Author:
Itan Yuval,Shang Lei,Boisson Bertrand,Patin Etienne,Bolze Alexandre,Moncada-Vélez Marcela,Scott Eric,Ciancanelli Michael J.,Lafaille Fabien G.,Markle Janet G.,Martinez-Barricarte Ruben,de Jong Sarah Jill,Kong Xiao-Fei,Nitschke Patrick,Belkadi Aziz,Bustamante Jacinta,Puel Anne,Boisson-Dupuis Stéphanie,Stenson Peter D.,Gleeson Joseph G.,Cooper David N.,Quintana-Murci Lluis,Claverie Jean-Michel,Zhang Shen-Ying,Abel Laurent,Casanova Jean-Laurent
Abstract
The protein-coding exome of a patient with a monogenic disease contains about 20,000 variants, only one or two of which are disease causing. We found that 58% of rare variants in the protein-coding exome of the general population are located in only 2% of the genes. Prompted by this observation, we aimed to develop a gene-level approach for predicting whether a given human protein-coding gene is likely to harbor disease-causing mutations. To this end, we derived the gene damage index (GDI): a genome-wide, gene-level metric of the mutational damage that has accumulated in the general population. We found that the GDI was correlated with selective evolutionary pressure, protein complexity, coding sequence length, and the number of paralogs. We compared GDI with the leading gene-level approaches, genic intolerance, and de novo excess, and demonstrated that GDI performed best for the detection of false positives (i.e., removing exome variants in genes irrelevant to disease), whereas genic intolerance and de novo excess performed better for the detection of true positives (i.e., assessing de novo mutations in genes likely to be disease causing). The GDI server, data, and software are freely available to noncommercial users from lab.rockefeller.edu/casanova/GDI.
Publisher
Proceedings of the National Academy of Sciences
Cited by
208 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献