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Science (New York, N.Y.) Feb 2015According to the disease module hypothesis, the cellular components associated with a disease segregate in the same neighborhood of the human interactome, the map of...
According to the disease module hypothesis, the cellular components associated with a disease segregate in the same neighborhood of the human interactome, the map of biologically relevant molecular interactions. Yet, given the incompleteness of the interactome and the limited knowledge of disease-associated genes, it is not obvious if the available data have sufficient coverage to map out modules associated with each disease. Here we derive mathematical conditions for the identifiability of disease modules and show that the network-based location of each disease module determines its pathobiological relationship to other diseases. For example, diseases with overlapping network modules show significant coexpression patterns, symptom similarity, and comorbidity, whereas diseases residing in separated network neighborhoods are phenotypically distinct. These tools represent an interactome-based platform to predict molecular commonalities between phenotypically related diseases, even if they do not share primary disease genes.
Topics: Comorbidity; Disease; Genetic Predisposition to Disease; Humans; Information Services; Protein Interaction Maps
PubMed: 25700523
DOI: 10.1126/science.1257601 -
Cell Jun 2018The evidence that most adult-onset common diseases have a polygenic genetic architecture fully consistent with robust biological systems supported by multiple back-up... (Review)
Review
The evidence that most adult-onset common diseases have a polygenic genetic architecture fully consistent with robust biological systems supported by multiple back-up mechanisms is now overwhelming. In this context, we consider the recent "omnigenic" or "core genes" model. A key assumption of the model is that there is a relatively small number of core genes relevant to any disease. While intuitively appealing, this model may underestimate the biological complexity of common disease, and therefore, the goal to discover core genes should not guide experimental design. We consider other implications of polygenicity, concluding that a focus on patient stratification is needed to achieve the goals of precision medicine.
Topics: Disease; Genome-Wide Association Study; Humans; Models, Genetic; Multifactorial Inheritance; Precision Medicine
PubMed: 29906445
DOI: 10.1016/j.cell.2018.05.051 -
Briefings in Bioinformatics Jul 2018Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern...
Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.
Topics: Computational Biology; Disease; Gene Expression Profiling; Gene Expression Regulation; Gene Regulatory Networks; Genes; Humans; Phenotype
PubMed: 28077403
DOI: 10.1093/bib/bbw139 -
International Journal of Molecular... Nov 2021Functional genomics applied in clinical disease diagnosis and prognosis allow the achievement of the progress in all aspects of biology in health and disease [...].
Functional genomics applied in clinical disease diagnosis and prognosis allow the achievement of the progress in all aspects of biology in health and disease [...].
Topics: Disease; Genetic Markers; Genetic Predisposition to Disease; Genomics; Humans; Prognosis; Risk Factors
PubMed: 34884749
DOI: 10.3390/ijms222312944 -
Nature Medicine Jun 2023Alcohol consumption accounts for ~3 million annual deaths worldwide, but uncertainty persists about its relationships with many diseases. We investigated the...
Alcohol consumption accounts for ~3 million annual deaths worldwide, but uncertainty persists about its relationships with many diseases. We investigated the associations of alcohol consumption with 207 diseases in the 12-year China Kadoorie Biobank of >512,000 adults (41% men), including 168,050 genotyped for ALDH2- rs671 and ADH1B- rs1229984 , with >1.1 million ICD-10 coded hospitalized events. At baseline, 33% of men drank alcohol regularly. Among men, alcohol intake was positively associated with 61 diseases, including 33 not defined by the World Health Organization as alcohol-related, such as cataract (n = 2,028; hazard ratio 1.21; 95% confidence interval 1.09-1.33, per 280 g per week) and gout (n = 402; 1.57, 1.33-1.86). Genotype-predicted mean alcohol intake was positively associated with established (n = 28,564; 1.14, 1.09-1.20) and new alcohol-associated (n = 16,138; 1.06, 1.01-1.12) diseases, and with specific diseases such as liver cirrhosis (n = 499; 2.30, 1.58-3.35), stroke (n = 12,176; 1.38, 1.27-1.49) and gout (n = 338; 2.33, 1.49-3.62), but not ischemic heart disease (n = 8,408; 1.04, 0.94-1.14). Among women, 2% drank alcohol resulting in low power to assess associations of self-reported alcohol intake with disease risks, but genetic findings in women suggested the excess male risks were not due to pleiotropic genotypic effects. Among Chinese men, alcohol consumption increased multiple disease risks, highlighting the need to strengthen preventive measures to reduce alcohol intake.
Topics: Adult; Female; Humans; Male; Alcohol Drinking; Aldehyde Dehydrogenase, Mitochondrial; East Asian People; Ethanol; Genotype; Gout; Risk Factors; Disease; China
PubMed: 37291211
DOI: 10.1038/s41591-023-02383-8 -
Nature Genetics May 2018Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the 'no...
Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the 'no horizontal pleiotropy' assumption can cause severe bias in MR. We developed the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that the MR-PRESSO test is best suited when horizontal pleiotropy occurs in <50% of instruments. Next we applied the MR-PRESSO test, along with several other MR tests, to complex traits and diseases and found that horizontal pleiotropy (i) was detectable in over 48% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from -131% to 201%; (iii) induced false-positive causal relationships in up to 10% of relationships; and (iv) could be corrected in some but not all instances.
Topics: Disease; Genetic Pleiotropy; Genetic Predisposition to Disease; Genetic Variation; Humans
PubMed: 29686387
DOI: 10.1038/s41588-018-0099-7 -
Nature Reviews. Neuroscience Jan 2008In response to a peripheral infection, innate immune cells produce pro-inflammatory cytokines that act on the brain to cause sickness behaviour. When activation of the... (Review)
Review
In response to a peripheral infection, innate immune cells produce pro-inflammatory cytokines that act on the brain to cause sickness behaviour. When activation of the peripheral immune system continues unabated, such as during systemic infections, cancer or autoimmune diseases, the ensuing immune signalling to the brain can lead to an exacerbation of sickness and the development of symptoms of depression in vulnerable individuals. These phenomena might account for the increased prevalence of clinical depression in physically ill people. Inflammation is therefore an important biological event that might increase the risk of major depressive episodes, much like the more traditional psychosocial factors.
Topics: Animals; Brain; Cytokines; Depression; Disease; Humans; Immune System; Inflammation
PubMed: 18073775
DOI: 10.1038/nrn2297 -
Cells Sep 2021Almost 25 years have passed since a mutation of a formin gene, , was identified as being responsible for a human inherited disorder: a form of sensorineural hearing... (Review)
Review
Almost 25 years have passed since a mutation of a formin gene, , was identified as being responsible for a human inherited disorder: a form of sensorineural hearing loss. Since then, our knowledge of the links between formins and disease has deepened considerably. Mutations of and six other formin genes (, , , , and ) have been identified as the genetic cause of a variety of inherited human disorders, including intellectual disability, renal disease, peripheral neuropathy, thrombocytopenia, primary ovarian insufficiency, hearing loss and cardiomyopathy. In addition, alterations in formin genes have been associated with a variety of pathological conditions, including developmental defects affecting the heart, nervous system and kidney, aging-related diseases, and cancer. This review summarizes the most recent discoveries about the involvement of formin alterations in monogenic disorders and other human pathological conditions, especially cancer, with which they have been associated. In vitro results and experiments in modified animal models are discussed. Finally, we outline the directions for future research in this field.
Topics: Disease; Female; Formins; Humans; Male
PubMed: 34685534
DOI: 10.3390/cells10102554 -
BMC Bioinformatics Aug 2016Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects....
BACKGROUND
Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature.
RESULTS
Among function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively.
CONCLUSIONS
Integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http:// www.digintelli.com:8000/ .
Topics: Algorithms; Data Mining; Disease; Genetic Association Studies; Humans; MEDLINE; Protein Interaction Maps
PubMed: 27578323
DOI: 10.1186/s12859-016-1205-4 -
Cold Spring Harbor Perspectives in... Feb 2016Genetic causes for human disorders are being discovered at an unprecedented pace. A growing subclass of disease-causing mutations involves changes in the epigenome or in... (Review)
Review
Genetic causes for human disorders are being discovered at an unprecedented pace. A growing subclass of disease-causing mutations involves changes in the epigenome or in the abundance and activity of proteins that regulate chromatin structure. This article focuses on research that has uncovered human diseases that stem from such epigenetic deregulation. Disease may be caused by direct changes in epigenetic marks, such as DNA methylation, commonly found to affect imprinted gene regulation. Also described are disease-causing genetic mutations in epigenetic modifiers that either affect chromatin in trans or have a cis effect in altering chromatin configuration.
Topics: Disease; Epigenesis, Genetic; Humans
PubMed: 26834142
DOI: 10.1101/cshperspect.a019497