DIVAN: accurate identification of non-coding disease-specific risk variants using multi-omics profiles

• Type of Material: Article
• Authors: Li Chen, Emory University; Peng Jin, Emory University; Zhaohui Qin, Emory University
• Language: English
• Date: 2016-12-06
• Publisher: BioMed Central
• Publication Version: Final Published Version
• Copyright Statement: © 2016 The Author(s). The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s13059-016-1112-z
• Title of Journal or Parent Work: Genome Biology
• ISSN: 1465-6906
• Volume: 17
• Issue: 1
• Start Page: 252
• End Page: 252
• Grant/Funding Information: The project was partially supported by R01 NS079625 and R01 MH102690 grants from National Institute of Health (PJ) and P01 GM085354 grant from National Institute of Health (ZSQ).
• Keywords: Disease-specific; Science & Technology; Genetics & Heredity; Ensemble learning; Feature selection; Life Sciences & Biomedicine; Non-coding variants; Variant annotation; Epigenomics; Histone marks; Biotechnology & Applied Microbiology
• Research Categories: Biology, Bioinformatics; Health Sciences, General; Biology, Genetics

Abstract

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.