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Gu J, Xu J, Jiao A, Cai N, Gu T, Wu P, Cheng X, Chen B, Chen Y, Liu X. Comprehensive analysis of single-cell transcriptomics and genetic factors reveals the mechanisms and preventive strategies for the progression from pulmonary fibrosis to lung cancer. Int Immunopharmacol 2024; 140:112803. [PMID: 39094357 DOI: 10.1016/j.intimp.2024.112803] [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: 04/25/2024] [Revised: 06/24/2024] [Accepted: 07/23/2024] [Indexed: 08/04/2024]
Abstract
BACKGROUND Pulmonary fibrosis (PF) leads to excessive deposition of fibrous connective tissue in the lungs, increasing the risk of lung cancer due to the enhanced activity of fibroblasts (FBs). Fibroblast-mediated collagen fiber deposition creates a tumor-like microenvironment, laying the foundation for tumorigenesis. Clinically, numerous cases of lung cancer induced by pulmonary fibrosis have been observed. In recent years, the study of nucleotide point mutations, which provide more detailed insights than gene expression, has made significant advancements, offering new perspectives for clinical research. METHODS We initially employed Mendelian randomization to ascertain that the initial stage of lung cancer induced by PF belongs to small cell lung cancer (SCLC). Subsequently, pulmonary neuroendocrine cells (PNECs) were identified by using pseudo-time series analysis as cell clusters with carcinogenic potential. We categorized FBs into four groups according to their cellular metabolism, and then analyzed the cellular communication between FBs and PNECs, as well as changes in intracellular pathways of PNECs. Additionally, we examined the characteristic genome of FBs which is significantly associated with PF and investigated the impact of FBs on immune cells in the PF microenvironment. Finally, we explored strategies for preventing the progression from PF to lung cancer. RESULTS The genetic features of cells with carcinogenic potential in PF tissues were revealed, characterized by upregulation of Achaete-Scute Family BHLH Transcription Factor 1 (ASCL1), Homeobox B2 (HOXB2), Teashirt Zinc Finger Homeobox 2 (TSHZ2), Insulinoma-associated 1 (INSM1), and reduced activity of RE1 Silencing Transcription Factor (REST). FBs characterized by high glycolysis and low tricarboxylic acid (TCA) cycling played a key role in the progression of PF. The microenvironment of PF resembles the tumor microenvironment, providing a conducive immunosuppressive environment for the occurrence of cancer cells. In dendritic cells, rs9265808 is a susceptibility locus for progression from pulmonary fibrosis to lung cancer, mutations at this locus increase the expression of Complement Factor B (CFB), and excessive activation of the complement pathway is a crucial factor leading to lung cancer development in patients with pulmonary fibrosis. Ensuring adequate nutritional supply and physical function is one of the effective measures to prevent progression from pulmonary fibrosis to lung cancer. CONCLUSION CFB promotes lung cancer occurrence by inducing the accumulation and polarization of a large number of monocytes/macrophages in the lungs, driving disease progression by reducing the physical fitness of patients with pulmonary fibrosis.
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Affiliation(s)
- Jinghua Gu
- School of Life Sciences, Anhui Medical University, Hefei 230032, China; The First Clinical Medical College of Anhui Medical University, Hefei 230032, China; Henan International Joint Laboratory of Non-coding RNA and Metabolism in Cancer, Henan Provincial Key Laboratory of Long Non-coding RNA and Cancer Metabolism, Translational Research Institute of Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jiansheng Xu
- The First Clinical Medical College of Anhui Medical University, Hefei 230032, China
| | - Annan Jiao
- The First Clinical Medical College of Anhui Medical University, Hefei 230032, China; The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
| | - Ningning Cai
- The First Clinical Medical College of Anhui Medical University, Hefei 230032, China
| | - Tianrui Gu
- School of Pharmacy, Zhejiang University, Hangzhou 310058, China
| | - Pengcheng Wu
- School of Life Sciences, Anhui Medical University, Hefei 230032, China
| | - Xinyu Cheng
- School of Life Sciences, Anhui Medical University, Hefei 230032, China
| | - Bo Chen
- The First Clinical Medical College of Anhui Medical University, Hefei 230032, China; The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China.
| | - Yang Chen
- The First Clinical Medical College of Anhui Medical University, Hefei 230032, China; The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China.
| | - Xiaoying Liu
- School of Life Sciences, Anhui Medical University, Hefei 230032, China; Henan International Joint Laboratory of Non-coding RNA and Metabolism in Cancer, Henan Provincial Key Laboratory of Long Non-coding RNA and Cancer Metabolism, Translational Research Institute of Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, Henan, China.
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Lin Z, Xiong J, Yang J, Huang Y, Li J, Zhao G, Li B. A comprehensive analysis of the health effects associated with smoking in the largest population using UK Biobank genotypic and phenotypic data. Heliyon 2024; 10:e35649. [PMID: 39220930 PMCID: PMC11365339 DOI: 10.1016/j.heliyon.2024.e35649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 07/29/2024] [Accepted: 08/01/2024] [Indexed: 09/04/2024] Open
Abstract
Background Smoking is a widespread behavior, while the relationship between smoking and various diseases remains a topic of debate. Objective We conducted analysis to further examine the identified associations and assess potential causal relationships. Methods We utilized seven single nucleotide polymorphisms (SNPs) known to be linked to smoking extracting genotype data from the UK Biobank, a large-scale biomedical repository encompassing comprehensive health-related and genetic information of European descent. Phenome-wide association study (PheWAS) analysis was conducted to map the association of genetically predicted smoking status with 1,549 phenotypes. The associations identified in the PheWAS were then meticulously examined through two-sample Mendelian randomization (MR) analysis, utilizing data from the UK Biobank (n = 487,365) and the Sequencing Consortium of Alcohol and Nicotine Use (GSCAN) (n = 337,334). This approach allowed us to comprehensively characterize the links between smoking and disease patterns. Results The PheWAS analysis produced 34 phenotypes that demonstrated significant associations with smoking (P = 0.05/1460). Importantly, sickle cell anemia and type 2 diabetes exhibited the most significant SNPs (both 85.71% significant SNPs). Furthermore, the MR analyses provided compelling evidence supporting causal associations between smoking and the risk of following diseases: obstructive chronic bronchitis (IVW: Beta = 0.48, 95% confidence interval (CI) 0.36-0.61, P = 1.62×10-13), cancer of the bronchus (IVW: Beta = 0.92, 95% CI 0.68-1.17, P = 2.02×10-13), peripheral vascular disease (IVW: Beta = 1.09, 95% CI 0.71-1.46, P = 1.63×10-8), emphysema (IVW: Beta = 1.63, 95% CI 0.90-2.36, P = 1.29×10-5), pneumococcal pneumonia (IVW: Beta = 0.30, 95% CI 0.11-0.49, P = 1.60×10-3), chronic airway obstruction (IVW: Beta = 0.83, 95% CI 0.30-1.36, P = 2.00×10-3) and type 2 diabetes (IVW: Beta = 0.53, 95% CI 0.16-0.90, P = 5.08×10-3). Conclusion This study affirms causal relationships between smoking and obstructive chronic bronchitis, cancer of the bronchus, peripheral vascular disease, emphysema, pneumococcal pneumonia, chronic airway obstruction, type 2 diabetes, in the European population. These findings highlight the broad health impacts of smoking and support smoking cessation efforts.
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Affiliation(s)
- Zixun Lin
- The Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China
| | - Jiayi Xiong
- The Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Jiaqi Yang
- The Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Yuanfeng Huang
- The Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Jinchen Li
- The Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Centre for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Bioinformatics Centre, Furong Laboratory, Changsha, Hunan, 410008, China
| | - Guihu Zhao
- The Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Bin Li
- The Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
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Alwan H, Luan J, Williamson A, Carrasco-Zanini J, Stewart ID, Wareham NJ, Langenberg C, Pietzner M. Testing for a causal role of thyroid hormone measurements within the normal range on human metabolism and diseases: a systematic Mendelian randomization. EBioMedicine 2024; 107:105306. [PMID: 39191175 DOI: 10.1016/j.ebiom.2024.105306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 08/09/2024] [Accepted: 08/09/2024] [Indexed: 08/29/2024] Open
Abstract
BACKGROUND Variation in thyroid function parameters within the normal range has been observationally associated with adverse health outcomes. Whether those associations reflect causal effects is largely unknown. METHODS We systematically tested associations between genetic differences in thyrotropin (TSH) and free thyroxine (FT4) within the normal range and more than 1100 diseases and more than 6000 molecular traits (metabolites and proteins) in three large population-based cohorts. This was performed by combining individual and summary level genetic data and using polygenic scores and Mendelian randomization (MR) methods. We performed a phenome-wide MR study in the OpenGWAS database covering thousands of complex phenotypes and diseases. FINDINGS Genetically predicted TSH or FT4 levels within the normal range were predominately associated with thyroid-related outcomes, like goitre. The few extra-thyroidal outcomes that were found to be associated with genetic liability towards high but normal TSH levels included atrial fibrillation (odds ratio = 0.92, p-value = 2.13 × 10-3), thyroid cancer (odds ratio = 0.57, p-value = 2.97 × 10-4), and specific biomarkers, such as sex hormone binding globulin (β = -0.046, p-value = 1.33 × 10-6) and total cholesterol (β = 0.027, p-value = 5.80 × 10-3). INTERPRETATION In contrast to previous studies that have described the association with thyroid hormone levels and disease outcomes, our genetic approach finds little evidence of an association between genetic differences in thyroid function within the normal range and non-thyroidal phenotypes. The association described in previous studies may be explained by reverse causation and confounding. FUNDING This research was funded by the Swiss National Science Foundation (P1BEP3_200041). The Fenland study (DOI 10.22025/2017.10.101.00001) is funded by the Medical Research Council (MC_UU_12015/1, MC_PC_13046 and MC_UU_00006/1). The EPIC-Norfolk study (DOI 10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1, MC-UU_12015/1, MC_PC_13048 and MC_UU_00006/1).
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Affiliation(s)
- Heba Alwan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; University of Bern, Institute of Primary Health Care (BIHAM), Bern, Switzerland; University of Bern, Graduate School for Health Sciences, Bern, Switzerland.
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Alice Williamson
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Julia Carrasco-Zanini
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Isobel D Stewart
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK; Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK; Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
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Yamamoto Y, Shirai Y, Edahiro R, Kumanogoh A, Okada Y. Large-scale cross-trait genetic analysis highlights shared genetic backgrounds of autoimmune diseases. Immunol Med 2024:1-10. [PMID: 39171621 DOI: 10.1080/25785826.2024.2394258] [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: 06/25/2024] [Accepted: 08/15/2024] [Indexed: 08/23/2024] Open
Abstract
Disorders associated with the immune system burden multiple organs, although the shared biology exists across the diseases. Preceding family-based studies reveal that immune diseases are heritable to varying degrees, providing the basis for immunogenomics. The recent cost reduction in genetic analysis intensively promotes biobank-scale studies and the development of frameworks for statistical genetics. The accumulating multi-layer omics data, including genome-wide association studies (GWAS) and RNA-sequencing at single-cell resolution, enable us to dissect the genetic backgrounds of immune-related disorders. Although autoimmune and allergic diseases are generally categorized into different disease categories, epidemiological studies reveal the high incidence of autoimmune and allergic disease complications, suggesting the shared genetics and biology between the disease categories. Biobank resources and consortia cover multiple immune-related disorders to accumulate phenome-wide associations of genetic variants and enhance researchers to analyze the shared and heterogeneous genetic backgrounds. The emerging post-GWAS and integrative multi-omics analyses provide genetic and biological insights into the multicategorical disease associations.
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Affiliation(s)
- Yuji Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- RIKEN Center for Integrative Medical Sciences, Laboratory for Systems Genetics, Yokohama, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- RIKEN Center for Integrative Medical Sciences, Laboratory for Systems Genetics, Yokohama, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Japan
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Japan
- Japan Agency for Medical Research and Development, Core Research for Evolutional Science and Technology (AMED-CREST), Tokyo, Japan
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- RIKEN Center for Integrative Medical Sciences, Laboratory for Systems Genetics, Yokohama, Japan
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Japan
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Japan
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Breeyear JH, Mitchell SL, Nealon CL, Hellwege JN, Charest B, Khakharia A, Halladay CW, Yang J, Garriga GA, Wilson OD, Basnet TB, Hung AM, Reaven PD, Meigs JB, Rhee MK, Sun Y, Lynch MG, Sobrin L, Brantley MA, Sun YV, Wilson PW, Iyengar SK, Peachey NS, Phillips LS, Edwards TL, Giri A. Development of electronic health record based algorithms to identify individuals with diabetic retinopathy. J Am Med Inform Assoc 2024:ocae213. [PMID: 39158361 DOI: 10.1093/jamia/ocae213] [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: 01/08/2024] [Revised: 07/17/2024] [Accepted: 07/30/2024] [Indexed: 08/20/2024] Open
Abstract
OBJECTIVES To develop, validate, and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHRs). MATERIALS AND METHODS We developed and validated electronic health record (EHR)-based algorithms to identify DR cases and individuals with type I or II diabetes without DR (controls) in 3 independent EHR systems: Vanderbilt University Medical Center Synthetic Derivative (VUMC), the VA Northeast Ohio Healthcare System (VANEOHS), and Massachusetts General Brigham (MGB). Cases were required to meet 1 of the following 3 criteria: (1) 2 or more dates with any DR ICD-9/10 code documented in the EHR, (2) at least one affirmative health-factor or EPIC code for DR along with an ICD9/10 code for DR on a different day, or (3) at least one ICD-9/10 code for any DR occurring within 24 hours of an ophthalmology examination. Criteria for controls included affirmative evidence for diabetes as well as an ophthalmology examination. RESULTS The algorithms, developed and evaluated in VUMC through manual chart review, resulted in a positive predictive value (PPV) of 0.93 for cases and negative predictive value (NPV) of 0.91 for controls. Implementation of algorithms yielded similar metrics in VANEOHS (PPV = 0.94; NPV = 0.86) and lower in MGB (PPV = 0.84; NPV = 0.76). In comparison, the algorithm for DR implemented in Phenome-wide association study (PheWAS) in VUMC yielded similar PPV (0.92) but substantially reduced NPV (0.48). Implementation of the algorithms to the Million Veteran Program identified over 62 000 DR cases with genetic data including 14 549 African Americans and 6209 Hispanics with DR. CONCLUSIONS/DISCUSSION We demonstrate the robustness of the algorithms at 3 separate healthcare centers, with a minimum PPV of 0.84 and substantially improved NPV than existing automated methods. We strongly encourage independent validation and incorporation of features unique to each EHR to enhance algorithm performance for DR cases and controls.
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Affiliation(s)
- Joseph H Breeyear
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
| | - Sabrina L Mitchell
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Cari L Nealon
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, OH 44106, United States
| | - Jacklyn N Hellwege
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, United States
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Brian Charest
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02111, United States
| | - Anjali Khakharia
- VA Atlanta Healthcare System, Decatur, GA 30033, United States
- Department of Medicine and Geriatrics, Emory University School of Medicine, Atlanta, GA 30307, United States
| | | | - Janine Yang
- Department of Ophthalmology, Mass Eye and Ear Infirmary, Harvard Medical School, Boston, MA 02114, United States
| | - Gustavo A Garriga
- Division of Quantitative and Clinical Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Otis D Wilson
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Til B Basnet
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, United States
- Division of Quantitative and Clinical Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Adriana M Hung
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Peter D Reaven
- Phoenix VA Health Care System, Phoenix, AZ 85012, United States
- College of Medicine, University of Arizona, Phoenix, AZ 85721, United States
| | - James B Meigs
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, United States
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Mary K Rhee
- VA Atlanta Healthcare System, Decatur, GA 30033, United States
- Division of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30307, United States
| | - Yang Sun
- Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, United States
| | - Mary G Lynch
- VA Atlanta Healthcare System, Decatur, GA 30033, United States
| | - Lucia Sobrin
- Department of Ophthalmology, Mass Eye and Ear Infirmary, Harvard Medical School, Boston, MA 02114, United States
| | - Milam A Brantley
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, United States
| | - Yan V Sun
- VA Atlanta Healthcare System, Decatur, GA 30033, United States
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA 30307, United States
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30307, United States
| | - Peter W Wilson
- VA Atlanta Healthcare System, Decatur, GA 30033, United States
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30307, United States
| | - Sudha K Iyengar
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, OH 44106, United States
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, United States
| | - Neal S Peachey
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, OH 44106, United States
- Cole Eye Institute, Cleveland Clinic, Cleveland, OH 44106, United States
- Department of Ophthalmology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH 44195, United States
| | - Lawrence S Phillips
- VA Atlanta Healthcare System, Decatur, GA 30033, United States
- Division of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30307, United States
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
| | - Ayush Giri
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- VA Tennessee Valley Healthcare System (626), Nashville, TN 37212, United States
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, United States
- Division of Quantitative and Clinical Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
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Chen SY, Chen YC, Liu TY, Chang KC, Chang SS, Wu N, Lee Wu D, Dunlap RK, Chan CJ, Yang JS, Liao CC, Tsai FJ. Novel Genes Associated with Atrial Fibrillation and the Predictive Models for AF Incorporating Polygenic Risk Scores and PheWAS-Derived Risk Factors. Can J Cardiol 2024:S0828-282X(24)00851-1. [PMID: 39142603 DOI: 10.1016/j.cjca.2024.07.029] [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: 05/12/2024] [Revised: 07/26/2024] [Accepted: 07/26/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Atrial fibrillation (AF), the most common atrial arrhythmia, presents varied clinical manifestations. Despite the identification of genetic loci associated with AF, particularly in specific populations, research within Asian ethnicities remains limited. This study aimed to develop predictive models for AF using AF-associated single-nucleotide polymorphisms (SNPs) from a Genome-Wide Association Study (GWAS) on a substantial cohort of Taiwanese individuals, evaluating the models' predictive efficacy. METHODS Involving 75,121 subjects, including 5,694 AF patients and 69,427 normal controls with GWAS data, the study merged polygenic risk scores (PRS) from AF-associated SNPs with Phenome-wide association study (PheWAS)-derived risk factors. Advanced statistical and machine learning techniques were employed to develop and evaluate AF predictive models for discrimination and calibration. RESULTS The study identified the top 30 significant SNPs associated with AF, predominantly on chromosomes 10 and 16, implicating genes like NEURL1, SH3PXD2A, INA, NT5C2, STN1, and ZFHX3. Notably, INA, NT5C2, and STN1 were newly linked to AF. The GWAS predictive power using PRS-CS analysis for AF exhibited an area under the curve (AUC) of 0.600 (P < 0.001), improving to 0.855 (P < 0.001) after adjusting for age and gender. PheWAS analysis showed the top 10 diseases associated with these genes were circulatory system diseases. CONCLUSIONS Integrating genetic and phenotypic data enhanced the accuracy and clinical relevance of AF predictive models. The findings suggest promise for refining AF risk assessment, enabling personalized interventions, and reducing AF-related morbidity and mortality burdens.
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Affiliation(s)
- Shih-Yin Chen
- School of Chinese Medicine, China Medical University, Taichung, 404, Taiwan; Genetics Center, Department of Medical Research, China Medical University Hospital, Taichung 404, Taiwan
| | - Yu-Chia Chen
- Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, 404, Taiwan
| | - Ting-Yuan Liu
- Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, 404, Taiwan
| | - Kuan-Cheng Chang
- Division of Cardiovascular Medicine, Department of Medicine, China Medical University Hospital, Taichung 404, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung 404, Taiwan
| | - Shih-Sheng Chang
- Division of Cardiovascular Medicine, Department of Medicine, China Medical University Hospital, Taichung 404, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung 404, Taiwan
| | - Ning Wu
- Department of Biological Sciences, Southeastern Oklahoma State University, Durant, OK 74701, USA
| | - Donald Lee Wu
- Department of Internal Medicine, University of Oklahoma Health Sciences Center, Tulsa, OK 74104, USA
| | - Rylee Kay Dunlap
- College of Osteopathic Medicine, Oklahoma State University Center for Health Sciences, Tulsa, OK 74107, USA
| | - Chia-Jung Chan
- Genetics Center, Department of Medical Research, China Medical University Hospital, Taichung 404, Taiwan
| | - Jai-Sing Yang
- Genetics Center, Department of Medical Research, China Medical University Hospital, Taichung 404, Taiwan
| | - Chi Chou Liao
- Genetics Center, Department of Medical Research, China Medical University Hospital, Taichung 404, Taiwan
| | - Fuu-Jen Tsai
- School of Chinese Medicine, China Medical University, Taichung, 404, Taiwan; Genetics Center, Department of Medical Research, China Medical University Hospital, Taichung 404, Taiwan; Department of Medical Genetics, China Medical University Hospital, Taichung 404, Taiwan.
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7
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Jiang F, Wang L, Ying H, Sun J, Zhao J, Lu Y, Bian Z, Chen J, Fang A, Zhang X, Larsson SC, Mantzoros CS, Wang W, Yuan S, Ding Y, Li X. Multisystem health comorbidity networks of metabolic dysfunction-associated steatotic liver disease. MED 2024:S2666-6340(24)00295-2. [PMID: 39116870 DOI: 10.1016/j.medj.2024.07.013] [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: 08/28/2023] [Revised: 04/09/2024] [Accepted: 07/11/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND The global burden of metabolic dysfunction-associated steatotic liver disease (MASLD) is growing, but its subsequent health consequences have not been thoroughly examined. METHODS A phenome-wide association study was conducted to map the associations of MASLD with 948 unique clinical outcomes among 361,021 Europeans in the UK Biobank. Disease trajectory and comorbidity analyses were applied to visualize the sequential patterns of multiple comorbidities related to the occurrence of MASLD. The associations jointly verified by observational and polygenic phenome-wide analyses were further replicated by two-sample Mendelian randomization analysis using data from the FinnGen study and international consortia. FINDINGS The observational and polygenic phenome-wide association study revealed the associations of MASLD with 96 intrahepatic and extrahepatic diseases, including circulatory, metabolic, genitourinary, neurological, gastrointestinal, and hematologic diseases. Sequential patterns of MASLD-related extrahepatic comorbidities were primarily found in circulatory, metabolic, and inflammatory diseases. Mendelian randomization analyses supported the causal associations between MASLD and the risk of several intrahepatic disorders, metabolic diseases, cardio-cerebrovascular disease, and ascites but found no associations with neurological diseases. CONCLUSIONS This study elucidated multisystem comorbidities and health consequences of MASLD, contributing to the development of combination interventions targeting distinct pathways for health promotion among patients with MASLD. FUNDING X.L. was funded by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001) and the National Nature Science Foundation of China (82204019) and Y.D. was funded by the Key Project of Traditional Chinese Medicine Science and Technology Plan of Zhejiang Province (GZY-ZJ-KJ-24077) and the National Natural Science Foundation of China (82001673 and 82272860).
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Affiliation(s)
- Fangyuan Jiang
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Lijuan Wang
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China; Centre for Global Health, Usher Institute, the University of Edinburgh, Edinburgh, UK
| | - Haochao Ying
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Sun
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianhui Zhao
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Ying Lu
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Zilong Bian
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Jie Chen
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Aiping Fang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Xuehong Zhang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Susanna C Larsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Unit of Medical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Christos S Mantzoros
- Division of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Section of Endocrinology, Boston VA Healthcare System, Harvard Medical School, Boston, MA, USA
| | - Weilin Wang
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shuai Yuan
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
| | - Yuan Ding
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Xue Li
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
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8
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Wiens J, Spector-Bagdady K, Mukherjee B. Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care. Annu Rev Genomics Hum Genet 2024; 25:141-159. [PMID: 38724019 DOI: 10.1146/annurev-genom-010323-010230] [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] [Indexed: 08/29/2024]
Abstract
Significant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.
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Affiliation(s)
- Jenna Wiens
- Division of Computer Science and Engineering, College of Engineering, University of Michigan, Ann Arbor, Michigan, USA;
| | - Kayte Spector-Bagdady
- Department of Obstetrics and Gynecology and Center for Bioethics and Social Sciences in Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
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9
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Singh J, Li N, Ashrafi E, Thao LTP, Curtis DJ, Wood EM, McQuilten ZK. Clonal hematopoiesis of indeterminate potential as a prognostic factor: a systematic review and meta-analysis. Blood Adv 2024; 8:3771-3784. [PMID: 38838228 PMCID: PMC11298876 DOI: 10.1182/bloodadvances.2024013228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/06/2024] [Accepted: 05/20/2024] [Indexed: 06/07/2024] Open
Abstract
ABSTRACT With advances in sequencing, individuals with clonal hematopoiesis of indeterminate potential (CHIP) are increasingly being identified, making it essential to understand its prognostic implications. We conducted a systematic review of studies comparing the risk of clinical outcomes in individuals with and without CHIP. We searched MEDLINE and EMBASE and included original research reporting an outcome risk measure in individuals with CHIP, adjusted for the effect of age. From the 3305 studies screened, we included 88 studies with 45 to 470 960 participants. Most studies had a low-to-moderate risk of bias in all domains of the Quality in Prognostic Factor Studies tool. Random-effects meta-analyses were performed for outcomes reported in at least 3 studies. CHIP conferred an increased risk of all-cause mortality (hazard ratio [HR], 1.34; 95% confidence interval, 1.19-1.50), cancer mortality (HR, 1.46; 1.13-1.88), composite cardiovascular events (HR, 1.40; 1.19-1.65), coronary heart disease (HR, 1.76; 1.27-2.44), stroke (HR, 1.16; 1.05-1.28), heart failure (HR, 1.27; 1.15-1.41), hematologic malignancy (HR, 4.28; 2.29-7.98), lung cancer (HR, 1.40; 1.27-1.54), renal impairment (HR, 1.25; 1.18-1.33) and severe COVID-19 (odds ratio [OR], 1.46; 1.18-1.80). CHIP was not associated with cardiovascular mortality (HR, 1.09; 0.97-1.22), except in the subgroup analysis restricted to larger clones (HR, 1.31; 1.12-1.54). Isolated DNMT3A mutations did not increase the risk of myeloid malignancy, all-cause mortality, or renal impairment. The reasons for heterogeneity between studies included differences in definitions and measurements of CHIP and the outcomes, and populations studied. In summary, CHIP is associated with diverse clinical outcomes, with clone size, specific gene, and inherent patient characteristics important mediators of risk.
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Affiliation(s)
- Jasmine Singh
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- Department of Haematology, Fiona Stanley Hospital, Perth, Australia
- Australian Centre for Blood Diseases, Monash University, Melbourne, Australia
| | - Nancy Li
- Department of Haematology, Eastern Health, Melbourne, Australia
| | - Elham Ashrafi
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Le Thi Phuong Thao
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - David J. Curtis
- Australian Centre for Blood Diseases, Monash University, Melbourne, Australia
- Department of Clinical Haematology, Alfred Health, Melbourne, Australia
| | - Erica M. Wood
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- Department of Haematology, Monash Health, Melbourne, Australia
| | - Zoe K. McQuilten
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- Department of Haematology, Monash Health, Melbourne, Australia
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10
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Chen Y, Wang G, Chen J, Wang C, Dong X, Chang HM, Yuan S, Zhao Y, Mu L. Genetic and Epigenetic Landscape for Drug Development in Polycystic Ovary Syndrome. Endocr Rev 2024; 45:437-459. [PMID: 38298137 DOI: 10.1210/endrev/bnae002] [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: 11/13/2023] [Revised: 12/26/2023] [Accepted: 01/23/2024] [Indexed: 02/02/2024]
Abstract
The treatment of polycystic ovary syndrome (PCOS) faces challenges as all known treatments are merely symptomatic. The US Food and Drug Administration has not approved any drug specifically for treating PCOS. As the significance of genetics and epigenetics rises in drug development, their pivotal insights have greatly enhanced the efficacy and success of drug target discovery and validation, offering promise for guiding the advancement of PCOS treatments. In this context, we outline the genetic and epigenetic advancement in PCOS, which provide novel insights into the pathogenesis of this complex disease. We also delve into the prospective method for harnessing genetic and epigenetic strategies to identify potential drug targets and ensure target safety. Additionally, we shed light on the preliminary evidence and distinctive challenges associated with gene and epigenetic therapies in the context of PCOS.
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Affiliation(s)
- Yi Chen
- Reproductive Medicine Center, Zhongshan Hospital, Fudan University, Shanghai 200032, China
- The First School of Medicine, Wenzhou Medical University, Wenzhou 325035, China
| | - Guiquan Wang
- Department of Reproductive Medicine, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen 361003, China
- Xiamen Key Laboratory of Reproduction and Genetics, Xiamen University, Xiamen 361023, China
| | - Jingqiao Chen
- The First School of Medicine, Wenzhou Medical University, Wenzhou 325035, China
| | - Congying Wang
- The Department of Cardiology, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang 322000, China
| | - Xi Dong
- Reproductive Medicine Center, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Hsun-Ming Chang
- Department of Obstetrics and Gynecology, China Medical University Hospital, Taichung 40400, Taiwan
| | - Shuai Yuan
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm 171 65, Sweden
| | - Yue Zhao
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China
- National Clinical Research Center for Obstetrics and Gynecology, Beijing 100007, China
- Key Laboratory of Assisted Reproduction, Ministry of Education, Peking University, Beijing 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Peking University, Beijing 100191, China
| | - Liangshan Mu
- Reproductive Medicine Center, Zhongshan Hospital, Fudan University, Shanghai 200032, China
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11
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Tang AS, Woldemariam SR, Miramontes S, Norgeot B, Oskotsky TT, Sirota M. Harnessing EHR data for health research. Nat Med 2024; 30:1847-1855. [PMID: 38965433 DOI: 10.1038/s41591-024-03074-8] [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: 01/03/2024] [Accepted: 05/17/2024] [Indexed: 07/06/2024]
Abstract
With the increasing availability of rich, longitudinal, real-world clinical data recorded in electronic health records (EHRs) for millions of patients, there is a growing interest in leveraging these records to improve the understanding of human health and disease and translate these insights into clinical applications. However, there is also a need to consider the limitations of these data due to various biases and to understand the impact of missing information. Recognizing and addressing these limitations can inform the design and interpretation of EHR-based informatics studies that avoid confusing or incorrect conclusions, particularly when applied to population or precision medicine. Here we discuss key considerations in the design, implementation and interpretation of EHR-based informatics studies, drawing from examples in the literature across hypothesis generation, hypothesis testing and machine learning applications. We outline the growing opportunities for EHR-based informatics studies, including association studies and predictive modeling, enabled by evolving AI capabilities-while addressing limitations and potential pitfalls to avoid.
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Affiliation(s)
- Alice S Tang
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah R Woldemariam
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Silvia Miramontes
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | | | - Tomiko T Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA.
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12
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Zhang DD, He XY, Yang L, Wu BS, Fu Y, Liu WS, Guo Y, Fei CJ, Kang JJ, Feng JF, Cheng W, Tan L, Yu JT. Exome sequencing identifies novel genetic variants associated with varicose veins. PLoS Genet 2024; 20:e1011339. [PMID: 38980841 PMCID: PMC11233024 DOI: 10.1371/journal.pgen.1011339] [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: 09/30/2023] [Accepted: 06/13/2024] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND Varicose veins (VV) are one of the common human diseases, but the role of genetics in its development is not fully understood. METHODS We conducted an exome-wide association study of VV using whole-exome sequencing data from the UK Biobank, and focused on common and rare variants using single-variant association analysis and gene-level collapsing analysis. FINDINGS A total of 13,823,269 autosomal genetic variants were obtained after quality control. We identified 36 VV-related independent common variants mapping to 34 genes by single-variant analysis and three rare variant genes (PIEZO1, ECE1, FBLN7) by collapsing analysis, and most associations between genes and VV were replicated in FinnGen. PIEZO1 was the closest gene associated with VV (P = 5.05 × 10-31), and it was found to reach exome-wide significance in both single-variant and collapsing analyses. Two novel rare variant genes (ECE1 and METTL21A) associated with VV were identified, of which METTL21A was associated only with females. The pleiotropic effects of VV-related genes suggested that body size, inflammation, and pulmonary function are strongly associated with the development of VV. CONCLUSIONS Our findings highlight the importance of causal genes for VV and provide new directions for treatment.
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Affiliation(s)
- Dan-Dan Zhang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei-Shi Liu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu Guo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chen-Jie Fei
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ju-Jiao Kang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
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13
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Ashton JJ, Satsangi J, Uhlig HH. Intestinal Inflammation and Extraintestinal Disease: Understanding Dynamic Risk. Gastroenterology 2024; 167:205-208. [PMID: 38604541 DOI: 10.1053/j.gastro.2024.03.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/21/2024] [Accepted: 03/24/2024] [Indexed: 04/13/2024]
Affiliation(s)
- James J Ashton
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, United Kingdom; Department of Paediatric Gastroenterology, Southampton Children's Hospital, Southampton, United Kingdom
| | - Jack Satsangi
- Translational Gastroenterology Unit, University of Oxford, Oxford, United Kingdom
| | - Holm H Uhlig
- Translational Gastroenterology Unit, University of Oxford, Oxford, United Kingdom; Biomedical Research Centre, University of Oxford, Oxford, United Kingdom; Department of Paediatrics, University of Oxford, Oxford, United Kingdom.
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14
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Huang X, Shen R, Zheng Z. Unraveling genetic threads: Identifying novel therapeutic targets for allergic rhinitis through Mendelian randomization. World Allergy Organ J 2024; 17:100927. [PMID: 39040085 PMCID: PMC11261789 DOI: 10.1016/j.waojou.2024.100927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/23/2024] [Accepted: 06/07/2024] [Indexed: 07/24/2024] Open
Abstract
Background Allergic rhinitis (AR) is a pervasive global health issue, and currently, there is a scarcity of targeted drug therapies available. This study aims to identify potential druggable target genes for AR using Mendelian randomization (MR) analysis. Methods MR analysis was conducted to assess the causal effect of expression quantitative trait loci (eQTL) in the blood on AR. Data on AR were collected from 2 datasets: FinnGen(R9) (11,009 cases and 359,149 controls) and UK Biobank (25,486 cases and 87,097 controls). Colocalization analysis was utilized to assess the common causal genetic variations between the identified drug target genes and AR. We also employed available genome-wide association studies (GWAS) data to gauge the impact of druggable genes on AR biomarkers and other allergic diseases. Results This study employs MR to analyze the relationship between 3410 druggable genes and AR. After Bonferroni correction, 10 genes were found to be significantly associated with AR risk (P < 0.05/3410). Colocalization analysis revealed a significant causal relationship between the expression variation of CFL1 and EFEMP2 genes and AR, sharing direct causal variants (colocalization probability PP.H3 + PP.H4 > 0.8), highlighting their importance as potential therapeutic targets for AR. The CFL1 gene showed a causal link with levels of thymic stromal lymphopoietin (TSLP), eosinophil count, and interleukin-13 (IL-13) (P = 0.016, 7.45E-16, 0.00091, respectively). EFEMP2 was also causally related to eosinophil count, IL-13, and interleukin-17 (IL-17) (P = 0.00012, 0.00091, 0.032, respectively). PheWAS analysis revealed significant associations of CFL1 with asthma, whereas EFEMP2 showed associations with both asthma and eczema. Protein-Protein Interaction (PPI) network analysis further unveiled the direct interactions of EFEMP2 and CFL1 with proteins related to immune regulation and inflammatory responses, with 77.64% of the network consisting of direct bindings, indicating their key roles in modulating AR-related immune and inflammatory responses. Notably, there was an 8.01% significant correlation between immune-related pathways and genes involved in inflammatory responses. Conclusion These genes present notable associations with AR biomarkers and other autoimmune diseases, offering valuable targets for developing new AR therapies.
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Affiliation(s)
- Xuerong Huang
- Department of Neonatology, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, Fujian 361003, China
- Xiamen Key Laboratory of Perinatal-Neonatal Infection, Xiamen, Fujian 361003, China
- Xiamen Clinical Research Center for Perinatal Medicine, Xiamen, Fujian 361003, China
| | - Ruoyi Shen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Zhi Zheng
- Department of Neonatology, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, Fujian 361003, China
- Xiamen Key Laboratory of Perinatal-Neonatal Infection, Xiamen, Fujian 361003, China
- Xiamen Clinical Research Center for Perinatal Medicine, Xiamen, Fujian 361003, China
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15
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Chou WH, Chen LC, Wong HSC, Chao CH, Chu HW, Chang WC. Phenomic landscape and pharmacogenomic implications for HLA region in a Taiwan Han Chinese population. Biomark Res 2024; 12:46. [PMID: 38702819 PMCID: PMC11067262 DOI: 10.1186/s40364-024-00591-z] [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: 01/10/2024] [Accepted: 04/18/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND The human leukocyte antigen (HLA) genes, exhibiting significant genetic diversity, are associated with susceptibility to various clinical diseases and diverse in drug responses. High costs of HLA sequencing and the population-specific architecture of this genetic region necessitate the establishment of a population-specific HLA imputation reference panel. Moreover, there is a lack of understanding about the genetic and phenotypic landscape of HLA variations within the Taiwanese population. METHODS We created models for a Taiwanese-specific HLA imputation reference panel. These models were trained with the array genotype data and HLA sequencing data from 845 Taiwanese subjects. HLA imputation was applied for 59,448 Taiwanese subjects to characterize the HLA allele and haplotype frequencies. Additionally, a phenome-wide association study (PheWAS) was conducted to identify the phenotypes associated with HLA variations. The association of the biallelic HLA variants with the binary and quantitative traits were evaluated with additive logistic and linear regression models, respectively. Furthermore, an omnibus test with likelihood-ratio test was applied for each HLA amino acid position in the multiallelic HLA amino acid polymorphisms to compare the difference between a fitted model and a null model following a χ2 distribution of n-1 degree of freedom at a position with n residues. Finally, we estimated the prevalence of adverse drug reactions (ADR)-related HLA alleles in the Taiwanese population. RESULTS In this study, the reference panel models displayed remarkable accuracy, with averages of 99.3%, 98.9%, and 99.1% for 2-, 4-, 6-digit alleles of the eight classical HLA genes, respectively. For PheWAS, a total of 18,136 significant associations with HLA variants across 26 phenotypes are identified (p < 5×10-8), highlighting the pleiotropy feature of the HLA region. Among the independent signals, 15 are novel, including the association of HLA-B pos 138 variation with ankylosing spondylitis (AS), and rs9266290 and rs9266292 with allergy. Through an analysis spanning the entire HLA region, we identified clusters of phenotype correlations. Finally, the carriers of pharmacogenomic related HLA alleles, including HLA-C*01:02 (35.86%), HLA-B*58:01 (20.9%), and HLA-B*15:02 (8.38%), were characterized in the Taiwanese general population. CONCLUSIONS We successfully delivered the HLA imputation for 59,448 Taiwanese subjects and characterized the genetic and phenotypic landscapes of the HLA variations. In addition, we quantified the estimated prevalence of the ADR-related HLA alleles in the Taiwanese population. The developed HLA imputation reference panel could be used for estimation of population HLA allele frequencies, which can facilitate further studies in the role of HLA variants in a wider range of phenotypes in the population.
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Affiliation(s)
- Wan-Hsuan Chou
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Lu-Chun Chen
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Henry Sung-Ching Wong
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Ching-Hsuan Chao
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Hou-Wei Chu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Wei-Chiao Chang
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan.
- Master Program in Clinical Genomics and Proteomics, School of Pharmacy, Taipei Medical University, Taipei, Taiwan.
- Integrative Research Center for Critical Care, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
- Department of Pharmacy, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
- Department of Pharmacology, National Defense Medical Center, Taipei, Taiwan.
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Zambrano Chaves JM, Lenchik L, Gallegos IO, Blankemeier L, Rubin DL, Willis MH, Chaudhari AS, Boutin RD. Abdominal CT metrics in 17,646 patients reveal associations between myopenia, myosteatosis, and medical phenotypes: a phenome-wide association study. EBioMedicine 2024; 103:105116. [PMID: 38636199 PMCID: PMC11031722 DOI: 10.1016/j.ebiom.2024.105116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 03/29/2024] [Accepted: 03/30/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Deep learning facilitates large-scale automated imaging evaluation of body composition. However, associations of body composition biomarkers with medical phenotypes have been underexplored. Phenome-wide association study (PheWAS) techniques search for medical phenotypes associated with biomarkers. A PheWAS integrating large-scale analysis of imaging biomarkers and electronic health record (EHR) data could discover previously unreported associations and validate expected associations. Here we use PheWAS methodology to determine the association of abdominal CT-based skeletal muscle metrics with medical phenotypes in a large North American cohort. METHODS An automated deep learning pipeline was used to measure skeletal muscle index (SMI; biomarker of myopenia) and skeletal muscle density (SMD; biomarker of myosteatosis) from abdominal CT scans of adults between 2012 and 2018. A PheWAS was performed with logistic regression using patient sex and age as covariates to assess for associations between CT-derived muscle metrics and 611 common EHR-derived medical phenotypes. PheWAS P values were considered significant at a Bonferroni corrected threshold (α = 0.05/1222). FINDINGS 17,646 adults (mean age, 56 years ± 19 [SD]; 57.5% women) were included. CT-derived SMI was significantly associated with 268 medical phenotypes; SMD with 340 medical phenotypes. Previously unreported associations with the highest magnitude of significance included higher SMI with decreased cardiac dysrhythmias (OR [95% CI], 0.59 [0.55-0.64]; P < 0.0001), decreased epilepsy (OR, 0.59 [0.50-0.70]; P < 0.0001), and increased elevated prostate-specific antigen (OR, 1.84 [1.47-2.31]; P < 0.0001), and higher SMD with decreased decubitus ulcers (OR, 0.36 [0.31-0.42]; P < 0.0001), sleep disorders (OR, 0.39 [0.32-0.47]; P < 0.0001), and osteomyelitis (OR, 0.43 [0.36-0.52]; P < 0.0001). INTERPRETATION PheWAS methodology reveals previously unreported associations between CT-derived biomarkers of myopenia and myosteatosis and EHR medical phenotypes. The high-throughput PheWAS technique applied on a population scale can generate research hypotheses related to myopenia and myosteatosis and can be adapted to research possible associations of other imaging biomarkers with hundreds of EHR medical phenotypes. FUNDING National Institutes of Health, Stanford AIMI-HAI pilot grant, Stanford Precision Health and Integrated Diagnostics, Stanford Cardiovascular Institute, Stanford Center for Digital Health, and Stanford Knight-Hennessy Scholars.
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Affiliation(s)
- Juan M Zambrano Chaves
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiology, Stanford University, Stanford, CA, USA
| | - Leon Lenchik
- Department of Diagnostic Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Isabel O Gallegos
- Department of Computer Science, (IOG), Stanford University, Stanford, CA, USA
| | - Louis Blankemeier
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Daniel L Rubin
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiology, Stanford University, Stanford, CA, USA
| | - Marc H Willis
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Akshay S Chaudhari
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiology, Stanford University, Stanford, CA, USA
| | - Robert D Boutin
- Department of Radiology, Stanford University, Stanford, CA, USA.
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17
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Truong B, Hull LE, Ruan Y, Huang QQ, Hornsby W, Martin H, van Heel DA, Wang Y, Martin AR, Lee SH, Natarajan P. Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases. CELL GENOMICS 2024; 4:100523. [PMID: 38508198 PMCID: PMC11019356 DOI: 10.1016/j.xgen.2024.100523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/15/2023] [Accepted: 02/20/2024] [Indexed: 03/22/2024]
Abstract
Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10-5) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10-6) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.
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Affiliation(s)
- Buu Truong
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Leland E Hull
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge Street, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Yunfeng Ruan
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Qin Qin Huang
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - Whitney Hornsby
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Hilary Martin
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - David A van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Ying Wang
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Alicia R Martin
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA 5000, Australia
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
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18
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Choi E, Song J, Lee Y, Jeong Y, Jang W. Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study. Hum Genomics 2024; 18:34. [PMID: 38566255 PMCID: PMC10985920 DOI: 10.1186/s40246-024-00591-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 02/27/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Male-pattern baldness (MPB) is the most common cause of hair loss in men. It can be categorized into three types: type 2 (T2), type 3 (T3), and type 4 (T4), with type 1 (T1) being considered normal. Although various MPB-associated genetic variants have been suggested, a comprehensive study for linking these variants to gene expression regulation has not been performed to the best of our knowledge. RESULTS In this study, we prioritized MPB-related tissue panels using tissue-specific enrichment analysis and utilized single-tissue panels from genotype-tissue expression version 8, as well as cross-tissue panels from context-specific genetics. Through a transcriptome-wide association study and colocalization analysis, we identified 52, 75, and 144 MPB associations for T2, T3, and T4, respectively. To assess the causality of MPB genes, we performed a conditional and joint analysis, which revealed 10, 11, and 54 putative causality genes for T2, T3, and T4, respectively. Finally, we conducted drug repositioning and identified potential drug candidates that are connected to MPB-associated genes. CONCLUSIONS Overall, through an integrative analysis of gene expression and genotype data, we have identified robust MPB susceptibility genes that may help uncover the underlying molecular mechanisms and the novel drug candidates that may alleviate MPB.
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Affiliation(s)
- Eunyoung Choi
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea
| | - Jaeseung Song
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea
| | - Yubin Lee
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea
| | - Yeonbin Jeong
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea
| | - Wonhee Jang
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea.
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19
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Tran TC, Schlueter DJ, Zeng C, Mo H, Carroll RJ, Denny JC. PheWAS analysis on large-scale biobank data with PheTK. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.12.24302720. [PMID: 38410487 PMCID: PMC10896413 DOI: 10.1101/2024.02.12.24302720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Summary With the rapid growth of genetic data linked to electronic health record data in huge cohorts, large-scale phenome-wide association study (PheWAS), have become powerful discovery tools in biomedical research. PheWAS is an analysis method to study phenotype associations utilizing longitudinal electronic health record (EHR) data. Previous PheWAS packages were developed mostly in the days of smaller biobanks and with earlier PheWAS approaches. PheTK was designed to simplify analysis and efficiently handle biobank-scale data. PheTK uses multithreading and supports a full PheWAS workflow including extraction of data from OMOP databases and Hail matrix tables as well as PheWAS analysis for both phecode version 1.2 and phecodeX. Benchmarking results showed PheTK took 64% less time than the R PheWAS package to complete the same workflow. PheTK can be run locally or on cloud platforms such as the All of Us Researcher Workbench ( All of Us ) or the UK Biobank (UKB) Research Analysis Platform (RAP). Availability and implementation The PheTK package is freely available on the Python Package Index (PyPi) and on GitHub under GNU Public License (GPL-3) at https://github.com/nhgritctran/PheTK . It is implemented in Python and platform independent. The demonstration workspace for All of Us will be made available in the future as a featured workspace. Contact PheTK@mail.nih.gov.
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20
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Ball RL, Bogue MA, Liang H, Srivastava A, Ashbrook DG, Lamoureux A, Gerring MW, Hatoum AS, Kim MJ, He H, Emerson J, Berger AK, Walton DO, Sheppard K, El Kassaby B, Castellanos F, Kunde-Ramamoorthy G, Lu L, Bluis J, Desai S, Sundberg BA, Peltz G, Fang Z, Churchill GA, Williams RW, Agrawal A, Bult CJ, Philip VM, Chesler EJ. GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis. Genome Res 2024; 34:145-159. [PMID: 38290977 PMCID: PMC10903950 DOI: 10.1101/gr.278157.123] [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: 08/08/2023] [Accepted: 01/10/2024] [Indexed: 02/01/2024]
Abstract
Hundreds of inbred mouse strains and intercross populations have been used to characterize the function of genetic variants that contribute to disease. Thousands of disease-relevant traits have been characterized in mice and made publicly available. New strains and populations including consomics, the collaborative cross, expanded BXD, and inbred wild-derived strains add to existing complex disease mouse models, mapping populations, and sensitized backgrounds for engineered mutations. The genome sequences of inbred strains, along with dense genotypes from others, enable integrated analysis of trait-variant associations across populations, but these analyses are hampered by the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense variant resource by harmonizing multiple data sets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extendable to other model organisms. The result is a web- and programmatically accessible data service called GenomeMUSter, comprising single-nucleotide variants covering 657 strains at 106.8 million segregating sites. Interoperation with phenotype databases, analytic tools, and other resources enable a wealth of applications, including multitrait, multipopulation meta-analysis. We show this in cross-species comparisons of type 2 diabetes and substance use disorder meta-analyses, leveraging mouse data to characterize the likely role of human variant effects in disease. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.
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Affiliation(s)
- Robyn L Ball
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA;
| | - Molly A Bogue
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | | | - Anuj Srivastava
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, USA
| | - David G Ashbrook
- University of Tennessee Health Science Center, Memphis, Tennessee 38163, USA
| | | | | | - Alexander S Hatoum
- Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri 63130, USA
- Artificial Intelligence and the Internet of Things Institute, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Matthew J Kim
- University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Hao He
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | - Jake Emerson
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | | | | | | | | | | | | | - Lu Lu
- University of Tennessee Health Science Center, Memphis, Tennessee 38163, USA
| | - John Bluis
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | - Sejal Desai
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | | | - Gary Peltz
- Department of Anesthesia, Pain and Perioperative Medicine, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Zhuoqing Fang
- Department of Anesthesia, Pain and Perioperative Medicine, Stanford University School of Medicine, Stanford, California 94305, USA
| | | | - Robert W Williams
- University of Tennessee Health Science Center, Memphis, Tennessee 38163, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Carol J Bult
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
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21
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Koonce TY, Giuse DA, Williams AM, Blasingame MN, Krump PA, Su J, Giuse NB. Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition. JMIR Med Inform 2024; 12:e53516. [PMID: 38289670 PMCID: PMC10865202 DOI: 10.2196/53516] [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: 10/10/2023] [Revised: 12/15/2023] [Accepted: 01/04/2024] [Indexed: 02/01/2024] Open
Abstract
Implementing artificial intelligence to extract insights from large, real-world clinical data sets can supplement and enhance knowledge management efforts for health sciences research and clinical care. At Vanderbilt University Medical Center (VUMC), the in-house developed Word Cloud natural language processing system extracts coded concepts from patient records in VUMC's electronic health record repository using the Unified Medical Language System terminology. Through this process, the Word Cloud extracts the most prominent concepts found in the clinical documentation of a specific patient or population. The Word Cloud provides added value for clinical care decision-making and research. This viewpoint paper describes a use case for how the VUMC Center for Knowledge Management leverages the condition-disease associations represented by the Word Cloud to aid in the knowledge generation needed to inform the interpretation of phenome-wide association studies.
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Affiliation(s)
- Taneya Y Koonce
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Dario A Giuse
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Annette M Williams
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Mallory N Blasingame
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Poppy A Krump
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jing Su
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Nunzia B Giuse
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
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22
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Wu L, Li S, Wu C, Wu S, Lin Y, Wei D. Causal relationship between systemic lupus erythematosus and primary liver cirrhosis based on two-sample bidirectional Mendelian randomization and transcriptome overlap analysis. Arthritis Res Ther 2024; 26:10. [PMID: 38167341 PMCID: PMC10762944 DOI: 10.1186/s13075-023-03235-z] [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: 10/22/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Overlapping cases of systemic lupus erythematosus (SLE) and primary biliary cirrhosis (PBC) are rare and have not yet been fully proven to be accidental or have a common genetic basis. METHODS Two-sample bidirectional Mendelian randomization (MR) analysis was applied to explore the potential causal relationship between SLE and PBC. The heterogeneity and reliability of MR analysis were evaluated through Cochran's Q-test and sensitivity test, respectively. Next, transcriptome overlap analysis of SLE and PBC was performed using the Gene Expression Omnibus database to identify the potential mechanism of hub genes. Finally, based on MR analysis, the potential causal relationship between hub genes and SLE or PBC was validated again. RESULTS The MR analysis results indicated that SLE and PBC were both high-risk factors for the occurrence and development of the other party. On the one hand, MR analysis had heterogeneity, and on the other hand, it also had robustness. Nine hub genes were identified through transcriptome overlap analysis, and machine learning algorithms were used to verify their high recognition efficiency for SLE patients. Finally, based on MR analysis, it was verified that there was no potential causal relationship between the central gene SOCS3 and SLE, but it was a high-risk factor for the potential risk of PBC. CONCLUSION The two-sample bidirectional MR analysis revealed that SLE and PBC were high-risk factors for each other, indicating that they had similar genetic bases, which could to some extent overcome the limitation of insufficient overlap in case samples of SLE and PBC. The analysis of transcriptome overlapping hub genes provided a theoretical basis for the potential mechanisms and therapeutic targets of SLE with PBC overlapping cases.
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Affiliation(s)
- Linyong Wu
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, Guangdong, 525000, People's Republic of China
| | - Songhua Li
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, Guangdong, 525000, People's Republic of China.
| | - Chaojun Wu
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, Guangdong, 525000, People's Republic of China
| | - Shaofeng Wu
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, Guangdong, 525000, People's Republic of China
| | - Yan Lin
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, Guangdong, 525000, People's Republic of China
| | - Dayou Wei
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, Guangdong, 525000, People's Republic of China.
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23
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Cao S, Li H, Xin J, Jin Z, Zhang Z, Li J, Zhu Y, Su L, Huang P, Jiang L, Du M, Christiani DC. Identification of genetic profile and biomarkers involved in acute respiratory distress syndrome. Intensive Care Med 2024; 50:46-55. [PMID: 37922010 PMCID: PMC11167213 DOI: 10.1007/s00134-023-07248-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 10/08/2023] [Indexed: 11/05/2023]
Abstract
PURPOSE The purpose of this study was to profile genetic causal factors of acute respiratory distress syndrome (ARDS) and early predict patients at high ARDS risk. METHODS We performed a phenome-wide Mendelian Randomization analysis through summary statistics of an ARDS genome-wide association study (1250 cases and 1583 controls of European ancestry) and 33,150 traits. Transcriptomic data from human blood and lung tissues of a preclinical mouse model were used to validate biomarkers, which were further used to construct a prediction model and nomogram. RESULTS A total of 1736 traits, including 1223 blood RNA, 159 plasma proteins, and 354 non-gene phenotypes (classified by Biochemistry, Anthropometry, Disease, Nutrition and Habit, Immunology, and Treatment), exhibited a potentially causal relationship with ARDS development, which were accessible through a user-friendly interface platform called CARDS (Causal traits for Acute Respiratory Distress Syndrome). Regarding candidate blood RNA, four genes were validated, namely TMEM176B, SLC2A5, CDC45, and VSIG8, showing differential expression in blood of ARDS patients compared to controls, as well as dynamic expression in mouse lung tissues. Importantly, the addition of four blood genes and five immune cell proportions significantly improved the prediction performance of ARDS development, with 0.791 of the area under the curve from receiver-operator characteristic, compared to 0.725 for the basic model consisting of Acute Physiology and Chronic Health Evaluation (APACHE) III Score, sex, body mass index, bacteremia, and sepsis. A model-based nomogram was also developed for the clinical practice. CONCLUSION This study identifies a wide range of ARDS relevant factors and develops a promising prediction model, enhancing early clinical management and intervention for ARDS development.
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Affiliation(s)
- Shurui Cao
- School of Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Huiqin Li
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Junyi Xin
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zhenghao Jin
- School of Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zhengyu Zhang
- School of Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jiawei Li
- School of Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yukun Zhu
- School of Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Li Su
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
| | - Peipei Huang
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
- Department of Emergency, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Lei Jiang
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
- Department of Emergency, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Mulong Du
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA.
| | - David C Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA.
- Pulmonary and Critical Care Unit, Department of Medicine, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
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24
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Gao YN, Coombes B, Ryu E, Pazdernik V, Jenkins G, Pendegraft R, Biernacka J, Olfson M. Phenotypic distinctions in depression and anxiety: a comparative analysis of comorbid and isolated cases. Psychol Med 2023; 53:7766-7774. [PMID: 37403468 PMCID: PMC11251006 DOI: 10.1017/s0033291723001745] [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] [Indexed: 07/06/2023]
Abstract
BACKGROUND Anxiety and depression are frequently comorbid yet phenotypically distinct. This study identifies differences in the clinically observable phenome across a wide variety of physical and mental disorders comparing patients with diagnoses of depression without anxiety, anxiety without depression, or both depression and anxiety. METHODS Using electronic health records for 14 994 participants with depression and/or anxiety in the Mayo Clinic Biobank, a phenotype-based phenome-wide association study (Phe2WAS) was performed to test for differences between these groups across a broad range of clinical diagnoses observed in the electronic health record. Additional analyses were performed to determine the temporal sequencing of diagnoses. RESULTS Compared to patients diagnosed only with anxiety, those diagnosed only with depression were more likely to have diagnoses of obesity (OR 1.75; p = 1 × 10-27), sleep apnea (OR 1.71; p = 1 × 10-22), and type II diabetes (OR 1.74; p = 9 × 10-18). Compared to those diagnosed only with depression, those diagnosed only with anxiety were more likely to have diagnoses of palpitations (OR 1.91; p = 2 × 10-25), benign skin neoplasms (OR 1.61; p = 2 × 10-17), and cardiac dysrhythmias (OR 1.45; p = 2 × 10-12). Patients with comorbid depression and anxiety were more likely to have diagnoses of other mental health disorders, substance use disorders, sleep problems, and gastroesophageal reflux relative to isolated depression. CONCLUSIONS While depression and anxiety are closely related, this study suggests that phenotypic distinctions exist between depression and anxiety. Improving phenotypic characterization within the broad categories of depression and anxiety could improve the clinical assessment of depression and anxiety.
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Affiliation(s)
- Y Nina Gao
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, USA
| | - Brandon Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Euijung Ryu
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Vanessa Pazdernik
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Gregory Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Richard Pendegraft
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Joanna Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Mark Olfson
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, USA
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25
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Bi X, Liang W, Zhao Q, Wang J. SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data. Bioinformatics 2023; 39:btad662. [PMID: 37941450 PMCID: PMC10666204 DOI: 10.1093/bioinformatics/btad662] [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: 05/15/2023] [Revised: 10/17/2023] [Accepted: 11/03/2023] [Indexed: 11/10/2023] Open
Abstract
MOTIVATION Medical genomics faces significant challenges in interpreting disease phenotype and genetic heterogeneity. Despite the establishment of standardized disease phenotype databases, computational methods for predicting gene-phenotype associations still suffer from imbalanced category distribution and a lack of labeled data in small categories. RESULTS To address the problem of labeled-data scarcity, we propose a self-supervised learning strategy for gene-phenotype association prediction, called SSLpheno. Our approach utilizes an attributed network that integrates protein-protein interactions and gene ontology data. We apply a Laplacian-based filter to ensure feature smoothness and use self-supervised training to optimize node feature representation. Specifically, we calculate the cosine similarity of feature vectors and select positive and negative sample nodes for reconstruction training labels. We employ a deep neural network for multi-label classification of phenotypes in the downstream task. Our experimental results demonstrate that SSLpheno outperforms state-of-the-art methods, especially in categories with fewer annotations. Moreover, our case studies illustrate the potential of SSLpheno as an effective prescreening tool for gene-phenotype association identification. AVAILABILITY AND IMPLEMENTATION https://github.com/bixuehua/SSLpheno.
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Affiliation(s)
- Xuehua Bi
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
- Medical Engineering and Technology College, Xinjiang Medical University, Urumqi 830017, China
| | - Weiyang Liang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Qichang Zhao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
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Shuey MM, Stead WW, Aka I, Barnado AL, Bastarache JA, Brokamp E, Campbell M, Carroll RJ, Goldstein JA, Lewis A, Malow BA, Mosley JD, Osterman T, Padovani-Claudio DA, Ramirez A, Roden DM, Schuler BA, Siew E, Sucre J, Thomsen I, Tinker RJ, Van Driest S, Walsh C, Warner JL, Wells QS, Wheless L, Bastarache L. Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics. Bioinformatics 2023; 39:btad655. [PMID: 37930895 PMCID: PMC10627409 DOI: 10.1093/bioinformatics/btad655] [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: 06/09/2023] [Revised: 09/13/2023] [Indexed: 11/08/2023] Open
Abstract
MOTIVATION Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure. RESULTS Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure-including infectious disease, pregnancy, congenital anomalies, and neonatology-and is a more robust representation of the medical phenome for global use in discovery research. AVAILABILITY AND IMPLEMENTATION phecodeX is available at https://github.com/PheWAS/phecodeX.
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Affiliation(s)
- Megan M Shuey
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - William W Stead
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Ida Aka
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - April L Barnado
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Julie A Bastarache
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Elly Brokamp
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Meredith Campbell
- Department of Pediatrics, Virginia Commonwealth University, Richmond, VA 23219, United States
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Jeffrey A Goldstein
- Department of Pathology, Northwestern Feinberg School of Medicine, Chicago, IL 60611, United States
| | - Adam Lewis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Beth A Malow
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Jonathan D Mosley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Travis Osterman
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Dolly A Padovani-Claudio
- Department of Ophthalmology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Andrea Ramirez
- All of Us Research Program, National Institutes of Health, Bethesda, MD 20892, United States
| | - Dan M Roden
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Bryce A Schuler
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Edward Siew
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Jennifer Sucre
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Isaac Thomsen
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Rory J Tinker
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Sara Van Driest
- All of Us Research Program, National Institutes of Health, Bethesda, MD 20892, United States
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Colin Walsh
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Jeremy L Warner
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Quinn S Wells
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Lee Wheless
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
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Graves LY, Schwartz KR, Shiff J, Chan ER, Galea M, Henzel MK, Olney C, Bogie KM. Genomic Biomarkers Can Provide a Deeper Understanding of Recurrent Pressure Injuries. Adv Skin Wound Care 2023; 36:534-539. [PMID: 37729163 PMCID: PMC10545060 DOI: 10.1097/asw.0000000000000041] [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: 05/08/2023] [Accepted: 07/12/2023] [Indexed: 09/22/2023]
Abstract
OBJECTIVE To identify genetic biomarkers predisposing individuals with spinal cord injury (SCI) to recurrent pressure injuries (PIs). METHODS Repeated measures of the transcriptome profile of veterans with SCI at three Veterans Spinal Cord Injuries and Disorders Centers. Exclusion criteria included having significant active systemic disease at time of enrollment. Researchers obtained comprehensive profiles of clinical and health factors and demographic information relevant to PI history at enrollment and at each follow-up visit by reviewing patients' medical charts. Whole blood samples were collected at 6- to 12-month intervals for 2 to 4 years. In addition to DNA profiling with whole genome sequencing of the patients, RNA sequencing was performed to assess pathways associated with PI risk. RESULTS Whole genome sequencing analysis identified 260 genes that showed increased prevalence of single-nucleotide variations in exonic regions with high (>20) combined annotation-dependent depletion scores between persons with high versus low intramuscular adipose tissue levels when cross-referenced with persons who had recurrent PIs. Gene set enrichment analysis using Hallmark and KEGG (Kyoto Encyclopedia of Genes and Genomes) gene sets of these candidate genes revealed enrichment in genes encoding proteins involved in fatty acid metabolism (P < .01). Further, RNA sequencing revealed upregulated activity in biological senescence pathways and downregulated activity in antimicrobial protection pathways. CONCLUSIONS Genomic biomarkers may complement electronic health records to support management of complex interactive health issues such as risk of recurrent PIs in people with SCI. These findings may also be leveraged for homogeneous phenotypic grouping of higher-risk individuals.
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Affiliation(s)
- Letitia Y Graves
- Letitia Y. Graves, PhD, RN, is Assistant Professor, School of Nursing, University of Texas Medical Branch, Galveston, Texas, and Research Health Scientist, Louis Stokes Cleveland Veterans Affairs Medical Center. Katelyn R. Schwartz, MPH, BSN, RN, is Research Nurse, Cleveland VA Medical Center, Cleveland, Ohio. Josie Shiff, MS, is Research Assistant, Cleveland VA Medical Research & Education Foundation. Ernest R. Chan, PhD, is Research Scientist, Case Western Reserve University, Cleveland. Marinella Galea, MD, is Chief, Spinal Cord Injuries and Disorders, James J. Peters VAMC, Bronx, New York, and Associate Professor, Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York. Mary K. Henzel, MD, PhD, is Assistant Chief, Spinal Cord Injuries and Disorders, and Spinal Cord Injury Physiatrist, Louis Stokes Cleveland VA Medical Center. Christine Olney, PhD, RN, is Nurse Scientist, Minneapolis VA Health Care System, Minneapolis, Minnesota. Kath M. Bogie, DPhil, FAIMBE, is Research Career Scientist, Louis Stokes Cleveland Veterans Affairs Medical Center, and Professor, Department of Orthopedics, Case Western Reserve University
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Bastarache L, Delozier S, Pandit A, He J, Lewis A, Annis AC, LeFaive J, Denny JC, Carroll RJ, Altman RB, Hughey JJ, Zawistowski M, Peterson JF. The phenotype-genotype reference map: Improving biobank data science through replication. Am J Hum Genet 2023; 110:1522-1533. [PMID: 37607538 PMCID: PMC10502848 DOI: 10.1016/j.ajhg.2023.07.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/24/2023] Open
Abstract
Population-scale biobanks linked to electronic health record data provide vast opportunities to extend our knowledge of human genetics and discover new phenotype-genotype associations. Given their dense phenotype data, biobanks can also facilitate replication studies on a phenome-wide scale. Here, we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments. PGRM phenotypes are standardized as phecodes, ensuring interoperability between biobanks. We applied the PGRM to five ancestry-specific cohorts from four independent biobanks and found evidence of robust replications across a wide array of phenotypes. We show how the PGRM can be used to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we use the PGRM to explore factors associated with replicability of GWAS results.
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Affiliation(s)
- Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Sarah Delozier
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anita Pandit
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Jing He
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam Lewis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Aubrey C Annis
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Jonathon LeFaive
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Russ B Altman
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Jacob J Hughey
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Matthew Zawistowski
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
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Gusev A. Germline mechanisms of immunotherapy toxicities in the era of genome-wide association studies. Immunol Rev 2023; 318:138-156. [PMID: 37515388 DOI: 10.1111/imr.13253] [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: 03/14/2023] [Accepted: 06/29/2023] [Indexed: 07/30/2023]
Abstract
Cancer immunotherapy has revolutionized the treatment of advanced cancers and is quickly becoming an option for early-stage disease. By reactivating the host immune system, immunotherapy harnesses patients' innate defenses to eradicate the tumor. By putatively similar mechanisms, immunotherapy can also substantially increase the risk of toxicities or immune-related adverse events (irAEs). Severe irAEs can lead to hospitalization, treatment discontinuation, lifelong immune complications, or even death. Many irAEs present with similar symptoms to heritable autoimmune diseases, suggesting that germline genetics may contribute to their onset. Recently, genome-wide association studies (GWAS) of irAEs have identified common germline associations and putative mechanisms, lending support to this hypothesis. A wide range of well-established GWAS methods can potentially be harnessed to understand the etiology of irAEs specifically and immunotherapy outcomes broadly. This review summarizes current findings regarding germline effects on immunotherapy outcomes and discusses opportunities and challenges for leveraging germline genetics to understand, predict, and treat irAEs.
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Affiliation(s)
- Alexander Gusev
- Division of Population Sciences, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA
- Division of Genetics, Brigham & Women's Hospital, Boston, Massachusetts, USA
- The Broad Institute, Cambridge, Massachusetts, USA
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30
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Khan AH, Bagley JR, LaPierre N, Gonzalez-Figueroa C, Spencer TC, Choudhury M, Xiao X, Eskin E, Jentsch JD, Smith DJ. Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in a panel of inbred and recombinant inbred mice. Cell Rep 2023; 42:112856. [PMID: 37481717 PMCID: PMC10530068 DOI: 10.1016/j.celrep.2023.112856] [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: 11/16/2022] [Revised: 06/06/2023] [Accepted: 07/10/2023] [Indexed: 07/25/2023] Open
Abstract
To identify addiction genes, we evaluate intravenous self-administration of cocaine or saline in 84 inbred and recombinant inbred mouse strains over 10 days. We integrate the behavior data with brain RNA-seq data from 41 strains. The self-administration of cocaine and that of saline are genetically distinct. We maximize power to map loci for cocaine intake by using a linear mixed model to account for this longitudinal phenotype while correcting for population structure. A total of 15 unique significant loci are identified in the genome-wide association study. A transcriptome-wide association study highlights the Trpv2 ion channel as a key locus for cocaine self-administration as well as identifying 17 additional genes, including Arhgef26, Slc18b1, and Slco5a1. We find numerous instances where alternate splice site selection or RNA editing altered transcript abundance. Our work emphasizes the importance of Trpv2, an ionotropic cannabinoid receptor, for the response to cocaine.
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Affiliation(s)
- Arshad H Khan
- Department of Molecular and Medical Pharmacology, Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Jared R Bagley
- Department of Psychology, Binghamton University, Binghamton, NY, USA
| | - Nathan LaPierre
- Department of Computer Science, UCLA, Los Angeles, CA 90095, USA
| | | | - Tadeo C Spencer
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA 90095, USA
| | - Mudra Choudhury
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA 90095, USA
| | - Xinshu Xiao
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA 90095, USA
| | - Eleazar Eskin
- Department of Computational Medicine, UCLA, Los Angeles, CA 90095, USA
| | - James D Jentsch
- Department of Psychology, Binghamton University, Binghamton, NY, USA
| | - Desmond J Smith
- Department of Molecular and Medical Pharmacology, Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA.
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Felsky D, Cannitelli A, Pipitone J. Whole Person Modeling: a transdisciplinary approach to mental health research. DISCOVER MENTAL HEALTH 2023; 3:16. [PMID: 37638348 PMCID: PMC10449734 DOI: 10.1007/s44192-023-00041-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 08/10/2023] [Indexed: 08/29/2023]
Abstract
The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.
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Affiliation(s)
- Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada
- Rotman Research Institute, Baycrest Hospital, Toronto, ON Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Alyssa Cannitelli
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Jon Pipitone
- Department of Psychiatry, Queen’s University, Kingston, ON Canada
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Susmitha P, Kumar P, Yadav P, Sahoo S, Kaur G, Pandey MK, Singh V, Tseng TM, Gangurde SS. Genome-wide association study as a powerful tool for dissecting competitive traits in legumes. FRONTIERS IN PLANT SCIENCE 2023; 14:1123631. [PMID: 37645459 PMCID: PMC10461012 DOI: 10.3389/fpls.2023.1123631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/08/2023] [Indexed: 08/31/2023]
Abstract
Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.
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Affiliation(s)
- Pusarla Susmitha
- Regional Agricultural Research Station, Acharya N.G. Ranga Agricultural University, Andhra Pradesh, India
| | - Pawan Kumar
- Department of Genetics and Plant Breeding, College of Agriculture, Chaudhary Charan Singh (CCS) Haryana Agricultural University, Hisar, India
| | - Pankaj Yadav
- Department of Bioscience and Bioengineering, Indian Institute of Technology, Rajasthan, India
| | - Smrutishree Sahoo
- Department of Genetics and Plant Breeding, School of Agriculture, Gandhi Institute of Engineering and Technology (GIET) University, Odisha, India
| | - Gurleen Kaur
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Manish K. Pandey
- Department of Genomics, Prebreeding and Bioinformatics, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India
| | - Varsha Singh
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Te Ming Tseng
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Sunil S. Gangurde
- Department of Plant Pathology, University of Georgia, Tifton, GA, United States
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Ball RL, Bogue MA, Liang H, Srivastava A, Ashbrook DG, Lamoureux A, Gerring MW, Hatoum AS, Kim M, He H, Emerson J, Berger AK, Walton DO, Sheppard K, Kassaby BE, Castellanos F, Kunde-Ramamoorthy G, Lu L, Bluis J, Desai S, Sundberg BA, Peltz G, Fang Z, Churchill GA, Williams RW, Agrawal A, Bult CJ, Philip VM, Chesler EJ. GenomeMUSter mouse genetic variation service enables multi-trait, multi-population data integration and analyses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.552506. [PMID: 37609331 PMCID: PMC10441370 DOI: 10.1101/2023.08.08.552506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Hundreds of inbred laboratory mouse strains and intercross populations have been used to functionalize genetic variants that contribute to disease. Thousands of disease relevant traits have been characterized in mice and made publicly available. New strains and populations including the Collaborative Cross, expanded BXD and inbred wild-derived strains add to set of complex disease mouse models, genetic mapping resources and sensitized backgrounds against which to evaluate engineered mutations. The genome sequences of many inbred strains, along with dense genotypes from others could allow integrated analysis of trait - variant associations across populations, but these analyses are not feasible due to the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense data resource by harmonizing multiple variant datasets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extensible to other model organism species. The result is a web- and programmatically-accessible data service called GenomeMUSter ( https://muster.jax.org ), comprising allelic data covering 657 strains at 106.8M segregating sites. Interoperation with phenotype databases, analytic tools and other resources enable a wealth of applications including multi-trait, multi-population meta-analysis. We demonstrate this in a cross-species comparison of the meta-analysis of Type 2 Diabetes and of substance use disorders, resulting in the more specific characterization of the role of human variant effects in light of mouse phenotype data. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.
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Stead WW, Lewis A, Giuse NB, Koonce TY, Bastarache L. Knowledgebase strategies to aid interpretation of clinical correlation research. J Am Med Inform Assoc 2023; 30:1257-1265. [PMID: 37164621 PMCID: PMC10280353 DOI: 10.1093/jamia/ocad078] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/09/2023] [Accepted: 04/25/2023] [Indexed: 05/12/2023] Open
Abstract
OBJECTIVE Knowledgebases are needed to clarify correlations observed in real-world electronic health record (EHR) data. We posit design principles, present a unifying framework, and report a test of concept. MATERIALS AND METHODS We structured a knowledge framework along 3 axes: condition of interest, knowledge source, and taxonomy. In our test of concept, we used hypertension as our condition of interest, literature and VanderbiltDDx knowledgebase as sources, and phecodes as our taxonomy. In a cohort of 832 566 deidentified EHRs, we modeled blood pressure and heart rate by sex and age, classified individuals by hypertensive status, and ran a Phenome-wide Association Study (PheWAS) for hypertension. We compared the correlations from PheWAS to the associations in our knowledgebase. RESULTS We produced PhecodeKbHtn: a knowledgebase comprising 167 hypertension-associated diseases, 15 of which were also negatively associated with blood pressure (pos+neg). Our hypertension PheWAS included 1914 phecodes, 129 of which were in the PhecodeKbHtn. Among the PheWAS association results, phecodes that were in PhecodeKbHtn had larger effect sizes compared with those phecodes not in the knowledgebase. DISCUSSION Each source contributed unique and additive associations. Models of blood pressure and heart rate by age and sex were consistent with prior cohort studies. All but 4 PheWAS positive and negative correlations for phecodes in PhecodeKbHtn may be explained by knowledgebase associations, hypertensive cardiac complications, or causes of hypertension independently associated with hypotension. CONCLUSION It is feasible to assemble a knowledgebase that is compatible with EHR data to aid interpretation of clinical correlation research.
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Affiliation(s)
- William W Stead
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adam Lewis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Nunzia B Giuse
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Taneya Y Koonce
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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35
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Qin N, Wang C, Hu Z. Genetic insights into lung function inform better management of respiratory diseases. Cell Rep Med 2023; 4:101041. [PMID: 37196630 DOI: 10.1016/j.xcrm.2023.101041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 04/18/2023] [Accepted: 04/18/2023] [Indexed: 05/19/2023]
Abstract
Shrine et al.1 conducted the largest multi-ancestry genome-wide meta-analysis of lung function and identified 1,020 signals associated with lung function. These provide novel insights into the genetic underpins of lung function and may inform better clinical management of respiratory disorders.
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Affiliation(s)
- Na Qin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166 China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166 China
| | - Cheng Wang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166 China; Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 211116 China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166 China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166 China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166 China; State Key Laboratory of Reproductive Medicine (Suzhou Centre), The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, 215002 China.
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Xiao T, Dong X, Lu Y, Zhou W. High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:204-215. [PMID: 37197647 PMCID: PMC10110825 DOI: 10.1007/s43657-022-00071-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 08/03/2022] [Accepted: 08/08/2022] [Indexed: 05/19/2023]
Abstract
Advances in genomic medicine have greatly improved our understanding of human diseases. However, phenome is not well understood. High-resolution and multidimensional phenotypes have shed light on the mechanisms underlying neonatal diseases in greater details and have the potential to optimize clinical strategies. In this review, we first highlight the value of analyzing traditional phenotypes using a data science approach in the neonatal population. We then discuss recent research on high-resolution, multidimensional, and structured phenotypes in neonatal critical diseases. Finally, we briefly introduce current technologies available for the analysis of multidimensional data and the value that can be provided by integrating these data into clinical practice. In summary, a time series of multidimensional phenome can improve our understanding of disease mechanisms and diagnostic decision-making, stratify patients, and provide clinicians with optimized strategies for therapeutic intervention; however, the available technologies for collecting multidimensional data and the best platform for connecting multiple modalities should be considered.
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Affiliation(s)
- Tiantian Xiao
- Division of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai, 201102 China
- Department of Neonatology, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610000 China
| | - Xinran Dong
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
| | - Yulan Lu
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
| | - Wenhao Zhou
- Division of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai, 201102 China
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
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Truong B, Hull LE, Ruan Y, Huang QQ, Hornsby W, Martin H, van Heel DA, Wang Y, Martin AR, Lee SH, Natarajan P. Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.21.23286110. [PMID: 36865265 PMCID: PMC9980241 DOI: 10.1101/2023.02.21.23286110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We propose PRSmix, a framework that evaluates and leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture. We applied PRSmix to 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% CI: [1.10; 1.3]; P-value = 9.17 × 10-5) and 1.19-fold (95% CI: [1.11; 1.27]; P-value = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI: [1.40; 2.04]; P-value = 7.58 × 10-6) and 1.42-fold (95% CI: [1.25; 1.59]; P-value = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously established cross-trait-combination method with scores from pre-defined correlated traits, we demonstrated that our method can improve prediction accuracy for coronary artery disease up to 3.27-fold (95% CI: [2.1; 4.44]; P-value after FDR correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.
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Affiliation(s)
- Buu Truong
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Leland E. Hull
- Division of General Internal Medicine, 100 Cambridge Street,
Massachusetts General Hospital, Boston, MA, 02114
- Department of Medicine, Harvard Medical School, 25 Shattuck
Street, Boston, MA 02115
| | - Yunfeng Ruan
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Qin Qin Huang
- Department of Human Genetics, Wellcome Sanger Institute,
Cambridge, UK
| | - Whitney Hornsby
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Hilary Martin
- Department of Human Genetics, Wellcome Sanger Institute,
Cambridge, UK
| | - David A. van Heel
- Blizard Institute, Barts and the London School of Medicine and
Dentistry, Queen Mary University of London, London, UK
| | - Ying Wang
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Stanley Center for Psychiatric Research, Broad Institute of
Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General
Hospital, Boston, MA, USA
| | - Alicia R. Martin
- Stanley Center for Psychiatric Research, Broad Institute of
Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General
Hospital, Boston, MA, USA
| | - S. Hong Lee
- Australian Centre for Precision Health, University of South
Australia Cancer Research Institute, University of South Australia, Adelaide, SA, 5000,
Australia
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
- Department of Medicine, Harvard Medical School, 25 Shattuck
Street, Boston, MA 02115
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Fan P, Zhang S, Wang W, Yang Z, Tan W, Li S, Zhu C, Hu D, Zhou X, Tian Z, Wang Y, Liu F, Huang W, Chen L. The design and implementation of natural population cohort study Biobank: A multiple-center project cooperation with medical consortia in Southwest China. Front Public Health 2022; 10:996169. [PMID: 36530701 PMCID: PMC9751194 DOI: 10.3389/fpubh.2022.996169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 10/31/2022] [Indexed: 12/04/2022] Open
Abstract
Objective The West China Hospital of Sichuan University collaborated with regional medical consortia in Sichuan Province to launch a natural population cohort study (NPCS) to investigate the health status of residents and collect public health data in southwest China. Methods Up to 80,000 participants will be enrolled by the NPCS from 11 regional medical consortia over five years. Individuals are invited to visit one of 11 participating medical consortia to fill out questionnaires, receive a free health exam, and donate biospecimens upon enrolment. All participating medical facilities adhered to standard operating procedures for collecting and processing biospecimens to ensure uniformity (serum, lithium heparinized plasma, ethylene diamine tetraacetie acid plasma, and buffy coat). The Electronic Data Capture System, Picture Archiving and Communication System, Laboratory Information Management System, Biospecimen Quality Control System, Biobank Information Management System, and will be used to sort and classify clinical indices, imaging data, laboratory parameters, pre-analytical variables, and biospecimen information, respectively. All quality assurance and quality control procedures in the NPCS biobank adhered to the "DAIDS Guidelines for Good Clinical Laboratory Practice Standards". This project will integrate high-dimensional multi-omics data, laboratory data, clinical data, questionnaire data, and environmental risk factors. Results An estimated 2,240,000 aliquots of the sample will be stored by the end of the study. These samples are linked with comprehensively collected clinical indices, imaging data, and laboratory parameters. Big data analysis can be implemented to create predictive algorithms, explore pathogenesis mechanisms, uncover potential biomarkers, and provide information on public health. Conclusions NPCS will provide an integrative approach to research risk factors and pathogenesis of major chronic or endemic diseases in Sichuan Province and provide key scientific evidence to support the formulation of health management policies in China.
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Affiliation(s)
- Ping Fan
- West China Biobanks and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Shu Zhang
- West China Biobanks and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Weiya Wang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Zongze Yang
- West China Biobanks and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Weiwei Tan
- West China Biobanks and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Shujun Li
- West China Biobanks and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Chenxing Zhu
- Department of Clinical Research Management, West China Hospital, Sichuan University, Chengdu, China
| | - Dan Hu
- Department of Clinical Research Management, West China Hospital, Sichuan University, Chengdu, China
| | - Xinran Zhou
- West China Biobanks and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Zixuan Tian
- West China Biobanks and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Yaxi Wang
- West China Biobanks and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Fang Liu
- Department of Clinical Research Management, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Huang
- West China Biobanks and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China,West China Centre of Excellence for Pancreatitis, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, China,Institutes for Systems Genetics & Immunology and Inflammation, Frontiers Science Centre for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China,*Correspondence: Wei Huang
| | - Lei Chen
- Department of Clinical Research Management, West China Hospital, Sichuan University, Chengdu, China,Lei Chen
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Liu LY, Gu Q, Hu X, Fan J, Liu XZ. Potential Mediators of Causal Associations of Circulating Triglycerides With Blood Pressure: Evidence From Genetic and Observational Data. Hypertension 2022; 79:2439-2447. [DOI: 10.1161/hypertensionaha.122.19510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Background:
Existing evidence indicates that elevated triglycerides may affect blood pressure, but the underlying mechanisms are not fully understood. Herein, we aim to identify the intermediaries of associations of triglyceride with systolic blood pressure and diastolic blood pressure using the Mendelian randomization (MR) framework.
Methods:
Triglyceride-associated single nucleotide polymorphisms were extracted and used to match phenotypes in PhenoScanner. From the broad spectrum of possible triglyceride-associated traits, potential mediators linking triglyceride to blood pressure were screened out by MR and MR-based mediation analysis. Moreover, cross-sectional observational data of 206 341 adults were used to validate the mediators identified at the genetic level.
Results:
Among the nearly 100 raw phenotypes matched by 313 triglyceride-associated single nucleotide polymorphisms, 39 traits were filtered and integrated into subsequent analysis. By further filtering using MR analysis, only pulse rate and lymphocyte count (LC) were identified as independent mediators. MR-based mediation analysis showed that genetically predicted LC could mediate 9.2% of the association of triglyceride with systolic blood pressure; genetically predicted pulse rate and LC could mediate 18.3% and 17.6% of the association of triglyceride with DBP, respectively. Observational data also support the mediating role of pulse rate and LC.
Conclusions:
The current findings highlighted the mediating role of pulse rate and LC on the causal pathway from triglyceride to blood pressure and may contribute to a better understanding of the pathogenic mechanism by which high triglyceride affects other cardiometabolic factors.
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Affiliation(s)
- Lian Yong Liu
- Department of Endocriology, Punan Hospital of Pudong New District, Shanghai, China (L.Y.L.)
| | - Qing Gu
- Department of Endocrinology, Shidong Hospital, University of Shanghai for Science and Technology, China (Q.G., X.H.)
| | - Xue Hu
- Department of Endocrinology, Shidong Hospital, University of Shanghai for Science and Technology, China (Q.G., X.H.)
| | - Jie Fan
- Zhejiang Police College, Hangzhou, China (J.F.)
| | - Xing Zhen Liu
- Hangzhou Aeronautical Sanatorium for Special Service of China Air Force, China (X.Z.L.)
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40
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Tebani A, Bekri S. [The promise of omics in the precision medicine era]. Rev Med Interne 2022; 43:649-660. [PMID: 36041909 DOI: 10.1016/j.revmed.2022.07.009] [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: 06/10/2022] [Accepted: 07/12/2022] [Indexed: 10/15/2022]
Abstract
The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.
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Affiliation(s)
- A Tebani
- UNIROUEN, Inserm U1245, Department of Metabolic Biochemistry, Normandie University, CHU Rouen, 76000 Rouen, France.
| | - S Bekri
- UNIROUEN, Inserm U1245, Department of Metabolic Biochemistry, Normandie University, CHU Rouen, 76000 Rouen, France
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Patel B, Kleeman SO, Neavin D, Powell J, Baskozos G, Ng M, Ahmed WUR, Bennett DL, Schmid AB, Furniss D, Wiberg A. Shared genetic susceptibility between trigger finger and carpal tunnel syndrome: a genome-wide association study. THE LANCET. RHEUMATOLOGY 2022; 4:e556-e565. [PMID: 36043126 PMCID: PMC7613465 DOI: 10.1016/s2665-9913(22)00180-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background Trigger finger and carpal tunnel syndrome are the two most common non-traumatic connective tissue disorders of the hand. Both of these conditions frequently co-occur, often in patients with rheumatoid arthritis. However, this phenotypic association is poorly understood. Hypothesising that the co-occurrence of trigger finger and carpal tunnel syndrome might be explained by shared germline predisposition, we aimed to identify a specific genetic locus associated with both diseases. Methods In this genome-wide association study (GWAS), we identified 2908 patients with trigger finger and 436579 controls from the UK Biobank prospective cohort. We conducted a case-control GWAS for trigger finger, followed by co-localisation analyses with carpal tunnel syndrome summary statistics. To identify putative causal variants and establish their biological relevance, we did fine-mapping analyses and expression quantitative trait loci (eQTL) analyses, using fibroblasts from healthy donors (n=79) and tenosynovium samples from patients with carpal tunnel syndrome (n=77). We conducted a Cox regression for time to trigger finger and carpal tunnel syndrome diagnosis against plasma IGF-1 concentrations in the UK Biobank cohort. Findings Phenome-wide analyses confirmed a marked association between carpal tunnel syndrome and trigger finger in the participants from UK Biobank (odds ratio [OR] 11·97, 95% CI 11·1-13·0; p<1 × 10-300). GWAS for trigger finger identified five independent loci, including one locus, DIRC3, that was co-localised with carpal tunnel syndrome and could be fine-mapped to rs62175241 (0·76, 0·68-0·84; p=5·03 × 10-13). eQTL analyses found a fibroblast-specific association between the protective T allele of rs62175241 and increased DIRC3 and IGFBP5 expression. Increased plasma IGF-1 concentrations were associated with both carpal tunnel syndrome and trigger finger in participants from UK Biobank (hazard ratio >1·04, p<0·02). Interpretation In this GWAS, the DIRC3 locus on chromosome 2 was significantly associated with both carpal tunnel syndrome and trigger finger, possibly explaining their co-occurrence. The disease-protective allele of rs62175241 was associated with increased expression of long non-coding RNA DIRC3 and its transcriptional target, IGBP5, an antagonist of IGF-1 signalling. These findings suggest a model in which IGF-1 is a driver of both carpal tunnel syndrome and trigger finger, and in which the DIRC3-IGFBP5 axis directly antagonises fibroblastic IGF-1 signalling. Funding Wellcome Trust, National Institute for Health Research, Medical Research Council.
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Affiliation(s)
- Benjamin Patel
- Department of Plastic and Reconstructive Surgery, Southmead Hospital, North Bristol NHS Trust, Bristol, UK
| | | | - Drew Neavin
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Joseph Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia; UNSW Cellular Genomics Futures Institute, University of New South Wales, NSW, Australia
| | - Georgios Baskozos
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Michael Ng
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
| | - Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
| | - David L Bennett
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Annina B Schmid
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Dominic Furniss
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
| | - Akira Wiberg
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
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mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights. Metabolites 2022; 12:metabo12060526. [PMID: 35736459 PMCID: PMC9230867 DOI: 10.3390/metabo12060526] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/25/2022] Open
Abstract
Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies.
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Mascalchi M, Santorelli FM. The Strange Case of the Multiple MRI Phenotypes of RFC1 Mutation. THE CEREBELLUM 2022; 22:478-481. [PMID: 35359253 DOI: 10.1007/s12311-022-01401-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/28/2022] [Indexed: 11/25/2022]
Affiliation(s)
- Mario Mascalchi
- Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, Viale Pieraccini 6, 50130, Florence, Italy.
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