Predicting regulatory variants with composite statistic

Mulin Jun Li, Zhicheng Pan, Zipeng Liu, Jiexing Wu, Panwen Wang, Yun Zhu, Feng Xu, Zhengyuan Xia, Pak Chung Sham, Jean Pierre A. Kocher, Miaoxin Li, Jun S. Liu, Junwen Wang

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2729-2736
Number of pages8
Issue number18
StatePublished - Sep 15 2016

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


Dive into the research topics of 'Predicting regulatory variants with composite statistic'. Together they form a unique fingerprint.

Cite this