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Lu Y, Xu K, Maydanchik N, Kang B, Pierce BL, Yang F, Chen LS. An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues. Am J Hum Genet 2024; 111:1736-1749. [PMID: 39053459 DOI: 10.1016/j.ajhg.2024.06.012] [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: 02/03/2024] [Revised: 06/11/2024] [Accepted: 06/24/2024] [Indexed: 07/27/2024] Open
Abstract
Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context- or tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers insights into disease mechanisms.
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Affiliation(s)
- Yihao Lu
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Ke Xu
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA
| | - Nathaniel Maydanchik
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Bowei Kang
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Brandon L Pierce
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Fan Yang
- Yau Mathematical Sciences Center, Tsinghua University, Beijing, China; Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, China.
| | - Lin S Chen
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA.
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2
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Jin YJ, Wu XY, An ZY. The Application of Mendelian Randomization in Cardiovascular Disease Risk Prediction: Current Status and Future Prospects. Rev Cardiovasc Med 2024; 25:262. [PMID: 39139440 PMCID: PMC11317336 DOI: 10.31083/j.rcm2507262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 03/05/2024] [Accepted: 03/11/2024] [Indexed: 08/15/2024] Open
Abstract
Cardiovascular disease (CVD), a leading cause of death and disability worldwide, and is associated with a wide range of risk factors, and genetically associated conditions. While many CVDs are preventable and early detection alongside treatment can significantly mitigate complication risks, current prediction models for CVDs need enhancements for better accuracy. Mendelian randomization (MR) offers a novel approach for estimating the causal relationship between exposure and outcome by using genetic variation in quasi-experimental data. This method minimizes the impact of confounding variables by leveraging the random allocation of genes during gamete formation, thereby facilitating the integration of new predictors into risk prediction models to refine the accuracy of prediction. In this review, we delve into the theory behind MR, as well as the strengths, applications, and limitations behind this emerging technology. A particular focus will be placed on MR application to CVD, and integration into CVD prediction frameworks. We conclude by discussing the inclusion of various populations and by offering insights into potential areas for future research and refinement.
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Affiliation(s)
- Yi-Jing Jin
- Peking University Health Science Center, 100191 Beijing, China
- Department of Cardiology, Peking University First Hospital, 100034
Beijing, China
| | - Xing-Yuan Wu
- Peking University Health Science Center, 100191 Beijing, China
| | - Zhuo-Yu An
- Peking University Health Science Center, 100191 Beijing, China
- Peking University Institute of Hematology, Peking University People's
Hospital, 100044 Beijing, China
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3
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Chen J, Shen L, Wu T, Yang Y. Unraveling the significance of AGPAT4 for the pathogenesis of endometriosis via a multi-omics approach. Hum Genet 2024:10.1007/s00439-024-02681-2. [PMID: 38850428 DOI: 10.1007/s00439-024-02681-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 05/26/2024] [Indexed: 06/10/2024]
Abstract
Endometriosis is characterized by the ectopic proliferation of endometrial cells, posing considerable diagnostic and therapeutic challenges. Our study investigates AGPAT4's involvement in endometriosis pathogenesis, aiming to unveil new therapeutic targets. Our investigation by analyzing eQTL data from GWAS for preliminary screening. Subsequently, within the GEO dataset, we utilized four machine learning algorithms to precisely identify risk-associated genes. Gene validity was confirmed through five Mendelian Randomization methods. AGPAT4 expression was measured by Single-Cell Analysis, ELISA and immunohistochemistry. We investigated AGPAT4's effect on endometrial stromal cells using RNA interference, assessing cell proliferation, invasion, and migration with CCK8, wound-healing, and transwell assays. Protein expression was analyzed by western blot, and AGPAT4 interactions were explored using AutoDock. Our investigation identified 11 genes associated with endometriosis risk, with AGPAT4 and COMT emerging as pivotal biomarkers through machine learning analysis. AGPAT4 exhibited significant upregulation in both ectopic tissues and serum samples from patients with endometriosis. Reduced expression of AGPAT4 was observed to detrimentally impact the proliferation, invasion, and migration capabilities of endometrial stromal cells, concomitant with diminished expression of key signaling molecules such as Wnt3a, β-Catenin, MMP-9, and SNAI2. Molecular docking analyses further underscored a substantive interaction between AGPAT4 and Wnt3a.Our study highlights AGPAT4's key role in endometriosis, influencing endometrial stromal cell behavior, and identifies AGPAT4 pathways as promising therapeutic targets for this condition.
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Affiliation(s)
- Jun Chen
- Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Licong Shen
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Tingting Wu
- Department of Cardiovasology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Yongwen Yang
- Department of Clinical Laboratory, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, China.
- National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
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Oh W, Jung J, Joo JWJ. MR-GGI: accurate inference of gene-gene interactions using Mendelian randomization. BMC Bioinformatics 2024; 25:192. [PMID: 38750431 PMCID: PMC11094870 DOI: 10.1186/s12859-024-05808-4] [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] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 05/09/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Researchers have long studied the regulatory processes of genes to uncover their functions. Gene regulatory network analysis is one of the popular approaches for understanding these processes, requiring accurate identification of interactions among the genes to establish the gene regulatory network. Advances in genome-wide association studies and expression quantitative trait loci studies have led to a wealth of genomic data, facilitating more accurate inference of gene-gene interactions. However, unknown confounding factors may influence these interactions, making their interpretation complicated. Mendelian randomization (MR) has emerged as a valuable tool for causal inference in genetics, addressing confounding effects by estimating causal relationships using instrumental variables. In this paper, we propose a new statistical method, MR-GGI, for accurately inferring gene-gene interactions using Mendelian randomization. RESULTS MR-GGI applies one gene as the exposure and another as the outcome, using causal cis-single-nucleotide polymorphisms as instrumental variables in the inverse-variance weighted MR model. Through simulations, we have demonstrated MR-GGI's ability to control type 1 error and maintain statistical power despite confounding effects. MR-GGI performed the best when compared to other methods using the F1 score on the DREAM5 dataset. Additionally, when applied to yeast genomic data, MR-GGI successfully identified six clusters. Through gene ontology analysis, we have confirmed that each cluster in our study performs distinct functional roles by gathering genes with specific functions. CONCLUSION These findings demonstrate that MR-GGI accurately inferences gene-gene interactions despite the confounding effects in real biological environments.
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Affiliation(s)
- Wonseok Oh
- Department of Industrial Pharmacy, Dongguk University-Seoul, Seoul, 04620, South Korea
| | - Junghyun Jung
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Hollywood, CA, USA
| | - Jong Wha J Joo
- Department of Computer Science and Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea.
- Division of AI Software Convergence, Dongguk University-Seoul, Seoul, 04620, South Korea.
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Lu Y, Xu K, Kang B, Pierce BL, Yang F, Chen LS. An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.04.24303731. [PMID: 38496462 PMCID: PMC10942526 DOI: 10.1101/2024.03.04.24303731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context/tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease-relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers new insights into disease mechanisms.
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6
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Lorincz-Comi N, Yang Y, Zhu X. simmrd: An open-source tool to perform simulations in Mendelian randomization. Genet Epidemiol 2024; 48:59-73. [PMID: 38263619 DOI: 10.1002/gepi.22544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 01/25/2024]
Abstract
Mendelian randomization (MR) has become a popular tool for inferring causality of risk factors on disease. There are currently over 45 different methods available to perform MR, reflecting this extremely active research area. It would be desirable to have a standard simulation environment to objectively evaluate the existing and future methods. We present simmrd, an open-source software for performing simulations to evaluate the performance of MR methods in a range of scenarios encountered in practice. Researchers can directly modify the simmrd source code so that the research community may arrive at a widely accepted framework for researchers to evaluate the performance of different MR methods.
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Affiliation(s)
- Noah Lorincz-Comi
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yihe Yang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
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Chepelev I, Harley IT, Harley JB. Modeling of horizontal pleiotropy identifies possible causal gene expression in systemic lupus erythematosus. FRONTIERS IN LUPUS 2023; 1:1234578. [PMID: 37799268 PMCID: PMC10554754 DOI: 10.3389/flupu.2023.1234578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
Background Systemic lupus erythematosus (SLE) is a chronic autoimmune condition with complex causes involving genetic and environmental factors. While genome-wide association studies (GWASs) have identified genetic loci associated with SLE, the functional genomic elements responsible for disease development remain largely unknown. Mendelian Randomization (MR) is an instrumental variable approach to causal inference based on data from observational studies, where genetic variants are employed as instrumental variables (IVs). Methods This study utilized a two-step strategy to identify causal genes for SLE. In the first step, the classical MR method was employed, assuming the absence of horizontal pleiotropy, to estimate the causal effect of gene expression on SLE. In the second step, advanced probabilistic MR methods (PMR-Egger, MRAID, and MR-MtRobin) were applied to the genes identified in the first step, considering horizontal pleiotropy, to filter out false positives. PMR-Egger and MRAID analyses utilized whole blood expression quantitative trait loci (eQTL) and SLE GWAS summary data, while MR-MtRobin analysis used an independent eQTL dataset from multiple immune cell types along with the same SLE GWAS data. Results The initial MR analysis identified 142 genes, including 43 outside of chromosome 6. Subsequently, applying the advanced MR methods reduced the number of genes with significant causal effects on SLE to 66. PMR-Egger, MRAID, and MR-MtRobin, respectively, identified 13, 7, and 16 non-chromosome 6 genes with significant causal effects. All methods identified expression of PHRF1 gene as causal for SLE. A comprehensive literature review was conducted to enhance understanding of the functional roles and mechanisms of the identified genes in SLE development. Conclusions The findings from the three MR methods exhibited overlapping genes with causal effects on SLE, demonstrating consistent results. However, each method also uncovered unique genes due to different modelling assumptions and technical factors, highlighting the complementary nature of the approaches. Importantly, MRAID demonstrated a reduced percentage of causal genes from the Major Histocompatibility complex (MHC) region on chromosome 6, indicating its potential in minimizing false positive findings. This study contributes to unraveling the mechanisms underlying SLE by employing advanced probabilistic MR methods to identify causal genes, thereby enhancing our understanding of SLE pathogenesis.
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Affiliation(s)
- Iouri Chepelev
- Research Service, US Department of Veterans Affairs Medical Center, Cincinnati, OH, United States
- Cincinnati Education and Research for Veterans Foundation, Cincinnati, OH, United States
| | - Isaac T.W. Harley
- US Department of Veterans Affairs Medical Center, Aurora, CO, United States
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO, United States
- Division of Rheumatology, Department of Medicine, University of Colorado School of Medicine, Aurora, CO, United States
| | - John B. Harley
- Research Service, US Department of Veterans Affairs Medical Center, Cincinnati, OH, United States
- Cincinnati Education and Research for Veterans Foundation, Cincinnati, OH, United States
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Lin Z, Xue H, Pan W. Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data. PLoS Genet 2023; 19:e1010762. [PMID: 37200398 PMCID: PMC10231771 DOI: 10.1371/journal.pgen.1010762] [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: 01/12/2023] [Revised: 05/31/2023] [Accepted: 04/25/2023] [Indexed: 05/20/2023] Open
Abstract
Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it would be useful to infer the direct causal effect between any two of many traits (by accounting for indirect or mediating effects through other traits). For this purpose we propose a two-step approach: we first apply an extended MR method to infer (i.e. both estimate and test) a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. Simulation studies showed much better performance of our proposed method than existing ones. We applied the method to 17 large-scale GWAS summary datasets (with median N = 256879 and median #IVs = 48) to infer the causal networks of both total and direct effects among 11 common cardiometabolic risk factors, 4 cardiometabolic diseases (coronary artery disease, stroke, type 2 diabetes, atrial fibrillation), Alzheimer's disease and asthma, identifying some interesting causal pathways. We also provide an R Shiny app (https://zhaotongl.shinyapps.io/cMLgraph/) for users to explore any subset of the 17 traits of interest.
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Affiliation(s)
- Zhaotong Lin
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Haoran Xue
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America
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9
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Ding M. A Two-stage Linear Mixed Model (TS-LMM) for Summary-data-based Multivariable Mendelian Randomization. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.25.23289099. [PMID: 37162968 PMCID: PMC10168515 DOI: 10.1101/2023.04.25.23289099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Multivariable Mendelian randomization (MVMR) methods provide a strategy for applying genome-wide summary statistics to assess simultaneous causal effects of multiple risk factors on a disease outcome. In contrast to univariate MR methods that assumes no horizonal pleiotropy (genetic variants only associate with one risk factor), MVMR allows for genetic variants associate with multiple risk factors and models pleiotropy by including summary statistics with risk factors as multiple variables into the regression model. Here, we propose a two-stage linear mixed model (TS-LMM) for MVMR that accounts for variance of summary statistics not only in outcome, but also in all of the risk factors. In stage I, we apply linear mixed model to treat variance in summary statistics of disease as fixed-/random-effects, while accounting for covariance between genetic variants due to linkage disequilibrium (LD). Particularly, we use an iteratively re-weighted least squares algorithm to obtain estimates for the random-effects. In stage II, we account for variance in summary statistics of multiple risk factors simultaneously by applying measurement error correction methods that take into consideration LD between genetic variants and correlation between summary statistics of risk factors. We compared our MVMR approach to other approaches in a simulation study. When most of the instrumental variables (IVs) were strong, our model generated the highest coverage of true causal associations, the highest power of detecting significant causal associations, and the lowest false positive rate of identifying null causal effect for a range of scenarios that varied correlation (weak, strong) between summary statistics of risk factors and LD among genetic variants (weak LD [γ 2 ≤0.1], moderate LD [0.1< γ 2 ≤0.5]). When the proportion of strong IVs was reduced, our model showed performances comparable to MVMR-Egger and MVMR-IVW. The more accurate inference of our model in the presence of correlation among risk factors supports potential wide application to -omics data that are commonly multi dimensional and correlated, as shown in application to determinants of longevity, where our method nominated a specific significant lipoprotein subfraction for causal association from a panel of 10 lipoprotein cholesterol measures. The robustness of our model to correlation structure suggests that in practice we can allow moderate LD in selection of IVs, thereby potentially leveraging genome-wide summary data in a more effective manner. Our model is implemented in 'TS_LMM' macro in R.
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10
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Aman A, Slob EAW, Ward J, Cullen B, Graham N, Lyall DM, Sattar N, Strawbridge RJ. Investigating the potential impact of PCSK9-inhibitors on mood disorders using eQTL-based Mendelian randomization. PLoS One 2022; 17:e0279381. [PMID: 36580462 PMCID: PMC9799310 DOI: 10.1371/journal.pone.0279381] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 12/06/2022] [Indexed: 12/30/2022] Open
Abstract
Prescription of PCSK9-inhibitors has increased in recent years but not much is known about its off-target effects. PCSK9-expression is evident in non-hepatic tissues, notably the brain, and genetic variation in the PCSK9 locus has recently been shown to be associated with mood disorder-related traits. We investigated whether PCSK9 inhibition, proxied by a genetic reduction in expression of PCSK9 mRNA, might have a causal adverse effect on mood disorder-related traits. We used genetic variants in the PCSK9 locus associated with reduced PCSK9 expression (eQTLs) in the European population from GTEx v8 and examined the effect on PCSK9 protein levels and three mood disorder-related traits (major depressive disorder, mood instability, and neuroticism), using summary statistics from the largest European ancestry genome-wide association studies. We conducted summary-based Mendelian randomization analyses to estimate the causal effects, and attempted replication using data from eQTLGen, Brain-eMETA, and the CAGE consortium. We found that genetically reduced PCSK9 gene-expression levels were significantly associated with reduced PCSK9 protein levels but not with increased risk of mood disorder-related traits. Further investigation of nearby genes demonstrated that reduced USP24 gene-expression levels was significantly associated with increased risk of mood instability (p-value range = 5.2x10-5-0.03), and neuroticism score (p-value range = 2.9x10-5-0.02), but not with PCSK9 protein levels. Our results suggest that genetic variation in this region acts on mood disorders through a PCSK9-independent pathway, and therefore PCSK9-inhibitors are unlikely to have an adverse impact on mood disorder-related traits.
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Affiliation(s)
- Alisha Aman
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
- School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
- * E-mail:
| | - Eric A. W. Slob
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Erasmus University Rotterdam Institute for Behaviour and Biology, Erasmus School of Economics, Rotterdam, The Netherlands
| | - Joey Ward
- School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Breda Cullen
- School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Nicholas Graham
- School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Donald M. Lyall
- School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
| | - Rona J. Strawbridge
- School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
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11
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Lin Z, Deng Y, Pan W. Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model. PLoS Genet 2021; 17:e1009922. [PMID: 34793444 PMCID: PMC8639093 DOI: 10.1371/journal.pgen.1009922] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 12/02/2021] [Accepted: 11/02/2021] [Indexed: 11/19/2022] Open
Abstract
With the increasing availability of large-scale GWAS summary data on various traits, Mendelian randomization (MR) has become commonly used to infer causality between a pair of traits, an exposure and an outcome. It depends on using genetic variants, typically SNPs, as instrumental variables (IVs). The inverse-variance weighted (IVW) method (with a fixed-effect meta-analysis model) is most powerful when all IVs are valid; however, when horizontal pleiotropy is present, it may lead to biased inference. On the other hand, Egger regression is one of the most widely used methods robust to (uncorrelated) pleiotropy, but it suffers from loss of power. We propose a two-component mixture of regressions to combine and thus take advantage of both IVW and Egger regression; it is often both more efficient (i.e. higher powered) and more robust to pleiotropy (i.e. controlling type I error) than either IVW or Egger regression alone by accounting for both valid and invalid IVs respectively. We propose a model averaging approach and a novel data perturbation scheme to account for uncertainties in model/IV selection, leading to more robust statistical inference for finite samples. Through extensive simulations and applications to the GWAS summary data of 48 risk factor-disease pairs and 63 genetically uncorrelated trait pairs, we showcase that our proposed methods could often control type I error better while achieving much higher power than IVW and Egger regression (and sometimes than several other new/popular MR methods). We expect that our proposed methods will be a useful addition to the toolbox of Mendelian randomization for causal inference.
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Affiliation(s)
- Zhaotong Lin
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Yangqing Deng
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America
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12
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Adam Y, Samtal C, Brandenburg JT, Falola O, Adebiyi E. Performing post-genome-wide association study analysis: overview, challenges and recommendations. F1000Res 2021; 10:1002. [PMID: 35222990 PMCID: PMC8847724 DOI: 10.12688/f1000research.53962.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/22/2021] [Indexed: 12/17/2022] Open
Abstract
Genome-wide association studies (GWAS) provide huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis. Finally, we include a custom pGWAS pipeline to guide new users when performing their research.
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Affiliation(s)
- Yagoub Adam
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun, 112233, Nigeria
| | - Chaimae Samtal
- Laboratory of Biotechnology, Environment, Agri-food and Health, Sidi Mohammed Ben Abdellah University, Fez, Fez-Meknes, 30000, Morocco
| | - Jean-tristan Brandenburg
- Sydney Brenner Institute for Molecular Bioscience (SBIMB), University of the Witwatersrand, Johannesburg, South Africa
| | - Oluwadamilare Falola
- Laboratory of Biotechnology, Environment, Agri-food and Health, Sidi Mohammed Ben Abdellah University, Fez, Fez-Meknes, 30000, Morocco
| | - Ezekiel Adebiyi
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun, 112233, Nigeria
- Computer & Information Sciences, Covenant University, Ota, Ogun, 112233, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence, Covenant University, Ota, Ogun, 112233, Nigeria
- Applied Bioinformatics Division, German Cancer Center DKFZ - Heidelberg University, Heidelberg, Baden-Württemberg, 69120, Germany
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Abstract
The field of medical and population genetics in stroke is moving at a rapid pace and has led to unanticipated opportunities for discovery and clinical applications. Genome-wide association studies have highlighted the role of specific pathways relevant to etiologically defined subtypes of stroke and to stroke as a whole. They have further offered starting points for the exploration of novel pathways and pharmacological strategies in experimental systems. Mendelian randomization studies continue to provide insights in the causal relationships between exposures and outcomes and have become a useful tool for predicting the efficacy and side effects of drugs. Additional applications that have emerged from recent discoveries include risk prediction based on polygenic risk scores and pharmacogenomics. Among the topics currently moving into focus is the genetics of stroke outcome. While still at its infancy, this field is expected to boost the development of neuroprotective agents. We provide a brief overview on recent progress in these areas.
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Affiliation(s)
- Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Nathalie Beaufort
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Stephanie Debette
- University of Bordeaux, INSERM, Bordeaux Population Health Center, UMR1219, Team VINTAGE, F-33000 Bordeaux, France
- Bordeaux University Hospital, Department of Neurology, Institute of Neurodegenerative Diseases, F-33000 Bordeaux, France
| | - Christopher D. Anderson
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
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Zhu A, Matoba N, Wilson EP, Tapia AL, Li Y, Ibrahim JG, Stein JL, Love MI. MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity. PLoS Genet 2021; 17:e1009455. [PMID: 33872308 PMCID: PMC8084342 DOI: 10.1371/journal.pgen.1009455] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 04/29/2021] [Accepted: 02/26/2021] [Indexed: 11/18/2022] Open
Abstract
Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus's estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.
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Affiliation(s)
- Anqi Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Nana Matoba
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Emma P. Wilson
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Amanda L. Tapia
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Joseph G. Ibrahim
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Michael I. Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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Yang F, Gleason KJ, Wang J, Duan J, He X, Pierce BL, Chen LS. CCmed: Cross-condition mediation analysis for identifying replicable trans-associations mediated by cis-gene expression. Bioinformatics 2021; 37:2513-2520. [PMID: 33647928 PMCID: PMC8428610 DOI: 10.1093/bioinformatics/btab139] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 02/25/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Trans-acting expression quantitative trait loci (eQTLs) collectively explain a substantial proportion of expression variation, yet are challenging to detect and replicate since their effects are often individually weak. A large proportion of genetic effects on distal genes are mediated through cisgene expression. Cis-association (between SNP and cis-gene) and gene-gene correlation conditional on SNP genotype could establish trans-association (between SNP and trans-gene). Both cis-association and gene-gene conditional correlation have effects shared across relevant tissues and conditions, and transassociations mediated by cis-gene expression also have effects shared across relevant conditions. RESULTS . We proposed a Cross-Condition Mediation analysis method (CCmed) for detecting cis-mediated trans-associations with replicable effects in relevant conditions/studies. CCmed integrates cis-association and gene-gene conditional correlation statistics from multiple tissues/studies. Motivated by the bimodal effect-sharing patterns of eQTLs, we proposed two variations of CCmed, CCmedmost and CCmedspec for detecting cross-tissue and tissue-specific trans-associations, respectively. We analyzed data of 13 brain tissues from the Genotype-Tissue Expression (GTEx) project, and identified trios with cis-mediated transassociations across brain tissues, many of which showed evidence of trans-association in two replication studies. We also identified trans-genes associated with schizophrenia loci in at least two brain tissues. AVAILABILITY AND IMPLEMENTATION CCmed software is available at http://github.com/kjgleason/CCmed. SUPPLEMENTARY INFORMATION Supplementary Material are available at Bioinformatics online.
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Affiliation(s)
- Fan Yang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 E. 17th Place, Aurora, Colorado, 80045, USA
| | - Kevin J Gleason
- Department of Public Health Sciences, University of Chicago, 5841 South Maryland Ave MC2000, Chicago, IL, 60637, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, 7135 Public Health, 130 DeSoto Street, Pittsburgh, PA, 15261, USA
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University HealthSystem, 1001 University Place, Evanston, IL, 60201, USA.,Department of Psychiatry and Behavioral Neuroscience, 5841 S Maryland Ave, Chicago MC3077, Chicago, IL, 60637, USA
| | - Xin He
- Heart Center, Turku University Hospital and University of Turku, Turku, Finland
| | - Brandon L Pierce
- Department of Public Health Sciences, University of Chicago, 5841 South Maryland Ave MC2000, Chicago, IL, 60637, USA.,Heart Center, Turku University Hospital and University of Turku, Turku, Finland
| | - Lin S Chen
- Department of Public Health Sciences, University of Chicago, 5841 South Maryland Ave MC2000, Chicago, IL, 60637, USA
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