Roadmap to determine the point mutations involved in cardiomyopathy disorder: a Bayesian approach. 2013

Ambuj Kumar, and Vidya Rajendran, and Rao Sethumadhavan, and Rituraj Purohit
Bioinformatics Division, School of Bio Sciences and Technology, Vellore Institute of Technology University, Vellore 632014, Tamil Nadu, India.

Determining the deleterious non-synonymous single nucleotide polymorphisms (nsSNPs), that might be involved in inducing disease-associated phenomena, is now among the most important field of computational genomic research. The rapid evolution in sequencing technologies has now outranged the limit of available sequence databases and has out-fledged the amount of SNP data that are yet to be characterized. In this article we have performed a comprehensive analysis of deleterious nsSNPs in MyH7 gene associated with cardiomyopathy cases using a set of computational platforms. We implemented a set of computational SNP analysis platforms along with the Bayesian calculations in order to filter the most likely mutation that might be associated with cardiomyopathy associated disorders. The Bayesian calculation depicted 27 fold rises in the likelihood score for causing cardiomyopathy disorder when MyH7 gene mutations were compiled. Furthermore, we reported E466Q mutation in MyH7 motor domain that showed increase in the amyloid propensity of protein, as well as a significant level of pathogenicity was also observed. The prediction roadmap followed in this article has showed a notable range of accuracy and can be used for determining cardiomyopathy associated nsSNPs for other candidate genes.

UI MeSH Term Description Entries
D009202 Cardiomyopathies A group of diseases in which the dominant feature is the involvement of the CARDIAC MUSCLE itself. Cardiomyopathies are classified according to their predominant pathophysiological features (DILATED CARDIOMYOPATHY; HYPERTROPHIC CARDIOMYOPATHY; RESTRICTIVE CARDIOMYOPATHY) or their etiological/pathological factors (CARDIOMYOPATHY, ALCOHOLIC; ENDOCARDIAL FIBROELASTOSIS). Myocardial Disease,Myocardial Diseases,Myocardial Diseases, Primary,Myocardial Diseases, Secondary,Myocardiopathies,Primary Myocardial Disease,Cardiomyopathies, Primary,Cardiomyopathies, Secondary,Primary Myocardial Diseases,Secondary Myocardial Diseases,Cardiomyopathy,Cardiomyopathy, Primary,Cardiomyopathy, Secondary,Disease, Myocardial,Disease, Primary Myocardial,Disease, Secondary Myocardial,Diseases, Myocardial,Diseases, Primary Myocardial,Diseases, Secondary Myocardial,Myocardial Disease, Primary,Myocardial Disease, Secondary,Myocardiopathy,Primary Cardiomyopathies,Primary Cardiomyopathy,Secondary Cardiomyopathies,Secondary Cardiomyopathy,Secondary Myocardial Disease
D010641 Phenotype The outward appearance of the individual. It is the product of interactions between genes, and between the GENOTYPE and the environment. Phenotypes
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000483 Alleles Variant forms of the same gene, occupying the same locus on homologous CHROMOSOMES, and governing the variants in production of the same gene product. Allelomorphs,Allele,Allelomorph
D000682 Amyloid A fibrous protein complex that consists of proteins folded into a specific cross beta-pleated sheet structure. This fibrillar structure has been found as an alternative folding pattern for a variety of functional proteins. Deposits of amyloid in the form of AMYLOID PLAQUES are associated with a variety of degenerative diseases. The amyloid structure has also been found in a number of functional proteins that are unrelated to disease. Amyloid Fibril,Amyloid Fibrils,Amyloid Substance,Fibril, Amyloid,Fibrils, Amyloid,Substance, Amyloid
D001499 Bayes Theorem A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result. Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes
D015894 Genome, Human The complete genetic complement contained in the DNA of a set of CHROMOSOMES in a HUMAN. The length of the human genome is about 3 billion base pairs. Human Genome,Genomes, Human,Human Genomes
D017354 Point Mutation A mutation caused by the substitution of one nucleotide for another. This results in the DNA molecule having a change in a single base pair. Mutation, Point,Mutations, Point,Point Mutations
D060388 Support Vector Machine SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples. Support Vector Network,Machine, Support Vector,Machines, Support Vector,Network, Support Vector,Networks, Support Vector,Support Vector Machines,Support Vector Networks,Vector Machine, Support,Vector Machines, Support,Vector Network, Support,Vector Networks, Support
D018995 Myosin Heavy Chains The larger subunits of MYOSINS. The heavy chains have a molecular weight of about 230 kDa and each heavy chain is usually associated with a dissimilar pair of MYOSIN LIGHT CHAINS. The heavy chains possess actin-binding and ATPase activity. Myosin Heavy Chain,Heavy Chain, Myosin,Heavy Chains, Myosin

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