Xiang Zhou is an John G. Searle Assistant Professor of Biostatistics. He received his M.S. in Statistics and Ph.D. in Neurobiology from Duke University in 2010, and completed a postdoctoral training in Human Genetics at the University of Chicago afterwards. He was a William H. Kruskal Instructor in the Department of Statistics at the University of Chicago before he joined the faculty at the University of Michigan in 2014. His research focuses on developing statistical methods and computational tools for genetic and genomic studies. These studies often involve large-scale and high-dimensional data; examples include genome-wide association studies and various functional genomic sequencing studies such as bulk and single cell RNA sequencing and bisulfite sequencing. By developing novel analytic methods, he seeks 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.
- Ph.D., Neurobiology, Duke University, 2010
- M.S., Statistics, Duke University, 2009
- B.S., Biology, Peking University, 2004
- Mengjie Chen and Xiang Zhou (2018). VIPER: Variability-preserving imputation for accurate gene expression recovery in single cell RNA sequencing studies. Genome Biology. 19:196.
- Xingjie Hao, Ping Zeng, Shujun Zhang and Xiang Zhou (2018). Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies. PLoS Genetics. e1007186.
- Ping Zeng and Xiang Zhou (2017). Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models. Nature Communications. 8: 456.
- Lorin Crawford, Ping Zeng, Sayan Mukherjee and Xiang Zhou (2017). Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits. PLoS Genetics. e1006869.
- Xiang Zhou (2017). A unified framework for variance component estimation with summary statistics in genome-wide association studies. Annals of Applied Statistics. 11(4): 2027-2051.
- Shiquan Sun, Michelle Hood, Laura Scott, Qinke Peng, Sayan Mukherjee, Jenny Tung and Xiang Zhou (2017). Differential expression analysis for RNAseq using Poisson mixed models. Nucleic Acids Research. 45(11): e106.
- Amanda J. Lea, Jenny Tung and Xiang Zhou (2015). A flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data. PLoS Genetics. 11: e1005650.
- Xiang Zhou, Carolyn Cain, Marsha Myrthil, Noah Lewellen, Katelyn Michelini, Emily Davenport, Matthew Stephens, Jonathan Pritchard and Yoav Gilad (2014). Epigenetic modifications are associated with inter-species gene expression variation in primates. Genome Biology. 15:547
- Xiang Zhou and Matthew Stephens (2014). Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nature Methods. 11(4): 407–409.
- Xiang Zhou, Peter Carbonetto and Matthew Stephens (2013). Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genetics. 9(2): e1003264.
- Xiang Zhou and Matthew Stephens (2012). Genome-wide efficient mixed-model analysis for association studies. Nature Genetics. 44: 821–824.