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Yuan D, Wu J, Li S, Zhou X, Zhang R, Zhang Y. Causal relationships between serum albumin, neuroticism and suicidal ideation in depressed patients: A Mendelian randomization study. Heliyon 2024; 10:e30718. [PMID: 38765065 PMCID: PMC11098842 DOI: 10.1016/j.heliyon.2024.e30718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/28/2024] [Accepted: 05/02/2024] [Indexed: 05/21/2024] Open
Abstract
Although serum albumin and neuroticism have revealed a strong association with suicidal ideation in individuals with depression, the causal relationship between them is uncertain. This study analyzed the causal association of serum albumin, neuroticism and suicidal ideation using large-scale GWAS data and Univariable Mendelian Randomization (UVMR) methods. The Multivariable MR (MVMR) analysis was used to explore the causal pathways. UVMR analysis revealed that genetically determined serum albumin is causally associated with neuroticism (β = -0.006 S.D.; 95 % CI: 0.009, -0.002; p = 0.003) and suicidal ideation (β = 0.009 S.D.; 95 % CI: 0.001, 0.016; p = 0.037); and that neuroticism mediates 100 % of the causal association between serum albumin and suicidal ideation in individuals with depression. These findings suggest genetic evidence for the causal effect of serum albumin on suicidal ideation in depressed patients and the significant mediation effect of neuroticism on this causal association. This study proves the protective role of serum albumin for neuroticism and the riskiness of personality traits for suicidal ideation in individuals with depression.
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Affiliation(s)
- Dongling Yuan
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jialing Wu
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Shansi Li
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Xiao Zhou
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Ruoyi Zhang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yi Zhang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
- Medical Psychological Institute of Central South University, Central South University, Changsha, China
- National Clinical Research Center on Mental Disorders (Xiangya), Changsha, China
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Xi Y, Wen R, Zhang R, Dong Q, Hou S, Zhang S. Genetic evidence supporting a causal role of Janus kinase 2 in prostate cancer: a Mendelian randomization study. Aging Male 2023; 26:2257300. [PMID: 37706641 DOI: 10.1080/13685538.2023.2257300] [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: 05/15/2023] [Revised: 09/04/2023] [Accepted: 09/06/2023] [Indexed: 09/15/2023] Open
Abstract
BACKGROUND Janus kinase-2 (JAK2) inhibitors are now being tried in basic research and clinical practice in prostate cancer (PCa). However, the causal relationship between JAK2 and PCa has not been uniformly described. Here, we examined the cause-effect relation between JAK2 and PCa. METHODS Two-sample Mendelian randomization (MR) analysis of genetic variation data of JAK2, PCa from IEU OpenGWAS Project was performed by inverse variance weighted, MR-Egger, and weighted median. Cochran's Q heterogeneity test and MR-Egger multiplicity analysis were performed to normalize the MR analysis results to reduce the effect of bias on the results. RESULTS Five instrumental variables were identified for further MR analysis. Specifically, combining the inverse variance-weighted (OR: 1.0009, 95% CI: 1.0001-1.0015, p = 0.02) and weighted median (OR: 1.0009, 95% CI: 1.0000-1.0017, p = 0.03). Sensitivity analysis showed that there was no heterogeneity (p = 0.448) and horizontal multiplicity (p = 0.770) among the instrumental variables. CONCLUSIONS We found JAK2 was associated with the development of PCa and was a risk factor for PCa, which might be instructive for the use of JAK2 inhibitors in PCa patients.
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Affiliation(s)
- Yujia Xi
- Department of Urology, The Second Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, China
- Shanxi Provincial Key Laboratory of Rheumatism Immune Microecology, Taiyuan, PR China
- Key Laboratory of Cellular Physiology at Shanxi Medical University, Ministry of Education, Taiyuan, PR China
| | - Rui Wen
- Shanxi Provincial Key Laboratory of Rheumatism Immune Microecology, Taiyuan, PR China
- Key Laboratory of Cellular Physiology at Shanxi Medical University, Ministry of Education, Taiyuan, PR China
| | - Ran Zhang
- Shanxi Provincial Key Laboratory of Rheumatism Immune Microecology, Taiyuan, PR China
- Key Laboratory of Cellular Physiology at Shanxi Medical University, Ministry of Education, Taiyuan, PR China
| | - Qirui Dong
- Shanxi Provincial Key Laboratory of Rheumatism Immune Microecology, Taiyuan, PR China
- Key Laboratory of Cellular Physiology at Shanxi Medical University, Ministry of Education, Taiyuan, PR China
| | - Sijia Hou
- Shanxi Provincial Key Laboratory of Rheumatism Immune Microecology, Taiyuan, PR China
- Key Laboratory of Cellular Physiology at Shanxi Medical University, Ministry of Education, Taiyuan, PR China
- Department of Neurology, The First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, China
| | - Shengxiao Zhang
- Shanxi Provincial Key Laboratory of Rheumatism Immune Microecology, Taiyuan, PR China
- Key Laboratory of Cellular Physiology at Shanxi Medical University, Ministry of Education, Taiyuan, PR China
- Department of Rheumatology, The Second Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, China
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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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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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Xue H, Shen X, Pan W. Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data. J Am Stat Assoc 2023; 118:1525-1537. [PMID: 37808547 PMCID: PMC10557939 DOI: 10.1080/01621459.2023.2183127] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 02/14/2023] [Indexed: 02/24/2023]
Abstract
Transcriptome-wide association studies (TWAS) have recently emerged as a popular tool to discover (putative) causal genes by integrating an outcome GWAS dataset with another gene expression/transcriptome GWAS (called eQTL) dataset. In our motivating and target application, we'd like to identify causal genes for low-density lipoprotein cholesterol (LDL), which is crucial for developing new treatments for hyperlipidemia and cardiovascular diseases. The statistical principle underlying TWAS is (two-sample) two-stage least squares (2SLS) using multiple correlated SNPs as instrumental variables (IVs); it is closely related to typical (two-sample) Mendelian randomization (MR) using independent SNPs as IVs, which is expected to be impractical and lower-powered for TWAS (and some other) applications. However, often some of the SNPs used may not be valid IVs, e.g. due to the widespread pleiotropy of their direct effects on the outcome not mediated through the gene of interest, leading to false conclusions by TWAS (or MR). Building on recent advances in sparse regression, we propose a robust and efficient inferential method to account for both hidden confounding and some invalid IVs via two-stage constrained maximum likelihood (2ScML), an extension of 2SLS. We first develop the proposed method with individual-level data, then extend it both theoretically and computationally to GWAS summary data for the most popular two-sample TWAS design, to which almost all existing robust IV regression methods are however not applicable. We show that the proposed method achieves asymptotically valid statistical inference on causal effects, demonstrating its wider applicability and superior finite-sample performance over the standard 2SLS/TWAS (and MR). We apply the methods to identify putative causal genes for LDL by integrating large-scale lipid GWAS summary data with eQTL data.
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Affiliation(s)
- Haoran Xue
- School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455
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Boehm FJ, Zhou X. Statistical methods for Mendelian randomization in genome-wide association studies: A review. Comput Struct Biotechnol J 2022; 20:2338-2351. [PMID: 35615025 PMCID: PMC9123217 DOI: 10.1016/j.csbj.2022.05.015] [Citation(s) in RCA: 101] [Impact Index Per Article: 50.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/08/2022] [Accepted: 05/09/2022] [Indexed: 11/15/2022] Open
Abstract
Genome-wide association studies have yielded thousands of associations for many common diseases and disease-related complex traits. The identified associations made it possible to identify the causal risk factors underlying diseases and investigate the causal relationships among complex traits through Mendelian randomization. Mendelian randomization is a form of instrumental variable analysis that uses SNP associations from genome-wide association studies as instruments to study and uncover causal relationships between complex traits. By leveraging SNP genotypes as instrumental variables, or proxies, for the exposure complex trait, investigators can tease out causal effects from observational data, provided that necessary assumptions are satisfied. We discuss below the development of Mendelian randomization methods in parallel with the growth of genome-wide association studies. We argue that the recent availability of GWAS summary statistics for diverse complex traits has motivated new Mendelian randomization methods with relaxed causality assumptions and that this area continues to offer opportunities for robust biological discoveries.
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Affiliation(s)
- Frederick J. Boehm
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
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Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munafò MR, Palmer T, Schooling CM, Wallace C, Zhao Q, Smith GD. Mendelian randomization. NATURE REVIEWS. METHODS PRIMERS 2022; 2:6. [PMID: 37325194 PMCID: PMC7614635 DOI: 10.1038/s43586-021-00092-5] [Citation(s) in RCA: 450] [Impact Index Per Article: 225.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/21/2021] [Indexed: 06/17/2023]
Abstract
Mendelian randomization (MR) is a term that applies to the use of genetic variation to address causal questions about how modifiable exposures influence different outcomes. The principles of MR are based on Mendel's laws of inheritance and instrumental variable estimation methods, which enable the inference of causal effects in the presence of unobserved confounding. In this Primer, we outline the principles of MR, the instrumental variable conditions underlying MR estimation and some of the methods used for estimation. We go on to discuss how the assumptions underlying an MR study can be assessed and give methods of estimation that are robust to certain violations of these assumptions. We give examples of a range of studies in which MR has been applied, the limitations of current methods of analysis and the outlook for MR in the future. The difference between the assumptions required for MR analysis and other forms of non-interventional epidemiological studies means that MR can be used as part of a triangulation across multiple sources of evidence for causal inference.
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Affiliation(s)
- Eleanor Sanderson
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Michael V. Holmes
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hyunseung Kang
- Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA
| | - Jean Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Marcus R. Munafò
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- School of Psychological Science, University of Bristol, Bristol, UK
- National Institute for Health Research (NIHR), Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Tom Palmer
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - C. Mary Schooling
- School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- School of Public Health, City University of New York, New York, USA
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), University of Cambridge, Cambridge, UK
| | - Qingyuan Zhao
- Statistical Laboratory, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research (NIHR), Biomedical Research Centre, University of Bristol, Bristol, UK
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