Publication Date:
2021-10-13
Description:
Understanding the impact of non-coding sequence variants on complex diseases is an essential problem. 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.
Print ISSN:
1467-5463
Electronic ISSN:
1477-4054
Topics:
Biology
,
Computer Science
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