Biological relevance of computationally predicted pathogenicity of noncoding variants.

ABSTRACT: Computational prediction of the phenotypic propensities of noncoding single nucleotide variants typically combines annotation of genomic, functional and evolutionary attributes into a single score. Here, we evaluate if the claimed excellent accuracies of these predictions translate into high rates of success in addressing questions important in biological research, such as fine mapping causal variants, distinguishing pathogenic allele(s) at a given position, and prioritizing variants for genetic risk assessment. A significant disconnect is found to exist between the statistical modelling and biological performance of predictive approaches. We discuss fundamental reasons underlying these deficiencies and suggest that future improvements of computational predictions need to address confoundi

SUBMITTER: Liu L 

PROVIDER: S-EPMC6338804 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature