Additional value of screening for minor genes and copy number variants in hypertrophic cardiomyopathy

Introduction: Hypertrophic cardiomyopathy (HCM) is the most prevalent inherited heart disease. Next-generation sequencing (NGS) is the preferred genetic test, but the diagnostic value of screening for minor and candidate genes, and the role of copy number variants (CNVs) deserves further evaluation. Methods: Three hundred and eighty-seven consecutive unrelated patients with HCM were screened for genetic variants in the 5 most frequent genes (MYBPC3, MYH7, TNNT2, TNNI3 and TPM1) using Sanger sequencing (N = 84) or NGS (N = 303). In the NGS cohort we analyzed 20 additional minor or candidate genes, and applied a proprietary bioinformatics algorithm for detecting CNVs. Additionally, the rate and classification of TTN variants in HCM were compared with 427 patients without structural heart disease. Results: The percentage of patients with pathogenic/likely pathogenic (P/LP) variants in the main genes was 33.3%, without significant differences between the Sanger sequencing and NGS cohorts. The screening for 20 additional genes revealed LP variants in ACTC1, MYL2, MYL3, TNNC1, GLA and PRKAG2 in 12 patients. This approach resulted in more inconclusive tests (36.0% vs. 9.6%, p<0.001), mostly due to variants of unknown significance (VUS) in TTN. The detection rate of rare variants in TTN was not significantly different to that found in the group of patients without structural heart disease. In the NGS cohort, 4 patients (1.3%) had pathogenic CNVs: 2 deletions in MYBPC3 and 2 deletions involving the complete coding region of PLN. Conclusions: A small percentage of HCM cases without point mutations in the 5 main genes are explained by P/LP variants in minor or candidate genes and CNVs. Screening for variants in TTN in HCM patients drastically increases the number of inconclusive tests, and shows a rate of VUS that is similar to patients without structural heart disease, suggesting that this gene should not be analyzed for clinical purposes in HCM

Statistical Quantification of Individual Differences (SQuID): an educational and statistical tool for understanding multilevel phenotypic data in linear mixed models

Phenotypic variation exists in and at all levels of biological organization: variation exists among species, among-individuals within-populations, and in the case of l within-populations abile traits, within-individuals. Mixed-effects models represent ideal tools to quantify multilevel measurements of traits and are being increasingly used in evolutionary ecology. Mixed-effects models are relatively complex, and two main issues may be hampering their proper usage: (i) the relatively few educational resources available to teach new users how to implement and interpret them and (ii) the lack of tools to ensure that the statistical parameters of interest are correctly estimated. In this paper, we introduce Statistical Quantification of Individual Differences (SQuID), a simulation-based tool that can be used for research and educational purposes. SQuID creates a virtual world inhabited by subjects whose phenotypes are generated by a user-defined phenotypic equation, which allows easy translation of biological hypotheses into quantifiable parameters. Statistical Quantification of Individual Differences currently models normally distributed traits with linear predictors, but SQuID is subject to further development and will adapt to handle more complex scenarios in the future. The current framework is suitable for performing simulation studies, determining optimal sampling designs for user-specific biological problems and making simulation-based inferences to aid in the interpretation of empirical studies. Statistical Quantification of Individual Differences is also a teaching tool for biologists interested in learning, or teaching others, how to implement and interpret linear mixed-effects models when studying the processes causing phenotypic variation. Interface-based modules allow users to learn about these issues. As research on effects of sampling designs continues, new issues will be implemented in new modules, including nonlinear and non-Gaussian data.Peer Reviewe

Melatonin synthesis under calcium constraint in gilthead sea bream (Sparus auratus L.)

Contains fulltext : 69197.pdf ( ) (Open Access)7 p

Genetic analysis, in silico prediction, and family segregation in long QT syndrome

The heritable cardiovascular disorder long QT syndrome (LQTS), characterized by prolongation of the QT interval on electrocardiogram, carries a high risk of sudden cardiac death. We sought to add new data to the existing knowledge of genetic mutations contributing to LQTS to both expand our understanding of its genetic basis and assess the value of genetic testing in clinical decision-making. Direct sequencing of the five major contributing genes, KCNQ1, KCNH2, SCN5A, KCNE1, and KCNE2, was performed in a cohort of 115 non-related LQTS patients. Pathogenicity of the variants was analyzed using family segregation, allele frequency from public databases, conservation analysis, and Condel and Provean in silico predictors. Phenotype-genotype correlations were analyzed statistically. Sequencing identified 36 previously described and 18 novel mutations. In 51.3% of the index cases, mutations were found, mostly in KCNQ1, KCNH2, and SCN5A; 5.2% of cases had multiple mutations. Pathogenicity analysis revealed 39 mutations as likely pathogenic, 12 as VUS, and 3 as non-pathogenic. Clinical analysis revealed that 75.6% of patients with QTc≥500 ms were genetically confirmed. Our results support the use of genetic testing of KCNQ1, KCNH2, and SCN5A as part of the diagnosis of LQTS and to help identify relatives at risk of SCD. Further, the genetic tools appear more valuable as disease severity increases. However, the identification of genetic variations in the clinical investigation of single patients using bioinformatic tools can produce erroneous conclusions regarding pathogenicity. Therefore segregation studies are key to determining causality.European Journal of Human Genetics advance online publication, 26 March 2014; doi:10.1038/ejhg.2014.54