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Kontou PI, Bagos PG. The goldmine of GWAS summary statistics: a systematic review of methods and tools. BioData Min 2024; 17:31. [PMID: 39238044 PMCID: PMC11375927 DOI: 10.1186/s13040-024-00385-x] [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: 02/09/2024] [Accepted: 08/27/2024] [Indexed: 09/07/2024] Open
Abstract
Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.
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Affiliation(s)
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131, Lamia, Greece.
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Chen G, Jin Y, Chu C, Zheng Y, Chen Y, Zhu X. Genetic prediction of blood metabolites mediating the relationship between gut microbiota and Alzheimer's disease: a Mendelian randomization study. Front Microbiol 2024; 15:1414977. [PMID: 39224217 PMCID: PMC11366617 DOI: 10.3389/fmicb.2024.1414977] [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: 04/10/2024] [Accepted: 07/22/2024] [Indexed: 09/04/2024] Open
Abstract
Background Observational studies have suggested an association between gut microbiota and Alzheimer's disease (AD); however, the causal relationship remains unclear, and the role of blood metabolites in this association remains elusive. Purpose To elucidate the causal relationship between gut microbiota and AD and to investigate whether blood metabolites serve as potential mediators. Materials and methods Univariable Mendelian randomization (UVMR) analysis was employed to assess the causal relationship between gut microbiota and AD, while multivariable MR (MVMR) was utilized to mitigate confounding factors. Subsequently, a two-step mediation MR approach was employed to explore the role of blood metabolites as potential mediators. We primarily utilized the inverse variance-weighted method to evaluate the causal relationship between exposure and outcome, and sensitivity analyses including Contamination mixture, Maximum-likelihood, Debiased inverse-variance weighted, MR-Egger, Bayesian Weighted Mendelian randomization, and MR pleiotropy residual sum and outlier were conducted to address pleiotropy. Results After adjustment for reverse causality and MVMR correction, class Actinobacteria (OR: 1.03, 95% CI: 1.01-1.06, p = 0.006), family Lactobacillaceae (OR: 1.03, 95% CI: 1.00-1.05, p = 0.017), genus Lachnoclostridium (OR: 1.03, 95% CI: 1.00-1.06, p = 0.019), genus Ruminiclostridium9 (OR: 0.97, 95% CI: 0.94-1.00, p = 0.027) and genus Ruminiclostridium6 (OR: 1.03, 95% CI: 1.01-1.05, p = 0.009) exhibited causal effects on AD. Moreover, 1-ribosyl-imidazoleacetate levels (-6.62%), Metabolonic lactone sulfate levels (2.90%), and Nonadecanoate (19:0) levels (-12.17%) mediated the total genetic predictive effects of class Actinobacteria on AD risk. Similarly, 2-stearoyl-GPE (18:0) levels (-9.87%), Octadecanedioylcarnitine (C18-DC) levels (4.44%), 1-(1-enyl-stearoyl)-2-oleoyl-GPE (p-18:0/18:1) levels (38.66%), and X-23639 levels (13.28%) respectively mediated the total genetic predictive effects of family Lactobacillaceae on AD risk. Furthermore, Hexadecanedioate (C16-DC) levels (5.45%) mediated the total genetic predictive effects of genus Ruminiclostridium 6 on AD risk; Indole-3-carboxylate levels (13.91%), X-13431 levels (7.08%), Alpha-ketoglutarate to succinate ratio (-13.91%), 3-phosphoglycerate to glycerate ratio (15.27%), and Succinate to proline ratio (-14.64%) respectively mediated the total genetic predictive effects of genus Ruminiclostridium 9 on AD risk. Conclusion Our mediation MR analysis provides genetic evidence suggesting the potential mediating role of blood metabolites in the causal relationship between gut microbiota and AD. Further large-scale randomized controlled trials are warranted to validate the role of blood metabolites in the specific mechanisms by which gut microbiota influence AD.
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Affiliation(s)
- Guanglei Chen
- Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
| | - Yaxian Jin
- The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
| | - Cancan Chu
- Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
| | - Yuhao Zheng
- Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
| | - Yunzhi Chen
- Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
| | - Xing Zhu
- Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
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Bouttle K, Ingold N, O’Mara TA. Using Genetics to Investigate Relationships between Phenotypes: Application to Endometrial Cancer. Genes (Basel) 2024; 15:939. [PMID: 39062718 PMCID: PMC11276418 DOI: 10.3390/genes15070939] [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: 06/25/2024] [Revised: 07/14/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024] Open
Abstract
Genome-wide association studies (GWAS) have accelerated the exploration of genotype-phenotype associations, facilitating the discovery of replicable genetic markers associated with specific traits or complex diseases. This narrative review explores the statistical methodologies developed using GWAS data to investigate relationships between various phenotypes, focusing on endometrial cancer, the most prevalent gynecological malignancy in developed nations. Advancements in analytical techniques such as genetic correlation, colocalization, cross-trait locus identification, and causal inference analyses have enabled deeper exploration of associations between different phenotypes, enhancing statistical power to uncover novel genetic risk regions. These analyses have unveiled shared genetic associations between endometrial cancer and many phenotypes, enabling identification of novel endometrial cancer risk loci and furthering our understanding of risk factors and biological processes underlying this disease. The current status of research in endometrial cancer is robust; however, this review demonstrates that further opportunities exist in statistical genetics that hold promise for advancing the understanding of endometrial cancer and other complex diseases.
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Affiliation(s)
| | | | - Tracy A. O’Mara
- Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia (N.I.)
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Lin Z, Pan W. A robust cis-Mendelian randomization method with application to drug target discovery. Nat Commun 2024; 15:6072. [PMID: 39025905 PMCID: PMC11258283 DOI: 10.1038/s41467-024-50385-y] [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: 08/04/2023] [Accepted: 07/08/2024] [Indexed: 07/20/2024] Open
Abstract
Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to investigate causal relationships between traits. Unlike conventional MR, cis-MR focuses on a single genomic region using only cis-SNPs. For example, using cis-pQTLs for a protein as exposure for a disease opens a cost-effective path for drug target discovery. However, few methods effectively handle pleiotropy and linkage disequilibrium (LD) of cis-SNPs. Here, we propose cisMR-cML, a method based on constrained maximum likelihood, robust to IV assumption violations with strong theoretical support. We further clarify the severe but largely neglected consequences of the current practice of modeling marginal, instead of conditional genetic effects, and only using exposure-associated SNPs in cis-MR analysis. Numerical studies demonstrated our method's superiority over other existing methods. In a drug-target analysis for coronary artery disease (CAD), including a proteome-wide application, we identified three potential drug targets, PCSK9, COLEC11 and FGFR1 for CAD.
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Affiliation(s)
- Zhaotong Lin
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, 55455, USA.
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA.
| | - Wei Pan
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, 55455, USA
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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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Peng J, Cai K, Chen G, Liu L, Peng L. Genetic evidence strengthens the bidirectional connection between gut microbiota and Shigella infection: insights from a two-sample Mendelian randomization study. Front Microbiol 2024; 15:1361927. [PMID: 38495509 PMCID: PMC10941758 DOI: 10.3389/fmicb.2024.1361927] [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: 12/28/2023] [Accepted: 02/21/2024] [Indexed: 03/19/2024] Open
Abstract
Background In recent investigations, substantial strides have been made in the precise modulation of the gut microbiota to prevent and treat a myriad of diseases. Simultaneously, the pressing issue of widespread antibiotic resistance and multidrug resistance resulting from Shigella infections demands urgent attention. Several studies suggest that the antagonistic influence of the gut microbiota could serve as a novel avenue for impeding the colonization of pathogenic microorganisms or treating Shigella infections. However, conventional research methodologies encounter inherent challenges in identifying antagonistic microbial agents against Shigella, necessitating a comprehensive and in-depth analysis of the causal relationship between Shigella infections and the gut microbiota. Materials and methods Utilizing the aggregated summary statistics from Genome-Wide Association Studies (GWAS), we conducted Mendelian Randomization (MR) analyses encompassing 18,340 participants to explore the interplay between the gut microbiota and Shigella infections. This investigation also involved 83 cases of Shigella infection patients and 336,396 control subjects. In the positive strand of our findings, we initially performed a preliminary analysis using the Inverse Variance Weighting (IVW) method. Subsequently, we undertook sensitivity analyses to assess the robustness of the results, addressing confounding factors' influence. This involved employing the Leave-One-Out method and scrutinizing funnel plots to ensure the reliability of the MR analysis outcomes. Conclusively, a reverse MR analysis was carried out, employing the Wald ratio method due to the exposure of individual Single Nucleotide Polymorphisms (SNPs). This was undertaken to explore the plausible associations between Shigella infections and genetically predicted compositions of the gut microbiota. Results In this study, we employed 2,818 SNPs associated with 211 species of gut microbiota as instrumental variables (IVs). Through IVW analysis, our positive MR findings revealed a significant negative correlation between the occurrence of Shigella infections and the phylum Tenericutes (OR: 0.18, 95% CI: 0.04-0.74, p = 0.02), class Mollicutes (OR: 0.18, 95% CI: 0.04-0.74, p = 0.02), genus Intestinimonas (OR: 0.16, 95% CI: 0.04-0.63, p = 0.01), genus Gordonibacter (OR: 0.39, 95% CI: 0.16-0.93, p = 0.03), and genus Butyrivibrio (OR: 0.44, 95% CI: 0.23-0.87, p = 0.02). Conversely, a positive correlation was observed between the occurrence of Shigella infections and genus Sutterella (OR: 10.16, 95% CI: 1.87-55.13, p = 0.01) and genus Alistipes (OR: 12.24, 95% CI: 1.71-87.34, p = 0.01). In sensitivity analyses, utilizing MR-Egger regression analysis and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) detection, all outcomes demonstrated robust stability. Simultaneously, in the reverse MR analysis, Shigella infections resulted in an upregulation of four bacterial genera and a downregulation of three bacterial genera. Conclusion In summation, the MR analysis outcomes corroborate the presence of bidirectional causal relationships between the gut microbiota and Shigella infections. This study not only unveils novel perspectives for the prevention and treatment of Shigella infections but also furnishes fresh insights into the mechanistic underpinnings of how the gut microbiota contributes to the pathogenesis of Shigella infections. Consequently, the established dual causal association holds promise for advancing our understanding and addressing the complexities inherent in the interplay between the gut microbiota and Shigella infections, thereby paving the way for innovative therapeutic interventions and preventive strategies in the realm of Shigella-related diseases.
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Affiliation(s)
- Jingyi Peng
- Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
| | - Kun Cai
- Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
| | - Guanglei Chen
- Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
| | - Linxiao Liu
- Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
| | - Lili Peng
- The First People’s Hospital of Hangzhou Lin’an District, Hangzhou, Zhejiang, China
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Pan T, Su L, Zhang Y, Yi F, Chen Y. Impact of gut microbiota on nonalcoholic fatty liver disease: insights from a leave-one-out cross-validation study. Front Microbiol 2024; 14:1320279. [PMID: 38260910 PMCID: PMC10801729 DOI: 10.3389/fmicb.2023.1320279] [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: 10/13/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction Enteric dysbacteriosis is strongly associated with nonalcoholic fatty liver disease (NAFLD). However, the underlying causal relationship remains unknown. Thus, the present study aimed to investigate the relationship between gut microbiota and NAFLD using Mendelian randomization (MR) and analyze the target genes potentially regulated by specific microbiota. Methods Bidirectional two-sample MR analysis was performed using inverse variance weighted (IVW) supplemented by MR-Egger, weighted median, simple mode, and weighted mode methods. Data were pooled from gut microbiota and NAFLD association studies. The least absolute shrinkage, selection operator regression, and the Support Vector Machine algorithm were used to identify genes regulated by these intestinal flora in NAFLD. The liver expression of these genes was verified in methionine choline-deficient (MCD) diet-fed mice. Results IVW results confirmed a causal relationship between eight specific gut microbes and NAFLD. Notably, the order Actinomycetales, NB1n, the family Actinomycetaceae, Oxalobacteraceae and the genus Ruminococcaceae UCG005 were positively correlated, whereas Lactobacillaceae, the Christensenellaceae R7 group, and Intestinibacter were negatively correlated with NAFLD onset. In NAFLD, these eight bacteria regulated four genes: colony-stimulating factor 2 receptor β, fucosyltransferase 2, 17-beta-hydroxysteroid dehydrogenase 14, and microtubule affinity regulatory kinase 3 (MAPK3). All genes, except MARK3, were differentially expressed in the liver tissues of MCD diet-fed mice. Discussion The abundance of eight gut microbiota species and NAFLD progression displayed a causal relationship based on the expression of the four target genes. Our findings contributed to the advancement of intestinal microecology-based diagnostic technologies and targeted therapies for NAFLD.
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Affiliation(s)
- Tongtong Pan
- Hepatology Diagnosis and Treatment Center, The First Affiliated Hospital of Wenzhou Medical University and Zhejiang Provincial Key Laboratory for Accurate Diagnosis and Treatment of Chronic Liver Diseases, Wenzhou, China
| | - Lihuang Su
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yiying Zhang
- Alberta Institute, Wenzhou Medical University, Wenzhou, China
| | - Fangfang Yi
- Hepatology Diagnosis and Treatment Center, The First Affiliated Hospital of Wenzhou Medical University and Zhejiang Provincial Key Laboratory for Accurate Diagnosis and Treatment of Chronic Liver Diseases, Wenzhou, China
| | - Yongping Chen
- Hepatology Diagnosis and Treatment Center, The First Affiliated Hospital of Wenzhou Medical University and Zhejiang Provincial Key Laboratory for Accurate Diagnosis and Treatment of Chronic Liver Diseases, Wenzhou, China
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Zhang X, Zhong Y, He K. The causal effects between selenium levels and the brain cortical structure: A two-sample Mendelian randomization study. Brain Behav 2023; 13:e3296. [PMID: 37904336 PMCID: PMC10726828 DOI: 10.1002/brb3.3296] [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: 06/26/2023] [Revised: 10/03/2023] [Accepted: 10/12/2023] [Indexed: 11/01/2023] Open
Abstract
Extensive research has demonstrated the critical role of selenium (Se) and selenoproteins in brain function and cognition. However, the impact of Se on brain cortical structure remains enigmatic. Therefore, this study used Mendelian randomization (MR) analysis to investigate the causal effect between Se levels and brain cortical structure. METHODS This study utilizes 11 genetic variants associated with Se level variations, extracted from a large-scale genome-wide association study (GWAS) encompassed circulating Se levels (n = 5477) and toenail Se levels (n = 4162) in the European population. Outcome data were sourced from the summary statistics of the ENIGMA Consortium, comprising GWAS data from 51,666 individuals. The variables include cortical surface area (SA), thickness (TH) at the global level, and 34 functional cortical regions evaluated by magnetic resonance imaging. The inverse-variance-weighted method was used as the primary estimate. Additionally, sensitivity analyses were conducted to detect potential violations of assumptions underlying MR. RESULTS At the global level, Se levels were not correlated with SA but showed a significant negative correlation with TH (β = -0.00485 mm, SE = 0.00192, p = .0115). Heterogeneity was observed across different brain regions, with positive correlations found between Se levels and the TH of the parahippocampal gyrus, superior frontal gyrus, and frontal pole, whereas negative correlations were found with the TH of the inferior parietal lobe and middle temporal lobe. Regarding SA, Se levels exhibit positive correlations with the pars triangularis, caudal anterior cingulate, inferior parietal lobe, and banks of the superior temporal sulcus. Conversely, negative correlations were observed with the medial orbitofrontal cortex, posterior cingulate gyrus, insula, and the middle, superior, and transverse gyrus of the temporal lobe. No pleiotropy was detected. RESULTS This MR study indicated that Se levels causally influence the brain cortical structure.
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Affiliation(s)
- Xiaowei Zhang
- Department of NeurosurgeryThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Yuqing Zhong
- The First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Kejun He
- Department of NeurosurgeryThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
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Li Y, Liang X, Lyu Y, Wang K, Han L, Wang Y, Sun J, Chi C. Association between the gut microbiota and nonalcoholic fatty liver disease: A two-sample Mendelian randomization study. Dig Liver Dis 2023; 55:1464-1471. [PMID: 37543433 DOI: 10.1016/j.dld.2023.07.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 06/25/2023] [Accepted: 07/11/2023] [Indexed: 08/07/2023]
Abstract
BACKGROUND Increasing studies have shown that there is a significant association between gut microbiota and non-alcoholic fatty liver disease. AIMS To show the potential association between gut microbiota and non-alcoholic fatty liver disease, we performed a two-sample Mendelian randomization analysis. METHODS We analyzed summary statistics from genome-wide association studies of gut microbiota and non-alcoholic fatty liver disease and conducted Mendelian randomization studies to evaluate relationships between these factors. RESULTS Of the 211 gut microbiota taxa examined, the inverse variance weighted method identified Lactobacillaceae (OR = 0.83, 95% CI = 0.72 - 0.95, P = 0.007), Christensenellaceae (OR = 0.74, 95% CI = 0.59 - 0.92, P = 0.007), and Intestinibacter (OR = 0.85, 95% CI = 0.73 - 0.99, P = 0.035) were negatively correlated with non-alcoholic fatty liver disease. And Coriobacteriia (OR = 1.22, 95% CI = 1.01 - 1.42, P = 0.038), Actinomycetales (OR = 1.25, 95% CI = 1.02 - 1.53, P = 0.031), Oxalobacteraceae (OR = 1.10, 95% CI = 1.01 - 1.21, P = 0.036), Ruminococcaceae_UCG005 (OR = 1.18, 95% CI = 1.01 - 1.38, P = 0.033) are positively associated with non-alcoholic fatty liver disease. CONCLUSIONS Our study found that the abundance of certain strains was associated with the progression of nonalcoholic fatty liver disease.
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Affiliation(s)
- Yu Li
- School of Nursing, Jining Medical University, Jining, 272067, China; School of Nursing, Weifang Medical University, Weifang, 261042, China
| | - Xifeng Liang
- School of Nursing, Jining Medical University, Jining, 272067, China; School of Nursing, Weifang Medical University, Weifang, 261042, China
| | - Yaning Lyu
- School of Nursing, Jining Medical University, Jining, 272067, China; School of Nursing, Weifang Medical University, Weifang, 261042, China
| | - Kexue Wang
- Department of Critical Care Medicine, The People's Hospital of Zhaoyuan City, Yantai 265400, China
| | - Linjing Han
- School of Nursing, Jining Medical University, Jining, 272067, China
| | - Yuhan Wang
- School of Nursing, Jining Medical University, Jining, 272067, China
| | - Jing Sun
- School of Medicine and Dentistry, Griffith University, Gold Coast, Queensland, 4222, Australia; Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Queensland, 4019, Australia.
| | - Cheng Chi
- School of Nursing, Jining Medical University, Jining, 272067, China.
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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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Lu H, Zhang S, Jiang Z, Zeng P. Leveraging trans-ethnic genetic risk scores to improve association power for complex traits in underrepresented populations. Brief Bioinform 2023:bbad232. [PMID: 37332016 DOI: 10.1093/bib/bbad232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 05/06/2023] [Accepted: 06/04/2023] [Indexed: 06/20/2023] Open
Abstract
Trans-ethnic genome-wide association studies have revealed that many loci identified in European populations can be reproducible in non-European populations, indicating widespread trans-ethnic genetic similarity. However, how to leverage such shared information more efficiently in association analysis is less investigated for traits in underrepresented populations. We here propose a statistical framework, trans-ethnic genetic risk score informed gene-based association mixed model (GAMM), by hierarchically modeling single-nucleotide polymorphism effects in the target population as a function of effects of the same trait in well-studied populations. GAMM powerfully integrates genetic similarity across distinct ancestral groups to enhance power in understudied populations, as confirmed by extensive simulations. We illustrate the usefulness of GAMM via the application to 13 blood cell traits (i.e. basophil count, eosinophil count, hematocrit, hemoglobin concentration, lymphocyte count, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, mean corpuscular volume, monocyte count, neutrophil count, platelet count, red blood cell count and total white blood cell count) in Africans of the UK Biobank (n = 3204) while utilizing genetic overlap shared in Europeans (n = 746 667) and East Asians (n = 162 255). We discovered multiple new associated genes, which had otherwise been missed by existing methods, and revealed that the trans-ethnic information indirectly contributed much to the phenotypic variance. Overall, GAMM represents a flexible and powerful statistical framework of association analysis for complex traits in underrepresented populations by integrating trans-ethnic genetic similarity across well-studied populations, and helps attenuate health inequities in current genetics research for people of minority populations.
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Affiliation(s)
- Haojie Lu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Zhou Jiang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
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12
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Dapas M, Dunaif A. Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification. Endocr Rev 2022; 43:927-965. [PMID: 35026001 PMCID: PMC9695127 DOI: 10.1210/endrev/bnac001] [Citation(s) in RCA: 82] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Indexed: 01/16/2023]
Abstract
Polycystic ovary syndrome (PCOS) is among the most common disorders in women of reproductive age, affecting up to 15% worldwide, depending on the diagnostic criteria. PCOS is characterized by a constellation of interrelated reproductive abnormalities, including disordered gonadotropin secretion, increased androgen production, chronic anovulation, and polycystic ovarian morphology. It is frequently associated with insulin resistance and obesity. These reproductive and metabolic derangements cause major morbidities across the lifespan, including anovulatory infertility and type 2 diabetes (T2D). Despite decades of investigative effort, the etiology of PCOS remains unknown. Familial clustering of PCOS cases has indicated a genetic contribution to PCOS. There are rare Mendelian forms of PCOS associated with extreme phenotypes, but PCOS typically follows a non-Mendelian pattern of inheritance consistent with a complex genetic architecture, analogous to T2D and obesity, that reflects the interaction of susceptibility genes and environmental factors. Genomic studies of PCOS have provided important insights into disease pathways and have indicated that current diagnostic criteria do not capture underlying differences in biology associated with different forms of PCOS. We provide a state-of-the-science review of genetic analyses of PCOS, including an overview of genomic methodologies aimed at a general audience of non-geneticists and clinicians. Applications in PCOS will be discussed, including strengths and limitations of each study. The contributions of environmental factors, including developmental origins, will be reviewed. Insights into the pathogenesis and genetic architecture of PCOS will be summarized. Future directions for PCOS genetic studies will be outlined.
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Affiliation(s)
- Matthew Dapas
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Andrea Dunaif
- Division of Endocrinology, Diabetes and Bone Disease, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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13
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Zhao H, Rasheed H, Nøst TH, Cho Y, Liu Y, Bhatta L, Bhattacharya A, Hemani G, Davey Smith G, Brumpton BM, Zhou W, Neale BM, Gaunt TR, Zheng J. Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases. CELL GENOMICS 2022; 2:None. [PMID: 36388766 PMCID: PMC9646482 DOI: 10.1016/j.xgen.2022.100195] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 06/06/2022] [Accepted: 09/21/2022] [Indexed: 11/07/2022]
Abstract
Proteome-wide Mendelian randomization (MR) shows value in prioritizing drug targets in Europeans but with limited evidence in other ancestries. Here, we present a multi-ancestry proteome-wide MR analysis based on cross-population data from the Global Biobank Meta-analysis Initiative (GBMI). We estimated the putative causal effects of 1,545 proteins on eight diseases in African (32,658) and European (1,219,993) ancestries and identified 45 and 7 protein-disease pairs with MR and genetic colocalization evidence in the two ancestries, respectively. A multi-ancestry MR comparison identified two protein-disease pairs with MR evidence in both ancestries and seven pairs with specific effects in the two ancestries separately. Integrating these MR signals with clinical trial evidence, we prioritized 16 pairs for investigation in future drug trials. Our results highlight the value of proteome-wide MR in informing the generalizability of drug targets for disease prevention across ancestries and illustrate the value of meta-analysis of biobanks in drug development.
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Affiliation(s)
- Huiling Zhao
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Humaria Rasheed
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Division of Medicine and Laboratory Sciences, University of Oslo, Oslo, Norway
| | - Therese Haugdahl Nøst
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Community Medicine, UIT The Arctic University of Norway, 9037 Tromsø, Norway
| | - Yoonsu Cho
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Yi Liu
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Institute of Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Global Biobank Meta-analysis Initiative
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Division of Medicine and Laboratory Sciences, University of Oslo, Oslo, Norway
- Department of Community Medicine, UIT The Arctic University of Norway, 9037 Tromsø, Norway
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Institute of Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7600 Levanger, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Ben Michael Brumpton
- Division of Medicine and Laboratory Sciences, University of Oslo, Oslo, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7600 Levanger, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Wei Zhou
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Benjamin M. Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Tom R. Gaunt
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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14
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Cheng Q, Zhang X, Chen LS, Liu J. Mendelian randomization accounting for complex correlated horizontal pleiotropy while elucidating shared genetic etiology. Nat Commun 2022; 13:6490. [PMID: 36310177 PMCID: PMC9618026 DOI: 10.1038/s41467-022-34164-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 10/17/2022] [Indexed: 12/25/2022] Open
Abstract
Mendelian randomization (MR) harnesses genetic variants as instrumental variables (IVs) to study the causal effect of exposure on outcome using summary statistics from genome-wide association studies. Classic MR assumptions are violated when IVs are associated with unmeasured confounders, i.e., when correlated horizontal pleiotropy (CHP) arises. Such confounders could be a shared gene or inter-connected pathways underlying exposure and outcome. We propose MR-CUE (MR with Correlated horizontal pleiotropy Unraveling shared Etiology and confounding), for estimating causal effect while identifying IVs with CHP and accounting for estimation uncertainty. For those IVs, we map their cis-associated genes and enriched pathways to inform shared genetic etiology underlying exposure and outcome. We apply MR-CUE to study the effects of interleukin 6 on multiple traits/diseases and identify several S100 genes involved in shared genetic etiology. We assess the effects of multiple exposures on type 2 diabetes across European and East Asian populations.
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Affiliation(s)
- Qing Cheng
- grid.443347.30000 0004 1761 2353Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan China ,grid.428397.30000 0004 0385 0924Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Xiao Zhang
- grid.428397.30000 0004 0385 0924Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Lin S. Chen
- grid.170205.10000 0004 1936 7822Department of Public Health Sciences, The University of Chicago, Chicago, IL USA
| | - Jin Liu
- Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore, Singapore.
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15
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Son N, Cui Y, Xi W. Association Between Telomere Length and Skin Cancer and Aging: A Mendelian Randomization Analysis. Front Genet 2022; 13:931785. [PMID: 35903361 PMCID: PMC9315360 DOI: 10.3389/fgene.2022.931785] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 06/20/2022] [Indexed: 11/23/2022] Open
Abstract
Background: Telomere shortening is a hallmark of cellular senescence. However, telomere length (TL)-related cellular senescence has varying effects in different cancers, resulting in a paradoxical relationship between senescence and cancer. Therefore, we used observational epidemiological studies to investigate the association between TL and skin cancer and aging, and to explore whether such a paradoxical relationship exists in skin tissue. Methods: This study employed two-sample Mendelian randomization (MR) to analyze the causal relationship between TL and skin cancer [melanoma and non-melanoma skin cancers (NMSCs)] and aging. We studied single nucleotide polymorphisms (SNPs) obtained from pooled data belonging to genome-wide association studies (GWAS) in the literature and biobanks. Quality control was performed using pleiotropy, heterogeneity, and sensitivity analyses. Results: We used five algorithms to analyze the causal relationship between TL and skin aging, melanoma, and NMSCs, and obtained consistent results. TL shortening reduced NMSC and melanoma susceptibility risk with specific odds ratios (ORs) of 1.0344 [95% confidence interval (CI): 1.0168–1.0524, p = 0.01] and 1.0127 (95% CI: 1.0046–1.0209, p = 6.36E-07), respectively. Conversely, TL shortening was validated to increase the odds of skin aging (OR = 0.96, 95% CI: 0.9332–0.9956, p = 0.03). Moreover, the MR-Egger, maximum likelihood, and inverse variance weighted (IVW) methods found significant heterogeneity among instrumental variable (IV) estimates (identified as MR-Egger skin aging Q = 76.72, p = 1.36E-04; melanoma Q = 97.10, p = 1.62E-07; NMSCsQ = 82.02, p = 1.90E-05). The leave-one-out analysis also showed that the SNP sensitivity was robust to each result. Conclusion: This study found that TL shortening may promote skin aging development and reduce the risk of cutaneous melanoma and NMSCs. The results provide a reference for future research on the causal relationship between skin aging and cancer in clinical practice.
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Affiliation(s)
| | | | - Wang Xi
- *Correspondence: Yankun Cui, ; Wang Xi,
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16
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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: 109] [Impact Index Per Article: 54.5] [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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17
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Wang A, Liu W, Liu Z. A two-sample robust Bayesian Mendelian Randomization method accounting for linkage disequilibrium and idiosyncratic pleiotropy with applications to the COVID-19 outcomes. Genet Epidemiol 2022; 46:159-169. [PMID: 35192729 PMCID: PMC9648496 DOI: 10.1002/gepi.22445] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 11/03/2021] [Accepted: 01/20/2022] [Indexed: 01/02/2023]
Abstract
Mendelian randomization (MR) is a statistical method exploiting genetic variants as instrumental variables to estimate the causal effect of modifiable risk factors on an outcome of interest. Despite wide uses of various popular two-sample MR methods based on genome-wide association study summary level data, however, those methods could suffer from potential power loss or/and biased inference when the chosen genetic variants are in linkage disequilibrium (LD), and also have relatively large direct effects on the outcome whose distribution might be heavy-tailed which is commonly referred to as the idiosyncratic pleiotropy phenomenon. To resolve those two issues, we propose a novel Robust Bayesian Mendelian Randomization (RBMR) model that uses the more robust multivariate generalized t $t$ -distribution to model such direct effects in a probabilistic model framework which can also incorporate the LD structure explicitly. The generalized t $t$ -distribution can be represented as a Gaussian scaled mixture so that our model parameters can be estimated by the expectation maximization (EM)-type algorithms. We compute the standard errors by calibrating the evidence lower bound using the likelihood ratio test. Through extensive simulation studies, we show that our RBMR has robust performance compared with other competing methods. We further apply our RBMR method to two benchmark data sets and find that RBMR has smaller bias and standard errors. Using our proposed RBMR method, we find that coronary artery disease is associated with increased risk of critically ill coronavirus disease 2019. We also develop a user-friendly R package RBMR (https://github.com/AnqiWang2021/RBMR) for public use.
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Affiliation(s)
- Anqi Wang
- Department of Statistics and Actuarial ScienceUniversity of Hong KongHong KongSARChina
| | - Wei Liu
- Department of Statistics and Actuarial ScienceUniversity of Hong KongHong KongSARChina
| | - Zhonghua Liu
- Department of Statistics and Actuarial ScienceUniversity of Hong KongHong KongSARChina
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18
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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: 484] [Impact Index Per Article: 242.0] [Reference Citation Analysis] [Abstract] [Grants] [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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19
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Tall AR, Thomas DG, Gonzalez-Cabodevilla AG, Goldberg IJ. Addressing dyslipidemic risk beyond LDL-cholesterol. J Clin Invest 2022; 132:148559. [PMID: 34981790 PMCID: PMC8718149 DOI: 10.1172/jci148559] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Despite the success of LDL-lowering drugs in reducing cardiovascular disease (CVD), there remains a large burden of residual disease due in part to persistent dyslipidemia characterized by elevated levels of triglyceride-rich lipoproteins (TRLs) and reduced levels of HDL. This form of dyslipidemia is increasing globally as a result of the rising prevalence of obesity and metabolic syndrome. Accumulating evidence suggests that impaired hepatic clearance of cholesterol-rich TRL remnants leads to their accumulation in arteries, promoting foam cell formation and inflammation. Low levels of HDL may associate with reduced cholesterol efflux from foam cells, aggravating atherosclerosis. While fibrates and fish oils reduce TRL, they have not been uniformly successful in reducing CVD, and there is a large unmet need for new approaches to reduce remnants and CVD. Rare genetic variants that lower triglyceride levels via activation of lipolysis and associate with reduced CVD suggest new approaches to treating dyslipidemia. Apolipoprotein C3 (APOC3) and angiopoietin-like 3 (ANGPTL3) have emerged as targets for inhibition by antibody, antisense, or RNAi approaches. Inhibition of either molecule lowers TRL but respectively raises or lowers HDL levels. Large clinical trials of such agents in patients with high CVD risk and elevated levels of TRL will be required to demonstrate efficacy of these approaches.
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Affiliation(s)
- Alan R Tall
- Division of Molecular Medicine, Department of Medicine, Columbia University, New York, New York, USA
| | - David G Thomas
- Division of Molecular Medicine, Department of Medicine, Columbia University, New York, New York, USA
| | - Ainara G Gonzalez-Cabodevilla
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA
| | - Ira J Goldberg
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA
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20
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Cheng Q, Qiu T, Chai X, Sun B, Xia Y, Shi X, Liu J. MR-Corr2: a two-sample Mendelian randomization method that accounts for correlated horizontal pleiotropy using correlated instrumental variants. Bioinformatics 2022; 38:303-310. [PMID: 34499127 DOI: 10.1093/bioinformatics/btab646] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 08/04/2021] [Accepted: 09/06/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Mendelian randomization (MR) is a valuable tool to examine the causal relationships between health risk factors and outcomes from observational studies. Along with the proliferation of genome-wide association studies, a variety of two-sample MR methods for summary data have been developed to account for horizontal pleiotropy (HP), primarily based on the assumption that the effects of variants on exposure (γ) and HP (α) are independent. In practice, this assumption is too strict and can be easily violated because of the correlated HP. RESULTS To account for this correlated HP, we propose a Bayesian approach, MR-Corr2, that uses the orthogonal projection to reparameterize the bivariate normal distribution for γ and α, and a spike-slab prior to mitigate the impact of correlated HP. We have also developed an efficient algorithm with paralleled Gibbs sampling. To demonstrate the advantages of MR-Corr2 over existing methods, we conducted comprehensive simulation studies to compare for both type-I error control and point estimates in various scenarios. By applying MR-Corr2 to study the relationships between exposure-outcome pairs in complex traits, we did not identify the contradictory causal relationship between HDL-c and CAD. Moreover, the results provide a new perspective of the causal network among complex traits. AVAILABILITY AND IMPLEMENTATION The developed R package and code to reproduce all the results are available at https://github.com/QingCheng0218/MR.Corr2. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qing Cheng
- School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China.,Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, 169857 Singapore
| | - Tingting Qiu
- School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China
| | - Xiaoran Chai
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
| | - Baoluo Sun
- Department of Statistics and Applied Probability, NUS, 117546 Singapore
| | - Yingcun Xia
- Department of Statistics and Applied Probability, NUS, 117546 Singapore
| | - Xingjie Shi
- Academy of Statistics and Interdisciplinary Sciences, Faculty of Economics and Management, East China Normal University, Shanghai 200062, China
| | - Jin Liu
- Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, 169857 Singapore
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21
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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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22
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Wang Y, Li T, Fu L, Yang S, Hu YQ. A Novel Method for Mendelian Randomization Analyses With Pleiotropy and Linkage Disequilibrium in Genetic Variants From Individual Data. Front Genet 2021; 12:634394. [PMID: 34322150 PMCID: PMC8312241 DOI: 10.3389/fgene.2021.634394] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 06/15/2021] [Indexed: 12/28/2022] Open
Abstract
Mendelian randomization makes use of genetic variants as instrumental variables to eliminate the influence induced by unknown confounders on causal estimation in epidemiology studies. However, with the soaring genetic variants identified in genome-wide association studies, the pleiotropy, and linkage disequilibrium in genetic variants are unavoidable and may produce severe bias in causal inference. In this study, by modeling the pleiotropic effect as a normally distributed random effect, we propose a novel mixed-effects regression model-based method PLDMR, pleiotropy and linkage disequilibrium adaptive Mendelian randomization, which takes linkage disequilibrium into account and also corrects for the pleiotropic effect in causal effect estimation and statistical inference. We conduct voluminous simulation studies to evaluate the performance of the proposed and existing methods. Simulation results illustrate the validity and advantage of the novel method, especially in the case of linkage disequilibrium and directional pleiotropic effects, compared with other methods. In addition, by applying this novel method to the data on Atherosclerosis Risk in Communications Study, we conclude that body mass index has a significant causal effect on and thus might be a potential risk factor of systolic blood pressure. The novel method is implemented in R and the corresponding R code is provided for free download.
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Affiliation(s)
- Yuquan Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Tingting Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Liwan Fu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Siqian Yang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yue-Qing Hu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China.,Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
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23
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Gleason KJ, Yang F, Chen LS. A robust two-sample transcriptome-wide Mendelian randomization method integrating GWAS with multi-tissue eQTL summary statistics. Genet Epidemiol 2021; 45:353-371. [PMID: 33834509 DOI: 10.1002/gepi.22380] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 01/25/2021] [Accepted: 02/08/2021] [Indexed: 02/06/2023]
Abstract
By treating genetic variants as instrumental variables (IVs), two-sample Mendelian randomization (MR) methods detect genetically regulated risk exposures for complex diseases using only summary statistics. When considering gene expression as exposure in transcriptome-wide MR (TWMR) analyses, the eQTLs (expression-quantitative-trait-loci) may have pleiotropic effects or be correlated with variants that have effects on disease not via expression, and the presence of those invalid IVs would lead to biased inference. Moreover, the number of eQTLs as IVs for a gene is generally limited, making the detection of invalid IVs challenging. We propose a method, "MR-MtRobin," for accurate TWMR inference in the presence of invalid IVs. By leveraging multi-tissue eQTL data in a mixed model, the proposed method makes identifiable the IV-specific random effects due to pleiotropy from estimation errors of eQTL summary statistics, and can provide accurate inference on the dependence (fixed effects) between eQTL and GWAS (genome-wide association study) effects in the presence of invalid IVs. Moreover, our method can improve power and precision in inference by selecting cross-tissue eQTLs as IVs that have improved consistency of effects across eQTL and GWAS data. We applied MR-MtRobin to detect genes associated with schizophrenia risk by integrating summary-level data from the Psychiatric Genomics Consortium and the Genotype-Tissue Expression project (V8).
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Affiliation(s)
- Kevin J Gleason
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Fan Yang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Lin S Chen
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
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24
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Zeng P, Dai J, Jin S, Zhou X. Aggregating multiple expression prediction models improves the power of transcriptome-wide association studies. Hum Mol Genet 2021; 30:939-951. [PMID: 33615361 DOI: 10.1093/hmg/ddab056] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 02/10/2021] [Accepted: 02/15/2021] [Indexed: 12/11/2022] Open
Abstract
Transcriptome-wide association study (TWAS) is an important integrative method for identifying genes that are causally associated with phenotypes. A key step of TWAS involves the construction of expression prediction models for every gene in turn using its cis-SNPs as predictors. Different TWAS methods rely on different models for gene expression prediction, and each such model makes a distinct modeling assumption that is often suitable for a particular genetic architecture underlying expression. However, the genetic architectures underlying gene expression vary across genes throughout the transcriptome. Consequently, different TWAS methods may be beneficial in detecting genes with distinct genetic architectures. Here, we develop a new method, HMAT, which aggregates TWAS association evidence obtained across multiple gene expression prediction models by leveraging the harmonic mean P-value combination strategy. Because each expression prediction model is suited to capture a particular genetic architecture, aggregating TWAS associations across prediction models as in HMAT improves accurate expression prediction and enables subsequent powerful TWAS analysis across the transcriptome. A key feature of HMAT is its ability to accommodate the correlations among different TWAS test statistics and produce calibrated P-values after aggregation. Through numerical simulations, we illustrated the advantage of HMAT over commonly used TWAS methods as well as ad hoc P-value combination rules such as Fisher's method. We also applied HMAT to analyze summary statistics of nine common diseases. In the real data applications, HMAT was on average 30.6% more powerful compared to the next best method, detecting many new disease-associated genes that were otherwise not identified by existing TWAS approaches. In conclusion, HMAT represents a flexible and powerful TWAS method that enjoys robust performance across a range of genetic architectures underlying gene expression.
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Affiliation(s)
- Ping Zeng
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.,Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Jing Dai
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Siyi Jin
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.,Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
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25
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Zhao Y, Sun L. On set‐based association tests: Insights from a regression using summary statistics. CAN J STAT 2020. [DOI: 10.1002/cjs.11584] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Yanyan Zhao
- Department of Statistical Sciences University of Toronto Toronto M5S 3G3 Ontario Canada
| | - Lei Sun
- Department of Statistical Sciences University of Toronto Toronto M5S 3G3 Ontario Canada
- Division of Biostatistics, Dalla Lana School of Public Health University of Toronto Toronto M5T 3M7 Ontario Canada
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