Yanpeng Wang
Research Subjects
Yanpeng Wang has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Lei Xu
School of Electronic and Communication Engineering, Shenzhen Polytechnic University , Shenzhen, 518055
1 shared publication - Yang Wang
Shenzhen Polytech, IoT Res Inst, Shenzhen 518055, Peoples R China
1 shared publication - Yingjie Guo
Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China; School of Computer and Information Technology, Shanxi University, Taiyuan, China; Key Laboratory of Evolutionary Science Intelligence of Shanxi Province, Taiyuan, Shanxi, China
1 shared publication - Zhen Liang
School of Life Science, Shanxi University, Taiyuan, China
1 shared publication - Zhian Yuan
Shanxi University
1 shared publication
Latest Publications
- Open Access
- 15 February 2022
- Published by Wiley in Computational and Mathematical Methods in Medicine
- Vol. 2022, 1-12
Maximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies
Interactions between genetic variants (epistasis) are ubiquitous in the model system and can significantly affect evolutionary adaptation, genetic mapping, and precision medical efforts. In this paper, we proposed a method for epistasis detection, called EpiMIC (epistasis detection through a maximal information coefficient (MIC)). MIC is a promising bivariate dependence measure explicitly designed for rapidly exploring various function types equally and for interpreting and comparing them on the same scale. Most epistasis detection approaches make assumptions about the form of the association between genetic variants, resulting in limited statistical performance. Based on the notion that if two SNPs do not interact, their joint distribution in all samples and in only cases should not be substantially different. We developed a statistic that utilizes the difference of MIC as a signal of epistasis and combined it with a permutation resampling strategy to estimate the empirical distribution of our statistic. Results of simulation and real-world data set showed that EpiMIC outperformed previous approaches for identifying epistasis at varying degrees of heredity.
- 15 February 2022
- Published by Wiley in Computational and Mathematical Methods in Medicine
- Vol. 2022, 1-12
Open AccessMaximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies
Interactions between genetic variants (epistasis) are ubiquitous in the model system and can significantly affect evolutionary adaptation, genetic mapping, and precision medical efforts. In this paper, we proposed a method for epistasis detection, called EpiMIC (epistasis detection through a maximal information coefficient (MIC)). MIC is a promising bivariate dependence measure explicitly designed for rapidly exploring various function types equally and for interpreting and comparing them on the same scale. Most epistasis detection approaches make assumptions about the form of the association between genetic variants, resulting in limited statistical performance. Based on the notion that if two SNPs do not interact, their joint distribution in all samples and in only cases should not be substantially different. We developed a statistic that utilizes the difference of MIC as a signal of epistasis and combined it with a permutation resampling strategy to estimate the empirical distribution of our statistic. Results of simulation and real-world data set showed that EpiMIC outperformed previous approaches for identifying epistasis at varying degrees of heredity.