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Genome Biol. 2017 Mar 16;18(1):52. doi: 10.1186/s13059-017-1177-3.

cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes.

Li MJ1,2,3, Li M4,5,6,7, Liu Z5,8, Yan B5,9, Pan Z5,10, Huang D11, Liang Q11, Ying D5, Xu F5,9, Yao H5,9, Wang P12, Kocher JA12, Xia Z8, Sham PC5,6, Liu JS13, Wang J14,15.

Author information

1
Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China. mulin0424.li@gmail.com.
2
Centre for Genomic Sciences, The University of Hong Kong, Hong Kong SAR, China. mulin0424.li@gmail.com.
3
Department of Statistics, Harvard University, Cambridge, Boston, MA, 02138-2901, USA. mulin0424.li@gmail.com.
4
Department of Medical Genetics, Center for Genome Research, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
5
Centre for Genomic Sciences, The University of Hong Kong, Hong Kong SAR, China.
6
Department of Psychiatry, The University of Hong Kong, Hong Kong SAR, China.
7
Centre for Reproduction, Development and Growth, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
8
Department of Anaesthesiology, The University of Hong Kong, Hong Kong SAR, China.
9
School of Biomedical Sciences, The University of Hong Kong, Hong Kong SAR, China.
10
Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, CA, 90095, USA.
11
Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
12
Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, 85259, USA.
13
Department of Statistics, Harvard University, Cambridge, Boston, MA, 02138-2901, USA. jliu@stat.harvard.edu.
14
Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, 85259, USA. wang.junwen@mayo.edu.
15
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, 85259, USA. wang.junwen@mayo.edu.

Abstract

It remains challenging to predict regulatory variants in particular tissues or cell types due to highly context-specific gene regulation. By connecting large-scale epigenomic profiles to expression quantitative trait loci (eQTLs) in a wide range of human tissues/cell types, we identify critical chromatin features that predict variant regulatory potential. We present cepip, a joint likelihood framework, for estimating a variant's regulatory probability in a context-dependent manner. Our method exhibits significant GWAS signal enrichment and is superior to existing cell type-specific methods. Furthermore, using phenotypically relevant epigenomes to weight the GWAS single-nucleotide polymorphisms, we improve the statistical power of the gene-based association test.

KEYWORDS:

Cell type-specific; Disease-susceptible gene; Epigenome; Regulatory variant; Variant prioritization

PMID:
28302177
PMCID:
PMC5356314
DOI:
10.1186/s13059-017-1177-3
[Indexed for MEDLINE]
Free PMC Article

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