Predicting the clinical impact of human mutation with deep neural networks
| Autor: | Nondas Fritzilas, Jinbo Xu, Yanjun Li, Hong Gao, Laksshman Sundaram, Jeremy F. McRae, Samskruthi Reddy Padigepati, John Shon, Jack A. Kosmicki, Xiaolin Li, Serafim Batzoglou, Kyle Kai-How Farh, Anindita Dutta, Jörg Hakenberg |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Primates medicine.medical_specialty Candidate gene Population Computational biology Biology medicine.disease_cause Genome 03 medical and health sciences Intellectual Disability Genetics medicine Animals Humans Exome Genetic Predisposition to Disease education Exome sequencing education.field_of_study Mutation Genome Human 030104 developmental biology Medical genetics Human genome Nerve Net |
| Zdroj: | Nature genetics. 50(8) |
| ISSN: | 1546-1718 |
| Popis: | Millions of human genomes and exomes have been sequenced, but their clinical applications remain limited due to the difficulty of distinguishing disease-causing mutations from benign genetic variation. Here we demonstrate that common missense variants in other primate species are largely clinically benign in human, enabling pathogenic mutations to be systematically identified by the process of elimination. Using hundreds of thousands of common variants from population sequencing of six non-human primate species, we train a deep neural network that identifies pathogenic mutations in rare disease patients with 88% accuracy and enables the discovery of 14 new candidate genes in intellectual disability at genome-wide significance. Cataloging common variation from additional primate species would improve interpretation for millions of variants of uncertain significance, further advancing the clinical utility of human genome sequencing. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink