Nós somos todos diferentes no DNA: análise da variação genética de codificação de proteínas

quinta-feira, agosto 18, 2016

Analysis of protein-coding genetic variation in 60,706 humans

Monkol Lek, Konrad J. Karczewski, Eric V. Minikel, Kaitlin E. Samocha, Eric Banks, Timothy Fennell, Anne H. O’Donnell-Luria, James S. Ware, Andrew J. Hill, Beryl B. Cummings, Taru Tukiainen, Daniel P. Birnbaum, Jack A. Kosmicki, Laramie E. Duncan, Karol Estrada, Fengmei Zhao, James Zou, Emma Pierce-Hoffman, Joanne Berghout, David N. Cooper, Nicole Deflaux, Mark DePristo, Ron Do, Jason Flannick, Menachem Fromer et al.

Affiliations Contributions Corresponding author

Nature 536, 285–291 (18 August 2016) doi:10.1038/nature19057

Received 19 October 2015 Accepted 24 June 2016 Published online 17 August 2016

Source/Fonte: STAT


Abstract• Introduction• The ExAC data set• Patterns of protein-coding variation• Inferring variant deleteriousness and gene constraint• ExAC improves variant interpretation in rare disease• Effect of rare protein-truncating variants• Discussion• Methods• References• Acknowledgements• Author information• Extended data figures and tables• Supplementary information

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human ‘knockout’ variants in protein-coding genes.

Subject terms: Genomics Medical genetics