclusions Instrumental variable methods can perform better than naïve regression and propensity scores. However, the assumptions need to be carefully considered and justified prior to conducting an analysis or performance may be worse than if the problem of unmeasured confounding had been ignored altogether.
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Affiliation(s)
- E R John
- Department of Health Sciences, University of Leicester, Leicester, UK.
| | - K R Abrams
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - C E Brightling
- Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - N A Sheehan
- Department of Health Sciences, University of Leicester, Leicester, UK
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Dahlqwist E, Kutalik Z, Sjölander A. Using instrumental variables to estimate the attributable fraction. Stat Methods Med Res 2019; 29:2063-2073. [PMID: 31640504 DOI: 10.1177/0962280219879175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In order to design efficient interventions aimed to improve public health, policy makers need to be provided with reliable information of the health burden of different risk factors. For this purpose, we are interested in the proportion of cases that could be prevented had some harmful exposure been eliminated from the population, i.e. the attributable fraction. The attributable fraction is a causal measure; thus, to estimate the attributable fraction from observational data, we have to make appropriate adjustment for confounding. However, some confounders may be unobserved, or even unknown to the investigator. A possible solution to this problem is to use instrumental variable analysis. In this work, we present how the attributable fraction can be estimated with instrumental variable methods based on the two-stage estimator or the G-estimator. One situation when the problem of unmeasuredconfounding may be particularly severe is when assessing the effect of low educational qualifications on coronary heart disease. By using Mendelian randomization, a special case of instrumental variable analysis, it has been claimed that low educational qualifications is a causal risk factor for coronary heart disease. We use Mendelian randomization to estimate the causal risk ratio and causal odds ratio of low educational qualifications as a risk factor for coronary heart disease with data from the UK Biobank. We compare the two-stage and G-estimator as well as the attributable fraction based on the two estimators. The plausibility of drawing causal conclusion in this analysis is thoroughly discussed and alternative genetic instrumental variables are tested.
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Affiliation(s)
- Elisabeth Dahlqwist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Zoltán Kutalik
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Arvid Sjölander
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Dong J, Gharahkhani P, Chow WH, Gammon MD, Liu G, Caldas C, Wu AH, Ye W, Onstad L, Anderson LA, Bernstein L, Pharoah PD, Risch HA, Corley DA, Fitzgerald RC, Iyer PG, Reid BJ, Lagergren J, Shaheen NJ, Vaughan TL, MacGregor S, Love S, Palles C, Tomlinson I, Gockel I, May A, Gerges C, Anders M, Böhmer AC, Becker J, Kreuser N, Thieme R, Noder T, Venerito M, Veits L, Schmidt T, Schmidt C, Izbicki JR, Hölscher AH, Lang H, Lorenz D, Schumacher B, Mayershofer R, Vashist Y, Ott K, Vieth M, Weismüller J, Nöthen MM, Moebus S, Knapp M, Peters WHM, Neuhaus H, Rösch T, Ell C, Jankowski J, Schumacher J, Neale RE, Whiteman DC, Thrift AP. No Association Between Vitamin D Status and Risk of Barrett's Esophagus or Esophageal Adenocarcinoma: A Mendelian Randomization Study. Clin Gastroenterol Hepatol 2019; 17:2227-2235.e1. [PMID: 30716477 PMCID: PMC6675666 DOI: 10.1016/j.cgh.2019.01.041] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 01/23/2019] [Accepted: 01/24/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND & AIMS Epidemiology studies of circulating concentrations of 25 hydroxy vitamin D (25(OH)D) and risk of esophageal adenocarcinoma (EAC) have produced conflicting results. We conducted a Mendelian randomization study to determine the associations between circulating concentrations of 25(OH)D and risks of EAC and its precursor, Barrett's esophagus (BE). METHODS We conducted a Mendelian randomization study using a 2-sample (summary data) approach. Six single-nucleotide polymorphisms (SNPs; rs3755967, rs10741657, rs12785878, rs10745742, rs8018720, and rs17216707) associated with circulating concentrations of 25(OH)D were used as instrumental variables. We collected data from 6167 patients with BE, 4112 patients with EAC, and 17,159 individuals without BE or EAC (controls) participating in the Barrett's and Esophageal Adenocarcinoma Consortium, as well as studies from Bonn, Germany, and Cambridge and Oxford, United Kingdom. Analyses were performed separately for BE and EAC. RESULTS Overall, we found no evidence for an association between genetically estimated 25(OH)D concentration and risk of BE or EAC. The odds ratio per 20 nmol/L increase in genetically estimated 25(OH)D concentration for BE risk estimated by combining the individual SNP association using inverse variance weighting was 1.21 (95% CI, 0.77-1.92; P = .41). The odds ratio for EAC risk, estimated by combining the individual SNP association using inverse variance weighting, was 0.68 (95% CI, 0.39-1.19; P = .18). CONCLUSIONS In a Mendelian randomization study, we found that low genetically estimated 25(OH)D concentrations were not associated with risk of BE or EAC.
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Affiliation(s)
- Jing Dong
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Puya Gharahkhani
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Wong-Ho Chow
- Department of Epidemiology, MD Anderson Cancer Center, Houston, Texas
| | - Marilie D Gammon
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina
| | - Geoffrey Liu
- Pharmacogenomic Epidemiology, Ontario Cancer Institute, Toronto, Ontario, Canada
| | - Carlos Caldas
- Cancer Research UK, Cambridge Institute, Cambridge, United Kingdom
| | - Anna H Wu
- Department of Preventive Medicine, University of Southern California, Norris Comprehensive Cancer Center, Los Angeles, California
| | - Weimin Ye
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Lynn Onstad
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Lesley A Anderson
- Centre for Public Health, Queen's University Belfast, Belfast, Northern Ireland
| | - Leslie Bernstein
- Department of Population Sciences, Beckman Research Institute and City of Hope Comprehensive Cancer Center, Duarte, California
| | - Paul D Pharoah
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom; Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Harvey A Risch
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut
| | - Douglas A Corley
- Division of Research, Kaiser Permanente Northern California, Oakland, California; San Francisco Medical Center, Kaiser Permanente Northern California, San Francisco, California
| | - Rebecca C Fitzgerald
- Medical Research Council Cancer Unit, Hutchison-Medical Research Council Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Prasad G Iyer
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Brian J Reid
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jesper Lagergren
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom
| | - Nicholas J Shaheen
- Division of Gastroenterology and Hepatology, School of Medicine, University of North Carolina, Chapel Hill, North Carolina
| | - Thomas L Vaughan
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Stuart MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Sharon Love
- Oxford Clinical Trials Research Unit, Centre for Statistics in Medicine, Oxford, United Kingdom
| | - Claire Palles
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Ian Tomlinson
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Ines Gockel
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Andrea May
- Department of Medicine II, Sana Klinikum, Offenbach, Germany
| | - Christian Gerges
- Department of Internal Medicine II, Evangelisches Krankenhaus, Düsseldorf, Germany
| | - Mario Anders
- Department of Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Department of Gastroenterology and Interdisciplinary Endoscopy, Vivantes Wenckebach-Klinikum, Berlin, Germany
| | - Anne C Böhmer
- Institute of Human Genetics, University of Bonn, Bonn, Germany; Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Jessica Becker
- Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-von-Guericke University Hospital, Magdeburg, Germany; Institute of Pathology, Klinikum Bayreuth, Bayreuth, Germany
| | - Nicole Kreuser
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Rene Thieme
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Tania Noder
- Department of Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Marino Venerito
- Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-von-Guericke University Hospital, Magdeburg, Germany
| | - Lothar Veits
- Institute of Pathology, Klinikum Bayreuth, Bayreuth, Germany
| | - Thomas Schmidt
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, Heidelberg, Germany
| | - Claudia Schmidt
- Department of General, Visceral and Cancer Surgery, University of Cologne, Cologne, Germany
| | - Jakob R Izbicki
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany
| | - Arnulf H Hölscher
- Department of General, Visceral and Cancer Surgery, University of Cologne, Cologne, Germany
| | - Hauke Lang
- Department of General, Visceral and Transplant Surgery, University Medical Center, University of Mainz, Mainz, Germany
| | - Dietmar Lorenz
- Department of General, Visceral and Thoracic Surgery, Klinikum Darmstadt, Darmstadt, Germany
| | - Brigitte Schumacher
- Department of Internal Medicine and Gastroenterology, Elisabeth Hospital, Essen, Germany
| | | | - Yogesh Vashist
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany; Kantonsspital Aarau, Aarau, Switzerland
| | - Katja Ott
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, Heidelberg, Germany; Department of General, Visceral and Thorax Surgery, RoMed Klinikum Rosenheim, Rosenheim, Germany
| | - Michael Vieth
- Institute of Pathology, Klinikum Bayreuth, Bayreuth, Germany
| | | | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany; Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Susanne Moebus
- Centre of Urban Epidemiology, Institute of Medical Informatics, Biometry and Epidemiology, University of Essen, Essen, Germany
| | - Michael Knapp
- Institute for Medical Biometry, Informatics, and Epidemiology, University of Bonn, Bonn, Germany
| | - Wilbert H M Peters
- Department of Gastroenterology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Horst Neuhaus
- Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - Thomas Rösch
- Department of Internal Medicine II, Evangelisches Krankenhaus, Düsseldorf, Germany
| | - Christian Ell
- Department of Medicine II, Sana Klinikum, Offenbach, Germany
| | | | - Johannes Schumacher
- Institute of Human Genetics, University of Bonn, Bonn, Germany; Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Rachel E Neale
- Cancer Aetiology and Prevention, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - David C Whiteman
- Cancer Control, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Aaron P Thrift
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas; Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas.
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Observational and genetic studies of short telomeres and Alzheimer’s disease in 67,000 and 152,000 individuals: a Mendelian randomization study. Eur J Epidemiol 2019; 35:147-156. [DOI: 10.1007/s10654-019-00563-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 09/10/2019] [Indexed: 02/06/2023]
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Efstathiadou A, Gill D, McGrane F, Quinn T, Dawson J. Genetically Determined Uric Acid and the Risk of Cardiovascular and Neurovascular Diseases: A Mendelian Randomization Study of Outcomes Investigated in Randomized Trials. J Am Heart Assoc 2019; 8:e012738. [PMID: 31438759 PMCID: PMC6755826 DOI: 10.1161/jaha.119.012738] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 07/12/2019] [Indexed: 02/07/2023]
Abstract
Background Higher serum uric acid levels are associated with cardiovascular and neurovascular disease, but whether these relationships are causal is not known. We applied Mendelian randomization approaches to assess the association between genetically determined uric acid levels and outcomes under study in large clinical trials. Methods and Results We used 28 genetic variants related to serum uric acid as instruments to perform a range of 2-sample Mendelian randomization methods. Our analysis had statistical power to detect clinically relevant effects of genetically determined serum uric acid levels on the considered clinical outcomes; cognitive function, Alzheimer disease, coronary heart disease, myocardial infarction, systolic blood pressure, and stroke. There was some suggestive evidence for an association between higher genetically determined serum uric acid and cognitive function. There was also some suggestive evidence of a relationship between coronary heart disease, systolic blood pressure, and the serum uric acid genetic instruments, but likely related to genetic pleiotropy. Overall, there was no consistent evidence of a clinically relevant effect of genetically determined serum uric acid on any of the considered outcomes. Conclusions This Mendelian randomization study does not support a clinically relevant causal effect of genetically determined serum urate on a range of cardiovascular and neurovascular outcomes. The weak association of genetically determined serum urate with coronary heart disease and systolic blood pressure may be because of pleiotropic effects. If urate lowering drugs such as allopurinol are found to affect these outcomes in clinical trials, then the effects may be mediated through urate independent mechanisms.
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Affiliation(s)
- Anthoula Efstathiadou
- Department of Biostatistics and EpidemiologySchool of Public HealthImperial College LondonLondonUnited Kingdom
| | - Dipender Gill
- Department of Biostatistics and EpidemiologySchool of Public HealthImperial College LondonLondonUnited Kingdom
| | - Frances McGrane
- Cardiovascular & Medical SciencesUniversity of GlasgowUnited Kingdom
| | - Terence Quinn
- Cardiovascular & Medical SciencesUniversity of GlasgowUnited Kingdom
| | - Jesse Dawson
- Cardiovascular & Medical SciencesUniversity of GlasgowUnited Kingdom
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DiazOrdaz K, Carpenter J. Local average treatment effects estimation via substantive model compatible multiple imputation. Biom J 2019; 61:1526-1540. [PMID: 31456263 DOI: 10.1002/bimj.201800345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 04/30/2019] [Accepted: 05/13/2019] [Indexed: 11/11/2022]
Abstract
Nonadherence to assigned treatment is common in randomized controlled trials (RCTs). Recently, there has been increased interest in estimating causal effects of treatment received, for example, the so-called local average treatment effect (LATE). Instrumental variables (IV) methods can be used for identification, with estimation proceeding either via fully parametric mixture models or two-stage least squares (TSLS). TSLS is popular but can be problematic for binary outcomes where the estimand of interest is a causal odds ratio. Mixture models are rarely used in practice, perhaps because of their perceived complexity and need for specialist software. Here, we propose using multiple imputation (MI) to impute the latent compliance class appearing in the mixture models. Since such models include an interaction term between the latent compliance class and randomized treatment, we use "substantive model compatible" MI (SMC MIC), which can additionally handle missing data in outcomes and other variables in the model, before fitting the mixture models via maximum likelihood to the MI data sets and combining results via Rubin's rules. We use simulations to compare the performance of SMC MIC to existing approaches and also illustrate the methods by reanalyzing an RCT in UK primary health. We show that SMC MIC can be more efficient than full Bayesian estimation when auxiliary variables are incorporated, and is superior to two-stage methods, especially for binary outcomes.
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Affiliation(s)
- Karla DiazOrdaz
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - James Carpenter
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
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Carslake D, Fraser A, May MT, Palmer T, Silventoinen K, Tynelius P, Lawlor DA, Davey Smith G. Associations of mortality with own blood pressure using son's blood pressure as an instrumental variable. Sci Rep 2019; 9:8986. [PMID: 31222129 PMCID: PMC6586810 DOI: 10.1038/s41598-019-45391-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 05/28/2019] [Indexed: 11/09/2022] Open
Abstract
High systolic blood pressure (SBP) causes cardiovascular disease (CVD) and is associated with mortality from other causes, but conventional multivariably-adjusted results may be confounded. Here we used a son’s SBP (>1 million Swedish men) as an instrumental variable for parental SBP and examined associations with parents’ cause-specific mortality, avoiding reverse causation. The hazard ratio for CVD mortality per SD (10.80 mmHg) of SBP was 1.49 (95% CI: 1.43, 1.56); SBP was positively associated with coronary heart disease and stroke. SBP was also associated positively with all-cause, diabetes and kidney cancer mortality, and negatively with external causes. Negative associations with respiratory-related mortality were probably confounded by smoking. Hazard ratios for other causes were imprecise or null. Diastolic blood pressure gave similar results to SBP. CVD hazard ratios were intermediate between those from conventional multivariable studies and Mendelian randomization and stronger than those from clinical trials, approximately consistent with an effect of exposure duration on effect sizes. Plots of parental mortality against offspring SBP were approximately linear, supporting calls for lower SBP targets. Results suggest that conventional multivariable analyses of mortality and SBP are not substantially confounded by reverse causation and confirm positive effects of SBP on all-cause, CVD and diabetes mortality.
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Affiliation(s)
- David Carslake
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK. .,Population Health Sciences, Bristol Medical School, Bristol, UK.
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Margaret T May
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Tom Palmer
- Department of Mathematics and Statistics, University of Lancaster, Lancaster, UK
| | - Karri Silventoinen
- Population Research Unit, Department of Social Research, University of Helsinki, Helsinki, Finland
| | - Per Tynelius
- Department of Public Health Sciences, Karolinska Institute, Stockholm, Sweden
| | - Debbie A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, Bristol, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, Bristol, UK
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Rothenhäusler D, Bühlmann P, Meinshausen N. Causal Dantzig: Fast inference in linear structural equation models with hidden variables under additive interventions. Ann Stat 2019. [DOI: 10.1214/18-aos1732] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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46
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Epidemiology, genetic epidemiology and Mendelian randomisation: more need than ever to attend to detail. Hum Genet 2019; 139:121-136. [PMID: 31134333 PMCID: PMC6942032 DOI: 10.1007/s00439-019-02027-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 05/09/2019] [Indexed: 12/02/2022]
Abstract
In the current era, with increasing availability of results from genetic association studies, finding genetic instruments for inferring causality in observational epidemiology has become apparently simple. Mendelian randomisation (MR) analyses are hence growing in popularity and, in particular, methods that can incorporate multiple instruments are being rapidly developed for these applications. Such analyses have enormous potential, but they all rely on strong, different, and inherently untestable assumptions. These have to be clearly stated and carefully justified for every application in order to avoid conclusions that cannot be replicated. In this article, we review the instrumental variable assumptions and discuss the popular linear additive structural model. We advocate the use of tests for the null hypothesis of ‘no causal effect’ and calculation of the bounds for a causal effect, whenever possible, as these do not rely on parametric modelling assumptions. We clarify the difference between a randomised trial and an MR study and we comment on the importance of validating instruments, especially when considering them for joint use in an analysis. We urge researchers to stand by their convictions, if satisfied that the relevant assumptions hold, and to interpret their results causally since that is the only reason for performing an MR analysis in the first place.
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Swanson SA, Hernán MA. The challenging interpretation of instrumental variable estimates under monotonicity. Int J Epidemiol 2019; 47:1289-1297. [PMID: 28379526 DOI: 10.1093/ije/dyx038] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/24/2017] [Indexed: 11/12/2022] Open
Abstract
Background Instrumental variable (IV) methods are often used to identify 'local' causal effects in a subgroup of the population of interest. Such 'local' effects may not be ideal for informing clinical or policy decision making. When the instrument is non-causal, additional difficulties arise for interpreting 'local' effects. Little attention has been paid to these difficulties, even though commonly proposed instruments in epidemiology are non-causal (e.g. proxies for physician's preference; genetic variants in some Mendelian randomization studies). Methods For IV estimates obtained from both causal and non-causal instruments under monotonicity, we present results to help investigators pose four questions about the local effect estimates obtained in their studies. (1) To what subgroup of the population does the effect pertain? Can we (2) estimate the size of or (3) describe the characteristics of this subgroup relative to the study population? (4) Can the sensitivity of the effect estimate to deviations from monotonicity be quantified? Results We show that the common interpretations and approaches for answering these four questions are generally only appropriate in the case of causal instruments. Conclusions Appropriate interpretation of an IV estimate under monotonicity as a 'local' effect critically depends on whether the proposed instrument is causal or non-causal. The results and formal proofs presented here can help in the transparent reporting of IV results and in enhancing the use of IV estimates in informing decision-making efforts.
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Affiliation(s)
- Sonja A Swanson
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Miguel A Hernán
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.,Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA
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48
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Genetic association and transcriptome integration identify contributing genes and tissues at cystic fibrosis modifier loci. PLoS Genet 2019; 15:e1008007. [PMID: 30807572 PMCID: PMC6407791 DOI: 10.1371/journal.pgen.1008007] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 03/08/2019] [Accepted: 02/06/2019] [Indexed: 01/09/2023] Open
Abstract
Cystic Fibrosis (CF) exhibits morbidity in several organs, including progressive lung disease in all patients and intestinal obstruction at birth (meconium ileus) in ~15%. Individuals with the same causal CFTR mutations show variable disease presentation which is partly attributed to modifier genes. With >6,500 participants from the International CF Gene Modifier Consortium, genome-wide association investigation identified a new modifier locus for meconium ileus encompassing ATP12A on chromosome 13 (min p = 3.83x10(-10)); replicated loci encompassing SLC6A14 on chromosome X and SLC26A9 on chromosome 1, (min p<2.2x10(-16), 2.81x10(-11), respectively); and replicated a suggestive locus on chromosome 7 near PRSS1 (min p = 2.55x10(-7)). PRSS1 is exclusively expressed in the exocrine pancreas and was previously associated with non-CF pancreatitis with functional characterization demonstrating impact on PRSS1 gene expression. We thus asked whether the other meconium ileus modifier loci impact gene expression and in which organ. We developed and applied a colocalization framework called the Simple Sum (SS) that integrates regulatory and genetic association information, and also contrasts colocalization evidence across tissues or genes. The associated modifier loci colocalized with expression quantitative trait loci (eQTLs) for ATP12A (p = 3.35x10(-8)), SLC6A14 (p = 1.12x10(-10)) and SLC26A9 (p = 4.48x10(-5)) in the pancreas, even though meconium ileus manifests in the intestine. The meconium ileus susceptibility locus on chromosome X appeared shifted in location from a previously identified locus for CF lung disease severity. Using the SS we integrated the lung disease association locus with eQTLs from nasal epithelia of 63 CF participants and demonstrated evidence of colocalization with airway-specific regulation of SLC6A14 (p = 2.3x10(-4)). Cystic Fibrosis is realizing the promise of personalized medicine, and identification of the contributing organ and understanding of tissue specificity for a gene modifier is essential for the next phase of personalizing therapeutic strategies.
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49
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Ward-Caviness CK, de Vries PS, Wiggins KL, Huffman JE, Yanek LR, Bielak LF, Giulianini F, Guo X, Kleber ME, Kacprowski T, Groß S, Petersman A, Davey Smith G, Hartwig FP, Bowden J, Hemani G, Müller-Nuraysid M, Strauch K, Koenig W, Waldenberger M, Meitinger T, Pankratz N, Boerwinkle E, Tang W, Fu YP, Johnson AD, Song C, de Maat MPM, Uitterlinden AG, Franco OH, Brody JA, McKnight B, Chen YDI, Psaty BM, Mathias RA, Becker DM, Peyser PA, Smith JA, Bielinski SJ, Ridker PM, Taylor KD, Yao J, Tracy R, Delgado G, Trompet S, Sattar N, Jukema JW, Becker LC, Kardia SLR, Rotter JI, März W, Dörr M, Chasman DI, Dehghan A, O’Donnell CJ, Smith NL, Peters A, Morrison AC. Mendelian randomization evaluation of causal effects of fibrinogen on incident coronary heart disease. PLoS One 2019; 14:e0216222. [PMID: 31075152 PMCID: PMC6510421 DOI: 10.1371/journal.pone.0216222] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 04/16/2019] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Fibrinogen is an essential hemostatic factor and cardiovascular disease risk factor. Early attempts at evaluating the causal effect of fibrinogen on coronary heart disease (CHD) and myocardial infraction (MI) using Mendelian randomization (MR) used single variant approaches, and did not take advantage of recent genome-wide association studies (GWAS) or multi-variant, pleiotropy robust MR methodologies. METHODS AND FINDINGS We evaluated evidence for a causal effect of fibrinogen on both CHD and MI using MR. We used both an allele score approach and pleiotropy robust MR models. The allele score was composed of 38 fibrinogen-associated variants from recent GWAS. Initial analyses using the allele score used a meta-analysis of 11 European-ancestry prospective cohorts, free of CHD and MI at baseline, to examine incidence CHD and MI. We also applied 2 sample MR methods with data from a prevalent CHD and MI GWAS. Results are given in terms of the hazard ratio (HR) or odds ratio (OR), depending on the study design, and associated 95% confidence interval (CI). In single variant analyses no causal effect of fibrinogen on CHD or MI was observed. In multi-variant analyses using incidence CHD cases and the allele score approach, the estimated causal effect (HR) of a 1 g/L higher fibrinogen concentration was 1.62 (CI = 1.12, 2.36) when using incident cases and the allele score approach. In 2 sample MR analyses that accounted for pleiotropy, the causal estimate (OR) was reduced to 1.18 (CI = 0.98, 1.42) and 1.09 (CI = 0.89, 1.33) in the 2 most precise (smallest CI) models, out of 4 models evaluated. In the 2 sample MR analyses for MI, there was only very weak evidence of a causal effect in only 1 out of 4 models. CONCLUSIONS A small causal effect of fibrinogen on CHD is observed using multi-variant MR approaches which account for pleiotropy, but not single variant MR approaches. Taken together, results indicate that even with large sample sizes and multi-variant approaches MR analyses still cannot exclude the null when estimating the causal effect of fibrinogen on CHD, but that any potential causal effect is likely to be much smaller than observed in epidemiological studies.
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Affiliation(s)
- Cavin K. Ward-Caviness
- Epidemiology II, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- * E-mail:
| | - Paul S. de Vries
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, United States of America
| | - Kerri L. Wiggins
- Department of Medicine, University of Washington, Health Sciences Bldg, Seattle, Washington, United States of America
| | - Jennifer E. Huffman
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA, United States of America
- The Framingham Heart Study, Framingham, MA, United States of America
| | - Lisa R. Yanek
- GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Broadway, Baltimore, MD, United States of America
| | - Lawrence F. Bielak
- Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Franco Giulianini
- Division of Preventative Medicine, Brigham and Women’s Hospital, Boston, MA, United States of America
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Marcus E. Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany
- Institute of Nutrition, Friedrich-Schiller University Jena, Jena, Germany
| | - Tim Kacprowski
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt University Greifswald, Griefswald, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Research Group Computational Systems Medicine, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany
| | - Stefan Groß
- DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Ferdinand-Sauerbruch-Straße, Greifswald, Germany
| | - Astrid Petersman
- Institute of Clinical Chemistry and Laboratory Medicine, University of Medicine Griefswald, Ferdinand-Sauerbruch-Straße, Greifswald, Germany
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Fernando P. Hartwig
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Jack Bowden
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Martina Müller-Nuraysid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
- Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Wolfgang Koenig
- DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
- Department of Internal Medicine II, University of Ulm Medical Center, Ulm, Germany
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | - Melanie Waldenberger
- Epidemiology II, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Meitinger
- DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Nathan Pankratz
- University of Minnesota School of Medicine, Minneapolis, MN, United States of America
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, United States of America
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, United States of America
| | - Weihong Tang
- University of Minnesota School of Public Health, Minneapolis, MN, United States of America
| | - Yi-Ping Fu
- Office of Biostatistics Research, Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States of America
| | - Andrew D. Johnson
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA, United States of America
- The Framingham Heart Study, Framingham, MA, United States of America
| | - Ci Song
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA, United States of America
- The Framingham Heart Study, Framingham, MA, United States of America
| | - Moniek P. M. de Maat
- Department of Hematology, Erasmus University Medical Center, Rotterdam, CND, Netherlands
| | - André G. Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, CN, Netherlands
| | - Oscar H. Franco
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jennifer A. Brody
- Department of Medicine, University of Washington, Health Sciences Bldg, Seattle, Washington, United States of America
| | - Barbara McKnight
- Department of Biostatistics, University of Washington, Health Sciences Bldg, Seattle, WA, United States of America
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Bruce M. Psaty
- Department of Medicine, University of Washington, Health Sciences Bldg, Seattle, Washington, United States of America
- Department of Epidemiology, University of Washington, Health Sciences Bldg, Seattle, WA, United States of America
- Department of Health Services, University of Washington, Health Sciences Bldg, Seattle, WA, United States of America
- Group Health Research Institute, Group Health Cooperative, Seattle, WA, United States of America
| | - Rasika A. Mathias
- GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Broadway, Baltimore, MD, United States of America
- Division of Allergy and Clinical Immunology, Department of Medicine, Johns Hopkins University School of Medicine, N. Broadway, Baltimore, MD, United States of America
| | - Diane M. Becker
- GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Broadway, Baltimore, MD, United States of America
| | - Patricia A. Peyser
- Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jennifer A. Smith
- Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Suzette J. Bielinski
- Department of Epidemiology, Mayo Clinic, Rochester, MN, United States of America
| | - Paul M. Ridker
- Division of Preventative Medicine, Brigham and Women’s Hospital, Boston, MA, United States of America
| | - Kent D. Taylor
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Jie Yao
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Russell Tracy
- Pathology and Laboratory Medicine, The University of Vermont College of Medicine, Col Research Facility, Burlington, VT, United States of America
| | - Graciela Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany
| | - Stella Trompet
- Department of Hematology, Erasmus University Medical Center, Rotterdam, CND, Netherlands
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
| | - Naveed Sattar
- BHF Glasgow Cardiovascular Research Centre, Faculty of Medicine, Glasgow, United Kingdom
| | - J. Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, Netherlands
| | - Lewis C. Becker
- GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Broadway, Baltimore, MD, United States of America
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, N. Broadway, Baltimore, MD, United States of America
| | - Sharon L. R. Kardia
- Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Winfried März
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Marcus Dörr
- DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Ferdinand-Sauerbruch-Straße, Greifswald, Germany
| | - Daniel I. Chasman
- Division of Preventative Medicine, Brigham and Women’s Hospital, Boston, MA, United States of America
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology and Biostatistics, Imperial College London, Norfolk Place, London, United Kingdom
| | - Christopher J. O’Donnell
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA, United States of America
- Cardiology Section Administration, Boston VA Healthcare System, West Roxbury, MA, United States of America
| | - Nicholas L. Smith
- Department of Epidemiology, University of Washington, Health Sciences Bldg, Seattle, WA, United States of America
- Group Health Research Institute, Group Health Cooperative, Seattle, WA, United States of America
- Seattle Epidemiologic Research and Information Center, Department of Veteran Affairs Office of Research and Development, Columbian Way, Seattle, WA, United States of America
| | - Annette Peters
- Epidemiology II, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, United States of America
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50
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Dai JY, Peters U, Wang X, Kocarnik J, Chang-Claude J, Slattery ML, Chan A, Lemire M, Berndt SI, Casey G, Song M, Jenkins MA, Brenner H, Thrift AP, White E, Hsu L. Diagnostics for Pleiotropy in Mendelian Randomization Studies: Global and Individual Tests for Direct Effects. Am J Epidemiol 2018; 187:2672-2680. [PMID: 30188971 PMCID: PMC6269243 DOI: 10.1093/aje/kwy177] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2017] [Revised: 05/03/2018] [Accepted: 05/09/2018] [Indexed: 12/29/2022] Open
Abstract
Diagnosing pleiotropy is critical for assessing the validity of Mendelian randomization (MR) analyses. The popular MR-Egger method evaluates whether there is evidence of bias-generating pleiotropy among a set of candidate genetic instrumental variables. In this article, we propose a statistical method-global and individual tests for direct effects (GLIDE)-for systematically evaluating pleiotropy among the set of genetic variants (e.g., single nucleotide polymorphisms (SNPs)) used for MR. As a global test, simulation experiments suggest that GLIDE is nearly uniformly more powerful than the MR-Egger method. As a sensitivity analysis, GLIDE is capable of detecting outliers in individual variant-level pleiotropy, in order to obtain a refined set of genetic instrumental variables. We used GLIDE to analyze both body mass index and height for associations with colorectal cancer risk in data from the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry (multiple studies). Among the body mass index-associated SNPs and the height-associated SNPs, several individual variants showed evidence of pleiotropy. Removal of these potentially pleiotropic SNPs resulted in attenuation of respective estimates of the causal effects. In summary, the proposed GLIDE method is useful for sensitivity analyses and improves the validity of MR.
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Affiliation(s)
- James Y Dai
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
| | - Xiaoyu Wang
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jonathan Kocarnik
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Genetic Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, Utah
| | - Andrew Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Mathieu Lemire
- Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Graham Casey
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California
| | - Mingyang Song
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Mark A Jenkins
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center and National Center for Tumor Diseases, Heidelberg, Germany
- German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Aaron P Thrift
- Department of Medicine and Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Emily White
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
| | - Li Hsu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
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