1
|
Shen N, Ba Q, Lu Y. Causal Relationships between Polyunsaturated Fatty Acids and Colon Polyps: A Two-Sample Mendelian Randomization Study. Nutrients 2024; 16:2033. [PMID: 38999781 PMCID: PMC11243184 DOI: 10.3390/nu16132033] [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: 05/27/2024] [Revised: 06/14/2024] [Accepted: 06/24/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Epidemiological studies have shown that fatty acids, especially polyunsaturated fatty acids (PUFAs), influence colorectal carcinogenesis. Colon polyps, particularly those identified as precancerous, are a frequently encountered phenomenon associated with PUFAs. However, the results are inconsistent. Therefore, we investigated the effect of PUFAs on colon polyps in individuals of European ancestry. METHODS Single nucleotide polymorphisms correlating with PUFAs and colon polyps were derived from extensive genome-wide association studies, encompassing a discovery cohort of 135,006 samples and a corresponding validation set with 114,999 samples. Causality was assessed by employing a range of techniques, such as inverse variance weighted (IVW), weighted median, MR-Egger, and simple and weighted modes. The analysis was complemented with sensitivity checks using leave-one-out and heterogeneity evaluation through MR-IVW and Cochran's Q. RESULTS A thorough analysis was performed to examine the causal effects of PUFAs on the development of colon polyps. The findings indicated that levels of Omega-3 fatty acids (OR = 1.0014, 95% CI 1.0004-1.0024, p = 0.004), the ratio of Docosahexaenoic acid (DHA)/total fatty acids (FAs) (DHA/totalFA; OR = 1.0015, 95% CI 1.0002-1.0028, p = 0.023), and the ratio of Omega-3/totalFA (Omega-3/totalFA; OR = 1.0013, 95% CI 1.0003-1.0022, p = 0.010) were identified as biomarkers associated with an increased risk of colon polyps. Conversely, the ratio of Omega-6/Omega-3 (OR = 0.9986, 95% CI 0.9976-0.9995, p = 0.003) and the ratio of Linoleic acid (LA)/totalFA (LA/totalFA; OR = 0.9981, 95% CI 0.9962-0.9999, p = 0.044) were negatively associated with susceptibility to colon polyps. The MR-Egger and MR-IVW analysis revealed that pleiotropy and heterogeneity did not significantly impact the outcomes. CONCLUSION This study has uncovered a possible adverse effect of PUFAs, notably Omega-3, on the formation of colon polyps. Elevated Omega-3 levels were correlated with a heightened risk of colon polyps.
Collapse
Affiliation(s)
| | | | - Yanjun Lu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (N.S.); (Q.B.)
| |
Collapse
|
2
|
Jin J, Qi G, Yu Z, Chatterjee N. Mendelian randomization analysis using multiple biomarkers of an underlying common exposure. Biostatistics 2024:kxae006. [PMID: 38459704 DOI: 10.1093/biostatistics/kxae006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 11/16/2023] [Accepted: 02/05/2024] [Indexed: 03/10/2024] Open
Abstract
Mendelian randomization (MR) analysis is increasingly popular for testing the causal effect of exposures on disease outcomes using data from genome-wide association studies. In some settings, the underlying exposure, such as systematic inflammation, may not be directly observable, but measurements can be available on multiple biomarkers or other types of traits that are co-regulated by the exposure. We propose a method for MR analysis on latent exposures (MRLE), which tests the significance for, and the direction of, the effect of a latent exposure by leveraging information from multiple related traits. The method is developed by constructing a set of estimating functions based on the second-order moments of GWAS summary association statistics for the observable traits, under a structural equation model where genetic variants are assumed to have indirect effects through the latent exposure and potentially direct effects on the traits. Simulation studies show that MRLE has well-controlled type I error rates and enhanced power compared to single-trait MR tests under various types of pleiotropy. Applications of MRLE using genetic association statistics across five inflammatory biomarkers (CRP, IL-6, IL-8, TNF-α, and MCP-1) provide evidence for potential causal effects of inflammation on increasing the risk of coronary artery disease, colorectal cancer, and rheumatoid arthritis, while standard MR analysis for individual biomarkers fails to detect consistent evidence for such effects.
Collapse
Affiliation(s)
- Jin Jin
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, United States
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104-6021, United States
| | - Guanghao Qi
- Department of Biomedical Engineering, Johns Hopkins University, 720 Rutland Avenue, Baltimore, MD 21205, United States
- Department of Biostatistics, University of Washington, 3980 15th Avenue NE, Seattle, WA 98195-1617, United States
| | - Zhi Yu
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, 415 Main St Cambridge, MA 02142, United States
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, United States
- Department of Oncology, School of Medicine, Johns Hopkins University, 733 N Broadway, Baltimore, MD 21205, United States
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Lee T, George CD, Jiang C, Asgari MM, Nijsten T, Pardo LM, Choquet H. Association between lifetime smoking and cutaneous squamous cell carcinoma: A 2-sample Mendelian randomization study. JAAD Int 2024; 14:69-76. [PMID: 38274396 PMCID: PMC10808986 DOI: 10.1016/j.jdin.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/22/2023] [Indexed: 01/27/2024] Open
Abstract
Background/Purpose Cutaneous squamous cell carcinoma (cSCC) is one of the most common malignancies worldwide. While several environmental risk factors for cSCC are well established, there is conflicting evidence on cigarette smoking (and its potential causal effect) and cSCC risk. Furthermore, it is unclear if these potential associations represent causal, modifiable risk factors for cSCC development. This study aims to assess the nature of the associations between cigarette smoking traits (smoking initiation, amount smoked, and lifetime smoking exposure) and cSCC risk using two-sample Mendelian randomization analyses. Methods Genetic instruments, based on common genetic variants associated with cigarette smoking traits (P < 5 × 10-8), were derived from published genome-wide association studies (GWASs). For cSCC, we used GWAS summary statistics from the Kaiser Permanente GERA cohort (7701 cSCC cases and 60,167 controls; all non-Hispanic Whites). Results We found modest evidence that genetically determined lifetime smoking was associated with cSCC (inverse-variance weighted method: OR[95% CI] = 1.47[1.09-1.98]; P = .012), suggesting it may be a causal risk factor for cSCC. We did not detect any evidence of association between genetically determined smoking initiation or amount smoked and cSCC risk. Conclusion Study findings highlight the importance of smoking prevention and may support risk-stratified cSCC screening strategies based on carcinogen exposure and other genetic and clinical information.
Collapse
Affiliation(s)
| | - Christopher D. George
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Chen Jiang
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Maryam M. Asgari
- Department of Dermatology, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Tamar Nijsten
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Luba M. Pardo
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| |
Collapse
|
5
|
Deng L, Fu S, Yu K. Bias and mean squared error in Mendelian randomization with invalid instrumental variables. Genet Epidemiol 2024; 48:27-41. [PMID: 37970963 DOI: 10.1002/gepi.22541] [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: 05/12/2023] [Revised: 10/09/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023]
Abstract
Mendelian randomization (MR) is a statistical method that utilizes genetic variants as instrumental variables (IVs) to investigate causal relationships between risk factors and outcomes. Although MR has gained popularity in recent years due to its ability to analyze summary statistics from genome-wide association studies (GWAS), it requires a substantial number of single nucleotide polymorphisms (SNPs) as IVs to ensure sufficient power for detecting causal effects. Unfortunately, the complex genetic heritability of many traits can lead to the use of invalid IVs that affect both the risk factor and the outcome directly or through an unobserved confounder. This can result in biased and imprecise estimates, as reflected by a larger mean squared error (MSE). In this study, we focus on the widely used two-stage least squares (2SLS) method and derive formulas for its bias and MSE when estimating causal effects using invalid IVs. Using those formulas, we identify conditions under which the 2SLS estimate is unbiased and reveal how the independent or correlated pleiotropic effects influence the accuracy and precision of the 2SLS estimate. We validate these formulas through extensive simulation studies and demonstrate the application of those formulas in an MR study to evaluate the causal effect of the waist-to-hip ratio on various sleeping patterns. Our results can aid in designing future MR studies and serve as benchmarks for assessing more sophisticated MR methods.
Collapse
Affiliation(s)
- Lu Deng
- School of Statistics and Data Science, Nankai University, Tianjin, China
| | - Sheng Fu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Liu Z, Qin Y, Wu T, Tubbs JD, Baum L, Mak TSH, Li M, Zhang YD, Sham PC. Reciprocal causation mixture model for robust Mendelian randomization analysis using genome-scale summary data. Nat Commun 2023; 14:1131. [PMID: 36854672 PMCID: PMC9975185 DOI: 10.1038/s41467-023-36490-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 02/03/2023] [Indexed: 03/02/2023] Open
Abstract
Mendelian randomization using GWAS summary statistics has become a popular method to infer causal relationships across complex diseases. However, the widespread pleiotropy observed in GWAS has made the selection of valid instrumental variables problematic, leading to possible violations of Mendelian randomization assumptions and thus potentially invalid inferences concerning causation. Furthermore, current MR methods can examine causation in only one direction, so that two separate analyses are required for bi-directional analysis. In this study, we propose a ststistical framework, MRCI (Mixture model Reciprocal Causation Inference), to estimate reciprocal causation between two phenotypes simultaneously using the genome-scale summary statistics of the two phenotypes and reference linkage disequilibrium information. Simulation studies, including strong correlated pleiotropy, showed that MRCI obtained nearly unbiased estimates of causation in both directions, and correct Type I error rates under the null hypothesis. In applications to real GWAS data, MRCI detected significant bi-directional and uni-directional causal influences between common diseases and putative risk factors.
Collapse
Affiliation(s)
- Zipeng Liu
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.,State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China.,Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yiming Qin
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.,State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China.,Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Tian Wu
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Justin D Tubbs
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Larry Baum
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.,State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China.,Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Timothy Shin Heng Mak
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Miaoxin Li
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. .,Zhongshan School of Medicine, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China. .,Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou, China.
| | - Yan Dora Zhang
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. .,Department of Statistics & Actuarial Science, Faculty of Science, The University of Hong Kong, Hong Kong SAR, China.
| | - Pak Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. .,State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China. .,Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
| |
Collapse
|
8
|
Choquet H, Khawaja AP, Jiang C, Yin J, Melles RB, Glymour MM, Hysi PG, Jorgenson E. Association Between Myopic Refractive Error and Primary Open-Angle Glaucoma: A 2-Sample Mendelian Randomization Study. JAMA Ophthalmol 2022; 140:864-871. [PMID: 35900730 PMCID: PMC9335248 DOI: 10.1001/jamaophthalmol.2022.2762] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/31/2022] [Indexed: 11/14/2022]
Abstract
Importance Refractive error (RE) is the most common form of visual impairment, and myopic RE is associated with an increased risk of primary open-angle glaucoma (POAG). Whether this association represents a causal role of RE in the etiology of POAG remains unknown. Objective To evaluate shared genetic influences and investigate the association of myopic RE with the risk for POAG. Design, Setting, and Participants Observational analyses were used to evaluate the association between mean spherical equivalent (MSE) RE (continuous trait) or myopia (binary trait) and POAG risk in individuals from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. To quantify genetic overlap, genome-wide genetic correlation analyses were performed using genome-wide association studies (GWAS) of MSE RE or myopia and POAG from GERA. Potential causal effects were assessed between MSE RE and POAG using 2-sample Mendelian randomization. Genetic variants associated with MSE RE were derived using GWAS summary statistics from a GWAS of RE conducted in 102 117 UK Biobank participants. For POAG, we used GWAS summary statistics from our previous GWAS (3836 POAG cases and 48 065 controls from GERA). Data analyses occurred between July 2020 and October 2021. Main Outcomes and Measure Our main outcome was POAG risk as odds ratio (OR) caused by per-unit difference in MSE RE (in diopters). Results Our observational analyses included data for 54 755 non-Hispanic White individuals (31 926 [58%] females and 22 829 [42%] males). Among 4047 individuals with POAG, mean (SD) age was 73.64 (9.20) years; mean (SD) age of the 50 708 controls was 65.38 (12.24) years. Individuals with POAG had a lower refractive MSE and were more likely to have myopia or high myopia compared with the control participants (40.2% vs 34.1%, P = 1.31 × 10-11 for myopia; 8.5% vs 6.8%, P = .004 for high myopia). Our genetic correlation analyses demonstrated that POAG was genetically correlated with MSE RE (rg, -0.24; SE, 0.06; P = 3.90 × 10-5), myopia (rg, 0.21; SE, 0.07; P = .004), and high myopia (rg, 0.23; SE, 0.09; P = .01). Genetically assessed refractive MSE was negatively associated with POAG risk (inverse-variance weighted model: OR per diopter more hyperopic MSE = 0.94; 95% CI, 0.89-0.99; P = .01). Conclusions and Relevance These findings demonstrate a shared genetic basis and an association between myopic RE and POAG risk. This may support population POAG risk stratification and screening strategies, based on RE information.
Collapse
Affiliation(s)
- Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Anthony P. Khawaja
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Chen Jiang
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Jie Yin
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Ronald B. Melles
- Department of Ophthalmology, Kaiser Permanente Northern California, Redwood City
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Pirro G. Hysi
- King’s College London, Section of Ophthalmology, School of Life Course Sciences, London, United Kingdom
- King’s College London, Department of Twin Research and Genetic Epidemiology, London, United Kingdom
- University College London, Great Ormond Street Hospital Institute of Child Health, London, United Kingdom
| | | |
Collapse
|
9
|
Yuan Z, Liu L, Guo P, Yan R, Xue F, Zhou X. Likelihood-based Mendelian randomization analysis with automated instrument selection and horizontal pleiotropic modeling. SCIENCE ADVANCES 2022; 8:eabl5744. [PMID: 35235357 PMCID: PMC8890724 DOI: 10.1126/sciadv.abl5744] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 01/05/2022] [Indexed: 05/03/2023]
Abstract
Mendelian randomization (MR) is a common tool for identifying causal risk factors underlying diseases. Here, we present a method, MR with automated instrument determination (MRAID), for effective MR analysis. MRAID borrows ideas from fine-mapping analysis to model an initial set of candidate single-nucleotide polymorphisms that are in potentially high linkage disequilibrium with each other and automatically selects among them the suitable instruments for causal inference. MRAID also explicitly models both uncorrelated and correlated horizontal pleiotropic effects that are widespread for complex trait analysis. MRAID achieves both tasks through a joint likelihood framework and relies on a scalable sampling-based algorithm to compute calibrated P values. Comprehensive and realistic simulations show that MRAID can provide calibrated type I error control and reduce false positives while being more powerful than existing approaches. We illustrate the benefits of MRAID for an MR screening analysis across 645 trait pairs in U.K. Biobank, identifying multiple lifestyle causal risk factors of cardiovascular disease-related traits.
Collapse
Affiliation(s)
- Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - 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
| |
Collapse
|
10
|
Brown BC, Knowles DA. Welch-weighted Egger regression reduces false positives due to correlated pleiotropy in Mendelian randomization. Am J Hum Genet 2021; 108:2319-2335. [PMID: 34861175 DOI: 10.1016/j.ajhg.2021.10.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 10/19/2021] [Indexed: 02/01/2023] Open
Abstract
Modern population-scale biobanks contain simultaneous measurements of many phenotypes, providing unprecedented opportunity to study the relationship between biomarkers and disease. However, inferring causal effects from observational data is notoriously challenging. Mendelian randomization (MR) has recently received increased attention as a class of methods for estimating causal effects using genetic associations. However, standard methods result in pervasive false positives when two traits share a heritable, unobserved common cause. This is the problem of correlated pleiotropy. Here, we introduce a flexible framework for simulating traits with a common genetic confounder that generalizes recently proposed models, as well as a simple approach we call Welch-weighted Egger regression (WWER) for estimating causal effects. We show in comprehensive simulations that our method substantially reduces false positives due to correlated pleiotropy while being fast enough to apply to hundreds of phenotypes. We apply our method first to a subset of the UK Biobank consisting of blood traits and inflammatory disease, and then to a broader set of 411 heritable phenotypes. We detect many effects with strong literature support, as well as numerous behavioral effects that appear to stem from physician advice given to people at high risk for disease. We conclude that WWER is a powerful tool for exploratory data analysis in ever-growing databases of genotypes and phenotypes.
Collapse
Affiliation(s)
- Brielin C Brown
- Data Science Institute, Columbia University, New York, NY 10027, USA; New York Genome Center, New York, NY 10013, USA.
| | - David A Knowles
- Data Science Institute, Columbia University, New York, NY 10027, USA; New York Genome Center, New York, NY 10013, USA; Department of Computer Science, Columbia University, New York, NY 10027, USA; Department of Systems Biology, Columbia University, New York, NY 10027, USA.
| |
Collapse
|
11
|
Peng P, Chen Z, Zhang X, Guo Z, Dong F, Xu Y, He Y, Guo D, Wan F. Investigating Causal Relationships Between Psychiatric Traits and Intracranial Aneurysms: A Bi-directional Two-Sample Mendelian Randomization Study. Front Genet 2021; 12:741429. [PMID: 34737764 PMCID: PMC8560679 DOI: 10.3389/fgene.2021.741429] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 10/07/2021] [Indexed: 11/26/2022] Open
Abstract
Background Despite psychiatric traits were associated with intracranial aneurysms (IAs) in observational studies, their causal relationships remain largely undefined. We aimed to assess the causality between psychiatric traits and IAs. Methods We firstly collected the genome-wide association statistics of IAs (sample size, n = 79,429) and ten psychiatric traits from Europeans, including insomnia (n = 1,331,010), mood instability (n = 363,705), anxiety disorder (n = 83,566), major depressive disorder (MDD) (n = 480,359), subjective wellbeing (n = 388,538), attention deficit/hyperactivity disorder (ADHD) (n = 53,293), autism spectrum disorder (ASD) (n = 46,350), bipolar disorder (BIP) (n = 51,710), schizophrenia (SCZ) (n = 105,318), and neuroticism (n = 168,105). We then conducted a series of bi-directional two-sample Mendelian randomization (MR) analyses, of which the Robust Adjusted Profile Score (RAPS) was the primary method to estimate the causal effects between these psychiatric traits and IAs. Results We found that insomnia exhibited a significant risk effect on IAs with the odds ratio (OR) being 1.22 (95% CI: 1.11–1.34, p = 4.61 × 10–5) from the RAPS method. There was suggestive evidence for risk effect of mood instability on IAs (RAPS, OR = 4.16, 95% CI: 1.02–17.00, p = 0.047). However, no clear evidence of causal effects on IAs for the rest eight psychiatric traits (anxiety disorder, MDD, subjective wellbeing, ADHD, ASD, BIP, SCZ, and neuroticism) was identified. In the reverse MR analyses, no causal effects of IAs on psychiatric traits were found. Conclusions Our findings provide strong evidence for a causal risk effect of insomnia on IAs and suggestive evidence for mood instability as a causal risk effect on IAs. These results could inform the prevention and clinical intervention of IAs.
Collapse
Affiliation(s)
- Peng Peng
- Department of Neurosurgery, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zirong Chen
- Department of Neurosurgery, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaolin Zhang
- Department of Neurosurgery, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhongyin Guo
- Department of Neurosurgery, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fangyong Dong
- Department of Neurosurgery, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Xu
- Department of Neurosurgery, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yue He
- Department of Neurosurgery, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dongsheng Guo
- Department of Neurosurgery, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Wan
- Department of Neurosurgery, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
12
|
Yu Z, Coresh J, Qi G, Grams M, Boerwinkle E, Snieder H, Teumer A, Pattaro C, Köttgen A, Chatterjee N, Tin A. A bidirectional Mendelian randomization study supports causal effects of kidney function on blood pressure. Kidney Int 2020; 98:708-716. [PMID: 32454124 DOI: 10.1016/j.kint.2020.04.044] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 03/12/2020] [Accepted: 04/09/2020] [Indexed: 12/11/2022]
Abstract
Blood pressure and kidney function have a bidirectional relation. Hypertension has long been considered as a risk factor for kidney function decline. However, whether intensive blood pressure control could promote kidney health has been uncertain. The kidney is known to have a major role in affecting blood pressure through sodium extraction and regulating electrolyte balance. This bidirectional relation makes causal inference between these two traits difficult. Therefore, to examine the causal relations between these two traits, we performed two-sample Mendelian randomization analyses using summary statistics of large-scale genome-wide association studies. We selected genetic instruments more likely to be specific for kidney function using meta-analyses of complementary kidney function biomarkers (glomerular filtration rate estimated from serum creatinine [eGFRcr], and blood urea nitrogen from the CKDGen Consortium). Systolic and diastolic blood pressure summary statistics were from the International Consortium for Blood Pressure and UK Biobank. Significant evidence supported the causal effects of higher kidney function on lower blood pressure. Based on the mode-based Mendelian randomization method, the effect estimates for one standard deviation (SD) higher in log-transformed eGFRcr was -0.17 SD unit (95 % confidence interval: -0.09 to -0.24) in systolic blood pressure and -0.15 SD unit (95% confidence interval: -0.07 to -0.22) in diastolic blood pressure. In contrast, the causal effects of blood pressure on kidney function were not statistically significant. Thus, our results support causal effects of higher kidney function on lower blood pressure and suggest preventing kidney function decline can reduce the public health burden of hypertension.
Collapse
Affiliation(s)
- Zhi Yu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - Guanghao Qi
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Morgan Grams
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA
| | - Harold Snieder
- Department of Epidemiology, University Medical Centre Groningen, Groningen, The Netherlands
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Cristian Pattaro
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Centre - University of Freiburg, Freiburg, Germany
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA; Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA.
| |
Collapse
|