17347645722

Applications:
Bioinformatics, Biological Sciences, Epidemiology, Genetics, Genomics, Precision Health
Methodologies:
Algorithms, Bayesian Methods, Causal Inference, Classification, Data Quality, Deep Learning, Heterogeneous Data Integration, High-Dimensional Data Analysis, Longitudinal Data Analysis, Missing Data and Imputation, Network Analysis, Predictive Modeling, Sparse Data Analysis, Statistical Analysis and Simulation, Statistical Inference, Statistical Modeling
Relevant Projects:

NIH, NSF, CZI


Xiang Zhou

John G Searle Assistant Professor

Biostatistics, School of Public Health

My research is focused on developing efficient and effective statistical and computational methods for genetic and genomic studies. These studies often involve large-scale and high-dimensional data; examples include genome-wide association studies, epigenome-wide association studies, and various functional genomic sequencing studies such as bulk and single cell RNAseq, bisulfite sequencing, ChIPseq, ATACseq etc. Our method development is often application oriented and specifically targeted for practical applications of these large-scale genetic and genomic studies, thus is not restricted in a particular methodology area. Our previous and current methods include, but are not limited to, Bayesian methods, mixed effects models, factor analysis models, sparse regression models, deep learning algorithms, clustering algorithms, integrative methods, spatial statistics, and efficient computational algorithms. By developing novel analytic methods, I seek to extract important information from these data and to advance our understanding of the genetic basis of phenotypic variation for various human diseases and disease related quantitative traits.

A statistical method recently developed in our group aims to identify tissues that are relevant to diseases or disease related complex traits, through integrating tissue specific omics studies (e.g. ROADMAP project) with genome-wide association studies (GWASs). Heatmap displays the rank of 105 tissues (y-axis) in terms of their relevance for each of the 43 GWAS traits (x-axis) evaluated by our method. Traits are organized by hierarchical clustering. Tissues are organized into ten tissue groups.