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Dilated cardiomyopathy (DCM) is a major cause of sudden cardiac death and heart failure, and it is characterized by genetic and clinical heterogeneity, even for some patients with a very poor clinical prognosis; in the majority of cases, DCM necessitates a heart transplant. Genetic mutations have long been considered to be associated with this disease. At present, mutations in over 50 genes related to DCM have been documented. This study was carried out to elucidate the characteristics of gene mutations in patients with DCM. The candidate genes that may cause DCM include MYBPC3, MYH6, MYH7, LMNA, TNNT2, TNNI3, MYPN, MYL3, TPM1, SCN5A, DES, ACTC1 and RBM20. Using next-generation sequencing (NGS) and subsequent mutation confirmation with traditional capillary Sanger sequencing analysis, possible causative non-synonymous mutations were identified in ~57% (12/21) of patients with DCM. As a result, 7 novel mutations (MYPN, p.E630K; TNNT2, p.G180A; MYH6, p.R1047C; TNNC1, p.D3V; DES, p.R386H; MYBPC3, p.C1124F; and MYL3, p.D126G), 3 variants of uncertain significance (RBM20, p.R1182H; MYH6, p.T1253M; and VCL, p.M209L), and 2 known mutations (MYH7, p.A26V and MYBPC3, p.R160W) were revealed to be associated with DCM. The mutations were most frequently found in the sarcomere (MYH6, MYBPC3, MYH7, TNNC1, TNNT2 and MYL3) and cytoskeletal (MYPN, DES and VCL) genes. As genetic testing is a useful tool in the clinical management of disease, testing for pathogenic mutations is beneficial to the treatment of patients with DCM and may assist in predicting disease risk for their family members before the onset of symptoms.
Pubmed ID: 26458567 RIS Download
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International collaboration producing an extensive public catalog of human genetic variation, including SNPs and structural variants, and their haplotype contexts, in an effort to provide a foundation for investigating the relationship between genotype and phenotype. The genomes of about 2500 unidentified people from about 25 populations around the world were sequenced using next-generation sequencing technologies. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared. The results of the study are freely and publicly accessible to researchers worldwide. The consortium identified the following populations whose DNA will be sequenced: Yoruba in Ibadan, Nigeria; Japanese in Tokyo; Chinese in Beijing; Utah residents with ancestry from northern and western Europe; Luhya in Webuye, Kenya; Maasai in Kinyawa, Kenya; Toscani in Italy; Gujarati Indians in Houston; Chinese in metropolitan Denver; people of Mexican ancestry in Los Angeles; and people of African ancestry in the southwestern United States. The goal Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing. All samples from the 1000 genomes are available as lymphoblastoid cell lines (LCLs) and LCL derived DNA from the Coriell Cell Repository as part of the NHGRI Catalog. The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via their mirrored ftp sites. ftp://ftp.1000genomes.ebi.ac.uk ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes
View all literature mentionsCurated database of known (published) gene lesions responsible for human inherited disease.
View all literature mentionsA dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.
View all literature mentionsEvaluates disease-causing potential of sequence alterations.
View all literature mentionsData analysis service to predict whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids. SIFT can be applied to naturally occurring nonsynonymous polymorphisms and laboratory-induced missense mutations. (entry from Genetic Analysis Software) Web service is also available.
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