Advancing, promoting and sharing knowledge of health through excellence in teaching, clinical practice and research into the prevention and treatment of illness

Network-Informed Gene Ranking Tackles Genetic Heterogeneity in Exome-Sequencing Studies of Monogenic Disease.

Dand, N; Schulz, R; Weale, ME; Southgate, L; Oakey, RJ; Simpson, MA; Schlitt, T (2015) Network-Informed Gene Ranking Tackles Genetic Heterogeneity in Exome-Sequencing Studies of Monogenic Disease. Hum Mutat, 36 (12). pp. 1135-1144. ISSN 1098-1004
SGUL Authors: Southgate, Laura

PDF Published Version
Available under License Creative Commons Attribution.

Download (685kB) | Preview


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

Item Type: Article
Additional Information: © 2015 The Authors. **Human Mutation published by Wiley Periodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution License (, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Mendelian, NGS, genetic heterogeneity, interaction networks, monogenic, next generation sequencing, rare disease, variant prioritization, whole-exome sequencing, Computational Biology, Computer Simulation, Epistasis, Genetic, Exome, Gene Regulatory Networks, Genetic Association Studies, Genetic Diseases, Inborn, Genetic Heterogeneity, High-Throughput Nucleotide Sequencing, Humans, Protein Interaction Mapping, Software, Web Browser, Humans, Genetic Diseases, Inborn, Protein Interaction Mapping, Computational Biology, Epistasis, Genetic, Genetic Heterogeneity, Computer Simulation, Software, Gene Regulatory Networks, Genetic Association Studies, High-Throughput Nucleotide Sequencing, Exome, Web Browser, whole-exome sequencing, next generation sequencing, NGS, rare disease, monogenic, Mendelian, genetic heterogeneity, variant prioritization, interaction networks, 0604 Genetics, 1103 Clinical Sciences, Genetics & Heredity
SGUL Research Institute / Research Centre: Academic Structure > Molecular and Clinical Sciences Research Institute (MCS)
Journal or Publication Title: Hum Mutat
ISSN: 1098-1004
Language: eng
10 November 2015Published
7 October 2015Published Online
9 September 2015Accepted
Publisher License: Creative Commons: Attribution 4.0
Project IDFunderFunder ID
MC_PC_14105Medical Research Council
PG/13/35/30236British Heart Foundation
PubMed ID: 26394720
Web of Science ID: WOS:000364788500003
Go to PubMed abstract
Publisher's version:

Actions (login required)

Edit Item Edit Item