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Bioinformatics. 2016 Sep 15;32(18):2729-36. doi: 10.1093/bioinformatics/btw288. Epub 2016 Jun 6.

Predicting regulatory variants with composite statistic.

Author information

1
Department of Statistics, Harvard University, Cambridge, Boston, 02138-2901 MA, USA, Centre for Genomic Sciences.
2
Centre for Genomic Sciences, Department of Psychiatry.
3
Centre for Genomic Sciences, Department of Anaesthesiology.
4
Department of Statistics, Harvard University, Cambridge, Boston, 02138-2901 MA, USA.
5
Centre for Genomic Sciences.
6
Centre for Genomic Sciences, School of Biomedical Sciences.
7
Department of Anaesthesiology.
8
Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA and.
9
Centre for Genomic Sciences, Department of Psychiatry, Centre for Reproduction, Development and Growth, LKS Faculty of Medicine, the University of Hong Kong, Hong Kong SAR, China.
10
Department of Statistics, Harvard University, Cambridge, Boston, 02138-2901 MA, USA, Center for Statistical Science, Tsinghua University, Beijing 100084, China and.
11
Centre for Genomic Sciences, Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA and Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA.

Abstract

MOTIVATION:

Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction.

RESULTS:

Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance.

AVAILABILITY AND IMPLEMENTATION:

We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS CONTACT: wang.junwen@mayo.edu, jliu@stat.harvard.edu, or limx54@gmail.com

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
27273672
PMCID:
PMC6280872
DOI:
10.1093/bioinformatics/btw288
[Indexed for MEDLINE]
Free PMC Article

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