Introduction

Genetic heterogeneity presents a significant challenge for the identification of monogenic disease genes. Whole-exome sequencing generates a large number of candidate disease-causing variants and typical analyses rely on deleterious variants being observed in the same gene across several unrelated affected individuals. This is less likely to occur for genetically heterogeneous diseases, making more advanced analysis methods necessary. To address this need, we present HetRank, a flexible gene-ranking method that incorporates interaction network data. We first show that different genes underlying the same monogenic disease are frequently connected in protein interaction networks. This motivates the central premise of HetRank: those genes carrying potentially pathogenic variants and whose network neighbors do so in other affected individuals are strong candidates for follow-up study. By simulating 1,000 exome sequencing studies (20,000 exomes in total), we model varying degrees of genetic heterogeneity and show that HetRank consistently prioritizes more disease-causing genes than existing analysis methods. We also demonstrate a proof-of-principle application of the method to prioritize genes causing Adams-Oliver syndrome, a genetically heterogeneous rare disease. An implementation of HetRank in R is available via the Website http://sourceforge.net/p/hetrank/.

Publications

  1. Network-Informed Gene Ranking Tackles Genetic Heterogeneity in Exome-Sequencing Studies of Monogenic Disease.
    Cite this
    Dand N, Schulz R, Weale ME, Southgate L, Oakey RJ, Simpson MA, Schlitt T, 2015-12-01 - Human mutation

Credits

  1. Nick Dand
    Developer

    Division of Genetics and Molecular Medicine, King's College London

  2. Reiner Schulz
    Developer

    Division of Genetics and Molecular Medicine, King's College London

  3. Michael E Weale
    Developer

    Division of Genetics and Molecular Medicine, King's College London

  4. Laura Southgate
    Developer

    Barts and The London School of Medicine and Dentistry, Queen Mary University of London

  5. Rebecca J Oakey
    Developer

    Division of Genetics and Molecular Medicine, King's College London

  6. Michael A Simpson
    Developer

    Division of Genetics and Molecular Medicine, King's College London

  7. Thomas Schlitt
    Investigator

    Institute for Mathematical and Molecular Biomedicine, King's College London

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Summary
AccessionBT001638
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByThomas Schlitt