ScienceGenomicsPeer-reviewed journal article
The landscape of tolerated genetic variation in humans and primates
Hong Chao GaoiD, Tobias Hamp, Jeffrey M. EdeiD, Joshua G. SchraiberiD, Jeremy F. McRaeiD, Moriel Singer‐BerkiD, Yanshen Yang, Anastasia S. D. DietrichiD, Petko P. FizieviD, Lukas F. K. KudernaiD, Laksshman SundaramiD, Yibing WuiD, Aashish N. AdhikariiD, Yair FieldiD, Chen Chen, Serafim BatzoglouiD, François AguetiD, Gabrielle LemireiD, Rebecca ReimersiD, Daniel J. BalickiD, Mareike C. JaniakiD, Martin KuhlwilmiD, Joseph Daniel OrkiniD, Shivakumara ManuiD, Alejandro ValenzuelaiD, , Kyle Kai‐How FarhiD
Abstract
Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole genome sequencing data for 809 individuals from 233 primate species, and identified 4.3 million common protein-altering variants with orthologs in human. We show that these variants can be inferred to have non-deleterious effects in human based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases. One Sentence SummaryDeep learning classifier trained on 4.3 million common primate missense variants predicts variant pathogenicity in humans.
- organism: human
- organism: multiple species
- assay: whole-genome sequencing
- research focus: variant effect prediction
- approach: deep learning
- Citations
- 183
- FWCI
- 30.33
- Field percentile
- top 1%
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