Network‐Informed Gene Ranking Tackles Genetic Heterogeneity in Exome‐Sequencing Studies of Monogenic Disease. Issue 12 (7th October 2015)

• Record Type: Journal Article
• Authors: Dand, Nick; Schulz, Reiner; Weale, Michael E.; Southgate, Laura; Oakey, Rebecca J.; Simpson, Michael A.; Schlitt, Thomas
• Is Part Of: Human mutation. Volume 36:Issue 12(2015:Dec.)
• Page Start: 1135
• Page End: 1144
• Publication Date: 2015-10-07
• Subjects: whole‐exome sequencing -- next generation sequencing -- NGS -- rare disease -- monogenic -- Mendelian -- genetic heterogeneity -- variant prioritization -- interaction networks; Human chromosome abnormalities -- Periodicals; Mutation (Biology) -- Periodicals; 616.04205
• DOI: 10.1002/humu.22906
• Languages: English
• ISSNs: 1059-7794

Abstract

Abstract : Genetic heterogeneity can impede efforts to identify the genes responsible for monogenic diseases through whole exome sequencing. To address this, we present a flexible gene‐ranking method which incorporates independent data from interaction networks to prioritize multiple functionally‐related genes. We demonstrate the utility of our tool, HetRank, through simulations and application to real disease data. ABSTRACT: 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 Websitehttp://sourceforge.net/p/hetrank/ .