Abstract:
:Key challenges for human genetics, precision medicine and evolutionary biology include deciphering the regulatory code of gene expression and understanding the transcriptional effects of genome variation. However, this is extremely difficult because of the enormous scale of the noncoding mutation space. We developed a deep learning-based framework, ExPecto, that can accurately predict, ab initio from a DNA sequence, the tissue-specific transcriptional effects of mutations, including those that are rare or that have not been observed. We prioritized causal variants within disease- or trait-associated loci from all publicly available genome-wide association studies and experimentally validated predictions for four immune-related diseases. By exploiting the scalability of ExPecto, we characterized the regulatory mutation space for human RNA polymerase II-transcribed genes by in silico saturation mutagenesis and profiled > 140 million promoter-proximal mutations. This enables probing of evolutionary constraints on gene expression and ab initio prediction of mutation disease effects, making ExPecto an end-to-end computational framework for the in silico prediction of expression and disease risk.
journal_name
Nat Genetjournal_title
Nature geneticsauthors
Zhou J,Theesfeld CL,Yao K,Chen KM,Wong AK,Troyanskaya OGdoi
10.1038/s41588-018-0160-6subject
Has Abstractpub_date
2018-08-01 00:00:00pages
1171-1179issue
8eissn
1061-4036issn
1546-1718pii
10.1038/s41588-018-0160-6journal_volume
50pub_type
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