A general framework for estimating the relative pathogenicity of human genetic variants View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-03

AUTHORS

Martin Kircher, Daniela M Witten, Preti Jain, Brian J O'Roak, Gregory M Cooper, Jay Shendure

ABSTRACT

Current methods for annotating and interpreting human genetic variation tend to exploit a single information type (for example, conservation) and/or are restricted in scope (for example, to missense changes). Here we describe Combined Annotation-Dependent Depletion (CADD), a method for objectively integrating many diverse annotations into a single measure (C score) for each variant. We implement CADD as a support vector machine trained to differentiate 14.7 million high-frequency human-derived alleles from 14.7 million simulated variants. We precompute C scores for all 8.6 billion possible human single-nucleotide variants and enable scoring of short insertions-deletions. C scores correlate with allelic diversity, annotations of functionality, pathogenicity, disease severity, experimentally measured regulatory effects and complex trait associations, and they highly rank known pathogenic variants within individual genomes. The ability of CADD to prioritize functional, deleterious and pathogenic variants across many functional categories, effect sizes and genetic architectures is unmatched by any current single-annotation method. More... »

PAGES

310-315

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/ng.2892

DOI

http://dx.doi.org/10.1038/ng.2892

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050728268

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24487276


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