Capturing all disease-causing mutations for clinical and research use: Toward an effortless system for the Human Variome Project

Cotton, Richard G.H.; Aqeel, Aida I. Al; Al-Mulla, Fahd; Carrera, Paola; Claustres, Mireille; Ekong, Rosemary; Hyland, Valentine J.; Macrae, Finlay; Marafie, Makia J.; Paalman, Mark H.; Patrinos, George P.; Qi, Ming; Ramesar, Raj; Scott, Rodney J.; Sijmons, Rolf H.; Sobrido, María Jesús; Vihinen, Mauno

doi:10.1097/gim.0b013e3181c371c5

Cited by 40 publications

(22 citation statements)

References 17 publications

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“…Locus specific databases (LSDBs) are the most reliable source for disease-related data. Although lots of variation data are listed in LSDBs, it would be necessary to capture to databases all the cases from clinical and research laboratories [25,26]. …”

Section: Principles Of Methods Evaluationmentioning

confidence: 99%

How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis

2012

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BackgroundPrediction methods are increasingly used in biosciences to forecast diverse features and characteristics. Binary two-state classifiers are the most common applications. They are usually based on machine learning approaches. For the end user it is often problematic to evaluate the true performance and applicability of computational tools as some knowledge about computer science and statistics would be needed.ResultsInstructions are given on how to interpret and compare method evaluation results. For systematic method performance analysis is needed established benchmark datasets which contain cases with known outcome, and suitable evaluation measures. The criteria for benchmark datasets are discussed along with their implementation in VariBench, benchmark database for variations. There is no single measure that alone could describe all the aspects of method performance. Predictions of genetic variation effects on DNA, RNA and protein level are important as information about variants can be produced much faster than their disease relevance can be experimentally verified. Therefore numerous prediction tools have been developed, however, systematic analyses of their performance and comparison have just started to emerge.ConclusionsThe end users of prediction tools should be able to understand how evaluation is done and how to interpret the results. Six main performance evaluation measures are introduced. These include sensitivity, specificity, positive predictive value, negative predictive value, accuracy and Matthews correlation coefficient. Together with receiver operating characteristics (ROC) analysis they provide a good picture about the performance of methods and allow their objective and quantitative comparison. A checklist of items to look at is provided. Comparisons of methods for missense variant tolerance, protein stability changes due to amino acid substitutions, and effects of variations on mRNA splicing are presented.

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Section: Principles Of Methods Evaluationmentioning

confidence: 99%

How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis

2012

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“…Considerable assistance in care is achieved through collection of mutations for diagnostic laboratories, clinicians and researchers. 9 Recently, the genetic causes of NSHL in Jews, Japanese and Chinese populations have been described. [10][11][12] In this survey, we emphasized the importance of identifying the genetic bases of NSHL and summarized the published data on the frequency of gene mutations in various loci studied in Iran; we provided counseling criteria for the patients.…”

Section: Introductionmentioning

confidence: 99%

Genetic causes of nonsyndromic hearing loss in Iran in comparison with other populations

et al. 2010

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Hearing loss (HL) is the most prevalent sensory defect affecting 1 in 500 neonates. Genetic factors are involved in half of the cases. The extreme heterogeneity of HL makes it difficult to analyze and determine the accurate genetic causes of the impairment. Up to now, 10 genes, namely, GJB2, GJB6, SLC26A4, TECTA, PJVK, Col11A2, Myo15A, TMC1, RDX and microRNA (miR-183), have been studied in an Iranian population. The prevalence of HL in Iran was estimated to be 2-3 times higher than that in other parts of the world. Here, the most common bases of congenital nonsyndromic hearing loss (NSHL) are discussed. We reviewed GJB2, GJB6 (large deletion), TECTA, SLC26A4 and PEJVK mutations, and studied their frequencies and distributions in different ethnic groups in 1934, 500, 121, 80 and 34 unrelated families throughout Iran, respectively. GJB2 mutation was the most common factor causing NSHL, with a mean frequency of 18.17% in the Iranian population. The importance of Iran's geographical location in the migration pathway from west to east through the silk route was also highlighted. SLC26A4 and TECTA mutations were the second and third main reasons of HL and accounted for up to 10 and 4% of prelingual HL in Iran, respectively. Mutations in GJB2, SLC26, TECTA and PJVK genes have an important role in HL in Iran and a screening test should be generated for better intervention and diagnosis programs.

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“…Although the challenges in developing mutation databases for ataxia genetics are by no means unique, they are particularly well aligned to those outlined by the Human Variome Project (HVP), and its neurogenetics consortium, which aims to develop a global collaboration for the collection, storage, interpretation, and sharing of genetic variation [Cotton et al, 2009]. Recent meetings of the HVP neurogenetics consortium [Haworth et al, 2010] have determined that global access to comprehensive repositories of genetic variant data were particularly apposite for neurogenetics due to the large number of disease genes, significant genetic heterogeneity, clinical variability, and complex genotype-phenotype relationships in neurological disorders.…”

Section: Neurogenetics Databasesmentioning

confidence: 99%

The inherited ataxias: Genetic heterogeneity, mutation databases, and future directions in research and clinical diagnostics

2012

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ABSTRACT:The inherited cerebellar ataxias are a diverse group of clinically and genetically heterogeneous neurodegenerative disorders. Inheritance patterns of these disorders can be complex with autosomal dominant, autosomal recessive, X-linked, and mitochondrial inheritance demonstrated by one or more ataxic syndromes. The broad range of mutation types found in inherited ataxia contributes to the complex genetic etiology of these disorders. The majority of inherited ataxias are caused by repeat expansions; however, conventional mutations are important causes of the rarer dominant and recessive ataxias. Advances in sequencing technology have allowed for much broader testing of these rare ataxia genes. This is relevant to the aims of the Human Variome Project, which aims to collate and store gene variation data through mutation databases. Variant data is currently located in a range of public and commercial resources. Few locus-specific databases have been created to catalogue variation in the dominant ataxia genes although there are several databases for some recessive genes. Developing these resources will facilitate a better understanding of the complex genotypephenotype relationships in these disorders and assist interpretation of gene variants as testing for rarer ataxia genes becomes commonplace.

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Capturing all disease-causing mutations for clinical and research use: Toward an effortless system for the Human Variome Project

Cited by 40 publications

References 17 publications

How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis

How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis

Genetic causes of nonsyndromic hearing loss in Iran in comparison with other populations

The inherited ataxias: Genetic heterogeneity, mutation databases, and future directions in research and clinical diagnostics

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