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Genetic analyses implicate complex links between adult testosterone levels and health and disease. COMMUNICATIONS MEDICINE 2023; 3:4. [PMID: 36653534 PMCID: PMC9849476 DOI: 10.1038/s43856-022-00226-0] [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: 09/17/2021] [Accepted: 12/07/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Testosterone levels are linked with diverse characteristics of human health, yet, whether these associations reflect correlation or causation remains debated. Here, we provide a broad perspective on the role of genetically determined testosterone on complex diseases in both sexes. METHODS Leveraging genetic and health registry data from the UK Biobank and FinnGen (total N = 625,650), we constructed polygenic scores (PGS) for total testosterone, sex-hormone binding globulin (SHBG) and free testosterone, associating these with 36 endpoints across different disease categories in the FinnGen. These analyses were combined with Mendelian Randomization (MR) and cross-sex PGS analyses to address causality. RESULTS We show testosterone and SHBG levels are intricately tied to metabolic health, but report lack of causality behind most associations, including type 2 diabetes (T2D). Across other disease domains, including 13 behavioral and neurological diseases, we similarly find little evidence for a substantial contribution from normal variation in testosterone levels. We nonetheless find genetically predicted testosterone affects many sex-specific traits, with a pronounced impact on female reproductive health, including causal contribution to PCOS-related traits like hirsutism and post-menopausal bleeding (PMB). We also illustrate how testosterone levels associate with antagonistic effects on stroke risk and reproductive endpoints between the sexes. CONCLUSIONS Overall, these findings provide insight into how genetically determined testosterone correlates with several health parameters in both sexes. Yet the lack of evidence for a causal contribution to most traits beyond sex-specific health underscores the complexity of the mechanisms linking testosterone levels to disease risk and sex differences.
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Haga SB. Revisiting Secondary Information Related to Pharmacogenetic Testing. Front Genet 2021; 12:741395. [PMID: 34659361 PMCID: PMC8517135 DOI: 10.3389/fgene.2021.741395] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 09/13/2021] [Indexed: 12/22/2022] Open
Abstract
Incidental or secondary findings have been a major part of the discussion of genomic medicine research and clinical applications. For pharmacogenetic (PGx) testing, secondary findings arise due to the pleiotropic effects of pharmacogenes, often related to their endogenous functions. Unlike the guidelines that have been developed for whole exome or genome sequencing applications for management of secondary findings (though slightly different from PGx testing in that these refer to detection of variants in multiple genes, some with clinical significance and actionability), no corresponding guidelines have been developed for PGx clinical laboratories. Nonetheless, patient and provider education will remain key components of any PGx testing program to minimize adverse responses related to secondary findings.
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Wang L, Zhang X, Meng X, Koskeridis F, Georgiou A, Yu L, Campbell H, Theodoratou E, Li X. Methodology in phenome-wide association studies: a systematic review. J Med Genet 2021; 58:720-728. [PMID: 34272311 DOI: 10.1136/jmedgenet-2021-107696] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 05/27/2021] [Indexed: 11/04/2022]
Abstract
Phenome-wide association study (PheWAS) has been increasingly used to identify novel genetic associations across a wide spectrum of phenotypes. This systematic review aims to summarise the PheWAS methodology, discuss the advantages and challenges of PheWAS, and provide potential implications for future PheWAS studies. Medical Literature Analysis and Retrieval System Online (MEDLINE) and Excerpta Medica Database (EMBASE) databases were searched to identify all published PheWAS studies up until 24 April 2021. The PheWAS methodology incorporating how to perform PheWAS analysis and which software/tool could be used, were summarised based on the extracted information. A total of 1035 studies were identified and 195 eligible articles were finally included. Among them, 137 (77.0%) contained 10 000 or more study participants, 164 (92.1%) defined the phenome based on electronic medical records data, 140 (78.7%) used genetic variants as predictors, and 73 (41.0%) conducted replication analysis to validate PheWAS findings and almost all of them (94.5%) received consistent results. The methodology applied in these PheWAS studies was dissected into several critical steps, including quality control of the phenome, selecting predictors, phenotyping, statistical analysis, interpretation and visualisation of PheWAS results, and the workflow for performing a PheWAS was established with detailed instructions on each step. This study provides a comprehensive overview of PheWAS methodology to help practitioners achieve a better understanding of the PheWAS design, to detect understudied or overstudied outcomes, and to direct their research by applying the most appropriate software and online tools for their study data structure.
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Affiliation(s)
- Lijuan Wang
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xiaomeng Zhang
- Centre for Global Health, The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | - Xiangrui Meng
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Fotios Koskeridis
- Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Epirus, Greece
| | - Andrea Georgiou
- Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Epirus, Greece
| | - Lili Yu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Harry Campbell
- Centre for Global Health, The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | - Evropi Theodoratou
- Centre for Global Health, The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK.,Cancer Research UK Edinburgh Centre, The University of Edinburgh MRC Institute of Genetics and Molecular Medicine, Edinburgh, UK
| | - Xue Li
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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Ouellette TW, Wright GE, Drögemöller BI, Ross CJ, Carleton BC. Integrating disease and drug-related phenotypes for improved identification of pharmacogenomic variants. Pharmacogenomics 2021; 22:251-261. [PMID: 33769074 DOI: 10.2217/pgs-2020-0130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Aim: To improve the identification and interpretation of pharmacogenetic variants through the integration of disease and drug-related traits. Materials & methods: We hypothesized that integrating genome-wide disease and pharmacogenomic data may drive new insights into drug toxicity and response by identifying shared genetic architecture. Pleiotropic variants were identified using a methodological framework incorporating colocalization analysis. Results: Using genome-wide association studies summary statistics from the UK Biobank, European Bioinformatics Institute genome-wide association studies catalog and the Pharmacogenomics Research Network, we validated pleiotropy at the ABCG2 locus between allopurinol response and gout and identified novel pleiotropy between antihypertensive-induced new-onset diabetes, Crohn's disease and inflammatory bowel disease at the IL18RAP/SLC9A4 locus. Conclusion: New mechanistic insights and genetic loci can be uncovered by identifying pleiotropy between disease and drug-related traits.
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Affiliation(s)
- Tom W Ouellette
- BC Children's Hospital Research Institute, Vancouver, BC, V5Z 4H4, Canada
| | - Galen Eb Wright
- BC Children's Hospital Research Institute, Vancouver, BC, V5Z 4H4, Canada.,Division of Translational Therapeutics, Department of Pediatrics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Britt I Drögemöller
- BC Children's Hospital Research Institute, Vancouver, BC, V5Z 4H4, Canada.,Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Colin Jd Ross
- BC Children's Hospital Research Institute, Vancouver, BC, V5Z 4H4, Canada.,Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Bruce C Carleton
- BC Children's Hospital Research Institute, Vancouver, BC, V5Z 4H4, Canada.,Division of Translational Therapeutics, Department of Pediatrics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
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5
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Lukkunaprasit T, Rattanasiri S, Turongkaravee S, Suvannang N, Ingsathit A, Attia J, Thakkinstian A. The association between genetic polymorphisms in ABCG2 and SLC2A9 and urate: an updated systematic review and meta-analysis. BMC MEDICAL GENETICS 2020; 21:210. [PMID: 33087043 PMCID: PMC7580000 DOI: 10.1186/s12881-020-01147-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 10/13/2020] [Indexed: 02/08/2023]
Abstract
Background Replication studies showed conflicting effects of ABCG2 and SLC2A9 polymorphisms on gout and serum urate. This meta-analysis therefore aimed to pool their effects across studies. Methods Studies were located from MEDLINE and Scopus from inception to 17th June 2018. Observational studies in adults with any polymorphism in ABCG2 or SLC2A9, and outcome including gout, hyperuricemia, and serum urate were included for pooling. Data extractions were performed by two independent reviewers. Genotype effects were pooled stratified by ethnicity using a mixed-effect logistic model and a multivariate meta-analysis for dichotomous and continuous outcomes. Results Fifty-two studies were included in the analysis. For ABCG2 polymorphisms, mainly studied in Asians, carrying 1–2 minor-allele-genotypes of rs2231142 and rs72552713 were respectively about 2.1–4.5 and 2.5–3.9 times higher odds of gout than non-minor-allele-genotypes. The two rs2231142-risk-genotypes also had higher serum urate about 11–18 μmol/l. Conversely, carrying 1–2 minor alleles of rs2231137 was about 36–57% significantly lower odds of gout. For SLC2A9 polymorphisms, mainly studied in Caucasians, carrying 1–2 minor alleles of rs1014290, rs6449213, rs6855911, and rs7442295 were about 25–43%, 31–62%, 33–64%, and 35–65% significantly lower odds of gout than non-minor-allele-genotypes. In addition, 1–2 minor-allele-genotypes of the latter three polymorphisms had significantly lower serum urate about 20–49, 21–51, and 18–54 μmol/l than non-minor-allele-genotypes. Conclusions Our findings should be useful in identifying patients at risk for gout and high serum urate and these polymorphisms may be useful in personalized risk scores. Trial registration PROSPERO registration number: CRD42018105275. Supplementary information The online version contains supplementary material available at 10.1186/s12881-020-01147-2.
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Affiliation(s)
- Thitiya Lukkunaprasit
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, 270 Rama VI Rd., Ratchathewi, Bangkok, 10400, Thailand.,Department of Pharmacology, College of Pharmacy, Rangsit University, Pathum Thani, Thailand
| | - Sasivimol Rattanasiri
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, 270 Rama VI Rd., Ratchathewi, Bangkok, 10400, Thailand.
| | - Saowalak Turongkaravee
- Social and Administrative Pharmacy Excellence Research (SAPER) Unit, Department of Pharmacy, Faculty of Pharmacy, Mahidol University, Bangkok, Thailand
| | - Naravut Suvannang
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, 270 Rama VI Rd., Ratchathewi, Bangkok, 10400, Thailand
| | - Atiporn Ingsathit
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, 270 Rama VI Rd., Ratchathewi, Bangkok, 10400, Thailand
| | - John Attia
- Centre for Clincial Epidemiology and Biostatistics, School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, and Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Ammarin Thakkinstian
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, 270 Rama VI Rd., Ratchathewi, Bangkok, 10400, Thailand
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Pendergrass SA, Buyske S, Jeff JM, Frase A, Dudek S, Bradford Y, Ambite JL, Avery CL, Buzkova P, Deelman E, Fesinmeyer MD, Haiman C, Heiss G, Hindorff LA, Hsu CN, Jackson RD, Lin Y, Le Marchand L, Matise TC, Monroe KR, Moreland L, North KE, Park SL, Reiner A, Wallace R, Wilkens LR, Kooperberg C, Ritchie MD, Crawford DC. A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans. PLoS One 2019; 14:e0226771. [PMID: 31891604 PMCID: PMC6938343 DOI: 10.1371/journal.pone.0226771] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 12/03/2019] [Indexed: 12/11/2022] Open
Abstract
We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.
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Affiliation(s)
| | - Steven Buyske
- Department of Statistics, Rutgers University, Piscataway, New Jersey, United States of America
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Janina M. Jeff
- Illumina, Inc., San Diego, California, United States of America
| | - Alex Frase
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Scott Dudek
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Yuki Bradford
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Jose-Luis Ambite
- Information Sciences Institute; University of Southern California, Marina del Rey, California, United States of America
| | - Christy L. Avery
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Petra Buzkova
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Ewa Deelman
- Information Sciences Institute; University of Southern California, Marina del Rey, California, United States of America
| | | | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Gerardo Heiss
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Lucia A. Hindorff
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Chun-Nan Hsu
- Center for Research in Biological Systems, Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America
| | | | - Yi Lin
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
| | - Tara C. Matise
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Kristine R. Monroe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Larry Moreland
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Kari E. North
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Sungshim L. Park
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Alex Reiner
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
| | - Robert Wallace
- Departments of Epidemiology and Internal Medicine, University of Iowa, Iowa City, Iowa, United States of America
| | - Lynne R. Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Marylyn D. Ritchie
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Dana C. Crawford
- Cleveland Institute for Computational Biology, Cleveland, Ohio, United States of America
- Departments of Population and Quantitative Health Sciences and Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
- * E-mail:
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7
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Sinnott JA, Cai F, Yu S, Hejblum BP, Hong C, Kohane IS, Liao KP. PheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies. J Am Med Inform Assoc 2019; 25:1359-1365. [PMID: 29788308 DOI: 10.1093/jamia/ocy056] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Accepted: 04/23/2018] [Indexed: 12/24/2022] Open
Abstract
Objective Standard approaches for large scale phenotypic screens using electronic health record (EHR) data apply thresholds, such as ≥2 diagnosis codes, to define subjects as having a phenotype. However, the variation in the accuracy of diagnosis codes can impair the power of such screens. Our objective was to develop and evaluate an approach which converts diagnosis codes into a probability of a phenotype (PheProb). We hypothesized that this alternate approach for defining phenotypes would improve power for genetic association studies. Methods The PheProb approach employs unsupervised clustering to separate patients into 2 groups based on diagnosis codes. Subjects are assigned a probability of having the phenotype based on the number of diagnosis codes. This approach was developed using simulated EHR data and tested in a real world EHR cohort. In the latter, we tested the association between low density lipoprotein cholesterol (LDL-C) genetic risk alleles known for association with hyperlipidemia and hyperlipidemia codes (ICD-9 272.x). PheProb and thresholding approaches were compared. Results Among n = 1462 subjects in the real world EHR cohort, the threshold-based p-values for association between the genetic risk score (GRS) and hyperlipidemia were 0.126 (≥1 code), 0.123 (≥2 codes), and 0.142 (≥3 codes). The PheProb approach produced the expected significant association between the GRS and hyperlipidemia: p = .001. Conclusions PheProb improves statistical power for association studies relative to standard thresholding approaches by leveraging information about the phenotype in the billing code counts. The PheProb approach has direct applications where efficient approaches are required, such as in Phenome-Wide Association Studies.
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Affiliation(s)
| | - Fiona Cai
- Stuyvesant High School, New York City, NY, USA
| | - Sheng Yu
- Center for Statistical Science, Tsinghua University, Beijing, China.,Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Boris P Hejblum
- Univ. Bordeaux, ISPED, Inserm BPH 1219, Inria SISTM, Bordeaux, France
| | - Chuan Hong
- Department of Biostatistics, Harvard University, Boston, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Children's Hospital Boston, Boston, MA, USA
| | - Katherine P Liao
- Department of Medicine, Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, MA, USA
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8
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O’Mara TA, Batra J, Glubb D. Editorial: Establishing Genetic Pleiotropy to Identify Common Pharmacological Agents for Common Diseases. Front Pharmacol 2019; 10:1038. [PMID: 31572202 PMCID: PMC6753863 DOI: 10.3389/fphar.2019.01038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 08/16/2019] [Indexed: 11/13/2022] Open
Affiliation(s)
- Tracy A. O’Mara
- Molecular Cancer Epidemiology Laboratory, Genetics and Computational Biology Department, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jyotsna Batra
- Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Dylan Glubb
- Molecular Cancer Epidemiology Laboratory, Genetics and Computational Biology Department, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
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9
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Verma A, Bang L, Miller JE, Zhang Y, Lee MTM, Zhang Y, Byrska-Bishop M, Carey DJ, Ritchie MD, Pendergrass SA, Kim D. Human-Disease Phenotype Map Derived from PheWAS across 38,682 Individuals. Am J Hum Genet 2019; 104:55-64. [PMID: 30598166 PMCID: PMC6323551 DOI: 10.1016/j.ajhg.2018.11.006] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 11/12/2018] [Indexed: 12/17/2022] Open
Abstract
Phenome-wide association studies (PheWASs) have been a useful tool for testing associations between genetic variations and multiple complex traits or diagnoses. Linking PheWAS-based associations between phenotypes and a variant or a genomic region into a network provides a new way to investigate cross-phenotype associations, and it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy. We created a network of associations from one of the largest PheWASs on electronic health record (EHR)-derived phenotypes across 38,682 unrelated samples from the Geisinger's biobank; the samples were genotyped through the DiscovEHR project. We computed associations between 632,574 common variants and 541 diagnosis codes. Using these associations, we constructed a "disease-disease" network (DDN) wherein pairs of diseases were connected on the basis of shared associations with a given genetic variant. The DDN provides a landscape of intra-connections within the same disease classes, as well as inter-connections across disease classes. We identified clusters of diseases with known biological connections, such as autoimmune disorders (type 1 diabetes, rheumatoid arthritis, and multiple sclerosis) and cardiovascular disorders. Previously unreported relationships between multiple diseases were identified on the basis of genetic associations as well. The network approach applied in this study can be used to uncover interactions between diseases as a result of their shared, potentially pleiotropic SNPs. Additionally, this approach might advance clinical research and even clinical practice by accelerating our understanding of disease mechanisms on the basis of similar underlying genetic associations.
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Affiliation(s)
- Anurag Verma
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Lisa Bang
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA 17821, USA
| | - Jason E Miller
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yanfei Zhang
- Genomic Medicine Institute, Geisinger, Danville, PA 17821, USA
| | | | - Yu Zhang
- Department of Statistics, The Pennsylvania State University, University Park, PA 16802, USA
| | - Marta Byrska-Bishop
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA 17821, USA
| | - David J Carey
- Weis Center for Research, Geisinger, Danville, PA 17821, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Sarah A Pendergrass
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA 17821, USA
| | - Dokyoon Kim
- The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA; Biomedical and Translational Informatics Institute, Geisinger, Danville, PA 17821, USA.
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