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      Identification of novel mutations in Chinese Hans with autosomal dominant polycystic kidney disease

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          Abstract

          Background

          Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited renal disease with an incidence of 1 in 400 to 1000. The disease is genetically heterogeneous, with two genes identified: PKD1 (16p13.3) and PKD2 (4q21). Molecular diagnosis of the disease in at-risk individuals is complicated due to the structural complexity of PKD1 gene and the high diversity of the mutations. This study is the first systematic ADPKD mutation analysis of both PKD1 and PKD2 genes in Chinese patients using denaturing high-performance liquid chromatography (DHPLC).

          Methods

          Both PKD1 and PKD2 genes were mutation screened in each proband from 65 families using DHPLC followed by DNA sequencing. Novel variations found in the probands were checked in their family members available and 100 unrelated normal controls. Then the pathogenic potential of the variations of unknown significance was examined by evolutionary comparison, effects of amino acid substitutions on protein structure, and effects of splice site alterations using online mutation prediction resources.

          Results

          A total of 92 variations were identified, including 27 reported previously. Definitely pathogenic mutations (ten frameshift, ten nonsense, two splicing defects and one duplication) were identified in 28 families, and probably pathogenic mutations were found in an additional six families, giving a total detection level of 52.3% (34/65). About 69% (20/29) of the mutations are first reported with a recurrent mutation rate of 31%.

          Conclusions

          Mutation study of PKD1 and PKD2 genes in Chinese Hans with ADPKD may contribute to a better understanding of the genetic diversity between different ethnic groups and enrich the mutation database. Besides, evaluating the pathogenic potential of novel variations should also facilitate the clinical diagnosis and genetic counseling of the disease.

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          Most cited references36

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          Mutation nomenclature extensions and suggestions to describe complex mutations: a discussion.

          Consistent gene mutation nomenclature is essential for efficient and accurate reporting, testing, and curation of the growing number of disease mutations and useful polymorphisms being discovered in the human genome. While a codified mutation nomenclature system for simple DNA lesions has now been adopted broadly by the medical genetics community, it is inherently difficult to represent complex mutations in a unified manner. In this article, suggestions are presented for reporting just such complex mutations. Copyright 2000 Wiley-Liss, Inc.
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            Improved splice site detection in Genie.

            We present an improved splice site predictor for the genefinding program Genie. Genie is based on a generalized Hidden Markov Model (GHMM) that describes the grammar of a legal parse of a multi-exon gene in a DNA sequence. In Genie, probabilities are estimated for gene features by using dynamic programming to combine information from multiple content and signal sensors, including sensors that integrate matches to homologous sequences from a database. One of the hardest problems in genefinding is to determine the complete gene structure correctly. The splice site sensors are the key signal sensors that address this problem. We replaced the existing splice site sensors in Genie with two novel neural networks based on dinucleotide frequencies. Using these novel sensors, Genie shows significant improvements in the sensitivity and specificity of gene structure identification. Experimental results in tests using a standard set of annotated genes showed that Genie identified 86% of coding nucleotides correctly with a specificity of 85%, versus 80% and 84% in the older system. In further splice site experiments, we also looked at correlations between splice site scores and intron and exon lengths, as well as at the effect of distance to the nearest splice site on false positive rates.
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              PKD2, a gene for polycystic kidney disease that encodes an integral membrane protein.

              A second gene for autosomal dominant polycystic kidney disease was identified by positional cloning. Nonsense mutations in this gene (PKD2) segregated with the disease in three PKD2 families. The predicted 968-amino acid sequence of the PKD2 gene product has six transmembrane spans with intracellular amino- and carboxyl-termini. The PKD2 protein has amino acid similarity with PKD1, the Caenorhabditis elegans homolog of PKD1, and the family of voltage-activated calcium (and sodium) channels, and it contains a potential calcium-binding domain.
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                Author and article information

                Journal
                BMC Med Genet
                BMC Med. Genet
                BMC Medical Genetics
                BioMed Central
                1471-2350
                2011
                20 December 2011
                : 12
                : 164
                Affiliations
                [1 ]Department of Medical Genetics, and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
                [2 ]Department of Nephrology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
                [3 ]Center for Clinical Molecular Medicine, Children's Hospital, Chongqing Medical University, Chongqing, 400014, P. R. China
                Article
                1471-2350-12-164
                10.1186/1471-2350-12-164
                3341574
                22185115
                9a97820c-b0e7-4034-a21f-e6dd7d6f33b6
                Copyright ©2011 Yu et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 July 2011
                : 20 December 2011
                Categories
                Research Article

                Genetics
                Genetics

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