Loading...
Biological relevance of computationally predicted pathogenicity of noncoding variants
Liu, L ; Sanderford, MD ; Patel, R ; Chandrashekar, P ; Gibson, G ; Kumar, S
Liu, L
Sanderford, MD
Patel, R
Chandrashekar, P
Gibson, G
Kumar, S
Citations
Altmetric:
Genre
Journal Article
Date
2019-12-01
Advisor
Committee member
Group
Department
Permanent link to this record
Collections
Research Projects
Organizational Units
Journal Issue
DOI
10.1038/s41467-018-08270-y
Abstract
© 2019, The Author(s). 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 confounding of allelic, positional and regional effects as well as imbalance of the proportion of true positive variants in candidate lists.
Description
Citation
Citation to related work
Springer Science and Business Media LLC
Has part
Nature Communications
ADA compliance
For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu