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DIVAN: accurate identification of non-coding disease-specific risk variants using multi-omics profiles
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DIVAN: accurate identification of non-coding disease-specific risk variants using multi-omics profiles
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Journal Article
DIVAN: accurate identification of non-coding disease-specific risk variants using multi-omics profiles
2016
Overview
Understanding the link between non-coding sequence variants, identified in genome-wide association studies, and the pathophysiology of complex diseases remains challenging due to a lack of annotations in non-coding regions. To overcome this, we developed DIVAN, a novel feature selection and ensemble learning framework, which identifies disease-specific risk variants by leveraging a comprehensive collection of genome-wide epigenomic profiles across cell types and factors, along with other static genomic features. DIVAN accurately and robustly recognizes non-coding disease-specific risk variants under multiple testing scenarios; among all the features, histone marks, especially those marks associated with repressed chromatin, are often more informative than others.
Publisher
BioMed Central,Springer Nature B.V
Subject
/ as Revealed Through Genomics
/ Biomedical and Life Sciences
/ Cancer
/ Computational Biology - methods
/ Disease
/ DNA
/ Genes
/ Genome-wide association studies
/ Genome-Wide Association Study
/ Genomes
/ histones
/ Humans
/ Method
/ Microbial Genetics and Genomics
/ Mutation
/ Polymorphism, Single Nucleotide
/ Prostate
/ risk
