Faculty Profile

Xiang  Zhou, PhD

Xiang Zhou, PhD

  • Associate Professor of Biostatistics
  • 4166 SPH II
  • 1415 Washington Heights
  • Ann Arbor, Michigan 48109-2029

Xiang Zhou is an Associate Professor of Biostatistics. He received his M.S. in Statistics and PhD 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.

  • PhD, Neurobiology, Duke University, 2010
  • M.S., Statistics, Duke University, 2009
  • B.S., Biology, Peking University, 2004

  • Zheng Li, Wei Zhao, Lulu Shang, Thomas H Mosley, Sharon LR Kardia, Jennifer A. Smith, and Xiang Zhou (2022). METRO: Multi-ancestry transcriptome-wide association studies for powerful gene-trait association detection. American Journal of Human Genetics. 109: 783-801.
  • Sheng Yang, and Xiang Zhou (2022). PGS-Server: Accuracy, robustness, and transferability of polygenic score methods for biobank scale studies. Briefings in Bioinformatics. 22: bbac039.
  • Jialu Hu, Mengjie Chen, and Xiang Zhou (2022). Effective and scalable single-cell data alignment with non-linear canonical correlation analysis. Nucleic Acids Research. 50: e21.
  • Zhongshang Yuan, Lu Liu, Ping Guo, Ran Yan, Fuzhong Xue, and Xiang Zhou (2022). Likelihood based Mendelian randomization analysis with automated instrument selection and horizontal pleiotropic modeling. Science Advances. 8: eabl5744.
  • Jiaqiang Zhu, Shiquan Sun, and Xiang Zhou (2021). SPARK-X: Non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies. Genome Biology. 22: 184.
  • Lu Wang, Boran Gao, Yue Fan, Fuzhong Xue, and Xiang Zhou (2021). Mendelian randomization under the omnigenic architecture. Briefings in Bioinformatics. 22: bbab322.
  • Ying Ma, and Xiang Zhou (2021). Genetic prediction of complex traits with polygenic scores: A statistical review. Trends in Genetics. 37: 995-1011.
  • Lu Liu, Ping Zeng, Fuzhong Xue, Zhongshang Yuan, and Xiang Zhou (2021). Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization. American Journal of Human Genetics. 108: 240-256.
  • Boran Gao, Can Yang, Jin Liu, and Xiang Zhou (2021). Accurate genetic and environmental covariance estimation with composite likelihood in genome-wide association studies. PLOS Genetics. 17: e1009293
  • Zhongshang Yuan, Huanhuan Zhu, Ping Zeng, Sheng Yang, Shiquan Sun, Can Yang, Jin Liu, and Xiang Zhou (2020). Testing and controlling for horizontal pleiotropy with the probabilistic Mendelian randomization in transcriptome-wide association studies. Nature Communications. 11: 3861.
  • Sheng Yang, and Xiang Zhou (2020). Accurate and scalable construction of polygenic scores in large biobank data sets. American Journal of Human Genetics. 106: 679-693.
  • Lulu Shang, Jennifer A. Smith, and Xiang Zhou (2020). Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies. PLOS Genetics. 16: e1008734.
  • Lulu Shang, Jennifer A. Smith, Wei Zhao, Minjung Kho, Stephen T. Turner, Thomas H. Mosley, Sharon L.R. Kardia, and Xiang Zhou (2020). Genetic architecture of gene expression in European and African Americans: An eQTL mapping study in GENOA. American Journal of Human Genetics. 106: 496-512.
  • Ying Ma, Shiquan Sun, Xuequn Shang, Evan T. Keller, Mengjie Chen and Xiang Zhou (2020). Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-Seq studies. Nature Communications. 11: 1585.
  • Shiquan Sun, Jiaqiang Zhu and Xiang Zhou (2020). Statistical analysis of spatial expression pattern for spatially resolved transcriptomic studies. Nature Methods. 17(2):193-200
  • Shiquan Sun, Jiaqiang Zhu, Ying Ma and Xiang Zhou (2019). Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. Genome Biology. 20: 269.

  • Yue Fan, Tauras P. Vilgalys, Shiquan Sun, Qinke Peng, Jenny Tung and Xiang Zhou (2019). IMAGE: High-powered detection of genetic effects on DNA methylation using integrated methylation QTL mapping and allele-specific analysis. Genome Biology. 20: 220.

  • Ping Zeng, Ting Wang, Junnian Zheng and Xiang Zhou (2019). Causal association of type 2 diabetes on amyotrophic lateral sclerosis: New evidence from Mendelian randomization using GWAS summary statistics. BMC Medicine. 17: 225.

  • 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.