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Žukauskaitė G, Domarkienė I, Rančelis T, Kavaliauskienė I, Baronas K, Kučinskas V, Ambrozaitytė L. Putative protective genomic variation in the Lithuanian population. Genet Mol Biol 2024; 47:e20230030. [PMID: 38626572 PMCID: PMC11021042 DOI: 10.1590/1678-4685-gmb-2023-0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 01/01/2024] [Indexed: 04/18/2024] Open
Abstract
Genomic effect variants associated with survival and protection against complex diseases vary between populations due to microevolutionary processes. The aim of this study was to analyse diversity and distribution of effect variants in a context of potential positive selection. In total, 475 individuals of Lithuanian origin were genotyped using high-throughput scanning and/or sequencing technologies. Allele frequency analysis for the pre-selected effect variants was performed using the catalogue of single nucleotide polymorphisms. Comparison of the pre-selected effect variants with variants in primate species was carried out to ascertain which allele was derived and potentially of protective nature. Recent positive selection analysis was performed to verify this protective effect. Four variants having significantly different frequencies compared to European populations were identified while two other variants reached borderline significance. Effect variant in SLC30A8 gene may potentially protect against type 2 diabetes. The existing paradox of high rates of type 2 diabetes in the Lithuanian population and the relatively high frequencies of potentially protective genome variants against it indicate a lack of knowledge about the interactions between environmental factors, regulatory regions, and other genome variation. Identification of effect variants is a step towards better understanding of the microevolutionary processes, etiopathogenetic mechanisms, and personalised medicine.
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Affiliation(s)
- Gabrielė Žukauskaitė
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
| | - Ingrida Domarkienė
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
| | - Tautvydas Rančelis
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
| | - Ingrida Kavaliauskienė
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
| | - Karolis Baronas
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
| | - Vaidutis Kučinskas
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
| | - Laima Ambrozaitytė
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
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Erdogdu B, Varabyou A, Hicks SC, Salzberg SL, Pertea M. Detecting differential transcript usage in complex diseases with SPIT. Cell Rep Methods 2024; 4:100736. [PMID: 38508189 PMCID: PMC10985272 DOI: 10.1016/j.crmeth.2024.100736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 12/21/2023] [Accepted: 02/27/2024] [Indexed: 03/22/2024]
Abstract
Differential transcript usage (DTU) plays a crucial role in determining how gene expression differs among cells, tissues, and developmental stages, contributing to the complexity and diversity of biological systems. In abnormal cells, it can also lead to deficiencies in protein function and underpin disease pathogenesis. Analyzing DTU via RNA sequencing (RNA-seq) data is vital, but the genetic heterogeneity in populations with complex diseases presents an intricate challenge due to diverse causal events and undetermined subtypes. Although the majority of common diseases in humans are categorized as complex, state-of-the-art DTU analysis methods often overlook this heterogeneity in their models. We therefore developed SPIT, a statistical tool that identifies predominant subgroups in transcript usage within a population along with their distinctive sets of DTU events. This study provides comprehensive assessments of SPIT's methodology and applies it to analyze brain samples from individuals with schizophrenia, revealing previously unreported DTU events in six candidate genes.
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Affiliation(s)
- Beril Erdogdu
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA.
| | - Ales Varabyou
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Stephanie C Hicks
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
| | - Steven L Salzberg
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Mihaela Pertea
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA; Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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Abdullahi T, Singh R, Eickhoff C. Learning to Make Rare and Complex Diagnoses With Generative AI Assistance: Qualitative Study of Popular Large Language Models. JMIR Med Educ 2024; 10:e51391. [PMID: 38349725 PMCID: PMC10900078 DOI: 10.2196/51391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 11/07/2023] [Accepted: 12/11/2023] [Indexed: 02/15/2024]
Abstract
BACKGROUND Patients with rare and complex diseases often experience delayed diagnoses and misdiagnoses because comprehensive knowledge about these diseases is limited to only a few medical experts. In this context, large language models (LLMs) have emerged as powerful knowledge aggregation tools with applications in clinical decision support and education domains. OBJECTIVE This study aims to explore the potential of 3 popular LLMs, namely Bard (Google LLC), ChatGPT-3.5 (OpenAI), and GPT-4 (OpenAI), in medical education to enhance the diagnosis of rare and complex diseases while investigating the impact of prompt engineering on their performance. METHODS We conducted experiments on publicly available complex and rare cases to achieve these objectives. We implemented various prompt strategies to evaluate the performance of these models using both open-ended and multiple-choice prompts. In addition, we used a majority voting strategy to leverage diverse reasoning paths within language models, aiming to enhance their reliability. Furthermore, we compared their performance with the performance of human respondents and MedAlpaca, a generative LLM specifically designed for medical tasks. RESULTS Notably, all LLMs outperformed the average human consensus and MedAlpaca, with a minimum margin of 5% and 13%, respectively, across all 30 cases from the diagnostic case challenge collection. On the frequently misdiagnosed cases category, Bard tied with MedAlpaca but surpassed the human average consensus by 14%, whereas GPT-4 and ChatGPT-3.5 outperformed MedAlpaca and the human respondents on the moderately often misdiagnosed cases category with minimum accuracy scores of 28% and 11%, respectively. The majority voting strategy, particularly with GPT-4, demonstrated the highest overall score across all cases from the diagnostic complex case collection, surpassing that of other LLMs. On the Medical Information Mart for Intensive Care-III data sets, Bard and GPT-4 achieved the highest diagnostic accuracy scores, with multiple-choice prompts scoring 93%, whereas ChatGPT-3.5 and MedAlpaca scored 73% and 47%, respectively. Furthermore, our results demonstrate that there is no one-size-fits-all prompting approach for improving the performance of LLMs and that a single strategy does not universally apply to all LLMs. CONCLUSIONS Our findings shed light on the diagnostic capabilities of LLMs and the challenges associated with identifying an optimal prompting strategy that aligns with each language model's characteristics and specific task requirements. The significance of prompt engineering is highlighted, providing valuable insights for researchers and practitioners who use these language models for medical training. Furthermore, this study represents a crucial step toward understanding how LLMs can enhance diagnostic reasoning in rare and complex medical cases, paving the way for developing effective educational tools and accurate diagnostic aids to improve patient care and outcomes.
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Affiliation(s)
- Tassallah Abdullahi
- Department of Computer Science, Brown University, Providence, RI, United States
| | - Ritambhara Singh
- Department of Computer Science, Brown University, Providence, RI, United States
- Center for Computational Molecular Biology, Brown University, Providence, RI, United States
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Johansson Å, Andreassen OA, Brunak S, Franks PW, Hedman H, Loos RJ, Meder B, Melén E, Wheelock CE, Jacobsson B. Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification. J Intern Med 2023; 294:378-396. [PMID: 37093654 PMCID: PMC10523928 DOI: 10.1111/joim.13640] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.
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Affiliation(s)
- Åsa Johansson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala university, Sweden
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopment Research, University of Oslo, Oslo, Norway
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
- Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2200 Copenhagen, Denmark
| | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Sweden
- Novo Nordisk Foundation, Denmark
| | - Harald Hedman
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | - Ruth J.F. Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin Meder
- Precision Digital Health, Cardiogenetics Center Heidelberg, Department of Cardiology, University Of Heidelberg, Germany
| | - Erik Melén
- Department of Clinical Sciences and Education, Södersjukhuset, Karolinska Institutet, Stockholm
- Sachś Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden
| | - Craig E Wheelock
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Bo Jacobsson
- Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Obstetrics and Gynaecology, Sahlgrenska University Hospital, Göteborg, Sweden
- Department of Genetics and Bioinformatics, Domain of Health Data and Digitalisation, Institute of Public Health, Oslo, Norway
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Zhang Y, Barupal DK, Fan S, Gao B, Zhu C, Flenniken AM, McKerlie C, Nutter LMJ, Lloyd KCK, Fiehn O. Sexual Dimorphism of the Mouse Plasma Metabolome Is Associated with Phenotypes of 30 Gene Knockout Lines. Metabolites 2023; 13:947. [PMID: 37623890 PMCID: PMC10456929 DOI: 10.3390/metabo13080947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/03/2023] [Accepted: 08/08/2023] [Indexed: 08/26/2023] Open
Abstract
Although metabolic alterations are observed in many monogenic and complex genetic disorders, the impact of most mammalian genes on cellular metabolism remains unknown. Understanding the effect of mouse gene dysfunction on metabolism can inform the functions of their human orthologues. We investigated the effect of loss-of-function mutations in 30 unique gene knockout (KO) lines on plasma metabolites, including genes coding for structural proteins (11 of 30), metabolic pathway enzymes (12 of 30) and protein kinases (7 of 30). Steroids, bile acids, oxylipins, primary metabolites, biogenic amines and complex lipids were analyzed with dedicated mass spectrometry platforms, yielding 827 identified metabolites in male and female KO mice and wildtype (WT) controls. Twenty-two percent of 23,698 KO versus WT comparison tests showed significant genotype effects on plasma metabolites. Fifty-six percent of identified metabolites were significantly different between the sexes in WT mice. Many of these metabolites were also found to have sexually dimorphic changes in KO lines. We used plasma metabolites to complement phenotype information exemplified for Dhfr, Idh1, Mfap4, Nek2, Npc2, Phyh and Sra1. The association of plasma metabolites with IMPC phenotypes showed dramatic sexual dimorphism in wildtype mice. We demonstrate how to link metabolomics to genotypes and (disease) phenotypes. Sex must be considered as critical factor in the biological interpretation of gene functions.
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Affiliation(s)
- Ying Zhang
- West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA
- Department of Chemistry, University of California Davis, Davis, CA 95616, USA
| | - Dinesh K. Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Sili Fan
- West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA
| | - Bei Gao
- School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Chao Zhu
- College of Medicine & Nursing, Dezhou University, Dezhou 253023, China
| | - Ann M. Flenniken
- The Centre for Phenogenomics, Toronto, ON M5T 3H7, Canada; (A.M.F.); (C.M.); (L.M.J.N.)
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Colin McKerlie
- The Centre for Phenogenomics, Toronto, ON M5T 3H7, Canada; (A.M.F.); (C.M.); (L.M.J.N.)
- The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Lauryl M. J. Nutter
- The Centre for Phenogenomics, Toronto, ON M5T 3H7, Canada; (A.M.F.); (C.M.); (L.M.J.N.)
- The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Kevin C. Kent Lloyd
- Department of Surgery, School of Medicine, and Mouse Biology Program, University of California Davis, Davis, CA 95616, USA;
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA
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6
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Cvijovic M, Polster A. Network medicine: facilitating a new view on complex diseases. Front Bioinform 2023; 3:1163445. [PMID: 37293293 PMCID: PMC10244535 DOI: 10.3389/fbinf.2023.1163445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 05/15/2023] [Indexed: 06/10/2023] Open
Abstract
Complex diseases are prevalent medical conditions which are characterized by inter-patient heterogeneity with regards to symptom profiles, disease trajectory, comorbidities, and treatment response. Their pathophysiology involves a combination of genetic, environmental, and psychosocial factors. The intricacies of complex diseases, encompassing different levels of biological organization in the context of environmental and psychosocial factors, makes them difficult to study, understand, prevent, and treat. The field of network medicine has progressed our understanding of these complex mechanisms and highlighted mechanistic overlap between diagnoses as well as patterns of symptom co-occurrence. These observations call into question the traditional conception of complex diseases, where diagnoses are treated as distinct entities, and prompts us to reconceptualize our nosological models. Thus, this manuscript presents a novel model, in which the individual disease burden is determined as a function of molecular, physiological, and pathological factors simultaneously, and represented as a state vector. In this conceptualization the focus shifts from identifying the underlying pathophysiology of diagnosis cohorts towards identifying symptom-determining traits in individual patients. This conceptualization facilitates a multidimensional approach to understanding human physiology and pathophysiology in the context of complex diseases. This may provide a useful concept to address both the significant interindividual heterogeneity of diagnose cohorts as well as the lack of clear distinction between diagnoses, health, and disease, thus facilitating the progression towards personalized medicine.
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Affiliation(s)
- Marija Cvijovic
- Department of Applied Mathematics and Statistics, University of Gothenburg, Gothenburg, Sweden
| | - Annikka Polster
- Division of Systems and Synthetic Biology, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
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Woerner J, Sriram V, Nam Y, Verma A, Kim D. Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network. medRxiv 2023:2023.05.11.23289852. [PMID: 37293013 PMCID: PMC10246076 DOI: 10.1101/2023.05.11.23289852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Many diseases exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between disease phenotypes and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and diabetic retinopathy. Triglycerides, another blood lipid with known genetics causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.
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Affiliation(s)
- Jakob Woerner
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Vivek Sriram
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yonghyun Nam
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anurag Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
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Casotti MC, Meira DD, Alves LNR, Bessa BGDO, Campanharo CV, Vicente CR, Aguiar CC, Duque DDA, Barbosa DG, dos Santos EDVW, Garcia FM, de Paula F, Santana GM, Pavan IP, Louro LS, Braga RFR, Trabach RSDR, Louro TS, de Carvalho EF, Louro ID. Translational Bioinformatics Applied to the Study of Complex Diseases. Genes (Basel) 2023; 14:419. [PMID: 36833346 PMCID: PMC9956936 DOI: 10.3390/genes14020419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/29/2023] [Accepted: 01/31/2023] [Indexed: 02/10/2023] Open
Abstract
Translational Bioinformatics (TBI) is defined as the union of translational medicine and bioinformatics. It emerges as a major advance in science and technology by covering everything, from the most basic database discoveries, to the development of algorithms for molecular and cellular analysis, as well as their clinical applications. This technology makes it possible to access the knowledge of scientific evidence and apply it to clinical practice. This manuscript aims to highlight the role of TBI in the study of complex diseases, as well as its application to the understanding and treatment of cancer. An integrative literature review was carried out, obtaining articles through several websites, among them: PUBMED, Science Direct, NCBI-PMC, Scientific Electronic Library Online (SciELO), and Google Academic, published in English, Spanish, and Portuguese, indexed in the referred databases and answering the following guiding question: "How does TBI provide a scientific understanding of complex diseases?" An additional effort is aimed at the dissemination, inclusion, and perpetuation of TBI knowledge from the academic environment to society, helping the study, understanding, and elucidating of complex disease mechanics and their treatment.
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Affiliation(s)
- Matheus Correia Casotti
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Débora Dummer Meira
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Lyvia Neves Rebello Alves
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | | | - Camilly Victória Campanharo
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Creuza Rachel Vicente
- Departamento de Medicina Social, Universidade Federal do Espírito Santo, Vitória 29040-090, Espírito Santo, Brazil
| | - Carla Carvalho Aguiar
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Daniel de Almeida Duque
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Débora Gonçalves Barbosa
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | | | - Fernanda Mariano Garcia
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Flávia de Paula
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Gabriel Mendonça Santana
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Isabele Pagani Pavan
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Luana Santos Louro
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Raquel Furlani Rocon Braga
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Raquel Silva dos Reis Trabach
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Thomas Santos Louro
- Escola Superior de Ciências da Santa Casa de Misericórdia de Vitória (EMESCAM), Vitória 29027-502, Espírito Santo, Brazil
| | - Elizeu Fagundes de Carvalho
- Instituto de Biologia Roberto Alcantara Gomes (IBRAG), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro 20551-030, Rio de Janeiro, Brazil
| | - Iúri Drumond Louro
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
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Politi C, Roumeliotis S, Tripepi G, Spoto B. Sample Size Calculation in Genetic Association Studies: A Practical Approach. Life (Basel) 2023; 13. [PMID: 36676184 DOI: 10.3390/life13010235] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/21/2022] [Accepted: 01/11/2023] [Indexed: 01/19/2023]
Abstract
Genetic association studies, testing the relationship between genetic variants and disease status, are useful tools for identifying genes that grant susceptibility to complex disorders. In such studies, an inadequate sample size may provide unreliable results: a small sample is unable to accurately describe the population, whereas a large sample makes the study expensive and complex to run. However, in genetic association studies, the sample size calculation is often overlooked or inadequately assessed for the small number of parameters included. In light of this, herein we list and discuss the role of the statistical and genetic parameters to be considered in the sample size calculation, show examples reporting incorrect estimation and, by using a genetic software program, we provide a practical approach for the assessment of the adequate sample size in a hypothetical study aimed at analyzing a gene-disease association.
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Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion. Brief Bioinform 2022; 23:6696143. [PMID: 36094095 DOI: 10.1093/bib/bbac397] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/19/2022] [Accepted: 08/15/2022] [Indexed: 12/14/2022] Open
Abstract
MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction.
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Affiliation(s)
- Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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11
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Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models. Brief Bioinform 2022; 23:6712303. [PMID: 36151749 DOI: 10.1093/bib/bbac407] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 08/11/2022] [Accepted: 08/20/2022] [Indexed: 12/14/2022] Open
Abstract
Currently, there exist no generally accepted strategies of evaluating computational models for microRNA-disease associations (MDAs). Though K-fold cross validations and case studies seem to be must-have procedures, the value of K, the evaluation metrics, and the choice of query diseases as well as the inclusion of other procedures (such as parameter sensitivity tests, ablation studies and computational cost reports) are all determined on a case-by-case basis and depending on the researchers' choices. In the current review, we include a comprehensive analysis on how 29 state-of-the-art models for predicting MDAs were evaluated. Based on the analytical results, we recommend a feasible evaluation workflow that would suit any future model to facilitate fair and systematic assessment of predictive performance.
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Affiliation(s)
- Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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12
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Zhao H, Rasheed H, Nøst TH, Cho Y, Liu Y, Bhatta L, Bhattacharya A, Hemani G, Davey Smith G, Brumpton BM, Zhou W, Neale BM, Gaunt TR, Zheng J. Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases. Cell Genom 2022; 2:None. [PMID: 36388766 PMCID: PMC9646482 DOI: 10.1016/j.xgen.2022.100195] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 06/06/2022] [Accepted: 09/21/2022] [Indexed: 11/07/2022]
Abstract
Proteome-wide Mendelian randomization (MR) shows value in prioritizing drug targets in Europeans but with limited evidence in other ancestries. Here, we present a multi-ancestry proteome-wide MR analysis based on cross-population data from the Global Biobank Meta-analysis Initiative (GBMI). We estimated the putative causal effects of 1,545 proteins on eight diseases in African (32,658) and European (1,219,993) ancestries and identified 45 and 7 protein-disease pairs with MR and genetic colocalization evidence in the two ancestries, respectively. A multi-ancestry MR comparison identified two protein-disease pairs with MR evidence in both ancestries and seven pairs with specific effects in the two ancestries separately. Integrating these MR signals with clinical trial evidence, we prioritized 16 pairs for investigation in future drug trials. Our results highlight the value of proteome-wide MR in informing the generalizability of drug targets for disease prevention across ancestries and illustrate the value of meta-analysis of biobanks in drug development.
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Affiliation(s)
- Huiling Zhao
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Humaria Rasheed
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Division of Medicine and Laboratory Sciences, University of Oslo, Oslo, Norway
| | - Therese Haugdahl Nøst
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Community Medicine, UIT The Arctic University of Norway, 9037 Tromsø, Norway
| | - Yoonsu Cho
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Yi Liu
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Institute of Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Global Biobank Meta-analysis Initiative
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Division of Medicine and Laboratory Sciences, University of Oslo, Oslo, Norway
- Department of Community Medicine, UIT The Arctic University of Norway, 9037 Tromsø, Norway
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Institute of Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7600 Levanger, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Ben Michael Brumpton
- Division of Medicine and Laboratory Sciences, University of Oslo, Oslo, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7600 Levanger, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Wei Zhou
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Benjamin M. Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Tom R. Gaunt
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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13
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Ma Y, Patil S, Zhou X, Mukherjee B, Fritsche LG. ExPRSweb: An online repository with polygenic risk scores for common health-related exposures. Am J Hum Genet 2022; 109:1742-1760. [PMID: 36152628 PMCID: PMC9606385 DOI: 10.1016/j.ajhg.2022.09.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 08/31/2022] [Indexed: 01/25/2023] Open
Abstract
Complex traits are influenced by genetic risk factors, lifestyle, and environmental variables, so-called exposures. Some exposures, e.g., smoking or lipid levels, have common genetic modifiers identified in genome-wide association studies. Because measurements are often unfeasible, exposure polygenic risk scores (ExPRSs) offer an alternative to study the influence of exposures on various phenotypes. Here, we collected publicly available summary statistics for 28 exposures and applied four common PRS methods to generate ExPRSs in two large biobanks: the Michigan Genomics Initiative and the UK Biobank. We established ExPRSs for 27 exposures and demonstrated their applicability in phenome-wide association studies and as predictors for common chronic conditions. Especially the addition of multiple ExPRSs showed, for several chronic conditions, an improvement compared to prediction models that only included traditional, disease-focused PRSs. To facilitate follow-up studies, we share all ExPRS constructs and generated results via an online repository called ExPRSweb.
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Affiliation(s)
- Ying Ma
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Snehal Patil
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lars G Fritsche
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA.
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14
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Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models. Brief Bioinform 2022; 23:6686738. [PMID: 36056743 DOI: 10.1093/bib/bbac358] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/24/2022] [Accepted: 07/30/2022] [Indexed: 12/12/2022] Open
Abstract
Since the problem proposed in late 2000s, microRNA-disease association (MDA) predictions have been implemented based on the data fusion paradigm. Integrating diverse data sources gains a more comprehensive research perspective, and brings a challenge to algorithm design for generating accurate, concise and consistent representations of the fused data. After more than a decade of research progress, a relatively simple algorithm like the score function or a single computation layer may no longer be sufficient for further improving predictive performance. Advanced model design has become more frequent in recent years, particularly in the form of reasonably combing multiple algorithms, a process known as model fusion. In the current review, we present 29 state-of-the-art models and introduce the taxonomy of computational models for MDA prediction based on model fusion and non-fusion. The new taxonomy exhibits notable changes in the algorithmic architecture of models, compared with that of earlier ones in the 2017 review by Chen et al. Moreover, we discuss the progresses that have been made towards overcoming the obstacles to effective MDA prediction since 2017 and elaborated on how future models can be designed according to a set of new schemas. Lastly, we analysed the strengths and weaknesses of each model category in the proposed taxonomy and proposed future research directions from diverse perspectives for enhancing model performance.
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Affiliation(s)
- Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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15
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Song S, Jiang W, Zhang Y, Hou L, Zhao H. Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflation. Am J Hum Genet 2022; 109:802-811. [PMID: 35421325 PMCID: PMC9118121 DOI: 10.1016/j.ajhg.2022.03.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/18/2022] [Indexed: 12/17/2022] Open
Abstract
Heritability is a fundamental concept in genetic studies, measuring the genetic contribution to complex traits and bringing insights about disease mechanisms. The advance of high-throughput technologies has provided many resources for heritability estimation. Linkage disequilibrium (LD) score regression (LDSC) estimates both heritability and confounding biases, such as cryptic relatedness and population stratification, among single-nucleotide polymorphisms (SNPs) by using only summary statistics released from genome-wide association studies. However, only partial information in the LD matrix is utilized in LDSC, leading to loss in precision. In this study, we propose LD eigenvalue regression (LDER), an extension of LDSC, by making full use of the LD information. Compared to state-of-the-art heritability estimating methods, LDER provides more accurate estimates of SNP heritability and better distinguishes the inflation caused by polygenicity and confounding effects. We demonstrate the advantages of LDER both theoretically and with extensive simulations. We applied LDER to 814 complex traits from UK Biobank, and LDER identified 363 significantly heritable phenotypes, among which 97 were not identified by LDSC.
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Affiliation(s)
- Shuang Song
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Wei Jiang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Yiliang Zhang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Lin Hou
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing 100084, China; MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA.
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16
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Trapecar M. Multiorgan microphysiological systems as tools to interrogate interorgan crosstalk and complex diseases. FEBS Lett 2021; 596:681-695. [PMID: 34923635 DOI: 10.1002/1873-3468.14260] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 12/14/2022]
Abstract
Metabolic and inflammatory disorders such as autoimmune and neurodegenerative diseases are increasing at alarming rates. Many of these are not tissue-specific occurrences but complex, systemic pathologies of unknown origin for which no cure exists. Such complexity obscures causal relationships among factors regulating disease progression. Emerging technologies mimicking human physiology, such as microphysiological systems (MPSs), offer new possibilities to provide clarity in systemic metabolic and inflammatory diseases. Controlled interaction of multiple MPSs and the scalability of biological complexity in MPSs, supported by continuous multiomic monitoring, might hold the key to identifying novel relationships between interorgan crosstalk, metabolism, and immunity. In this perspective, I aim to discuss the current state of modeling multiorgan physiology and evaluate current opportunities and challenges.
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Affiliation(s)
- Martin Trapecar
- Department of Medicine, Johns Hopkins University School of Medicine, Institute for Fundamental Biomedical Research, Johns Hopkins All Children's Hospital, St. Petersburg, FL, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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17
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Xiao Q, Dai J, Luo J. A survey of circular RNAs in complex diseases: databases, tools and computational methods. Brief Bioinform 2021; 23:6407737. [PMID: 34676391 DOI: 10.1093/bib/bbab444] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/21/2021] [Accepted: 09/28/2021] [Indexed: 01/22/2023] Open
Abstract
Circular RNAs (circRNAs) are a category of novelty discovered competing endogenous non-coding RNAs that have been proved to implicate many human complex diseases. A large number of circRNAs have been confirmed to be involved in cancer progression and are expected to become promising biomarkers for tumor diagnosis and targeted therapy. Deciphering the underlying relationships between circRNAs and diseases may provide new insights for us to understand the pathogenesis of complex diseases and further characterize the biological functions of circRNAs. As traditional experimental methods are usually time-consuming and laborious, computational models have made significant progress in systematically exploring potential circRNA-disease associations, which not only creates new opportunities for investigating pathogenic mechanisms at the level of circRNAs, but also helps to significantly improve the efficiency of clinical trials. In this review, we first summarize the functions and characteristics of circRNAs and introduce some representative circRNAs related to tumorigenesis. Then, we mainly investigate the available databases and tools dedicated to circRNA and disease studies. Next, we present a comprehensive review of computational methods for predicting circRNA-disease associations and classify them into five categories, including network propagating-based, path-based, matrix factorization-based, deep learning-based and other machine learning methods. Finally, we further discuss the challenges and future researches in this field.
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Affiliation(s)
- Qiu Xiao
- Hunan Normal University and Hunan Xiangjiang Artificial Intelligence Academy, Changsha, China
| | - Jianhua Dai
- Hunan Normal University and Hunan Xiangjiang Artificial Intelligence Academy, Changsha, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
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18
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Chowdhury D, Zhou X, Li B, Zhang Y, Cheung WK, Lu A, Zhang L. Editorial: Predicting High-Risk Individuals for Common Diseases Using Multi-Omics and Epidemiological Data. Front Genet 2021; 12:737598. [PMID: 34484310 PMCID: PMC8416410 DOI: 10.3389/fgene.2021.737598] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 07/26/2021] [Indexed: 01/16/2023] Open
Affiliation(s)
- Debajyoti Chowdhury
- Computational Medicine Lab, Hong Kong Baptist University, Kowloon Tong, Hong Kong.,School of Chinese Medicine, Institute of Integrated Bioinformedicine and Translational Sciences, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Xin Zhou
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Bailiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, United States
| | - Yuanwei Zhang
- The Chinese Academy of Sciences Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at the Microscale, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China
| | - William K Cheung
- Department of Computer Science, Faculty of Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Aiping Lu
- Computational Medicine Lab, Hong Kong Baptist University, Kowloon Tong, Hong Kong.,School of Chinese Medicine, Institute of Integrated Bioinformedicine and Translational Sciences, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Lu Zhang
- Computational Medicine Lab, Hong Kong Baptist University, Kowloon Tong, Hong Kong.,Department of Computer Science, Faculty of Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
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19
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Liebermann A, Schweiger J, Edelhoff D, Schwerin C. Innovative tooth-colored CAD/CAM polycarbonate splint design for prosthetic rehabilitation of a young ectodermal dysplasia patient with permanent tooth aplasia. Quintessence Int 2021; 52:694-704. [PMID: 34076383 DOI: 10.3290/j.qi.b1492063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Ectodermal dysplasia (ED) is one of the congenitally syndromic diseases with dental anomalies. This syndrome manifests in various forms and usually requires early restorative therapy to restore esthetics and function in young patients. The restorative approaches vary greatly and range from minimally invasive shape corrections using composite build-ups and removable partial or complete dental prostheses, to implantologic solutions, always based on the syndromic expression, the age of the patient, the residual growth, as well as the dentition itself. The present case report presents an innovative prosthetic management of a young patient suffering from ED with permanent tooth aplasia and persistent primary teeth using maxillomandibular individually veneered tooth-colored CAD/CAM polycarbonate splints. The CAD phase has been significantly improved by including the analysis of 3D face scans. This advanced technical development makes it possible to avoid any time-consuming try-in and start directly with the splint production, ensuring a much faster complete rehabilitation and support for the young patient.
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20
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Chawla A, Nagy C, Turecki G. Chromatin Profiling Techniques: Exploring the Chromatin Environment and Its Contributions to Complex Traits. Int J Mol Sci 2021; 22:7612. [PMID: 34299232 PMCID: PMC8305586 DOI: 10.3390/ijms22147612] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 07/09/2021] [Accepted: 07/13/2021] [Indexed: 01/04/2023] Open
Abstract
The genetic architecture of complex traits is multifactorial. Genome-wide association studies (GWASs) have identified risk loci for complex traits and diseases that are disproportionately located at the non-coding regions of the genome. On the other hand, we have just begun to understand the regulatory roles of the non-coding genome, making it challenging to precisely interpret the functions of non-coding variants associated with complex diseases. Additionally, the epigenome plays an active role in mediating cellular responses to fluctuations of sensory or environmental stimuli. However, it remains unclear how exactly non-coding elements associate with epigenetic modifications to regulate gene expression changes and mediate phenotypic outcomes. Therefore, finer interrogations of the human epigenomic landscape in associating with non-coding variants are warranted. Recently, chromatin-profiling techniques have vastly improved our understanding of the numerous functions mediated by the epigenome and DNA structure. Here, we review various chromatin-profiling techniques, such as assays of chromatin accessibility, nucleosome distribution, histone modifications, and chromatin topology, and discuss their applications in unraveling the brain epigenome and etiology of complex traits at tissue homogenate and single-cell resolution. These techniques have elucidated compositional and structural organizing principles of the chromatin environment. Taken together, we believe that high-resolution epigenomic and DNA structure profiling will be one of the best ways to elucidate how non-coding genetic variations impact complex diseases, ultimately allowing us to pinpoint cell-type targets with therapeutic potential.
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Affiliation(s)
- Anjali Chawla
- Integrated Program in Neuroscience, McGill University, 845 Sherbrooke St W, Montreal, QC H3A 0G4, Canada;
- McGill Group for Suicide Studies, Department of Psychiatry, Douglas Mental Health University Institute, McGill University, 6875 LaSalle Blvd, Verdun, QC H4H 1R3, Canada;
| | - Corina Nagy
- McGill Group for Suicide Studies, Department of Psychiatry, Douglas Mental Health University Institute, McGill University, 6875 LaSalle Blvd, Verdun, QC H4H 1R3, Canada;
- Genome Quebec Innovation Centre, Department of Human Genetics, McGill University, 845 Sherbrooke St W, Montreal, QC H3A 0G4, Canada
| | - Gustavo Turecki
- Integrated Program in Neuroscience, McGill University, 845 Sherbrooke St W, Montreal, QC H3A 0G4, Canada;
- McGill Group for Suicide Studies, Department of Psychiatry, Douglas Mental Health University Institute, McGill University, 6875 LaSalle Blvd, Verdun, QC H4H 1R3, Canada;
- Genome Quebec Innovation Centre, Department of Human Genetics, McGill University, 845 Sherbrooke St W, Montreal, QC H3A 0G4, Canada
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21
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McDonough CW. Pharmacogenomics in Cardiovascular Diseases. Curr Protoc 2021; 1:e189. [PMID: 34232575 DOI: 10.1002/cpz1.189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Cardiovascular pharmacogenomics is the study and identification of genomic markers that are associated with variability in cardiovascular drug response, cardiovascular drug-related outcomes, or cardiovascular drug-related adverse events. This overview presents an introduction and historical background to cardiovascular pharmacogenomics, and a protocol for designing a cardiovascular pharmacogenomics study. Important considerations are also included for constructing a cardiovascular pharmacogenomics phenotype, designing the replication or validation strategy, common statistical approaches, and how to put the results in context with the cardiovascular drug or cardiovascular disease under investigation. © 2021 Wiley Periodicals LLC. Basic Protocol: Designing a cardiovascular pharmacogenomics study.
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Affiliation(s)
- Caitrin W McDonough
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida
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22
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Migliore L, Nicolì V, Stoccoro A. Gender Specific Differences in Disease Susceptibility: The Role of Epigenetics. Biomedicines 2021; 9:652. [PMID: 34200989 PMCID: PMC8228628 DOI: 10.3390/biomedicines9060652] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/02/2021] [Accepted: 06/05/2021] [Indexed: 01/08/2023] Open
Abstract
Many complex traits or diseases, such as infectious and autoimmune diseases, cancer, xenobiotics exposure, neurodevelopmental and neurodegenerative diseases, as well as the outcome of vaccination, show a differential susceptibility between males and females. In general, the female immune system responds more efficiently to pathogens. However, this can lead to over-reactive immune responses, which may explain the higher presence of autoimmune diseases in women, but also potentially the more adverse effects of vaccination in females compared with in males. Many clinical and epidemiological studies reported, for the SARS-CoV-2 infection, a gender-biased differential response; however, the majority of reports dealt with a comparable morbidity, with males, however, showing higher COVID-19 adverse outcomes. Although gender differences in immune responses have been studied predominantly within the context of sex hormone effects, some other mechanisms have been invoked: cellular mosaicism, skewed X chromosome inactivation, genes escaping X chromosome inactivation, and miRNAs encoded on the X chromosome. The hormonal hypothesis as well as other mechanisms will be examined and discussed in the light of the most recent epigenetic findings in the field, as the concept that epigenetics is the unifying mechanism in explaining gender-specific differences is increasingly emerging.
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Affiliation(s)
- Lucia Migliore
- Department of Translational Research and of New Surgical and Medical Technologies, Medical School, University of Pisa, 56126 Pisa, Italy; (V.N.); (A.S.)
- Department of Laboratory Medicine, Azienda Ospedaliero Universitaria Pisana, 56124 Pisa, Italy
| | - Vanessa Nicolì
- Department of Translational Research and of New Surgical and Medical Technologies, Medical School, University of Pisa, 56126 Pisa, Italy; (V.N.); (A.S.)
| | - Andrea Stoccoro
- Department of Translational Research and of New Surgical and Medical Technologies, Medical School, University of Pisa, 56126 Pisa, Italy; (V.N.); (A.S.)
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23
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Dolci LS, Perone RC, Di Gesù R, Kurakula M, Gualandi C, Zironi E, Gazzotti T, Tondo MT, Pagliuca G, Gostynska N, Baldassarro VA, Cescatti M, Giardino L, Focarete ML, Calzà L, Passerini N, Bolognesi ML. Design and In Vitro Study of a Dual Drug-Loaded Delivery System Produced by Electrospinning for the Treatment of Acute Injuries of the Central Nervous System. Pharmaceutics 2021; 13:848. [PMID: 34201089 DOI: 10.3390/pharmaceutics13060848] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/28/2021] [Accepted: 06/03/2021] [Indexed: 12/16/2022] Open
Abstract
Vascular and traumatic injuries of the central nervous system are recognized as global health priorities. A polypharmacology approach that is able to simultaneously target several injury factors by the combination of agents having synergistic effects appears to be promising. Herein, we designed a polymeric delivery system loaded with two drugs, ibuprofen (Ibu) and thyroid hormone triiodothyronine (T3) to in vitro release the suitable amount of the anti-inflammation and the remyelination drug. As a production method, electrospinning technology was used. First, Ibu-loaded micro (diameter circa 0.95–1.20 µm) and nano (diameter circa 0.70 µm) fibers were produced using poly(l-lactide) PLLA and PLGA with different lactide/glycolide ratios (50:50, 75:25, and 85:15) to select the most suitable polymer and fiber diameter. Based on the in vitro release results and in-house knowledge, PLLA nanofibers (mean diameter = 580 ± 120 nm) loaded with both Ibu and T3 were then successfully produced by a co-axial electrospinning technique. The in vitro release studies demonstrated that the final Ibu/T3 PLLA system extended the release of both drugs for 14 days, providing the target sustained release. Finally, studies in cell cultures (RAW macrophages and neural stem cell-derived oligodendrocyte precursor cells—OPCs) demonstrated the anti-inflammatory and promyelinating efficacy of the dual drug-loaded delivery platform.
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24
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Ginsberg SD, Neubert TA, Sharma S, Digwal CS, Yan P, Timbus C, Wang T, Chiosis G. Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease. FEBS J 2021; 289:2047-2066. [PMID: 34028172 DOI: 10.1111/febs.16031] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 04/23/2021] [Accepted: 05/20/2021] [Indexed: 12/22/2022]
Abstract
The increasingly appreciated prevalence of complicated stressor-to-phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell- and tissue-specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor-induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context-dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single-alteration perspective in disease to a comprehensive network-based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment.
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Affiliation(s)
- Stephen D Ginsberg
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, USA.,Departments of Psychiatry, Neuroscience & Physiology, The NYU Neuroscience Institute, New York University Grossman School of Medicine, NY, USA
| | - Thomas A Neubert
- Kimmel Center for Biology and Medicine at the Skirball Institute, NYU School of Medicine, New York, NY, USA
| | - Sahil Sharma
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY, USA
| | - Chander S Digwal
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY, USA
| | - Pengrong Yan
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY, USA
| | - Calin Timbus
- Department of Mathematics, Technical University of Cluj-Napoca, CJ, Romania
| | - Tai Wang
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY, USA
| | - Gabriela Chiosis
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY, USA.,Breast Cancer Medicine Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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25
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Lu Y, Wu Y, Liu Y, Li Y, Jing R, Li M. Prediction of disease-associated functional variants in noncoding regions through a comprehensive analysis by integrating datasets and features. Hum Mutat 2021; 42:667-684. [PMID: 33822436 DOI: 10.1002/humu.24203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 02/01/2021] [Accepted: 03/31/2021] [Indexed: 02/01/2023]
Abstract
One of the greatest challenges in human genetics is deciphering the link between functional variants in noncoding sequences and the pathophysiology of complex diseases. To address this issue, many methods have been developed to sort functional single-nucleotide variants (SNVs) for neutral SNVs in noncoding regions. In this study, we integrated well-established features and commonly used datasets and merged them into large-scale datasets based on a random forest model, which yielded promising performance and outperformed some cutting-edge approaches. Our analyses of feature importance and data coverage also provide certain clues for future research in enhancing the prediction of functional noncoding SNVs.
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Affiliation(s)
- Yu Lu
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
| | - Yiming Wu
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Yuan Liu
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
| | - Yizhou Li
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
| | - Runyu Jing
- College of Cybersecurity, Sichuan University, Chengdu, Sichuan, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
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26
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Secolin R, Gonsales MC, Rocha CS, Naslavsky M, De Marco L, Bicalho MAC, Vazquez VL, Zatz M, Silva WA, Lopes-Cendes I. Exploring a Region on Chromosome 8p23.1 Displaying Positive Selection Signals in Brazilian Admixed Populations: Additional Insights Into Predisposition to Obesity and Related Disorders. Front Genet 2021; 12:636542. [PMID: 33841501 PMCID: PMC8027303 DOI: 10.3389/fgene.2021.636542] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/04/2021] [Indexed: 11/13/2022] Open
Abstract
We recently reported a deviation of local ancestry on the chromosome (ch) 8p23.1, which led to positive selection signals in a Brazilian population sample. The deviation suggested that the genetic variability of candidate genes located on ch 8p23.1 may have been evolutionarily advantageous in the early stages of the admixture process. In the present work, we aim to extend the previous work by studying additional Brazilian admixed individuals and examining DNA sequencing data from the ch 8p23.1 candidate region. Thus, we inferred the local ancestry of 125 exomes from individuals born in five towns within the Southeast region of Brazil (São Paulo, Campinas, Barretos, and Ribeirão Preto located in the state of São Paulo and Belo Horizonte, the capital of the state of Minas Gerais), and compared to data from two public Brazilian reference genomic databases, BIPMed and ABraOM, and with information from the 1000 Genomes Project phase 3 and gnomAD databases. Our results revealed that ancestry is similar among individuals born in the five Brazilian towns assessed; however, an increased proportion of sub-Saharan African ancestry was observed in individuals from Belo Horizonte. In addition, individuals from the five towns considered, as well as those from the ABRAOM dataset, had the same overrepresentation of Native-American ancestry on the ch 8p23.1 locus that was previously reported for the BIPMed reference sample. Sequencing analysis of ch 8p23.1 revealed the presence of 442 non-synonymous variants, including frameshift, inframe deletion, start loss, stop gain, stop loss, and splicing site variants, which occurred in 24 genes. Among these genes, 13 were associated with obesity, type II diabetes, lipid levels, and waist circumference (PRAG1, MFHAS1, PPP1R3B, TNKS, MSRA, PRSS55, RP1L1, PINX1, MTMR9, FAM167A, BLK, GATA4, and CTSB). These results strengthen the hypothesis that a set of variants located on ch 8p23.1 that result from positive selection during early admixture events may influence obesity-related disease predisposition in admixed individuals of the Brazilian population. Furthermore, we present evidence that the exploration of local ancestry deviation in admixed individuals may provide information with the potential to be translated into health care improvement.
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Affiliation(s)
- Rodrigo Secolin
- Department of Medical Genetics and Genomic Medicine, Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas - UNICAMP, Campinas, Brazil
| | - Marina C Gonsales
- Department of Medical Genetics and Genomic Medicine, Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas - UNICAMP, Campinas, Brazil
| | - Cristiane S Rocha
- Department of Medical Genetics and Genomic Medicine, Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas - UNICAMP, Campinas, Brazil
| | - Michel Naslavsky
- Departament of Genetics and Evolutive Biology, Human Genome and Stem Cell Research Center, Institute of Bioscience, University of São Paulo (USP), São Paulo, Brazil
| | - Luiz De Marco
- Department of Surgery, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Maria A C Bicalho
- Department of Clinical Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Vinicius L Vazquez
- Molecular Oncology Research Center (CPOM) - Barretos Cancer Hospital, Barretos, Brazil
| | - Mayana Zatz
- Departament of Genetics and Evolutive Biology, Human Genome and Stem Cell Research Center, Institute of Bioscience, University of São Paulo (USP), São Paulo, Brazil
| | - Wilson A Silva
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo at Ribeirão Preto (USP), Ribeirão Preto, Brazil
| | - Iscia Lopes-Cendes
- Department of Medical Genetics and Genomic Medicine, Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas - UNICAMP, Campinas, Brazil
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27
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Zhang B, Chiu CY, Yuan F, Sang T, Cook RJ, Wilson AF, Bailey-Wilson JE, Chew EY, Xiong M, Fan R. Gene-based analysis of bi-variate survival traits via functional regressions with applications to eye diseases. Genet Epidemiol 2021; 45:455-470. [PMID: 33645812 DOI: 10.1002/gepi.22381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 01/15/2021] [Accepted: 02/08/2021] [Indexed: 11/12/2022]
Abstract
Genetic studies of two related survival outcomes of a pleiotropic gene are commonly encountered but statistical models to analyze them are rarely developed. To analyze sequencing data, we propose mixed effect Cox proportional hazard models by functional regressions to perform gene-based joint association analysis of two survival traits motivated by our ongoing real studies. These models extend fixed effect Cox models of univariate survival traits by incorporating variations and correlation of multivariate survival traits into the models. The associations between genetic variants and two survival traits are tested by likelihood ratio test statistics. Extensive simulation studies suggest that type I error rates are well controlled and power performances are stable. The proposed models are applied to analyze bivariate survival traits of left and right eyes in the age-related macular degeneration progression.
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Affiliation(s)
- Bingsong Zhang
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Chi-Yang Chiu
- Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA.,Computational and Statistical Genomics Branch, National Human Genome, Research Institute, National Institutes of Health (NIH), Baltimore, Maryland, USA
| | - Fang Yuan
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Kunming Medical University, Kunming, People's Republic of China
| | - Tian Sang
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA.,School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, China
| | - Richard J Cook
- Department of Statistics and Actuarial Science, Waterloo, Ontario, Canada
| | - Alexander F Wilson
- Computational and Statistical Genomics Branch, National Human Genome, Research Institute, National Institutes of Health (NIH), Baltimore, Maryland, USA
| | - Joan E Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome, Research Institute, National Institutes of Health (NIH), Baltimore, Maryland, USA
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, NIH, Bethesda, Maryland, USA
| | - Momiao Xiong
- Human Genetics Center, University of Texas-Houston, Houston, Texas, USA
| | - Ruzong Fan
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA.,Computational and Statistical Genomics Branch, National Human Genome, Research Institute, National Institutes of Health (NIH), Baltimore, Maryland, USA
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28
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Zhou W, Chen Z, Lu A, Liu Z. Systems Pharmacology-Based Strategy to Explore the Pharmacological Mechanisms of Citrus Peel (Chenpi) for Treating Complicated Diseases. Am J Chin Med 2021; 49:391-411. [PMID: 33622210 DOI: 10.1142/s0192415x2150018x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Citri Reticulatae Pericarpium (CRP), also known as Chenpi in Chinese, is the dry mature peel of Citrus reticulata Blanco or its cultivated varieties. CRP as the health-care food and dietary supplement has been widely used in various diseases. However, the potential pharmacological mechanisms of CRP to predict and treat various diseases have not yet been fully elucidated. A systems pharmacology-based approach is developed by integrating absorption, distribution, metabolism, and excretion screening, multiple target fishing, network pharmacology, as well as pathway analysis to comprehensively dissect the potential mechanism of CRP for therapy of various diseases. The results showed that 39 bioactive components and 121 potential protein targets were identified from CRP. The 121 targets are closely related to various diseases of the cardiovascular system, respiratory system, gastrointestinal system, etc. These targets are further mapped to compound-target, target-disease, and target-pathway networks to clarify the therapeutic mechanism of CRP at the system level. The current study sheds light on a promising way for promoting the discovery of new botanical drugs.
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Affiliation(s)
- Wei Zhou
- Department of Respirology & Allergy, Third Affiliated Hospital of Shenzhen University, Shenzhen Key Laboratory of Allergy & Immunology, Shenzhen University School of Medicine, Shenzhen University, Shenzhen, P. R. China.,State Key Laboratory of Respiratory Disease for Allergy at Shenzhen University, Shenzhen Key Laboratory of Allergy & Immunology, Shenzhen University School of Medicine, Shenzhen University, Shenzhen, P. R. China
| | - Ziyi Chen
- Musculoskeletal Research Laboratory, Department of Orthopaedics & Traumatology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, P. R. China
| | - Aiping Lu
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, P. R. China
| | - Zhigang Liu
- Department of Respirology & Allergy, Third Affiliated Hospital of Shenzhen University, Shenzhen Key Laboratory of Allergy & Immunology, Shenzhen University School of Medicine, Shenzhen University, Shenzhen, P. R. China.,State Key Laboratory of Respiratory Disease for Allergy at Shenzhen University, Shenzhen Key Laboratory of Allergy & Immunology, Shenzhen University School of Medicine, Shenzhen University, Shenzhen, P. R. China
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29
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Song Q, Lei Y, Shao L, Li W, Kong Q, Lin Z, Qin X, Wei W, Hou F, Li J, Guo X, Mao Y, Cao Y, Liu Z, Zheng L, Liang R, Jiang Y, Liu Y, Zhang L, Yang J, Lau YL, Zhang Y, Ban B, Wang YF, Yang W. Genome-wide association study on Northern Chinese identifies KLF2, DOT1L and STAB2 associated with systemic lupus erythematosus. Rheumatology (Oxford) 2021; 60:4407-4417. [PMID: 33493351 DOI: 10.1093/rheumatology/keab016] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 12/10/2020] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVES To identify novel genetic loci associated with systemic lupus erythematosus (SLE) and to evaluate potential genetic differences between ethnic Chinese and European populations in SLE susceptibility. METHODS A new genome-wide association study (GWAS) was conducted from Jining, North China, on 1506 individuals (512 SLE cases and 994 matched healthy controls). The association results were meta-analysed with existing data on Chinese populations from Hong Kong, Guangzhou and Central China, as well as GWAS results from four cohorts of European ancestry. A total of 26 774 individuals (9310 SLE cases and 17 464 controls) were included in this study. RESULTS Meta-analysis on four Chinese cohorts identifies KLF2 as a novel locus associated with SLE [rs2362475; odds ratio (OR) = 0.85, P=2.00E-09]. KLF2 is likely an Asian-specific locus as no evidence of association was detected in the four European cohorts (OR = 0.98, P =0.58), with evidence of heterogeneity (P=0.0019) between the two ancestral groups. Meta-analyses of results from both Chinese and Europeans identify STAB2 (rs10082873; OR= 0.89, P=4.08E-08) and DOT1L (rs4807205; OR= 1.12, P=8.17E-09) as trans-ancestral association loci, surpassing the genome-wide significance. CONCLUSIONS We identified three loci associated with SLE, with KLF2 a likely Chinese-specific locus, highlighting the importance of studying diverse populations in SLE genetics. We hypothesize that DOT1L and KLF2 are plausible SLE treatment targets, with inhibitors of DOT1L and inducers of KLF2 already available clinically.
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Affiliation(s)
- Qin Song
- Department of Rheumatology and Lupus Research Institute, The Affiliated Hospital of Jining Medical University
| | - Yao Lei
- Collaborative Innovation Center for Birth Defect Research and Transformation of Shandong Province, Jining Medical University, Shandong.,Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | - Li Shao
- Department of Rheumatology and Lupus Research Institute, The Affiliated Hospital of Jining Medical University
| | - Weiyang Li
- Collaborative Innovation Center for Birth Defect Research and Transformation of Shandong Province, Jining Medical University, Shandong
| | - Qingsheng Kong
- Collaborative Innovation Center for Birth Defect Research and Transformation of Shandong Province, Jining Medical University, Shandong
| | - Zhiming Lin
- Department of Rheumatology, The Third Affiliated Hospital of Sun Yat-Sen University
| | - Xiao Qin
- Collaborative Innovation Center for Birth Defect Research and Transformation of Shandong Province, Jining Medical University, Shandong
| | - Wei Wei
- Collaborative Innovation Center for Birth Defect Research and Transformation of Shandong Province, Jining Medical University, Shandong
| | - Fei Hou
- Collaborative Innovation Center for Birth Defect Research and Transformation of Shandong Province, Jining Medical University, Shandong
| | - Jian Li
- Department of Rheumatology and Lupus Research Institute, The Affiliated Hospital of Jining Medical University
| | - Xianghua Guo
- Department of Rheumatology and Lupus Research Institute, The Affiliated Hospital of Jining Medical University
| | - Yujing Mao
- Department of Rheumatology and Lupus Research Institute, The Affiliated Hospital of Jining Medical University
| | - Yujie Cao
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | - Zhongyi Liu
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | - Lichuan Zheng
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | - Rui Liang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | - Yuping Jiang
- Department of Rheumatology and Lupus Research Institute, The Affiliated Hospital of Jining Medical University
| | - Yan Liu
- Department of Rheumatology and Lupus Research Institute, The Affiliated Hospital of Jining Medical University
| | - Lili Zhang
- Department of Rheumatology and Lupus Research Institute, The Affiliated Hospital of Jining Medical University
| | - Jing Yang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | - Yu Lung Lau
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | - Yan Zhang
- Department of Pediatric Surgery, Guangzhou Institute of Pediatrics, Guangdong Provincial Key Laboratory of Research in Structural Birth Defect Disease, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou
| | - Bo Ban
- Department of Endocrinology, Affiliated Hospital of Jining Medical University, Jining Medical University.,Chinese Research Center for Behavior Medicine in Growth and Development, Shandong
| | - Yong-Fei Wang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR.,Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China
| | - Wanling Yang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR
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30
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Abedi V, Li J, Shivakumar MK, Avula V, Chaudhary DP, Shellenberger MJ, Khara HS, Zhang Y, Lee MTM, Wolk DM, Yeasin M, Hontecillas R, Bassaganya-Riera J, Zand R. Increasing the Density of Laboratory Measures for Machine Learning Applications. J Clin Med 2020; 10:E103. [PMID: 33396741 DOI: 10.3390/jcm10010103] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 12/23/2020] [Accepted: 12/25/2020] [Indexed: 12/12/2022] Open
Abstract
Background. The imputation of missingness is a key step in Electronic Health Records (EHR) mining, as it can significantly affect the conclusions derived from the downstream analysis in translational medicine. The missingness of laboratory values in EHR is not at random, yet imputation techniques tend to disregard this key distinction. Consequently, the development of an adaptive imputation strategy designed specifically for EHR is an important step in improving the data imbalance and enhancing the predictive power of modeling tools for healthcare applications. Method. We analyzed the laboratory measures derived from Geisinger’s EHR on patients in three distinct cohorts—patients tested for Clostridioides difficile (Cdiff) infection, patients with a diagnosis of inflammatory bowel disease (IBD), and patients with a diagnosis of hip or knee osteoarthritis (OA). We extracted Logical Observation Identifiers Names and Codes (LOINC) from which we excluded those with 75% or more missingness. The comorbidities, primary or secondary diagnosis, as well as active problem lists, were also extracted. The adaptive imputation strategy was designed based on a hybrid approach. The comorbidity patterns of patients were transformed into latent patterns and then clustered. Imputation was performed on a cluster of patients for each cohort independently to show the generalizability of the method. The results were compared with imputation applied to the complete dataset without incorporating the information from comorbidity patterns. Results. We analyzed a total of 67,445 patients (11,230 IBD patients, 10,000 OA patients, and 46,215 patients tested for C. difficile infection). We extracted 495 LOINC and 11,230 diagnosis codes for the IBD cohort, 8160 diagnosis codes for the Cdiff cohort, and 2042 diagnosis codes for the OA cohort based on the primary/secondary diagnosis and active problem list in the EHR. Overall, the most improvement from this strategy was observed when the laboratory measures had a higher level of missingness. The best root mean square error (RMSE) difference for each dataset was recorded as −35.5 for the Cdiff, −8.3 for the IBD, and −11.3 for the OA dataset. Conclusions. An adaptive imputation strategy designed specifically for EHR that uses complementary information from the clinical profile of the patient can be used to improve the imputation of missing laboratory values, especially when laboratory codes with high levels of missingness are included in the analysis.
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31
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Chen X, Lin Y, Qu Q, Ning B, Chen H, Cai L. A Multi-Source Data Fusion Framework for Revealing the Regulatory Mechanism of Breast Cancer Immune Evasion. Front Genet 2020; 11:595324. [PMID: 33304391 PMCID: PMC7693564 DOI: 10.3389/fgene.2020.595324] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 09/30/2020] [Indexed: 01/03/2023] Open
Abstract
For precision medicine, there is an enormous need to understand the immune evasion mechanism of tumor development, especially when tumor heterogeneity significantly affects the effect of immunotherapy. Recognizing the subtypes of breast cancer based on the immune-related genes helps to understand the immune escape pathways dominated by different subtypes, so as to implement effective treatment measures for different subtypes. For that, we used non-negative matrix factorization and consistent clustering algorithm on The Cancer Genome Atlas RNA-seq breast cancer data and recognized 4 subtypes according to the curated immune-related genes. Then, we conducted differential expression analysis between each subtype of breast cancer and normal tissue of RNA-seq data from non-cancer individuals collected by the Genotype-Tissue Expression to find out subtype-related immune genes. After that, we carried out correlation analysis between copy number variants (CNV) and mRNA of immune genes and investigated the regulatory mechanism of the immune genes, which cannot be explained by CNV based on ATAC-seq data. The experimental results reveal that CDH1 and PVRL2 are potential for immune evasion in all 4 subgroups. The expression variations of CDH1 can be mainly explained by its CNV, while the expression variation of PVRL2 is more likely regulated by transcript factors.
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Affiliation(s)
- Xia Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.,School of Basic Education, Changsha Aeronautical Vocational and Technical College, Changsha, China
| | - Yexiong Lin
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Qiang Qu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Bin Ning
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Haowen Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Lijun Cai
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
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32
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Kafaie S, Xu L, Hu T. Statistical methods with exhaustive search in the identification of gene-gene interactions for colorectal cancer. Genet Epidemiol 2020; 45:222-234. [PMID: 33231893 DOI: 10.1002/gepi.22372] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 10/10/2020] [Accepted: 11/09/2020] [Indexed: 12/16/2022]
Abstract
Though additive forms of heritability are primarily studied in genetics, nonlinear, non-additive gene-gene interactions, that is, epistasis, could explain a portion of the missing heritability in complex human diseases including cancer. In recent years, powerful computational methods have been introduced to understand multivariable genetic factors of these complex human diseases in extremely high-dimensional genome-wide data. In this study, we investigated the performance of three powerful methods, BOolean Operation-based Screening and Testing (BOOST), FastEpistasis, and Tree-based Epistasis Association Mapping (TEAM) to identify interacting genetic risk factors of colorectal cancer (CRC) for genome-wide association studies (GWAS). After quality-control based data preprocessing, we applied these three algorithms to a CRC GWAS data set, and selected the top-ranked 100 single-nucleotide polymorphism (SNP) pairs identified by each method (251 SNPs in total), among which 74 pairs were common between FastEpistasis and BOOST. The identified SNPs by BOOST, FastEpistasis, and TEAM mapped to 58, 57, and 62 genes, respectively. Some genes highlighted by our study, including MACF1, USP49, SMAD2, SMAD3, TGFBR1, and RHOA, have been detected in previous CRC-related research. We also identified some new genes with potential biological relevance to CRC such as CCDC32. Furthermore, we constructed the network of these top SNP pairs for three methods, and the patterns identified in the networks show that some SNPs including rs2412531, rs349699, and rs17142011 play a crucial role in the classification of disease status in our study.
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Affiliation(s)
- Somayeh Kafaie
- Department of Computer Science, Memorial University, St. John's, Newfoundland, Canada
| | - Ling Xu
- Department of Computer Science, Memorial University, St. John's, Newfoundland, Canada
| | - Ting Hu
- Department of Computer Science, Memorial University, St. John's, Newfoundland, Canada.,School of Computing, Queen's University, Kingston, Ontario, Canada
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33
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Affiliation(s)
- Ting Hu
- Memorial University of Newfoundland, St. John's, NL, Canada.,Queen's University, Kingston, ON, Canada
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34
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Abstract
Current genetic studies of monogenic and complex bone diseases have broadened our understanding of disease pathophysiology, highlighting the need for medical interventions and treatments tailored to the characteristics of patients. As genomic research progresses, novel insights into the molecular mechanisms are starting to provide support to clinical decision-making; now offering ample opportunities for disease screening, diagnosis, prognosis and treatment. Drug targets holding mechanisms with genetic support are more likely to be successful. Therefore, implementing genetic information to the drug development process and a molecular redefinition of skeletal disease can help overcoming current shortcomings in pharmaceutical research, including failed attempts and appalling costs. This review summarizes the achievements of genetic studies in the bone field and their application to clinical care, illustrating the imminent advent of the genomic medicine era.
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35
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Zhao Y, Ning Y, Zhang F, Ding M, Wen Y, Shi L, Wang K, Lu M, Sun J, Wu M, Cheng B, Ma M, Zhang L, Cheng S, Shen H, Tian Q, Guo X, Deng HW. PCA-based GRS analysis enhances the effectiveness for genetic correlation detection. Brief Bioinform 2020; 20:2291-2298. [PMID: 30169568 DOI: 10.1093/bib/bby075] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 07/30/2018] [Accepted: 08/01/2018] [Indexed: 01/10/2023] Open
Abstract
Genetic risk score (GRS, also known as polygenic risk score) analysis is an increasingly popular method for exploring genetic architectures and relationships of complex diseases. However, complex diseases are usually measured by multiple correlated phenotypes. Analyzing each disease phenotype individually is likely to reduce statistical power due to multiple testing correction. In order to conquer the disadvantage, we proposed a principal component analysis (PCA)-based GRS analysis approach. Extensive simulation studies were conducted to compare the performance of PCA-based GRS analysis and traditional GRS analysis approach. Simulation results observed significantly improved performance of PCA-based GRS analysis compared to traditional GRS analysis under various scenarios. For the sake of verification, we also applied both PCA-based GRS analysis and traditional GRS analysis to a real Caucasian genome-wide association study (GWAS) data of bone geometry. Real data analysis results further confirmed the improved performance of PCA-based GRS analysis. Given that GWAS have flourished in the past decades, our approach may help researchers to explore the genetic architectures and relationships of complex diseases or traits.
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Affiliation(s)
- Yan Zhao
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Yujie Ning
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China.,Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Feng Zhang
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Miao Ding
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Yan Wen
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Liang Shi
- Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Kunpeng Wang
- Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Mengnan Lu
- Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Jingyan Sun
- Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Menglu Wu
- Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Bolun Cheng
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Mei Ma
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Lu Zhang
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Shiqiang Cheng
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Hui Shen
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, China
| | - Qing Tian
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, China
| | - Xiong Guo
- School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Hong-Wen Deng
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, China
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36
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Li Y, Ma A, Mathé EA, Li L, Liu B, Ma Q. Elucidation of Biological Networks across Complex Diseases Using Single-Cell Omics. Trends Genet 2020; 36:951-966. [PMID: 32868128 DOI: 10.1016/j.tig.2020.08.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 07/29/2020] [Accepted: 08/04/2020] [Indexed: 12/14/2022]
Abstract
Single-cell multimodal omics (scMulti-omics) technologies have made it possible to trace cellular lineages during differentiation and to identify new cell types in heterogeneous cell populations. The derived information is especially promising for computing cell-type-specific biological networks encoded in complex diseases and improving our understanding of the underlying gene regulatory mechanisms. The integration of these networks could, therefore, give rise to a heterogeneous regulatory landscape (HRL) in support of disease diagnosis and drug therapeutics. In this review, we provide an overview of this field and pay particular attention to how diverse biological networks can be inferred in a specific cell type based on integrative methods. Then, we discuss how HRL can advance our understanding of regulatory mechanisms underlying complex diseases and aid in the prediction of prognosis and therapeutic responses. Finally, we outline challenges and future trends that will be central to bringing the field of HRL in complex diseases forward.
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Affiliation(s)
- Yang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Ewy A Mathé
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health (NIH), Rockville, MD, 20892, USA
| | - Lang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China.
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA.
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37
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Abstract
Most complex diseases involve genetic and environmental risk factors, engage multiple cells and tissues, and follow a polygenic or omnigenic model depicting numerous genes contributing to pathophysiology. These multidimensional complexities pose challenges to traditional approaches that examine individual factors. In turn, multitissue multiomics systems biology has emerged to comprehensively elucidate within- and cross-tissue molecular networks underlying gene-by-environment interactions and contributing to complex diseases. The power of systems biology in retrieving novel insights and formulating new hypotheses has been well documented. However, the field faces various challenges that call for debate and action. In this opinion article, I discuss the concepts, benefits, current state, and challenges of the field and point to the next steps toward network-based systems medicine.
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Affiliation(s)
- Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA.
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38
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Lin DY, Zeng D, Couper D. A general framework for integrative analysis of incomplete multiomics data. Genet Epidemiol 2020; 44:646-664. [PMID: 32691502 DOI: 10.1002/gepi.22328] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 03/05/2020] [Accepted: 05/29/2020] [Indexed: 12/21/2022]
Abstract
There is a tremendous current interest in measuring multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation profiles, metabolic profiles, protein expressions) on a large number of subjects. Although genotypes are typically available for all study subjects, other data types may be measured only on a subset of subjects due to cost or other constraints. In addition, quantitative omics measurements, such as metabolite levels and protein expressions, are subject to detection limits in that the measurements below (or above) certain thresholds are not detectable. In this article, we propose a rigorous and powerful approach to handle missing values and detection limits in integrative analysis of multiomics data. We relate quantitative omics variables to genetic variants and other variables through linear regression models and relate phenotypes to quantitative omics variables and other variables through generalized linear models. We derive the joint-likelihood for the two sets of models by allowing arbitrary patterns of missing values and detection limits for quantitative omics variables. We carry out maximum-likelihood estimation through computationally fast and stable algorithms. The resulting estimators are approximately unbiased and statistically efficient. An application to a major study on chronic obstructive lung disease yielded new biological insights.
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Affiliation(s)
- Dan-Yu Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
| | - David Couper
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
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39
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Abstract
Ultraconserved elements (UCEs) are among the most popular DNA markers for phylogenomic analysis. In at least three of five placental mammalian genomes (human, dog, cow, mouse, and rat), 2189 UCEs of at least 200 bp in length that are identical have been identified. Most of these regions have not yet been functionally annotated, and their associations with diseases remain largely unknown. This is an important knowledge gap in human genomics with regard to UCE roles in physiologically critical functions, and by extension, their relevance for shared susceptibilities to common complex diseases across several mammalian organisms in the event of their polymorphic variations. In the present study, we remapped the genomic locations of these UCEs to the latest human genome assembly, and examined them for documented polymorphisms in sequenced human genomes. We identified 29,983 polymorphisms within analyzed UCEs, but revealed that a vast majority exhibits very low minor allele frequencies. Notably, only 112 of the identified polymorphisms are associated with a phenotype in the Ensembl genome browser. Through literature analyses, we confirmed associations of 37 (i.e., out of the 112) polymorphisms within 23 UCEs with 25 diseases and phenotypic traits, including, muscular dystrophies, eye diseases, and cancers (e.g., familial adenomatous polyposis). Most reports of UCE polymorphism-disease associations appeared to be not cognizant that their candidate polymorphisms were actually within UCEs. The present study offers strategic directions and knowledge gaps for future computational and experimental work so as to better understand the thus far intriguing and puzzling role(s) of UCEs in mammalian genomes.
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Affiliation(s)
- Anamarija Habic
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia
| | - John S Mattick
- School of Biotechnology and Biomolecular Science, University of New South Wales, Sydney, Australia.,Green Templeton College, University of Oxford, Oxford, United Kingdom
| | - George Adrian Calin
- Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas.,The Center for RNA Interference and Noncoding RNAs, The University of Texas M.D. Anderson Cancer Center, Houston, Texas
| | - Rok Krese
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia
| | - Janez Konc
- National Institute of Chemistry, Ljubljana, Slovenia
| | - Tanja Kunej
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia
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40
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Herholt A, Galinski S, Geyer PE, Rossner MJ, Wehr MC. Multiparametric Assays for Accelerating Early Drug Discovery. Trends Pharmacol Sci 2020; 41:318-335. [PMID: 32223968 DOI: 10.1016/j.tips.2020.02.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 02/21/2020] [Accepted: 02/27/2020] [Indexed: 02/07/2023]
Abstract
Drug discovery campaigns are hampered by substantial attrition rates largely due to a lack of efficacy and safety reasons associated with candidate drugs. This is true in particular for genetically complex diseases, where insufficient knowledge of the modulatory actions of candidate drugs on targets and entire target pathways further adds to the problem of attrition. To better profile compound actions on targets, potential off-targets, and disease-linked pathways, new innovative technologies need to be developed that can elucidate the complex cellular signaling networks in health and disease. Here, we discuss progress in genetically encoded multiparametric assays and mass spectrometry (MS)-based proteomics, which both represent promising toolkits to profile multifactorial actions of drug candidates in disease-relevant cellular systems to promote drug discovery and personalized medicine.
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Affiliation(s)
- Alexander Herholt
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nussbaumstr. 7, 80336 Munich, Germany; Systasy Bioscience GmbH, Balanstr. 6, 81669, Munich, Germany
| | - Sabrina Galinski
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nussbaumstr. 7, 80336 Munich, Germany; Systasy Bioscience GmbH, Balanstr. 6, 81669, Munich, Germany
| | - Philipp E Geyer
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Planegg, Germany; NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark; OmicEra Diagnostics GmbH, Am Klopferspitz 19, 82152, Planegg, Germany
| | - Moritz J Rossner
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nussbaumstr. 7, 80336 Munich, Germany
| | - Michael C Wehr
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nussbaumstr. 7, 80336 Munich, Germany; Systasy Bioscience GmbH, Balanstr. 6, 81669, Munich, Germany.
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41
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Tiwary BK. Computational medicine: quantitative modeling of complex diseases. Brief Bioinform 2020; 21:429-440. [PMID: 30698665 DOI: 10.1093/bib/bbz005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 12/21/2018] [Accepted: 12/26/2018] [Indexed: 12/18/2022] Open
Abstract
Biological complex systems are composed of numerous components that interact within and across different scales. The ever-increasing generation of high-throughput biomedical data has given us an opportunity to develop a quantitative model of nonlinear biological systems having implications in health and diseases. Multidimensional molecular data can be modeled using various statistical methods at different scales of biological organization, such as genome, transcriptome and proteome. I will discuss recent advances in the application of computational medicine in complex diseases such as network-based studies, genome-scale metabolic modeling, kinetic modeling and support vector machines with specific examples in the field of cancer, psychiatric disorders and type 2 diabetes. The recent advances in translating these computational models in diagnosis and identification of drug targets of complex diseases are discussed, as well as the challenges researchers and clinicians are facing in taking computational medicine from the bench to bedside.
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Affiliation(s)
- Basant K Tiwary
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Pondicherry, India
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42
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Cheng S, Ma M, Zhang L, Liu L, Cheng B, Qi X, Liang C, Li P, Kafle OP, Wen Y, Zhang F. CGSEA: A Flexible Tool for Evaluating the Associations of Chemicals with Complex Diseases. G3 (Bethesda) 2020; 10:945-9. [PMID: 31937547 DOI: 10.1534/g3.119.400945] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The etiology of many human complex diseases or traits involves interactions between chemicals and genes that regulate important physiological processes. It has been well documented that chemicals can contribute to disease development through affecting gene expression in vivo. In this study, we developed a flexible tool CGSEA for scanning the candidate chemicals associated with complex diseases or traits. CGSEA only need genome-wide summary level data, such as transcriptome-wide association studies (TWAS) and mRNA expression profiles. CGSEA was applied to the GWAS summaries of attention deficiency/hyperactive disorder, (ADHD), autism spectrum disorder (ASD) and cervical cancer. CGSEA identified several significant chemicals, which have been demonstrated to be involved in the development or treatment of ADHD, ASD and cervical cancer. The CGSEA program and user manual are available at https://github.com/ChengSQXJTU/CGSEA.
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43
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Joshi V, Upadhyay A, Prajapati VK, Mishra A. How autophagy can restore proteostasis defects in multiple diseases? Med Res Rev 2020; 40:1385-1439. [PMID: 32043639 DOI: 10.1002/med.21662] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 01/03/2020] [Accepted: 01/28/2020] [Indexed: 12/12/2022]
Abstract
Cellular evolution develops several conserved mechanisms by which cells can tolerate various difficult conditions and overall maintain homeostasis. Autophagy is a well-developed and evolutionarily conserved mechanism of catabolism, which endorses the degradation of foreign and endogenous materials via autolysosome. To decrease the burden of the ubiquitin-proteasome system (UPS), autophagy also promotes the selective degradation of proteins in a tightly regulated way to improve the physiological balance of cellular proteostasis that may get perturbed due to the accumulation of misfolded proteins. However, the diverse as well as selective clearance of unwanted materials and regulations of several cellular mechanisms via autophagy is still a critical mystery. Also, the failure of autophagy causes an increase in the accumulation of harmful protein aggregates that may lead to neurodegeneration. Therefore, it is necessary to address this multifactorial threat for in-depth research and develop more effective therapeutic strategies against lethal autophagy alterations. In this paper, we discuss the most relevant and recent reports on autophagy modulations and their impact on neurodegeneration and other complex disorders. We have summarized various pharmacological findings linked with the induction and suppression of autophagy mechanism and their promising preclinical and clinical applications to provide therapeutic solutions against neurodegeneration. The conclusion, key questions, and future prospectives sections summarize fundamental challenges and their possible feasible solutions linked with autophagy mechanism to potentially design an impactful therapeutic niche to treat neurodegenerative diseases and imperfect aging.
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Affiliation(s)
- Vibhuti Joshi
- Cellular and Molecular Neurobiology Unit, Indian Institute of Technology Jodhpur, Karwar, India
| | - Arun Upadhyay
- Cellular and Molecular Neurobiology Unit, Indian Institute of Technology Jodhpur, Karwar, India
| | - Vijay K Prajapati
- Department of Biochemistry, School of Life Sciences, Central University of Rajasthan, Ajmer, India
| | - Amit Mishra
- Cellular and Molecular Neurobiology Unit, Indian Institute of Technology Jodhpur, Karwar, India
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44
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Gemmati D, Varani K, Bramanti B, Piva R, Bonaccorsi G, Trentini A, Manfrinato MC, Tisato V, Carè A, Bellini T. " Bridging the Gap" Everything that Could Have Been Avoided If We Had Applied Gender Medicine, Pharmacogenetics and Personalized Medicine in the Gender-Omics and Sex-Omics Era. Int J Mol Sci 2019; 21:E296. [PMID: 31906252 DOI: 10.3390/ijms21010296] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 12/21/2019] [Accepted: 12/30/2019] [Indexed: 02/06/2023] Open
Abstract
Gender medicine is the first step of personalized medicine and patient-centred care, an essential development to achieve the standard goal of a holistic approach to patients and diseases. By addressing the interrelation and integration of biological markers (i.e., sex) with indicators of psychological/cultural behaviour (i.e., gender), gender medicine represents the crucial assumption for achieving the personalized health-care required in the third millennium. However, ‘sex’ and ‘gender’ are often misused as synonyms, leading to frequent misunderstandings in those who are not deeply involved in the field. Overall, we have to face the evidence that biological, genetic, epigenetic, psycho-social, cultural, and environmental factors mutually interact in defining sex/gender differences, and at the same time in establishing potential unwanted sex/gender disparities. Prioritizing the role of sex/gender in physiological and pathological processes is crucial in terms of efficient prevention, clinical signs’ identification, prognosis definition, and therapy optimization. In this regard, the omics-approach has become a powerful tool to identify sex/gender-specific disease markers, with potential benefits also in terms of socio-psychological wellbeing for each individual, and cost-effectiveness for National Healthcare systems. “Being a male or being a female” is indeed important from a health point of view and it is no longer possible to avoid “sex and gender lens” when approaching patients. Accordingly, personalized healthcare must be based on evidence from targeted research studies aimed at understanding how sex and gender influence health across the entire life span. The rapid development of genetic tools in the molecular medicine approaches and their impact in healthcare is an example of highly specialized applications that have moved from specialists to primary care providers (e.g., pharmacogenetic and pharmacogenomic applications in routine medical practice). Gender medicine needs to follow the same path and become an established medical approach. To face the genetic, molecular and pharmacological bases of the existing sex/gender gap by means of omics approaches will pave the way to the discovery and identification of novel drug-targets/therapeutic protocols, personalized laboratory tests and diagnostic procedures (sex/gender-omics). In this scenario, the aim of the present review is not to simply resume the state-of-the-art in the field, rather an opportunity to gain insights into gender medicine, spanning from molecular up to social and psychological stances. The description and critical discussion of some key selected multidisciplinary topics considered as paradigmatic of sex/gender differences and sex/gender inequalities will allow to draft and design strategies useful to fill the existing gap and move forward.
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45
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Abstract
Genome-wide variation data with millions of genetic markers have become commonplace. However, the potential for interpretation and application of these data for clinical assessment of outcomes of interest, and prediction of disease risk, is currently not fully realized. Many common complex diseases now have numerous, well-established risk loci and likely harbor many genetic determinants with effects too small to be detected at genome-wide levels of statistical significance. A simple and intuitive approach for converting genetic data to a predictive measure of disease susceptibility is to aggregate the effects of these loci into a single measure, the genetic risk score. Here, we describe some common methods and software packages for calculating genetic risk scores and polygenic risk scores, with focus on studies of common complex diseases. We review the basic information needed, as well as important considerations for constructing genetic risk scores, including specific requirements for phenotypic and genetic data, and limitations in their application. © 2019 by John Wiley & Sons, Inc.
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Affiliation(s)
- Robert P. Igo
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Tyler G. Kinzy
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Jessica N. Cooke Bailey
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
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46
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Cui H, Srinivasan S, Korkin D. Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases. Genes (Basel) 2019; 10:E933. [PMID: 31731769 PMCID: PMC6895925 DOI: 10.3390/genes10110933] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 11/04/2019] [Accepted: 11/11/2019] [Indexed: 11/16/2022] Open
Abstract
Rapid progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein-protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers.
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Affiliation(s)
- Hongzhu Cui
- Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Suhas Srinivasan
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
| | - Dmitry Korkin
- Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
- Computer Science Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA
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47
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Abstract
Consumption of fructose has dramatically increased in past few decades in children and adults. Increasing evidence indicates that added sugars (particularly fructose) have adverse effects on metabolism and lead to numerous cardiometabolic diseases. Although both fructose and glucose are components of sucrose and high fructose corn syrup, the sugars have different metabolic fates in the human body and the effects of fructose on health are thought to be more adverse than glucose. Studies have also shown that the metabolic effects of fructose differ between individuals based on their genetic background, as individuals with specific SNPs and risk alleles seem to be more susceptible to the adverse metabolic effects of fructose. The current review discusses the metabolic effects of fructose on key complex diseases and discusses the heterogeneity in metabolic responses to dietary fructose in humans.
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Affiliation(s)
- Ruixue Hou
- Department of Nutrition and UNC Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
| | - Chinmayee Panda
- Department of Nutrition and UNC Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
| | - V Saroja Voruganti
- Department of Nutrition and UNC Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
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48
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Abstract
Variants within non-coding genomic regions can greatly affect disease. In recent years, increasing focus has been given to these variants, and how they can alter regulatory elements, such as enhancers, transcription factor binding sites and DNA methylation regions. Such variants can be considered regulatory variants. Concurrently, much effort has been put into establishing international consortia to undertake large projects aimed at discovering regulatory elements in different tissues, cell lines and organisms, and probing the effects of genetic variants on regulation by measuring gene expression. Here, we describe methods and techniques for discovering disease-associated non-coding variants using sequencing technologies. We then explain the computational procedures that can be used for annotating these variants using the information from the aforementioned projects, and prediction of their putative effects, including potential pathogenicity, based on rule-based and machine learning approaches. We provide the details of techniques to validate these predictions, by mapping chromatin-chromatin and chromatin-protein interactions, and introduce Clustered Regularly Interspaced Short Palindromic Repeats-Associated Protein 9 (CRISPR-Cas9) technology, which has already been used in this field and is likely to have a big impact on its future evolution. We also give examples of regulatory variants associated with multiple complex diseases. This review is aimed at bioinformaticians interested in the characterization of regulatory variants, molecular biologists and geneticists interested in understanding more about the nature and potential role of such variants from a functional point of views, and clinicians who may wish to learn about variants in non-coding genomic regions associated with a given disease and find out what to do next to uncover how they impact on the underlying mechanisms.
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Affiliation(s)
- Elena Rojano
- Department of Molecular Biology and Biochemistry, University of Malaga (UMA), 29010 Malaga, Spain
| | - Pedro Seoane
- Department of Molecular Biology and Biochemistry, University of Malaga (UMA), 29010 Malaga, Spain
| | - Juan A G Ranea
- CIBER de Enfermedades Raras, ISCIII, Madrid, Spain and Department of Molecular Biology and Biochemistry, University of Malaga (UMA), 29010 Malaga, Spain
| | - James R Perkins
- Research laboratory, IBIMA-Regional University Hospital of Malaga, UMA, Malaga 29009, Spain
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49
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Chiu CY, Zhang B, Wang S, Shao J, Lakhal-Chaieb ML, Cook RJ, Wilson AF, Bailey-Wilson JE, Xiong M, Fan R. Gene-based association analysis of survival traits via functional regression-based mixed effect cox models for related samples. Genet Epidemiol 2019; 43:952-965. [PMID: 31502722 DOI: 10.1002/gepi.22254] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 06/26/2019] [Accepted: 07/16/2019] [Indexed: 01/09/2023]
Abstract
The importance to integrate survival analysis into genetics and genomics is widely recognized, but only a small number of statisticians have produced relevant work toward this study direction. For unrelated population data, functional regression (FR) models have been developed to test for association between a quantitative/dichotomous/survival trait and genetic variants in a gene region. In major gene association analysis, these models have higher power than sequence kernel association tests. In this paper, we extend this approach to analyze censored traits for family data or related samples using FR based mixed effect Cox models (FamCoxME). The FamCoxME model effect of major gene as fixed mean via functional data analysis techniques, the local gene or polygene variations or both as random, and the correlation of pedigree members by kinship coefficients or genetic relationship matrix or both. The association between the censored trait and the major gene is tested by likelihood ratio tests (FamCoxME FR LRT). Simulation results indicate that the LRT control the type I error rates accurately/conservatively and have good power levels when both local gene or polygene variations are modeled. The proposed methods were applied to analyze a breast cancer data set from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). The FamCoxME provides a new tool for gene-based analysis of family-based studies or related samples.
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Affiliation(s)
- Chi-Yang Chiu
- Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Bingsong Zhang
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia
| | - Shuqi Wang
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia
| | - Jingyi Shao
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia
| | | | - Richard J Cook
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Alexander F Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Joan E Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Momiao Xiong
- Department of Biostatistics, Human Genetics Center, University of Texas-Houston, Houston, Texas
| | - Ruzong Fan
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia
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50
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Dong SS, Guo Y, Yang TL. Addressing the Missing Heritability Problem With the Help of Regulatory Features. Evol Bioinform Online 2019; 15:1176934319860861. [PMID: 31320792 PMCID: PMC6610400 DOI: 10.1177/1176934319860861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 06/10/2019] [Indexed: 12/02/2022] Open
Abstract
Genome-wide association studies (GWASs) have successfully identified thousands of
susceptibility loci for human complex diseases. However, missing heritability is still a
challenging problem. Considering most GWAS loci are located in regulatory elements, we
recently developed a pipeline named functional disease-associated single-nucleotide
polymorphisms (SNPs) prediction (FDSP), to predict novel susceptibility loci for complex
diseases based on the interpretation of regulatory features and published GWAS results
with machine learning. When applied to type 2 diabetes and hypertension, the predicted
susceptibility loci by FDSP were proved to be capable of explaining additional
heritability. In addition, potential target genes of the predicted positive SNPs were
significantly enriched in disease-related pathways. Our results suggested that taking
regulatory features into consideration might be a useful way to address the missing
heritability problem. We hope FDSP could offer help for the identification of novel
susceptibility loci for complex diseases.
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Affiliation(s)
- Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, P. R. China
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