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Tan X, Xiao Z, Wen Y, Liu H, Yu W. Advancing allergic rhinitis research through phenome-wide association studies: Insights from known genetic loci. World Allergy Organ J 2025; 18:101014. [PMID: 39807185 PMCID: PMC11728958 DOI: 10.1016/j.waojou.2024.101014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 10/14/2024] [Accepted: 11/24/2024] [Indexed: 01/16/2025] Open
Abstract
Background Allergic rhinitis (AR) is a common chronic respiratory disease that can lead to the development of various other conditions. Although genetic risk loci associated with AR have been reported, the connections between these loci and AR comorbidities or other diseases remain unclear. Methods This study conducted a phenome-wide association study (PheWAS) using known AR risk loci to explore the impact of known AR risk variants on a broad spectrum of phenotypes. Subsequently, linkage disequilibrium score regression (LDSC) and bidirectional two-sample mendelian randomization (TSMR) analyses were used to further analyze the genetic correlation and causal relationships between significant and potentially related phenotypes and AR. Results The PheWAS analysis indicated significant associations between asthma, eczema, nasal polyps, hypothyroidism, and AR risk variants. Additionally, potential associations were observed with ulcerative colitis, psoriasis, chalazion, pernicious anemia, glaucoma, multiple sclerosis, arthritis, prostate cancer, varicose veins of lower extremities, and heart attack. LDSC analysis showed that only asthma, eczema, and nasal polyps have significant positive genetic correlations with AR. Furthermore, TSMR analysis revealed causal relationships between AR and asthma, eczema, and nasal polyps. Conclusion This study highlights the impact of AR risk loci on a variety of diseases. By revealing new associations and shared genetic pathways, our findings provide valuable insights into the pathophysiology of AR and pave the way for more effective targeted interventions to manage AR and its related diseases.
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Affiliation(s)
- Xingling Tan
- Institute of Life Science, Chongqing Medical University, Chongqing, China
| | - Zhouyouyou Xiao
- Department of Audiology and Speech Rehabilitation, Chongqing Medical University, Chongqing, China
| | - Yao Wen
- Institute of Life Science, Chongqing Medical University, Chongqing, China
| | - Han Liu
- Institute of Life Science, Chongqing Medical University, Chongqing, China
| | - Wei Yu
- Institute of Life Science, Chongqing Medical University, Chongqing, China
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Cai X, Li H, Cao X, Ma X, Zhu W, Xu L, Yang S, Yu R, Huang P. Integrating transcriptomic and polygenic risk scores to enhance predictive accuracy for ischemic stroke subtypes. Hum Genet 2025; 144:43-54. [PMID: 39551887 DOI: 10.1007/s00439-024-02717-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2024] [Accepted: 11/11/2024] [Indexed: 11/19/2024]
Abstract
Ischemic stroke (IS), characterized by complex etiological diversity, is a significant global health challenge. Recent advancements in genome-wide association studies (GWAS) and transcriptomic profiling offer promising avenues for enhanced risk prediction and understanding of disease mechanisms. GWAS summary statistics from the GIGASTROKE Consortium and genetic and phenotypic data from the UK Biobank (UKB) were used. Transcriptome-Wide Association Studies (TWAS) were conducted using FUSION to identify genes associated with IS and its subtypes across eight tissues. Colocalization analysis identified shared genetic variants influencing both gene expression and disease risk. Sum Transcriptome-Polygenic Risk Scores (STPRS) models were constructed by combining polygenic risk scores (PRS) and polygenic transcriptome risk scores (PTRS) using logistic regression. The predictive performance of STPRS was evaluated using the area under the curve (AUC). A Phenome-wide association study (PheWAS) explored associations between STPRS and various phenotypes. TWAS identified 34 susceptibility genes associated with IS and its subtypes. Colocalization analysis revealed 18 genes with a posterior probability (PP) H4 > 75% for joint expression quantitative trait loci (eQTL) and GWAS associations, highlighting their genetic relevance. The STPRS models demonstrated superior predictive accuracy compared to conventional PRS, showing significant associations with numerous UKB phenotypes, including atrial fibrillation and blood pressure. Integrating transcriptomic data with polygenic risk scores through STPRS enhances predictive accuracy for IS and its subtypes. This approach refines our understanding of the genetic and molecular landscape of stroke and paves the way for tailored preventive and therapeutic strategies.
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Affiliation(s)
- Xuehong Cai
- Department of Epidemiology, Center for Global Health, School of Public Health, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, 211166, China
| | - Haochang Li
- Department of Epidemiology, Center for Global Health, School of Public Health, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, 211166, China
| | - Xiaoxiao Cao
- Department of Epidemiology, Center for Global Health, School of Public Health, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, 211166, China
| | - Xinyan Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, 211166, China
| | - Wenhao Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, 211166, China
| | - Lei Xu
- Department of Epidemiology, Center for Global Health, School of Public Health, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, 211166, China
| | - Sheng Yang
- Department of Biostatistics, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, China
| | - Rongbin Yu
- Department of Epidemiology, Center for Global Health, School of Public Health, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, 211166, China.
| | - Peng Huang
- Department of Epidemiology, Center for Global Health, School of Public Health, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, 211166, China.
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Jeong HG, Jeon M, Ryu KJ, Kim J, Choe BY, Joo YY, Park H. Similar but Distinct Comorbidity Patterns Between Polycystic Ovary Syndrome and Endometriosis in Korean Women: A Nationwide Cohort Study. J Korean Med Sci 2024; 39:e284. [PMID: 39561807 DOI: 10.3346/jkms.2024.39.e284] [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: 03/21/2024] [Accepted: 08/15/2024] [Indexed: 11/21/2024] Open
Abstract
BACKGROUND Polycystic ovary syndrome (PCOS) and endometriosis are widely recognized as significant risk factors affecting the reproductive health of women. The underlying mechanisms impacting fertility may vary, potentially leading to divergent outcomes. We aimed to examine and contrast the prevalence patterns of diseases coexisting with PCOS and endometriosis, using a large-scale nationwide insurance claims data from Asian women of reproductive age. METHODS We analyzed health insurance and examination data of 157,662 Korean women aged 15-45 years, drawn from the Korea National Health Insurance Service-National Sample Cohort database. International Classification of Disease, Tenth Revision codes were mapped to phenome-wide association study codes (phecodes). Subsequently, multivariate logistic regression was performed to assess the comorbidity patterns among patients diagnosed with PCOS and endometriosis and healthy control groups. RESULTS Our analysis revealed that PCOS was correlated with a wider range of metabolic disorders and symptoms, such as hyperlipidemia, type 2 diabetes, various gastrointestinal (GI) issues, and an array of pregnancy-related complications. Conversely, endometriosis was more prevalent among benign neoplasms of female reproductive and digestive organs, endometrial hyperplasia, and angina pectoris. Notably, infertility and glaucoma demonstrated significant associations with both conditions. Furthermore, a comparison of symptom-related codes in women with endometriosis revealed a predominance of pain-related symptoms, whereas those with PCOS exhibited a broader spectrum, encompassing pain, pruritus, GI problems, cough, fever, menstrual cycle disorders, edema, and dizziness. CONCLUSION PCOS and endometriosis, which are prevalent gynecological disorders affecting similar age groups of women, rarely co-occur and exhibit unique comorbidity profiles. Tailored healthcare strategies that take into account these distinct patterns have the potential to enhance long-term healthcare outcomes of affected patients. Further research is required to elucidate the underlying mechanisms and contrasting comorbidity profiles between PCOS and endometriosis.
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Affiliation(s)
- Hye Gyeong Jeong
- Department of Obstetrics and Gynecology, Korea University College of Medicine, Seoul, Korea
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Minhyek Jeon
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ki-Jin Ryu
- Department of Obstetrics and Gynecology, Korea University College of Medicine, Seoul, Korea
| | - Jina Kim
- Department of Statistics, Korea University, Seoul, Korea
| | - Byeol Yi Choe
- Department of Obstetrics and Gynecology, Korea University College of Medicine, Seoul, Korea
| | - Yoonjung Yoonie Joo
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University, Seoul, Korea.
| | - Hyuntae Park
- Department of Obstetrics and Gynecology, Korea University College of Medicine, Seoul, Korea.
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Strosahl J, Ye K, Pazdro R. Novel insights into the pleiotropic health effects of growth differentiation factor 11 gained from genome-wide association studies in population biobanks. BMC Genomics 2024; 25:837. [PMID: 39237910 PMCID: PMC11378601 DOI: 10.1186/s12864-024-10710-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 08/14/2024] [Indexed: 09/07/2024] Open
Abstract
BACKGROUND Growth differentiation factor 11 (GDF11) is a member of the transforming growth factor-β (TGF-β) superfamily that has gained considerable attention over the last decade for its observed ability to reverse age-related deterioration of multiple tissues, including the heart. Yet as many researchers have struggled to confirm the cardioprotective and anti-aging effects of GDF11, the topic has grown increasingly controversial, and the field has reached an impasse. We postulated that a clearer understanding of GDF11 could be gained by investigating its health effects at the population level. METHODS AND RESULTS We employed a comprehensive strategy to interrogate results from genome-wide association studies in population Biobanks. Interestingly, phenome-wide association studies (PheWAS) of GDF11 tissue-specific cis-eQTLs revealed associations with asthma, immune function, lung function, and thyroid phenotypes. Furthermore, PheWAS of GDF11 genetic variants confirmed these results, revealing similar associations with asthma, immune function, lung function, and thyroid health. To complement these findings, we mined results from transcriptome-wide association studies, which uncovered associations between predicted tissue-specific GDF11 expression and the same health effects identified from PheWAS analyses. CONCLUSIONS In this study, we report novel relationships between GDF11 and disease, namely asthma and hypothyroidism, in contrast to its formerly assumed role as a rejuvenating factor in basic aging and cardiovascular health. We propose that these associations are mediated through the involvement of GDF11 in inflammatory signaling pathways. Taken together, these findings provide new insights into the health effects of GDF11 at the population level and warrant future studies investigating the role of GDF11 in these specific health conditions.
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Affiliation(s)
- Jessica Strosahl
- Department of Nutritional Sciences, University of Georgia, 305 Sanford Drive, Athens, GA, 30602, USA
| | - Kaixiong Ye
- Department of Genetics, University of Georgia, Athens, GA, 30602, USA
- Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA
| | - Robert Pazdro
- Department of Nutritional Sciences, University of Georgia, 305 Sanford Drive, Athens, GA, 30602, USA.
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Zhang B, You J, Rolls ET, Wang X, Kang J, Li Y, Zhang R, Zhang W, Wang H, Xiang S, Shen C, Jiang Y, Xie C, Yu J, Cheng W, Feng J. Identifying behaviour-related and physiological risk factors for suicide attempts in the UK Biobank. Nat Hum Behav 2024; 8:1784-1797. [PMID: 38956227 DOI: 10.1038/s41562-024-01903-x] [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: 10/22/2023] [Accepted: 04/29/2024] [Indexed: 07/04/2024]
Abstract
Suicide is a global public health challenge, yet considerable uncertainty remains regarding the associations of both behaviour-related and physiological factors with suicide attempts (SA). Here we first estimated polygenic risk scores (PRS) for SA in 334,706 UK Biobank participants and conducted phenome-wide association analyses considering 2,291 factors. We identified 246 (63.07%) behaviour-related and 200 (10.41%, encompassing neuroimaging, blood and metabolic biomarkers, and proteins) physiological factors significantly associated with SA-PRS, with robust associations observed in lifestyle factors and mental health. Further case-control analyses involving 3,558 SA cases and 149,976 controls mirrored behaviour-related associations observed with SA-PRS. Moreover, Mendelian randomization analyses supported a potential causal effect of liability to 58 factors on SA, such as age at first intercourse, neuroticism, smoking, overall health rating and depression. Notably, machine-learning classification models based on behaviour-related factors exhibited high discriminative accuracy in distinguishing those with and without SA (area under the receiver operating characteristic curve 0.909 ± 0.006). This study provides comprehensive insights into diverse risk factors for SA, shedding light on potential avenues for targeted prevention and intervention strategies.
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Affiliation(s)
- Bei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Jia You
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Oxford Centre for Computational Neuroscience, Oxford, UK
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Xiang Wang
- Medical Psychological Centre, The Second Xiangya Hospital, Central South University, Changsha, China
- Medical Psychological Institute, Central South University, Changsha, China
- China National Clinical Research Centre on Mental Disorders (Xiangya), Changsha, China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Yuzhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Ruohan Zhang
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Huifu Wang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Chun Shen
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Yuchao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Jintai Yu
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- MOE Frontiers Centre for Brain Science, Fudan University, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, 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, UK.
- MOE Frontiers Centre for Brain Science, Fudan University, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
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Yan Z, Yang J, Wei WT, Zhou ML, Mo DX, Wan X, Ma R, Wu MM, Huang JH, Liu YJ, Lv FH, Li MH. A time-resolved multi-omics atlas of transcriptional regulation in response to high-altitude hypoxia across whole-body tissues. Nat Commun 2024; 15:3970. [PMID: 38730227 PMCID: PMC11087590 DOI: 10.1038/s41467-024-48261-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 04/23/2024] [Indexed: 05/12/2024] Open
Abstract
High-altitude hypoxia acclimatization requires whole-body physiological regulation in highland immigrants, but the underlying genetic mechanism has not been clarified. Here we use sheep as an animal model for low-to-high altitude translocation. We generate multi-omics data including whole-genome sequences, time-resolved bulk RNA-Seq, ATAC-Seq and single-cell RNA-Seq from multiple tissues as well as phenotypic data from 20 bio-indicators. We characterize transcriptional changes of all genes in each tissue, and examine multi-tissue temporal dynamics and transcriptional interactions among genes. Particularly, we identify critical functional genes regulating the short response to hypoxia in each tissue (e.g., PARG in the cerebellum and HMOX1 in the colon). We further identify TAD-constrained cis-regulatory elements, which suppress the transcriptional activity of most genes under hypoxia. Phenotypic and transcriptional evidence indicate that antenatal hypoxia could improve hypoxia tolerance in offspring. Furthermore, we provide time-series expression data of candidate genes associated with human mountain sickness (e.g., BMPR2) and high-altitude adaptation (e.g., HIF1A). Our study provides valuable resources and insights for future hypoxia-related studies in mammals.
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Affiliation(s)
- Ze Yan
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, 100193, China
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Ji Yang
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, 100193, China
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Wen-Tian Wei
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, 100193, China
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Ming-Liang Zhou
- Sichuan Academy of Grassland Science, Chengdu, 611743, China
| | - Dong-Xin Mo
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, 100193, China
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Xing Wan
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, 100193, China
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Rui Ma
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, 100193, China
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Mei-Ming Wu
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, 100193, China
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jia-Hui Huang
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, 100193, China
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Ya-Jing Liu
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, 100193, China
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Feng-Hua Lv
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, 100193, China
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Meng-Hua Li
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing, 100193, China.
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
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Israr J, Alam S, Kumar A. System biology approaches for drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:221-245. [PMID: 38789180 DOI: 10.1016/bs.pmbts.2024.03.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design.
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Affiliation(s)
- Juveriya Israr
- Institute of Biosciences and Technology, Shri Ramswaroop Memorial University, Lucknow-Deva Road, Barabanki, Uttar Pradesh, India; Department of Biotechnology Era University, Lucknow, Uttar Pradesh, India
| | - Shabroz Alam
- Department of Biotechnology Era University, Lucknow, Uttar Pradesh, India
| | - Ajay Kumar
- Department of Biotechnology, Faculty of Engineering and Technology, Rama University, Mandhana, Kanpur, Uttar Pradesh, India.
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Wang L, Lu Y, Li D, Zhou Y, Yu L, Mesa Eguiagaray I, Campbell H, Li X, Theodoratou E. The landscape of the methodology in drug repurposing using human genomic data: a systematic review. Brief Bioinform 2024; 25:bbad527. [PMID: 38279645 PMCID: PMC10818097 DOI: 10.1093/bib/bbad527] [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: 07/17/2023] [Revised: 11/24/2023] [Accepted: 12/19/2023] [Indexed: 01/28/2024] Open
Abstract
The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.
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Affiliation(s)
- Lijuan Wang
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ying Lu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Doudou Li
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yajing Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lili Yu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ines Mesa Eguiagaray
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Xue Li
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, The University of Edinburgh MRC Institute of Genetics and Cancer, Edinburgh, UK
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9
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Song I, Thompson EW, Verma A, MacLean MT, Duda J, Elahi A, Tran R, Raghupathy P, Swago S, Hazim M, Bhattaru A, Schneider C, Vujkovic M, Torigian DA, Kahn CE, Gee JC, Borthakur A, Kripke CM, Carson CC, Carr R, Jehangir Q, Ko YA, Litt H, Rosen M, Mankoff DA, Schnall MD, Shou H, Chirinos J, Damrauer SM, Serper M, Chen J, Rader DJ, Witschey WRT, Sagreiya H. Clinical correlates of CT imaging-derived phenotypes among lean and overweight patients with hepatic steatosis. Sci Rep 2024; 14:53. [PMID: 38167550 PMCID: PMC10761858 DOI: 10.1038/s41598-023-49470-x] [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: 05/24/2023] [Accepted: 12/08/2023] [Indexed: 01/05/2024] Open
Abstract
The objective of this study is to define CT imaging derived phenotypes for patients with hepatic steatosis, a common metabolic liver condition, and determine its association with patient data from a medical biobank. There is a need to further characterize hepatic steatosis in lean patients, as its epidemiology may differ from that in overweight patients. A deep learning method determined the spleen-hepatic attenuation difference (SHAD) in Hounsfield Units (HU) on abdominal CT scans as a quantitative measure of hepatic steatosis. The patient cohort was stratified by BMI with a threshold of 25 kg/m2 and hepatic steatosis with threshold SHAD ≥ - 1 HU or liver mean attenuation ≤ 40 HU. Patient characteristics, diagnoses, and laboratory results representing metabolism and liver function were investigated. A phenome-wide association study (PheWAS) was performed for the statistical interaction between SHAD and the binary characteristic LEAN. The cohort contained 8914 patients-lean patients with (N = 278, 3.1%) and without (N = 1867, 20.9%) steatosis, and overweight patients with (N = 1863, 20.9%) and without (N = 4906, 55.0%) steatosis. Among all lean patients, those with steatosis had increased rates of cardiovascular disease (41.7 vs 27.8%), hypertension (86.7 vs 49.8%), and type 2 diabetes mellitus (29.1 vs 15.7%) (all p < 0.0001). Ten phenotypes were significant in the PheWAS, including chronic kidney disease, renal failure, and cardiovascular disease. Hepatic steatosis was found to be associated with cardiovascular, kidney, and metabolic conditions, separate from overweight BMI.
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Affiliation(s)
- Isabel Song
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Elizabeth W Thompson
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Anurag Verma
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew T MacLean
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey Duda
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Ameena Elahi
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Richard Tran
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Pavan Raghupathy
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Sophia Swago
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Mohamad Hazim
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Abhijit Bhattaru
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Carolin Schneider
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marijana Vujkovic
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Drew A Torigian
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Charles E Kahn
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - James C Gee
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Arijitt Borthakur
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Colleen M Kripke
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher C Carson
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rotonya Carr
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Medicine, School of Medicine, University of Washington, Seattle, WA, USA
| | - Qasim Jehangir
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yi-An Ko
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Harold Litt
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Mark Rosen
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - David A Mankoff
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Mitchell D Schnall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Julio Chirinos
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott M Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marina Serper
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jinbo Chen
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel J Rader
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Walter R T Witschey
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Hersh Sagreiya
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
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Pham K, Mulugeta A, Lumsden A, Hyppӧnen E. Genetically instrumented LDL-cholesterol lowering and multiple disease outcomes: A Mendelian randomization phenome-wide association study in the UK Biobank. Br J Clin Pharmacol 2023; 89:2992-3004. [PMID: 37208559 PMCID: PMC10952153 DOI: 10.1111/bcp.15793] [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: 07/24/2022] [Revised: 03/24/2023] [Accepted: 05/08/2023] [Indexed: 05/21/2023] Open
Abstract
AIMS Lipid-lowering medications are widely used to control blood cholesterol levels and manage a range of cardiovascular and lipid disorders. We aimed to explore the possible associations between LDL lowering and multiple disease outcomes or biomarkers. METHODS We performed a Mendelian randomization phenome-wide association study (MR-PheWAS) in 337 475 UK Biobank participants to test for associations between four proposed LDL-C-lowering genetic risk scores (PCSK9, HMGCR, NPC1L1 and LDLR) and 1135 disease outcomes, with follow-up MR analyses in 52 serum, urine, imaging and clinical biomarkers. We used inverse-variance weighted MR in the main analyses and complementary MR methods (weighted median, weighted mode, MR-Egger and MR-PRESSO) as sensitivity analyses. We accounted for multiple testing with false discovery rate correction (P < 2.0 × 10-4 for phecodes, P < 1.3 × 10-2 for biomarkers). RESULTS We found evidence for an association between genetically instrumented LDL lowering and 10 distinct disease outcomes, suggesting potential causality. All genetic instruments were associated with hyperlipidaemias and cardiovascular diseases in the expected directions. Biomarker analyses supported an effect of LDL-C lowering through PCSK9 on lung function (FEV [beta per 1 mg/dL lower LDL-C -1.49, 95% CI -2.21, -0.78]; FVC [-1.42, 95% CI -2.29, -0.54]) and through HMGCR on hippocampal volume (beta per 1 mg/dL lower LDL-C 6.09, 95% CI 1.74, 10.44). CONCLUSIONS We found genetic evidence to support both positive and negative effects of LDL-C lowering through all four LDL-C-lowering pathways. Future studies should further explore the effects of LDL-C lowering on lung function and changes in brain volume.
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Affiliation(s)
- Kitty Pham
- Australian Centre for Precision Health, Clinical & Health SciencesUniversity of South AustraliaAdelaideSouth AustraliaAustralia
| | - Anwar Mulugeta
- Australian Centre for Precision Health, Clinical & Health SciencesUniversity of South AustraliaAdelaideSouth AustraliaAustralia
- South Australian Health and Medical Research InstituteAdelaideSouth AustraliaAustralia
- Department of Pharmacology and Clinical Pharmacy, College of Health SciencesAddis Ababa UniversityAddis AbabaEthiopia
| | - Amanda Lumsden
- Australian Centre for Precision Health, Clinical & Health SciencesUniversity of South AustraliaAdelaideSouth AustraliaAustralia
- South Australian Health and Medical Research InstituteAdelaideSouth AustraliaAustralia
| | - Elina Hyppӧnen
- Australian Centre for Precision Health, Clinical & Health SciencesUniversity of South AustraliaAdelaideSouth AustraliaAustralia
- South Australian Health and Medical Research InstituteAdelaideSouth AustraliaAustralia
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11
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McCoy JCS, Spicer JI, Ibbini Z, Tills O. Phenomics as an approach to Comparative Developmental Physiology. Front Physiol 2023; 14:1229500. [PMID: 37645563 PMCID: PMC10461620 DOI: 10.3389/fphys.2023.1229500] [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: 05/26/2023] [Accepted: 07/24/2023] [Indexed: 08/31/2023] Open
Abstract
The dynamic nature of developing organisms and how they function presents both opportunity and challenge to researchers, with significant advances in understanding possible by adopting innovative approaches to their empirical study. The information content of the phenotype during organismal development is arguably greater than at any other life stage, incorporating change at a broad range of temporal, spatial and functional scales and is of broad relevance to a plethora of research questions. Yet, effectively measuring organismal development, and the ontogeny of physiological regulations and functions, and their responses to the environment, remains a significant challenge. "Phenomics", a global approach to the acquisition of phenotypic data at the scale of the whole organism, is uniquely suited as an approach. In this perspective, we explore the synergies between phenomics and Comparative Developmental Physiology (CDP), a discipline of increasing relevance to understanding sensitivity to drivers of global change. We then identify how organismal development itself provides an excellent model for pushing the boundaries of phenomics, given its inherent complexity, comparably smaller size, relative to adult stages, and the applicability of embryonic development to a broad suite of research questions using a diversity of species. Collection, analysis and interpretation of whole organismal phenotypic data are the largest obstacle to capitalising on phenomics for advancing our understanding of biological systems. We suggest that phenomics within the context of developing organismal form and function could provide an effective scaffold for addressing grand challenges in CDP and phenomics.
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Affiliation(s)
| | | | | | - Oliver Tills
- School of Biological and Marine Sciences, University of Plymouth, Plymouth, United Kingdom
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12
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Singhal P, Guare L, Morse C, Lucas A, Byrska-Bishop M, Guerraty MA, Kim D, Ritchie MD, Verma A. DETECT: Feature extraction method for disease trajectory modeling in electronic health records. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2023; 2023:487-496. [PMID: 37350926 PMCID: PMC10283148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Modeling with longitudinal electronic health record (EHR) data proves challenging given the high dimensionality, redundancy, and noise captured in EHR. In order to improve precision medicine strategies and identify predictors of disease risk in advance, evaluating meaningful patient disease trajectories is essential. In this study, we develop the algorithm DiseasE Trajectory fEature extraCTion (DETECT) for feature extraction and trajectory generation in high-throughput temporal EHR data. This algorithm can 1) simulate longitudinal individual-level EHR data, specified to user parameters of scale, complexity, and noise and 2) use a convergent relative risk framework to test intermediate codes occurring between specified index code(s) and outcome code(s) to determine if they are predictive features of the outcome. Temporal range can be specified to investigate predictors occurring during a specific period of time prior to onset of the outcome. We benchmarked our method on simulated data and generated real-world disease trajectories using DETECT in a cohort of 145,575 individuals diagnosed with hypertension in Penn Medicine EHR for severe cardiometabolic outcomes.
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Affiliation(s)
- Pankhuri Singhal
- Department of Genetics, University of Pennsylvania, Philadelphia, PA
| | - Lindsay Guare
- Department of Genetics, University of Pennsylvania, Philadelphia, PA
| | - Colleen Morse
- Department of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anastasia Lucas
- Department of Genetics, University of Pennsylvania, Philadelphia, PA
| | | | - Marie A Guerraty
- Department of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Institute of Biomedical Informatics, University of Pennsylvania, Philadelphia, PA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA
- Institute of Biomedical Informatics, University of Pennsylvania, Philadelphia, PA
- Center for Precision Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anurag Verma
- Department of Medicine, University of Pennsylvania, Philadelphia, PA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA
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Zhang F, Dou J, Zhao X, Luo H, Ma L, Wang L, Wang Y. Identification of Key Genes Associated with Heat Stress in Rats by Weighted Gene Co-Expression Network Analysis. Animals (Basel) 2023; 13:ani13101618. [PMID: 37238049 DOI: 10.3390/ani13101618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/28/2023] Open
Abstract
Heat stress has been a big challenge for animal survival and health due to global warming. However, the molecular processes driving heat stress response were unclear. In this study, we exposed the control group rats (n = 5) at 22 °C and the other three heat stress groups (five rats in each group) at 42 °C lasting 30, 60, and 120 min, separately. We performed RNA sequencing in the adrenal glands and liver and detected the levels of hormones related to heat stress in the adrenal gland, liver, and blood tissues. Weighted gene co-expression network analysis (WGCNA) was also performed. Results showed that rectal temperature and adrenal corticosterone levels were significantly negatively related to genes in the black module, which was significantly enriched in thermogenesis and RNA metabolism. The genes in the green-yellow module were strongly positively associated with rectal temperature and dopamine, norepinephrine, epinephrine, and corticosterone levels in the adrenal glands and were enriched in transcriptional regulatory activities under stress. Finally, 17 and 13 key genes in the black and green-yellow modules were identified, respectively, and shared common patterns of changes. Methyltransferase 3 (Mettl3), poly(ADP-ribose) polymerase 2 (Parp2), and zinc finger protein 36-like 1 (Zfp36l1) occupied pivotal positions in the protein-protein interaction network and were involved in a number of heat stress-related processes. Therefore, Parp2, Mettl3, and Zfp36l1 could be considered candidate genes for heat stress regulation. Our findings shed new light on the molecular processes underpinning heat stress.
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Affiliation(s)
- Fan Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jinhuan Dou
- College of Animal Science and Technology, Beijing University of Agriculture, Beijing 102206, China
| | - Xiuxin Zhao
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Hanpeng Luo
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Longgang Ma
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Lei Wang
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Yachun Wang
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
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Singh D, Singh A, Chawla PA. An overview of current strategies and future prospects in drug repurposing in tuberculosis. EXPLORATION OF MEDICINE 2023. [DOI: 10.37349/emed.2023.00125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
Abstract
A large number of the population faces mortality as an effect of tuberculosis (TB). The line of treatment in the management of TB faces a jolt with ever-increasing multi-drug resistance (DR) cases. Further, the drugs engaged in the treatment of TB are associated with different toxicities, such as renal and hepatic toxicity. Different combinations are sought for effective anti-tuberculosis (anti-TB) effects with a decrease in toxicity. In this regard, drug repurposing has been very promising in improving the efficacy of drugs by enhancement of bioavailability and widening the safety margin. The success in drug repurposing lies in specified binding and inhibition of a particular target in the drug molecule. Different drugs have been repurposed for various ailments like cancer, Alzheimer’s disease, acquired immunodeficiency syndrome (AIDS), hair loss, etc. Repurposing in anti-TB drugs holds great potential too. The use of whole-cell screening assays and the availability of large chemical compounds for testing against Mycobacterium tuberculosis poses a challenge in this development. The target-based discovery of sites has emerged in the form of phenotypic screening as ethionamide R (EthR) and malate synthase inhibitors are similar to pharmaceuticals. In this review, the authors have thoroughly described the drug repurposing techniques on the basis of pharmacogenomics and drug metabolism, pathogen-targeted therapy, host-directed therapy, and bioinformatics approaches for the identification of drugs. Further, the significance of repurposing of drugs elaborated on large databases has been revealed. The role of genomics and network-based methods in drug repurposing has been also discussed in this article.
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Affiliation(s)
- Dilpreet Singh
- Department of Pharmaceutics, ISF College of Pharmacy, Moga 142001, Punjab, India
| | - Amrinder Singh
- Department of Pharmaceutics, ISF College of Pharmacy, Moga 142001, Punjab, India
| | - Pooja A. Chawla
- Department of Pharmaceutical Chemistry, ISF College of Pharmacy, Moga 142001, Punjab, India
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Jujjavarapu C, Suri P, Pejaver V, Friedly J, Gold LS, Meier E, Cohen T, Mooney SD, Heagerty PJ, Jarvik JG. Predicting decompression surgery by applying multimodal deep learning to patients' structured and unstructured health data. BMC Med Inform Decis Mak 2023; 23:2. [PMID: 36609379 PMCID: PMC9824905 DOI: 10.1186/s12911-022-02096-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 12/29/2022] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Low back pain (LBP) is a common condition made up of a variety of anatomic and clinical subtypes. Lumbar disc herniation (LDH) and lumbar spinal stenosis (LSS) are two subtypes highly associated with LBP. Patients with LDH/LSS are often started with non-surgical treatments and if those are not effective then go on to have decompression surgery. However, recommendation of surgery is complicated as the outcome may depend on the patient's health characteristics. We developed a deep learning (DL) model to predict decompression surgery for patients with LDH/LSS. MATERIALS AND METHOD We used datasets of 8387 and 8620 patients from a prospective study that collected data from four healthcare systems to predict early (within 2 months) and late surgery (within 12 months after a 2 month gap), respectively. We developed a DL model to use patients' demographics, diagnosis and procedure codes, drug names, and diagnostic imaging reports to predict surgery. For each prediction task, we evaluated the model's performance using classical and generalizability evaluation. For classical evaluation, we split the data into training (80%) and testing (20%). For generalizability evaluation, we split the data based on the healthcare system. We used the area under the curve (AUC) to assess performance for each evaluation. We compared results to a benchmark model (i.e. LASSO logistic regression). RESULTS For classical performance, the DL model outperformed the benchmark model for early surgery with an AUC of 0.725 compared to 0.597. For late surgery, the DL model outperformed the benchmark model with an AUC of 0.655 compared to 0.635. For generalizability performance, the DL model outperformed the benchmark model for early surgery. For late surgery, the benchmark model outperformed the DL model. CONCLUSIONS For early surgery, the DL model was preferred for classical and generalizability evaluation. However, for late surgery, the benchmark and DL model had comparable performance. Depending on the prediction task, the balance of performance may shift between DL and a conventional ML method. As a result, thorough assessment is needed to quantify the value of DL, a relatively computationally expensive, time-consuming and less interpretable method.
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Affiliation(s)
- Chethan Jujjavarapu
- Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Box 358047, Seattle, WA, 98195, USA
| | - Pradeep Suri
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA
- Department of Rehabilitation Medicine, University of Washington, 1959 NE Pacific St, Seattle, WA, 98195, USA
| | - Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Janna Friedly
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA
- Department of Rehabilitation Medicine, University of Washington, 1959 NE Pacific St, Seattle, WA, 98195, USA
| | - Laura S Gold
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA
- Department of Radiology, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA
| | - Eric Meier
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA
- Department of Biostatistics, University of Washington, Box 357232, Seattle, WA, 98195-7232, USA
- Center for Biomedical Statistics, University of Washington, Seattle, WA, USA
| | - Trevor Cohen
- Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Box 358047, Seattle, WA, 98195, USA
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Box 358047, Seattle, WA, 98195, USA
| | - Patrick J Heagerty
- Department of Biostatistics, University of Washington, Box 357232, Seattle, WA, 98195-7232, USA
- Center for Biomedical Statistics, University of Washington, Seattle, WA, USA
| | - Jeffrey G Jarvik
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA.
- Department of Radiology, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA.
- Department of Neurological Surgery, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA.
- Department of Health Services, University of Washington, Box 357660, Seattle, WA, 98195-7660, USA.
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Li W, Mi S, Zhang J, Liu X, Chen S, Liu S, Feng X, Tang Y, Li Y, Liu L, Fang L, Zhang S, Yu Y. Integrating sperm cell transcriptome and seminal plasma metabolome to analyze the molecular regulatory mechanism of sperm motility in Holstein stud bulls. J Anim Sci 2023; 101:skad214. [PMID: 37366074 PMCID: PMC10355371 DOI: 10.1093/jas/skad214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 06/26/2023] [Indexed: 06/28/2023] Open
Abstract
Considering that artificial insemination is the most widely used assisted reproductive technique in the dairy industry, the semen quality of bulls is very important for selecting excellent stud bulls. Sperm motility is one of the important traits of semen quality, and related genes may be regulated by environmental factors. Seminal plasma can affect sperm cell transcriptome and further affect sperm motility through exosome or other processes. However, the molecular regulation mechanism of bull sperm motility has not been studied by combining the sperm cell transcriptome with seminal plasma metabolome. The number of motile sperm per ejaculate (NMSPE) is an integrated indicator for assessing sperm motility in stud bulls. In the present study, we selected 7 bulls with higher NMSPE (5,698.55 million +/- 945.40 million) as group H and 7 bulls with lower NMSPE (2,279.76 million +/- 1,305.69 million) as group L from 53 Holstein stud bulls. The differentially expressed genes (DEGs) in sperm cells were evaluated between the two groups (H vs. L). We conducted gene co-expression network analysis (WGCNA) on H and L groups of bulls, as well as two monozygotic twin Holstein bulls with different NMSPE values, to screen candidate genes for NMSPE. The regulatory effect of seminal plasma metabolome on the candidate genes of NMSPE was also investigated. A total of 1,099 DEGs were identified in the sperm cells of H and L groups. These DEGs were primarily concentrated in energy metabolism and sperm cell transcription. The significantly enriched Kyoto encyclopedia of genes and genomes (KEGG) pathways of the 57 differential metabolites were the aminoacyl-tRNA biosynthesis pathway and vitamin B6 metabolism pathway. Our study discovered 14 genes as the potential candidate markers for sperm motility, including FBXO39. We observed a broad correlation between transcriptome of sperm cells and seminal plasma metabolome, such as three metabolites, namely, mesaconic acid, 2-coumaric acid, and 4-formylaminoantipyrine, might regulate FBXO39 expression through potential pathways. The genes related to seminal plasma metabolites expressed in sperm cells are not only located near the quantitative trait loci of reproductive traits, but also enriched in the genome-wide association study signal of sire conception rate. Collectively, this study was the first to investigate the interplays among transcriptome of sperm cells and seminal plasma metabolome from Holstein stud bulls with different sperm motility.
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Affiliation(s)
- Wenlong Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, Department of Animal Breeding and Genetics, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Siyuan Mi
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, Department of Animal Breeding and Genetics, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jinning Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, Department of Animal Breeding and Genetics, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Xueqin Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, Department of Animal Breeding and Genetics, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Siqian Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, Department of Animal Breeding and Genetics, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Shuli Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, Department of Animal Breeding and Genetics, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Xia Feng
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, Department of Animal Breeding and Genetics, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Yongjie Tang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, Department of Animal Breeding and Genetics, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Yanhua Li
- Beijing Dairy Cattle Center, Qinghe’nanzhen Deshengmenwai Road, Beijing 100192, China
| | - Lin Liu
- Beijing Dairy Cattle Center, Qinghe’nanzhen Deshengmenwai Road, Beijing 100192, China
| | - Lingzhao Fang
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
- Center for Quantitative Genetics and Genomics (QGG), Aarhus University, Aarhus, Denmark
| | - Shengli Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, Department of Animal Breeding and Genetics, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Ying Yu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, Department of Animal Breeding and Genetics, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
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Wu BS, Zhang YR, Yang L, Zhang W, Deng YT, Chen SD, Feng JF, Cheng W, Yu JT. Polygenic Liability to Alzheimer's Disease Is Associated with a Wide Range of Chronic Diseases: A Cohort Study of 312,305 Participants. J Alzheimers Dis 2023; 91:437-447. [PMID: 36442194 DOI: 10.3233/jad-220740] [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] [Indexed: 11/24/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) patients rank among the highest levels of comorbidities compared to persons with other diseases. However, it is unclear whether the conditions are caused by shared pathophysiology due to the genetic pleiotropy for AD risk genes. OBJECTIVE To figure out the genetic pleiotropy for AD risk genes in a wide range of diseases. METHODS We estimated the polygenic risk score (PRS) for AD and tested the association between PRS and 16 ICD10 main chapters, 136 ICD10 level-1 chapters, and 377 diseases with cases more than 1,000 in 312,305 individuals without AD diagnosis from the UK Biobank. RESULTS After correction for multiple testing, AD PRS was associated with two main ICD10 chapters: Chapter IV (endocrine, nutritional and metabolic diseases) and Chapter VII (eye and adnexa disorders). When narrowing the definition of the phenotypes, positive associations were observed between AD PRS and other types of dementia (OR = 1.39, 95% CI [1.34, 1.45], p = 1.96E-59) and other degenerative diseases of the nervous system (OR = 1.18, 95% CI [1.13, 1.24], p = 7.74E-10). In contrast, we detected negative associations between AD PRS and diabetes mellitus, obesity, chronic bronchitis, other retinal disorders, pancreas diseases, and cholecystitis without cholelithiasis (ORs range from 0.94 to 0.97, FDR < 0.05). CONCLUSION Our study confirms several associations reported previously and finds some novel results, which extends the knowledge of genetic pleiotropy for AD in a range of diseases. Further mechanistic studies are necessary to illustrate the molecular mechanisms behind these associations.
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Affiliation(s)
- Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, 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, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Wei Cheng
- 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
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
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19
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Liang X, Cao X, Sha Q, Zhang S. HCLC-FC: A novel statistical method for phenome-wide association studies. PLoS One 2022; 17:e0276646. [PMID: 36350801 PMCID: PMC9645610 DOI: 10.1371/journal.pone.0276646] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 10/11/2022] [Indexed: 11/11/2022] Open
Abstract
The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association studies (PheWAS). In PheWAS, the whole phenome can be divided into numerous phenotypic categories according to the genetic architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test the association between one genetic variant and one phenotype at a time. In this article, we derived a novel and powerful multivariate method for PheWAS. The proposed method involves three steps. In the first step, we apply the bottom-up hierarchical clustering method to partition a large number of phenotypes into disjoint clusters within each phenotypic category. In the second step, the clustering linear combination method is used to combine test statistics within each category based on the phenotypic clusters and obtain p-values from each phenotypic category. In the third step, we propose a new false discovery rate (FDR) control approach. We perform extensive simulation studies to compare the performance of our method with that of other existing methods. The results show that our proposed method controls FDR very well and outperforms other methods we compared with. We also apply the proposed approach to a set of EMR-based phenotypes across more than 300,000 samples from the UK Biobank. We find that the proposed approach not only can well-control FDR at a nominal level but also successfully identify 1,244 significant SNPs that are reported to be associated with some phenotypes in the GWAS catalog. Our open-access tools and instructions on how to implement HCLC-FC are available at https://github.com/XiaoyuLiang/HCLCFC.
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Affiliation(s)
- Xiaoyu Liang
- Department of Preventive Medicine, Division of Biostatistics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
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20
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Bhattacharya A, Hirbo JB, Zhou D, Zhou W, Zheng J, Kanai M, Pasaniuc B, Gamazon ER, Cox NJ. Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative. CELL GENOMICS 2022; 2:100180. [PMID: 36341024 PMCID: PMC9631681 DOI: 10.1016/j.xgen.2022.100180] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 08/09/2022] [Accepted: 09/01/2022] [Indexed: 12/13/2022]
Abstract
The Global Biobank Meta-analysis Initiative (GBMI), through its diversity, provides a valuable opportunity to study population-wide and ancestry-specific genetic associations. However, with multiple ascertainment strategies and multi-ancestry study populations across biobanks, GBMI presents unique challenges in implementing statistical genetics methods. Transcriptome-wide association studies (TWASs) boost detection power for and provide biological context to genetic associations by integrating genetic variant-to-trait associations from genome-wide association studies (GWASs) with predictive models of gene expression. TWASs present unique challenges beyond GWASs, especially in a multi-biobank, meta-analytic setting. Here, we present the GBMI TWAS pipeline, outlining practical considerations for ancestry and tissue specificity, meta-analytic strategies, and open challenges at every step of the framework. We advise conducting ancestry-stratified TWASs using ancestry-specific expression models and meta-analyzing results using inverse-variance weighting, showing the least test statistic inflation. Our work provides a foundation for adding transcriptomic context to biobank-linked GWASs, allowing for ancestry-aware discovery to accelerate genomic medicine.
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Affiliation(s)
- Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Institute of Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jibril B. Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dan Zhou
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - the Global Biobank Meta-analysis Initiative
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Institute of Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Eric R. Gamazon
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Nancy J. Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
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21
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David S, Dorado G, Duarte EL, David-Bosne S, Trigueiro-Louro J, Rebelo-de-Andrade H. COVID-19: impact on Public Health and hypothesis-driven investigations on genetic susceptibility and severity. Immunogenetics 2022; 74:381-407. [PMID: 35348847 PMCID: PMC8961091 DOI: 10.1007/s00251-022-01261-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/14/2022] [Indexed: 12/12/2022]
Abstract
COVID-19 is a new complex multisystem disease caused by the novel coronavirus SARS-CoV-2. In slightly over 2 years, it infected nearly 500 million and killed 6 million human beings worldwide, causing an unprecedented coronavirus pandemic. Currently, the international scientific community is engaged in elucidating the molecular mechanisms of the pathophysiology of SARS-CoV-2 infection as a basis of scientific developments for the future control of COVID-19. Global exome and genome analysis efforts work to define the human genetics of protective immunity to SARS-CoV-2 infection. Here, we review the current knowledge regarding the SARS-CoV-2 infection, the implications of COVID-19 to Public Health and discuss genotype to phenotype association approaches that could be exploited through the selection of candidate genes to identify the genetic determinants of severe COVID-19.
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Affiliation(s)
- Susana David
- Departamento de Genética Humana, Instituto Nacional de Saúde Doutor Ricardo Jorge (INSA,IP), Lisboa, Portugal.
- Instituto de Investigação do Medicamento (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Lisboa, Portugal.
| | - Guillermo Dorado
- Atlántida Centro de Investigación y Desarrollo de Estudios Profesionales (CIDEP), Granada, Spain
| | - Elsa L Duarte
- MED-Instituto Mediterrâneo para a Agricultura, Ambiente e Desenvolvimento, Escola de Ciências e Tecnologia, Universidade de Évora, Évora, Portugal
| | | | - João Trigueiro-Louro
- Departamento de Doenças Infeciosas, INSA, IP, Lisboa, Portugal
- Host-Pathogen Interaction Unit, Instituto de Investigação do Medicamento (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Lisboa, Portugal
- Hospital Egas Moniz, Centro Hospitalar Lisboa Ocidental, Lisboa, Portugal
| | - Helena Rebelo-de-Andrade
- Departamento de Doenças Infeciosas, INSA, IP, Lisboa, Portugal
- Host-Pathogen Interaction Unit, Instituto de Investigação do Medicamento (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Lisboa, Portugal
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22
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Truong TTT, Panizzutti B, Kim JH, Walder K. Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders. Pharmaceutics 2022; 14:1464. [PMID: 35890359 PMCID: PMC9319329 DOI: 10.3390/pharmaceutics14071464] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/30/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.
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Affiliation(s)
- Trang T. T. Truong
- IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong 3220, Australia; (T.T.T.T.); (B.P.); (J.H.K.)
| | - Bruna Panizzutti
- IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong 3220, Australia; (T.T.T.T.); (B.P.); (J.H.K.)
| | - Jee Hyun Kim
- IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong 3220, Australia; (T.T.T.T.); (B.P.); (J.H.K.)
- Mental Health Theme, The Florey Institute of Neuroscience and Mental Health, Parkville 3010, Australia
| | - Ken Walder
- IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong 3220, Australia; (T.T.T.T.); (B.P.); (J.H.K.)
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23
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Rout M, Kour B, Vuree S, Lulu SS, Medicherla KM, Suravajhala P. Diabetes mellitus susceptibility with varied diseased phenotypes and its comparison with phenome interactome networks. World J Clin Cases 2022; 10:5957-5964. [PMID: 35949812 PMCID: PMC9254192 DOI: 10.12998/wjcc.v10.i18.5957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/02/2022] [Accepted: 04/22/2022] [Indexed: 02/06/2023] Open
Abstract
An emerging area of interest in understanding disease phenotypes is systems genomics. Complex diseases such as diabetes have played an important role towards understanding the susceptible genes and mutations. A wide number of methods have been employed and strategies such as polygenic risk score and allele frequencies have been useful, but understanding the candidate genes harboring those mutations is an unmet goal. In this perspective, using systems genomic approaches, we highlight the application of phenome-interactome networks in diabetes and provide deep insights. LINC01128, which we previously described as candidate for diabetes, is shown as an example to discuss the approach.
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Affiliation(s)
- Madhusmita Rout
- Department of Pediatrics, University of Oklahoma Health Sciences Centre, Oklahoma City, OK 73104, United States
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur 302001, Rajasthan, India
| | - Bhumandeep Kour
- Department of Biotechnology, Lovely Professional University, Phagwara 144001, Punjab, India
| | - Sugunakar Vuree
- Department of Biotechnology, Lovely Professional University, Phagwara 144001, Punjab, India
| | - Sajitha S Lulu
- Department of Biotechnology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Krishna Mohan Medicherla
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur 302001, Rajasthan, India
| | - Prashanth Suravajhala
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Vallikavu PO, Amritapuri, Clappana, Kollam 690525, Kerala, India
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24
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Dumancas GG, Harrison D, Rico JA, Zamora PRFC, Liwag AG, Villaruz JF, Guanzon MLVV, Ferraris HFD, Jalandoni PJB, Padernal WF, Villareal BNL, Maculada RA, Fernandez RMA, Villa FR, de Castro R. Are phenome-wide association studies feasible in a developing country? Trends Genet 2022; 38:885-888. [PMID: 35660028 DOI: 10.1016/j.tig.2022.05.003] [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: 03/07/2022] [Revised: 05/02/2022] [Accepted: 05/03/2022] [Indexed: 11/18/2022]
Abstract
Phenome-wide association studies (PheWASs), a powerful approach that examines phenotypes associated with a genetic marker, have been used extensively in highly developed countries. Although there may be a clear need for PheWAS in a developing country such as the Philippines, limitations related to resources and practicality would make conducting them a challenge.
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Affiliation(s)
- Gerard G Dumancas
- Department of Chemistry, Loyola Science Center, The University of Scranton, Scranton, PA 18510, USA; Balik Scientist Program, Department of Science and Technology-Philippine Council for Health Research and Development, Bicutan, Taguig City, 1631, Philippines.
| | - Destiny Harrison
- Department of Mathematics and Physical Sciences, Louisiana State University - Alexandria, Alexandria, LA 71302, USA
| | - Jonathan Adam Rico
- Center for Informatics, University of San Agustin, Gen. Luna St, Iloilo City, 5000, Philippines
| | | | - Aretha G Liwag
- West Visayas State University, Luna St, Lapaz, Iloilo City, 5000, Philippines
| | - Joselito F Villaruz
- West Visayas State University, Luna St, Lapaz, Iloilo City, 5000, Philippines
| | - Ma Luz Vicenta V Guanzon
- Corazon Locsin Montelibano Memorial Regional Hospital, Lacson St, Bacolod, Negros Occidental, 6100, Philippines
| | - Hans Francis D Ferraris
- Corazon Locsin Montelibano Memorial Regional Hospital, Lacson St, Bacolod, Negros Occidental, 6100, Philippines
| | | | | | | | | | | | | | - Romulo de Castro
- Center for Informatics, University of San Agustin, Gen. Luna St, Iloilo City, 5000, Philippines; Balik Scientist Program, Department of Science and Technology-Philippine Council for Health Research and Development, Bicutan, Taguig City, 1631, Philippines
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25
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Avery CL, Howard AG, Ballou AF, Buchanan VL, Collins JM, Downie CG, Engel SM, Graff M, Highland HM, Lee MP, Lilly AG, Lu K, Rager JE, Staley BS, North KE, Gordon-Larsen P. Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:55001. [PMID: 35533073 PMCID: PMC9084332 DOI: 10.1289/ehp9098] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 05/11/2023]
Abstract
Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.
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Affiliation(s)
- Christy L Avery
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Annie Green Howard
- Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Anna F Ballou
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Victoria L Buchanan
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jason M Collins
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Carolina G Downie
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Stephanie M Engel
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Moa P Lee
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Adam G Lilly
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Sociology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kun Lu
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Julia E Rager
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Brooke S Staley
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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26
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Lu C, Jin D, Palmer N, Fox K, Kohane IS, Smoller JW, Yu KH. Large-scale real-world data analysis identifies comorbidity patterns in schizophrenia. Transl Psychiatry 2022; 12:154. [PMID: 35410453 PMCID: PMC9001711 DOI: 10.1038/s41398-022-01916-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 11/23/2022] Open
Abstract
Schizophrenia affects >3.2 million people in the USA. However, its comorbidity patterns have not been systematically characterized in real-world populations. To address this gap, we conducted an observational study using a cohort of 86 million patients in a nationwide health insurance dataset. We identified participants with schizophrenia and those without schizophrenia matched by age, sex, and the first three digits of zip code. For each phenotype encoded in phecodes, we compared their prevalence in schizophrenia patients and the matched non-schizophrenic participants, and we performed subgroup analyses stratified by age and sex. Results show that anxiety, posttraumatic stress disorder, and substance abuse commonly occur in adolescents and young adults prior to schizophrenia diagnoses. Patients aged 60 and above are at higher risks of developing delirium, alcoholism, dementia, pelvic fracture, and osteomyelitis than their matched controls. Type 2 diabetes, sleep apnea, and eating disorders were more prevalent in women prior to schizophrenia diagnosis, whereas acute renal failure, rhabdomyolysis, and developmental delays were found at higher rates in men. Anxiety and obesity are more commonly seen in patients with schizoaffective disorders compared to patients with other types of schizophrenia. Leveraging a large-scale insurance claims dataset, this study identified less-known comorbidity patterns of schizophrenia and confirmed known ones. These comorbidity profiles can guide clinicians and researchers to take heed of early signs of co-occurring diseases.
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Affiliation(s)
- Chenyue Lu
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Di Jin
- grid.116068.80000 0001 2341 2786Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA USA
| | - Nathan Palmer
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Kathe Fox
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Isaac S. Kohane
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Jordan W. Smoller
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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27
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Kerley CI, Chaganti S, Nguyen TQ, Bermudez C, Cutting LE, Beason-Held LL, Lasko T, Landman BA. pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis. Neuroinformatics 2022; 20:483-505. [PMID: 34981404 PMCID: PMC9250547 DOI: 10.1007/s12021-021-09553-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/29/2021] [Indexed: 11/29/2022]
Abstract
Along with the increasing availability of electronic medical record (EMR) data, phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) have become a prominent, first-line method of analysis for uncovering the secrets of EMR. Despite this recent growth, there is a lack of approachable software tools for conducting these analyses on large-scale EMR cohorts. In this article, we introduce pyPheWAS, an open-source python package for conducting PheDAS and related analyses. This toolkit includes 1) data preparation, such as cohort censoring and age-matching; 2) traditional PheDAS analysis of ICD-9 and ICD-10 billing codes; 3) PheDAS analysis applied to a novel EMR phenotype mapping: current procedural terminology (CPT) codes; and 4) novelty analysis of significant disease-phenotype associations found through PheDAS. The pyPheWAS toolkit is approachable and comprehensive, encapsulating data prep through result visualization all within a simple command-line interface. The toolkit is designed for the ever-growing scale of available EMR data, with the ability to analyze cohorts of 100,000 + patients in less than 2 h. Through a case study of Down Syndrome and other intellectual developmental disabilities, we demonstrate the ability of pyPheWAS to discover both known and potentially novel disease-phenotype associations across different experiment designs and disease groups. The software and user documentation are available in open source at https://github.com/MASILab/pyPheWAS .
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Affiliation(s)
- Cailey I Kerley
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Shikha Chaganti
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Tin Q Nguyen
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Department of Special Education, Peabody College of Education and Human Development, Nashville, TN, USA
| | - Camilo Bermudez
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Laurie E Cutting
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Department of Special Education, Peabody College of Education and Human Development, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute On Aging, NIH, Baltimore, MD, USA
| | - Thomas Lasko
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
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28
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Abbas T, Chaturvedi G, Prakrithi P, Pathak AK, Kutum R, Dakle P, Narang A, Manchanda V, Patil R, Aggarwal D, Girase B, Srivastava A, Kapoor M, Gupta I, Pandey R, Juvekar S, Dash D, Mukerji M, Prasher B. Whole Exome Sequencing in Healthy Individuals of Extreme Constitution Types Reveals Differential Disease Risk: A Novel Approach towards Predictive Medicine. J Pers Med 2022; 12:jpm12030489. [PMID: 35330488 PMCID: PMC8952204 DOI: 10.3390/jpm12030489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 02/23/2022] [Indexed: 12/10/2022] Open
Abstract
Precision medicine aims to move from traditional reactive medicine to a system where risk groups can be identified before the disease occurs. However, phenotypic heterogeneity amongst the diseased and healthy poses a major challenge for identification markers for risk stratification and early actionable interventions. In Ayurveda, individuals are phenotypically stratified into seven constitution types based on multisystem phenotypes termed “Prakriti”. It enables the prediction of health and disease trajectories and the selection of health interventions. We hypothesize that exome sequencing in healthy individuals of phenotypically homogeneous Prakriti types might enable the identification of functional variations associated with the constitution types. Exomes of 144 healthy Prakriti stratified individuals and controls from two genetically homogeneous cohorts (north and western India) revealed differential risk for diseases/traits like metabolic disorders, liver diseases, and body and hematological measurements amongst healthy individuals. These SNPs differ significantly from the Indo-European background control as well. Amongst these we highlight novel SNPs rs304447 (IFIT5) and rs941590 (SERPINA10) that could explain differential trajectories for immune response, bleeding or thrombosis. Our method demonstrates the requirement of a relatively smaller sample size for a well powered study. This study highlights the potential of integrating a unique phenotyping approach for the identification of predictive markers and the at-risk population amongst the healthy.
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Affiliation(s)
- Tahseen Abbas
- Centre of Excellence for Applied Development of Ayurveda Prakriti and Genomics, CSIR Ayurgenomics Unit-TRISUTRA, CSIR-Institute of Genomics & Integrative Biology, Delhi 110020, India; (T.A.); (G.C.); (R.K.); (P.D.); (A.N.); (V.M.)
- Informatics and Big Data Unit, CSIR-Institute of Genomics & Integrative Biology, Mathura Road, Delhi 110020, India
- Academy of Scientific and Innovative Research, Ghaziabad 201002, India
| | - Gaura Chaturvedi
- Centre of Excellence for Applied Development of Ayurveda Prakriti and Genomics, CSIR Ayurgenomics Unit-TRISUTRA, CSIR-Institute of Genomics & Integrative Biology, Delhi 110020, India; (T.A.); (G.C.); (R.K.); (P.D.); (A.N.); (V.M.)
- Academy of Scientific and Innovative Research, Ghaziabad 201002, India
- Genomics and Molecular Medicine, CSIR-Institute of Genomics & Integrative Biology, Mathura Road, Delhi 110020, India; (P.P.); (A.K.P.)
| | - P. Prakrithi
- Genomics and Molecular Medicine, CSIR-Institute of Genomics & Integrative Biology, Mathura Road, Delhi 110020, India; (P.P.); (A.K.P.)
| | - Ankit Kumar Pathak
- Genomics and Molecular Medicine, CSIR-Institute of Genomics & Integrative Biology, Mathura Road, Delhi 110020, India; (P.P.); (A.K.P.)
| | - Rintu Kutum
- Centre of Excellence for Applied Development of Ayurveda Prakriti and Genomics, CSIR Ayurgenomics Unit-TRISUTRA, CSIR-Institute of Genomics & Integrative Biology, Delhi 110020, India; (T.A.); (G.C.); (R.K.); (P.D.); (A.N.); (V.M.)
- Informatics and Big Data Unit, CSIR-Institute of Genomics & Integrative Biology, Mathura Road, Delhi 110020, India
- Academy of Scientific and Innovative Research, Ghaziabad 201002, India
| | - Pushkar Dakle
- Centre of Excellence for Applied Development of Ayurveda Prakriti and Genomics, CSIR Ayurgenomics Unit-TRISUTRA, CSIR-Institute of Genomics & Integrative Biology, Delhi 110020, India; (T.A.); (G.C.); (R.K.); (P.D.); (A.N.); (V.M.)
| | - Ankita Narang
- Centre of Excellence for Applied Development of Ayurveda Prakriti and Genomics, CSIR Ayurgenomics Unit-TRISUTRA, CSIR-Institute of Genomics & Integrative Biology, Delhi 110020, India; (T.A.); (G.C.); (R.K.); (P.D.); (A.N.); (V.M.)
- Informatics and Big Data Unit, CSIR-Institute of Genomics & Integrative Biology, Mathura Road, Delhi 110020, India
| | - Vijeta Manchanda
- Centre of Excellence for Applied Development of Ayurveda Prakriti and Genomics, CSIR Ayurgenomics Unit-TRISUTRA, CSIR-Institute of Genomics & Integrative Biology, Delhi 110020, India; (T.A.); (G.C.); (R.K.); (P.D.); (A.N.); (V.M.)
| | - Rutuja Patil
- Vadu Rural Health Program, KEM Hospital Research Centre, Pune 412216, India; (R.P.); (D.A.); (B.G.); (A.S.); (S.J.)
| | - Dhiraj Aggarwal
- Vadu Rural Health Program, KEM Hospital Research Centre, Pune 412216, India; (R.P.); (D.A.); (B.G.); (A.S.); (S.J.)
| | - Bhushan Girase
- Vadu Rural Health Program, KEM Hospital Research Centre, Pune 412216, India; (R.P.); (D.A.); (B.G.); (A.S.); (S.J.)
| | - Ankita Srivastava
- Vadu Rural Health Program, KEM Hospital Research Centre, Pune 412216, India; (R.P.); (D.A.); (B.G.); (A.S.); (S.J.)
| | - Manav Kapoor
- Department of Neuroscience, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA;
| | - Ishaan Gupta
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India;
| | - Rajesh Pandey
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi 110007, India;
| | - Sanjay Juvekar
- Vadu Rural Health Program, KEM Hospital Research Centre, Pune 412216, India; (R.P.); (D.A.); (B.G.); (A.S.); (S.J.)
| | - Debasis Dash
- Informatics and Big Data Unit, CSIR-Institute of Genomics & Integrative Biology, Mathura Road, Delhi 110020, India
- Academy of Scientific and Innovative Research, Ghaziabad 201002, India
- Correspondence: (D.D.); (M.M.); (B.P.)
| | - Mitali Mukerji
- Centre of Excellence for Applied Development of Ayurveda Prakriti and Genomics, CSIR Ayurgenomics Unit-TRISUTRA, CSIR-Institute of Genomics & Integrative Biology, Delhi 110020, India; (T.A.); (G.C.); (R.K.); (P.D.); (A.N.); (V.M.)
- Academy of Scientific and Innovative Research, Ghaziabad 201002, India
- Genomics and Molecular Medicine, CSIR-Institute of Genomics & Integrative Biology, Mathura Road, Delhi 110020, India; (P.P.); (A.K.P.)
- Department of Bioscience and Bioengineering, Indian Institute of Technology Jodhpur, NH 62, Jodhpur 342037, India
- Correspondence: (D.D.); (M.M.); (B.P.)
| | - Bhavana Prasher
- Centre of Excellence for Applied Development of Ayurveda Prakriti and Genomics, CSIR Ayurgenomics Unit-TRISUTRA, CSIR-Institute of Genomics & Integrative Biology, Delhi 110020, India; (T.A.); (G.C.); (R.K.); (P.D.); (A.N.); (V.M.)
- Academy of Scientific and Innovative Research, Ghaziabad 201002, India
- Genomics and Molecular Medicine, CSIR-Institute of Genomics & Integrative Biology, Mathura Road, Delhi 110020, India; (P.P.); (A.K.P.)
- Correspondence: (D.D.); (M.M.); (B.P.)
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Fu M, Chang TS. Phenome-Wide Association Study of Polygenic Risk Score for Alzheimer's Disease in Electronic Health Records. Front Aging Neurosci 2022; 14:800375. [PMID: 35370621 PMCID: PMC8965623 DOI: 10.3389/fnagi.2022.800375] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 02/04/2022] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia and a growing public health burden in the United States. Significant progress has been made in identifying genetic risk for AD, but limited studies have investigated how AD genetic risk may be associated with other disease conditions in an unbiased fashion. In this study, we conducted a phenome-wide association study (PheWAS) by genetic ancestry groups within a large academic health system using the polygenic risk score (PRS) for AD. PRS was calculated using LDpred2 with genome-wide association study (GWAS) summary statistics. Phenotypes were extracted from electronic health record (EHR) diagnosis codes and mapped to more clinically meaningful phecodes. Logistic regression with Firth's bias correction was used for PRS phenotype analyses. Mendelian randomization was used to examine causality in significant PheWAS associations. Our results showed a strong association between AD PRS and AD phenotype in European ancestry (OR = 1.26, 95% CI: 1.13, 1.40). Among a total of 1,515 PheWAS tests within the European sample, we observed strong associations of AD PRS with AD and related phenotypes, which include mild cognitive impairment (MCI), memory loss, and dementias. We observed a phenome-wide significant association between AD PRS and gouty arthropathy (OR = 0.90, adjusted p = 0.05). Further causal inference tests with Mendelian randomization showed that gout was not causally associated with AD. We concluded that genetic predisposition of AD was negatively associated with gout, but gout was not a causal risk factor for AD. Our study evaluated AD PRS in a real-world EHR setting and provided evidence that AD PRS may help to identify individuals who are genetically at risk of AD and other related phenotypes. We identified non-neurodegenerative diseases associated with AD PRS, which is essential to understand the genetic architecture of AD and potential side effects of drugs targeting genetic risk factors of AD. Together, these findings expand our understanding of AD genetic and clinical risk factors, which provide a framework for continued research in aging with the growing number of real-world EHR linked with genetic data.
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Affiliation(s)
- Mingzhou Fu
- Movement Disorders Program, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Medical Informatics Home Area, Department of Bioinformatics, University of California, Los Angeles, Los Angeles, CA, United States
| | - Timothy S. Chang
- Movement Disorders Program, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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30
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Cao X, Wang X, Zhang S, Sha Q. Gene-based association tests using GWAS summary statistics and incorporating eQTL. Sci Rep 2022; 12:3553. [PMID: 35241742 PMCID: PMC8894384 DOI: 10.1038/s41598-022-07465-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 02/11/2022] [Indexed: 01/29/2023] Open
Abstract
Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with.
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Affiliation(s)
- Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA
| | - Xuexia Wang
- Department of Mathematics, University of North Texas, Denton, TX, USA
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA.
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31
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Choe EK, Shivakumar M, Verma A, Verma SS, Choi SH, Kim JS, Kim D. Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits. Sci Rep 2022; 12:1930. [PMID: 35121771 PMCID: PMC8817039 DOI: 10.1038/s41598-021-04580-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 12/17/2021] [Indexed: 11/09/2022] Open
Abstract
The expanding use of the phenome-wide association study (PheWAS) faces challenges in the context of using International Classification of Diseases billing codes for phenotype definition, imbalanced study population ethnicity, and constrained application of the results in research. We performed a PheWAS utilizing 136 deep phenotypes corroborated by comprehensive health check-ups in a Korean population, along with trans-ethnic comparisons through using the UK Biobank and Biobank Japan Project. Meta-analysis with Korean and Japanese population was done. The PheWAS associated 65 phenotypes with 14,101 significant variants (P < 4.92 × 10-10). Network analysis, visualization of cross-phenotype mapping, and causal inference mapping with Mendelian randomization were conducted. Among phenotype pairs from the genotype-driven cross-phenotype associations, we evaluated penetrance in correlation analysis using a clinical database. We focused on the application of PheWAS in order to make it robust and to aid the derivation of biological meaning post-PheWAS. This comprehensive analysis of PheWAS results based on a health check-up database will provide researchers and clinicians with a panoramic overview of the networks among multiple phenotypes and genetic variants, laying groundwork for the practical application of precision medicine.
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Affiliation(s)
- Eun Kyung Choe
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA.,Department of Surgery, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, South Korea
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA
| | - Anurag Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Shefali Setia Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Seung Ho Choi
- Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, South Korea
| | - Joo Sung Kim
- Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, South Korea. .,Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, 03080, South Korea.
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA. .,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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32
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System and network biology-based computational approaches for drug repositioning. COMPUTATIONAL APPROACHES FOR NOVEL THERAPEUTIC AND DIAGNOSTIC DESIGNING TO MITIGATE SARS-COV-2 INFECTION 2022. [PMCID: PMC9300680 DOI: 10.1016/b978-0-323-91172-6.00003-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Recent advances in computational biology have not only fastened the drug discovery process but have also proven to be a powerful tool for the search of existing molecules of therapeutic value for drug repurposing. The system biology-based drug repurposing approaches shorten the time and reduced the cost of the whole process when compared to de novo drug discovery. In the present pandemic situation, these computational approaches have emerged as a boon to tackle the COVID-19 associated morbidities and mortalities. In this chapter, we present the overview of system biology-based network system approaches which can be exploited for the drug repurposing of disease. Besides, we have included information on relevant repurposed drugs which are currently used for the treatment of COVID-19.
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33
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van Duijvenboden S, Ramírez J, Young WJ, Orini M, Mifsud B, Tinker A, Lambiase PD, Munroe PB. Genomic and pleiotropic analyses of resting QT interval identifies novel loci and overlap with atrial electrical disorders. Hum Mol Genet 2021; 30:2513-2523. [PMID: 34274964 PMCID: PMC8643508 DOI: 10.1093/hmg/ddab197] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 05/26/2021] [Accepted: 06/17/2021] [Indexed: 11/13/2022] Open
Abstract
The resting QT interval, an electrocardiographic (ECG) measure of ventricular myocardial repolarization, is a heritable risk marker of cardiovascular mortality, but the mechanisms remain incompletely understood. Previously reported candidate genes have provided insights into the regulatory mechanisms of the QT interval. However, there are still important knowledge gaps. We aimed to gain new insights by (i) providing new candidate genes, (ii) identifying pleiotropic associations with other cardiovascular traits, and (iii) scanning for sexually dimorphic genetic effects. We conducted a genome-wide association analysis for resting QT interval with ~9.8 million variants in 52 107 individuals of European ancestry without known cardiovascular disease from the UK Biobank. We identified 40 loci, 13 of which were novel, including 2 potential sex-specific loci, explaining ~11% of the trait variance. Candidate genes at novel loci were involved in myocardial structure and arrhythmogenic cardiomyopathy. Investigation of pleiotropic effects of QT interval variants using phenome-wide association analyses in 302 000 unrelated individuals from the UK Biobank and pairwise genome-wide comparisons with other ECG and cardiac imaging traits revealed genetic overlap with atrial electrical pathology. These findings provide novel insights into how abnormal myocardial repolarization and increased cardiovascular mortality may be linked.
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Affiliation(s)
- Stefan van Duijvenboden
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Julia Ramírez
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - William J Young
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, London EC1A 7BE, UK
| | - Michele Orini
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
| | - Borbala Mifsud
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha PO 34110, Qatar
| | - Andrew Tinker
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
- NIHR Barts Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Pier D Lambiase
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
- Barts Heart Centre, St Bartholomew’s Hospital, London EC1A 7BE, UK
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
- NIHR Barts Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
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34
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Zhang X, Yan C, Malin BA, Patel MB, Chen Y. Predicting next-day discharge via electronic health record access logs. J Am Med Inform Assoc 2021; 28:2670-2680. [PMID: 34592753 DOI: 10.1093/jamia/ocab211] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/21/2021] [Accepted: 09/15/2021] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE Hospital capacity management depends on accurate real-time estimates of hospital-wide discharges. Estimation by a clinician requires an excessively large amount of effort and, even when attempted, accuracy in forecasting next-day patient-level discharge is poor. This study aims to support next-day discharge predictions with machine learning by incorporating electronic health record (EHR) audit log data, a resource that captures EHR users' granular interactions with patients' records by communicating various semantics and has been neglected in outcome predictions. MATERIALS AND METHODS This study focused on the EHR data for all adults admitted to Vanderbilt University Medical Center in 2019. We learned multiple advanced models to assess the value that EHR audit log data adds to the daily prediction of discharge likelihood within 24 h and to compare different representation strategies. We applied Shapley additive explanations to identify the most influential types of user-EHR interactions for discharge prediction. RESULTS The data include 26 283 inpatient stays, 133 398 patient-day observations, and 819 types of user-EHR interactions. The model using the count of each type of interaction in the recent 24 h and other commonly used features, including demographics and admission diagnoses, achieved the highest area under the receiver operating characteristics (AUROC) curve of 0.921 (95% CI: 0.919-0.923). By contrast, the model lacking user-EHR interactions achieved a worse AUROC of 0.862 (0.860-0.865). In addition, 10 of the 20 (50%) most influential factors were user-EHR interaction features. CONCLUSION EHR audit log data contain rich information such that it can improve hospital-wide discharge predictions.
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Affiliation(s)
- Xinmeng Zhang
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Chao Yan
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Bradley A Malin
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Mayur B Patel
- Section of Surgical Sciences, Departments of Surgery & Neurosurgery, Division of Trauma, Surgical Critical Care, and Emergency General Surgery, Nashville, Tennessee, USA.,Geriatric Research and Education Clinical Center, Surgical Services, Veteran Affairs Tennessee Valley Healthcare System, Nashville, Tennessee, USA
| | - You Chen
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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35
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Daniels H, Jones KH, Heys S, Ford DV. Exploring the Use of Genomic and Routinely Collected Data: Narrative Literature Review and Interview Study. J Med Internet Res 2021; 23:e15739. [PMID: 34559060 PMCID: PMC8501405 DOI: 10.2196/15739] [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: 08/02/2019] [Revised: 10/01/2020] [Accepted: 07/15/2021] [Indexed: 11/13/2022] Open
Abstract
Background Advancing the use of genomic data with routinely collected health data holds great promise for health care and research. Increasing the use of these data is a high priority to understand and address the causes of disease. Objective This study aims to provide an outline of the use of genomic data alongside routinely collected data in health research to date. As this field prepares to move forward, it is important to take stock of the current state of play in order to highlight new avenues for development, identify challenges, and ensure that adequate data governance models are in place for safe and socially acceptable progress. Methods We conducted a literature review to draw information from past studies that have used genomic and routinely collected data and conducted interviews with individuals who use these data for health research. We collected data on the following: the rationale of using genomic data in conjunction with routinely collected data, types of genomic and routinely collected data used, data sources, project approvals, governance and access models, and challenges encountered. Results The main purpose of using genomic and routinely collected data was to conduct genome-wide and phenome-wide association studies. Routine data sources included electronic health records, disease and death registries, health insurance systems, and deprivation indices. The types of genomic data included polygenic risk scores, single nucleotide polymorphisms, and measures of genetic activity, and biobanks generally provided these data. Although the literature search showed that biobanks released data to researchers, the case studies revealed a growing tendency for use within a data safe haven. Challenges of working with these data revolved around data collection, data storage, technical, and data privacy issues. Conclusions Using genomic and routinely collected data holds great promise for progressing health research. Several challenges are involved, particularly in terms of privacy. Overcoming these barriers will ensure that the use of these data to progress health research can be exploited to its full potential.
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Affiliation(s)
- Helen Daniels
- Population Data Science, Swansea University, Swansea, United Kingdom
| | | | - Sharon Heys
- Population Data Science, Swansea University, Swansea, United Kingdom
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A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11188319] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Significant growth in Electronic Health Records (EHR) over the last decade has provided an abundance of clinical text that is mostly unstructured and untapped. This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. Named Entity Recognition (NER) and Relationship Extraction (RE) are key components of information extraction tasks in the clinical domain. In this paper, we highlight the present status of clinical NER and RE techniques in detail by discussing the existing proposed NLP models for the two tasks and their performances and discuss the current challenges. Our comprehensive survey on clinical NER and RE encompass current challenges, state-of-the-art practices, and future directions in information extraction from clinical text. This is the first attempt to discuss both of these interrelated topics together in the clinical context. We identified many research articles published based on different approaches and looked at applications of these tasks. We also discuss the evaluation metrics that are used in the literature to measure the effectiveness of the two these NLP methods and future research directions.
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Suri P, Stanaway IB, Zhang Y, Freidin MB, Tsepilov YA, Carrell DS, Williams FM, Aulchenko YS, Hakonarson H, Namjou B, Crosslin DR, Jarvik GP, Lee MT. Genome-wide association studies of low back pain and lumbar spinal disorders using electronic health record data identify a locus associated with lumbar spinal stenosis. Pain 2021; 162:2263-2272. [PMID: 33729212 PMCID: PMC8277660 DOI: 10.1097/j.pain.0000000000002221] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/15/2021] [Indexed: 12/30/2022]
Abstract
ABSTRACT Identifying genetic risk factors for lumbar spine disorders may lead to knowledge regarding underlying mechanisms and the development of new treatments. We conducted a genome-wide association study involving 100,811 participants with genotypes and longitudinal electronic health record data from the Electronic Medical Records and Genomics Network and Geisinger Health. Cases and controls were defined using validated algorithms and clinical diagnostic codes. Electronic health record-defined phenotypes included low back pain requiring healthcare utilization (LBP-HC), lumbosacral radicular syndrome (LSRS), and lumbar spinal stenosis (LSS). Genome-wide association study used logistic regression with additive genetic effects adjusting for age, sex, site-specific factors, and ancestry (principal components). A fixed-effect inverse-variance weighted meta-analysis was conducted. Genetic variants of genome-wide significance (P < 5 × 10-8) were carried forward for replication in an independent sample from UK Biobank. Phenotype prevalence was 48.8% for LBP-HC, 19.8% for LSRS, and 7.9% for LSS. No variants were significantly associated with LBP-HC. One locus was associated with LSRS (lead variant rs146153280:C>G, odds ratio [OR] = 1.17 for G, P = 2.1 × 10-9), but was not replicated. Another locus on chromosome 2 spanning GFPT1, NFU1, and AAK1 was associated with LSS (lead variant rs13427243:G>A, OR = 1.10 for A, P = 4.3 × 10-8) and replicated in UK Biobank (OR = 1.11, P = 5.4 × 10-5). This was the first genome-wide association study meta-analysis of lumbar spinal disorders using electronic health record data. We identified 2 novel associations with LSRS and LSS; the latter was replicated in an independent sample.
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Affiliation(s)
- Pradeep Suri
- Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System, 1660 S. Columbian Way, Seattle, WA 98108, USA
- Division of Rehabilitation Care Services, 1660 S. Columbian Way, Seattle, WA 98108, USA
- Clinical Learning, Evidence, and Research Center, University of Washington, 325 Ninth Avenue, Box 359612 Seattle, WA 98104, USA
- Department of Rehabilitation Medicine, University of Washington, 325 Ninth Avenue, Box 359612 Seattle, WA 98104, USA
| | - Ian B. Stanaway
- Department of Medicine (Medical Genetics), University of Washington Medical Center, 3720 15th Ave NE, Seattle, WA 98105, USA
| | - Yanfei Zhang
- Genomic Medicine Institute, Geisinger, 100 N. Academy Avenue, Danville, PA 17822, USA
| | - Maxim B. Freidin
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, London, SE1 7EH, UK
| | - Yakov A. Tsepilov
- Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, 1 Pirogova Street, Novosibirsk, 630090, Russia
- Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, 10 Lavrentiev Avenue, Novosibirsk, 630090, Russia
- PolyOmica, s’-Hetogenbosch,5237 PA, The Netherlands
| | - David S. Carrell
- Kaiser Permante Washington Health Research Institute, 1700 Minor Ave, Suite 1600, Seattle, WA 98101, USA
| | - Frances M.K. Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, London, SE1 7EH, UK
| | - Yurii S. Aulchenko
- PolyOmica, s’-Hetogenbosch,5237 PA, The Netherlands
- Kurchatov Genomics Center of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090, Russia
| | - Hakon Hakonarson
- Department of Pediatrics, Children’s Hospital of Philadelphia, 3615 Civic Center Blvd.Philadelphia, PA 19104, USA
| | - Bahram Namjou
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH 45229, USA
| | - David R. Crosslin
- Department of Biomedical Informatics and Education, University of Washington, 3720 15th Ave NE, Seattle, WA 98105, USA
| | - Gail P. Jarvik
- Department of Medicine (Medical Genetics), University of Washington Medical Center, 3720 15th Ave NE, Seattle, WA 98105, USA
| | - Ming Ta Lee
- Genomic Medicine Institute, Geisinger, 100 N. Academy Avenue, Danville, PA 17822, USA
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Zhou J, Sun Y, Huang W, Ye K. Altered Blood Cell Traits Underlie a Major Genetic Locus of Severe COVID-19. J Gerontol A Biol Sci Med Sci 2021; 76:e147-e154. [PMID: 33530099 PMCID: PMC7929197 DOI: 10.1093/gerona/glab035] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Indexed: 12/29/2022] Open
Abstract
Background The genetic locus 3p21.31 has been associated with severe coronavirus disease 2019 (COVID-19), but the underlying pathophysiological mechanism is unknown. Methods To identify intermediate traits associated with the 3p21.31 locus, we first performed a phenome-wide association study (PheWAS) with 923 phenotypes in 310,999 European individuals from the UK Biobank. For genes potentially regulated by the COVID-19 risk variant, we examined associations between their expression and the polygenic score (PGS) of 1,263 complex traits in a meta-analysis of 31,684 blood samples. For the prioritized blood cell traits, we tested their associations with age and sex in the same UK Biobank sample. Results Our PheWAS highlighted multiple blood cell traits to be associated with the COVID-19 risk variant, including monocyte count and percentage (p = 1.07×10 -8, 4.09×10 -13), eosinophil count and percentage (p = 5.73×10 -3, 2.20×10 -3), and neutrophil percentage (p = 3.23×10 -3). The PGS analysis revealed positive associations between the expression of candidate genes and genetically predicted counts of specific blood cells: CCR3 with eosinophil and basophil (p = 5.73×10 -21, 5.08×10 -19); CCR2 with monocytes (p = 2.40×10 -10); and CCR1 with monocytes and neutrophil (p = 1.78×10 -6, 7.17×10 -5). Additionally, we found that almost all examined white blood cell traits are significantly different across age and sex groups. Conclusions Our findings suggest that altered blood cell traits, especially those of monocyte, eosinophil, and neutrophil, may represent the mechanistic links between the genetic locus 3p21.31 and severe COVID-19. They may also underlie the increased risk of severe COVID-19 in older adults and men.
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Affiliation(s)
- Jingqi Zhou
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA.,School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - Yitang Sun
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA
| | - Weishan Huang
- Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, USA.,Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Kaixiong Ye
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA.,Institute of Bioinformatics, University of Georgia, Athens, GA, USA
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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: 2.3] [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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Moon CY, Schilder BM, Raj T, Huang KL. Phenome-wide and expression quantitative trait locus associations of coronavirus disease 2019 genetic risk loci. iScience 2021; 24:102550. [PMID: 34027315 PMCID: PMC8129787 DOI: 10.1016/j.isci.2021.102550] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 03/24/2021] [Accepted: 05/13/2021] [Indexed: 12/15/2022] Open
Abstract
While several genes and clinical traits have been associated with higher risk of severe coronavirus disease 2019 (COVID-19), how host genetic variants may interact with these parameters and contribute to severe disease is still unclear. Herein, we performed phenome-wide association study, tissue and immune-cell-specific expression quantitative trait locus (eQTL)/splicing quantitative trait locus, and colocalization analyses for genetic risk loci suggestively associated with severe COVID-19 with respiratory failure. Thirteen phenotypes/traits were associated with the severe COVID-19-associated loci at the genome-wide significance threshold, including monocyte counts, fat metabolism traits, and fibrotic idiopathic interstitial pneumonia. In addition, we identified tissue and immune subtype-specific eQTL associations affecting 48 genes, including several ones that may directly impact host immune responses, colocalized with the severe COVID-19 genome-wide association study associations, and showed altered expression in single-cell transcriptomes. Collectively, our work demonstrates that host genetic variations associated with multiple genes and traits show genetic pleiotropy with severe COVID-19 and may inform disease etiology.
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Affiliation(s)
- Chang Yoon Moon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Brian M. Schilder
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Nash Family Department of Neuroscience & Friedman Brain Institute, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Towfique Raj
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Nash Family Department of Neuroscience & Friedman Brain Institute, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kuan-lin Huang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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Application of network link prediction in drug discovery. BMC Bioinformatics 2021; 22:187. [PMID: 33845763 PMCID: PMC8042985 DOI: 10.1186/s12859-021-04082-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 03/16/2021] [Indexed: 11/10/2022] Open
Abstract
Background Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug–drug, drug–disease, and protein–protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches. Results We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and \documentclass[12pt]{minimal}
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\begin{document}$$LRW_5$$\end{document}LRW5 are the top 3 best performers on all five datasets. Conclusions This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug–drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks. Supplementary Information The online version supplementary material available at 10.1186/s12859-021-04082-y.
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Next Generation Exome Sequencing of Pediatric Asthma Identifies Rare and Novel Variants in Candidate Genes. DISEASE MARKERS 2021; 2021:8884229. [PMID: 33628342 PMCID: PMC7888305 DOI: 10.1155/2021/8884229] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 01/21/2021] [Accepted: 02/03/2021] [Indexed: 12/16/2022]
Abstract
Multiple genes have been implicated to have a role in asthma predisposition by association studies. Pediatric patients often manifest a more extensive form of this disease and a particularly severe disease course. It is likely that genetic predisposition could play a more substantial role in this group. This study is aimed at identifying the spectrum of rare and novel variation in known pediatric asthma susceptibility genes using whole exome sequencing analysis in nine individual cases of childhood onset allergic asthma. DNA samples from the nine children with a history of bronchial asthma diagnosis underwent whole exome sequencing on Ion Proton. For each patient, the entire complement of rare variation within strongly associated candidate genes was catalogued. The analysis showed 21 variants in the subjects, 13 had been previously identified, and 8 were novel. Also, among of which, nineteen were nonsynonymous and 2 were nonsense. With regard to the novel variants, the 2 nonsynonymous variants in the PRKG1 gene (PRKG1: p.C519W and PRKG1: p.G520W) were presented in 4 cases, and a nonsynonymous variant in the MAVS gene (MAVS: p.A45V) was identified in 3 cases. The variants we found in this study will enrich the variant spectrum and build up the database in the Saudi population. Novel eight variants were identified in the study which provides more evidence in the genetic susceptibility in asthma among Saudi children, providing a genetic screening map for the molecular genetic determinants of allergic disease in Saudi children, with the goal of reducing the impact of chronic diseases on the health and the economy. We believe that the advanced specified statistical filtration/annotation programs used in this study succeeded to release such results in a preliminary study, exploring the genetic map of that disease in Saudi children.
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Langlois PH, Schraw JM, Hoyt AT, Lupo PJ. Leveraging a phenome-wide approach to identify novel exposure-birth defect associations: A proof of concept using maternal smoking and a spectrum of birth defects. Birth Defects Res 2020; 113:439-445. [PMID: 33275842 DOI: 10.1002/bdr2.1851] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 11/07/2020] [Accepted: 11/08/2020] [Indexed: 11/06/2022]
Abstract
BACKGROUND There are often questions about the impact of exposures on a range of birth defects, but there are few rigorous approaches for evaluating these associations. Using maternal smoking as an example we applied a phenome-wide association study (PheWAS) approach to evaluate the impact of this exposure on a comprehensive range of birth defects. METHODS Cases were obtained from the Texas Birth Defects Registry for the period 1999-2015. A total of 127 birth defects were examined. Maternal smoking at any time during the pregnancy was ascertained from the vital record. Data were randomly divided into discovery (60%) and replication (40%) partitions. Poisson regression models were run in the discovery partition, using a Bonferroni threshold of p < 3.9×10-4 . Birth defects passing that threshold in adjusted models were evaluated in the replication partition using p < 0.05 to determine statistical significance. RESULTS Four birth defects were positively and significantly associated with maternal smoking in both partitions: cleft palate, anomalies of the pulmonary artery, other specified anomalies of the mouth and pharynx, and congenital hypertrophic pyloric stenosis. Obstructive defects of the renal pelvis and ureter showed consistent negative associations. All five defects exhibited significant dose-response relationships. CONCLUSIONS We demonstrated that a statistically rigorous approach can be applied to birth defects registry data to examine the association between specified exposures and a range of birth defects. While we confirmed previously reported associations, others were not validated, likely due to misclassification in exposure based on vital records.
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Affiliation(s)
- Peter H Langlois
- Division of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas School of Public Health - Austin Regional Campus, Austin, Texas, USA
| | - Jeremy M Schraw
- Center for Epidemiology and Population Health, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA.,Section of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA.,Texas Children's Cancer and Hematology Centers, Texas Children's Hospital, Houston, Texas, USA
| | - Adrienne T Hoyt
- University of Texas School of Public Health - Austin Regional Campus, Austin, Texas, USA
| | - Philip J Lupo
- Center for Epidemiology and Population Health, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA.,Section of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA.,Texas Children's Cancer and Hematology Centers, Texas Children's Hospital, Houston, Texas, USA
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A Comprehensive Assessment of the Associations Between Season of Conception and Birth Defects, Texas, 1999-2015. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17197120. [PMID: 33003294 PMCID: PMC7579376 DOI: 10.3390/ijerph17197120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 09/25/2020] [Accepted: 09/26/2020] [Indexed: 12/23/2022]
Abstract
Birth defects prevalence may vary seasonally, but previous studies have focused on a few commonly occurring phenotypes. We performed a phenome-wide association study (PheWAS) in order to evaluate the associations between season of conception and a broad range of birth defects. Date of conception was estimated for all livebirths and birth defect cases in Texas from 1999-2015 using data from vital records, provided by the Texas Department of State Health Services Center for Health Statistics. Birth defects diagnoses were obtained from the Texas Birth Defects Registry, a statewide, active surveillance system. We estimated prevalence ratios (PRs) for phenotypes with ≥50 cases according to conception in spring (March-May), summer (June-August) or fall (September-November) relative to winter (December-February), using Poisson regression. Season of conception was associated with 5% of birth defects studied in models adjusted for maternal age, education, race/ethnicity, and number of previous livebirths. Specifically, summer conception was associated with any monitored birth defect (PR 1.03, 95% CI 1.02-1.04) and five specific phenotypes, most notably Hirschsprung disease (PR 1.46, 95% CI 1.22-1.75). These findings suggest that seasonally variable exposures influence the development of several birth defects and may assist in identifying novel environmental risk factors.
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Comprehensive assessment of the associations between maternal diabetes and structural birth defects in offspring: a phenome-wide association study. Ann Epidemiol 2020; 53:14-20.e8. [PMID: 32920098 DOI: 10.1016/j.annepidem.2020.08.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 08/07/2020] [Accepted: 08/10/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE Our objective was to comprehensively evaluate the risk of a broad range of birth defects among offspring of women with diabetes, overall and stratified by pregestational versus gestational diagnosis, using the phenome-wide association (PheWAS) methodology. METHODS We performed a registry linkage study of all live births (>6,500,000) and birth defects cases (>290,000) in Texas, 1999-2015. We ascertained diabetes from birth and fetal death certificates. We calculated prevalence rate ratios (PRR) for phenotypes with ≥10 cases among exposed offspring (n = 130). RESULTS Diabetes was associated with the prevalence of any defect (PRR 1.40, 95% confidence interval [CI] 1.38-1.42), multiple defects (PRR 1.86, 95% CI 1.81-1.91), and 60 specific phenotypes, including novel (hypospadias, mitral stenosis) and previously reported phenotypes (renal a-/dysgenesis, spinal anomalies). Pregestational diabetes was a stronger risk factor for any defect (PRR 2.00, 95% CI 1.93-2.07), multiple defects (PRR 3.27, 95% CI 3.11-3.44), and the 60 specific phenotypes evaluated. Gestational diabetes was associated with any defect (PRR 1.21, 95% CI 1.19-1.23) and 47 specific birth defects phenotypes, although associations were weaker than for pregestational diabetes. CONCLUSIONS The PheWAS is an efficient way to identify risk factors for disease using population-based registry data. Pregestational diabetes is associated with a broader range of phenotypes than previously reported. Because diabetes is diagnosed in 1% of women prior to pregnancy and 6%-9% during pregnancy, our results highlight a significant public health concern.
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Quan Y, Zhang QY, Lv BM, Xu RF, Zhang HY. Genome-wide pathogenesis interpretation using a heat diffusion-based systems genetics method and implications for gene function annotation. Mol Genet Genomic Med 2020; 8:e1456. [PMID: 32869547 PMCID: PMC7549611 DOI: 10.1002/mgg3.1456] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 07/08/2020] [Accepted: 07/27/2020] [Indexed: 12/27/2022] Open
Abstract
Background Genetics is best dedicated to interpreting pathogenesis and revealing gene functions. The past decade has witnessed unprecedented progress in genetics, particularly in genome‐wide identification of disorder variants through Genome‐Wide Association Studies (GWAS) and Phenome‐Wide Association Studies (PheWAS). However, it is still a great challenge to use GWAS/PheWAS‐derived data to elucidate pathogenesis. Methods In this study, we used HotNet2, a heat diffusion‐based systems genetics algorithm, to calculate the networks for disease genes obtained from GWAS and PheWAS, with an attempt to get deeper insights into disease pathogenesis at a molecular level. Results Through HotNet2 calculation, significant networks for 202 (for GWAS) and 167 (for PheWAS) types of diseases were identified and evaluated, respectively. The GWAS‐derived disease networks exhibit a stronger biomedical relevance than PheWAS counterparts. Therefore, the GWAS‐derived networks were used for pathogenesis interpretation by integrating the accumulated biomedical information. As a result, the pathogenesis for 64 diseases was elucidated in terms of mutation‐caused abnormal transcriptional regulation, and 47 diseases were preliminarily interpreted in terms of mutation‐caused varied protein‐protein interactions. In addition, 3,802 genes (including 46 function‐unknown genes) were assigned with new functions by disease network information, some of which were validated through mice gene knockout experiments. Conclusions Systems genetics algorithm HotNet2 can efficiently establish genotype‐phenotype links at the level of biological networks. Compared with original GWAS/PheWAS results, HotNet2‐calculated disease‐gene associations have stronger biomedical significance, hence provide better interpretations for the pathogenesis of genome‐wide variants, and offer new insights into gene functions as well. These results are also helpful in drug development.
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Affiliation(s)
- Yuan Quan
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, China.,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Qing-Ye Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Bo-Min Lv
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Rui-Feng Xu
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, China
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
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47
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Turk JR, Deaton AM, Yin J, Stolina M, Felx M, Boyd G, Bienvenu JG, Varela A, Guillot M, Holdsworth G, Wolfreys A, Dwyer D, Kumar SV, de Koning EM, Qu Y, Engwall M, Locher K, Ward LD, Glaus C, He YD, Boyce RW. Nonclinical cardiovascular safety evaluation of romosozumab, an inhibitor of sclerostin for the treatment of osteoporosis in postmenopausal women at high risk of fracture. Regul Toxicol Pharmacol 2020; 115:104697. [PMID: 32590049 DOI: 10.1016/j.yrtph.2020.104697] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 05/20/2020] [Accepted: 05/29/2020] [Indexed: 12/26/2022]
Abstract
Romosozumab (EVENITY™ [romosozumab-aqqg in the US]) is a humanized monoclonal antibody that inhibits sclerostin and has been approved in several countries for the treatment of osteoporosis in postmenopausal women at high risk of fracture. Sclerostin is expressed in bone and aortic vascular smooth muscle (AVSM). Its function in AVSM is unclear but it has been proposed to inhibit vascular calcification, atheroprogression, and inflammation. An increased incidence of positively adjudicated serious cardiovascular adverse events driven by an increase in myocardial infarction and stroke was observed in romosozumab-treated subjects in a clinical trial comparing alendronate with romosozumab (ARCH; NCT01631214) but not in a placebo-controlled trial (FRAME; NCT01575834). To investigate the effects of sclerostin inhibition with sclerostin antibody on the cardiovascular system, a comprehensive nonclinical toxicology package with additional cardiovascular studies was conducted. Although pharmacodynamic effects were observed in the bone, there were no functional, morphological, or transcriptional effects on the cardiovascular system in animal models in the presence or absence of atherosclerosis. These nonclinical studies did not identify evidence that proves the association between sclerostin inhibition and adverse cardiovascular function, increased cardiovascular calcification, and atheroprogression.
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Affiliation(s)
- James R Turk
- Translational Safety and Bioanalytical Sciences, Amgen Research, Thousand Oaks, CA, USA.
| | - Aimee M Deaton
- Translational Safety and Bioanalytical Sciences, Amgen Research, Cambridge, MA, USA
| | - Jun Yin
- Genome Analysis Unit, Amgen Research, South San Francisco, CA, USA
| | - Marina Stolina
- Cardiometabolic Disorders Research, Amgen Research, Thousand Oaks, CA, USA
| | - Melanie Felx
- Charles River Laboratories Montreal ULC, Senneville, QC, Canada
| | - Gabrielle Boyd
- Charles River Laboratories Montreal ULC, Senneville, QC, Canada
| | | | - Aurore Varela
- Charles River Laboratories Montreal ULC, Senneville, QC, Canada
| | - Martin Guillot
- Charles River Laboratories Montreal ULC, Senneville, QC, Canada
| | | | | | - Denise Dwyer
- Cardiometabolic Disorders Research, Amgen Research, Thousand Oaks, CA, USA
| | - Sheetal V Kumar
- Translational Safety and Bioanalytical Sciences, Amgen Research, Cambridge, MA, USA
| | - Emily M de Koning
- Translational Safety and Bioanalytical Sciences, Amgen Research, Cambridge, MA, USA
| | - Yusheng Qu
- Translational Safety and Bioanalytical Sciences, Amgen Research, Thousand Oaks, CA, USA
| | - Michael Engwall
- Translational Safety and Bioanalytical Sciences, Amgen Research, Thousand Oaks, CA, USA
| | - Kathrin Locher
- Translational Safety and Bioanalytical Sciences, Amgen Research, South San Francisco, CA, USA
| | - Lucas D Ward
- Translational Safety and Bioanalytical Sciences, Amgen Research, Cambridge, MA, USA
| | - Charles Glaus
- Cardiometabolic Disorders Research, Amgen Research, Thousand Oaks, CA, USA
| | - Yudong D He
- Translational Safety and Bioanalytical Sciences, Amgen Research, Thousand Oaks, CA, USA; Genome Analysis Unit, Amgen Research, South San Francisco, CA, USA
| | - Rogely Waite Boyce
- Translational Safety and Bioanalytical Sciences, Amgen Research, Thousand Oaks, CA, USA
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48
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Jarada TN, Rokne JG, Alhajj R. A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J Cheminform 2020; 12:46. [PMID: 33431024 PMCID: PMC7374666 DOI: 10.1186/s13321-020-00450-7] [Citation(s) in RCA: 176] [Impact Index Per Article: 35.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/13/2020] [Indexed: 01/13/2023] Open
Abstract
Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an important role in optimizing the pre-clinical process of developing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repositioning relies on data for existing drugs and diseases the enormous growth of publicly available large-scale biological, biomedical, and electronic health-related data along with the high-performance computing capabilities have accelerated the development of computational drug repositioning approaches. Multidisciplinary researchers and scientists have carried out numerous attempts, with different degrees of efficiency and success, to computationally study the potential of repositioning drugs to identify alternative drug indications. This study reviews recent advancements in the field of computational drug repositioning. First, we highlight different drug repositioning strategies and provide an overview of frequently used resources. Second, we summarize computational approaches that are extensively used in drug repositioning studies. Third, we present different computing and experimental models to validate computational methods. Fourth, we address prospective opportunities, including a few target areas. Finally, we discuss challenges and limitations encountered in computational drug repositioning and conclude with an outline of further research directions.
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Affiliation(s)
- Tamer N Jarada
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
| | - Jon G Rokne
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
| | - Reda Alhajj
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada.
- Department of Computer Engineering, Istanbul Medipol University, Istanbul, Turkey.
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49
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Yan Z, Huang H, Freebern E, Santos DJA, Dai D, Si J, Ma C, Cao J, Guo G, Liu GE, Ma L, Fang L, Zhang Y. Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle. BMC Genomics 2020; 21:489. [PMID: 32680461 PMCID: PMC7367229 DOI: 10.1186/s12864-020-06909-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/13/2020] [Indexed: 01/12/2023] Open
Abstract
Background Ketosis is a common metabolic disease during the transition period in dairy cattle, resulting in long-term economic loss to the dairy industry worldwide. While genetic selection of resistance to ketosis has been adopted by many countries, the genetic and biological basis underlying ketosis is poorly understood. Results We collected a total of 24 blood samples from 12 Holstein cows, including 4 healthy and 8 ketosis-diagnosed ones, before (2 weeks) and after (5 days) calving, respectively. We then generated RNA-Sequencing (RNA-Seq) data and seven blood biochemical indicators (bio-indicators) from leukocytes and plasma in each of these samples, respectively. By employing a weighted gene co-expression network analysis (WGCNA), we detected that 4 out of 16 gene-modules, which were significantly engaged in lipid metabolism and immune responses, were transcriptionally (FDR < 0.05) correlated with postpartum ketosis and several bio-indicators (e.g., high-density lipoprotein and low-density lipoprotein). By conducting genome-wide association signal (GWAS) enrichment analysis among six common health traits (ketosis, mastitis, displaced abomasum, metritis, hypocalcemia and livability), we found that 4 out of 16 modules were genetically (FDR < 0.05) associated with ketosis, among which three were correlated with postpartum ketosis based on WGCNA. We further identified five candidate genes for ketosis, including GRINA, MAF1, MAFA, C14H8orf82 and RECQL4. Our phenome-wide association analysis (Phe-WAS) demonstrated that human orthologues of these candidate genes were also significantly associated with many metabolic, endocrine, and immune traits in humans. For instance, MAFA, which is involved in insulin secretion, glucose response, and transcriptional regulation, showed a significantly higher association with metabolic and endocrine traits compared to other types of traits in humans. Conclusions In summary, our study provides novel insights into the molecular mechanism underlying ketosis in cattle, and highlights that an integrative analysis of omics data and cross-species mapping are promising for illustrating the genetic architecture underpinning complex traits.
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Affiliation(s)
- Ze Yan
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Hetian Huang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.,College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, 450002, China
| | - Ellen Freebern
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Daniel J A Santos
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Dongmei Dai
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jingfang Si
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Chong Ma
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China
| | - Jie Cao
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China
| | - Gang Guo
- Beijing Sunlon Livestock Development Co Ltd., Beijing, 100076, China
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
| | - Yi Zhang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
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50
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Lau A, So HC. Turning genome-wide association study findings into opportunities for drug repositioning. Comput Struct Biotechnol J 2020; 18:1639-1650. [PMID: 32670504 PMCID: PMC7334463 DOI: 10.1016/j.csbj.2020.06.015] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 06/05/2020] [Accepted: 06/05/2020] [Indexed: 02/02/2023] Open
Abstract
Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored. The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations. Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.
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Affiliation(s)
- Alexandria Lau
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Hon-Cheong So
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Zoology Institute of Zoology and The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
- Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong SAR, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
- Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Corresponding author at: School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
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