Statistical Innovation for Biomedical Discovery
Welcome to the Zhao Lab in the Department of Mathematics & Statistics at York University (Toronto, Canada). Our research focuses on developing innovative statistical and computational methods for analyzing high-dimensional biological data, with applications in genomics, epigenomics, and precision health. By connecting methodological innovation with real-world biomedical problems, our research advances both fundamental statistical methodology and impactful health discoveries.
🧠Research Overview
We work at the interface of statistics, genomics, and machine learning, building models that make sense of high-dimensional, noisy, and biologically complex data.
Our current research themes include:
High-dimensional inference in genetic and epigenetic association
Regional DNA methylation QTL mapping using penalized and smooth functional modeling.Wearable omics and dynamic precision health
Integrating wearable sensor data with genomic, proteomic, and metabolomic features for disease phenotyping.
🔬 Recent Highlight: sparseSOMNiBUS
sparseSOMNiBUS (Sparse Smooth Omnibus Model for
Regional DNA Methylation QTL Mapping) is our newly published
method for identifying regional mQTLs using
high-resolution bisulfite sequencing data.
It unifies smooth functional modeling with sparse variable selection,
allowing accurate estimation of SNP–CpG associations across genomic
regions.
Reference
Zhao K, Yang AY, Oualkacha K, Zeng Y, Klein K, Hudson M, Colmegna I, Bernatsky S, Greenwood CMT.
A novel high-dimensional model for identifying regional DNA methylation QTLs.
Biostatistics, Volume 26, Issue 1, 2025, kxaf032.
https://doi.org/10.1093/biostatistics/kxaf032
📈 Model Illustration
sparseSOMNiBUS fits a penalized regression model
balancing smoothness and
sparsity.
The tuning parameter λ is selected via cross-validation using both the
minimum mean deviance (λ_min) and the 1-SE rule
(λ_1SE) to achieve model parsimony and interpretability.
🧑🔬 Join Us
We are always looking for motivated students and postdocs interested
in: - Statistical genetics and epigenomics
- Causal inference and Mendelian randomization
- High-dimensional and functional data analysis
- Integrative modeling of multi-omics and wearable data
If you are excited about developing new statistical methods
for biomedical discovery, feel free to reach out!
📧 kaiqiong@yorku.ca