8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Estimating prevalence for limb-girdle muscular dystrophy based on public sequencing databases

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references26

          • Record: found
          • Abstract: found
          • Article: not found

          The prevalence of cystic fibrosis in the European Union.

          This study combined a variety of methods to determine the prevalence of cystic fibrosis in the European Union. The results of literature reviews, surveys, and registry analyses revealed a mean prevalence of 0.737/10,000 in the 27 EU countries, which is similar to the value of 0.797 in the United States, and only one outlier, namely the Republic of Ireland at 2.98.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Performance evaluation of pathogenicity-computation methods for missense variants

            Abstract With expanding applications of next-generation sequencing in medical genetics, increasing computational methods are being developed to predict the pathogenicity of missense variants. Selecting optimal methods can accelerate the identification of candidate genes. However, the performances of different computational methods under various conditions have not been completely evaluated. Here, we compared 12 performance measures of 23 methods based on three independent benchmark datasets: (i) clinical variants from the ClinVar database related to genetic diseases, (ii) somatic variants from the IARC TP53 and ICGC databases related to human cancers and (iii) experimentally evaluated PPARG variants. Some methods showed different performances under different conditions, suggesting that they were not always applicable for different conditions. Furthermore, the specificities were lower than the sensitivities for most methods (especially, for the experimentally evaluated benchmark datasets), suggesting that more rigorous cutoff values are necessary to distinguish pathogenic variants. Furthermore, REVEL, VEST3 and the combination of both methods (i.e. ReVe) showed the best overall performances with all the benchmark data. Finally, we evaluated the performances of these methods with de novo mutations, finding that ReVe consistently showed the best performance. We have summarized the performances of different methods under various conditions, providing tentative guidance for optimal tool selection.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The molecular basis of β-thalassemia.

              Swee Thein (2013)
              The β-thalassemias are characterized by a quantitative deficiency of β-globin chains underlaid by a striking heterogeneity of molecular defects. Although most of the molecular lesions involve the structural β gene directly, some down-regulate the gene through distal cis effects, and rare trans-acting mutations have also been identified. Most β-thalassemias are inherited in a Mendelian recessive fashion but there is a subgroup of β-thalassemia alleles that behave as dominant negatives. Unraveling the molecular basis of β-thalassemia has provided a paradigm for understanding of much of human genetics.
                Bookmark

                Author and article information

                Journal
                Genetics in Medicine
                Genet Med
                Springer Science and Business Media LLC
                1098-3600
                1530-0366
                November 2019
                May 20 2019
                November 2019
                : 21
                : 11
                : 2512-2520
                Article
                10.1038/s41436-019-0544-8
                31105274
                a3d61ff0-1209-4f42-8c6a-df3e1069b65a
                © 2019

                http://www.springer.com/tdm

                History

                Comments

                Comment on this article