WEVar: a novel statistical learning framework for predicting noncoding regulatory variants.

ABSTRACT: Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies or quantitative trait locus analyses have identified variants associated with traits or molecular phenotypes, most of them are located in the noncoding regions, making the identification of causal variants a particular challenge. Existing computational approaches developed for prioritizing noncoding variants produce inconsistent and even conflicting results. To address these challenges, we propose a novel statistical learning framework, which directly integrates the precomputed functional scores from representative scoring methods. It will maximize the usage of integrated methods by automatically learning the relative contribution of each method and produce an ensembl

SUBMITTER: Wang Y 

PROVIDER: S-EPMC8574971 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature