Predicting regulatory variants with composite statistic.
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.



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.



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 data are available at Bioinformatics online.
Department of Statistics, Harvard University, Cambridge, Boston, 02138-2901 MA, USA, Centre for Genomic Sciences.
Mulin Jun
MJ
Li
Centre for Genomic Sciences, Department of Psychiatry.
Zhicheng
Z
Pan
Centre for Genomic Sciences, Department of Anaesthesiology.
Zipeng
Z
Liu
Department of Statistics, Harvard University, Cambridge, Boston, 02138-2901 MA, USA.
Jiexing
J
Wu
Centre for Genomic Sciences.
Panwen
P
Wang
Centre for Genomic Sciences, School of Biomedical Sciences.
Yun
Y
Zhu
Centre for Genomic Sciences.
Feng
F
Xu
Department of Anaesthesiology.
Zhengyuan
Z
Xia
Centre for Genomic Sciences, Department of Psychiatry.
Pak Chung
PC
Sham
Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA and.
Jean-Pierre A
JP
Kocher
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.
Miaoxin
M
Li
Department of Statistics, Harvard University, Cambridge, Boston, 02138-2901 MA, USA, Center for Statistical Science, Tsinghua University, Beijing 100084, China and.
Jun S
JS
Liu
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.
Junwen
J
Wang
D000465:Algorithms; D005075:Biological Evolution; D014644:Genetic Variation; D006801:Humans; D008962:Models, Theoretical; D058977:Molecular Sequence Annotation; D022661:RNA, Untranslated; D012984:Software
D022661:RNA, Untranslated