1
|
Wang Z, Li S, Cai G, Gao Y, Yang H, Li Y, Liang J, Zhang S, Hu J, Zheng J. Mendelian randomization analysis identifies druggable genes and drugs repurposing for chronic obstructive pulmonary disease. Front Cell Infect Microbiol 2024; 14:1386506. [PMID: 38660492 PMCID: PMC11039854 DOI: 10.3389/fcimb.2024.1386506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 03/22/2024] [Indexed: 04/26/2024] Open
Abstract
Background Chronic obstructive pulmonary disease (COPD) is a prevalent condition that significantly impacts public health. Unfortunately, there are few effective treatment options available. Mendelian randomization (MR) has been utilized to repurpose existing drugs and identify new therapeutic targets. The objective of this study is to identify novel therapeutic targets for COPD. Methods Cis-expression quantitative trait loci (cis-eQTL) were extracted for 4,317 identified druggable genes from genomics and proteomics data of whole blood (eQTLGen) and lung tissue (GTEx Consortium). Genome-wide association studies (GWAS) data for doctor-diagnosed COPD, spirometry-defined COPD (Forced Expiratory Volume in one second [FEV1]/Forced Vital Capacity [FVC] <0.7), and FEV1 were obtained from the cohort of FinnGen, UK Biobank and SpiroMeta consortium. We employed Summary-data-based Mendelian Randomization (SMR), HEIDI test, and colocalization analysis to assess the causal effects of druggable gene expression on COPD and lung function. The reliability of these druggable genes was confirmed by eQTL two-sample MR and protein quantitative trait loci (pQTL) SMR, respectively. The potential effects of druggable genes were assessed through the phenome-wide association study (PheWAS). Information on drug repurposing for COPD was collected from multiple databases. Results A total of 31 potential druggable genes associated with doctor-diagnosed COPD, spirometry-defined COPD, and FEV1 were identified through SMR, HEIDI test, and colocalization analysis. Among them, 22 genes (e.g., MMP15, PSMA4, ERBB3, and LMCD1) were further confirmed by eQTL two-sample MR and protein SMR analyses. Gene-level PheWAS revealed that ERBB3 expression might reduce inflammation, while GP9 and MRC2 were associated with other traits. The drugs Montelukast (targeting the MMP15 gene) and MARIZOMIB (targeting the PSMA4 gene) may reduce the risk of spirometry-defined COPD. Additionally, an existing small molecule inhibitor of the APH1A gene has the potential to increase FEV1. Conclusions Our findings identified 22 potential drug targets for COPD and lung function. Prioritizing clinical trials that target these identified druggable genes with existing drugs or novel medications will be beneficial for the development of COPD treatments.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | - Jinping Zheng
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China
| |
Collapse
|
2
|
Li R, Benz L, Duan R, Denny JC, Hakonarson H, Mosley JD, Smoller JW, Wei WQ, Ritchie MD, Moore JH, Chen Y. mixWAS: An efficient distributed algorithm for mixed-outcomes genome-wide association studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24301073. [PMID: 38260403 PMCID: PMC10802662 DOI: 10.1101/2024.01.09.24301073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Genome-wide association studies (GWAS) have been instrumental in identifying genetic associations for various diseases and traits. However, uncovering genetic underpinnings among traits beyond univariate phenotype associations remains a challenge. Multi-phenotype associations (MPA), or genetic pleiotropy, offer important insights into shared genes and pathways among traits, enhancing our understanding of genetic architectures of complex diseases. GWAS of biobank-linked electronic health record (EHR) data are increasingly being utilized to identify MPA among various traits and diseases. However, methodologies that can efficiently take advantage of distributed EHR to detect MPA are still lacking. Here, we introduce mixWAS, a novel algorithm that efficiently and losslessly integrates multiple EHRs via summary statistics, allowing the detection of MPA among mixed phenotypes while accounting for heterogeneities across EHRs. Simulations demonstrate that mixWAS outperforms the widely used MPA detection method, Phenome-wide association study (PheWAS), across diverse scenarios. Applying mixWAS to data from seven EHRs in the US, we identified 4,534 MPA among blood lipids, BMI, and circulatory diseases. Validation in an independent EHR data from UK confirmed 97.7% of the associations. mixWAS fundamentally improves the detection of MPA and is available as a free, open-source software.
Collapse
Affiliation(s)
- Ruowang Li
- Department of Computational Biomedicine, Cedars-Sinai Medical Center
| | - Luke Benz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health
| | - Hakon Hakonarson
- Division of Human Genetics, Children's Hospital of Philadelphia
- Center for Applied Genomics, Children's Hospital of Philadelphia
- Department of Pediatrics, University of Pennsylvania, Perelman School of Medicine
| | - Jonathan D Mosley
- Department of Medicine, Vanderbilt University Medical Center
- Department of Biomedical Informatics, Vanderbilt University Medical Center
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| |
Collapse
|
3
|
Adami LNG, Moysés-Oliveira M, Souza-Cunha LA, Vasco MB, Tufik S, Andersen ML. Lipid metabolism and neuromuscular junction as common pathways underlying the genetic basis of erectile dysfunction and obstructive sleep apnea. Int J Impot Res 2023:10.1038/s41443-023-00795-1. [PMID: 37990110 DOI: 10.1038/s41443-023-00795-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 10/18/2023] [Accepted: 11/06/2023] [Indexed: 11/23/2023]
Abstract
Erectile dysfunction (ED) incidence is higher in patients with obstructive sleep apnea (OSA). Studies have suggested that ED and OSA may activate similar pathways; however, few have investigated the links between their underlying genotypic profiles. Therefore, we conducted an in-silico analysis to test whether ED and OSA share genetic variants of risk and to identify any molecular, cellular and biological interactions between them. Two gene lists were manually curated through a literature review based on a PUBMED search, which resulted in one gene list associated with ED (total of 205 genes) and the other with OSA (total of 2622 genes). Between those gene sets, 35 were common for both lists (Fisher exact test, p-value = 0.027). The Protein-protein interaction (PPI) analysis using the intersect list as input showed that 3 of them had direct interactions (LPL, DGKB and PLCB1). In addition, the biological function of the genes contained in the intersect list suggested that pathways related to lipid metabolism and the neuromuscular junction were commonly found in the genetic basis of ED and OSA. From the shared genes between both conditions, the biological pathways highlighted in this study may serve as preliminary findings for future functional investigations on OSA and ED association.
Collapse
Affiliation(s)
- Luana N G Adami
- Sleep Institute, São Paulo, Brazil
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil
| | | | | | - Matheus Brandão Vasco
- Departamento de Cirurgia, Disciplina de Urologia, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Sergio Tufik
- Sleep Institute, São Paulo, Brazil
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Monica L Andersen
- Sleep Institute, São Paulo, Brazil.
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil.
| |
Collapse
|
4
|
Jeong R, Bulyk ML. Blood cell traits' GWAS loci colocalization with variation in PU.1 genomic occupancy prioritizes causal noncoding regulatory variants. CELL GENOMICS 2023; 3:100327. [PMID: 37492098 PMCID: PMC10363807 DOI: 10.1016/j.xgen.2023.100327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/10/2023] [Accepted: 04/25/2023] [Indexed: 07/27/2023]
Abstract
Genome-wide association studies (GWASs) have uncovered numerous trait-associated loci across the human genome, most of which are located in noncoding regions, making interpretation difficult. Moreover, causal variants are hard to statistically fine-map at many loci because of widespread linkage disequilibrium. To address this challenge, we present a strategy utilizing transcription factor (TF) binding quantitative trait loci (bQTLs) for colocalization analysis to identify trait associations likely mediated by TF occupancy variation and to pinpoint likely causal variants using motif scores. We applied this approach to PU.1 bQTLs in lymphoblastoid cell lines and blood cell trait GWAS data. Colocalization analysis revealed 69 blood cell trait GWAS loci putatively driven by PU.1 occupancy variation. We nominate PU.1 motif-altering variants as the likely shared causal variants at 51 loci. Such integration of TF bQTL data with other GWAS data may reveal transcriptional regulatory mechanisms and causal noncoding variants underlying additional complex traits.
Collapse
Affiliation(s)
- Raehoon Jeong
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
| | - Martha L. Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
5
|
Sofer T, Kurniansyah N, Murray M, Ho YL, Abner E, Esko T, Huffman JE, Cho K, Wilson PWF, Gottlieb DJ. Genome-wide association study of obstructive sleep apnoea in the Million Veteran Program uncovers genetic heterogeneity by sex. EBioMedicine 2023; 90:104536. [PMID: 36989840 PMCID: PMC10065974 DOI: 10.1016/j.ebiom.2023.104536] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) for obstructive sleep apnoea (OSA) are limited due to the underdiagnosis of OSA, leading to misclassification of OSA, which consequently reduces statistical power. We performed a GWAS of OSA in the Million Veteran Program (MVP) of the U.S. Department of Veterans Affairs (VA) healthcare system, where OSA prevalence is close to its true population prevalence. METHODS We performed GWAS of 568,576 MVP participants, stratified by biological sex and by harmonized race/ethnicity and genetic ancestry (HARE) groups of White, Black, Hispanic, and Asian individuals. We considered both BMI adjusted (BMI-adj) and unadjusted (BMI-unadj) models. We replicated associations in independent datasets, and analysed the heterogeneity of OSA genetic associations across HARE and sex groups. We finally performed a larger meta-analysis GWAS of MVP, FinnGen, and the MGB Biobank, totalling 916,696 individuals. FINDINGS MVP participants are 91% male. OSA prevalence is 21%. In MVP there were 18 and 6 genome-wide significant loci in BMI-unadj and BMI-adj analyses, respectively, corresponding to 21 association regions. Of these, 17 were not previously reported in association with OSA, and 13 replicated in FinnGen (False Discovery Rate p-value < 0.05). There were widespread significant differences in genetic effects between men and women, but less so across HARE groups. Meta-analysis of MVP, FinnGen, and MGB biobank revealed 17 additional, previously unreported, genome-wide significant regions. INTERPRETATION Sex differences in genetic associations with OSA are widespread, likely associated with multiple OSA risk factors. OSA shares genetic underpinnings with several sleep phenotypes, suggesting shared aetiology and causal pathways. FUNDING Described in acknowledgements.
Collapse
Affiliation(s)
- Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Michael Murray
- Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA
| | - Erik Abner
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA; VA Palo Alto Health Care System, Palo Alto, CA, USA; Palo Alto Veterans Institute for Research, Palo Alto, CA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA; Division of Aging, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Daniel J Gottlieb
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA
| |
Collapse
|