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Zhang Y, Ni Y, An H, Li L, Ren Y. Multidimensional plasma lipid composition and its causal association with type 2 diabetes mellitus: A Mendelian randomization study. Nutr Metab Cardiovasc Dis 2024; 34:2075-2084. [PMID: 38866614 DOI: 10.1016/j.numecd.2024.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/15/2024] [Accepted: 05/03/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND AND AIM Recent research extends our knowledge of plasma lipid species, building on established links between serum lipid levels and Type 2 Diabetes Mellitus (T2DM) risk. Identifying the causal roles of these lipid species is key to improving T2DM risk assessment. METHODS AND RESULTS This study employs Mendelian randomization (MR) to investigate the causal relationship between 179 lipid species across 13 lipid categories and T2DM. Summary-level data were sourced from genome-wide association studies. The primary analytical methods included the inverse variance weighted (IVW) approach and the Wald ratio, complemented by a series of sensitivity analyses to ensure the robustness of results. The IVW analysis reveals a significant causal association between elevated levels of ceramide (d40:2) (OR = 1.071, 95% CI 1.034-1.109, P = 1.36 × 10-4), sphingomyelin (d38:1) (OR = 1.052, 95% CI 1.028-1.077, P = 1.80 × 10-5), and triacylglycerol (56:8) (OR = 1.174, 95% CI 1.108-1.243, P = 4.65 × 10-8), and an increased risk of T2DM. Conversely, Wald ratio analysis indicates that higher levels of phosphatidylcholine (O-16:1_16:0) (OR = 0.928, 95% CI 0.892-0.966, P = 2.37 × 10-4), phosphatidylcholine (O-16:1_20:4) (OR = 0.932, 95% CI 0.897-0.967, P = 2.37 × 10-4), and phosphatidylcholine (O-18:2_20:4) (OR = 0.872, 95% CI 0.812-0.935, P = 1.24 × 10-4) are significantly associated with a reduced risk of T2DM. Furthermore, suggestive causal evidence for 22 additional lipid species was identified. CONCLUSIONS This MR study establishes a causal relationship between specific lipid classes in modulating the risk of T2DM. It offers new insights for risk assessment and potential therapeutic targets in T2DM.
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Affiliation(s)
- Youqian Zhang
- Department of Endocrinology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei Province, China; Health Science Center, Yangtze University, Jingzhou, Hubei Province, China
| | - Yao Ni
- Department of Dermatovenereology, Chengdu Second People's Hospital, Chengdu, Sichuan Province, China
| | - Hui An
- Health Science Center, Yangtze University, Jingzhou, Hubei Province, China
| | - Lin Li
- Department of Endocrinology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei Province, China.
| | - Yanrui Ren
- Department of Endocrinology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei Province, China.
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Zhang X, Lu Q, Luo Y, Wang L, Tian Y, Luo X. The causal relationship between major depression disorder and thyroid diseases: A Mendelian randomization study and mediation analysis. J Affect Disord 2024; 359:287-299. [PMID: 38788859 DOI: 10.1016/j.jad.2024.05.097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 04/23/2024] [Accepted: 05/19/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND Studies have been conducted on the relationship between depression and thyroid diseases and function, its causal relationship remains unclear. METHODS Using summary statistics of genome-wide association studies of European and East Asian ancestry, we conducted 2-sample bidirectional Mendelian randomization to estimate the association between MDD and thyroid function (European: normal range TSH, T4, T3, fT4, TPOAb levels and TPOAb-positives; East Asian: T4) and thyroid diseases (hypothyroidism, hyperthyroidism, and Hashimoto's thyroiditis), and used Mediation analysis to evaluate potential mediators (alcohol intake, antidepressant) of the association and calculate the mediated proportions. RESULTS It was observed a significant causal association between MDD on hypothyroidism (P = 8.94 × 10-5), hyperthyroidism (P = 8.68 × 10-3), and hashimoto's thyroiditis (P = 3.97 × 10-5) among European ancestry, which was mediated by Alcohol intake (alcohol intake versus 10 years previously for hypothyroidism (P = 0.026), hashimoto's thyroiditis (P = 0.042), and alcohol intake frequency for hypothyroidism (P = 0.015)) and antidepressant (for hypothyroidism (P = 0.008), hashimoto's thyroiditis (P = 0.010)), but not among East Asian ancestry (PMDD-hypothyroidism = 0.016, but β direction was different; PMDD-hyperthyroidism = 0.438; PMDD-hashimoto's thyroiditis = 0.496). There was no evidence for bidirectional causal association between thyroid function mentioned above and MDD among both ancestry (all P > 0.05). CONCLUSION We importantly observed a significant causal association between MDD on risk of hypothyroidism, hyperthyroidism, and hashimoto's thyroiditis among European ancestry, and Alcohol intake and antidepressant as mediators for prevention of hypothyroidism, hashimoto's thyroiditis attributable to MDD.
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Affiliation(s)
- Xu Zhang
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China.
| | - Qiao Lu
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China
| | - Yiping Luo
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China
| | - Luyao Wang
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China
| | - Yuan Tian
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China
| | - Xuemei Luo
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China
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Chen S, Sun S, Cai M, Zhou Z, Ma Y, Zhou Z, Wang F, Liu J, Song W, Liu Y, Huang K, Yang Q, Guo Y. A metabolome-wide Mendelian randomization study prioritizes causal circulating metabolites for reproductive disorders including primary ovarian insufficiency, polycystic ovary syndrome, and abnormal spermatozoa. J Ovarian Res 2024; 17:166. [PMID: 39143642 PMCID: PMC11325614 DOI: 10.1186/s13048-024-01486-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 07/27/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Accumulating studies have highlighted the significant role of circulating metabolomics in the etiology of reproductive system disorders. However, the causal effects between genetically determined metabolites (GDMs) and reproductive diseases, including primary ovarian insufficiency (POI), polycystic ovary syndrome (PCOS), and abnormal spermatozoa (AS), still await thorough clarification. METHODS With the currently most comprehensive genome-wide association studies (GWAS) data of metabolomics, systematic two-sample Mendelian randomization (MR) analyses were conducted to disclose causal associations between 1,091 blood metabolites and 309 metabolite ratios with reproductive disorders. The inverse-variance weighted (IVW) method served as the primary analysis approach, and multiple effective MR methods were employed as complementary analyses including MR-Egger, weighted median, constrained maximum likelihood (cML-MA), contamination mixture method, robust adjusted profile score (MR-RAPS), and debiased inverse-variance weighted method. Heterogeneity and pleiotropy were assessed via MR-Egger intercept and Cochran's Q statistical analysis. Outliers were detected by Radial MR and MR-PRESSO methods. External replication and metabolic pathway analysis were also conducted. RESULTS Potential causal associations of 63 GDMs with POI were unearthed, and five metabolites with strong causal links to POI were emphasized. Two metabolic pathways related to the pathogenesis of POI were pinpointed. Suggestive causal effects of 70 GDMs on PCOS were detected, among which 7 metabolites stood out for strong causality with elevated PCOS risk. Four metabolic pathways associated with PCOS mechanisms were recognized. For AS, 64 GDMs as potential predictive biomarkers were identified, particularly highlighting two metabolites for their strong causal connections with AS. Three pathways underneath the AS mechanism were identified. Multiple assessments were conducted to further confirm the reliability and robustness of our causal inferences. CONCLUSION By extensively assessing the causal implications of circulating GDMs on reproductive system disorders, our study underscores the intricate and pivotal role of metabolomics in reproductive ill-health, laying a theoretical foundation for clinical strategies from metabolic insights.
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Affiliation(s)
- Shuang Chen
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Shihao Sun
- Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Mingshu Cai
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Zhaokai Zhou
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Yuan Ma
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Zihan Zhou
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Fang Wang
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Jinhao Liu
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Wenyan Song
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Yu Liu
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Kai Huang
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Qingling Yang
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
| | - Yihong Guo
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
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Hu X, Cai M, Xiao J, Wan X, Wang Z, Zhao H, Yang C. Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics. Am J Hum Genet 2024; 111:1717-1735. [PMID: 39059387 PMCID: PMC11339627 DOI: 10.1016/j.ajhg.2024.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/26/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices. Our study focused on the following crucial aspects: type I error control in the presence of various confounding scenarios (e.g., population stratification, pleiotropy, and family-level confounders like assortative mating), the accuracy of causal effect estimates, replicability, and power. By comprehensively evaluating the performance of compared methods over one thousand exposure-outcome trait pairs, our study not only provides valuable insights into the performance and limitations of the compared methods but also offers practical guidance for researchers to choose appropriate MR methods for causal inference.
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Affiliation(s)
- Xianghong Hu
- School of Mathematical Sciences, Institute of Statistical Sciences, Shenzhen University, Shenzhen 518060, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China; Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China
| | - Mingxuan Cai
- Department of Biostatistics, City University of Hong Kong, Hong Kong, China
| | - Jiashun Xiao
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China
| | - Xiaomeng Wan
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China; Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China
| | - Zhiwei Wang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China; Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA.
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China; Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Big Data Bio-Intelligence Lab, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
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Zhao B, Li Y, Fan Z, Wu Z, Shu J, Yang X, Yang Y, Wang X, Li B, Wang X, Copana C, Yang Y, Lin J, Li Y, Stein JL, O'Brien JM, Li T, Zhu H. Eye-brain connections revealed by multimodal retinal and brain imaging genetics. Nat Commun 2024; 15:6064. [PMID: 39025851 PMCID: PMC11258354 DOI: 10.1038/s41467-024-50309-w] [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: 06/23/2023] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes. We identified novel phenotypic and genetic links between retinal imaging biomarkers and brain structure and function measures from multimodal magnetic resonance imaging (MRI), with many associations involving the primary visual cortex and visual pathways. Retinal imaging biomarkers shared genetic influences with brain diseases and complex traits in 65 genomic regions, with 18 showing genetic overlap with brain MRI traits. Mendelian randomization suggests bidirectional genetic causal links between retinal structures and neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, our findings reveal the genetic basis for eye-brain connections, suggesting that retinal images can help uncover genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA.
- Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Penn Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Yilin Yang
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT, 06511, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joan M O'Brien
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Medicine Center for Ophthalmic Genetics in Complex Diseases, Philadelphia, PA, 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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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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Zhang Y, Su Y, Tang Z, Li L. The impact of cannabis use on erectile dysfunction and sex hormones: a Mendelian randomization analysis. Int J Impot Res 2024:10.1038/s41443-024-00925-3. [PMID: 38834872 DOI: 10.1038/s41443-024-00925-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/22/2024] [Accepted: 05/31/2024] [Indexed: 06/06/2024]
Abstract
Previous study has highlighted an association between cannabis use (CU) and an increased risk of erectile dysfunction (ED), potentially due to indirect effects on sex hormonal balance. However, the evidence remains controversial, and the causal relationship is unclear. This study utilized genome-wide association study (GWAS) data to investigate the causal relationships between cannabis use disorder (CUD), lifetime cannabis use (LCU), and ED, as well as levels of sex hormones including estradiol (E2), bioavailable testosterone (BT), follicle-stimulating hormone (FSH), and luteinizing hormone (LH) through Mendelian randomization (MR) analysis. The primary method of analysis was the inverse variance weighted (IVW) method. Data from the FinnGen and UK Biobank were used for replication and meta-analysis. The results indicated no causal relationship between genetically predicted CUD (OR = 0.97, 95% CI 0.87-1.10, P = 0.66) and LCU (OR = 1.13, 95% CI 0.84-1.50, P = 0.42) with the risk of ED. The meta-analysis provided consistent evidence (P > 0.05). No causal relationships were found between CUD and LCU with E2(CUD: β = 0.00, 95% CI 0.00-0.01, P = 0.37; LCU: β = 0.00, 95% CI -0.02-0.01, P = 0.62), BT (CUD: β = 0.00, 95% CI -0.03-0.02, P = 0.90; LCU: β = 0.02, 95% CI -0.04-0.09, P = 0.46), FSH (CUD: β = 0.01, 95% CI -0.18-0.20, P = 0.92; LCU: β = 0.01, 95% CI -0.44-0.47, P = 0.95), and LH (CUD: β = 0.01, 95% CI -0.18-0.21, P = 0.90; LCU: β = 0.13, 95% CI -0.22-0.49, P = 0.46). Sensitivity analyses detected no evidence of horizontal pleiotropy or heterogeneity, ensuring the robustness of the results. In conclusion, this MR analysis did not provide evidence supporting a causal relationship between CU and ED or sex hormone levels.
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Affiliation(s)
- Youqian Zhang
- Department of Endocrinology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei Province, China
- Health Science Center, Yangtze University, Jingzhou, Hubei Province, China
| | - Yue Su
- Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Zitian Tang
- Health Science Center, Yangtze University, Jingzhou, Hubei Province, China
| | - Lin Li
- Department of Endocrinology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei Province, China.
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Patel A, DiTraglia FJ, Zuber V, Burgess S. Selecting Invalid Instruments to Improve Mendelian Randomization with Two-Sample Summary Data. Ann Appl Stat 2024; 18:23-aoas1856. [PMID: 38737575 PMCID: PMC7615940 DOI: 10.1214/23-aoas1856] [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] [Indexed: 05/14/2024]
Abstract
Mendelian randomization (MR) is a widely-used method to estimate the causal relationship between a risk factor and disease. A fundamental part of any MR analysis is to choose appropriate genetic variants as instrumental variables. Genome-wide association studies often reveal that hundreds of genetic variants may be robustly associated with a risk factor, but in some situations investigators may have greater confidence in the instrument validity of only a smaller subset of variants. Nevertheless, the use of additional instruments may be optimal from the perspective of mean squared error even if they are slightly invalid; a small bias in estimation may be a price worth paying for a larger reduction in variance. For this purpose, we consider a method for "focused" instrument selection whereby genetic variants are selected to minimise the estimated asymptotic mean squared error of causal effect estimates. In a setting of many weak and locally invalid instruments, we propose a novel strategy to construct confidence intervals for post-selection focused estimators that guards against the worst case loss in asymptotic coverage. In empirical applications to: (i) validate lipid drug targets; and (ii) investigate vitamin D effects on a wide range of outcomes, our findings suggest that the optimal selection of instruments does not involve only a small number of biologically-justified instruments, but also many potentially invalid instruments.
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Affiliation(s)
| | | | - Verena Zuber
- Department of Biostatistics and Epidemiology, Imperial College London
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge
- Cardiovascular Epidemiology Unit, University of Cambridge
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9
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Xu Z, Ning F, Zhang X, Wang Q, Zhang Y, Guo Y, Jia H. Deciphering the brain-gut axis: elucidating the link between cerebral cortex structures and functional gastrointestinal disorders via integrated Mendelian randomization. Front Neurosci 2024; 18:1398412. [PMID: 38841096 PMCID: PMC11152161 DOI: 10.3389/fnins.2024.1398412] [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: 03/09/2024] [Accepted: 04/30/2024] [Indexed: 06/07/2024] Open
Abstract
Background Observational studies have suggested associations between functional gastrointestinal disorders (FGIDs) and variations in the cerebral cortex. However, the causality of these relationships remains unclear, confounded by anxiety and depression. To clarify these causal relationships and explore the mediating roles of anxiety and depression, we applied univariate, multivariable, and mediation Mendelian randomization (MR) analyses. Method We utilized genome-wide association study (GWAS) summary data from the FinnGen database and the ENIGMA consortium, identifying genetic variants associated with irritable bowel syndrome (IBS), functional dyspepsia (FD), and cerebral cortex structures. Data on anxiety and depression came from FinnGen and a large meta-analysis. Utilizing a bidirectional univariate MR approach, we explored correlations between FD, IBS, and cortex variations. Then, independent effects were assessed through multivariable MR. A meta-analysis of these results, incorporating data from two cohorts, aimed to increase precision. We also explored the potential mediating roles of anxiety and depression. Results Our findings indicate a negative causal correlation between FD and the thickness of the rostral anterior cingulate cortex (rACC) across both global and regional adjustments (β = -0.142, 95% confidence interval (CI): -0.209 to-0.074, P.FDR = 0.004; β = -0.112, 95%CI: -0.163 to-0.006, P.FDR = 0.003) and a positive causal correlation with the globally adjusted thickness of the superior frontal gyrus (SFG) (β = 0.107, 95%CI: 0.062 to 0.153, P.FDR = 0.001). The causal correlation with the rACC persisted after multiple variable adjustments (β = -0.137, 95% CI: -0.187 to-0.087, P.FDR = 1.81 × 10-5; β = -0.109, 95%CI: -0.158 to-0.06, P.FDR = 0.002). A significant causal association was found between globally adjusted surface area of the caudal anterior cingulate cortex (cACC) and IBS (odds ratio = 1.267, 95%CI: 1.128 to 1.424, P.FDR = 0.02). The analysis showed that neither anxiety nor depression mediated the relationship between FGIDs and cerebral cortex structures. Conclusion Our research provides significant MR evidence of a bidirectional causal relationship between FGIDs and the cerebral cortex structures. This evidence not only confirms the two-way communication along the brain-gut axis but also illuminates the underlying pathophysiology, paving the way for identifying potential therapeutic approaches.
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Affiliation(s)
- Zhiwei Xu
- School of Acupuncture and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Fenglan Ning
- Department of Acupuncture and Rehabilitation, Longkou Traditional Chinese Medicine Hospital, Yantai, Shandong, China
| | - Xuecheng Zhang
- Department of Proctology, China-Japan Friendship Hospital, Beijing, China
| | - Qi Wang
- Department of Acupuncture, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Yimei Zhang
- Department of Acupuncture, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Yiting Guo
- Department of Radiology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Hongling Jia
- Department of Acupuncture, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
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10
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Ni Y, Zhang D, Tang W, Xiang L, Cheng X, Zhang Y, Feng Y. Body mass index, smoking behavior, and depression mediated the effects of schizophrenia on chronic obstructive pulmonary disease: trans-ethnic Mendelian-randomization analysis. Front Psychiatry 2024; 15:1405107. [PMID: 38846919 PMCID: PMC11155452 DOI: 10.3389/fpsyt.2024.1405107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 05/09/2024] [Indexed: 06/09/2024] Open
Abstract
Background Previous studies have highlighted the association between schizophrenia (SCZ) and chronic obstructive pulmonary disease (COPD), yet the causal relationship remains unestablished. Methods Under the genome-wide significance threshold (P<5×10-8), data from individuals of European (EUR) and East Asian (EAS) ancestries with SCZ were selected for analysis. Univariable Mendelian randomization (MR) explored the causal relationship between SCZ and COPD. Linkage disequilibrium score (LDSC) regression was used to calculate genetic correlation, while multivariable and mediation MR further investigated the roles of six confounding factors and their mediating effects. The primary method utilized was inverse-variance weighted (IVW), complemented by a series of sensitivity analyses and false discovery rate (FDR) correction. Results LDSC analysis revealed a significant genetic correlation between SCZ and COPD within EUR ancestry (rg = 0.141, P = 6.16×10-7), with no such correlation found in EAS ancestry. IVW indicated a significant causal relationship between SCZ and COPD in EUR ancestry (OR = 1.042, 95% CI 1.013-1.071, P = 0.003, PFDR = 0.015). Additionally, replication datasets provide evidence of consistent causal associations(P < 0.05 & PFDR < 0.05). Multivariable and mediation MR analyses identified body mass index (BMI)(Mediation effect: 50.57%, P = 0.02), age of smoking initiation (Mediation effect: 27.42%, P = 0.02), and major depressive disorder (MDD) (Mediation effect: 60.45%, P = 6.98×10-5) as partial mediators of this causal relationship. No causal associations were observed in EAS (OR = 0.971, 95% CI 0.875-1.073, P = 0.571, PFDR = 0.761) ancestry. No causal associations were found in the reverse analysis across the four ancestries (P > 0.05 & PFDR > 0.05). Conclusions This study confirmed a causal relationship between SCZ and the risk of COPD in EUR ancestry, with BMI, smoking, and MDD serving as key mediators. Future research on a larger scale is necessary to validate the generalizability of these findings across other ancestries.
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Affiliation(s)
- Yao Ni
- Department of Dermatovenereology, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
| | - DaWei Zhang
- Department of Clinical Laboratory, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
- Department of Clinical Laboratory, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wenlong Tang
- Department of Dermatovenereology, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
| | - Liming Xiang
- Department of Dermatovenereology, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
| | - Xiaoding Cheng
- Department of Dermatovenereology, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
| | - Youqian Zhang
- Health Science Center, Yangtze University, Jingzhou, Hubei, China
| | - Yanyan Feng
- Department of Dermatovenereology, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
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11
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Yu X, Chen Y, Chen J, Fan Y, Lu H, Wu D, Xu Y. Shared genetic architecture between autoimmune disorders and B-cell acute lymphoblastic leukemia: insights from large-scale genome-wide cross-trait analysis. BMC Med 2024; 22:161. [PMID: 38616254 PMCID: PMC11017616 DOI: 10.1186/s12916-024-03385-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 04/08/2024] [Indexed: 04/16/2024] Open
Abstract
BACKGROUND To study the shared genetic structure between autoimmune diseases and B-cell acute lymphoblastic leukemia (B-ALL) and identify the shared risk loci and genes and genetic mechanisms involved. METHODS Based on large-scale genome-wide association study (GWAS) summary-level data sets, we observed genetic overlaps between autoimmune diseases and B-ALL, and cross-trait pleiotropic analysis was performed to detect shared pleiotropic loci and genes. A series of functional annotation and tissue-specific analysis were performed to determine the influence of pleiotropic genes. The heritability enrichment analysis was used to detect crucial immune cells and tissues. Finally, bidirectional Mendelian randomization (MR) methods were utilized to investigate the casual associations. RESULTS Our research highlighted shared genetic mechanisms between seven autoimmune disorders and B-ALL. A total of 73 pleiotropic loci were identified at the genome-wide significance level (P < 5 × 10-8), 16 of which had strong evidence of colocalization. We demonstrated that several loci have been previously reported (e.g., 17q21) and discovered some novel loci (e.g., 10p12, 5p13). Further gene-level identified 194 unique pleiotropic genes, for example IKZF1, GATA3, IKZF3, GSDMB, and ORMDL3. Pathway analysis determined the key role of cellular response to cytokine stimulus, B cell activation, and JAK-STAT signaling pathways. SNP-level and gene-level tissue enrichment suggested that crucial role pleiotropic mechanisms involved in the spleen, whole blood, and EBV-transformed lymphocytes. Also, hyprcoloc and stratified LD score regression analyses revealed that B cells at different developmental stages may be involved in mechanisms shared between two different diseases. Finally, two-sample MR analysis determined causal effects of asthma and rheumatoid arthritis on B-ALL. CONCLUSIONS Our research proved shared genetic architecture between autoimmune disorders and B-ALL and shed light on the potential mechanism that might involve in.
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Affiliation(s)
- Xinghao Yu
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Collaborative Innovation Center of Hematology, Institute of Blood and Marrow Transplantation, Soochow University, Suzhou, China
| | - Yiyin Chen
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Collaborative Innovation Center of Hematology, Institute of Blood and Marrow Transplantation, Soochow University, Suzhou, China
| | - Jia Chen
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yi Fan
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Huimin Lu
- Department of Outpatient and Emergency, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Depei Wu
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China.
- Collaborative Innovation Center of Hematology, Institute of Blood and Marrow Transplantation, Soochow University, Suzhou, China.
| | - Yang Xu
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China.
- Collaborative Innovation Center of Hematology, Institute of Blood and Marrow Transplantation, Soochow University, Suzhou, China.
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12
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Shi YM, Ou D, Li JT, Bao L, Liu XD, Zhang W, Ding H. Genetically Predicted Apolipoprotein E Levels with the Risk of Panvascular Diseases: A Mendelian Randomization Study. Cardiovasc Toxicol 2024; 24:385-395. [PMID: 38536640 DOI: 10.1007/s12012-024-09846-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 02/28/2024] [Indexed: 04/07/2024]
Abstract
The aim of this study was to comprehensively assess the causal relationship between the overall genetic effect of circulating ApoE levels and panvascular lesions using newer genome-wide association data and two-sample bidirectional Mendelian randomization (MR) analysis. Two-way MR using single-nucleotide polymorphisms of circulating ApoE as instrumental variables was performed using the highest-priority Genome-wide association study (GWAS) data, with factor-adjusted and data-corrected statistics, to estimate causal associations between circulating ApoE levels and 10 pan-vascular diseases in > 500,000 UK Biobank participants, > 400,000 participants of Finnish ancestry, and numerous participants in a consortium of predominantly European ancestry. Meta-analysis was conducted to assess positive results. After correcting for statistical results, elevated circulating ApoE levels were shown to have a significant protective effect against Cerebral ischemia (CI) [IVW odds ratio (OR) 0.888, 95% Confidence Interval (CI): 0.823-0.958, p = 2.3 × 10-3], Coronary heart disease [IVW OR 0.950,95% CI: 0.924-0.976, p = 2.0 × 10-4] had a significant protective effect and potentially suggestive protective causality against Angina pectoris [IVW odds ratio (OR) 0.961, 95%CI: 0.931-0.991, p = 1.1 × 10-2]. There was a potential causal effect for increased risk of Heart failure (HF) [IVW ratio (OR) 1.040, 95%CI: 1.006-1.060, p = 1.8 × 10-2]. (Bonferroni threshold p < 0.0026, PFDR < 0.05) Reverse MR analysis did not reveal significant evidence of a causal effect of PVD on changes in circulating ApoE levels. Meta-analysis increases reliability of results. Elevated circulating ApoE levels were particularly associated with an increased risk of heart failure. Elevated ApoE levels reduce the risk of cerebral ischemia, coronary heart disease, and angina pectoris, reflecting a protective effect. The possible pathophysiological role of circulating ApoE levels in the development of panvascular disease is emphasized.
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Affiliation(s)
- Yi-Ming Shi
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine On Prevention andTreatmentof Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China
| | - Dian Ou
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine On Prevention andTreatmentof Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China
| | - Jia-Ting Li
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine On Prevention andTreatmentof Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China
| | - Le Bao
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine On Prevention andTreatmentof Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China
| | - Xiao-Dan Liu
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine On Prevention andTreatmentof Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China
| | - Wei Zhang
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine On Prevention andTreatmentof Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China.
| | - Huang Ding
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine On Prevention andTreatmentof Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China.
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13
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Min Q, Chen Y, Geng H, Gao Q, Zhang X, Xu M. Causal relationship between PCOS and related sex hormones with oral inflammatory diseases: a bidirectional Mendelian randomization study. Front Endocrinol (Lausanne) 2024; 14:1282056. [PMID: 38264282 PMCID: PMC10803436 DOI: 10.3389/fendo.2023.1282056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 12/01/2023] [Indexed: 01/25/2024] Open
Abstract
Background Observational studies have identified a strong association between polycystic ovary syndrome (PCOS) and hormone levels related to oral inflammatory diseases. To better understand the relationship between them, we conducted an analysis using a two-sample Mendelian randomization (MR) approach. Methods We gathered summary statistical data from previously published genome-wide association studies (GWAS) on PCOS and three sex hormones (AMH, Estradiol, LH) along with four oral inflammatory diseases (painful gums, loose teeth, mouth ulcers, and toothache). We selected single nucleotide polymorphisms (SNPs) as instrumental variables and employed four types of MR analysis methods to evaluate causal relationships between exposure and outcome. Finally, the robustness of our results was further validated through sensitivity tests and reverse MR. Results We observed that PCOS could increase the risk of mouth ulcers (ORIVW= 1.0013, 95%CI: 1.0001-1.0025, PIVW = 0.0278), painful gums (ORIVW= 1.0015, 95%CI:1.0003-1.0027, PIVW = 0.0163), and loose teeth (ORIVW= 1.0014, 95%CI: 1.0001-1.0027, PIVW = 0.0328). Moreover, LH was also found to increase the risk of mouth ulcers (ORIVW= 1.0031, 95%CI: 0.0001-1.0062, PIVW = 0.0457). MR-Egger regression, weighted mode, and WE indicated similar results. Additionally, we discovered no causal link between PCOS and toothache (PIVW>0.05), LH and painful gums, loose teeth, or toothache (PIVW>0.05), or AMH and Estradiol level with any of the four oral diseases (PIVW>0.05). Conclusion Our research provides new insights and references for exploring the effects of PCOS and related hormones on oral inflammatory lesions. For patients with PCOS, especially those with elevated LH levels, early intervention measures should be taken to prevent the occurrence of oral inflammatory diseases.
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Affiliation(s)
- Qiusi Min
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Yi Chen
- Department of Gynecology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, China
| | - Hongling Geng
- Department of Gynecology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, China
| | - Qian Gao
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Xueying Zhang
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Min Xu
- Department of Gynecology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, China
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14
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Huo J, Xu Y, Chen X, Yu J, Zhao L. Inverse association between type 2 diabetes and hepatocellular carcinoma in East Asian populations. Front Endocrinol (Lausanne) 2024; 14:1308561. [PMID: 38234424 PMCID: PMC10791969 DOI: 10.3389/fendo.2023.1308561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024] Open
Abstract
Aims To investigate the potential association between type 2 diabetes (T2D) and hepatocellular carcinoma (HCC) in East Asian populations using Mendelian randomization (MR) analyses. Methods Bidirectional Mendelian randomization (MR) studies were conducted using summary statistics from genome-wide association studies (GWAS) related to T2D and HCC. The potential effects of confounders such as chronic hepatitis B, chronic hepatitis C, body mass index, and alcohol intake frequency were corrected using a multivariate MR study. Various MR methods, including the inverse variance weighted (IVW) approach, were used to estimate the associations between T2D and HCC. Sensitivity analysis and assessment of heterogeneity were performed to ensure the robustness of the results. Results In the forward MR study, the IVW approach of MR analysis suggested an inverse association between T2D and HCC, with a risk odds ratio of 0.8628 (95% confidence interval [CI], 0.7888-0.9438). Furthermore, even after adjusting for BMI, chronic hepatitis B, and alcohol intake frequency, this study still supports the inverse association between T2D and HCC. Additional MR methods provided further support for this relationship. Sensitivity analysis and assessment of heterogeneity confirmed the robustness of the results. The reverse MR analysis did not show a clear impact of genetic liability to HCC on reduced risk of T2D(OR=0.9788; 95% CI, 0.9061-1.0574). Conclusion This study provides evidence of an inverse association between T2D and HCC in East Asian populations using MR analysis. Further studies are warranted to validate these findings.
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Affiliation(s)
- Jinlong Huo
- Department of General Surgery, Digestive Disease Hospital, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
- Department of Breast and Thyroid Surgery, The Third Affiliated Hospital of Zunyi Medical University (The First People’s Hospital of Zunyi), Zunyi, Guizhou, China
| | - Yaxuan Xu
- Department of General Surgery, Digestive Disease Hospital, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Xingqi Chen
- Department of General Surgery, Digestive Disease Hospital, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Jie Yu
- Department of General Surgery, Digestive Disease Hospital, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Lijin Zhao
- Department of General Surgery, Digestive Disease Hospital, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
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15
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Patel A, Gill D, Newcombe P, Burgess S. Conditional inference in cis-Mendelian randomization using weak genetic factors. Biometrics 2023; 79:3458-3471. [PMID: 37337418 PMCID: PMC7615409 DOI: 10.1111/biom.13888] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/25/2023] [Indexed: 06/21/2023]
Abstract
Mendelian randomization (MR) is a widely used method to estimate the causal effect of an exposure on an outcome by using genetic variants as instrumental variables. MR analyses that use variants from only a single genetic region (cis-MR) encoding the protein target of a drug are able to provide supporting evidence for drug target validation. This paper proposes methods for cis-MR inference that use many correlated variants to make robust inferences even in situations, where those variants have only weak effects on the exposure. In particular, we exploit the highly structured nature of genetic correlations in single gene regions to reduce the dimension of genetic variants using factor analysis. These genetic factors are then used as instrumental variables to construct tests for the causal effect of interest. Since these factors may often be weakly associated with the exposure, size distortions of standard t-tests can be severe. Therefore, we consider two approaches based on conditional testing. First, we extend results of commonly-used identification-robust tests for the setting where estimated factors are used as instruments. Second, we propose a test which appropriately adjusts for first-stage screening of genetic factors based on their relevance. Our empirical results provide genetic evidence to validate cholesterol-lowering drug targets aimed at preventing coronary heart disease.
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Affiliation(s)
- Ashish Patel
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
| | - Dipender Gill
- Department of Epidemiology and BiostatisticsImperial College LondonLondonUK
- Chief Scientific Advisor OfficeResearch and Early DevelopmentNovo Nordisk, CopenhagenDenmark
| | - Paul Newcombe
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
| | - Stephen Burgess
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Cardiovascular Epidemiology UnitUniversity of CambridgeCambridgeUK
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Walker JT, Saunders DC, Rai V, Chen HH, Orchard P, Dai C, Pettway YD, Hopkirk AL, Reihsmann CV, Tao Y, Fan S, Shrestha S, Varshney A, Petty LE, Wright JJ, Ventresca C, Agarwala S, Aramandla R, Poffenberger G, Jenkins R, Mei S, Hart NJ, Phillips S, Kang H, Greiner DL, Shultz LD, Bottino R, Liu J, Below JE, Parker SCJ, Powers AC, Brissova M. Genetic risk converges on regulatory networks mediating early type 2 diabetes. Nature 2023; 624:621-629. [PMID: 38049589 DOI: 10.1038/s41586-023-06693-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 09/28/2023] [Indexed: 12/06/2023]
Abstract
Type 2 diabetes mellitus (T2D), a major cause of worldwide morbidity and mortality, is characterized by dysfunction of insulin-producing pancreatic islet β cells1,2. T2D genome-wide association studies (GWAS) have identified hundreds of signals in non-coding and β cell regulatory genomic regions, but deciphering their biological mechanisms remains challenging3-5. Here, to identify early disease-driving events, we performed traditional and multiplexed pancreatic tissue imaging, sorted-islet cell transcriptomics and islet functional analysis of early-stage T2D and control donors. By integrating diverse modalities, we show that early-stage T2D is characterized by β cell-intrinsic defects that can be proportioned into gene regulatory modules with enrichment in signals of genetic risk. After identifying the β cell hub gene and transcription factor RFX6 within one such module, we demonstrated multiple layers of genetic risk that converge on an RFX6-mediated network to reduce insulin secretion by β cells. RFX6 perturbation in primary human islet cells alters β cell chromatin architecture at regions enriched for T2D GWAS signals, and population-scale genetic analyses causally link genetically predicted reduced RFX6 expression with increased T2D risk. Understanding the molecular mechanisms of complex, systemic diseases necessitates integration of signals from multiple molecules, cells, organs and individuals, and thus we anticipate that this approach will be a useful template to identify and validate key regulatory networks and master hub genes for other diseases or traits using GWAS data.
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Affiliation(s)
- John T Walker
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Diane C Saunders
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Vivek Rai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Hung-Hsin Chen
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Peter Orchard
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chunhua Dai
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yasminye D Pettway
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Alexander L Hopkirk
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Conrad V Reihsmann
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yicheng Tao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Simin Fan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Shristi Shrestha
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Arushi Varshney
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan J Wright
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christa Ventresca
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Samir Agarwala
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Radhika Aramandla
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Greg Poffenberger
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Regina Jenkins
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shaojun Mei
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nathaniel J Hart
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sharon Phillips
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dale L Greiner
- Department of Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Medical School, Worcester, MA, USA
| | | | - Rita Bottino
- Imagine Pharma, Devon, PA, USA
- Institute of Cellular Therapeutics, Allegheny-Singer Research Institute, Allegheny Health Network, Pittsburgh, PA, USA
| | - Jie Liu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephen C J Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
| | - Alvin C Powers
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA.
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- VA Tennessee Valley Healthcare System, Nashville, TN, USA.
| | - Marcela Brissova
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
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Patel A, Ye T, Xue H, Lin Z, Xu S, Woolf B, Mason AM, Burgess S. MendelianRandomization v0.9.0: updates to an R package for performing Mendelian randomization analyses using summarized data. Wellcome Open Res 2023; 8:449. [PMID: 37915953 PMCID: PMC10616660 DOI: 10.12688/wellcomeopenres.19995.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/16/2023] [Indexed: 11/03/2023] Open
Abstract
The MendelianRandomization package is a software package written for the R software environment that implements methods for Mendelian randomization based on summarized data. In this manuscript, we describe functions that have been added or edited in the package since version 0.5.0, when we last described the package and its contents. The main additions to the package since that time are: 1) new robust methods for performing Mendelian randomization, particularly in the cases of bias from weak instruments and/or winner's curse, and pleiotropic variants, 2) methods for performing Mendelian randomization with correlated variants using dimension reduction to summarize large numbers of highly correlated variants into a limited set of principal components, 3) functions for calculating first-stage F statistics, representing instrument strength, in both univariable and multivariable contexts, and with uncorrelated and correlated genetic variants. We also discuss some pragmatic issues relating to the use of correlated variants in Mendelian randomization.
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Affiliation(s)
- Ashish Patel
- MRC Biostatistics Unit, University of Cambridge, Cambridge, England, CB2 0SR, UK
| | - Ting Ye
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Haoran Xue
- Department of Biostatistics, City University of Hong Kong, Hong Kong, Hong Kong
- Division of Biostatistics, School of Public Health, University of Minnesota Duluth, Duluth, Minnesota, USA
| | - Zhaotong Lin
- Division of Biostatistics, School of Public Health, University of Minnesota Duluth, Duluth, Minnesota, USA
- Department of Statistics, Florida State University, Tallahassee, Florida, USA
| | - Siqi Xu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Benjamin Woolf
- MRC Biostatistics Unit, University of Cambridge, Cambridge, England, CB2 0SR, UK
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, England, UK
- School of Psychological Science, University of Bristol, Bristol, England, UK
| | - Amy M. Mason
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, England, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, England, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, England, CB2 0SR, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, England, UK
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Patel A, Ye T, Xue H, Lin Z, Xu S, Woolf B, Mason AM, Burgess S. MendelianRandomization v0.9.0: updates to an R package for performing Mendelian randomization analyses using summarized data. Wellcome Open Res 2023; 8:449. [PMID: 37915953 PMCID: PMC10616660 DOI: 10.12688/wellcomeopenres.19995.2] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/16/2023] [Indexed: 08/21/2024] Open
Abstract
The MendelianRandomization package is a software package written for the R software environment that implements methods for Mendelian randomization based on summarized data. In this manuscript, we describe functions that have been added or edited in the package since version 0.5.0, when we last described the package and its contents. The main additions to the package since that time are: 1) new robust methods for performing Mendelian randomization, particularly in the cases of bias from weak instruments and/or winner's curse, and pleiotropic variants, 2) methods for performing Mendelian randomization with correlated variants using dimension reduction to summarize large numbers of highly correlated variants into a limited set of principal components, 3) functions for calculating first-stage F statistics, representing instrument strength, in both univariable and multivariable contexts, and with uncorrelated and correlated genetic variants. We also discuss some pragmatic issues relating to the use of correlated variants in Mendelian randomization.
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Affiliation(s)
- Ashish Patel
- MRC Biostatistics Unit, University of Cambridge, Cambridge, England, CB2 0SR, UK
| | - Ting Ye
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Haoran Xue
- Department of Biostatistics, City University of Hong Kong, Hong Kong, Hong Kong
- Division of Biostatistics, School of Public Health, University of Minnesota Duluth, Duluth, Minnesota, USA
| | - Zhaotong Lin
- Division of Biostatistics, School of Public Health, University of Minnesota Duluth, Duluth, Minnesota, USA
- Department of Statistics, Florida State University, Tallahassee, Florida, USA
| | - Siqi Xu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Benjamin Woolf
- MRC Biostatistics Unit, University of Cambridge, Cambridge, England, CB2 0SR, UK
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, England, UK
- School of Psychological Science, University of Bristol, Bristol, England, UK
| | - Amy M. Mason
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, England, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, England, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, England, CB2 0SR, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, England, UK
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Li J, Zhang J, Kong B, Chen L, Yuan J, He M, Wang Y, Wei S, Chen W, Tang Y, Zhu X, Yao P. Abdominal obesity mediates the causal relationship between depression and the risk of gallstone disease: retrospective cohort study and Mendelian randomization analyses. J Psychosom Res 2023; 174:111474. [PMID: 37689051 DOI: 10.1016/j.jpsychores.2023.111474] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 08/13/2023] [Accepted: 08/22/2023] [Indexed: 09/11/2023]
Abstract
OBJECTIVE Our study aimed to explore the causal effect of depression on the risk of gallstone disease, and the mediation effects of metabolic traits. METHODS A retrospective cohort study on Chinese elderly from the Dongfeng-Tongji cohort (including 18,141 individuals) was conducted to estimate the adverse effect of probable depression on the risk of gallstone disease. Two-sample Mendelian randomization was performed in European and East-Asian ancestries, to verify the causal relationship between major depression and gallstone disease. We further applied two-step Mendelian randomization to explore the mediation effects of metabolic traits. RESULTS In the cohort study, probable depression was associated with an increased risk of gallstone disease within 5 years, with RR (95% CI) of 1.33 (1.12, 1.58) in multivariable regression, and 1.34 (1.11, 1.61) following propensity score weighting. Bidirectional Mendelian randomization in European ancestry revealed a positive causal effect (OR: 1.21; 95% CI: 1.07 to 1.37) of genetically predicted major depression liability on gallstone disease, based on the inverse variance weighted method. Little evidence was presented from other complementary approaches, and the analysis in East-Asian ancestry (IVW estimated OR: 1.03; 95% CI: 0.92 to 1.15). The indirect effect via waist circumference and HDL-C were 1.06 (95% CI: 1.02 to 1.10) and 1.01 (95% CI: 1.00 to 1.01) respectively, which mediated 25.8% and 3.78% of the causal relationship. CONCLUSIONS Our study suggested a higher risk of gallstone disease in the population with probable depression, while the two-sample Mendelian randomization provided weak evidence for the causal relationship, which was moderately mediated by abdominal obesity.
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Affiliation(s)
- Jingxi Li
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jun Zhang
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Bingxuan Kong
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Li Chen
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jing Yuan
- Institute of Occupational Medicine and Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Meian He
- Institute of Occupational Medicine and Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Youjie Wang
- Institute of Occupational Medicine and Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Sheng Wei
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Weihong Chen
- Institute of Occupational Medicine and Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yuhan Tang
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xinhong Zhu
- School of Nursing, Hubei University of Chinese Medicine, Wuhan 430070, China
| | - Ping Yao
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
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20
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Gu J, Yan GM, Kong XL, Zhang YY, Huang LH, Lu HM. Assessing the causal relationship between immune traits and systemic lupus erythematosus by bi-directional Mendelian randomization analysis. Mol Genet Genomics 2023; 298:1493-1503. [PMID: 37845373 DOI: 10.1007/s00438-023-02071-9] [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: 10/04/2022] [Accepted: 09/23/2023] [Indexed: 10/18/2023]
Abstract
Previous studies have observed relationships between immune cells and systemic lupus erythematosus (SLE), but their causal links remain undetermined. Based on the public available genome-wide association studies (GWAS) summary statistics, we conducted two-sample Mendelian randomization (MR) to evaluate the associations between 731 immune phenotypes and SLE pairs. Pairwise pleiotropy analysis was performed to identify pleiotropic genes for significant immunophenotype-SLE pairs. A comprehensive gene function analysis was undertaken to explore the mechanisms of immune cells in SLE. By using the instrumental variables extracted from GWAS data, we observed that increased levels of five immune phenotypes were causally associated with SLE risk (FDR < 0.05), that were CD20 on IgD+ CD38- naïve, BAFF-R on IgD+ CD38dim, CD39+ secreting Treg AC, CD14- CD16+ monocyte AC, and HLA DR on CD14+ monocyte. Pairwise gene-based analyses identified a total of 38 pleiotropic genes for 5 significant pairs identified and gene set enrichment analysis revealed the involvement of the identified pleiotropic genes in complex pathways (i.e., systemic lupus erythematosus, an integral component of luminal side of endoplasmic reticulum membrane, C-type lectin receptor signaling pathway and regulation of hormone secretion). This study demonstrates that the immune response influences the progression of SLE in a complex pattern. These findings greatly improve our understanding of the interaction between immune response and SLE risk and also aid in the design of therapeutic strategies from an immunological perspective.
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Affiliation(s)
- Jie Gu
- Department of Outpatient and Emergency, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People's Republic of China
| | - Guan-Min Yan
- Department of Outpatient and Emergency, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People's Republic of China
| | - Xiao-Lin Kong
- Department of Outpatient and Emergency, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People's Republic of China
| | - Yin-Ying Zhang
- Department of Outpatient and Emergency, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People's Republic of China
| | - Li-Hong Huang
- Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China.
| | - Hui-Min Lu
- Department of Outpatient and Emergency, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People's Republic of China.
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21
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Liu Z, Ye T, Sun B, Schooling M, Tchetgen ET. Mendelian randomization mixed-scale treatment effect robust identification and estimation for causal inference. Biometrics 2023; 79:2208-2219. [PMID: 35950778 DOI: 10.1111/biom.13735] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 08/02/2022] [Indexed: 11/28/2022]
Abstract
Standard Mendelian randomization (MR) analysis can produce biased results if the genetic variant defining an instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment variable. We provide novel identification conditions for the causal effect of a treatment in the presence of unmeasured confounding by leveraging a possibly invalid IV for which both the IV independence and exclusion restriction assumptions may be violated. The proposed Mendelian randomization mixed-scale treatment effect robust identification (MR MiSTERI) approach relies on (i) an assumption that the treatment effect does not vary with the possibly invalid IV on the additive scale; (ii) that the confounding bias does not vary with the possibly invalid IV on the odds ratio scale; and (iii) that the residual variance for the outcome is heteroskedastic with respect to the possibly invalid IV. Although assumptions (i) and (ii) have, respectively, appeared in the IV literature, assumption (iii) has not; we formally establish that their conjunction can identify a causal effect even with an invalid IV. MR MiSTERI is shown to be particularly advantageous in the presence of pervasive heterogeneity of pleiotropic effects on the additive scale. We propose a simple and consistent three-stage estimator that can be used as a preliminary estimator to a carefully constructed efficient one-step-update estimator. In order to incorporate multiple, possibly correlated, and weak invalid IVs, a common challenge in MR studies, we develop a MAny Weak Invalid Instruments (MR MaWII MiSTERI) approach for strengthened identification and improved estimation accuracy. Both simulation studies and UK Biobank data analysis results demonstrate the robustness of the proposed methods.
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Affiliation(s)
- Zhonghua Liu
- Department of Biostatistics, Columbia University, New York, New York, USA
| | - Ting Ye
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Baoluo Sun
- Department of Statistics and Data Science, National University of Singapore, Singapore
| | - Mary Schooling
- CUNY Graduate School of Public Health and Health Policy, New York, New York, USA
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
| | - Eric Tchetgen Tchetgen
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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22
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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: 150] [Impact Index Per Article: 150.0] [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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Jiang T, Gill D, Butterworth AS, Burgess S. An empirical investigation into the impact of winner's curse on estimates from Mendelian randomization. Int J Epidemiol 2023; 52:1209-1219. [PMID: 36573802 PMCID: PMC10396423 DOI: 10.1093/ije/dyac233] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 12/08/2022] [Indexed: 12/28/2022] Open
Abstract
INTRODUCTION Genetic associations for variants identified through genome-wide association studies (GWASs) tend to be overestimated in the original discovery data set as, if the association was underestimated, the variant may not have been detected. This bias, known as winner's curse, can affect Mendelian randomization estimates, but its severity and potential impact are unclear. METHODS We performed an empirical investigation to assess the potential bias from winner's curse in practice. We considered Mendelian randomization estimates for the effect of body mass index (BMI) on coronary artery disease risk. We randomly divided a UK Biobank data set 100 times into three equally sized subsets. The first subset was treated as the 'discovery GWAS'. We compared genetic associations estimated in the discovery GWAS to those estimated in the other subsets for each of the 100 iterations. RESULTS For variants associated with BMI at P < 5 × 10-8 in at least one iteration, genetic associations with BMI were up to 5-fold greater in iterations in which the variant was associated with BMI at P < 5 × 10-8 compared with its mean association across all iterations. If the minimum P-value for association with BMI was P = 10-13 or lower, then this inflation was <25%. Mendelian randomization estimates were affected by winner's curse bias. However, bias did not materially affect results; all analyses indicated a deleterious effect of BMI on coronary artery disease risk. CONCLUSIONS Winner's curse can bias Mendelian randomization estimates, although its practical impact may not be substantial. If avoiding sample overlap is infeasible, analysts should consider performing a sensitivity analysis based on variants strongly associated with the exposure.
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Affiliation(s)
- Tao Jiang
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Chief Scientific Advisor Office, Research and Early Development, Novo Nordisk, Copenhagen, Denmark
| | - Adam S Butterworth
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Stephen Burgess
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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Guo X, Tang P, Zhang L, Li R. Tobacco and alcohol consumption and the risk of frailty and falling: a Mendelian randomisation study. J Epidemiol Community Health 2023; 77:349-354. [PMID: 37001985 DOI: 10.1136/jech-2022-219855] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 03/22/2023] [Indexed: 04/03/2023]
Abstract
BackgroundEpidemiological data have suggested that tobacco and alcohol consumption were associated with the risk of frailty and falling, but it is yet unclear whether these associations are of a causal nature. Thus, we conducted two-sample Mendelian randomisation analysis using genetic instruments to determine the causal associations of tobacco and alcohol consumption on frailty and falls.MethodsIndependent instrumental variables strongly (p<5E–09) associated with tobacco and alcohol consumption were obtained from the genome-wide association study (GWAS) and Sequencing Consortium of Alcohol and Nicotine use (up to 2 669 029 participants). Summary statistics of the frailty index (FI, N=175 226) and falling risk (N=451 179) were from the two latest published GWAS datasets on FI and falling risk.ResultsUsing the inverse-variance weighted method, our results showed that genetically determined initiation of smoking was significantly associated with an increased FI (β=0.34, 95% CI=0.29 to 0.40, p=5.48E–33) and risk of falling (OR=1.39, 95% CI=1.30 to 1.50, p=1.01E–20). In addition, the age of initiation of smoking and cigarettes consumption per day was negatively and positively associated with both FI and falls, respectively. Current smokers were prone to having a higher FI and falling risk than individuals who quit smoking. There was no significant causal association between alcohol use and the risk of frailty and falling. Similar results were obtained using other statistical approaches with good stability.ConclusionsOur findings demonstrate that tobacco use, but not alcohol drinking, significantly increases the risk of frailty and falling. Future studies are warranted to clarify the underlying physiopathological mechanisms.
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Affiliation(s)
- Xingzhi Guo
- Department of Geriatric Neurology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China
- Shaanxi Provincial Clinical Research Center for Geriatric Medicine, Xi'an, Shaanxi, China
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Peng Tang
- Department of Geriatric Neurology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China
- Shaanxi Provincial Clinical Research Center for Geriatric Medicine, Xi'an, Shaanxi, China
| | - Lina Zhang
- Department of Geriatric Neurology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China
- Shaanxi Provincial Clinical Research Center for Geriatric Medicine, Xi'an, Shaanxi, China
| | - Rui Li
- Department of Geriatric Neurology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China
- Shaanxi Provincial Clinical Research Center for Geriatric Medicine, Xi'an, Shaanxi, China
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shaanxi, China
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26
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Wang K. Support Interval for Two-Sample Summary Data-Based Mendelian Randomization. Genes (Basel) 2023; 14:211. [PMID: 36672952 PMCID: PMC9859138 DOI: 10.3390/genes14010211] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 12/31/2022] [Accepted: 01/04/2023] [Indexed: 01/18/2023] Open
Abstract
The summary-data-based Mendelian randomization (SMR) method is gaining popularity in estimating the causal effect of an exposure on an outcome. In practice, the instrument SNP is often selected from the genome-wide association study (GWAS) on the exposure but no correction is made for such selection in downstream analysis, leading to a biased estimate of the effect size and invalid inference. We address this issue by using the likelihood derived from the sampling distribution of the estimated SNP effects in the exposure GWAS and the outcome GWAS. This likelihood takes into account how the instrument SNPs are selected. Since the effective sample size is 1, the asymptotic theory does not apply. We use a support for a profile likelihood as an interval estimate of the causal effect. Simulation studies indicate that this support has robust coverage while the confidence interval implied by the SMR method has lower-than-nominal coverage. Furthermore, the variance of the two-stage least squares estimate of the causal effect is shown to be the same as the variance used for SMR for one-sample data when there is no selection.
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Affiliation(s)
- Kai Wang
- Department of Biostatistics, University of Iowa, 145 N Riverside Dr., Iowa City, IA 52242, USA
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27
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Zhu X, Huang S, Kang W, Chen P, Liu J. Associations between polyunsaturated fatty acid concentrations and Parkinson's disease: A two-sample Mendelian randomization study. Front Aging Neurosci 2023; 15:1123239. [PMID: 36909950 PMCID: PMC9992541 DOI: 10.3389/fnagi.2023.1123239] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 02/02/2023] [Indexed: 02/24/2023] Open
Abstract
Introduction Observational studies demonstrated controversial effect of polyunsaturated fatty acids (PUFAs) on Parkinson's disease (PD) with limited causality evidence. Randomized control trials showed possible improvement in PD symptoms with PUFA supplement but had small study population and limited intervention time. Methods A two-sample Mendelian randomization was designed to evaluate the causal relevance between PUFAs and PD, using genetic variants of PUFAs as instrumental variables and PD data from the largest genome-wide association study as outcome. Inverse variance weighted (IVW) method was applied to obtain the primary outcome. Mendelian randomization Egger regression, weighted median and weighted mode methods were exploited to assist result analyses. Strict Mendelian randomization and multivariable Mendelian randomization (MVMR) were used to estimate direct effects of PUFAs on PD, eliminating pleiotropic effect. Debiased inverse variance weighted estimator was implemented when weak instrument bias was introduced into the analysis. A variety of sensitivity analyses were utilized to assess validity of the results. Results Our study included 33,674 PD cases and 449,056 controls. Higher plasma level of arachidonic acid (AA) was associated with a 3% increase of PD risk per 1-standard deviation (SD) increase of AA (IVW; Odds ratio (OR)=1.03 [95% confidence interval (CI) 1.01-1.04], P = 2.24E-04). After MVMR (IVW; OR=1.03 [95% CI 1.02-1.04], P =6.15E-08) and deletion of pleiotropic single-nucleotide polymorphisms overlapping with other lipids (IVW; OR=1.03 [95% CI 1.01-1.05], P =5.88E-04), result was still significant. Increased level of eicosapentaenoic acid (EPA) showed possible relevance with increased PD risk after adjustment of pleiotropy (MVMR; OR=1.05 [95% CI 1.01-1.08], P =5.40E-03). Linoleic acid (LA), docosahexaenoic acid (DHA), docosapentaenoic acid (DPA) and alpha-linolenic acid (ALA) were found not causally relevant to PD risk. Various sensitivity analyses verified the validity of our results. In conclusion, our findings from Mendelian randomization suggested that elevated levels of AA and possibly EPA might be linked to a higher risk of PD. No association between PD risk and LA, DHA, DPA, or ALA was found. Discussion The odds ratio for plasma AA and PD risk was weak. It is important to approach our results with caution in clinical practice and to conduct additional studies on the relationship between PUFAs and PD risk.
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Affiliation(s)
- Xue Zhu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sijia Huang
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenyan Kang
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Peizhan Chen
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Liu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Co-innovation Center of Neuroregeneration, Nantong University, Nantong, China
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28
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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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29
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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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30
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Yang Z, Schooling CM, Kwok MK. Credible Mendelian Randomization Studies in the Presence of Selection Bias Using Control Exposures. Front Genet 2021; 12:729326. [PMID: 34899831 PMCID: PMC8652250 DOI: 10.3389/fgene.2021.729326] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 11/01/2021] [Indexed: 11/26/2022] Open
Abstract
Selection bias is increasingly acknowledged as a limitation of Mendelian randomization (MR). However, few methods exist to assess this issue. We focus on two plausible causal structures relevant to MR studies and illustrate the data-generating process underlying selection bias via simulation studies. We conceptualize the use of control exposures to validate MR estimates derived from selected samples by detecting potential selection bias and reproducing the exposure-outcome association of primary interest based on subject matter knowledge. We discuss the criteria for choosing the control exposures. We apply the proposal in an MR study investigating the potential effect of higher transferrin with stroke (including ischemic and cardioembolic stroke) using transferrin saturation and iron status as control exposures. Theoretically, selection bias affects associations of genetic instruments with the outcome in selected samples, violating the exclusion-restriction assumption and distorting MR estimates. Our applied example showing inconsistent effects of genetically predicted higher transferrin and higher transferrin saturation on stroke suggests the potential selection bias. Furthermore, the expected associations of genetically predicted higher iron status on stroke and longevity indicate no systematic selection bias. The routine use of control exposures in MR studies provides a valuable tool to validate estimated causal effects. Like the applied example, an antagonist, decoy, or exposure with similar biological activity as the exposure of primary interest, which has the same potential selection bias sources as the exposure-outcome association, is suggested as the control exposure. An additional or a validated control exposure with a well-established association with the outcome is also recommended to explore possible systematic selection bias.
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Affiliation(s)
- Zhao Yang
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - C. Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Graduate School of Public Health and Health Policy, City University of New York, New York, NY, United States
| | - Man Ki Kwok
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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