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1.
Nat Methods ; 16(9): 843-852, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31471613

RESUMO

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.


Assuntos
Biologia Computacional/métodos , Doença/genética , Redes Reguladoras de Genes , Estudo de Associação Genômica Ampla , Modelos Biológicos , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , Algoritmos , Perfilação da Expressão Gênica , Humanos , Fenótipo , Mapas de Interação de Proteínas
2.
BMC Bioinformatics ; 20(1): 396, 2019 Jul 17.
Artigo em Inglês | MEDLINE | ID: mdl-31315558

RESUMO

BACKGROUND: Since the number of known lncRNA-disease associations verified by biological experiments is quite limited, it has been a challenging task to uncover human disease-related lncRNAs in recent years. Moreover, considering the fact that biological experiments are very expensive and time-consuming, it is important to develop efficient computational models to discover potential lncRNA-disease associations. RESULTS: In this manuscript, a novel Collaborative Filtering model called CFNBC for inferring potential lncRNA-disease associations is proposed based on Naïve Bayesian Classifier. In CFNBC, an original lncRNA-miRNA-disease tripartite network is constructed first by integrating known miRNA-lncRNA associations, miRNA-disease associations and lncRNA-disease associations, and then, an updated lncRNA-miRNA-disease tripartite network is further constructed through applying the item-based collaborative filtering algorithm on the original tripartite network. Finally, based on the updated tripartite network, a novel approach based on the Naïve Bayesian Classifier is proposed to predict potential associations between lncRNAs and diseases. The novelty of CFNBC lies in the construction of the updated lncRNA-miRNA-disease tripartite network and the introduction of the item-based collaborative filtering algorithm and Naïve Bayesian Classifier, which guarantee that CFNBC can be applied to predict potential lncRNA-disease associations efficiently without entirely relying on known miRNA-disease associations. Simulation results show that CFNBC can achieve a reliable AUC of 0.8576 in the Leave-One-Out Cross Validation (LOOCV), which is considerably better than previous state-of-the-art results. Moreover, case studies of glioma, colorectal cancer and gastric cancer demonstrate the excellent prediction performance of CFNBC as well. CONCLUSIONS: According to simulation results, due to the satisfactory prediction performance, CFNBC may be an excellent addition to biomedical researches in the future.


Assuntos
Doença/genética , RNA Longo não Codificante/metabolismo , Algoritmos , Teorema de Bayes , Neoplasias Colorretais/genética , Simulação por Computador , Glioma/genética , Humanos , MicroRNAs/metabolismo , Neoplasias Gástricas/genética
3.
BMC Bioinformatics ; 20(1): 404, 2019 Jul 25.
Artigo em Inglês | MEDLINE | ID: mdl-31345171

RESUMO

BACKGROUND: It has been shown that the deregulation of miRNAs is associated with the development and progression of many human diseases. To reduce time and cost of biological experiments, a number of algorithms have been proposed for predicting miRNA-disease associations. However, the existing methods rarely investigated the cause-and-effect mechanism behind these associations, which hindered further biomedical follow-ups. RESULTS: In this study, we presented a CCA-based model in which the possible molecular causes of miRNA-disease associations were comprehensively revealed by extracting correlated sets of genes and diseases based on the co-occurrence of miRNAs in target gene profiles and disease profiles. Our method directly suggested the underlying genes involved, which could be used for experimental tests and confirmation. The inference of associated diseases of a new miRNA was made by taking into account the weight vectors of the extracted sets. We extracted 60 pairs of correlated sets from 404 miRNAs with two profiles for 2796 target genes and 362 diseases. The extracted diseases could be considered as possible outcomes of miRNAs regulating the target genes which appeared in the same set, some of which were supported by independent source of information. Furthermore, we tested our method on the 404 miRNAs under the condition of 5-fold cross validations and received an AUC value of 0.84606. Finally, we extensively inferred miRNA-disease associations for 100 new miRNAs and some interesting prediction results were validated by established databases. CONCLUSIONS: The encouraging results demonstrated that our method could provide a biologically relevant prediction and interpretation of associations between miRNAs and diseases, which were of great usefulness when guiding biological experiments for scientific research.


Assuntos
Algoritmos , Biologia Computacional/métodos , Doença/genética , Estudos de Associação Genética , MicroRNAs/genética , Bases de Dados Genéticas , Humanos , MicroRNAs/metabolismo , Modelos Genéticos
5.
Nature ; 571(7766): 489-499, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-31341302

RESUMO

Epigenetic research has accelerated rapidly in the twenty-first century, generating justified excitement and hope, but also a degree of hype. Here we review how the field has evolved over the last few decades and reflect on some of the recent advances that are changing our understanding of biology. We discuss the interplay between epigenetics and DNA sequence variation as well as the implications of epigenetics for cellular memory and plasticity. We consider the effects of the environment and both intergenerational and transgenerational epigenetic inheritance on biology, disease and evolution. Finally, we present some new frontiers in epigenetics with implications for human health.


Assuntos
Doença/genética , Epigênese Genética/genética , Epigenômica/tendências , Interação Gene-Ambiente , Envelhecimento/genética , Animais , Cromatina/genética , Cromatina/metabolismo , Metilação de DNA/genética , Variação Genética/genética , Humanos , Neoplasias/genética
6.
BMC Bioinformatics ; 20(Suppl 12): 313, 2019 Jun 20.
Artigo em Inglês | MEDLINE | ID: mdl-31216978

RESUMO

BACKGROUND: Schizophrenia and autism are examples of polygenic diseases caused by a multitude of genetic variants, many of which are still poorly understood. Recently, both diseases have been associated with disrupted neuron motility and migration patterns, suggesting that aberrant cell motility is a phenotype for these neurological diseases. RESULTS: We formulate the POLYGENIC DISEASE PHENOTYPE Problem which seeks to identify candidate disease genes that may be associated with a phenotype such as cell motility. We present a machine learning approach to solve this problem for schizophrenia and autism genes within a brain-specific functional interaction network. Our method outperforms peer semi-supervised learning approaches, achieving better cross-validation accuracy across different sets of gold-standard positives. We identify top candidates for both schizophrenia and autism, and select six genes labeled as schizophrenia positives that are predicted to be associated with cell motility for follow-up experiments. CONCLUSIONS: Candidate genes predicted by our method suggest testable hypotheses about these genes’ role in cell motility regulation, offering a framework for generating predictions for experimental validation.


Assuntos
Movimento Celular/genética , Doença/genética , Redes Reguladoras de Genes , Herança Multifatorial/genética , Algoritmos , Transtorno Autístico/genética , Estudos de Associação Genética , Humanos , Aprendizado de Máquina , Fenótipo , Curva ROC , Reprodutibilidade dos Testes , Esquizofrenia/genética
7.
Genome Biol ; 20(1): 105, 2019 06 03.
Artigo em Inglês | MEDLINE | ID: mdl-31155008

RESUMO

BACKGROUND: DNA methylation is thought to be an important determinant of human phenotypic variation, but its inherent cell type specificity has impeded progress on this question. At exceptional genomic regions, interindividual variation in DNA methylation occurs systemically. Like genetic variants, systemic interindividual epigenetic variants are stable, can influence phenotype, and can be assessed in any easily biopsiable DNA sample. We describe an unbiased screen for human genomic regions at which interindividual variation in DNA methylation is not tissue-specific. RESULTS: For each of 10 donors from the NIH Genotype-Tissue Expression (GTEx) program, CpG methylation is measured by deep whole-genome bisulfite sequencing of genomic DNA from tissues representing the three germ layer lineages: thyroid (endoderm), heart (mesoderm), and brain (ectoderm). We develop a computational algorithm to identify genomic regions at which interindividual variation in DNA methylation is consistent across all three lineages. This approach identifies 9926 correlated regions of systemic interindividual variation (CoRSIVs). These regions, comprising just 0.1% of the human genome, are inter-correlated over long genomic distances, associated with transposable elements and subtelomeric regions, conserved across diverse human ethnic groups, sensitive to periconceptional environment, and associated with genes implicated in a broad range of human disorders and phenotypes. CoRSIV methylation in one tissue can predict expression of associated genes in other tissues. CONCLUSIONS: In addition to charting a previously unexplored molecular level of human individuality, this atlas of human CoRSIVs provides a resource for future population-based investigations into how interindividual epigenetic variation modulates risk of disease.


Assuntos
Metilação de DNA , Epigênese Genética , Genoma Humano , Idoso , Encéfalo/metabolismo , Estudos de Casos e Controles , Criança , Doença/genética , Feminino , Gâmbia , Variação Genética , Humanos , Masculino , Pessoa de Meia-Idade , Miocárdio/metabolismo , Gravidez , Fenômenos Fisiológicos da Nutrição Pré-Natal , Estações do Ano , Glândula Tireoide/metabolismo
8.
Methods Mol Biol ; 1975: 427-454, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31062321

RESUMO

The field of cell fate engineering is contingent on tools that can quantitatively assess the efficacy of cell fate engineering protocols and experiments. CellNet is such a cell fate assessment tool that utilizes network biology to both evaluate and suggest candidate transcriptional regulatory modifications to improve the similarity of an engineered population to its corresponding in vivo target population. CellNet takes in expression profiles in the form of RNA-sequencing data and generates several metrics of cell identity and protocol efficacy. In this chapter, we demonstrate how to (1) preprocess raw RNA-sequencing data to generate an expression matrix, (2) train CellNet using preprocessed expression matrices, and (3) apply CellNet to a query study and interpret its results. We demonstrate the utility of CellNet for analysis of iPSC disease modeling studies, which we evaluate through the lens of cell fate engineering.


Assuntos
Engenharia Celular/métodos , Linhagem da Célula , Biologia Computacional/métodos , Doença/genética , Regulação da Expressão Gênica , Células-Tronco Pluripotentes Induzidas/citologia , Software , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Humanos , Células-Tronco Pluripotentes Induzidas/fisiologia
9.
PLoS Comput Biol ; 15(5): e1007052, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-31075101

RESUMO

Protein domains are basic functional units of proteins. Many protein domains are pervasive among diverse biological processes, yet some are associated with specific pathways. Human complex diseases are generally viewed as pathway-level disorders. Therefore, we hypothesized that pathway-specific domains could be highly informative for human diseases. To test the hypothesis, we developed a network-based scoring scheme to quantify specificity of domain-pathway associations. We first generated domain profiles for human proteins, then constructed a co-pathway protein network based on the associations between domain profiles. Based on the score, we classified human protein domains into pathway-specific domains (PSDs) and non-specific domains (NSDs). We found that PSDs contained more pathogenic variants than NSDs. PSDs were also enriched for disease-associated mutations that disrupt protein-protein interactions (PPIs) and tend to have a moderate number of domain interactions. These results suggest that mutations in PSDs are likely to disrupt within-pathway PPIs, resulting in functional failure of pathways. Finally, we demonstrated the prediction capacity of PSDs for disease-associated genes with experimental validations in zebrafish. Taken together, the network-based quantitative method of modeling domain-pathway associations presented herein suggested underlying mechanisms of how protein domains associated with specific pathways influence mutational impacts on diseases via perturbations in within-pathway PPIs, and provided a novel genomic feature for interpreting genetic variants to facilitate the discovery of human disease genes.


Assuntos
Doença/etiologia , Domínios Proteicos , Mapas de Interação de Proteínas , Animais , Animais Geneticamente Modificados , Biologia Computacional , Doença da Artéria Coronariana/etiologia , Doença da Artéria Coronariana/genética , Doença da Artéria Coronariana/metabolismo , Doença/genética , Predisposição Genética para Doença , Variação Genética , Estudo de Associação Genômica Ampla , Humanos , Modelos Animais , Modelos Biológicos , Mutação , Polimorfismo de Nucleotídeo Único , Domínios Proteicos/genética , Mapeamento de Interação de Proteínas , Mapas de Interação de Proteínas/genética , Peixe-Zebra/genética
10.
PLoS Comput Biol ; 15(5): e1007022, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-31091224

RESUMO

Chemicals interact with genes in the process of disease development and treatment. Although much biomedical research has been performed to understand relationships among genes, chemicals, and diseases, which have been reported in biomedical articles in Medline, there are few studies that extract disease-gene-chemical relationships from biomedical literature at a PubMed scale. In this study, we propose a deep learning model based on bidirectional long short-term memory to identify the evidence sentences of relationships among genes, chemicals, and diseases from Medline abstracts. Then, we develop the search engine DigChem to enable disease-gene-chemical relationship searches for 35,124 genes, 56,382 chemicals, and 5,675 diseases. We show that the identified relationships are reliable by comparing them with manual curation and existing databases. DigChem is available at http://gcancer.org/digchem.


Assuntos
Distúrbios Induzidos Quimicamente/etiologia , Distúrbios Induzidos Quimicamente/genética , Doença/etiologia , Doença/genética , Ferramenta de Busca , Resumos e Indexação como Assunto , Biologia Computacional , Mineração de Dados , Bases de Dados Factuais , Bases de Dados Genéticas , Aprendizado Profundo , Feminino , Humanos , MEDLINE , Masculino , Redes Neurais (Computação) , PubMed
11.
Stud Health Technol Inform ; 258: 189-193, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30942743

RESUMO

We present a method called SMDG (Single Multi-Disease Genes) for systematic discovery of monogenic causes of multi-diseases. Multi-disease conditions, quite common in older populations, are difficult to treat due to missing their precise medical guidelines and need for attention of multiple health care providers. Finding monogenic causes of these diseases would enable introducing new therapeutic approaches, focused on the remediation of mutations of single genes. SMDG is based on the hierarchical divisive clustering of electronic medical records (EMR) that include genetic data, and on the analysis of the public gene-to-disease and gene-to-gene repositories. The method was tested on the database of the Harvard Personal Genome Project (PGP), the gene-to-disease repository DisGeNET and the gene-to-gene interactions repository BioGRID. It identified possible new monogenic causes of selected multi-diseases, which were confirmed as valid hypotheses by examining related research papers.


Assuntos
Bases de Dados Genéticas , Doença , Registros Eletrônicos de Saúde , Análise por Conglomerados , Bases de Dados Factuais , Doença/genética , Humanos
12.
Int J Mol Sci ; 20(7)2019 Apr 05.
Artigo em Inglês | MEDLINE | ID: mdl-30959806

RESUMO

Abstract: Deciphering the code of cis-regulatory element (CRE) is one of the core issues of current biology. As an important category of CRE, enhancers play crucial roles in gene transcriptional regulations in a distant manner. Further, the disruption of an enhancer can cause abnormal transcription and, thus, trigger human diseases, which means that its accurate identification is currently of broad interest. Here, we introduce an innovative concept, i.e., abelian complexity function (ACF), which is a more complex extension of the classic subword complexity function, for a new coding of DNA sequences. After feature selection by an upper bound estimation and integration with DNA composition features, we developed an enhancer prediction model with hybrid abelian complexity features (HACF). Compared with existing methods, HACF shows consistently superior performance on three sources of enhancer datasets. We tested the generalization ability of HACF by scanning human chromosome 22 to validate previously reported super-enhancers. Meanwhile, we identified novel candidate enhancers which have supports from enhancer-related ENCODE ChIP-seq signals. In summary, HACF improves current enhancer prediction and may be beneficial for further prioritization of functional noncoding variants.


Assuntos
Biologia Computacional/métodos , Sequências Reguladoras de Ácido Nucleico/genética , Algoritmos , Sequência de Bases , Cromossomos Humanos Par 22/genética , Doença/genética , Elementos Facilitadores Genéticos , Entropia , Éxons/genética , Humanos , Íntrons/genética , Regiões Promotoras Genéticas/genética
13.
Methods Mol Biol ; 1970: 101-120, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30963491

RESUMO

MicroRNAs are small noncoding RNA molecules with great importance in regulating a large number of diverse biological processes in health and disease. MicroRNAs can bind to both coding and noncoding RNAs and regulate their stability and expression. Genetic variants and somatic mutations may alter microRNA sequences and their target sites and therefore impact microRNA-target recognition. Aberrant microRNA-target interactions have been associated with many diseases. In recent years, computational resources have been developed for retrieving, annotating, and analyzing the impact of mutations on microRNA-target recognition. In this chapter, we provide an overview on the computational analysis of mutations impacting microRNA target recognition, followed by a detailed tutorial on how to use three major Web-based bioinformatics resources: PolymiRTS ( http://compbio.uthsc.edu/miRSNP ), a database of genetic variants impacting microRNA target recognition; SomamiR ( http://compbio.uthsc.edu/SomamiR ), a database of somatic mutations affecting the interactions between microRNAs and their targets in mRNAs and noncoding RNAs; and miR2GO ( http://compbio.uthsc.edu/miR2GO ), a computational tool for knowledge-based functional analysis of genetic variants and somatic mutations in microRNA seed regions.


Assuntos
Biomarcadores/análise , Biologia Computacional/métodos , Doença/genética , MicroRNAs/genética , Mutação , Polimorfismo de Nucleotídeo Único , RNA Mensageiro/genética , Software , Regulação da Expressão Gênica , Humanos , MicroRNAs/metabolismo , RNA Mensageiro/metabolismo
14.
Int J Mol Sci ; 20(7)2019 Mar 29.
Artigo em Inglês | MEDLINE | ID: mdl-30934865

RESUMO

One of the known potential effects of disease-causing amino acid substitutions in proteins is to modulate protein-protein interactions (PPIs). To interpret such variants at the molecular level and to obtain useful information for prediction purposes, it is important to determine whether they are located at protein-protein interfaces, which are composed of two main regions, core and rim, with different evolutionary conservation and physicochemical properties. Here we have performed a structural, energetics and computational analysis of interactions between proteins hosting mutations related to diseases detected in newborn screening. Interface residues were classified as core or rim, showing that the core residues contribute the most to the binding free energy of the PPI. Disease-causing variants are more likely to occur at the interface core region rather than at the interface rim (p < 0.0001). In contrast, neutral variants are more often found at the interface rim or at the non-interacting surface rather than at the interface core region. We also found that arginine, tryptophan, and tyrosine are over-represented among mutated residues leading to disease. These results can enhance our understanding of disease at molecular level and thus contribute towards personalized medicine by helping clinicians to provide adequate diagnosis and treatments.


Assuntos
Biologia Computacional/métodos , Doença/genética , Mutação/genética , Proteínas/genética , Sequência de Aminoácidos , Substituição de Aminoácidos , Humanos , Recém-Nascido , Doenças do Recém-Nascido/diagnóstico , Doenças do Recém-Nascido/genética , Simulação de Acoplamento Molecular , Proteínas Mutantes/química , Triagem Neonatal , Ligação Proteica , Subunidades Proteicas/química , Globinas beta/química
15.
Int J Mol Sci ; 20(7)2019 Mar 27.
Artigo em Inglês | MEDLINE | ID: mdl-30934684

RESUMO

Modern sequencing technologies provide an unprecedented amount of data of single-nucleotide variations occurring in coding regions and leading to changes in the expressed protein sequences. A significant fraction of these single-residue variations is linked to disease onset and collected in public databases. In recent years, many scientific studies have been focusing on the dissection of salient features of disease-related variations from different perspectives. In this work, we complement previous analyses by updating a dataset of disease-related variations occurring in proteins with 3D structure. Within this dataset, we describe functional and structural features that can be of interest for characterizing disease-related variations, including major chemico-physical properties, the strength of association to disease of variation types, their effect on protein stability, their location on the protein structure, and their distribution in Pfam structural/functional protein models. Our results support previous findings obtained in different data sets and introduce Pfam models as possible fingerprints of patterns of disease related single-nucleotide variations.


Assuntos
Doença/genética , Proteínas Mutantes/química , Proteínas Mutantes/metabolismo , Mutação/genética , Bases de Dados de Proteínas , Humanos , Domínios Proteicos , Solventes
16.
Hum Genet ; 138(4): 425-435, 2019 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-30941497

RESUMO

Heritability is the most commonly used measure of genetic contribution to disease outcomes. Being the fraction of the variance of latent trait liability attributable to genetic factors, heritability of binary traits is a difficult technical concept that is sometimes misinterpreted as the more-easily understandable concept of attributable fraction. In this paper we use the liability threshold model to describe the analytical relationship between heritability and attributable fraction. Towards this end, we consider a hypothetical intervention that is aimed to reduce the genetic risk of the disease for a specified target group of the population. We show how the relation between the heritability and the attributable fraction depends on the disease prevalence, the intervention effect and the size of the target group. We use two real examples to illustrate the practical implications of our theoretical results.


Assuntos
Predisposição Genética para Doença/epidemiologia , Modelos Genéticos , Modelos Estatísticos , Herança Multifatorial , Característica Quantitativa Herdável , Causalidade , Doença/etiologia , Doença/genética , Humanos , Fenótipo , Densidade Demográfica , Prevalência , Fatores de Risco , Tamanho da Amostra
17.
Eur J Epidemiol ; 34(3): 279-300, 2019 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-30887376

RESUMO

The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites.


Assuntos
Estudos de Coortes , Doença/genética , Predisposição Genética para Doença , Adolescente , Adulto , Criança , Pré-Escolar , Feminino , Previsões , Humanos , Lactente , Recém-Nascido , Masculino , Fenótipo , Gravidez , Projetos de Pesquisa , Reino Unido/epidemiologia
18.
J Physiol Sci ; 69(3): 433-451, 2019 May.
Artigo em Inglês | MEDLINE | ID: mdl-30868372

RESUMO

An organism stems from assemblies of a variety of cells and proteins. This complex system serves as a unit, and it exhibits highly sophisticated functions in response to exogenous stimuli that change over time. The complete sequencing of the entire human genome has allowed researchers to address the enigmas of life and disease at the gene- or molecular-based level. The consequence of such studies is the rapid accumulation of a multitude of data at multiple levels, ranging from molecules to the whole body, that has necessitated the development of entirely new concepts, tools, and methodologies to analyze and integrate these data. This necessity has given birth to systems biology, an advanced theoretical and practical research framework that has totally changed the directions of not only basic life science but also medicine. During the symposium of the 95th Annual Meeting of The Physiological Society of Japan 2018, five researchers reported on their respective studies on systems biology. The topics included reactions of drugs, ion-transport architecture in an epithelial system, multi-omics in renal disease, cardiac electrophysiological systems, and a software platform for computer simulation. In this review article these authors have summarized recent achievements in the field and discuss next-generation studies on health and disease.


Assuntos
Doença/genética , Biologia de Sistemas/métodos , Animais , Biologia Computacional/métodos , Simulação por Computador , Humanos , Japão , Pesquisa , Software
19.
Genet Epidemiol ; 43(5): 522-531, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-30888715

RESUMO

Case-control genome-wide association studies (CC-GWAS) might provide valuable clues to the underlying pathophysiologic mechanisms of complex diseases, such as neurodegenerative disease and cancer. A commonly overlooked complication is that multiple distinct disease states might present with the same set of symptoms and hence share a clinical diagnosis. These disease states can only be distinguished based on a biomarker evaluation that might not be feasible in the whole set of cases in the large number of samples that are typically needed for CC-GWAS. Instead, the biomarkers are measured on a subset of cases. Or an external reliability study estimates the frequencies of the disease states of interest within the clinically diagnosed set of cases. These frequencies often vary by the genetic and/or nongenetic variables. We derive a simple approximation that relates the genetic effect estimates obtained in a traditional logistic regression model with the clinical diagnosis as the outcome variable to the genetic effect estimates in the relationship to the true disease state of interest. We performed simulation studies to assess the accuracy of the approximation that we have derived. We next applied the derived approximation to the analysis of the genetic basis of the innate immune system of Alzheimer's disease.


Assuntos
Doença/genética , Modelos Genéticos , Doença de Alzheimer/genética , Doença de Alzheimer/imunologia , Viés , Estudos de Casos e Controles , Simulação por Computador , Estudo de Associação Genômica Ampla , Humanos , Imunidade Inata/genética , Polimorfismo de Nucleotídeo Único/genética
20.
Nat Commun ; 10(1): 1338, 2019 03 22.
Artigo em Inglês | MEDLINE | ID: mdl-30902979

RESUMO

Allele-specific protein-RNA binding is an essential aspect that may reveal functional genetic variants (GVs) mediating post-transcriptional regulation. Recently, genome-wide detection of in vivo binding of RNA-binding proteins is greatly facilitated by the enhanced crosslinking and immunoprecipitation (eCLIP) method. We developed a new computational approach, called BEAPR, to identify allele-specific binding (ASB) events in eCLIP-Seq data. BEAPR takes into account crosslinking-induced sequence propensity and variations between replicated experiments. Using simulated and actual data, we show that BEAPR largely outperforms often-used count analysis methods. Importantly, BEAPR overcomes the inherent overdispersion problem of these methods. Complemented by experimental validations, we demonstrate that the application of BEAPR to ENCODE eCLIP-Seq data of 154 proteins helps to predict functional GVs that alter splicing or mRNA abundance. Moreover, many GVs with ASB patterns have known disease relevance. Overall, BEAPR is an effective method that helps to address the outstanding challenge of functional interpretation of GVs.


Assuntos
Alelos , Variação Genética , Proteínas de Ligação a RNA/metabolismo , RNA/genética , Regiões 3' não Traduzidas/genética , Motivos de Aminoácidos , Sequência de Bases , Biologia Computacional , Simulação por Computador , Doença/genética , Predisposição Genética para Doença , Células Hep G2 , Humanos , Células K562 , Polimorfismo de Nucleotídeo Único/genética , Ligação Proteica , Locos de Características Quantitativas/genética , RNA Helicases/metabolismo , Processamento de RNA/genética , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , Reprodutibilidade dos Testes , Transativadores/metabolismo
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