Mendelian Inheritance In Man Database Research Articles

Emery-Dreifuss muscular dystrophy.

Emery‐Dreifuss muscular dystrophy (EDMD) is a rare muscular dystrophy, but is particularly important to diagnose due to frequent life‐threatening cardiac complications. EDMD classically presents with muscle weakness, early contractures, cardiac conduction abnormalities and cardiomyopathy, although the presence and severity of these manifestations vary by subtype and individual. Associated genes include EMD, LMNA, SYNE1, SYNE2, FHL1, TMEM43, SUN1, SUN2, and TTN, encoding emerin, lamin A/C, nesprin‐1, nesprin‐2, FHL1, LUMA, SUN1, SUN2, and titin, respectively. The Online Mendelian Inheritance in Man database recognizes subtypes 1 through 7, which captures most but not all of the associated genes. Genetic diagnosis is essential whenever available, but traditional diagnostic tools can help steer the evaluation toward EDMD and assist with interpretation of equivocal genetic test results. Management is primarily supportive, but it is important to monitor patients closely, especially for potential cardiac complications. There is a high potential for progress in the treatment of EDMD in the coming years.

Toward a Pathway-Driven Clinical-Molecular Framework for Classifying Autism Spectrum Disorders

BackgroundThe current classification system of neurodevelopmental disorders is based on clinical criteria; however, this method alone fails to incorporate what is now known about genomic similarities and differences between closely related clinical neurodevelopmental disorders. Here we present an alternative clinical molecular classification system of neurodevelopmental disorders based on shared molecular and cellular pathways, using syndromes with autistic features as examples. MethodsUsing the Online Mendelian Inheritance in Man database, we identified 83 syndromes that had “autism” as a feature of disease, which in combination were associated with 69 autism disease-causing genes. Using annotation terms generated from the DAVID annotation tool, we grouped each gene and its associated autism syndrome into three biological pathways: ion transport, cellular synaptic function, and transcriptional regulation. ResultsThe majority of the autism syndromes we analyzed (54 of 83) enriched for processes related to transcriptional regulation and were associated with more non-neurologic symptoms and co-morbid psychiatric disease when compared with the other two pathways studied. Disorders with disrupted cellular synaptic function had significantly more motor-related symptoms when compared with the other groups of disorders. ConclusionOur pathway-based classification system identified unique clinical characteristics within each group that may help guide clinical diagnosis, prognosis, and treatment. These results suggest that shifting current clinical classification of autism disorders toward molecularly driven, pathway-related diagnostic groups such as this may more precisely guide clinical decision making and may be informative for future clinical trial and drug development approaches.

National Center for Biotechnology Information (NCBI) database search for rubber plant Hevea brasiliensis

The present study describes search National Centre for Biotechnology Information (NCBI) databases (GQuery) on rubber plant Hevea brasiliensis Mull. Arg. NCBI databases search (https://www.ncbi.nlm.nih.gov/) was employed to generate a number of valuable information. Results found in 26 databases for H. brasiliensis. All databases of the rubber plant consist of literature, genes, genes, protein genomes, and chemical properties of H. brasiliensis. It is noteworthy no information concerning genetics. The literature contained bookshelf, MeSH (Medical Subject Headings), National Library of Medicine Catalogue, PubMed, and PubMed Central. Health comprised only OMIM (Online Mendelian Inheritance in Man database), Gene consists of EST, Gene, GEO DataSets, and PopSet. No Genetics data available for H. brasiliensis. Proteins feature contained Identical Protein Groups, Protein Clusters, Sparcle, and Structure. Genomes included Assembly, BioCollections, BioProject, BioSample, Genome, GSS, Nucleotide, Probe, SRA, and Taxonomy. The chemicals property denoted BioSystems and PubChem BioAssay. The present work provides essential information relating to biotechnology.

Systematic Review of Studies That Have Evaluated Screening Tests in Relatives of Patients Affected by Nonsyndromic Thoracic Aortic Disease.

BackgroundNonsyndromic thoracic aortic diseases (NS‐TADs) are often silent entities until they present as life‐threatening emergencies. Despite familial inheritance being common, screening is not the current standard of care in NS‐TADs. We sought to determine the incidence of aortic diseases, the predictive accuracy of available screening tests, and the effectiveness of screening programs in relatives of patients affected by NS‐TADs.Methods and ResultsA systematic literature search on PubMed/MEDLINE, Embase, and the Cochrane Library was conducted from inception to the end of December 2017. The search was supplemented with the Online Mendelian Inheritance in Man database. A total of 53 studies were included, and a total of 2696 NS‐TAD relatives were screened. Screening was genetic in 49% of studies, followed by imaging techniques in 11% and a combination of the 2 in 40%. Newly affected individuals were identified in 33%, 24%, and 15% of first‐, second‐, and third‐degree relatives, respectively. Familial NS‐TADs were primarily attributed to single‐gene mutations, expressed in an autosomal dominant pattern with incomplete penetrance. Specific gene mutations were observed in 25% of the screened families. Disease subtype and genetic mutations stratified patients with respect to age of presentation, aneurysmal location, and aortic diameter before dissection. Relatives of patients with sporadic NS‐TADs were also found to be affected. No studies evaluated the predictive accuracy of imaging or genetic screening tests, or the clinical or cost‐effectiveness of an NS‐TAD screening program.ConclusionsFirst‐ and second‐degree relatives of patients affected by both familial and sporadic NS‐TADs may benefit from personalized screening programs.

Untangling the complexity of limb-girdle muscular dystrophies.

The limb-girdle muscular dystrophies (LGMDs) are a group of genetically heterogeneous, autosomal inherited muscular dystrophies with a childhood to adult onset, manifesting with hip- and shoulder-girdle muscle weakness. When the term LGMD was first conceptualized in 1954, it was thought to be a single entity. Currently, there are 8 autosomal dominant (LGMD1A-1H) and 26 autosomal recessive (LGMD2A-2Z) variants according to the Online Mendelian Inheritance in Man database. In addition, there are other genetically identified muscular dystrophies with an LGMD phenotype not yet classified as LGMD. This highlights the entanglement of LGMDs, which represents an area in continuous expansion. Herein we aim to simplify the complexity of LGMDs by subgrouping them on the basis of the underlying defective protein and impaired function. Muscle Nerve 58: 167-177, 2018.

Molecular differences between mature and immature dental pulp cells: Bioinformatics and preliminary results.

Although previous studies have demonstrated that dental pulp stem cells (DPSCs) from mature and immature teeth exhibit potential for multi-directional differentiation, the molecular and biological difference between the DPSCs from mature and immature permanent teeth has not been fully investigated. In the present study, 500 differentially expressed genes from dental pulp cells (DPCs) in mature and immature permanent teeth were obtained from the Gene Expression Omnibus online database. Based on bioinformatics analysis using the Database for Annotation, Visualization and Integrated Discovery, these genes were divided into a number of subgroups associated with immunity, inflammation and cell signaling. The results of the present study suggest that immune features, response to infection and cell signaling may be different in DPCs from mature and immature permanent teeth; furthermore, DPCs from immature permanent teeth may be more suitable for use in tissue engineering or stem cell therapy. The Online Mendelian Inheritance in Man database stated that Sonic Hedgehog (SHH), a differentially expressed gene in DPCs from mature and immature permanent teeth, serves a crucial role in the development of craniofacial tissues, including teeth, which further confirmed that SHH may cause DPCs from mature and immature permanent teeth to exhibit different biological characteristics. The Search Tool for the Retrieval of Interacting Genes/Proteins database revealed that SHH has functional protein associations with a number of other proteins, including Glioma-associated oncogene (GLI)1, GLI2, growth arrest-specific protein 1, bone morphogenetic protein (BMP)2 and BMP4, in mice and humans. It was also demonstrated that SHH may interact with other genes to regulate the biological characteristics of DPCs. The results of the present study may provide a useful reference basis for selecting suitable DPSCs and molecules for the treatment of these cells to optimize features for tissue engineering or stem cell therapy. Quantitative polymerase chain reaction should be performed to confirm the differential expression of these genes prior to the beginning of a functional study.

A network pharmacology approach to investigate the pharmacological effects of Guizhi Fuling Wan on uterine fibroids.

To investigate the pharmacological mechanism of Guizhi Fuling Wan (GFW) in the treatment of uterine fibroids, a network pharmacology approach was used. Information on GFW compounds was collected from traditional Chinese medicine (TCM) databases, and input into PharmMapper to identify the compound targets. Genes associated with uterine fibroids genes were then obtained from the GeneCards and Online Mendelian Inheritance in Man databases. The interaction data of the targets and other human proteins was also collected from the STRING and IntAct databases. The target data were input into the Database for Annotation, Visualization and Integrated Discovery for gene ontology (GO) and pathway enrichment analyses. Networks of the above information were constructed and analyzed using Cytoscape. The following networks were compiled: A compound-compound target network of GFW; a herb-compound target-uterine fibroids target network of GWF; and a compound target-uterine fibroids target-other human proteins protein-protein interaction network, which were subjected to GO and pathway enrichment analyses. According to this approach, a number of novel signaling pathways and biological processes underlying the effects of GFW on uterine fibroids were identified, including the negative regulation of smooth muscle cell proliferation, apoptosis, and the Ras, wingless-type, epidermal growth factor and insulin-like growth factor-1 signaling pathways. This network pharmacology approach may aid the systematical study of herbal formulae and make TCM drug discovery more predictable.

Genetic Profiling and Comorbidities of Zika Infection.

The difficulty in distinguishing infection by Zika virus (ZIKV) from other flaviviruses is a global health concern, particularly given the high risk of neurologic complications (including Guillain-Barré syndrome [GBS]) with ZIKV infection. We developed quantitative frameworks to compare and explore infectome, diseasome, and comorbidity of ZIKV infections. We analyzed gene expression microarray and RNA-Seq data from ZIKV, West Nile fever (WNF), chikungunya, dengue, yellow fever, Japanese encephalitis virus, GBS, and control datasets. Using neighborhood-based benchmarking and multilayer network topology, we constructed relationship networks based on the Online Mendelian Inheritance in Man database and our identified significant genes. ZIKV infections showed dysregulation in expression of 929 genes. Forty-seven genes were highly expressed in both ZIKV and dengue infections. However, ZIKV shared <15 significant transcripts with other flavivirus infections. Notably, dysregulation of MAFB and SELENBP1 was common to ZIKV, dengue, and GBS infection; ATF5, TNFAIP3, and BAMB1 were common to ZIKV, dengue, and WNF; and NAMPT and PMAlP1 were common to ZIKV, GBS, and WNF. Phylogenetic, ontologic, and pathway analyses showed that ZIKV infection most resembles dengue fever. We have developed methodologies to investigate disease mechanisms and predictions for infectome, diseasome, and comorbidities quantitatively, and identified particular similarities between ZIKV and dengue infections.

Identification of key genes in colorectal cancer using random walk with restart.

As the most common type of cancer and the second leading cause of cancer-associated mortality, colorectal cancer (CRC) has received increasing attention. The aim of the present study was to investigate the mechanisms of CRC by analyzing the microarray dataset, GSE32323. The GSE32323 dataset was downloaded from the Gene Expression Omnibus, and included 17pairs of matched cancer and normal colorectal tissue samples. The differentially expressed genes (DEGs) were screened using the Linear Models for Microarray Data package and a search of CRC genes, also denoted as seed genes, was performed using the Online Mendelian Inheritance in Man database. Subsequently, the protein‑protein interaction (PPI) network was downloaded from the Search Tool for the Retrieval of Interacting Genes database and the sub‑network (CRC.PPI) of the DEGs and seed genes were obtained. In addition, the top 50nodes with highest affinity scores in the CRC.PPI were identified using random walk with restart analysis. The potential functions of the DEGs included in the top 50nodes were analyzed using the Database for Annotation, Visualization and Integrated Discovery online tool. Using the Drug Gene Interaction database, drug‑gene interaction analysis was performed to identify antineoplastic drug interacts with genes. A total of 1,640DEGs between the CRC and normal samples were screened. The obtained seed genes included cyclinD1 (CCND1) and aurora kinase A (AURKA). The enriched functions for the 31DEGs in the PPI network of the top 50nodes were predominantly associated with cell cycle. The DEGs may function in CRC by interacting with other genes in the PPI network of the top 50nodes, for example, DEP domain‑containing MTOR‑interacting protein (DEPTOR)‑CCND1, AURKA‑breast carcinoma amplified sequence‑1 (BCAS1), CCND1‑BCAS1, CCND1‑neural precursor cell expressed developmentally downregulated9 (NEDD9) and CCND1‑mitogen‑activated protein kinase kinase2 (MAP2K2). Only three DEGs (CCND1, AURKA and DEPTOR) had interactions with their corresponding antineoplastic drugs. Taken together, DEPTOR,AURKA,CCND1, BCAS1,NEDD9 and MAP2K2 may act in CRC.

Deciphering the genetic and modular connections between coronary heart disease, idiopathic pulmonary arterial hypertension and pulmonary heart disease

Coronary heart disease (CHD), idiopathic pulmonary arterial hypertension (IPAH) and pulmonary heart disease (PHD) are circulatory system diseases that may simultaneously emerge in a patient and they are often treated together in clinical practice. However, the molecular mechanisms connecting these three diseases remain unclear. In order to determine the multidimensional characteristic correlations between these three diseases based on genomic networks to aid in medical decision-making, genes from the Online Mendelian Inheritance in Man database were obtained, and applied network construction and modularized analysis were conducted. Functional enrichment analysis was conducted to explore the associations between overlapping genes, modules and pathways. A total of 29 overlapping genes and 3 common modules were identifed for the 3 diseases. Glycosphingolipid biosynthesis and the arachidonic acid metabolism are common pathways, and the biosynthetic process is suggested to be the major function involved in the three diseases. The current study reported, to the best of our knowledge for the first time, the role of glycosphingolipid biosynthesis in IPAH and PHD. The present study provided an improved understanding of the pathological mechanisms underlying CHD, IPAH and PHD. The overlapping genes, modules and pathways suggest novel areas for further research, and drug targets. The observations of the current study additionally suggest that drug indications can be broadened because of the presence of common targets.

A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics.

BackgroundGenome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives.MethodsOur tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes.ResultsWe retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants.ConclusionsOur integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0261-8) contains supplementary material, which is available to authorized users.

Construction of a protein-protein interaction network of Wilms' tumor and pathway prediction of molecular complexes.

Wilms' tumor (WT), or nephroblastoma, is the most common malignant renal cancer that affects the pediatric population. Great progress has been achieved in the treatment of WT, but it cannot be cured at present. Nonetheless, a protein-protein interaction network of WT should provide some new ideas and methods. The purpose of this study was to analyze the protein-protein interaction network of WT. We screened the confirmed disease-related genes using the Online Mendelian Inheritance in Man database, created a protein-protein interaction network based on biological function in the Cytoscape software, and detected molecular complexes and relevant pathways that may be included in the network. The results showed that the protein-protein interaction network of WT contains 654 nodes, 1544 edges, and 5 molecular complexes. Among them, complex 1 is predicted to be related to the Jak-STAT signaling pathway, regulation of hematopoiesis by cytokines, cytokine-cytokine receptor interaction, cytokine and inflammatory responses, and hematopoietic cell lineage pathways. Molecular complex 4 shows a correlation of WT with colorectal cancer and the ErbB signaling pathway. The proposed method can provide the bioinformatic foundation for further elucidation of the mechanisms of WT development.

Novel genetic causes for cerebral visual impairment.

Cerebral visual impairment (CVI) is a major cause of low vision in children due to impairment in projection and/or interpretation of the visual input in the brain. Although acquired causes for CVI are well known, genetic causes underlying CVI are largely unidentified. DNAs of 25 patients with CVI and intellectual disability, but without acquired (eg, perinatal) damage, were investigated by whole-exome sequencing. The data were analyzed for de novo, autosomal-recessive, and X-linked variants, and subsequently classified into known, candidate, or unlikely to be associated with CVI. This classification was based on the Online Mendelian Inheritance in Man database, literature reports, variant characteristics, and functional relevance of the gene. After classification, variants in four genes known to be associated with CVI (AHDC1, NGLY1, NR2F1, PGAP1) in 5 patients (20%) were identified, establishing a conclusive genetic diagnosis for CVI. In addition, in 11 patients (44%) with CVI, variants in one or more candidate genes were identified (ACP6, AMOT, ARHGEF10L, ATP6V1A, DCAF6, DLG4, GABRB2, GRIN1, GRIN2B, KCNQ3, KCTD19, RERE, SLC1A1, SLC25A16, SLC35A2, SOX5, UFSP2, UHMK1, ZFP30). Our findings show that diverse genetic causes underlie CVI, some of which will provide insight into the biology underlying this disease process.

Convergence and divergence of genetic and modular networks between diabetes and breast cancer.

Diabetes mellitus (DM) and breast cancer (BC) can simultaneously occur in the same patient populations, but the molecular relationship between them remains unknown. In this study, we constructed genetic networks and used modularized analysis approaches to investigate the multi-dimensional characteristics of two diseases and one disease subtype. A text search engine (Agilent Literature Search 2.71) and MCODE software were applied to validate potential subnetworks and to divide the modules, respectively. A total of 793 DM-related genes, 386 type 2 diabetes (T2DM) genes and 873 BC-related genes were identified from the Online Mendelian Inheritance in Man database. For DM and BC, a total of 99 overlapping genes, 9 modules, 29 biological processes and 7 pathways were identified. Meanwhile, for T2DM and BC, 56 overlapping genes, 5 modules, 20 biological processes and 12 pathways were identified. Based on the Gene Ontology functional enrichment analysis of the top 10 non-overlapping modules of the two diseases, 10 biological functions and 5 pathways overlapped between them. The glycosphingolipid and lysosome pathways verified molecular mechanisms of cell death related to both DM and BC. We also identified new biological functions of dopamine receptors and four signalling pathways (Parkinson's disease, Alzheimer's disease, Huntington's disease and long-term depression) related to both diseases; these warrant further investigation. Our results illustrate the landscape of the novel molecular substructures between DM and BC, which may support a new model for complex disease classification and rational therapies for multiple diseases.

Predicting of disease genes for gestational diabetes mellitus based on network and functional consistency

ObjectiveGestational diabetes mellitus (GDM) is a world-widely prevalent disease with adverse outcomes. This study aims to identify its disease genes through bioinformatics analysis. Study designThe raw gene expression profiling (ID: GSE19649) was downloaded from Gene Expression Omnibus database, including 3 GDM and 2 healthy control specimens. Then limma package in R was utilized to identify differentially expressed genes (DEGs, criteria: p value <0.05 and |log2 FC|>1). Simultaneously, known disease genes of GDM were downloaded from Online Mendelian Inheritance in Man database. Then, DEGs and known disease genes were uploaded to STRING to investigate their protein–protein interactions (PPIs). Gene pairs with confidence score >0.8 were utilized to construct PPI network. Furthermore, pathway and functional enrichment analyses were performed through KOBAS (criterion: p value <0.05) and DAVID (The Database for Annotation, Visualization and Integrated Discovery) software (criterion: false discovery rate <0.05), respectively. ResultsA total of 404 DEGs were identified, including 273 up-regulated and 131 down-regulated DEGs. Moreover, 68 known disease genes of GDM were obtained. Then, 190 gene pairs were identified to significantly interact with each other. After deleting PPIs between DEGs, PPI network was constructed, consisting of 115 gene pairs. Furthermore, genes in PPI network were significantly enriched in 10 functions and 8 pathways. ConclusionBased on PPI network and functional consistency, 6 candidate genes of GDM were considered to be candidate disease genes of GDM, including CYP1A1, LEPR, ESR1, GYS2, AGRP, and CACNA1G. However, further studies are required to validate these results.

Copy number variations in spermatogenic failure patients with chromosomal abnormalities and unexplained azoospermia.

Male infertility is mostly caused by spermatogenic failure. Currently, routine genetic analyses of unexplained azoospermia or oligozoospermia are limited to the investigation of Y chromosomal microdeletions and chromosome karyotype analyses. The aim of this study was to find spermatogenic failure genes in patients with chromosomal abnormalities and unexplained azoospermia caused by copy number variations in order to provide a theoretical basis for further research. Spermatogenic failure patients consisting of 13 males with chromosomal abnormalities and 20 with unexplained azoospermia were enrolled. The subjects underwent high-throughput genome-wide sequencing to find copy number variants (CNVs), and the results were analyzed using the Database of Genomic Variants, Online Mendelian Inheritance in Man database, and PubMed. The results showed that 16 CNVs were detected in 11 patients with chromosome abnormalities, and 26 CNVs were found in 16 males with azoospermia. Our data showed CNV-involved loci including: three times on 11p11.12 and 14q11.2 and twice on 6p21.32, 13q11, 15q11.11, 16p12.2, and 21q22.3. Some CNVs may involve changes in genetic structure and function or gene mutations, which may affect gene expression in testicular tissues and lead to spermatogenic failure. The involved genes include EDDM3A, EDDM3B, HLA-DRB1, HLA-DQA1, POTE B, GOLGA8C, DNMT3L, ALF, NPHP1, NRG1, RID2, ADAMTS20, TWF1, COX10, MAK, and DNEL1. By applying high throughput genome-wide sequencing to determine CNVs, we provide a number of candidate genes possibly contributing to spermatogenic failure.

Significant Overlapping Modules and Biological Processes Between Stroke and Coronary Heart Disease

The systematical associations between stroke and coronary heart disease (CHD) remain controversial and uncertain. Network construction and modularized analysis have become powerful tools in the field of systems biology research, which can help us to mine the multidimensional characters of correlation between the two diseases in depth. A total of 218 stroke-related and 204 CHD-related genes were identified via the Online Mendelian Inheritance in Man database; text searching engine (Agilent Literature Search 2.71) and MCODE software were employed for network construction and module division, respectively. Finally, 67, 21, 7, and 196 overlapping genes, hubs, modules and modular functions were identified between stroke and CHD, respectively. The overlapping genes, which should be responsible for the similar phenotypes, highlighted the molecular signatures of the two linked diseases. Additionally, the analysis of modules and their functional annotations showed potential dependent and independent risk factors, such as atherosclerosis, cholesterol homeostasis, plasma lipoprotein particle remodeling and response to oxidative stress, etc. Moreover, the potential mechanisms by which the same biological process activating pathological cascade or risk component-based shared pathway between stroke and CHD were uncovered, which may provide useful insights for further drug development and cost saving.

Construction of a protein-protein interaction network for chronic myelocytic leukemia and pathway prediction of molecular complexes.

Chronic myelocytic leukemia is a disease that threatens both adults and children. Great progress has been achieved in treatment but protein-protein interaction networks underlining chronic myelocytic leukemia are less known. To develop a protein-protein interaction network for chronic myelocytic leukemia based on gene expression and to predict biological pathways underlying molecular complexes in the network. Genes involved in chronic myelocytic leukemia were selected from OMIM database. Literature mining was performed by Agilent Literature Search plugin and a protein-protein interaction network of chronic myelocytic leukemia was established by Cytoscape. The molecular complexes in the network were detected by Clusterviz plugin and pathway enrichment of molecular complexes were performed by DAVID online. There are seventy-nine chronic myelocytic leukemia genes in the Mendelian Inheritance In Man Database. The protein-protein interaction network of chronic myelocytic leukemia contained 638 nodes, 1830 edges and perhaps 5 molecular complexes. Among them, complex 1 is involved in pathways that are related to cytokine secretion, cytokine-receptor binding, cytokine receptor signaling, while complex 3 is related to biological behavior of tumors which can provide the bioinformatic foundation for further understanding the mechanisms of chronic myelocytic leukemia.

Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes.

Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO) analysis highlighted the role of functional diversity for such diseases.

Common variants in Mendelian kidney disease genes and their association with renal function.

Many common genetic variants identified by genome-wide association studies for complex traits map to genes previously linked to rare inherited Mendelian disorders. A systematic analysis of common single-nucleotide polymorphisms (SNPs) in genes responsible for Mendelian diseases with kidney phenotypes has not been performed. We thus developed a comprehensive database of genes for Mendelian kidney conditions and evaluated the association between common genetic variants within these genes and kidney function in the general population. Using the Online Mendelian Inheritance in Man database, we identified 731 unique disease entries related to specific renal search terms and confirmed a kidney phenotype in 218 of these entries, corresponding to mutations in 258 genes. We interrogated common SNPs (minor allele frequency >5%) within these genes for association with the estimated GFR in 74,354 European-ancestry participants from the CKDGen Consortium. However, the top four candidate SNPs (rs6433115 at LRP2, rs1050700 at TSC1, rs249942 at PALB2, and rs9827843 at ROBO2) did not achieve significance in a stage 2 meta-analysis performed in 56,246 additional independent individuals, indicating that these common SNPs are not associated with estimated GFR. The effect of less common or rare variants in these genes on kidney function in the general population and disease-specific cohorts requires further research.