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Luo P, Guo R, Gao D, Zhang Q. Causal relationship between sex hormones and cutaneous melanoma: a two-sample Mendelian randomized study. Melanoma Res 2024; 34:408-418. [PMID: 38842104 DOI: 10.1097/cmr.0000000000000983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
This study aimed to elucidate the genetic aspects of the relationship between sex hormones and cutaneous melanoma risk, providing valuable insights into this complex association. In this study, we used estradiol, bioavailable testosterone, sex hormone-binding globulin, and total testosterone as the exposure and melanoma as the outcome for two-sample Mendelian randomization analysis. In this study, a random-effects inverse-variance weighting (IVW) model was used as the main analysis model, and the corresponding weighted median, simple mode, weighted mode, and Mendelian randomization‒Egger methods were used as supplementary methods. We assessed both heterogeneity and horizontal pleiotropy in our study, scrutinizing whether the analysis results were affected by any individual single nucleotide polymorphism. The random-effects IVW method indicated that estradiol [odds ratio (OR), 1.000; 95% confidence interval (CI), 0.998-1.003; P = 0.658], bioavailable testosterone (OR = 1.001, 95% CI, 0.999-1.003; P = 0.294), sex hormone-binding globulin (IVW: OR, 1.000; 95% CI, 0.998-1.003; P = 0.658), and total testosterone (IVW: OR, 1.002; 95% CI, 0.999-1.005; P = 0.135) were not genetically linked to cutaneous melanoma. No analyses exhibited heterogeneity, horizontal pleiotropy, or deviations. We were unable to find genetic evidence for a causal relationship between sex hormones and the occurrence of cutaneous melanoma in this study. These results are limited by sample size and population, so the causal relationship between sex hormones and cutaneous melanoma needs to be further studied.
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Affiliation(s)
- Pan Luo
- Department of Auricular Reconstruction, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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2
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Cheng D, Li J, Liu L, Yu K, Duy Le T, Liu J. Discovering Ancestral Instrumental Variables for Causal Inference From Observational Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:11542-11552. [PMID: 37018092 DOI: 10.1109/tnnls.2023.3262848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Instrumental variable (IV) is a powerful approach to inferring the causal effect of a treatment on an outcome of interest from observational data even when there exist latent confounders between the treatment and the outcome. However, existing IV methods require that an IV is selected and justified with domain knowledge. An invalid IV may lead to biased estimates. Hence, discovering a valid IV is critical to the applications of IV methods. In this article, we study and design a data-driven algorithm to discover valid IVs from data under mild assumptions. We develop the theory based on partial ancestral graphs (PAGs) to support the search for a set of candidate ancestral IVs (AIVs), and for each possible AIV, the identification of its conditioning set. Based on the theory, we propose a data-driven algorithm to discover a pair of IVs from data. The experiments on synthetic and real-world datasets show that the developed IV discovery algorithm estimates accurate estimates of causal effects in comparison with the state-of-the-art IV-based causal effect estimators.
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3
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Lorincz-Comi N, Yang Y, Li G, Zhu X. MRBEE: A bias-corrected multivariable Mendelian randomization method. HGG ADVANCES 2024; 5:100290. [PMID: 38582968 PMCID: PMC11053334 DOI: 10.1016/j.xhgg.2024.100290] [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: 11/17/2023] [Revised: 04/01/2024] [Accepted: 04/01/2024] [Indexed: 04/08/2024] Open
Abstract
Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes, which is becoming increasingly popular because of its ability to handle summary statistics from genome-wide association studies. However, existing MR approaches often suffer the bias from weak instrumental variables, horizontal pleiotropy and sample overlap. We introduce MRBEE (MR using bias-corrected estimating equation), a multivariable MR method capable of simultaneously removing weak instrument and sample overlap bias and identifying horizontal pleiotropy. Our extensive simulations and real data analyses reveal that MRBEE provides nearly unbiased estimates of causal effects, well-controlled type I error rates and higher power than comparably robust methods and is computationally efficient. Our real data analyses result in consistent causal effect estimates and offer valuable guidance for conducting multivariable MR studies, elucidating the roles of pleiotropy, and identifying total 42 horizontal pleiotropic loci missed previously that are associated with myopia, schizophrenia, and coronary artery disease.
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Affiliation(s)
- Noah Lorincz-Comi
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Yihe Yang
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Gen Li
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
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4
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Luo P, Gao D, Zhang Q. Genetic causal relationship between gut microbiota and basal cell carcinoma: A two-sample mendelian randomization study. Skin Res Technol 2024; 30:e13804. [PMID: 38895789 PMCID: PMC11187847 DOI: 10.1111/srt.13804] [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: 05/14/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024]
Abstract
OBJECTIVE Research has previously established connections between the intestinal microbiome and the progression of some cancers. However, there is a noticeable gap in the literature in regard to using Mendelian randomisation (MR) to delve into potential causal relationships between the gut microbiota (GM) and basal cell carcinoma (BCC). Therefore, the purpose of our study was to use MR to explore the causal relationship between four kinds of GM (Bacteroides, Streptococcus, Proteobacteria and Lachnospiraceae) and BCC. METHODS We used genome-wide association study (GWAS) data and MR to explore the causal relationship between four kinds of GM and BCC. This study primarily employed the random effect inverse variance weighted (IVW) model for analysis, as complemented by additional methods including the simple mode, weighted median, weighted mode and MR‒Egger methods. We used heterogeneity and horizontal multiplicity to judge the reliability of each analysis. MR-PRESSO was mainly used to detect and correct outliers. RESULTS The random-effects IVW results showed that Bacteroides (OR = 0.936, 95% CI = 0.787-1.113, p = 0.455), Streptococcus (OR = 0.974, 95% CI = 0.875-1.083, p = 0.629), Proteobacteria (OR = 1.113, 95% CI = 0.977-1.267, p = 0.106) and Lachnospiraceae (OR = 1.027, 95% CI = 0.899-1.173, p = 0.688) had no genetic causal relationship with BCC. All analyses revealed no horizontal pleiotropy, heterogeneity or outliers. CONCLUSION We found that Bacteroides, Streptococcus, Proteobacteria and Lachnospiraceae do not increase the incidence of BCC at the genetic level, which provides new insight for the study of GM and BCC.
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Affiliation(s)
- Pan Luo
- Department of Comprehensive Plastic SurgeryPlastic Surgery HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Dejin Gao
- Department of Comprehensive Plastic SurgeryPlastic Surgery HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Qingguo Zhang
- Department of Comprehensive Plastic SurgeryPlastic Surgery HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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5
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Orihara S, Goto A, Taguri M. Valid instrumental variable selection method using negative control outcomes and constructing efficient estimator. Biom J 2024; 66:e2300113. [PMID: 38801216 DOI: 10.1002/bimj.202300113] [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: 04/22/2023] [Revised: 02/29/2024] [Accepted: 03/18/2024] [Indexed: 05/29/2024]
Abstract
In observational studies, instrumental variable (IV) methods are commonly applied when there are unmeasured covariates. In Mendelian randomization, constructing an allele score using many single nucleotide polymorphisms is often implemented; however, estimating biased causal effects by including some invalid IVs poses some risks. Invalid IVs are those IV candidates that are associated with unobserved variables. To solve this problem, we developed a novel strategy using negative control outcomes (NCOs) as auxiliary variables. Using NCOs, we are able to select only valid IVs and exclude invalid IVs without knowing which of the instruments are invalid. We also developed a new two-step estimation procedure and proved the semiparametric efficiency of our estimator. The performance of our proposed method was superior to some previous methods through simulations. Subsequently, we applied the proposed method to the UK Biobank dataset. Our results demonstrate that the use of an auxiliary variable, such as an NCO, enables the selection of valid IVs with assumptions different from those used in previous methods.
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Affiliation(s)
- Shunichiro Orihara
- Department of Health Data Science, Tokyo Medical University, Tokyo, Japan
- Graduate School of Data Science, Yokohama City University, Kanagawa, Japan
| | - Atsushi Goto
- Graduate School of Data Science, Yokohama City University, Kanagawa, Japan
| | - Masataka Taguri
- Department of Health Data Science, Tokyo Medical University, Tokyo, Japan
- Graduate School of Data Science, Yokohama City University, Kanagawa, Japan
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6
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Huang Z, Huang J, Leung CK, Zhang CJ, Akinwunmi B, Ming WK. Hemorrhoidal disease and its genetic association with depression, bipolar disorder, anxiety disorders, and schizophrenia: a bidirectional mendelian randomization study. Hum Genomics 2024; 18:27. [PMID: 38509615 PMCID: PMC10956248 DOI: 10.1186/s40246-024-00588-7] [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: 11/24/2023] [Accepted: 02/21/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Hemorrhoids and psychiatric disorders exhibit high prevalence rates and a tendency for relapse in epidemiological studies. Despite this, limited research has explored their correlation, and these studies are often subject to reverse causality and residual confounding. We conducted a Mendelian randomization (MR) analysis to comprehensively investigate the association between several mental illnesses and hemorrhoidal disease. METHODS Genetic associations for four psychiatric disorders and hemorrhoidal disease were obtained from large consortia, the FinnGen study, and the UK Biobank. Genetic variants associated with depression, bipolar disorder, anxiety disorders, schizophrenia, and hemorrhoidal disease at the genome-wide significance level were selected as instrumental variables. Screening for potential confounders in genetic instrumental variables using PhenoScanner V2. Bidirectional MR estimates were employed to assess the effects of four psychiatric disorders on hemorrhoidal disease. RESULTS Our analysis revealed a significant association between genetically predicted depression and the risk of hemorrhoidal disease (IVW, OR=1.20,95% CI=1.09 to 1.33, P <0.001). We found no evidence of associations between bipolar disorder, anxiety disorders, schizophrenia, and hemorrhoidal disease. Inverse MR analysis provided evidence for a significant association between genetically predicted hemorrhoidal disease and depression (IVW, OR=1.07,95% CI=1.04 to 1.11, P <0.001). CONCLUSIONS This study offers MR evidence supporting a bidirectional causal relationship between depression and hemorrhoidal disease.
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Affiliation(s)
- Zhiguang Huang
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China
| | - Jian Huang
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Chun Kai Leung
- Department of Public and International Affairs, City University of Hong Kong, Hong Kong SAR, China
| | - Casper Jp Zhang
- School of Public Health, The University of Hong Kong, Hong Kong SAR, China
| | - Babatunde Akinwunmi
- Maternal-Fetal Medicine Unit, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Wai-Kit Ming
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China.
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7
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Lorincz-Comi N, Yang Y, Zhu X. simmrd: An open-source tool to perform simulations in Mendelian randomization. Genet Epidemiol 2024; 48:59-73. [PMID: 38263619 DOI: 10.1002/gepi.22544] [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: 09/09/2023] [Revised: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 01/25/2024]
Abstract
Mendelian randomization (MR) has become a popular tool for inferring causality of risk factors on disease. There are currently over 45 different methods available to perform MR, reflecting this extremely active research area. It would be desirable to have a standard simulation environment to objectively evaluate the existing and future methods. We present simmrd, an open-source software for performing simulations to evaluate the performance of MR methods in a range of scenarios encountered in practice. Researchers can directly modify the simmrd source code so that the research community may arrive at a widely accepted framework for researchers to evaluate the performance of different MR methods.
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Affiliation(s)
- Noah Lorincz-Comi
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yihe Yang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
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8
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Sun J, Zhou J, Gong Y, Pang C, Ma Y, Zhao J, Yu Z, Zhang Y. Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference. Hum Genet 2024:10.1007/s00439-024-02640-x. [PMID: 38381161 DOI: 10.1007/s00439-024-02640-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/05/2024] [Indexed: 02/22/2024]
Abstract
Mendelian randomization is a powerful method for inferring causal relationships. However, obtaining suitable genetic instrumental variables is often challenging due to gene interaction, linkage, and pleiotropy. We propose Bayesian network-based Mendelian randomization (BNMR), a Bayesian causal learning and inference framework using individual-level data. BNMR employs the random graph forest, an ensemble Bayesian network structural learning process, to prioritize candidate genetic variants and select appropriate instrumental variables, and then obtains a pleiotropy-robust estimate by incorporating a shrinkage prior in the Bayesian framework. Simulations demonstrate BNMR can efficiently reduce the false-positive discoveries in variant selection, and outperforms existing MR methods in terms of accuracy and statistical power in effect estimation. With application to the UK Biobank, BNMR exhibits its capacity in handling modern genomic data, and reveals the causal relationships from hematological traits to blood pressures and psychiatric disorders. Its effectiveness in handling complex genetic structures and modern genomic data highlights the potential to facilitate real-world evidence studies, making it a promising tool for advancing our understanding of causal mechanisms.
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Affiliation(s)
- Jianle Sun
- Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Zhou
- Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Yuqiao Gong
- Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Chongchen Pang
- Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Yanran Ma
- Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Jian Zhao
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China.
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China.
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9
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Li Y, Wong KY, Howard AG, Gordon-Larsen P, Highland HM, Graff M, North KE, Downie CG, Avery CL, Yu B, Young KL, Buchanan VL, Kaplan R, Hou L, Joyce BT, Qi Q, Sofer T, Moon JY, Lin DY. Mendelian randomization with incomplete measurements on the exposure in the Hispanic Community Health Study/Study of Latinos. HGG ADVANCES 2024; 5:100245. [PMID: 37817410 PMCID: PMC10628889 DOI: 10.1016/j.xhgg.2023.100245] [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: 05/02/2023] [Revised: 10/02/2023] [Accepted: 10/02/2023] [Indexed: 10/12/2023] Open
Abstract
Mendelian randomization has been widely used to assess the causal effect of a heritable exposure variable on an outcome of interest, using genetic variants as instrumental variables. In practice, data on the exposure variable can be incomplete due to high cost of measurement and technical limits of detection. In this paper, we propose a valid and efficient method to handle both unmeasured and undetectable values of the exposure variable in one-sample Mendelian randomization analysis with individual-level data. We estimate the causal effect of the exposure variable on the outcome using maximum likelihood estimation and develop an expectation maximization algorithm for the computation of the estimator. Simulation studies show that the proposed method performs well in making inference on the causal effect. We apply our method to the Hispanic Community Health Study/Study of Latinos, a community-based prospective cohort study, and estimate the causal effect of several metabolites on phenotypes of interest.
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Affiliation(s)
- Yilun Li
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kin Yau Wong
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
| | - Annie Green Howard
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Penny Gordon-Larsen
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Carolina G Downie
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Christy L Avery
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Kristin L Young
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Victoria L Buchanan
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Lifang Hou
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Brian Thomas Joyce
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Tamar Sofer
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Jee-Young Moon
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Dan-Yu Lin
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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10
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Grant AJ, Burgess S. A Bayesian approach to Mendelian randomization using summary statistics in the univariable and multivariable settings with correlated pleiotropy. Am J Hum Genet 2024; 111:165-180. [PMID: 38181732 PMCID: PMC10806746 DOI: 10.1016/j.ajhg.2023.12.002] [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: 05/22/2023] [Revised: 12/01/2023] [Accepted: 12/01/2023] [Indexed: 01/07/2024] Open
Abstract
Mendelian randomization uses genetic variants as instrumental variables to make causal inferences on the effect of an exposure on an outcome. Due to the recent abundance of high-powered genome-wide association studies, many putative causal exposures of interest have large numbers of independent genetic variants with which they associate, each representing a potential instrument for use in a Mendelian randomization analysis. Such polygenic analyses increase the power of the study design to detect causal effects; however, they also increase the potential for bias due to instrument invalidity. Recent attention has been given to dealing with bias caused by correlated pleiotropy, which results from violation of the "instrument strength independent of direct effect" assumption. Although methods have been proposed that can account for this bias, a number of restrictive conditions remain in many commonly used techniques. In this paper, we propose a Bayesian framework for Mendelian randomization that provides valid causal inference under very general settings. We propose the methods MR-Horse and MVMR-Horse, which can be performed without access to individual-level data, using only summary statistics of the type commonly published by genome-wide association studies, and can account for both correlated and uncorrelated pleiotropy. In simulation studies, we show that the approach retains type I error rates below nominal levels even in high-pleiotropy scenarios. We demonstrate the proposed approaches in applied examples in both univariable and multivariable settings, some with very weak instruments.
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Affiliation(s)
- Andrew J Grant
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; Sydney School of Public Health, University of Sydney, Sydney, NSW, Australia.
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
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11
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Chen S, Lin Z, Shen X, Li L, Pan W. Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data. Genet Epidemiol 2023; 47:585-599. [PMID: 37573486 PMCID: PMC10840616 DOI: 10.1002/gepi.22535] [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: 10/24/2022] [Revised: 06/19/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023]
Abstract
We propose structural equation models (SEMs) as a general framework to infer causal networks for metabolites and other complex traits. Traditionally SEMs are used only for individual-level data under the assumption that all instrumental variables (IVs) are valid. To overcome these limitations, we propose both one- and two-sample approaches for causal network inference based on SEMs that can: (1) perform causal analysis and discover causal relationships among multiple traits; (2) account for the possible presence of some invalid IVs; (3) allow for data analysis using only genome-wide association studies (GWAS) summary statistics when individual-level data are not available; (4) consider the possibility of bidirectional relationships between traits. Our method employs a simple stepwise selection to identify invalid IVs, thus avoiding false positives while possibly increasing true discoveries based on two-stage least squares (2SLS). We use both real GWAS data and simulated data to demonstrate the superior performance of our method over the standard 2SLS/SEMs. For real data analysis, our proposed approach is applied to a human blood metabolite GWAS summary data set to uncover putative causal relationships among the metabolites; we also identify some metabolites (putative) causal to Alzheimer's disease (AD), which, along with the inferred causal metabolite network, suggest some possible pathways of metabolites involved in AD.
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Affiliation(s)
- Siyi Chen
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455
| | - Zhaotong Lin
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, MN 55455
| | - Ling Li
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455
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12
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Cheng D, Li J, Liu L, Yu K, Duy Le T, Liu J. Toward Unique and Unbiased Causal Effect Estimation From Data With Hidden Variables. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:6108-6120. [PMID: 34995195 DOI: 10.1109/tnnls.2021.3133337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Causal effect estimation from observational data is a crucial but challenging task. Currently, only a limited number of data-driven causal effect estimation methods are available. These methods either provide only a bound estimation of causal effects of treatment on the outcome or generate a unique estimation of the causal effect but making strong assumptions on data and having low efficiency. In this article, we identify a problem setting with the Cause Or Spouse of the treatment Only (COSO) variable assumption and propose an approach to achieving a unique and unbiased estimation of causal effects from data with hidden variables. For the approach, we have developed the theorems to support the discovery of the proper covariate sets for confounding adjustment (adjustment sets). Based on the theorems, two algorithms are proposed for finding the proper adjustment sets from data with hidden variables to obtain unbiased and unique causal effect estimation. Experiments with synthetic datasets generated using five benchmark Bayesian networks and four real-world datasets have demonstrated the efficiency and effectiveness of the proposed algorithms, indicating the practicability of the identified problem setting and the potential of the proposed approach in real-world applications.
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13
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Seng LL, Liu CT, Wang J, Li J. Instrumental variable model average with applications in Mendelian randomization. Stat Med 2023; 42:3547-3567. [PMID: 37476915 DOI: 10.1002/sim.9819] [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: 12/02/2022] [Revised: 04/20/2023] [Accepted: 05/29/2023] [Indexed: 07/22/2023]
Abstract
Mendelian randomization is a technique used to examine the causal effect of a modifiable exposure on a trait using an observational study by utilizing genetic variants. The use of many instruments can help to improve the estimation precision but may suffer bias when the instruments are weakly associated with the exposure. To overcome the difficulty of high-dimensionality, we propose a model average estimator which involves using different subsets of instruments (single nucleotide polymorphisms, SNPs) to predict the exposure in the first stage, followed by weighting the submodels' predictions using penalization by common penalty functions such as least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). The model averaged predictions are then used as a genetically predicted exposure to obtain the estimation of the causal effect on the response in the second stage. The novelty of our model average estimator also lies in that it allows the number of submodels and the submodels' sizes to grow with the sample size. The practical performance of the estimator is examined in a series of numerical studies. We apply the proposed method on a real genetic dataset investigating the relationship between stature and blood pressure.
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Affiliation(s)
- Loraine Liping Seng
- Department of Statistics and Data Science, National University of Singapore, Singapore
- Duke-NUS Graduate Medical School, National University of Singapore, Singapore
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- Department of Statistics, National Cheng Kung University, Tainan, Taiwan
| | - Jingli Wang
- School of Statistics and Data Science, Nankai University, China
| | - Jialiang Li
- Department of Statistics and Data Science, National University of Singapore, Singapore
- Duke-NUS Graduate Medical School, National University of Singapore, Singapore
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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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15
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Yu B, Zachrisson HD, Cheesman R, Ystrom E, Nes RB. Boys with overweight status lagged behind girls with overweight status in reading: evidence from mendelian randomization. J Clin Epidemiol 2023; 159:199-205. [PMID: 37156340 DOI: 10.1016/j.jclinepi.2023.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 04/05/2023] [Accepted: 05/01/2023] [Indexed: 05/10/2023]
Abstract
OBJECTIVES We investigated the relationship between childhood weight status and academic achievement across sexes and different school subjects in Norway. STUDY DESIGN AND SETTING We used data from the Norwegian Mother, Father and Child Cohort Study (MoBa), which includes genetic data (N = 13,648, 8-year-old children). We employed within-family mendelian randomization, using a body mass index (BMI) polygenic risk score as an instrument to address unobserved heterogeneity. RESULTS Contrary to most previous findings, we observed that overweight status (including obesity) has more detrimental effects on reading achievement in boys than in girls; the test scores of boys with overweight were about a standard deviation lower than those of normal weight boys, and the negative effects on reading achievement became stronger in the later grade. CONCLUSION Previous obesity prevention studies have mainly targeted girls, based on the assumption that the obesity penalty is greater for girls. Our findings highlight that particular attention to boys with overweight may help reduce the existing gender gap in academic achievement.
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Affiliation(s)
- Baeksan Yu
- Department of Education, Gwangju National University of Education, Gwangju, South Korea.
| | | | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Eivind Ystrom
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Ragnhild Bang Nes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway; Department of Mental Health and Suicide, Norwegian Institute of Public Health, Oslo, Norway; Department of Philosophy, Classics, and History of Arts and Ideas, University of Oslo, Oslo, Norway
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16
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Lorincz-Comi N, Yang Y, Li G, Zhu X. MRBEE: A novel bias-corrected multivariable Mendelian Randomization method. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.10.523480. [PMID: 37066391 PMCID: PMC10103949 DOI: 10.1101/2023.01.10.523480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes and can apply to summary data from genome-wide association studies (GWAS). Since GWAS summary statistics are subject to estimation errors, most existing MR approaches suffer from measurement error bias, whose scale and direction are influenced by weak instrumental variables and GWAS sample overlap, respectively. We introduce MRBEE (MR using Bias-corrected Estimating Equation), a novel multivariable MR method capable of simultaneously removing measurement error bias and identifying horizontal pleiotropy. In simulations, we showed that MRBEE is capable of effectively removing measurement error bias in the presence of weak instrumental variables and sample overlap. In two independent real data analyses, we discovered that the causal effect of BMI on coronary artery disease risk is entirely mediated by blood pressure, and that existing MR methods may underestimate the causal effect of cannabis use disorder on schizophrenia risk compared to MRBEE. MRBEE possesses significant potential for advancing genetic research by providing a valuable tool to study causality between multiple risk factors and disease outcomes, particularly as a large number of GWAS summary statistics become publicly available.
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Affiliation(s)
- Noah Lorincz-Comi
- Department of Population and Quantitative Health Sciences, School of Medicine
- Case Western Reserve University, Cleveland, OH 44106, USA June 12, 2023
| | - Yihe Yang
- Department of Population and Quantitative Health Sciences, School of Medicine
- Case Western Reserve University, Cleveland, OH 44106, USA June 12, 2023
| | - Gen Li
- Department of Population and Quantitative Health Sciences, School of Medicine
- Case Western Reserve University, Cleveland, OH 44106, USA June 12, 2023
| | - Xiaofeng Zhu
- This work was supported by grant HG011052 (to X.Z.) from the National Human Genome Research Institute (NHGRI), USA.
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17
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Kang H. Summarising multiple bounds of the average causal effect in Mendelian randomisation. Paediatr Perinat Epidemiol 2023; 37:338-340. [PMID: 36970788 DOI: 10.1111/ppe.12973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 03/16/2023] [Indexed: 05/10/2023]
Affiliation(s)
- Hyunseung Kang
- Department of Statistics, University of Wisconsin - Madison, Madison, Wisconsin, USA
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18
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Xue H, Shen X, Pan W. Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data. J Am Stat Assoc 2023; 118:1525-1537. [PMID: 37808547 PMCID: PMC10557939 DOI: 10.1080/01621459.2023.2183127] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 02/14/2023] [Indexed: 02/24/2023]
Abstract
Transcriptome-wide association studies (TWAS) have recently emerged as a popular tool to discover (putative) causal genes by integrating an outcome GWAS dataset with another gene expression/transcriptome GWAS (called eQTL) dataset. In our motivating and target application, we'd like to identify causal genes for low-density lipoprotein cholesterol (LDL), which is crucial for developing new treatments for hyperlipidemia and cardiovascular diseases. The statistical principle underlying TWAS is (two-sample) two-stage least squares (2SLS) using multiple correlated SNPs as instrumental variables (IVs); it is closely related to typical (two-sample) Mendelian randomization (MR) using independent SNPs as IVs, which is expected to be impractical and lower-powered for TWAS (and some other) applications. However, often some of the SNPs used may not be valid IVs, e.g. due to the widespread pleiotropy of their direct effects on the outcome not mediated through the gene of interest, leading to false conclusions by TWAS (or MR). Building on recent advances in sparse regression, we propose a robust and efficient inferential method to account for both hidden confounding and some invalid IVs via two-stage constrained maximum likelihood (2ScML), an extension of 2SLS. We first develop the proposed method with individual-level data, then extend it both theoretically and computationally to GWAS summary data for the most popular two-sample TWAS design, to which almost all existing robust IV regression methods are however not applicable. We show that the proposed method achieves asymptotically valid statistical inference on causal effects, demonstrating its wider applicability and superior finite-sample performance over the standard 2SLS/TWAS (and MR). We apply the methods to identify putative causal genes for LDL by integrating large-scale lipid GWAS summary data with eQTL data.
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Affiliation(s)
- Haoran Xue
- School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455
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19
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A bidirectional Mendelian randomized study of classical blood lipids and venous thrombosis. Sci Rep 2023; 13:3904. [PMID: 36890190 PMCID: PMC9995644 DOI: 10.1038/s41598-023-31067-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 03/06/2023] [Indexed: 03/10/2023] Open
Abstract
There is still some controversy about the relationship between lipids and venous thrombosis (VTE). A bidirectional Mendelian randomization (MR) study was conducted to clarify the causal relationship between three classical lipids (low-density lipoprotein (LDL), high-density lipoprotein (HDL) and triglycerides (TGs)) and venous thromboembolism (VTE) (deep venous thrombosis (DVT) and pulmonary embolism (PE)). Three classical lipids and VTE were analysed by bidirectional Mendelian randomization (MR). We used the random effect inverse variance weighted (IVW) model as the main analysis model and the weighted median method, simple mode method, weighted mode method and MR-Egger methods as supplementary methods. The leave-one-out test was used to determine the influence of outliers. The heterogeneity was calculated by using Cochran Q statistics in the MR-Egger and IVW methods. The intercept term in the MR‒Egger regression was used to indicate whether horizontal pleiotropy affected the results of the MR analysis. In addition, MR-PRESSO identified outlier single-nucleotide polymorphisms (SNPs) and obtained a stable result by removing outlier SNPs and then performing MR analysis. When we used three classical lipids (LDL, HDL and TGs) as exposure variables, no causal relationship between them and VTE (DVT and PE) was found. In addition, we did not find significant causal effects of VTE on the three classical lipids in reverse MR analysis. There is no significant causal relationship between three classical lipids (LDL, HDL and TGs) and VTE (DVT and PE) from a genetic point of view.
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20
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Shi D, Wang Y, Zhang Z, Cao Y, Hu YQ. MR-BOIL: Causal inference in one-sample Mendelian randomization for binary outcome with integrated likelihood method. Genet Epidemiol 2023; 47:332-357. [PMID: 36808763 DOI: 10.1002/gepi.22520] [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: 08/17/2022] [Revised: 12/14/2022] [Accepted: 02/01/2023] [Indexed: 02/21/2023]
Abstract
Mendelian randomization is a statistical method for inferring the causal relationship between exposures and outcomes using an economics-derived instrumental variable approach. The research results are relatively complete when both exposures and outcomes are continuous variables. However, due to the noncollapsing nature of the logistic model, the existing methods inherited from the linear model for exploring binary outcome cannot take the effect of confounding factors into account, which leads to biased estimate of the causal effect. In this article, we propose an integrated likelihood method MR-BOIL to investigate causal relationships for binary outcomes by treating confounders as latent variables in one-sample Mendelian randomization. Under the assumption of a joint normal distribution of the confounders, we use expectation maximization algorithm to estimate the causal effect. Extensive simulations demonstrate that the estimator of MR-BOIL is asymptotically unbiased and that our method improves statistical power without inflating type I error rate. We then apply this method to analyze the data from Atherosclerosis Risk in Communications Study. The results show that MR-BOIL can better identify plausible causal relationships with high reliability, compared with the unreliable results of existing methods. MR-BOIL is implemented in R and the corresponding R code is provided for free download.
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Affiliation(s)
- Dapeng Shi
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
| | - Yuquan Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Ziyong Zhang
- Department of Statistics and Data Science, School of Management, Fudan University, Shanghai, China
| | - Yunlong Cao
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yue-Qing Hu
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China.,State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
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21
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Wang S, Kang H. Weak-instrument robust tests in two-sample summary-data Mendelian randomization. Biometrics 2022; 78:1699-1713. [PMID: 34213007 DOI: 10.1111/biom.13524] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 06/07/2021] [Accepted: 06/17/2021] [Indexed: 12/30/2022]
Abstract
Mendelian randomization (MR) has been a popular method in genetic epidemiology to estimate the effect of an exposure on an outcome using genetic variants as instrumental variables (IV), with two-sample summary-data MR being the most popular. Unfortunately, instruments in MR studies are often weakly associated with the exposure, which can bias effect estimates and inflate Type I errors. In this work, we propose test statistics that are robust under weak-instrument asymptotics by extending the Anderson-Rubin, Kleibergen, and the conditional likelihood ratio test in econometrics to two-sample summary-data MR. We also use the proposed Anderson-Rubin test to develop a point estimator and to detect invalid instruments. We conclude with a simulation and an empirical study and show that the proposed tests control size and have better power than existing methods with weak instruments.
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Affiliation(s)
- Sheng Wang
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Hyunseung Kang
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, USA
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22
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Pölsterl S, Wachinger C. Identification of causal effects of neuroanatomy on cognitive decline requires modeling unobserved confounders. Alzheimers Dement 2022; 19:1994-2005. [PMID: 36419215 DOI: 10.1002/alz.12825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 11/27/2022]
Abstract
INTRODUCTION Carrying out a randomized controlled trial to estimate the causal effects of regional brain atrophy due to Alzheimer's disease (AD) is impossible. Instead, we must estimate causal effects from observational data. However, this generally requires knowing and having recorded all confounders, which is often unrealistic. METHODS We provide an approach that leverages the dependencies among multiple neuroanatomical measures to estimate causal effects from observational neuroimaging data without the need to know and record all confounders. RESULTS Our analyses of N = 732 $N=732$ subjects from the Alzheimer's Disease Neuroimaging Initiative demonstrate that using our approach results in biologically meaningful conclusions, whereas ignoring unobserved confounding yields results that conflict with established knowledge on cognitive decline due to AD. DISCUSSION The findings provide evidence that the impact of unobserved confounding can be substantial. To ensure trustworthy scientific insights, future AD research can account for unobserved confounding via the proposed approach.
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Affiliation(s)
- Sebastian Pölsterl
- The Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
| | - Christian Wachinger
- The Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany.,Technical University of Munich, School of Medicine, Department of Radiology, Munich, Germany
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23
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Kang H. Discussion on “Instrumented difference‐in‐differences” by Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy & Dylan S. Small. Biometrics 2022. [DOI: 10.1111/biom.13784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 07/11/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Hyunseung Kang
- Department of Statistics University of Wisconsin‐Madison Madison USA
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24
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St.-Pierre J, Zhang X, Lu T, Jiang L, Loffree X, Wang L, Bhatnagar S, Greenwood CMT. Considering strategies for SNP selection in genetic and polygenic risk scores. Front Genet 2022; 13:900595. [PMID: 36819922 PMCID: PMC9930898 DOI: 10.3389/fgene.2022.900595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 10/05/2022] [Indexed: 02/04/2023] Open
Abstract
Genetic risk scores (GRS) and polygenic risk scores (PRS) are weighted sums of, respectively, several or many genetic variant indicator variables. Although they are being increasingly proposed for clinical use, the best ways to construct them are still actively debated. In this commentary, we present several case studies illustrating practical challenges associated with building or attempting to improve score performance when there is expected to be heterogeneity of disease risk between cohorts or between subgroups of individuals. Specifically, we contrast performance associated with several ways of selecting single nucleotide polymorphisms (SNPs) for inclusion in these scores. By considering GRS and PRS as predictors that are measured with error, insights into their strengths and weaknesses may be obtained, and SNP selection approaches play an important role in defining such errors.
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Affiliation(s)
- Julien St.-Pierre
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
| | - Xinyi Zhang
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada,Quantitative Life Sciences, McGill University, Montréal, QC, Canada
| | - Lai Jiang
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada
| | - Xavier Loffree
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada,Department of Statistics and Actuarial Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Linbo Wang
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Sahir Bhatnagar
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
| | - Celia M. T. Greenwood
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada,Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada,Quantitative Life Sciences, McGill University, Montréal, QC, Canada,Gerald Bronfman Department of Oncology, McGill University, Montréal, QC, Canada,*Correspondence: Celia M. T. Greenwood,
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25
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Hartley AE, Power GM, Sanderson E, Smith GD. A Guide for Understanding and Designing Mendelian Randomization Studies in the Musculoskeletal Field. JBMR Plus 2022; 6:e10675. [PMID: 36248277 PMCID: PMC9549705 DOI: 10.1002/jbm4.10675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 08/08/2022] [Indexed: 11/10/2022] Open
Abstract
Mendelian randomization (MR) is an increasingly popular component of an epidemiologist's toolkit, used to provide evidence of a causal effect of one trait (an exposure, eg, body mass index [BMI]) on an outcome trait or disease (eg, osteoarthritis). Identifying these effects is important for understanding disease etiology and potentially identifying targets for therapeutic intervention. MR uses genetic variants as instrumental variables for the exposure, which should not be influenced by the outcome or confounding variables, overcoming key limitations of traditional epidemiological analyses. For MR to generate a valid estimate of effect, key assumptions must be met. In recent years, there has been a rapid rise in MR methods that aim to test, or are robust to violations of, these assumptions. In this review, we provide an overview of MR for a non-expert audience, including an explanation of these key assumptions and how they are often tested, to aid a better reading and understanding of the MR literature. We highlight some of these new methods and how they can be useful for specific methodological challenges in the musculoskeletal field, including for conditions or traits that share underlying biological pathways, such as bone and joint disease. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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Affiliation(s)
- April E Hartley
- MRC‐Integrative Epidemiology UnitPopulation Health Sciences, Bristol Medical SchoolBristolUK
| | - Grace M Power
- MRC‐Integrative Epidemiology UnitPopulation Health Sciences, Bristol Medical SchoolBristolUK
| | - Eleanor Sanderson
- MRC‐Integrative Epidemiology UnitPopulation Health Sciences, Bristol Medical SchoolBristolUK
| | - George Davey Smith
- MRC‐Integrative Epidemiology UnitPopulation Health Sciences, Bristol Medical SchoolBristolUK
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26
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Denault WRP, Bohlin J, Page CM, Burgess S, Jugessur A. Cross-fitted instrument: A blueprint for one-sample Mendelian randomization. PLoS Comput Biol 2022; 18:e1010268. [PMID: 36037248 PMCID: PMC9462731 DOI: 10.1371/journal.pcbi.1010268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 09/09/2022] [Accepted: 05/31/2022] [Indexed: 11/18/2022] Open
Abstract
Bias from weak instruments may undermine the ability to estimate causal effects in instrumental variable regression (IVR). We present here a new approach to handling weak instrument bias through the application of a new type of instrumental variable coined 'Cross-Fitted Instrument' (CFI). CFI splits the data at random and estimates the impact of the instrument on the exposure in each partition. These estimates are then used to perform an IVR on each partition. We adapt CFI to the Mendelian randomization (MR) setting and term this adaptation 'Cross-Fitting for Mendelian Randomization' (CFMR). We show that, even when using weak instruments, CFMR is, at worst, biased towards the null, which makes it a conservative one-sample MR approach. In particular, CFMR remains conservative even when the two samples used to perform the MR analysis completely overlap, whereas current state-of-the-art approaches (e.g., MR RAPS) display substantial bias in this setting. Another major advantage of CFMR lies in its use of all of the available data to select genetic instruments, which maximizes statistical power, as opposed to traditional two-sample MR where only part of the data is used to select the instrument. Consequently, CFMR is able to enhance statistical power in consortia-led meta-analyses by enabling a conservative one-sample MR to be performed in each cohort prior to a meta-analysis of the results across all the cohorts. In addition, CFMR enables a cross-ethnic MR analysis by accounting for ethnic heterogeneity, which is particularly important in meta-analyses where the participating cohorts may have different ethnicities. To our knowledge, none of the current MR approaches can account for such heterogeneity. Finally, CFMR enables the application of MR to exposures that are either rare or difficult to measure, which would normally preclude their analysis in the regular two-sample MR setting.
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Affiliation(s)
- William R. P. Denault
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Jon Bohlin
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Christian M. Page
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Astanand Jugessur
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
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27
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Hu X, Zhao J, Lin Z, Wang Y, Peng H, Zhao H, Wan X, Yang C. Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics. Proc Natl Acad Sci U S A 2022; 119:e2106858119. [PMID: 35787050 PMCID: PMC9282238 DOI: 10.1073/pnas.2106858119] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 03/22/2022] [Indexed: 01/07/2023] Open
Abstract
Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false-positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here, we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which 1) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and 2) allows the inclusion of more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects.
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Affiliation(s)
- Xianghong Hu
- Department of Mathematics, The Hong Kong University of Science and Technology, The Hong Kong Special Administrative Region, China
| | - Jia Zhao
- Department of Mathematics, The Hong Kong University of Science and Technology, The Hong Kong Special Administrative Region, China
| | - Zhixiang Lin
- Department of Statistics, The Chinese University of Hong Kong, The Hong Kong Special Administrative Region, China
| | - Yang Wang
- Department of Mathematics, The Hong Kong University of Science and Technology, The Hong Kong Special Administrative Region, China
| | - Heng Peng
- Department of Mathematics, Hong Kong Baptist University, The Hong Kong Special Administrative Region, China
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520
| | - Xiang Wan
- Research Center for Intelligent Systems in Big Data, Shen Zhen Research Institute of Big Data, Shen Zhen 518172, China
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, The Hong Kong Special Administrative Region, China
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Hasegawa RB, Small DS. Estimating Malaria Vaccine Efficacy in the Absence of a Gold Standard Case Definition: Mendelian Factorial Design. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2020.1863222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Raiden B. Hasegawa
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA
| | - Dylan S. Small
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA
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Yang Q, Sanderson E, Tilling K, Borges MC, Lawlor DA. Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization. Eur J Epidemiol 2022; 37:683-700. [PMID: 35622304 PMCID: PMC9329407 DOI: 10.1007/s10654-022-00874-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 04/18/2022] [Indexed: 12/19/2022]
Abstract
With the increasing size and number of genome-wide association studies, individual single nucleotide polymorphisms are increasingly found to associate with multiple traits. Many different mechanisms could result in proposed genetic IVs for an exposure of interest being associated with multiple non-exposure traits, some of which could bias MR results. We describe and illustrate, through causal diagrams, a range of scenarios that could result in proposed IVs being related to non-exposure traits in MR studies. These associations could occur due to five scenarios: (i) confounding, (ii) vertical pleiotropy, (iii) horizontal pleiotropy, (iv) reverse causation and (v) selection bias. For each of these scenarios we outline steps that could be taken to explore the underlying mechanism and mitigate any resulting bias in the MR estimation. We recommend MR studies explore possible IV-non-exposure associations across a wider range of traits than is usually the case. We highlight the pros and cons of relying on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic or other biasing paths via known traits. We apply our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in UK Biobank.
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Affiliation(s)
- Qian Yang
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
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30
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Zhao A, Lee Y, Small DS, Karmakar B. Evidence factors from multiple, possibly invalid, instrumental variables. Ann Stat 2022. [DOI: 10.1214/21-aos2148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Anqi Zhao
- Department of Statistics and Data Science, National University of Singapore
| | - Youjin Lee
- Department of Biostatistics, Brown University
| | - Dylan S. Small
- Department of Statistics and Data Science, University of Pennsylvania
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Guo Z, Ćevid D, Bühlmann P. Doubly debiased lasso: High-dimensional inference under hidden confounding. Ann Stat 2022; 50:1320-1347. [DOI: 10.1214/21-aos2152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Zijian Guo
- Department of Statistics, Rutgers University
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32
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Boehm FJ, Zhou X. Statistical methods for Mendelian randomization in genome-wide association studies: A review. Comput Struct Biotechnol J 2022; 20:2338-2351. [PMID: 35615025 PMCID: PMC9123217 DOI: 10.1016/j.csbj.2022.05.015] [Citation(s) in RCA: 101] [Impact Index Per Article: 50.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/08/2022] [Accepted: 05/09/2022] [Indexed: 11/15/2022] Open
Abstract
Genome-wide association studies have yielded thousands of associations for many common diseases and disease-related complex traits. The identified associations made it possible to identify the causal risk factors underlying diseases and investigate the causal relationships among complex traits through Mendelian randomization. Mendelian randomization is a form of instrumental variable analysis that uses SNP associations from genome-wide association studies as instruments to study and uncover causal relationships between complex traits. By leveraging SNP genotypes as instrumental variables, or proxies, for the exposure complex trait, investigators can tease out causal effects from observational data, provided that necessary assumptions are satisfied. We discuss below the development of Mendelian randomization methods in parallel with the growth of genome-wide association studies. We argue that the recent availability of GWAS summary statistics for diverse complex traits has motivated new Mendelian randomization methods with relaxed causality assumptions and that this area continues to offer opportunities for robust biological discoveries.
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Affiliation(s)
- Frederick J. Boehm
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
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33
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Yue M, Li J, Sun B. Conditional sparse boosting for high-dimensional instrumental variable estimation. J STAT COMPUT SIM 2022. [DOI: 10.1080/00949655.2022.2056739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Mu Yue
- Singapore University of Technology and Design, Singapore, Singapore
| | - Jialiang Li
- National University of Singapore, Duke University-NUS Graduate Medical School, Singapore Eye Research Institute, Singapore, Singapore
| | - Baoluo Sun
- National University of Singapore, Singapore, Singapore
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34
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Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models. ENTROPY 2022; 24:e24040512. [PMID: 35455175 PMCID: PMC9024820 DOI: 10.3390/e24040512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 04/01/2022] [Accepted: 04/03/2022] [Indexed: 01/27/2023]
Abstract
This paper investigates the problem of selecting instrumental variables relative to a target causal influence X→Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders. We propose a necessary condition for detecting variables that cannot serve as instrumental variables. Unlike many existing conditions for continuous variables, i.e., that at least two or more valid instrumental variables are present in the system, our condition is designed with a single instrumental variable. We then characterize the graphical implications of our condition in linear non-Gaussian acyclic causal models. Given that the existing graphical criteria for the instrument validity are not directly testable given observational data, we further show whether and how such graphical criteria can be checked by exploiting our condition. Finally, we develop a method to select the set of candidate instrumental variables given observational data. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method.
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35
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Edwards CH, Vie GÅ, Kinge JM. Body mass index and healthcare costs: using genetic variants from the HUNT study as instrumental variables. BMC Health Serv Res 2022; 22:396. [PMID: 35337320 PMCID: PMC8957125 DOI: 10.1186/s12913-022-07597-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 01/27/2022] [Indexed: 12/01/2022] Open
Abstract
Background Past studies have found associations between obesity and healthcare costs, however, these studies have suffered from bias due to omitted variables, reverse causality, and measurement error. Methods We used genetic variants related to body mass index (BMI) as instruments for BMI; thereby exploiting the natural randomization of genetic variants that occurs at conception. We used data on measured height and weight, genetic information, and sociodemographic factors from the Nord-Trøndelag Health Studies (HUNT), and individual-level registry data on healthcare costs, educational level, registration status, and biological relatives. We studied associations between BMI and general practitioner (GP)-, specialist-, and total healthcare costs in the Norwegian setting using instrumental variable (IV) regressions, and compared our findings with effect estimates from ordinary least squares (OLS) regressions. The sensitivity of our findings to underlying IV-assumptions was explored using two-sample Mendelian randomization methods, non-linear analyses, sex-, healthcare provider-, and age-specific analyses, within-family analyses, and outlier removal. We also conducted power calculations to assess the likelihood of detecting an effect given our sample 60,786 individuals. Results We found that increased BMI resulted in significantly higher GP costs; however, the IV-based effect estimate was smaller than the OLS-based estimate. We found no evidence of an association between BMI and specialist or total healthcare costs. Conclusions Elevated BMI leads to higher GP costs, and more studies are needed to understand the causal mechanisms between BMI and specialist costs. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-022-07597-z.
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Affiliation(s)
- Christina Hansen Edwards
- Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway. .,Centre for Fertility and Health & Centre for Disease Burden, Norwegian Institute of Public Health, Oslo, Norway.
| | - Gunnhild Åberge Vie
- Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jonas Minet Kinge
- Centre for Fertility and Health & Centre for Disease Burden, Norwegian Institute of Public Health, Oslo, Norway.,Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway
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36
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Boddy S, Islam M, Moll T, Kurz J, Burrows D, McGown A, Bhargava A, Julian TH, Harvey C, Marshall JNG, Hall BPC, Allen SP, Kenna KP, Sanderson E, Zhang S, Ramesh T, Snyder MP, Shaw PJ, McDermott C, Cooper-Knock J. Unbiased metabolome screen leads to personalized medicine strategy for amyotrophic lateral sclerosis. Brain Commun 2022; 4:fcac069. [PMID: 35441136 PMCID: PMC9010771 DOI: 10.1093/braincomms/fcac069] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 11/29/2021] [Accepted: 03/15/2022] [Indexed: 11/17/2022] Open
Abstract
Amyotrophic lateral sclerosis is a rapidly progressive neurodegenerative disease that affects 1/350 individuals in the United Kingdom. The cause of amyotrophic lateral sclerosis is unknown in the majority of cases. Two-sample Mendelian randomization enables causal inference between an exposure, such as the serum concentration of a specific metabolite, and disease risk. We obtained genome-wide association study summary statistics for serum concentrations of 566 metabolites which were population matched with a genome-wide association study of amyotrophic lateral sclerosis. For each metabolite, we performed Mendelian randomization using an inverse variance weighted estimate for significance testing. After stringent Bonferroni multiple testing correction, our unbiased screen revealed three metabolites that were significantly linked to the risk of amyotrophic lateral sclerosis: Estrone-3-sulphate and bradykinin were protective, which is consistent with literature describing a male preponderance of amyotrophic lateral sclerosis and a preventive effect of angiotensin-converting enzyme inhibitors which inhibit the breakdown of bradykinin. Serum isoleucine was positively associated with amyotrophic lateral sclerosis risk. All three metabolites were supported by robust Mendelian randomization measures and sensitivity analyses; estrone-3-sulphate and isoleucine were confirmed in a validation amyotrophic lateral sclerosis genome-wide association study. Estrone-3-sulphate is metabolized to the more active estradiol by the enzyme 17β-hydroxysteroid dehydrogenase 1; further, Mendelian randomization demonstrated a protective effect of estradiol and rare variant analysis showed that missense variants within HSD17B1, the gene encoding 17β-hydroxysteroid dehydrogenase 1, modify risk for amyotrophic lateral sclerosis. Finally, in a zebrafish model of C9ORF72-amyotrophic lateral sclerosis, we present evidence that estradiol is neuroprotective. Isoleucine is metabolized via methylmalonyl-CoA mutase encoded by the gene MMUT in a reaction that consumes vitamin B12. Multivariable Mendelian randomization revealed that the toxic effect of isoleucine is dependent on the depletion of vitamin B12; consistent with this, rare variants which reduce the function of MMUT are protective against amyotrophic lateral sclerosis. We propose that amyotrophic lateral sclerosis patients and family members with high serum isoleucine levels should be offered supplementation with vitamin B12.
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Affiliation(s)
- Sarah Boddy
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Mahjabin Islam
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Tobias Moll
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Julian Kurz
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - David Burrows
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Alexander McGown
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Anushka Bhargava
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Thomas H Julian
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Calum Harvey
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Jack NG Marshall
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Benjamin PC Hall
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Scott P Allen
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Kevin P Kenna
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Eleanor Sanderson
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
| | - Sai Zhang
- Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Tennore Ramesh
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Michael P Snyder
- Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Pamela J Shaw
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Christopher McDermott
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Johnathan Cooper-Knock
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
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37
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Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munafò MR, Palmer T, Schooling CM, Wallace C, Zhao Q, Smith GD. Mendelian randomization. NATURE REVIEWS. METHODS PRIMERS 2022; 2:6. [PMID: 37325194 PMCID: PMC7614635 DOI: 10.1038/s43586-021-00092-5] [Citation(s) in RCA: 467] [Impact Index Per Article: 233.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/21/2021] [Indexed: 06/17/2023]
Abstract
Mendelian randomization (MR) is a term that applies to the use of genetic variation to address causal questions about how modifiable exposures influence different outcomes. The principles of MR are based on Mendel's laws of inheritance and instrumental variable estimation methods, which enable the inference of causal effects in the presence of unobserved confounding. In this Primer, we outline the principles of MR, the instrumental variable conditions underlying MR estimation and some of the methods used for estimation. We go on to discuss how the assumptions underlying an MR study can be assessed and give methods of estimation that are robust to certain violations of these assumptions. We give examples of a range of studies in which MR has been applied, the limitations of current methods of analysis and the outlook for MR in the future. The difference between the assumptions required for MR analysis and other forms of non-interventional epidemiological studies means that MR can be used as part of a triangulation across multiple sources of evidence for causal inference.
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Affiliation(s)
- Eleanor Sanderson
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Michael V. Holmes
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hyunseung Kang
- Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA
| | - Jean Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Marcus R. Munafò
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- School of Psychological Science, University of Bristol, Bristol, UK
- National Institute for Health Research (NIHR), Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Tom Palmer
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - C. Mary Schooling
- School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- School of Public Health, City University of New York, New York, USA
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), University of Cambridge, Cambridge, UK
| | - Qingyuan Zhao
- Statistical Laboratory, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research (NIHR), Biomedical Research Centre, University of Bristol, Bristol, UK
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Elghazy AM, Elsaeid AM, Refaat M, Youssef MM. Biochemical studies of adiponectin gene polymorphism in patients with obesity in Egyptians. Arch Physiol Biochem 2022; 128:43-50. [PMID: 31502880 DOI: 10.1080/13813455.2019.1662451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Adiponectin gene polymorphisms have recently been reported to be associated with obesity. In Egypt, obesity has expanded especially with the changing nourishment propensities and the inexorably inactive ways of life, with almost 70 percent of the Egyptian populations being obese. AIM To assess the relationship of the adiponectin gene (ADIPOQ) polymorphism in patients with Obesity in Egyptians. SUBJECTS AND METHODS This study included 100 patients with obesity and 97 random controls. RESULTS Adiponectin rs1501299 polymorphism showed significant difference cases with different obesity grades where the T/T genotype was relatively higher in higher classes of obesity (Class II and III; (66.7% and 55.6% respectively, p = 000), which determines the susceptibility to obesity. CONCLUSION Adiponectin rs1501299 polymorphism might be a candidate gene, which determines the susceptibility to obesity. Larger studies are necessary to confirm these findings in various populations.
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Affiliation(s)
- Ahmed M Elghazy
- Biochemistry Division, Chemistry Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - Afaf M Elsaeid
- Genetics unit, Department of Pediatrics, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Manar Refaat
- Biochemistry Division, Chemistry Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - Magdy M Youssef
- Biochemistry Division, Chemistry Department, Faculty of Science, Mansoura University, Mansoura, Egypt
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Abstract
Mendelian randomization (MR) is a method of studying the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with the specific exposures of interest. MR provides a more robust understanding of the influence of these exposures on outcomes because germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure-outcome associations. The genetic variant can therefore be used as a tool to link the proposed risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches. We describe the scope of MR, highlighting the range of applications being made possible as genetic data sets and resources become larger and more freely available. We outline the MR approach in detail, covering concepts, assumptions, and estimation methods. We cover some common misconceptions, provide strategies for overcoming violation of assumptions, and discuss future prospects for extending the clinical applicability, methodological innovations, robustness, and generalizability of MR findings.
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Affiliation(s)
- Rebecca C Richmond
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, United Kingdom
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol BS1 3NU, United Kingdom
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40
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Miao W, Hu W, Ogburn EL, Zhou X. Identifying effects of multiple treatments in the presence of unmeasured confounding. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2021.2023551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Wang Miao
- Department of Probability and Statistics, Peking University, Beijing, PRC
| | - Wenjie Hu
- Department of Probability and Statistics, Peking University, Beijing, PRC
| | - Elizabeth L. Ogburn
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Xiaohua Zhou
- Department of Biostatistics and Beijing International Center for Mathematical Research, Peking University, Beijing, PRC
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41
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Fan Q, Yu W. Adaptive k-class estimation in high-dimensional linear models. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2019.1634206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Qingliang Fan
- The Wang Yanan Institute of Studies in Economics (WISE) and School of Economics, Xiamen University, Xiamen, China
| | - Wanlu Yu
- Jinshan College of Fujian Agriculture and Forestry University, Fujian, China
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42
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Gkatzionis A, Burgess S, Conti DV, Newcombe PJ. Bayesian variable selection with a pleiotropic loss function in Mendelian randomization. Stat Med 2021; 40:5025-5045. [PMID: 34155684 PMCID: PMC8446304 DOI: 10.1002/sim.9109] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 04/17/2021] [Accepted: 06/07/2021] [Indexed: 01/04/2023]
Abstract
Mendelian randomization is the use of genetic variants as instruments to assess the existence of a causal relationship between a risk factor and an outcome. A Mendelian randomization analysis requires a set of genetic variants that are strongly associated with the risk factor and only associated with the outcome through their effect on the risk factor. We describe a novel variable selection algorithm for Mendelian randomization that can identify sets of genetic variants which are suitable in both these respects. Our algorithm is applicable in the context of two-sample summary-data Mendelian randomization and employs a recently proposed theoretical extension of the traditional Bayesian statistics framework, including a loss function to penalize genetic variants that exhibit pleiotropic effects. The algorithm offers robust inference through the use of model averaging, as we illustrate by running it on a range of simulation scenarios and comparing it against established pleiotropy-robust Mendelian randomization methods. In a real-data application, we study the effect of systolic and diastolic blood pressure on the risk of suffering from coronary heart disease (CHD). Based on a recent large-scale GWAS for blood pressure, we use 395 genetic variants for systolic and 391 variants for diastolic blood pressure. Both traits are shown to have significant risk-increasing effects on CHD risk.
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Affiliation(s)
- Apostolos Gkatzionis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - David V. Conti
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
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Dummy endogenous treatment effect estimation using high‐dimensional instrumental variables. CAN J STAT 2021. [DOI: 10.1002/cjs.11648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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44
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Windmeijer F, Liang X, Hartwig FP, Bowden J. The confidence interval method for selecting valid instrumental variables. J R Stat Soc Series B Stat Methodol 2021. [DOI: 10.1111/rssb.12449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Frank Windmeijer
- Department of Statistics and Nuffield College University of Oxford Oxford UK
- MRC Integrative Epidemiology Unit University of Bristol Bristol UK
| | - Xiaoran Liang
- Department of Economics University of Bristol Bristol UK
| | - Fernando P. Hartwig
- MRC Integrative Epidemiology Unit University of Bristol Bristol UK
- Center for Epidemiological Research University of Pelotas Pelotas Brazil
| | - Jack Bowden
- MRC Integrative Epidemiology Unit University of Bristol Bristol UK
- College of Medicine and Health University of Exeter Exeter UK
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45
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Grant AJ, Burgess S. Pleiotropy robust methods for multivariable Mendelian randomization. Stat Med 2021; 40:5813-5830. [PMID: 34342032 PMCID: PMC7612169 DOI: 10.1002/sim.9156] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 07/21/2021] [Accepted: 07/22/2021] [Indexed: 12/02/2022]
Abstract
Mendelian randomization is a powerful tool for inferring the presence, or otherwise, of causal effects from observational data. However, the nature of genetic variants is such that pleiotropy remains a barrier to valid causal effect estimation. There are many options in the literature for pleiotropy robust methods when studying the effects of a single risk factor on an outcome. However, there are few pleiotropy robust methods in the multivariable setting, that is, when there are multiple risk factors of interest. In this article we introduce three methods which build on common approaches in the univariable setting: MVMR-Robust; MVMR-Median; and MVMR-Lasso. We discuss the properties of each of these methods and examine their performance in comparison to existing approaches in a simulation study. MVMR-Robust is shown to outperform existing outlier robust approaches when there are low levels of pleiotropy. MVMR-Lasso provides the best estimation in terms of mean squared error for moderate to high levels of pleiotropy, and can provide valid inference in a three sample setting. MVMR-Median performs well in terms of estimation across all scenarios considered, and provides valid inference up to a moderate level of pleiotropy. We demonstrate the methods in an applied example looking at the effects of intelligence, education and household income on the risk of Alzheimer’s disease.
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Affiliation(s)
- Andrew J Grant
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.,Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
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46
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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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47
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Barry C, Liu J, Richmond R, Rutter MK, Lawlor DA, Dudbridge F, Bowden J. Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data. PLoS Genet 2021; 17:e1009703. [PMID: 34370750 PMCID: PMC8376220 DOI: 10.1371/journal.pgen.1009703] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 08/19/2021] [Accepted: 07/08/2021] [Indexed: 11/18/2022] Open
Abstract
Over the last decade the availability of SNP-trait associations from genome-wide association studies has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification. In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. These estimates are then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method's performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes. Our approach can be viewed as a generalization of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our work serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias.
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Affiliation(s)
- Ciarrah Barry
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Junxi Liu
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Rebecca Richmond
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Martin K. Rutter
- Division of Diabetes, Endocrinology and Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- Diabetes, Endocrinology and Metabolism Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Deborah A. Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
| | - Jack Bowden
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Exeter Diabetes Group (ExCEED), College of Medicine and Health, University of Exeter, Exeter, United Kingdom
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48
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Zhu X. Mendelian randomization and pleiotropy analysis. QUANTITATIVE BIOLOGY 2021; 9:122-132. [PMID: 34386270 PMCID: PMC8356909 DOI: 10.1007/s40484-020-0216-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 05/16/2020] [Accepted: 05/21/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Mendelian randomization (MR) analysis has become popular in inferring and estimating the causality of an exposure on an outcome due to the success of genome wide association studies. Many statistical approaches have been developed and each of these methods require specific assumptions. RESULTS In this article, we review the pros and cons of these methods. We use an example of high-density lipoprotein cholesterol on coronary artery disease to illuminate the challenges in Mendelian randomization investigation. CONCLUSION The current available MR approaches allow us to study causality among risk factors and outcomes. However, novel approaches are desirable for overcoming multiple source confounding of risk factors and an outcome in MR analysis.
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Affiliation(s)
- Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
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49
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Wang Y, Li T, Fu L, Yang S, Hu YQ. A Novel Method for Mendelian Randomization Analyses With Pleiotropy and Linkage Disequilibrium in Genetic Variants From Individual Data. Front Genet 2021; 12:634394. [PMID: 34322150 PMCID: PMC8312241 DOI: 10.3389/fgene.2021.634394] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 06/15/2021] [Indexed: 12/28/2022] Open
Abstract
Mendelian randomization makes use of genetic variants as instrumental variables to eliminate the influence induced by unknown confounders on causal estimation in epidemiology studies. However, with the soaring genetic variants identified in genome-wide association studies, the pleiotropy, and linkage disequilibrium in genetic variants are unavoidable and may produce severe bias in causal inference. In this study, by modeling the pleiotropic effect as a normally distributed random effect, we propose a novel mixed-effects regression model-based method PLDMR, pleiotropy and linkage disequilibrium adaptive Mendelian randomization, which takes linkage disequilibrium into account and also corrects for the pleiotropic effect in causal effect estimation and statistical inference. We conduct voluminous simulation studies to evaluate the performance of the proposed and existing methods. Simulation results illustrate the validity and advantage of the novel method, especially in the case of linkage disequilibrium and directional pleiotropic effects, compared with other methods. In addition, by applying this novel method to the data on Atherosclerosis Risk in Communications Study, we conclude that body mass index has a significant causal effect on and thus might be a potential risk factor of systolic blood pressure. The novel method is implemented in R and the corresponding R code is provided for free download.
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Affiliation(s)
- Yuquan Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Tingting Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Liwan Fu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Siqian Yang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yue-Qing Hu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China.,Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
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50
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Xue H, Shen X, Pan W. Constrained maximum likelihood-based Mendelian randomization robust to both correlated and uncorrelated pleiotropic effects. Am J Hum Genet 2021; 108:1251-1269. [PMID: 34214446 PMCID: PMC8322939 DOI: 10.1016/j.ajhg.2021.05.014] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 05/25/2021] [Indexed: 12/23/2022] Open
Abstract
With the increasing availability of large-scale GWAS summary data on various complex traits and diseases, there have been tremendous interests in applications of Mendelian randomization (MR) to investigate causal relationships between pairs of traits using SNPs as instrumental variables (IVs) based on observational data. In spite of the potential significance of such applications, the validity of their causal conclusions critically depends on some strong modeling assumptions required by MR, which may be violated due to the widespread (horizontal) pleiotropy. Although many MR methods have been proposed recently to relax the assumptions by mainly dealing with uncorrelated pleiotropy, only a few can handle correlated pleiotropy, in which some SNPs/IVs may be associated with hidden confounders, such as some heritable factors shared by both traits. Here we propose a simple and effective approach based on constrained maximum likelihood and model averaging, called cML-MA, applicable to GWAS summary data. To deal with more challenging situations with many invalid IVs with only weak pleiotropic effects, we modify and improve it with data perturbation. Extensive simulations demonstrated that the proposed methods could control the type I error rate better while achieving higher power than other competitors. Applications to 48 risk factor-disease pairs based on large-scale GWAS summary data of 3 cardio-metabolic diseases (coronary artery disease, stroke, and type 2 diabetes), asthma, and 12 risk factors confirmed its superior performance.
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Affiliation(s)
- Haoran Xue
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.
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