Integrating population variation and protein structural analysis to improve clinical interpretation of missense variation: application to the WD40 domain

Roman A. Laskowski, Nidhi Tyagi, Diana Johnson, Shelagh Joss, Esther Kinning, Catherine McWilliam, Miranda Splitt, Janet M. Thornton, Helen V. Firth, Caroline F. Wright (Lead / Corresponding author)

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    17 Citations (Scopus)
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    Abstract

    We present a generic, multidisciplinary approach for improving our understanding of novel missense variants in recently discovered disease genes exhibiting genetic heterogeneity, by combining clinical and population genetics with protein structural analysis. Using six new de novo missense diagnoses in TBL1XR1 fromthe Deciphering Developmental Disorders study, together with population variation data, we show that the β-propeller structure of the ubiquitous WD40 domain provides a convincing way to discriminate between pathogenic and benign variation. Children with likely pathogenic mutations in this gene have severely delayed language development, often accompanied by intellectual disability, autism, dysmorphology and gastrointestinal problems. Amino acids affected by likely pathogenic missense mutations are either crucial for the stability of the fold, forming part of a highly conserved symmetrically repeating hydrogen-bonded tetrad, or located at the top face of the β-propeller, where 'hotspot' residues affect the binding of β-catenin to the TBLR1 protein. In contrast, those altered by population variation are significantly less likely to be spatially clustered towards the top face or to be at buried or highly conserved residues. This result is useful not only for interpreting benign and pathogenic missense variants in this gene, but also in other WD40 domains, many of which are associated with disease.
    Original languageEnglish
    Pages (from-to)927-935
    Number of pages9
    JournalHuman Molecular Genetics
    Volume25
    Issue number5
    Early online date5 Jan 2016
    DOIs
    Publication statusPublished - 1 Mar 2016

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