Research
Research Topics
Signal-detection methods for hunting disease genes
We develop and apply statistical signal-detection methods to help reveal genetic factors and mechanisms of human diseases. Based on the genetic signal patterns and data properties, we aim to optimize statistical methods that maximize the statistical power for detecting genes and genetic factors associated with health-related phenotypes. The challenges include weak signals, correlated data, confounding, mediation, heterogeneity, etc.
Our strategies:
- Optimal signal-detection.
- Data integration and inference.
Related publications
- Hong Zhang, Jiashun Jin and Zheyang Wu (2020) Distributions and power of optimal signal-detection statistics in finite case, IEEE Transaction On Signal Processing, Vol. 68, 1021--1033.
- Hong Zhang, Ming Liu, Jiashun Jin and Zheyang Wu (2022) Signal-noise ratio of genetic associations and \\ statistical power of SNP-set tests, Annals of Applied Statistics, in print.
- Hong Zhang and Zheyang Wu (2022) The generalized Fisher’s combination and accurate p-value calculation under dependence, Biometrics.
- Hong Zhang, Tiejun Tong, John E. Landers and Zheyang Wu (2020) TFisher: A powerful truncation and weighting procedure for combining p-values, Annals of Applied Statistics, 14(1):178--201.
- Zheyang Wu, Yiming Sun, Shiquan He, Judy Cho, Hongyu Zhao, and Jiashun Jin (2014) Detection Boundary and Higher Criticism for Weak and Sparse Genetic Effects, The Annals of Applied Statistics, 8(2): 824-851.
Theoretical and computational statistics
We develop statistical theory and computational methods to address practical challenges, such as data correlation, error control, discrete data, etc.
Our strategies:
- Reality-appropriate asymptotics
- Accurate distribution approximations for better error control and computing speedup
Related publications
- Hong Zhang, Judong Shen and Zheyang Wu (2022) An efficient and accurate approximation to the distribution of quadratic forms of Gaussian variables, Journal of Computational & Graphical Statistics.
- Hong Zhang, Tiejun Tong, John E. Landers and Zheyang Wu (2020) TFisher: A powerful truncation and weighting procedure for combining p-values, Annals of Applied Statistics, 14(1):178--201.
- Hong Zhang and Zheyang Wu (2022) The generalized Fisher’s combination and accurate p-value calculation under dependence, Biometrics.
- Zheyang Wu, Yiming Sun, Shiquan He, Judy Cho, Hongyu Zhao, and Jiashun Jin (2014) Detection Boundary and Higher Criticism for Weak and Sparse Genetic Effects, The Annals of Applied Statistics, 8(2): 824-851.
Genome-wide association studies of human diseases
We design and apply pipelines for analyzing common and rare variants from whole genome sequencing data. Current focuses are aging-related diseases: osteoporosis, neurodegenerative diseases, etc.
To address one of the challenges, the population-level weak effects of rare variants, our strategies are
- Optimal weak-signal detection.
- Optimal weighting by functional annotations of SNPs.
Related publications
- Xiaohui Chen, Hong Zhang, Ming Liu, Hong-Wen Deng, and Zheyang Wu (2022) Simultaneous detection of novel genes and SNPs by adaptive p-value combination, Frontiers in Genetics - Statistical Genetics and Methodology.
- Bradley N. Smith, et. al. (2014) Exome-wide rare variant analysis identifies TUBA4A mutations associated with familial ALS, Neuron. 84(2):324-31.
Predicting health-related outcomes
We study the methods for identifying predictors and design predictive models for forecasting health-related outcomes. For example, in collaboration with Prof. Dalin Tang, we aim to improve the prediction of cardiovascular events (such as carotid atherosclerotic plaque progression) based on 3D in vivo serial magnetic resonance imaging, mechanical testing, and computational fluid-structure interaction models. The purpose is to understand the role of mechanical force in plaque progression and vulnerability. This research has the potential to assist doctors in reducing the risk of actual stroke, reducing the number of unnecessary surgeries and healthcare costs, and improving quality of life.
A similar collaboration is with Prof. Songbai Ji in establishing an efficient neural health monitoring system using mechanical tissue responses, rather than impact forces alone, to assess the risk of concussion and the degree of neurocognitive alteration in football and other sports.
- Zheyang Wu, Chun Yang and Dalin Tang (2011)In Vivo Serial MRI-Based Models and Statistical Methods to Quantify Sensitivity and Specificity of Mechanical Predictors for Carotid Plaque Rupture: Location and Beyond Journal of Biomechanical Engineering 133(6):064503 (5 pages)
- Liang Wang, Zheyang Wu, Chun Yang, Jie Zheng, Richard Bach, David Muccigrosso, Kristen Billiar, Akiko Maehara, Gary S. Mintz, and Dalin Tang (2015) IVUS-Based FSI Models for Human Coronary Plaque Progression Study: Components, Correlation and Predictive Analysis Annals of Biomedical Engineering 43(1):107-121.
- Shaoju Wu, Wei Zhao, Zheyang Wu, Thomas McAllister, Jingwen Hu, Songbai Ji (2022) Approximating Subject-specific Brain Injury Models via Scaling Based on Head-brain Morphological Relationships Biomechanics and Modeling in Mechanobiology
- Wei Zhao, Zheyang Wu, Songbai Ji (2021) Displacement error propagation from em- bedded markers to brain strain Journal of Biomechanical Engineering. 143(10):101001
- Yunliang Cai, Shaoju Wu, Wei Zhao, Zhigang Li, Zheyang Wu, Songbai Ji (2018) Concussion classification via deep learning using whole-brain white matter fiber strains PLoS ONE 13(5):e0197992.