A FRAMEWORK FOR DETERMINING THE RELATIVE EFFECT OF GENETIC VARIANTS

摘要

Current methods for annotating and interpreting human genetic variation typically exploit only a single information type (e.g., conservation) and/or are restricted in scope (e.g., to missense changes). Here, a method for objectively integrating many diverse annotations into a single measure (integrated deleteriousness score, or C-score) for each variant is described. The method may be implemented as a support vector machine (SVM) trained to differentiate high-frequency human-derived alleles from simulated variants. C-scores were precomputed for all 8.6 billion possible human single- nucleotide variants and allow 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.