1
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Canida T, Ke H, Chen S, Ye Z, Ma T. Multivariate Bayesian variable selection for multi-trait genetic fine mapping. J R Stat Soc Ser C Appl Stat 2025; 74:331-351. [PMID: 40092670 PMCID: PMC11905884 DOI: 10.1093/jrsssc/qlae055] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/21/2024] [Accepted: 10/01/2024] [Indexed: 03/19/2025]
Abstract
Genome-wide association studies (GWAS) have identified thousands of single-nucleotide polymorphisms (SNPs) associated with complex traits, but determining the underlying causal variants remains challenging. Fine mapping aims to pinpoint the potentially causal variants from a large number of correlated SNPs possibly with group structure in GWAS-enriched genomic regions using variable selection approaches. In multi-trait fine mapping, we are interested in identifying the causal variants for multiple related traits. Existing multivariate variable selection methods for fine mapping select variables for all responses without considering the possible heterogeneity across different responses. Here, we develop a novel multivariate Bayesian variable selection method for multi-trait fine mapping to select causal variants from a large number of grouped SNPs that target at multiple correlated and possibly heterogeneous traits. Our new method is featured by its selection at multiple levels, incorporation of prior biological knowledge to guide selection and identification of best subset of traits the variants target at. We showed the advantage of our method over existing methods via comprehensive simulations that mimic typical fine-mapping settings and a real-world fine-mapping example in UK Biobank, where we identified critical causal variants potentially targeting at different subsets of addictive behaviours and risk factors.
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Affiliation(s)
- Travis Canida
- Department of Epidemiology and Biostatistics, University of Maryland, 4200 Valley Drive, College Park, MD 20742, USA
| | - Hongjie Ke
- Department of Epidemiology and Biostatistics, University of Maryland, 4200 Valley Drive, College Park, MD 20742, USA
| | - Shuo Chen
- Department of Epidemiology and Public Health, University of Maryland, 655 W. Baltimore Street, Baltimore, MD 21201, USA
| | - Zhenyao Ye
- Department of Epidemiology and Public Health, University of Maryland, 655 W. Baltimore Street, Baltimore, MD 21201, USA
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, University of Maryland, 4200 Valley Drive, College Park, MD 20742, USA
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2
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Kuksa PP, Ionita M, Carter L, Cifello J, Clark K, Valladares O, Leung YY, Wang LS. BTS: scalable Bayesian Tissue Score for prioritizing GWAS variants and their functional contexts across omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.10.30.621077. [PMID: 39975395 PMCID: PMC11838512 DOI: 10.1101/2024.10.30.621077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/21/2025]
Abstract
Motivation statistics from genome-wide association studies (GWAS) are widely used in fine-mapping and colocalization analyses to identify causal variants and their enrichment in functional contexts, such as affected cell types and genomic features. With the expansion of functional genomic (FG) datasets, which now include hundreds of thousands of tracks across various cell and tissue types, it is critical to establish scalable algorithms integrating thousands of diverse FG annotations with GWAS results. Results We propose BTS (Bayesian Tissue Score), a novel, highly efficient algorithm uniquely designed for 1) identifying affected cell types and functional elements (context-mapping) and 2) fine-mapping potentially causal variants in a context-specific manner using large collections of cell type-specific FG annotation tracks. BTS leverages GWAS summary statistics and annotation-specific Bayesian models to analyze genome-wide annotation tracks, including enhancers, open chromatin, and histone marks. We evaluated BTS on GWAS summary statistics for immune and cardiovascular traits, such as Inflammatory Bowel Disease (IBD), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Coronary Artery Disease (CAD). Our results demonstrate that BTS is over 100x more efficient in estimating functional annotation effects and context-specific variant fine-mapping compared to existing methods. Importantly, this large-scale Bayesian approach prioritizes both known and novel annotations, cell types, genomic regions, and variants and provides valuable biological insights into the functional contexts of these diseases. Availability and implementation Docker image is available at https://hub.docker.com/r/wanglab/bts with pre-installed BTS R package (https://bitbucket.org/wanglab-upenn/BTS-R) and BTS GWAS summary statistics analysis pipeline (https://bitbucket.org/wanglab-upenn/bts-pipeline).
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Affiliation(s)
- Pavel P. Kuksa
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matei Ionita
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Luke Carter
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jeffrey Cifello
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kaylyn Clark
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Otto Valladares
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yuk Yee Leung
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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3
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Woodward DJ, Thorp JG, Middeldorp CM, Akóṣílè W, Derks EM, Gerring ZF. Leveraging pleiotropy for the improved treatment of psychiatric disorders. Mol Psychiatry 2025; 30:705-721. [PMID: 39390223 PMCID: PMC11746150 DOI: 10.1038/s41380-024-02771-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 09/23/2024] [Accepted: 09/25/2024] [Indexed: 10/12/2024]
Abstract
Over 90% of drug candidates fail in clinical trials, while it takes 10-15 years and one billion US dollars to develop a single successful drug. Drug development is more challenging for psychiatric disorders, where disease comorbidity and complex symptom profiles obscure the identification of causal mechanisms for therapeutic intervention. One promising approach for determining more suitable drug candidates in clinical trials is integrating human genetic data into the selection process. Genome-wide association studies have identified thousands of replicable risk loci for psychiatric disorders, and sophisticated statistical tools are increasingly effective at using these data to pinpoint likely causal genes. These studies have also uncovered shared or pleiotropic genetic risk factors underlying comorbid psychiatric disorders. In this article, we argue that leveraging pleiotropic effects will provide opportunities to discover novel drug targets and identify more effective treatments for psychiatric disorders by targeting a common mechanism rather than treating each disease separately.
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Affiliation(s)
- Damian J Woodward
- Brain and Mental Health, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- School of Biomedical Science, Queensland University of Technology, Brisbane, QLD, Australia.
| | - Jackson G Thorp
- Brain and Mental Health, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Christel M Middeldorp
- Department of Child and Adolescent Psychiatry and Psychology, Amsterdam UMC, Amsterdam Reproduction and Development Research Institute, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Arkin Mental Health Care, Amsterdam, The Netherlands
- Levvel, Academic Center for Child and Adolescent Psychiatry, Amsterdam, The Netherlands
- Child Health Research Centre, University of Queensland, Brisbane, QLD, Australia
- Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Service, Brisbane, QLD, Australia
| | - Wọlé Akóṣílè
- Greater Brisbane Clinical School, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Eske M Derks
- Brain and Mental Health, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Zachary F Gerring
- Brain and Mental Health, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- Healthy Development and Ageing, Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia.
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4
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Richard D, Muthuirulan P, Young M, Yengo L, Vedantam S, Marouli E, Bartell E, Hirschhorn J, Capellini TD. Functional genomics of human skeletal development and the patterning of height heritability. Cell 2025; 188:15-32.e24. [PMID: 39549696 PMCID: PMC11724752 DOI: 10.1016/j.cell.2024.10.040] [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: 12/31/2023] [Revised: 08/01/2024] [Accepted: 10/21/2024] [Indexed: 11/18/2024]
Abstract
Underlying variation in height are regulatory changes to chondrocytes, cartilage cells comprising long-bone growth plates. Currently, we lack knowledge on epigenetic regulation and gene expression of chondrocytes sampled across the human skeleton, and therefore we cannot understand basic regulatory mechanisms controlling height biology. We first rectify this issue by generating extensive epigenetic and transcriptomic maps from chondrocytes sampled from different growth plates across developing human skeletons, discovering novel regulatory networks shaping human bone/joint development. Next, using these maps in tandem with height genome-wide association study (GWAS) signals, we disentangle the regulatory impacts that skeletal element-specific versus global-acting variants have on skeletal growth, revealing the prime importance of regulatory pleiotropy in controlling height variation. Finally, as height is highly heritable, and thus often the test case for complex-trait genetics, we leverage these datasets within a testable omnigenic model framework to discover novel chondrocyte developmental modules and peripheral-acting factors shaping height biology and skeletal growth.
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Affiliation(s)
- Daniel Richard
- Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | | | - Mariel Young
- Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Sailaja Vedantam
- Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA
| | - Eirini Marouli
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Eric Bartell
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Joel Hirschhorn
- Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Terence D Capellini
- Human Evolutionary Biology, Harvard University, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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5
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Wang J, Ouyang L, You T, Yang N, Xu X, Zhang W, Yang H, Yi X, Huang D, Zhou W, Li M. CAUSALdb2: an updated database for causal variants of complex traits. Nucleic Acids Res 2025; 53:D1295-D1301. [PMID: 39558176 PMCID: PMC11701604 DOI: 10.1093/nar/gkae1096] [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: 09/08/2024] [Revised: 10/16/2024] [Accepted: 10/25/2024] [Indexed: 11/20/2024] Open
Abstract
Unraveling the causal variants from genome wide association studies (GWASs) is pivotal for understanding genetic underpinnings of complex traits and diseases. Despite continuous efforts, tools to refine and prioritize GWAS signals need enhancement to address the direct causal implications of genetic variations. To overcome challenges related to statistical fine-mapping in identifying causal variants, CAUSALdb has been updated with novel features and comprehensive datasets, morphing into CAUSALdb2. This expanded repository integrates 15 057 updated GWAS summary statistics across 10 839 unique traits and implements both LD-based and LD-free fine-mapping approaches, including innovative applications of approximate Bayes Factor and SuSiE. Additionally, by incorporating larger LD reference panels such as TOPMED and UK Biobank, and integrating functional annotations via PolyFun, CAUSALdb2 enhances the accuracy and context of fine-mapping results. The database now supports interrogation of additional causal signals and offers sophisticated visualizations to aid researchers in deciphering complex genetic architectures. By facilitating a deeper and more precise characterisation of causal variants, CAUSALdb2 serves as a crucial tool for advancing the genetic analysis of complex diseases. Available freely, CAUSALdb2 continues to set benchmarks in the post-GWAS era, fostering the development of targeted diagnostics and therapeutics derived from responsible genetic research. Explore these advancements at http://mulinlab.org/causaldb.
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Affiliation(s)
- Jianhua Wang
- Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Department of Bioinformatics, Key Laboratory of Prevention and Control of Human Major Diseases (Ministry of Education), The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, China
| | - Liao Ouyang
- School of Materials and Environmental Engineering, Shenzhen Polytechnic University, Shenzhen, China
| | - Tianyi You
- Department of Bioinformatics, Key Laboratory of Prevention and Control of Human Major Diseases (Ministry of Education), The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, China
| | - Nianling Yang
- Department of Bioinformatics, Key Laboratory of Prevention and Control of Human Major Diseases (Ministry of Education), The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, China
| | - Xinran Xu
- Department of Bioinformatics, Key Laboratory of Prevention and Control of Human Major Diseases (Ministry of Education), The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, China
| | - Wenwen Zhang
- Department of Bioinformatics, Key Laboratory of Prevention and Control of Human Major Diseases (Ministry of Education), The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, China
| | - Hongxi Yang
- Department of Bioinformatics, Key Laboratory of Prevention and Control of Human Major Diseases (Ministry of Education), The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, China
| | - Xianfu Yi
- Department of Bioinformatics, Key Laboratory of Prevention and Control of Human Major Diseases (Ministry of Education), The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, China
| | - Dandan Huang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, China
| | - Wenhao Zhou
- Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Mulin Jun Li
- Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Department of Bioinformatics, Key Laboratory of Prevention and Control of Human Major Diseases (Ministry of Education), The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin, China
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6
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Li Y, Xiao J, Ming J, Zeng Y, Cai M. Funmap: integrating high-dimensional functional annotations to improve fine-mapping. Bioinformatics 2024; 41:btaf017. [PMID: 39799513 PMCID: PMC11769679 DOI: 10.1093/bioinformatics/btaf017] [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: 06/23/2024] [Revised: 12/20/2024] [Accepted: 01/09/2025] [Indexed: 01/15/2025] Open
Abstract
MOTIVATION Fine-mapping aims to prioritize causal variants underlying complex traits by accounting for the linkage disequilibrium of genome-wide association study risk locus. The expanding resources of functional annotations serve as auxiliary evidence to improve the power of fine-mapping. However, existing fine-mapping methods tend to generate many false positive results when integrating a large number of annotations. RESULTS In this study, we propose a unified method to integrate high-dimensional functional annotations with fine-mapping (Funmap). Funmap can effectively improve the power of fine-mapping by borrowing information from hundreds of functional annotations. Meanwhile, it relates the annotation to the causal probability with a random effects model that avoids the over-fitting issue, thereby producing a well-controlled false positive rate. Paired with a fast algorithm, Funmap enables scalable integration of a large number of annotations to facilitate prioritizing multiple causal single nucleotide polymorphisms. Our comprehensive simulations across a wide range of annotation relevance settings demonstrate that Funmap is the only method that produces well-calibrated false discovery rate under the setting of high-dimensional annotations while achieving better or comparable power gains as compared to existing methods. By integrating genome-wide association studies of 4 lipid traits with 187 functional annotations, Funmap consistently identified more variants that can be replicated in an independent cohort, achieving 15.5%-26.2% improvement over the runner-up in terms of replication rate. AVAILABILITY AND IMPLEMENTATION The Funmap software and all analysis code are available at https://github.com/LeeHITsz/Funmap.
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Affiliation(s)
- Yuekai Li
- Department of Biostatistics, City University of Hong Kong, Hong Kong, China
| | - Jiashun Xiao
- Shenzhen International Center for Industrial and Applied Mathematics, Shenzhen Research Institute of Big Data, Shenzhen 518172, China
| | - Jingsi Ming
- Academy of Statistics and Interdisciplinary Sciences, KLATASDS-MOE, East China Normal University, Shanghai 200062, China
| | - Yicheng Zeng
- Shenzhen International Center for Industrial and Applied Mathematics, Shenzhen Research Institute of Big Data, Shenzhen 518172, China
| | - Mingxuan Cai
- Department of Biostatistics, City University of Hong Kong, Hong Kong, China
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7
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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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8
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Zou Y, Carbonetto P, Xie D, Wang G, Stephens M. Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.14.536893. [PMID: 37425935 PMCID: PMC10327118 DOI: 10.1101/2023.04.14.536893] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
We introduce mvSuSiE, a multi-trait fine-mapping method for identifying putative causal variants from genetic association data (individual-level or summary data). mvSuSiE learns patterns of shared genetic effects from data, and exploits these patterns to improve power to identify causal SNPs. Comparisons on simulated data show that mvSuSiE is competitive in speed, power and precision with existing multi-trait methods, and uniformly improves on single-trait fine-mapping (SuSiE) in each trait separately. We applied mvSuSiE to jointly fine-map 16 blood cell traits using data from the UK Biobank. By jointly analyzing the traits and modeling heterogeneous effect sharing patterns, we discovered a much larger number of causal SNPs (>3,000) compared with single-trait fine-mapping, and with narrower credible sets. mvSuSiE also more comprehensively characterized the ways in which the genetic variants affect one or more blood cell traits; 68% of causal SNPs showed significant effects in more than one blood cell type.
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Affiliation(s)
- Yuxin Zou
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA
| | - Peter Carbonetto
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Dongyue Xie
- Department of Statistics, University of Chicago, Chicago, IL, USA
| | - Gao Wang
- Gertrude. H. Sergievsky Center, Department of Neurology, Columbia University, New York, NY, USA
| | - Matthew Stephens
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
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9
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Tan MMX, Lawton MA, Pollard MI, Brown E, Real R, Carrasco AM, Bekadar S, Jabbari E, Reynolds RH, Iwaki H, Blauwendraat C, Kanavou S, Hubbard L, Malek N, Grosset KA, Bajaj N, Barker RA, Burn DJ, Bresner C, Foltynie T, Wood NW, Williams-Gray CH, Andreassen OA, Toft M, Elbaz A, Artaud F, Brice A, Corvol JC, Aasly J, Farrer MJ, Nalls MA, Singleton AB, Williams NM, Ben-Shlomo Y, Hardy J, Hu MTM, Grosset DG, Shoai M, Pihlstrøm L, Morris HR. Genome-wide determinants of mortality and motor progression in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:113. [PMID: 38849413 PMCID: PMC11161485 DOI: 10.1038/s41531-024-00729-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 05/24/2024] [Indexed: 06/09/2024] Open
Abstract
There are 90 independent genome-wide significant genetic risk variants for Parkinson's disease (PD) but currently only five nominated loci for PD progression. The biology of PD progression is likely to be of central importance in defining mechanisms that can be used to develop new treatments. We studied 6766 PD patients, over 15,340 visits with a mean follow-up of between 4.2 and 15.7 years and carried out genome-wide survival studies for time to a motor progression endpoint, defined by reaching Hoehn and Yahr stage 3 or greater, and death (mortality). There was a robust effect of the APOE ε4 allele on mortality in PD. We also identified a locus within the TBXAS1 gene encoding thromboxane A synthase 1 associated with mortality in PD. We also report 4 independent loci associated with motor progression in or near MORN1, ASNS, PDE5A, and XPO1. Only the non-Gaucher disease causing GBA1 PD risk variant E326K, of the known PD risk variants, was associated with mortality in PD. Further work is needed to understand the links between these genomic variants and the underlying disease biology. However, these may represent new candidates for disease modification in PD.
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Affiliation(s)
- Manuela M X Tan
- Department of Neurology, Oslo University Hospital, Oslo, Norway.
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK.
- UCL Movement Disorders Centre, University College London, London, UK.
| | - Michael A Lawton
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Miriam I Pollard
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
| | - Emmeline Brown
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
| | - Raquel Real
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
| | - Alejandro Martinez Carrasco
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
| | - Samir Bekadar
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Departement of Neurology, Hôpital Pitié-Salpêtrière, Paris, France
| | - Edwin Jabbari
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
| | - Regina H Reynolds
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Hirotaka Iwaki
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica, Washington DC, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Cornelis Blauwendraat
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Sofia Kanavou
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Leon Hubbard
- Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Naveed Malek
- Department of Neurology, Institute of Neurological Sciences, Queen Elizabeth University Hospital, Glasgow, UK
| | - Katherine A Grosset
- Department of Neurology, Institute of Neurological Sciences, Queen Elizabeth University Hospital, Glasgow, UK
| | - Nin Bajaj
- Clinical Neurosciences, University of Nottingham, Nottingham, UK
| | - Roger A Barker
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
- John Van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - David J Burn
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Catherine Bresner
- Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Thomas Foltynie
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
| | - Nicholas W Wood
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
| | - Caroline H Williams-Gray
- John Van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Mathias Toft
- Department of Neurology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Alexis Elbaz
- Paris-Saclay University, UVSQ, Inserm, Gustave Roussy, "Exposome and Heredity" team, CESP, 94807, Villejuif, France
| | - Fanny Artaud
- Paris-Saclay University, UVSQ, Inserm, Gustave Roussy, "Exposome and Heredity" team, CESP, 94807, Villejuif, France
| | - Alexis Brice
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Departement of Neurology, Hôpital Pitié-Salpêtrière, Paris, France
| | - Jean-Christophe Corvol
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Departement of Neurology, Hôpital Pitié-Salpêtrière, Paris, France
| | - Jan Aasly
- Department of Neurology, St. Olavs Hospital, Trondheim, Norway
- Department of Neuromedicine and Movement Science (INB), Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Matthew J Farrer
- Department of Neurology, University of Florida, Gainesville, FL, USA
| | - Michael A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica, Washington DC, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Andrew B Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Nigel M Williams
- Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - John Hardy
- UCL Movement Disorders Centre, University College London, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
- Department of Neurodegenerative Diseases, Queen Square Institute of Neurology, University College London, London, UK
- Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute, University College London, London, UK
- National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre, London, UK
- Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Michele T M Hu
- Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, University of Oxford, Oxford, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Department of Clinical Neurology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Donald G Grosset
- School of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
| | - Maryam Shoai
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
- Department of Neurodegenerative Diseases, Queen Square Institute of Neurology, University College London, London, UK
- Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, London, UK
| | - Lasse Pihlstrøm
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Huw R Morris
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK.
- UCL Movement Disorders Centre, University College London, London, UK.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.
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10
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Li X, Zhou Z, Ma Y, Ding K, Xiao H, Chen D, Liu N. Shared Genetic Architectures between Coronary Artery Disease and Type 2 Diabetes Mellitus in East Asian and European Populations. Biomedicines 2024; 12:1243. [PMID: 38927450 PMCID: PMC11201280 DOI: 10.3390/biomedicines12061243] [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: 03/25/2024] [Revised: 05/23/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
Coronary artery disease (CAD) is a common comorbidity of type 2 diabetes mellitus (T2DM). However, the pathophysiology connecting these two phenotypes remains to be further understood. Combined analysis in multi-ethnic populations can help contribute to deepening our understanding of biological mechanisms caused by shared genetic loci. We applied genetic correlation analysis and then performed conditional and joint association analyses in Chinese, Japanese, and European populations to identify the genetic variants jointly associated with CAD and T2DM. Next, the associations between genes and the two traits were also explored. Finally, fine-mapping and functional enrichment analysis were employed to identify the potential causal variants and pathways. Genetic correlation results indicated significant genetic overlap between CAD and T2DM in the three populations. Over 10,000 shared signals were identified, and 587 were shared by East Asian and European populations. Fifty-six novel shared genes were found to have significant effects on both CAD and T2DM. Most loci were fine-mapped to plausible causal variant sets. Several similarities and differences of the involved genes in GO terms and KEGG pathways were revealed across East Asian and European populations. These findings highlight the importance of immunoregulation, neuroregulation, heart development, and the regulation of glucose metabolism in shared etiological mechanisms between CAD and T2DM.
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Affiliation(s)
- Xiaoyi Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Zechen Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Yujia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Kexin Ding
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Han Xiao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Dafang Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Na Liu
- Department of Neurology, Peking University Third Hospital, Beijing 100191, China
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11
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Chin IM, Gardell ZA, Corces MR. Decoding polygenic diseases: advances in noncoding variant prioritization and validation. Trends Cell Biol 2024; 34:465-483. [PMID: 38719704 DOI: 10.1016/j.tcb.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/12/2024] [Accepted: 03/21/2024] [Indexed: 06/09/2024]
Abstract
Genome-wide association studies (GWASs) provide a key foundation for elucidating the genetic underpinnings of common polygenic diseases. However, these studies have limitations in their ability to assign causality to particular genetic variants, especially those residing in the noncoding genome. Over the past decade, technological and methodological advances in both analytical and empirical prioritization of noncoding variants have enabled the identification of causative variants by leveraging orthogonal functional evidence at increasing scale. In this review, we present an overview of these approaches and describe how this workflow provides the groundwork necessary to move beyond associations toward genetically informed studies on the molecular and cellular mechanisms of polygenic disease.
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Affiliation(s)
- Iris M Chin
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Zachary A Gardell
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - M Ryan Corces
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
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12
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Liao Y, Yu H, Zhang Y, Lu Z, Sun Y, Guo L, Guo J, Kang Z, Feng X, Sun Y, Wang G, Su Z, Lu T, Yang Y, Li W, Lv L, Yan H, Zhang D, Yue W. Genome-wide association study implicates lipid pathway dysfunction in antipsychotic-induced weight gain: multi-ancestry validation. Mol Psychiatry 2024; 29:1857-1868. [PMID: 38336841 DOI: 10.1038/s41380-024-02447-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 01/21/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024]
Abstract
Antipsychotic-induced weight gain (AIWG) is a common side effect of antipsychotic medication and may contribute to diabetes and coronary heart disease. To expand the unclear genetic mechanism underlying AIWG, we conducted a two-stage genome-wide association study in Han Chinese patients with schizophrenia. The study included a discovery cohort of 1936 patients and a validation cohort of 534 patients, with an additional 630 multi-ancestry patients from the CATIE study for external validation. We applied Mendelian randomization (MR) analysis to investigate the relationship between AIWG and antipsychotic-induced lipid changes. Our results identified two novel genome-wide significant loci associated with AIWG: rs10422861 in PEPD (P = 1.373 × 10-9) and rs3824417 in PTPRD (P = 3.348 × 10-9) in Chinese Han samples. The association of rs10422861 was validated in the European samples. Fine-mapping and functional annotation revealed that PEPD and PTPRD are potentially causal genes for AIWG, with their proteins being prospective therapeutic targets. Colocalization analysis suggested that AIWG and type 2 diabetes (T2D) shared a causal variant in PEPD. Polygenic risk scores (PRSs) for AIWG and T2D significantly predicted AIWG in multi-ancestry samples. Furthermore, MR revealed a risky causal effect of genetically predicted changes in low-density lipoprotein cholesterol (P = 7.58 × 10-4) and triglycerides (P = 2.06 × 10-3) caused by acute-phase of antipsychotic treatment on AIWG, which had not been previously reported. Our model, incorporating antipsychotic-induced lipid changes, PRSs, and clinical predictors, significantly predicted BMI percentage change after 6-month antipsychotic treatment (AUC = 0.79, R2 = 0.332). Our results highlight that the mechanism of AIWG involves lipid pathway dysfunction and may share a genetic basis with T2D through PEPD. Overall, this study provides new insights into the pathogenesis of AIWG and contributes to personalized treatment of schizophrenia.
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Affiliation(s)
- Yundan Liao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Hao Yu
- Department of Psychiatry, Jining Medical University, Jining, Shandong, 272067, China
| | - Yuyanan Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China.
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China.
| | - Zhe Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Yaoyao Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Liangkun Guo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Jing Guo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Zhewei Kang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Xiaoyang Feng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Yutao Sun
- No.5 Hospital, Tangshan, Hebei, 063000, China
| | - Guishan Wang
- The Second Affiliated Hospital of Jining Medical College, Jining, 272051, China
| | - Zhonghua Su
- The Second Affiliated Hospital of Jining Medical College, Jining, 272051, China
| | - Tianlan Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Yongfeng Yang
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453002, Henan, China
| | - Wenqiang Li
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453002, Henan, China
| | - Luxian Lv
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453002, Henan, China
| | - Hao Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Dai Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
- Chinese Institute for Brain Research, Beijing, 102206, China
- Institute for Brain Research and Rehabilitation (IBRR), Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Weihua Yue
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China.
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China.
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13
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Rossen J, Shi H, Strober BJ, Zhang MJ, Kanai M, McCaw ZR, Liang L, Weissbrod O, Price AL. MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.13.24307291. [PMID: 38798542 PMCID: PMC11118590 DOI: 10.1101/2024.05.13.24307291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Leveraging data from multiple ancestries can greatly improve fine-mapping power due to differences in linkage disequilibrium and allele frequencies. We propose MultiSuSiE, an extension of the sum of single effects model (SuSiE) to multiple ancestries that allows causal effect sizes to vary across ancestries based on a multivariate normal prior informed by empirical data. We evaluated MultiSuSiE via simulations and analyses of 14 quantitative traits leveraging whole-genome sequencing data in 47k African-ancestry and 94k European-ancestry individuals from All of Us. In simulations, MultiSuSiE applied to Afr47k+Eur47k was well-calibrated and attained higher power than SuSiE applied to Eur94k; interestingly, higher causal variant PIPs in Afr47k compared to Eur47k were entirely explained by differences in the extent of LD quantified by LD 4th moments. Compared to very recently proposed multi-ancestry fine-mapping methods, MultiSuSiE attained higher power and/or much lower computational costs, making the analysis of large-scale All of Us data feasible. In real trait analyses, MultiSuSiE applied to Afr47k+Eur94k identified 579 fine-mapped variants with PIP > 0.5, and MultiSuSiE applied to Afr47k+Eur47k identified 44% more fine-mapped variants with PIP > 0.5 than SuSiE applied to Eur94k. We validated MultiSuSiE results for real traits via functional enrichment of fine-mapped variants. We highlight several examples where MultiSuSiE implicates well-studied or biologically plausible fine-mapped variants that were not implicated by other methods.
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14
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Nizamuddin S, Timmers HTM. greenPipes: an integrated data analysis pipeline for greenCUT&RUN and CUT&RUN genome-localization datasets. Bioinformatics 2024; 40:btae307. [PMID: 38718209 PMCID: PMC11112040 DOI: 10.1093/bioinformatics/btae307] [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: 01/29/2024] [Revised: 03/28/2024] [Accepted: 05/07/2024] [Indexed: 05/24/2024] Open
Abstract
MOTIVATION To study gene regulation through transcription factors and chromatin modifiers, a variety of genome-wide techniques are used. Recently, CUT&RUN-based technologies have become popular, but a pipeline for the comprehensive analysis of CUT&RUN datasets is currently lacking. Here, we present the "greenPipes" package, which includes fine-tuned parameters specifically for bioinformatic analyses of greenCUT&RUN and CUT&RUN datasets. greenPipes provides additional functionalities for data analysis and data integration with other -omics technologies, which are either not available in other pipelines developed for CUT&RUN datasets or scattered in the literature as individual packages. AVAILABILITY AND IMPLEMENTATION Source code and a manual of the greenPipes are freely available on GitHub website (https://github.com/snizam001/greenPipes). The test datasets, comprehensive annotation files, and other datasets are available at https://osf.io/ruhj9/. CONTACT n.sheikh@dkfz-heidelberg.de or m.timmers@dkfz-heidelberg.de. SUPPLEMENTARY INFORMATION The handbook of greenPipes is available online at Bioinformatics as Supplementary text.
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Affiliation(s)
- Sheikh Nizamuddin
- Department of Urology, Medical Center-University of Freiburg, Freiburg, 79016, Germany
- German Cancer Consortium (DKTK), partnersite Freiburg, a partnership between the DKFZ and Medical Center-University of Freiburg, Germany
| | - H T Marc Timmers
- Department of Urology, Medical Center-University of Freiburg, Freiburg, 79016, Germany
- German Cancer Consortium (DKTK), partnersite Freiburg, a partnership between the DKFZ and Medical Center-University of Freiburg, Germany
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15
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Chen H, Wang Z, Gong L, Wang Q, Chen W, Wang J, Ma X, Ding R, Li X, Zou X, Plass M, Lian C, Ni T, Wei GH, Li W, Deng L, Li L. A distinct class of pan-cancer susceptibility genes revealed by an alternative polyadenylation transcriptome-wide association study. Nat Commun 2024; 15:1729. [PMID: 38409266 PMCID: PMC10897204 DOI: 10.1038/s41467-024-46064-7] [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: 08/09/2023] [Accepted: 02/12/2024] [Indexed: 02/28/2024] Open
Abstract
Alternative polyadenylation plays an important role in cancer initiation and progression; however, current transcriptome-wide association studies mostly ignore alternative polyadenylation when identifying putative cancer susceptibility genes. Here, we perform a pan-cancer 3' untranslated region alternative polyadenylation transcriptome-wide association analysis by integrating 55 well-powered (n > 50,000) genome-wide association studies datasets across 22 major cancer types with alternative polyadenylation quantification from 23,955 RNA sequencing samples across 7,574 individuals. We find that genetic variants associated with alternative polyadenylation are co-localized with 28.57% of cancer loci and contribute a significant portion of cancer heritability. We further identify 642 significant cancer susceptibility genes predicted to modulate cancer risk via alternative polyadenylation, 62.46% of which have been overlooked by traditional expression- and splicing- studies. As proof of principle validation, we show that alternative alleles facilitate 3' untranslated region lengthening of CRLS1 gene leading to increased protein abundance and promoted proliferation of breast cancer cells. Together, our study highlights the significant role of alternative polyadenylation in discovering new cancer susceptibility genes and provides a strong foundational framework for enhancing our understanding of the etiology underlying human cancers.
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Affiliation(s)
- Hui Chen
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Zeyang Wang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Lihai Gong
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Qixuan Wang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Wenyan Chen
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Jia Wang
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Xuelian Ma
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Ruofan Ding
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Xing Li
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Xudong Zou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Mireya Plass
- Gene Regulation of Cell Identity Group, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet de Llobregat, Barcelona, 08908, Spain
- Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P-CMR[C], L'Hospitalet de Llobregat, Barcelona, 08908, Spain
- Center for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, 28029, Spain
| | - Cheng Lian
- Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, 200032, China
| | - Ting Ni
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, School of Life Sciences and Huashan Hospital, Fudan University, Shanghai, 200438, China
| | - Gong-Hong Wei
- Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, 200032, China
- Disease Networks Research Unit, Faculty of Biochemistry and Molecular Medicine & Biocenter Oulu, University of Oulu, Oulu, 90410, Finland
| | - Wei Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, The University of California, Irvine, CA, 92697, USA.
| | - Lin Deng
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.
| | - Lei Li
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.
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16
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Hodonsky CJ, Turner AW, Khan MD, Barrientos NB, Methorst R, Ma L, Lopez NG, Mosquera JV, Auguste G, Farber E, Ma WF, Wong D, Onengut-Gumuscu S, Kavousi M, Peyser PA, van der Laan SW, Leeper NJ, Kovacic JC, Björkegren JLM, Miller CL. Multi-ancestry genetic analysis of gene regulation in coronary arteries prioritizes disease risk loci. CELL GENOMICS 2024; 4:100465. [PMID: 38190101 PMCID: PMC10794848 DOI: 10.1016/j.xgen.2023.100465] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 09/07/2023] [Accepted: 11/19/2023] [Indexed: 01/09/2024]
Abstract
Genome-wide association studies (GWASs) have identified hundreds of risk loci for coronary artery disease (CAD). However, non-European populations are underrepresented in GWASs, and the causal gene-regulatory mechanisms of these risk loci during atherosclerosis remain unclear. We incorporated local ancestry and haplotypes to identify quantitative trait loci for expression (eQTLs) and splicing (sQTLs) in coronary arteries from 138 ancestrally diverse Americans. Of 2,132 eQTL-associated genes (eGenes), 47% were previously unreported in coronary artery; 19% exhibited cell-type-specific expression. Colocalization revealed subgroups of eGenes unique to CAD and blood pressure GWAS. Fine-mapping highlighted additional eGenes, including TBX20 and IL5. We also identified sQTLs for 1,690 genes, among which TOR1AIP1 and ULK3 sQTLs demonstrated the importance of evaluating splicing to accurately identify disease-relevant isoform expression. Our work provides a patient-derived coronary artery eQTL resource and exemplifies the need for diverse study populations and multifaceted approaches to characterize gene regulation in disease processes.
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Affiliation(s)
- Chani J Hodonsky
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Adam W Turner
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Mohammad Daud Khan
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Nelson B Barrientos
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Ruben Methorst
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nicolas G Lopez
- Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305, USA
| | - Jose Verdezoto Mosquera
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - Gaëlle Auguste
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Emily Farber
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Wei Feng Ma
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Medical Scientist Training Program, Department of Pathology, University of Virginia, Charlottesville, VA 22908, USA
| | - Doris Wong
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, 3000 CA Rotterdam, the Netherlands
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan, Ann Arbor, MI 48019, USA
| | - Sander W van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Nicholas J Leeper
- Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305, USA
| | - Jason C Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; St. Vincent's Clinical School, University of New South Wales, Sydney, NSW 2052, Australia
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Huddinge, Karolinska Institutet, 141 52 Huddinge, Sweden
| | - Clint L Miller
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA.
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17
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Zhang X, Jiang W, Zhao H. Integration of expression QTLs with fine mapping via SuSiE. PLoS Genet 2024; 20:e1010929. [PMID: 38271473 PMCID: PMC10846745 DOI: 10.1371/journal.pgen.1010929] [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: 08/22/2023] [Revised: 02/06/2024] [Accepted: 01/06/2024] [Indexed: 01/27/2024] Open
Abstract
Genome-wide association studies (GWASs) have achieved remarkable success in associating thousands of genetic variants with complex traits. However, the presence of linkage disequilibrium (LD) makes it challenging to identify the causal variants. To address this critical gap from association to causation, many fine-mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants, taking into account the underlying LD pattern. In this manuscript, we introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine-mapping, built on the sum of single-effects (SuSiE) regression model. Our new method, SuSiE2, connects two SuSiE models, one for eQTL analysis and one for genetic fine-mapping. This is achieved by first computing the posterior inclusion probabilities (PIPs) from an eQTL-based SuSiE model with the expression level of the candidate gene as the phenotype. These calculated PIPs are then utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. By prioritizing functional variants within the candidate region using eQTL information, SuSiE2 improves SuSiE by increasing the detection rate of causal SNPs and reducing the average size of credible sets. We compared the performance of SuSiE2 with other multi-trait fine-mapping methods with respect to power, coverage, and precision through simulations and applications to the GWAS results of Alzheimer's disease (AD) and body mass index (BMI). Our results demonstrate the better performance of SuSiE2, both when the in-sample linkage disequilibrium (LD) matrix and an external reference panel is used in inference.
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Affiliation(s)
- Xiangyu Zhang
- Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Wei Jiang
- Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Hongyu Zhao
- Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut, United States of America
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18
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Mo C, Ye Z, Pan Y, Zhang Y, Wu Q, Bi C, Liu S, Mitchell B, Kochunov P, Hong LE, Ma T, Chen S. An in-depth association analysis of genetic variants within nicotine-related loci: Meeting in middle of GWAS and genetic fine-mapping. Mol Cell Neurosci 2023; 127:103895. [PMID: 37634742 PMCID: PMC11128188 DOI: 10.1016/j.mcn.2023.103895] [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: 01/19/2023] [Revised: 08/21/2023] [Accepted: 08/24/2023] [Indexed: 08/29/2023] Open
Abstract
In the last two decades of Genome-wide association studies (GWAS), nicotine-dependence-related genetic loci (e.g., nicotinic acetylcholine receptor - nAChR subunit genes) are among the most replicable genetic findings. Although GWAS results have reported tens of thousands of SNPs within these loci, further analysis (e.g., fine-mapping) is required to identify the causal variants. However, it is computationally challenging for existing fine-mapping methods to reliably identify causal variants from thousands of candidate SNPs based on the posterior inclusion probability. To address this challenge, we propose a new method to select SNPs by jointly modeling the SNP-wise inference results and the underlying structured network patterns of the linkage disequilibrium (LD) matrix. We use adaptive dense subgraph extraction method to recognize the latent network patterns of the LD matrix and then apply group LASSO to select causal variant candidates. We applied this new method to the UK biobank data to identify the causal variant candidates for nicotine addiction. Eighty-one nicotine addiction-related SNPs (i.e.,-log(p) > 50) of nAChR were selected, which are highly correlated (average r2>0.8) although they are physically distant (e.g., >200 kilobase away) and from various genes. These findings revealed that distant SNPs from different genes can show higher LD r2 than their neighboring SNPs, and jointly contribute to a complex trait like nicotine addiction.
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Affiliation(s)
- Chen Mo
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Yezhi Pan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Yuan Zhang
- Department of Statistics, College of Arts and Sciences, Ohio State University, Columbus, Ohio, United States
| | - Qiong Wu
- Department of Mathematics, University of Maryland, College Park, Maryland, United States
| | - Chuan Bi
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Braxton Mitchell
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, United States
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19
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Zhou F, Soremekun O, Chikowore T, Fatumo S, Barroso I, Morris AP, Asimit JL. Leveraging information between multiple population groups and traits improves fine-mapping resolution. Nat Commun 2023; 14:7279. [PMID: 37949886 PMCID: PMC10638399 DOI: 10.1038/s41467-023-43159-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals. Its resolution can be improved by (i) leveraging information between traits; and (ii) exploiting differences in linkage disequilibrium structure between diverse population groups. Using association summary statistics, MGflashfm jointly fine-maps signals from multiple traits and population groups; MGfm uses an analogous framework to analyse each trait separately. We also provide a practical approach to fine-mapping with out-of-sample reference panels. In simulation studies we show that MGflashfm and MGfm are well-calibrated and that the mean proportion of causal variants with PP > 0.80 is above 0.75 (MGflashfm) and 0.70 (MGfm). In our analysis of four lipids traits across five population groups, MGflashfm gives a median 99% credible set reduction of 10.5% over MGfm. MGflashfm and MGfm only require summary level data, making them very useful fine-mapping tools in consortia efforts where individual-level data cannot be shared.
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Affiliation(s)
- Feng Zhou
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Opeyemi Soremekun
- The African Computational Genomic (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Segun Fatumo
- The African Computational Genomic (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), University of Exeter Medical School, Exeter, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK
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20
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Chen H, Luo H, Tian T, Li S, Jiang Y. Integrated Analyses of Single-Cell Transcriptome and Mendelian Randomization Reveal the Protective Role of Resistin in Sepsis Survival in Intensive Care Unit. Int J Mol Sci 2023; 24:14982. [PMID: 37834432 PMCID: PMC10573869 DOI: 10.3390/ijms241914982] [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: 09/03/2023] [Revised: 09/27/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
The high morbidity and mortality rates associated with sepsis highlight the challenges of finding specific remedies for this condition in the intensive care unit (ICU). This study aimed to explore the differentially expressed genes (DEGs) specific to cell types in sepsis and investigate the role of resistin in the survival of sepsis patients through Mendelian randomization (MR) analyses. We used single-cell and bulk transcriptome data to identify cell type-specific DEGs between sepsis and healthy controls. MR analyses were then conducted to investigate the causal relationships between resistin (one of the identified DEGs) levels and the survival of sepsis patients. Additionally, we utilized meQTL (methylation quantitative trait loci) to identify cytosine-phosphate-guanine (CpG) sites that may directly affect sepsis. We identified 560 cell type-specific DEGs between sepsis and healthy controls. Notably, we observed the upregulation of resistin levels in macrophages during sepsis. In bulk transcriptome, RETN is also upregulated in sepsis samples compared with healthy controls. MR analyses revealed a negative association existed between the expression of resistin, at both gene and protein levels, and the mortality or severity of sepsis patients in ICU. Moreover, there were no associations observed between resistin levels and death or organ failure due to other causes. We also identified three methylation CpG sites, located in RETN or its promoter region-cg06633066, cg22322184, and cg02346997-that directly affected both resistin protein levels and sepsis death in the ICU. Our findings suggest that resistin may provide feasible protection for sepsis patients, particularly those with severe cases, without serious side effects. Therefore, resistin could be a potential drug candidate for sepsis treatment. Additionally, we identified two CpG sites, cg06633066 and cg22322184, that were associated with RETN protein levels and sepsis death, providing novel insights into the underlying mechanisms of sepsis.
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Affiliation(s)
| | | | | | | | - Yong Jiang
- Guangdong Provincial Key Laboratory of Proteomics, State Key Laboratory of Organ Failure Research, Department of Pathophysiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China; (H.C.); (H.L.); (T.T.); (S.L.)
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21
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Luo J, Huang H, Qiao H, Tan J, Chen W, Zhang M, Ruiz-Linares A, Wang J, Yang Y, Jin L, Headon DJ, Wang S. GWASs Identify Genetic Loci Associated with Human Scalp Hair Whorl Direction. J Invest Dermatol 2023; 143:2065-2068.e10. [PMID: 37565938 DOI: 10.1016/j.jid.2023.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/07/2023] [Accepted: 04/10/2023] [Indexed: 08/12/2023]
Affiliation(s)
- Junyu Luo
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - He Huang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China; Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Science, Fudan University, Shanghai, China
| | - Hui Qiao
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Science, Fudan University, Shanghai, China
| | - Jingze Tan
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Science, Fudan University, Shanghai, China
| | - Wenyan Chen
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Manfei Zhang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China; Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Andrés Ruiz-Linares
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Science, Fudan University, Shanghai, China; Aix-Marseille Université, Centre National de la Recherche Scientifique (CNRS), EFS, Anthropologie Bio-Culturelle, Droit, Ethique et Santé (ADES), Marseille, France; Department of Genetics, Evolution and Environment, UCL Division of Biosciences, University College London, London, United Kingdom
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China; Fudan-Taizhou Institute of Health Sciences, Taizhou, China; Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences, Shanghai, China
| | - Yajun Yang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Science, Fudan University, Shanghai, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China; Fudan-Taizhou Institute of Health Sciences, Taizhou, China; Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences, Shanghai, China
| | - Denis J Headon
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
| | - Sijia Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
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22
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Als TD, Kurki MI, Grove J, Voloudakis G, Therrien K, Tasanko E, Nielsen TT, Naamanka J, Veerapen K, Levey DF, Bendl J, Bybjerg-Grauholm J, Zeng B, Demontis D, Rosengren A, Athanasiadis G, Bækved-Hansen M, Qvist P, Bragi Walters G, Thorgeirsson T, Stefánsson H, Musliner KL, Rajagopal VM, Farajzadeh L, Thirstrup J, Vilhjálmsson BJ, McGrath JJ, Mattheisen M, Meier S, Agerbo E, Stefánsson K, Nordentoft M, Werge T, Hougaard DM, Mortensen PB, Stein MB, Gelernter J, Hovatta I, Roussos P, Daly MJ, Mors O, Palotie A, Børglum AD. Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses. Nat Med 2023; 29:1832-1844. [PMID: 37464041 PMCID: PMC10839245 DOI: 10.1038/s41591-023-02352-1] [Citation(s) in RCA: 137] [Impact Index Per Article: 68.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 04/17/2023] [Indexed: 07/20/2023]
Abstract
Depression is a common psychiatric disorder and a leading cause of disability worldwide. Here we conducted a genome-wide association study meta-analysis of six datasets, including >1.3 million individuals (371,184 with depression) and identified 243 risk loci. Overall, 64 loci were new, including genes encoding glutamate and GABA receptors, which are targets for antidepressant drugs. Intersection with functional genomics data prioritized likely causal genes and revealed new enrichment of prenatal GABAergic neurons, astrocytes and oligodendrocyte lineages. We found depression to be highly polygenic, with ~11,700 variants explaining 90% of the single-nucleotide polymorphism heritability, estimating that >95% of risk variants for other psychiatric disorders (anxiety, schizophrenia, bipolar disorder and attention deficit hyperactivity disorder) were influencing depression risk when both concordant and discordant variants were considered, and nearly all depression risk variants influenced educational attainment. Additionally, depression genetic risk was associated with impaired complex cognition domains. We dissected the genetic and clinical heterogeneity, revealing distinct polygenic architectures across subgroups of depression and demonstrating significantly increased absolute risks for recurrence and psychiatric comorbidity among cases of depression with the highest polygenic burden, with considerable sex differences. The risks were up to 5- and 32-fold higher than cases with the lowest polygenic burden and the background population, respectively. These results deepen the understanding of the biology underlying depression, its disease progression and inform precision medicine approaches to treatment.
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Affiliation(s)
- Thomas D Als
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
- Center for Genomics and Personalized Medicine, Aarhus, Denmark.
| | - Mitja I Kurki
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jakob Grove
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Georgios Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J Peters VA Medical Center, Bronx, NY, USA
| | - Karen Therrien
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J Peters VA Medical Center, Bronx, NY, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elisa Tasanko
- Department of Psychology and Logopedics, SleepWell Research Program, University of Helsinki, Helsinki, Finland
| | - Trine Tollerup Nielsen
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Joonas Naamanka
- Department of Psychology and Logopedics, SleepWell Research Program, University of Helsinki, Helsinki, Finland
| | - Kumar Veerapen
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Daniel F Levey
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jonas Bybjerg-Grauholm
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Biao Zeng
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ditte Demontis
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Anders Rosengren
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
| | - Georgios Athanasiadis
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Barcelona, Spain
| | - Marie Bækved-Hansen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Per Qvist
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | | | | | | | - Katherine L Musliner
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research (NCRR), Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Department of Affective Disorders, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
- The Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Veera M Rajagopal
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Leila Farajzadeh
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Janne Thirstrup
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Bjarni J Vilhjálmsson
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - John J McGrath
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, Queensland, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Manuel Mattheisen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Sandra Meier
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Esben Agerbo
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research (NCRR), Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, CIRRAU, Aarhus University, Aarhus, Denmark
| | | | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Copenhagen, Capital Region of Denmark, Copenhagen University Hospital, Copenhagen, Denmark
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- Institute of Clinical Sciences and GLOBE Institute, LF Center for GeoGenetics, University of Copenhagen, Copenhagen, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Preben B Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research (NCRR), Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, CIRRAU, Aarhus University, Aarhus, Denmark
| | - Murray B Stein
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
- Departments of Psychiatry and Herbert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Iiris Hovatta
- Department of Psychology and Logopedics, SleepWell Research Program, University of Helsinki, Helsinki, Finland
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J Peters VA Medical Center, Bronx, NY, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Mark J Daly
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Psychosis Research Unit, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Anders D Børglum
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
- Center for Genomics and Personalized Medicine, Aarhus, Denmark.
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23
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Yang Z, Wang C, Liu L, Khan A, Lee A, Vardarajan B, Mayeux R, Kiryluk K, Ionita-Laza I. CARMA is a new Bayesian model for fine-mapping in genome-wide association meta-analyses. Nat Genet 2023; 55:1057-1065. [PMID: 37169873 DOI: 10.1038/s41588-023-01392-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 04/11/2023] [Indexed: 05/13/2023]
Abstract
Fine-mapping is commonly used to identify putative causal variants at genome-wide significant loci. Here we propose a Bayesian model for fine-mapping that has several advantages over existing methods, including flexible specification of the prior distribution of effect sizes, joint modeling of summary statistics and functional annotations and accounting for discrepancies between summary statistics and external linkage disequilibrium in meta-analyses. Using simulations, we compare performance with commonly used fine-mapping methods and show that the proposed model has higher power and lower false discovery rate (FDR) when including functional annotations, and higher power, lower FDR and higher coverage for credible sets in meta-analyses. We further illustrate our approach by applying it to a meta-analysis of Alzheimer's disease genome-wide association studies where we prioritize putatively causal variants and genes.
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Affiliation(s)
- Zikun Yang
- Department of Biostatistics, Columbia University, New York City, NY, USA
| | - Chen Wang
- Department of Biostatistics, Columbia University, New York City, NY, USA
- Division of Nephrology Department of Medicine College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Linxi Liu
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Atlas Khan
- Division of Nephrology Department of Medicine College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Annie Lee
- Department of Neurology College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Badri Vardarajan
- Department of Neurology College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Richard Mayeux
- Department of Neurology College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Krzysztof Kiryluk
- Division of Nephrology Department of Medicine College of Physicians and Surgeons, Columbia University, New York City, NY, USA
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24
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Wang Z, Zhao G, Li B, Fang Z, Chen Q, Wang X, Luo T, Wang Y, Zhou Q, Li K, Xia L, Zhang Y, Zhou X, Pan H, Zhao Y, Wang Y, Wang L, Guo J, Tang B, Xia K, Li J. Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:649-661. [PMID: 35272052 PMCID: PMC10787016 DOI: 10.1016/j.gpb.2022.02.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 12/28/2021] [Accepted: 02/27/2022] [Indexed: 06/14/2023]
Abstract
Non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences. However, it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods. To solve this issue, we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets: (1) rare germline variants from clinical relevant sequence variants (ClinVar), (2) rare somatic variants from Catalogue Of Somatic Mutations In Cancer (COSMIC), (3) common regulatory variants from curated expression quantitative trait locus (eQTL) data, and (4) disease-associated common variants from curated genome-wide association studies (GWAS). All 24 tested methods performed differently under various conditions, indicating varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve (AUROC) of 0.4481-0.8033 and poor for rare somatic variants from COSMIC (AUROC = 0.4984-0.7131), common regulatory variants from curated eQTL data (AUROC = 0.4837-0.6472), and disease-associated common variants from curated GWAS (AUROC = 0.4766-0.5188). We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder, and found that the combined annotation-dependent depletion (CADD) and context-dependent tolerance score (CDTS) methods showed better performance. Summarily, we assessed the performance of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.
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Affiliation(s)
- Zheng Wang
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Guihu Zhao
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Bin Li
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhenghuan Fang
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Qian Chen
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xiaomeng Wang
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Tengfei Luo
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Yijing Wang
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Qiao Zhou
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Kuokuo Li
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Lu Xia
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Yi Zhang
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xun Zhou
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Hongxu Pan
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yuwen Zhao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yige Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Lin Wang
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China; Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Jifeng Guo
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Beisha Tang
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Kun Xia
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Jinchen Li
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China; Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China.
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25
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McManus JN, Lovelett RJ, Lowengrub D, Christensen S. A unifying statistical framework to discover disease genes from GWASs. CELL GENOMICS 2023; 3:100264. [PMID: 36950381 PMCID: PMC10025450 DOI: 10.1016/j.xgen.2023.100264] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 09/07/2022] [Accepted: 01/19/2023] [Indexed: 03/10/2023]
Abstract
Genome-wide association studies (GWASs) identify genomic loci associated with complex traits, but it remains a challenge to identify the genes affected by causal genetic variants in these loci. Attempts to solve this challenge are frustrated by a number of compounding problems. Here, we show how to combine solutions to these problems into a unified mathematical framework. From this synthesis, it becomes possible to compute the probability that each gene in the genome is affected by a causal variant, given a particular trait, without making assumptions about the relevant cell types or tissues. We validate each component of the framework individually and in combination. When applied to large GWASs of human disease, the resulting paradigm can rediscover the majority of well-known disease genes. Moreover, it establishes human genetics support for many genes previously implicated only by clinical or preclinical evidence, and it uncovers a plethora of novel disease genes with compelling biological rationale.
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26
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Hodonsky CJ, Turner AW, Khan MD, Barrientos NB, Methorst R, Ma L, Lopez NG, Mosquera JV, Auguste G, Farber E, Ma WF, Wong D, Onengut-Gumuscu S, Kavousi M, Peyser PA, van der Laan SW, Leeper NJ, Kovacic JC, Björkegren JLM, Miller CL. Integrative multi-ancestry genetic analysis of gene regulation in coronary arteries prioritizes disease risk loci. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.09.23285622. [PMID: 36824883 PMCID: PMC9949190 DOI: 10.1101/2023.02.09.23285622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Genome-wide association studies (GWAS) have identified hundreds of genetic risk loci for coronary artery disease (CAD). However, non-European populations are underrepresented in GWAS and the causal gene-regulatory mechanisms of these risk loci during atherosclerosis remain unclear. We incorporated local ancestry and haplotype information to identify quantitative trait loci (QTL) for gene expression and splicing in coronary arteries obtained from 138 ancestrally diverse Americans. Of 2,132 eQTL-associated genes (eGenes), 47% were previously unreported in coronary arteries and 19% exhibited cell-type-specific expression. Colocalization analysis with GWAS identified subgroups of eGenes unique to CAD and blood pressure. Fine-mapping highlighted additional eGenes of interest, including TBX20 and IL5 . Splicing (s)QTLs for 1,690 genes were also identified, among which TOR1AIP1 and ULK3 sQTLs demonstrated the importance of evaluating splicing events to accurately identify disease-relevant gene expression. Our work provides the first human coronary artery eQTL resource from a patient sample and exemplifies the necessity of diverse study populations and multi-omic approaches to characterize gene regulation in critical disease processes. Study Design Overview
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27
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Zhou H, Arapoglou T, Li X, Li Z, Zheng X, Moore J, Asok A, Kumar S, Blue E, Buyske S, Cox N, Felsenfeld A, Gerstein M, Kenny E, Li B, Matise T, Philippakis A, Rehm HL, Sofia HJ, Snyder G, NHGRI Genome Sequencing Program Variant Functional Annotation Working Group, Weng Z, Neale B, Sunyaev S, Lin X. FAVOR: functional annotation of variants online resource and annotator for variation across the human genome. Nucleic Acids Res 2023; 51:D1300-D1311. [PMID: 36350676 PMCID: PMC9825437 DOI: 10.1093/nar/gkac966] [Citation(s) in RCA: 71] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/25/2022] [Accepted: 10/14/2022] [Indexed: 11/11/2022] Open
Abstract
Large biobank-scale whole genome sequencing (WGS) studies are rapidly identifying a multitude of coding and non-coding variants. They provide an unprecedented resource for illuminating the genetic basis of human diseases. Variant functional annotations play a critical role in WGS analysis, result interpretation, and prioritization of disease- or trait-associated causal variants. Existing functional annotation databases have limited scope to perform online queries and functionally annotate the genotype data of large biobank-scale WGS studies. We develop the Functional Annotation of Variants Online Resources (FAVOR) to meet these pressing needs. FAVOR provides a comprehensive multi-faceted variant functional annotation online portal that summarizes and visualizes findings of all possible nine billion single nucleotide variants (SNVs) across the genome. It allows for rapid variant-, gene- and region-level queries of variant functional annotations. FAVOR integrates variant functional information from multiple sources to describe the functional characteristics of variants and facilitates prioritizing plausible causal variants influencing human phenotypes. Furthermore, we provide a scalable annotation tool, FAVORannotator, to functionally annotate large-scale WGS studies and efficiently store the genotype and their variant functional annotation data in a single file using the annotated Genomic Data Structure (aGDS) format, making downstream analysis more convenient. FAVOR and FAVORannotator are available at https://favor.genohub.org.
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Affiliation(s)
- Hufeng Zhou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Theodore Arapoglou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Zilin Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Xiuwen Zheng
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Jill Moore
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | - Sushant Kumar
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Elizabeth E Blue
- Division of Medical Genetics, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Steven Buyske
- Department of Statistics, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Nancy Cox
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Eimear Kenny
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Tara Matise
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Anthony Philippakis
- Data Science Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Heidi J Sofia
- National Human Genome Research Institute, Bethesda, DC, USA
| | - Grace Snyder
- National Human Genome Research Institute, Bethesda, DC, USA
| | | | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Benjamin Neale
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Shamil R Sunyaev
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
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28
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McDonald MLN, Lakshman Kumar P, Srinivasasainagendra V, Nair A, Rocco AP, Wilson AC, Chiles JW, Richman JS, Pinson SA, Dennis RA, Jagadale V, Brown CJ, Pyarajan S, Tiwari HK, Bamman MM, Singh JA. Novel genetic loci associated with osteoarthritis in multi-ancestry analyses in the Million Veteran Program and UK Biobank. Nat Genet 2022; 54:1816-1826. [PMID: 36411363 DOI: 10.1038/s41588-022-01221-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 10/05/2022] [Indexed: 11/22/2022]
Abstract
Osteoarthritis is a common progressive joint disease. As no effective medical interventions are available, osteoarthritis often progresses to the end stage, in which only surgical options such as total joint replacement are available. A more thorough understanding of genetic influences of osteoarthritis is essential to develop targeted personalized approaches to treatment, ideally long before the end stage is reached. To date, there have been no large multiancestry genetic studies of osteoarthritis. Here, we leveraged the unique resources of 484,374 participants in the Million Veteran Program and UK Biobank to address this gap. Analyses included participants of European, African, Asian and Hispanic descent. We discovered osteoarthritis-associated genetic variation at 10 loci and replicated findings from previous osteoarthritis studies. We also present evidence that some osteoarthritis-associated regions are robust to population ancestry. Drug repurposing analyses revealed enrichment of targets of several medication classes and provide potential insight into the etiology of beneficial effects of antiepileptics on osteoarthritis pain.
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Affiliation(s)
- Merry-Lynn N McDonald
- Birmingham Veterans Affairs Health Care System (BVAHCS), Birmingham, AL, USA.
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, School of Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL, USA.
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA.
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Preeti Lakshman Kumar
- Birmingham Veterans Affairs Health Care System (BVAHCS), Birmingham, AL, USA
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, School of Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
| | - Vinodh Srinivasasainagendra
- Birmingham Veterans Affairs Health Care System (BVAHCS), Birmingham, AL, USA
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ashwathy Nair
- Birmingham Veterans Affairs Health Care System (BVAHCS), Birmingham, AL, USA
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, School of Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
| | - Alison P Rocco
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, School of Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
| | - Ava C Wilson
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, School of Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Joe W Chiles
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, School of Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
| | - Joshua S Richman
- Birmingham Veterans Affairs Health Care System (BVAHCS), Birmingham, AL, USA
- Department of Surgery, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sarah A Pinson
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, School of Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
| | - Richard A Dennis
- Central Arkansas Veterans Healthcare System (CAVHS), Little Rock, AR, USA
| | - Vivek Jagadale
- Central Arkansas Veterans Healthcare System (CAVHS), Little Rock, AR, USA
| | - Cynthia J Brown
- Birmingham Veterans Affairs Health Care System (BVAHCS), Birmingham, AL, USA
- Department of Medicine, Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | - Saiju Pyarajan
- Center for Data and Computational Sciences (C-DACS), Veterans Affairs Boston Healthcare System (VABHS), Boston, MA, USA
| | - Hemant K Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Marcas M Bamman
- Birmingham Veterans Affairs Health Care System (BVAHCS), Birmingham, AL, USA
- Department of Cell, Developmental, and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
- Florida Institute for Human & Machine Cognition, Pensacola, FL, USA
| | - Jasvinder A Singh
- Birmingham Veterans Affairs Health Care System (BVAHCS), Birmingham, AL, USA
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
- Division of Rheumatology and Clinical Immunology, Department of Medicine at the School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
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29
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Chen J, Wang L, De Jager PL, Bennett DA, Buchman AS, Yang J. A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease. HGG ADVANCES 2022; 3:100143. [PMID: 36204489 PMCID: PMC9530673 DOI: 10.1016/j.xhgg.2022.100143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 09/14/2022] [Indexed: 11/30/2022] Open
Abstract
Existing methods for integrating functional annotations in genome-wide association studies (GWASs) to fine-map and prioritize potential causal variants are limited to using non-overlapped categorical annotations or limited by the computation burden of modeling genome-wide variants. To overcome these limitations, we propose a scalable Bayesian functional GWAS method to account for multivariate quantitative functional annotations (BFGWAS_QUANT), accompanied by a scalable computation algorithm enabling joint modeling of genome-wide variants. Simulation studies validated the performance of BFGWAS_QUANT for accurately quantifying annotation enrichment and improving GWAS power. Applying BFGWAS_QUANT to study five Alzheimer disease (AD)-related phenotypes using individual-level GWAS data (n = ∼1,000), we found that histone modification annotations have higher enrichment than expression quantitative trait locus (eQTL) annotations for all considered phenotypes, with the highest enrichment in H3K27me3 (polycomb regression). We also found that cis-eQTLs in microglia had higher enrichment than eQTLs of bulk brain frontal cortex tissue for all considered phenotypes. A similar enrichment pattern was also identified using the International Genomics of Alzheimer's Project (IGAP) summary-level GWAS data of AD (n = ∼54,000). The strongest known APOE E4 risk allele was identified for all five phenotypes, and the APOE locus was validated using the IGAP data. BFGWAS_QUANT fine-mapped 32 significant variants from 1,073 genome-wide significant variants in the IGAP data. We also demonstrated that the polygenic risk scores (PRSs) using effect size estimates by BFGWAS_QUANT had a similar prediction accuracy as other methods assuming a sparse causal model. Overall, BFGWAS_QUANT is a useful GWAS tool for quantifying annotation enrichment and prioritizing potential causal variants.
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Affiliation(s)
- Junyu Chen
- Department of Epidemiology, Emory University School of Public Health, Atlanta, GA 30322, USA
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Lei Wang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Philip L. De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Aron S. Buchman
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
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30
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Dubé M, Chazara O, Lemaçon A, Asselin G, Provost S, Barhdadi A, Lemieux Perreault L, Mongrain I, Wang Q, Carss K, Paul DS, Cunningham JW, Rouleau J, Solomon SD, McMurray JJ, Yusuf S, Granger CB, Haefliger C, de Denus S, Tardif J. Pharmacogenomic study of heart failure and candesartan response from the CHARM programme. ESC Heart Fail 2022; 9:2997-3008. [PMID: 35736394 PMCID: PMC9715825 DOI: 10.1002/ehf2.14026] [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: 09/23/2021] [Revised: 03/21/2022] [Accepted: 06/03/2022] [Indexed: 11/07/2022] Open
Abstract
AIMS The Candesartan in Heart failure Assessment of Reduction in Mortality and morbidity (CHARM) programme consisted of three parallel, randomized, double-blind clinical trials comparing candesartan with placebo in patients with heart failure (HF) categorized according to left ventricular ejection fraction and tolerability to an angiotensin-converting enzyme inhibitor. We conducted a pharmacogenomic study of the CHARM trials with the objective of identifying genetic predictors of HF progression and of the efficacy and safety of treatment with candesartan. METHODS We performed genome-wide association studies in 2727 patients of European ancestry from CHARM-Overall and stratified by CHARM study according to preserved and reduced ejection fraction and according to assignment to the interventional treatment with candesartan. We tested genetic association with the composite endpoint of cardiovascular death or hospitalization for heart failure for drug efficacy in candesartan-treated patients and for HF progression using patients from both candesartan and placebo arms. The safety endpoints for response to candesartan were hyperkalaemia, renal dysfunction, hypotension, and change in systolic blood pressure between baseline and 6 weeks of treatment. To support our observations, we conducted a genome-wide gene-level collapsing analysis from whole-exome sequencing data with the composite cardiovascular endpoint. RESULTS We found that the A allele (14% allele frequency) of the genetic variant rs66886237 at 8p21.3 near the gene GFRA2 was associated with the composite cardiovascular endpoint in 1029 HF patients with preserved ejection fraction from the CHARM-Preserved study (hazard ratio: 1.91, 95% confidence interval: 1.55-2.35; P = 1.7 × 10-9 ). The association was independent of candesartan treatment, and the genetic variant was not associated with the cardiovascular endpoint in patients with reduced ejection fraction. None of the genome-wide association studies for candesartan safety or efficacy conducted in patients treated with candesartan passed the significance threshold. We found no significant association from the gene-level collapsing analysis. CONCLUSIONS We have identified a candidate genetic variant potentially predictive of the progression of heart failure in patients with preserved ejection fraction. The findings require further replication, and we cannot exclude the possibility that the results may be chance findings.
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Affiliation(s)
- Marie‐Pierre Dubé
- Montreal Heart InstituteMontrealCanada
- Université de Montréal Beaulieu‐Saucier Pharmacogenomics CentreMontrealCanada
- Université de Montréal, Faculty of Medicine, Department of medicineUniversité de MontréalMontrealCanada
| | - Olympe Chazara
- Centre for Genomics Research (CGR), Discovery Sciences, BioPharmaceuticals R&D, AstraZenecaCambridgeUK
| | - Audrey Lemaçon
- Montreal Heart InstituteMontrealCanada
- Université de Montréal Beaulieu‐Saucier Pharmacogenomics CentreMontrealCanada
- Université de Montréal, Faculty of Medicine, Department of medicineUniversité de MontréalMontrealCanada
| | - Géraldine Asselin
- Montreal Heart InstituteMontrealCanada
- Université de Montréal Beaulieu‐Saucier Pharmacogenomics CentreMontrealCanada
| | - Sylvie Provost
- Montreal Heart InstituteMontrealCanada
- Université de Montréal Beaulieu‐Saucier Pharmacogenomics CentreMontrealCanada
| | - Amina Barhdadi
- Montreal Heart InstituteMontrealCanada
- Université de Montréal Beaulieu‐Saucier Pharmacogenomics CentreMontrealCanada
| | | | - Ian Mongrain
- Montreal Heart InstituteMontrealCanada
- Université de Montréal Beaulieu‐Saucier Pharmacogenomics CentreMontrealCanada
| | - Quanli Wang
- Centre for Genomics Research (CGR), Discovery Sciences, BioPharmaceuticals R&D, AstraZenecaCambridgeUK
| | - Keren Carss
- Centre for Genomics Research (CGR), Discovery Sciences, BioPharmaceuticals R&D, AstraZenecaCambridgeUK
| | - Dirk S. Paul
- Centre for Genomics Research (CGR), Discovery Sciences, BioPharmaceuticals R&D, AstraZenecaCambridgeUK
| | | | - Jean Rouleau
- Montreal Heart InstituteMontrealCanada
- Université de Montréal, Faculty of Medicine, Department of medicineUniversité de MontréalMontrealCanada
| | - Scott D. Solomon
- Cardiovascular Division, Brigham and Women's HospitalHarvard Medical SchoolBostonMAUSA
| | | | - Salim Yusuf
- The Population Health Research Institute, Hamilton Health Sciences and the School of Rehabilitation ScienceMcMaster UniversityHamiltonONCanada
| | - Chris B. Granger
- Duke Clinical Research InstituteDuke University School of MedicineDurhamNCUSA
| | - Carolina Haefliger
- Centre for Genomics Research (CGR), Discovery Sciences, BioPharmaceuticals R&D, AstraZenecaCambridgeUK
| | - Simon de Denus
- Montreal Heart InstituteMontrealCanada
- Université de Montréal Beaulieu‐Saucier Pharmacogenomics CentreMontrealCanada
- Faculty of PharmacyUniversité de MontréalMontrealCanada
| | - Jean‐Claude Tardif
- Montreal Heart InstituteMontrealCanada
- Université de Montréal, Faculty of Medicine, Department of medicineUniversité de MontréalMontrealCanada
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Piras IS, Raju A, Don J, Schork NJ, Gerhard GS, DiStefano JK. Hepatic PEMT Expression Decreases with Increasing NAFLD Severity. Int J Mol Sci 2022; 23:ijms23169296. [PMID: 36012560 PMCID: PMC9409182 DOI: 10.3390/ijms23169296] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/12/2022] [Accepted: 08/16/2022] [Indexed: 11/16/2022] Open
Abstract
Choline deficiency causes hepatic fat accumulation, and is associated with a higher risk of nonalcoholic fatty liver disease (NAFLD) and more advanced NAFLD-related hepatic fibrosis. Reduced expression of hepatic phosphatidylethanolamine N-methyltransferase (PEMT), which catalyzes the production of phosphatidylcholine, causes steatosis, inflammation, and fibrosis in mice. In humans, common PEMT variants impair phosphatidylcholine synthesis, and are associated with NAFLD risk. We investigated hepatic PEMT expression in a large cohort of patients representing the spectrum of NAFLD, and examined the relationship between PEMT genetic variants and gene expression. Hepatic PEMT expression was reduced in NAFLD patients with inflammation and fibrosis (i.e., nonalcoholic steatohepatitis or NASH) compared to participants with normal liver histology (β = −1.497; p = 0.005). PEMT levels also declined with increasing severity of fibrosis with cirrhosis < incomplete cirrhosis < bridging fibrosis (β = −1.185; p = 0.011). Hepatic PEMT expression was reduced in postmenopausal women with NASH compared to those with normal liver histology (β = −3.698; p = 0.030). We detected a suggestive association between rs7946 and hepatic fibrosis (p = 0.083). Although none of the tested variants were associated with hepatic PEMT expression, computational fine mapping analysis indicated that rs4646385 may impact PEMT levels in the liver. Hepatic PEMT expression decreases with increasing severity of NAFLD in obese individuals and postmenopausal women, and may contribute to disease pathogenesis in a subset of NASH patients.
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Affiliation(s)
- Ignazio S. Piras
- Translational Genomics Research Institute, Phoenix, AZ 85004, USA
| | - Anish Raju
- Translational Genomics Research Institute, Phoenix, AZ 85004, USA
| | - Janith Don
- Translational Genomics Research Institute, Phoenix, AZ 85004, USA
| | | | - Glenn S. Gerhard
- Department of Medical Genetics and Molecular Biochemistry, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19122, USA
| | - Johanna K. DiStefano
- Translational Genomics Research Institute, Phoenix, AZ 85004, USA
- Correspondence:
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32
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Collins RL, Glessner JT, Porcu E, Lepamets M, Brandon R, Lauricella C, Han L, Morley T, Niestroj LM, Ulirsch J, Everett S, Howrigan DP, Boone PM, Fu J, Karczewski KJ, Kellaris G, Lowther C, Lucente D, Mohajeri K, Nõukas M, Nuttle X, Samocha KE, Trinh M, Ullah F, Võsa U, Hurles ME, Aradhya S, Davis EE, Finucane H, Gusella JF, Janze A, Katsanis N, Matyakhina L, Neale BM, Sanders D, Warren S, Hodge JC, Lal D, Ruderfer DM, Meck J, Mägi R, Esko T, Reymond A, Kutalik Z, Hakonarson H, Sunyaev S, Brand H, Talkowski ME. A cross-disorder dosage sensitivity map of the human genome. Cell 2022; 185:3041-3055.e25. [PMID: 35917817 PMCID: PMC9742861 DOI: 10.1016/j.cell.2022.06.036] [Citation(s) in RCA: 178] [Impact Index Per Article: 59.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 03/17/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023]
Abstract
Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, we aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome. We harmonized and meta-analyzed rCNVs from nearly one million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplo & pTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will provide broad utility for human disease research and clinical genetics.
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Affiliation(s)
- Ryan L Collins
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Medical Sciences and Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
| | - Joseph T Glessner
- Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, Division of Human Genetics, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Maarja Lepamets
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; Institute of Molecular and Cell Biology, University of Tartu, 51010 Tartu, Estonia
| | | | | | - Lide Han
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Theodore Morley
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | | | - Jacob Ulirsch
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Medical Sciences and Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Selin Everett
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Daniel P Howrigan
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Philip M Boone
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA
| | - Jack Fu
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Konrad J Karczewski
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Georgios Kellaris
- Advanced Center for Translational and Genetic Medicine, Stanley Manne Children's Research Institute, Lurie Children's Hospital, Chicago, IL 60611, USA; Departments of Pediatrics and Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Chelsea Lowther
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Diane Lucente
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Kiana Mohajeri
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Medical Sciences and Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Margit Nõukas
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; Institute of Molecular and Cell Biology, University of Tartu, 51010 Tartu, Estonia
| | - Xander Nuttle
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Kaitlin E Samocha
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Medical Sciences and Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10, UK
| | - Mi Trinh
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10, UK
| | - Farid Ullah
- Advanced Center for Translational and Genetic Medicine, Stanley Manne Children's Research Institute, Lurie Children's Hospital, Chicago, IL 60611, USA; Departments of Pediatrics and Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Matthew E Hurles
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10, UK
| | | | - Erica E Davis
- Advanced Center for Translational and Genetic Medicine, Stanley Manne Children's Research Institute, Lurie Children's Hospital, Chicago, IL 60611, USA; Departments of Pediatrics and Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Hilary Finucane
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - James F Gusella
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | | | - Nicholas Katsanis
- Advanced Center for Translational and Genetic Medicine, Stanley Manne Children's Research Institute, Lurie Children's Hospital, Chicago, IL 60611, USA; Departments of Pediatrics and Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | | | - Benjamin M Neale
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | | | - Jennelle C Hodge
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Dennis Lal
- Cologne Center for Genomics, University of Cologne, 51149 Cologne, Germany; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Douglas M Ruderfer
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Center for Precision Medicine, Department of Biomedical Informatics, and Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | | | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; Center for Primary Care and Public Health, University of Lausanne, 1015 Lausanne, Switzerland; Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
| | - Hakon Hakonarson
- Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, Division of Human Genetics, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Shamil Sunyaev
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Medical Sciences and Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Genetics, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Harrison Brand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Pediatric Surgical Research Laboratories, Massachusetts General Hospital, Boston, MA 02114, USA.
| | - Michael E Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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33
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Dey KK, Gazal S, van de Geijn B, Kim SS, Nasser J, Engreitz JM, Price AL. SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease. CELL GENOMICS 2022; 2:100145. [PMID: 35873673 PMCID: PMC9306342 DOI: 10.1016/j.xgen.2022.100145] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 04/03/2021] [Accepted: 05/27/2022] [Indexed: 12/11/2022]
Abstract
We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.
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Affiliation(s)
- Kushal K. Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Bryce van de Geijn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Genentech, South San Francisco, CA 94080, USA
| | - Samuel Sungil Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Joseph Nasser
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jesse M. Engreitz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- BASE Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford University School of Medicine, Stanford, CA 94304, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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34
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Zhou F, Butterworth AS, Asimit JL. Flashfm-ivis: interactive visualization for fine-mapping of multiple quantitative traits. Bioinformatics 2022; 38:4238-4242. [PMID: 35792838 PMCID: PMC9438951 DOI: 10.1093/bioinformatics/btac453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/31/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
SUMMARY flashfm-ivis provides a suite of interactive visualization plots to view potential causal genetic variants that underlie associations that are shared or distinct between multiple quantitative traits and compares results between single- and multi-trait fine-mapping. Unique features include network diagrams that show joint effects between variants for each trait and regional association plots that integrate fine-mapping results, all with user-controlled zoom features for an interactive exploration of potential causal variants across traits. AVAILABILITY AND IMPLEMENTATION flashfm-ivis is an open-source software under the MIT license. It is available as an interactive web-based tool (http://shiny.mrc-bsu.cam.ac.uk/apps/flashfm-ivis/) and as an R package. Code and documentation are available at https://github.com/fz-cambridge/flashfm-ivis and https://zenodo.org/record/6376244#.YjnarC-l2X0. Additional features can be downloaded as standalone R libraries to encourage reuse. SUPPLEMENTARY INFORMATION Supplementary information are available at Bioinformatics online.
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Affiliation(s)
- Feng Zhou
- To whom correspondence should be addressed. or
| | - Adam S Butterworth
- British Heart Foundation, Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK,National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics University of Cambridge, Cambridge CB1 8RN, UK,British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK,Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
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35
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Song YQ, Hu SD, Lin X, Meng XH, Wang X, Zhang YH, Peng C, Gong R, Xu T, Zhang T, Li CZ, Pan DY, Yang JY, Greenbaum J, Shen J, Deng HW. Identification of PDXDC1 as a novel pleiotropic susceptibility locus shared between lumbar spine bone mineral density and birth weight. J Mol Med (Berl) 2022; 100:723-734. [PMID: 35314877 PMCID: PMC9110509 DOI: 10.1007/s00109-021-02165-0] [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] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 10/13/2021] [Accepted: 11/04/2021] [Indexed: 02/04/2023]
Abstract
An increasing number of epidemiological studies have suggested that birth weight (BW) may be a determinant of bone health later in life, although the underlying genetic mechanism remains unclear. Here, we applied a pleiotropic conditional false discovery rate (cFDR) approach to the genome-wide association study (GWAS) summary statistics for lumbar spine bone mineral density (LS BMD) and BW, aiming to identify novel susceptibility variants shared between these two traits. We detected 5 novel potential pleiotropic loci which are located at or near 7 different genes (NTAN1, PDXDC1, CACNA1G, JAG1, FAT1P1, CCDC170, ESR1), among which PDXDC1 and FAT1P1 have not previously been linked to these phenotypes. To partially validate the findings, we demonstrated that the expression of PDXDC1 was dramatically reduced in ovariectomized (OVX) mice in comparison with sham-operated (SHAM) mice in both the growth plate and trabecula bone. Furthermore, immunohistochemistry assay with serial sections showed that both osteoclasts and osteoblasts express PDXDC1, supporting its potential role in bone metabolism. In conclusion, our study provides insights into some shared genetic mechanisms for BMD and BW as well as a novel potential therapeutic target for the prevention of OP in the early stages of the disease development. KEY MESSAGES : We investigated pleiotropy-informed enrichment between LS BMD and BW. We identified genetic variants related to both LS BMD and BW by utilizing a cFDR approach. PDXDC1 is a novel pleiotropic gene which may be related to both LS BMD and BW. Elevated expression of PDXDC1 is related to higher BMD and lower ratio n-6/n-3 PUFA indicating a bone protective effect of PDXDC1.
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Affiliation(s)
- Yu-Qian Song
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, China
| | - Shi-Di Hu
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, China
| | - Xu Lin
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, China
- Department of Endocrinology and Metabolism, Shunde Hospital of Southern Medical University, The First People's Hospital of Shunde Foshan), Foshan, Guangdong, China
| | - Xiang-He Meng
- School of Basic Medical Science, Central South University, Changsha, Hunan, 410013, People's Republic of China
| | - Xiao Wang
- Department of Endocrinology and Metabolism, Shunde Hospital of Southern Medical University, The First People's Hospital of Shunde Foshan), Foshan, Guangdong, China
| | - Yin-Hua Zhang
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, China
| | - Cheng Peng
- Department of Geriatrics, School of Medicine, National Clinical Key Specialty, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China
| | - Rui Gong
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, China
- Cadre Ward Endocrinology Dept, Gansu Provincial Hospital, Lanzhou, Gansu, 730000, China
| | - Tao Xu
- Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA, USA
| | - Tong Zhang
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, China
| | - Chen-Zhong Li
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, China
| | - Dao-Yan Pan
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, China
| | - Jia-Yi Yang
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, China
| | - Jonathan Greenbaum
- Tulane Center for Biomedical Informatics and Genomics, Department of Medicine, Tulane University, New Orleans, LA, USA
| | - Jie Shen
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, China.
- Department of Endocrinology and Metabolism, Shunde Hospital of Southern Medical University, The First People's Hospital of Shunde Foshan), Foshan, Guangdong, China.
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Department of Medicine, Tulane University, New Orleans, LA, USA.
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Yuan Z, Liu L, Guo P, Yan R, Xue F, Zhou X. Likelihood-based Mendelian randomization analysis with automated instrument selection and horizontal pleiotropic modeling. SCIENCE ADVANCES 2022; 8:eabl5744. [PMID: 35235357 PMCID: PMC8890724 DOI: 10.1126/sciadv.abl5744] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 01/05/2022] [Indexed: 05/03/2023]
Abstract
Mendelian randomization (MR) is a common tool for identifying causal risk factors underlying diseases. Here, we present a method, MR with automated instrument determination (MRAID), for effective MR analysis. MRAID borrows ideas from fine-mapping analysis to model an initial set of candidate single-nucleotide polymorphisms that are in potentially high linkage disequilibrium with each other and automatically selects among them the suitable instruments for causal inference. MRAID also explicitly models both uncorrelated and correlated horizontal pleiotropic effects that are widespread for complex trait analysis. MRAID achieves both tasks through a joint likelihood framework and relies on a scalable sampling-based algorithm to compute calibrated P values. Comprehensive and realistic simulations show that MRAID can provide calibrated type I error control and reduce false positives while being more powerful than existing approaches. We illustrate the benefits of MRAID for an MR screening analysis across 645 trait pairs in U.K. Biobank, identifying multiple lifestyle causal risk factors of cardiovascular disease-related traits.
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Affiliation(s)
- Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
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37
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Network-Based Approach to Repurpose Approved Drugs for COVID-19 by Integrating GWAS and Text Mining Data. Processes (Basel) 2022. [DOI: 10.3390/pr10020326] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The coronavirus disease 19 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has a rapidly increasing prevalence and has caused significant morbidity/mortality. Despite the availability of many vaccines that can offer widespread immunization, it is also important to reach effective treatment for COVID-19 patients. However, the development of novel drug therapeutics is usually a time-consuming and costly process, and therefore, repositioning drugs that were previously approved for other purposes could have a major impact on the fight against COVID-19. Here, we first identified lung-specific gene regulatory/interaction subnetworks (COVID-19-related genes modules) enriched for COVID-19-associated genes obtained from GWAS and text mining. We then screened the targets of 220 approved drugs from DrugBank, obtained their drug-induced gene expression profiles in the LINCS database, and constructed lung-specific drug-related gene modules. By applying an integrated network-based approach to quantify the interactions of the COVID-19-related gene modules and drug-related gene modules, we prioritized 13 approved drugs (e.g., alitretinoin, clocortolone, terazosin, doconexent, and pergolide) that could potentially be repurposed for the treatment of COVID-19. These findings provide important and timely insights into alternative therapeutic options that should be further explored as COVID-19 continues to spread.
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Zhang X, Li TY, Xiao HM, Ehrlich KC, Shen H, Deng HW, Ehrlich M. Epigenomic and Transcriptomic Prioritization of Candidate Obesity-Risk Regulatory GWAS SNPs. Int J Mol Sci 2022; 23:1271. [PMID: 35163195 PMCID: PMC8836216 DOI: 10.3390/ijms23031271] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/19/2022] [Accepted: 01/19/2022] [Indexed: 02/06/2023] Open
Abstract
Concern about rising rates of obesity has prompted searches for obesity-related single nucleotide polymorphisms (SNPs) in genome-wide association studies (GWAS). Identifying plausible regulatory SNPs is very difficult partially because of linkage disequilibrium. We used an unusual epigenomic and transcriptomic analysis of obesity GWAS-derived SNPs in adipose versus heterologous tissues. From 50 GWAS and 121,064 expanded SNPs, we prioritized 47 potential causal regulatory SNPs (Tier-1 SNPs) for 14 gene loci. A detailed examination of seven loci revealed that four (CABLES1, PC, PEMT, and FAM13A) had Tier-1 SNPs positioned so that they could regulate use of alternative transcription start sites, resulting in different polypeptides being generated or different amounts of an intronic microRNA gene being expressed. HOXA11 and long noncoding RNA gene RP11-392O17.1 had Tier-1 SNPs in their 3' or promoter region, respectively, and strong preferences for expression in subcutaneous versus visceral adipose tissue. ZBED3-AS1 had two intragenic Tier-1 SNPs, each of which could contribute to mediating obesity risk through modulating long-distance chromatin interactions. Our approach not only revealed especially credible novel regulatory SNPs, but also helped evaluate previously highlighted obesity GWAS SNPs that were candidates for transcription regulation.
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Affiliation(s)
- Xiao Zhang
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA; (X.Z.); (K.C.E.); (H.S.)
| | - Tian-Ying Li
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China; (T.-Y.L.); (H.-M.X.)
| | - Hong-Mei Xiao
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China; (T.-Y.L.); (H.-M.X.)
| | - Kenneth C. Ehrlich
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA; (X.Z.); (K.C.E.); (H.S.)
| | - Hui Shen
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA; (X.Z.); (K.C.E.); (H.S.)
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA; (X.Z.); (K.C.E.); (H.S.)
| | - Melanie Ehrlich
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA; (X.Z.); (K.C.E.); (H.S.)
- Tulane Cancer Center and Hayward Genetics Center, Tulane University, New Orleans, LA 70112, USA
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Schilder BM, Humphrey J, Raj T. echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline. Bioinformatics 2022; 38:536-539. [PMID: 34529038 PMCID: PMC10060715 DOI: 10.1093/bioinformatics/btab658] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 09/06/2021] [Accepted: 09/13/2021] [Indexed: 02/03/2023] Open
Abstract
SUMMARY echolocatoR integrates a diverse suite of statistical and functional fine-mapping tools to identify, test enrichment in, and visualize high-confidence causal consensus variants in any phenotype. It requires minimal input from users (a summary statistics file), can be run in a single R function, and provides extensive access to relevant datasets (e.g. reference linkage disequilibrium panels, quantitative trait loci, genome-wide annotations, cell-type-specific epigenomics), thereby enabling rapid, robust and scalable end-to-end fine-mapping investigations. AVAILABILITY AND IMPLEMENTATION echolocatoR is an open-source R package available through GitHub under the GNU General Public License (Version 3) license: https://github.com/RajLabMSSM/echolocatoR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Brian M Schilder
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
| | - Jack Humphrey
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
| | - Towfique Raj
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
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40
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Wang QS, Huang H. Methods for statistical fine-mapping and their applications to auto-immune diseases. Semin Immunopathol 2022; 44:101-113. [PMID: 35041074 PMCID: PMC8837575 DOI: 10.1007/s00281-021-00902-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 10/22/2021] [Indexed: 01/07/2023]
Abstract
Although genome-wide association studies (GWAS) have identified thousands of loci in the human genome that are associated with different traits, understanding the biological mechanisms underlying the association signals identified in GWAS remains challenging. Statistical fine-mapping is a method aiming to refine GWAS signals by evaluating which variant(s) are truly causal to the phenotype. Here, we review the types of statistical fine-mapping methods that have been widely used to date, with a focus on recently developed functionally informed fine-mapping (FIFM) methods that utilize functional annotations. We then systematically review the applications of statistical fine-mapping in autoimmune disease studies to highlight the value of statistical fine-mapping in biological contexts.
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Affiliation(s)
- Qingbo S Wang
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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41
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Schilder BM, Navarro E, Raj T. Multi-omic insights into Parkinson's Disease: From genetic associations to functional mechanisms. Neurobiol Dis 2021; 163:105580. [PMID: 34871738 PMCID: PMC10101343 DOI: 10.1016/j.nbd.2021.105580] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/17/2021] [Accepted: 12/02/2021] [Indexed: 02/07/2023] Open
Abstract
Genome-Wide Association Studies (GWAS) have elucidated the genetic components of Parkinson's Disease (PD). However, because the vast majority of GWAS association signals fall within non-coding regions, translating these results into an interpretable, mechanistic understanding of the disease etiology remains a major challenge in the field. In this review, we provide an overview of the approaches to prioritize putative causal variants and genes as well as summarise the primary findings of previous studies. We then discuss recent efforts to integrate multi-omics data to identify likely pathogenic cell types and biological pathways implicated in PD pathogenesis. We have compiled full summary statistics of cell-type, tissue, and phentoype enrichment analyses from multiple studies of PD GWAS and provided them in a standardized format as a resource for the research community (https://github.com/RajLabMSSM/PD_omics_review). Finally, we discuss the experimental, computational, and conceptual advances that will be necessary to fully elucidate the effects of functional variants and genes on cellular dysregulation and disease risk.
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Affiliation(s)
- Brian M Schilder
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom; UK Dementia Research Institute at Imperial College London, London, United Kingdom.
| | - Elisa Navarro
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Sección Departamental de Bioquímica y Biología Molecular, Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - Towfique Raj
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
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Srinivasan C, Phan BN, Lawler AJ, Ramamurthy E, Kleyman M, Brown AR, Kaplow IM, Wirthlin ME, Pfenning AR. Addiction-Associated Genetic Variants Implicate Brain Cell Type- and Region-Specific Cis-Regulatory Elements in Addiction Neurobiology. J Neurosci 2021; 41:9008-9030. [PMID: 34462306 PMCID: PMC8549541 DOI: 10.1523/jneurosci.2534-20.2021] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 06/18/2021] [Accepted: 07/10/2021] [Indexed: 12/14/2022] Open
Abstract
Recent large genome-wide association studies have identified multiple confident risk loci linked to addiction-associated behavioral traits. Most genetic variants linked to addiction-associated traits lie in noncoding regions of the genome, likely disrupting cis-regulatory element (CRE) function. CREs tend to be highly cell type-specific and may contribute to the functional development of the neural circuits underlying addiction. Yet, a systematic approach for predicting the impact of risk variants on the CREs of specific cell populations is lacking. To dissect the cell types and brain regions underlying addiction-associated traits, we applied stratified linkage disequilibrium score regression to compare genome-wide association studies to genomic regions collected from human and mouse assays for open chromatin, which is associated with CRE activity. We found enrichment of addiction-associated variants in putative CREs marked by open chromatin in neuronal (NeuN+) nuclei collected from multiple prefrontal cortical areas and striatal regions known to play major roles in reward and addiction. To further dissect the cell type-specific basis of addiction-associated traits, we also identified enrichments in human orthologs of open chromatin regions of female and male mouse neuronal subtypes: cortical excitatory, D1, D2, and PV. Last, we developed machine learning models to predict mouse cell type-specific open chromatin, enabling us to further categorize human NeuN+ open chromatin regions into cortical excitatory or striatal D1 and D2 neurons and predict the functional impact of addiction-associated genetic variants. Our results suggest that different neuronal subtypes within the reward system play distinct roles in the variety of traits that contribute to addiction.SIGNIFICANCE STATEMENT We combine statistical genetic and machine learning techniques to find that the predisposition to for nicotine, alcohol, and cannabis use behaviors can be partially explained by genetic variants in conserved regulatory elements within specific brain regions and neuronal subtypes of the reward system. Our computational framework can flexibly integrate open chromatin data across species to screen for putative causal variants in a cell type- and tissue-specific manner for numerous complex traits.
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Affiliation(s)
- Chaitanya Srinivasan
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - BaDoi N Phan
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Medical Scientist Training Program, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
| | - Alyssa J Lawler
- Department of Biological Sciences, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Easwaran Ramamurthy
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Michael Kleyman
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Ashley R Brown
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Irene M Kaplow
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Morgan E Wirthlin
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Andreas R Pfenning
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Department of Biological Sciences, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
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Hernández N, Soenksen J, Newcombe P, Sandhu M, Barroso I, Wallace C, Asimit JL. The flashfm approach for fine-mapping multiple quantitative traits. Nat Commun 2021; 12:6147. [PMID: 34686674 PMCID: PMC8536717 DOI: 10.1038/s41467-021-26364-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 10/04/2021] [Indexed: 11/08/2022] Open
Abstract
Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (flexible and shared information fine-mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Here we show flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits.
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Affiliation(s)
- N Hernández
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - J Soenksen
- Exeter Centre of Excellence for Diabetes Research (EXCEED), University of Exeter Medical School, Exeter, UK
- School of Life Sciences, University of Glasgow, Glasgow, UK
| | - P Newcombe
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - M Sandhu
- Dept of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - I Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), University of Exeter Medical School, Exeter, UK
| | - C Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), University of Cambridge, Cambridge, UK
| | - J L Asimit
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
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44
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The distribution of common-variant effect sizes. Nat Genet 2021; 53:1243-1249. [PMID: 34326547 DOI: 10.1038/s41588-021-00901-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 06/23/2021] [Indexed: 01/08/2023]
Abstract
The genetic effect-size distribution of a disease describes the number of risk variants, the range of their effect sizes and sample sizes that will be required to discover them. Accurate estimation has been a challenge. Here I propose Fourier Mixture Regression (FMR), validating that it accurately estimates real and simulated effect-size distributions. Applied to summary statistics for ten diseases (average [Formula: see text]), FMR estimates that 100,000-1,000,000 cases will be required for genome-wide significant SNPs to explain 50% of SNP heritability. In such large studies, genome-wide significance becomes increasingly conservative, and less stringent thresholds achieve high true positive rates if confounding is controlled. Across traits, polygenicity varies, but the range of their effect sizes is similar. Compared with effect sizes in the top 10% of heritability, including most discovered thus far, those in the bottom 10-50% are orders of magnitude smaller and more numerous, spanning a large fraction of the genome.
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Althouse AD, Below JE, Claggett BL, Cox NJ, de Lemos JA, Deo RC, Duval S, Hachamovitch R, Kaul S, Keith SW, Secemsky E, Teixeira-Pinto A, Roger VL. Recommendations for Statistical Reporting in Cardiovascular Medicine: A Special Report From the American Heart Association. Circulation 2021; 144:e70-e91. [PMID: 34032474 DOI: 10.1161/circulationaha.121.055393] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Statistical analyses are a crucial component of the biomedical research process and are necessary to draw inferences from biomedical research data. The application of sound statistical methodology is a prerequisite for publication in the American Heart Association (AHA) journal portfolio. The objective of this document is to summarize key aspects of statistical reporting that might be most relevant to the authors, reviewers, and readership of AHA journals. The AHA Scientific Publication Committee convened a task force to inventory existing statistical standards for publication in biomedical journals and to identify approaches suitable for the AHA journal portfolio. The experts on the task force were selected by the AHA Scientific Publication Committee, who identified 12 key topics that serve as the section headers for this document. For each topic, the members of the writing group identified relevant references and evaluated them as a resource to make the standards summarized herein. Each section was independently reviewed by an expert reviewer who was not part of the task force. Expert reviewers were also permitted to comment on other sections if they chose. Differences of opinion were adjudicated by consensus. The standards presented in this report are intended to serve as a guide for high-quality reporting of statistical analyses methods and results.
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Affiliation(s)
- Andrew D Althouse
- Center for Research on Health Care Data Center, Division of General Internal Medicine, University of Pittsburgh, PA (A.D.A.)
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN (J.E.B., N.J.C.)
| | - Brian L Claggett
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA (B.L.C., R.C.D.)
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN (J.E.B., N.J.C.)
| | - James A de Lemos
- Division of Cardiology, University of Texas Southwestern Medical Center, Dallas (J.A.d.L.)
| | - Rahul C Deo
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA (B.L.C., R.C.D.)
| | - Sue Duval
- Cardiovascular Division, University of Minnesota Medical School, Minneapolis (S.D.)
| | - Rory Hachamovitch
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic Foundation, OH (R.H.)
| | - Sanjay Kaul
- Department of Cardiology, Cedars-Sinai Medical Center, and the David Geffen School of Medicine, University of California, Los Angeles (S.K.)
| | - Scott W Keith
- Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College of Thomas Jefferson University, Philadelphia, PA (S.W.K.)
| | - Eric Secemsky
- Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA (E.S.)
| | - Armando Teixeira-Pinto
- School of Public Health, Faculty of Medicine and Health, University of Sydney, Australia (A.T.-P.)
| | - Veronique L Roger
- Department of Cardiovascular Diseases Medicine, Mayo Clinic College of Medicine, Rochester, MN (V.L.R.)
- now with Epidemiology and Community Health Branch National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD (V.L.R.)
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Fisher V, Sebastiani P, Cupples LA, Liu CT. ANNORE: Genetic fine mapping with functional annotation. Hum Mol Genet 2021; 31:32-40. [PMID: 34302344 DOI: 10.1093/hmg/ddab210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/30/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
Genome-wide association studies (GWAS) have successfully identified loci of the human genome implicated in numerous complex traits. However, the limitations of this study design make it difficult to identify specific causal variants or biological mechanisms of association. We propose a novel method, AnnoRE, which uses GWAS summary statistics, local correlation structure among genotypes, and functional annotation from external databases to prioritize the most plausible causal SNPs in each trait-associated locus. Our proposed method improves upon previous fine mapping approaches by estimating the effects of functional annotation from genome-wide summary statistics, allowing for the inclusion of many annotation categories. By implementing a multiple regression model with differential shrinkage via random effects, we avoid reductive assumptions on the number of causal SNPs per locus. Application of this method to a large GWAS meta-analysis of body mass index identified six loci with significant evidence in favor of one or more variants. In an additional 24 loci, one or two variants were strongly prioritized over others in the region. The use of functional annotation in genetic fine mapping studies helps to distinguish between variants in high LD, and to identify promising targets for follow-up studies.
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Affiliation(s)
- Virginia Fisher
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Paola Sebastiani
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA.,Tufts Medical Center, Boston, MA 02111, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
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Pluta J, Pyle LC, Nead KT, Wilf R, Li M, Mitra N, Weathers B, D'Andrea K, Almstrup K, Anson-Cartwright L, Benitez J, Brown CD, Chanock S, Chen C, Cortessis VK, Ferlin A, Foresta C, Gamulin M, Gietema JA, Grasso C, Greene MH, Grotmol T, Hamilton RJ, Haugen TB, Hauser R, Hildebrandt MAT, Johnson ME, Karlsson R, Kiemeney LA, Lessel D, Lothe RA, Loud JT, Loveday C, Martin-Gimeno P, Meijer C, Nsengimana J, Quinn DI, Rafnar T, Ramdas S, Richiardi L, Skotheim RI, Stefansson K, Turnbull C, Vaughn DJ, Wiklund F, Wu X, Yang D, Zheng T, Wells AD, Grant SFA, Rajpert-De Meyts E, Schwartz SM, Bishop DT, McGlynn KA, Kanetsky PA, Nathanson KL. Identification of 22 susceptibility loci associated with testicular germ cell tumors. Nat Commun 2021; 12:4487. [PMID: 34301922 PMCID: PMC8302763 DOI: 10.1038/s41467-021-24334-y] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 06/01/2021] [Indexed: 02/07/2023] Open
Abstract
Testicular germ cell tumors (TGCT) are the most common tumor in young white men and have a high heritability. In this study, the international Testicular Cancer Consortium assemble 10,156 and 179,683 men with and without TGCT, respectively, for a genome-wide association study. This meta-analysis identifies 22 TGCT susceptibility loci, bringing the total to 78, which account for 44% of disease heritability. Men with a polygenic risk score (PRS) in the 95th percentile have a 6.8-fold increased risk of TGCT compared to men with median scores. Among men with independent TGCT risk factors such as cryptorchidism, the PRS may guide screening decisions with the goal of reducing treatment-related complications causing long-term morbidity in survivors. These findings emphasize the interconnected nature of two known pathways that promote TGCT susceptibility: male germ cell development within its somatic niche and regulation of chromosomal division and structure, and implicate an additional biological pathway, mRNA translation.
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Affiliation(s)
- John Pluta
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Louise C Pyle
- Division of Human Genetics, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kevin T Nead
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rona Wilf
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nandita Mitra
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benita Weathers
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kurt D'Andrea
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kristian Almstrup
- Department of Growth and Reproduction, Rigshospitalet, Copenhagen, Denmark
| | - Lynn Anson-Cartwright
- Department of Surgery (Urology), University of Toronto and The Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Javier Benitez
- Human Genetics Group, Spanish National Cancer Centre (CNIO), Madrid, Spain
| | - Christopher D Brown
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, Clinical Genetics Branch, National Cancer Institute, Bethesda, MD, USA
| | - Chu Chen
- Program in Epidemiology, Fred Hutchinson Cancer Research Center; Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Victoria K Cortessis
- Departments of Preventive Medicine and Obstetrics and Gynecology, Keck School of Medicine at the University of Southern California, Los Angeles, CA, USA
| | - Alberto Ferlin
- Unit of Endocrinology and Metabolism, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Carlo Foresta
- Unit of Andrology and Reproductive Medicine, Department of Medicine, University of Padova, Padova, Italy
| | - Marija Gamulin
- Department of Oncology, Division of Medical Oncology, University Hospital Centre Zagreb, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Jourik A Gietema
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Chiara Grasso
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO-Piemonte, Turin, Italy
| | - Mark H Greene
- Division of Cancer Epidemiology and Genetics, Clinical Genetics Branch, National Cancer Institute, Bethesda, MD, USA
| | - Tom Grotmol
- Department of Research, Cancer Registry of Norway, Oslo, Norway
| | - Robert J Hamilton
- Department of Surgery (Urology), University of Toronto and The Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Trine B Haugen
- Faculty of Health Sciences, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - Russ Hauser
- Department of Environmental Health, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Matthew E Johnson
- Division of Human Genetics, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Davor Lessel
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ragnhild A Lothe
- Department of Molecular Oncology, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
- Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Jennifer T Loud
- Division of Cancer Epidemiology and Genetics, Clinical Genetics Branch, National Cancer Institute, Bethesda, MD, USA
| | - Chey Loveday
- Division of Genetics & Epidemiology, The Institute of Cancer Research, London, UK
| | | | - Coby Meijer
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Jérémie Nsengimana
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | - David I Quinn
- Division of Oncology, Keck School of Medicine at the University of Southern California, Los Angeles, CA, USA
| | | | - Shweta Ramdas
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lorenzo Richiardi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO-Piemonte, Turin, Italy
| | - Rolf I Skotheim
- Department of Molecular Oncology, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
- Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | | | - Clare Turnbull
- Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- William Harvey Research Institute, Queen Mary University, London, UK
| | - David J Vaughn
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Xifeng Wu
- School of Public Health, Zhejiang University, Zhejiang, China
| | - Daphne Yang
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tongzhang Zheng
- Department of Epidemiology, Brown School of Public Health, Brown University, Providence, RI, USA
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Struan F A Grant
- Division of Human Genetics, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Stephen M Schwartz
- Program in Epidemiology, Fred Hutchinson Cancer Research Center; Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - D Timothy Bishop
- Department of Haematology and Immunology, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Katherine A McGlynn
- Division of Cancer Epidemiology and Genetics, Clinical Genetics Branch, National Cancer Institute, Bethesda, MD, USA
| | - Peter A Kanetsky
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Katherine L Nathanson
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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48
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Boocock J, Leask M, Okada Y, Matsuo H, Kawamura Y, Shi Y, Li C, Mount DB, Mandal AK, Wang W, Cadzow M, Gosling AL, Major TJ, Horsfield JA, Choi HK, Fadason T, O'Sullivan J, Stahl EA, Merriman TR. Genomic dissection of 43 serum urate-associated loci provides multiple insights into molecular mechanisms of urate control. Hum Mol Genet 2021; 29:923-943. [PMID: 31985003 DOI: 10.1093/hmg/ddaa013] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 12/23/2019] [Accepted: 01/20/2020] [Indexed: 12/17/2022] Open
Abstract
High serum urate is a prerequisite for gout and associated with metabolic disease. Genome-wide association studies (GWAS) have reported dozens of loci associated with serum urate control; however, there has been little progress in understanding the molecular basis of the associated loci. Here, we employed trans-ancestral meta-analysis using data from European and East Asian populations to identify 10 new loci for serum urate levels. Genome-wide colocalization with cis-expression quantitative trait loci (eQTL) identified a further five new candidate loci. By cis- and trans-eQTL colocalization analysis, we identified 34 and 20 genes, respectively, where the causal eQTL variant has a high likelihood that it is shared with the serum urate-associated locus. One new locus identified was SLC22A9 that encodes organic anion transporter 7 (OAT7). We demonstrate that OAT7 is a very weak urate-butyrate exchanger. Newly implicated genes identified in the eQTL analysis include those encoding proteins that make up the dystrophin complex, a scaffold for signaling proteins and transporters at the cell membrane; MLXIP that, with the previously identified MLXIPL, is a transcription factor that may regulate serum urate via the pentose-phosphate pathway and MRPS7 and IDH2 that encode proteins necessary for mitochondrial function. Functional fine mapping identified six loci (RREB1, INHBC, HLF, UBE2Q2, SFMBT1 and HNF4G) with colocalized eQTL containing putative causal SNPs. This systematic analysis of serum urate GWAS loci identified candidate causal genes at 24 loci and a network of previously unidentified genes likely involved in control of serum urate levels, further illuminating the molecular mechanisms of urate control.
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Affiliation(s)
- James Boocock
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand.,Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Megan Leask
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.,Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | | | - Hirotaka Matsuo
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Saitama, Japan
| | - Yusuke Kawamura
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Saitama, Japan
| | - Yongyong Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiaric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Changgui Li
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - David B Mount
- Renal Division, Brigham and Women's Hospital, Harvard Medical School, Boston MA, USA.,Renal Division, VA Boston Healthcare System, Harvard Medical School, Boston MA, USA
| | - Asim K Mandal
- Renal Division, Brigham and Women's Hospital, Harvard Medical School, Boston MA, USA
| | - Weiqing Wang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, New York, NY, USA
| | - Murray Cadzow
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
| | - Anna L Gosling
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
| | - Tanya J Major
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
| | - Julia A Horsfield
- Department of Pathology, Otago Medical School, University of Otago, Dunedin, New Zealand
| | - Hyon K Choi
- Division of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tayaza Fadason
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | | | - Eli A Stahl
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, New York, NY, USA
| | - Tony R Merriman
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
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49
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Liu YQ, Liu Y, Zhang Q, Xiao T, Deng HW. Identification of Novel Pleiotropic SNPs Associated with Osteoporosis and Rheumatoid Arthritis. Calcif Tissue Int 2021; 109:17-31. [PMID: 33740106 PMCID: PMC8238865 DOI: 10.1007/s00223-021-00817-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/28/2021] [Indexed: 01/21/2023]
Abstract
Genome-wide association studies (GWASs) have identified hundreds of genetic loci for osteoporosis (OP) and rheumatoid arthritis (RA), individually, however, a large proportion of the total trait heritability remains unexplained. Previous studies demonstrated that these two diseases may share some common genetic determination and risk factors, but they were generally focused on individual trait and failed to identify the common variants that play key functional roles in the etiology of these two diseases. Here, we performed a conditional false discovery rate (cFDR) analysis to identify novel pleiotropic variants shared between them by integrating two independent GWASs with summary statistics for total body bone mineral density (TB-BMD, a major risk factor for osteoporosis) (n = 66,628) and RA (n = 58,284). A fine-mapping approach was also applied to identify the most probable causal variants with biological effects on both TB-BMD and RA. As a result, we found 47 independent pleiotropic SNPs shared between TB-BMD and RA, and 40 of them were validated in heel ultrasound estimated BMD (eBMD), femoral neck BMD (FN-BMD) or lumbar spine (LS-BMD). We detected one SNP (rs13299616) was novel and not identified by previous BMD or RA-related studies. Combined with fine-mapping and GWAS-eQTL colocalization analyses, our results suggested that locus 1p13.2 (including PTPN22, MAGI3, PHTF1, and RSBN1) was an important region to regulate TB-BMD and RA simultaneously. These findings provide new insights into the shared biological mechanisms and functional genetic determinants between OP and RA, and novel potential targets for treatment development.
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Affiliation(s)
- Ying-Qi Liu
- Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, 410011, China
| | - Yong Liu
- College of Life Sciences and Bioengineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Qiang Zhang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, China
| | - Tao Xiao
- Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, 410011, China
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal St., Suite 2001, New Orleans, 70112, USA.
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50
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Dubé MP, Lemaçon A, Barhdadi A, Lemieux Perreault LP, Oussaïd E, Asselin G, Provost S, Sun M, Sandoval J, Legault MA, Mongrain I, Dubois A, Valois D, Dedelis E, Lousky J, Choi J, Goulet E, Savard C, Chicoine LM, Cossette M, Chabot-Blanchet M, Guertin MC, de Denus S, Bouabdallaoui N, Marchand R, Bassevitch Z, Nozza A, Gaudet D, L'Allier PL, Hussin J, Boivin G, Busseuil D, Tardif JC. Genetics of symptom remission in outpatients with COVID-19. Sci Rep 2021; 11:10847. [PMID: 34035401 PMCID: PMC8149390 DOI: 10.1038/s41598-021-90365-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/11/2021] [Indexed: 01/09/2023] Open
Abstract
We conducted a genome-wide association study of time to remission of COVID-19 symptoms in 1723 outpatients with at least one risk factor for disease severity from the COLCORONA clinical trial. We found a significant association at 5p13.3 (rs1173773; P = 4.94 × 10-8) near the natriuretic peptide receptor 3 gene (NPR3). By day 15 of the study, 44%, 54% and 59% of participants with 0, 1, or 2 copies of the effect allele respectively, had symptom remission. In 851 participants not treated with colchicine (placebo), there was a significant association at 9q33.1 (rs62575331; P = 2.95 × 10-8) in interaction with colchicine (P = 1.19 × 10-5) without impact on risk of hospitalisations, highlighting a possibly shared mechanistic pathway. By day 15 of the study, 46%, 62% and 64% of those with 0, 1, or 2 copies of the effect allele respectively, had symptom remission. The findings need to be replicated and could contribute to the biological understanding of COVID-19 symptom remission.
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Affiliation(s)
- Marie-Pierre Dubé
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada. .,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada. .,Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Canada.
| | - Audrey Lemaçon
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada.,Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Canada
| | - Amina Barhdadi
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada
| | - Louis-Philippe Lemieux Perreault
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada
| | - Essaïd Oussaïd
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada
| | - Géraldine Asselin
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada
| | - Sylvie Provost
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada
| | - Maxine Sun
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada.,Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Canada
| | - Johanna Sandoval
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada
| | - Marc-André Legault
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada.,Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Université de Montréal, Montreal, Canada
| | - Ian Mongrain
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada
| | - Anick Dubois
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada
| | - Diane Valois
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada
| | - Emma Dedelis
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada
| | - Jennifer Lousky
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada
| | - Julie Choi
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada
| | - Elisabeth Goulet
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada
| | - Christiane Savard
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada
| | - Lea-Mei Chicoine
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada
| | - Mariève Cossette
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Montreal Health Innovations Coordinating Centre, Montreal, Canada
| | - Malorie Chabot-Blanchet
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Montreal Health Innovations Coordinating Centre, Montreal, Canada
| | - Marie-Claude Guertin
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Montreal Health Innovations Coordinating Centre, Montreal, Canada
| | - Simon de Denus
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, Canada.,Faculty of Pharmacy, Université de Montréal, Montreal, Canada
| | | | - Richard Marchand
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada
| | - Zohar Bassevitch
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Montreal Health Innovations Coordinating Centre, Montreal, Canada
| | - Anna Nozza
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Montreal Health Innovations Coordinating Centre, Montreal, Canada
| | - Daniel Gaudet
- Ecogene-21 and Department of Medicine, Université de Montréal, Chicoutimi, Canada
| | | | - Julie Hussin
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada.,Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Canada.,Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Université de Montréal, Montreal, Canada
| | - Guy Boivin
- Centre Hospitalier de l'Université Laval, Quebec City, Canada
| | - David Busseuil
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada
| | - Jean-Claude Tardif
- Montreal Heart Institute, 5000 Belanger Street, Montreal, H1T 1C8, Canada. .,Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Canada.
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