Disease category-specific annotation of variants using an ensemble learning framework

We present a novel ensemble learning framework-CASAVA, to predict genomic loci in terms of disease category-specific risk. Using disease-associated variants identified by GWAS as training data, and diverse sequencing-based genomics and epigenomics profiles as features, CASAVA provides risk prediction of 24 major categories of diseases throughout the human genome. Our studies showed that CASAVA scores at a genomic locus provide a reasonable prediction of the disease-specific and disease category-specific risk prediction for non-coding variants located within the locus. Taking MHC2TA and immune system diseases as an example, we demonstrate the potential of CASAVA in revealing variant-disease associations. A website (http://zhanglabtools.org/CASAVA) has been built to facilitate easily access to CASAVA scores.PMID:34643213 | DOI:10.1093/bib/bbab438

Source: Briefings in Bioinformatics - October 13, 2021 Category: Bioinformatics Authors: Zhen Cao Yanting Huang Ran Duan Peng Jin Zhaohui S Qin Shihua Zhang Source Type: research