Paralogous annotation of disease-causing variants in long QT syndrome genes

Authors

  • James S. Ware,

    Corresponding author
    1. Medical Research Council Clinical Sciences Centre, Imperial College London, London, United Kingdom
    • Medical Research Council Clinical Sciences Centre, Imperial College London, London, United Kingdom
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    • First two authors have contributed equally to this work.

  • Roddy Walsh,

    1. Cardiovascular Biomedical Research Unit, Royal Brompton and Harefield NHS Trust, London, United Kingdom
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    • First two authors have contributed equally to this work.

  • Fiona Cunningham,

    1. European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
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  • Ewan Birney,

    1. European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
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  • Stuart A. Cook

    1. Medical Research Council Clinical Sciences Centre, Imperial College London, London, United Kingdom
    2. Cardiovascular Biomedical Research Unit, Royal Brompton and Harefield NHS Trust, London, United Kingdom
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  • Communicated by Raymond Dalgleish

Abstract

Discriminating between rare benign and pathogenic variation is a key challenge in clinical genetics, particularly as increasing numbers of nonsynonymous single-nucleotide polymorphisms (SNPs) are identified in resequencing studies. Here, we describe an approach for the functional annotation of nonsynonymous variants that identifies functionally important, disease-causing residues across protein families using multiple sequence alignment. We applied the methodology to long QT syndrome (LQT) genes, which cause sudden death, and their paralogues, which largely cause neurological disease. This approach accurately classified known LQT disease-causing variants (positive predictive value = 98.4%) with a better performance than established bioinformatic methods. The analysis also identified 1078 new putative disease loci, which we incorporated along with known variants into a comprehensive and freely accessible long QT resource (http://cardiodb.org/Paralogue_Annotation/), based on newly created Locus Reference Genomic sequences (http://www.lrg-sequence.org/). We propose that paralogous annotation is widely applicable for Mendelian human disease genes. Hum Mutat 33:1188–1191, 2012. © 2012 Wiley Periodicals, Inc.

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