Skip to main content Skip to navigation
Washington State University Molecular Plant Sciences

Zhiwu Zhang


Associate Professor, Department of Crop and Soil Sciences
Ph.D. 1998, Michigan State University
Lab website
Curriculum Vitae (pdf)


Today biological scientists are facing more analytical and computational challenges than ever before. These challenges not only come from big data but also the complexity of overarching spatial, spectral, and temporal dimensions. We have been tackling these challenges through statistics, statistical learning, machine learning, and artificial intelligence. Ongoing research includes 1) Gene mapping in genome-wide association studies, 2) Molecular breeding through genomic selection or genomic prediction, and 3) Multiple dimensional complex data analyses using artificial intelligence such as deep learning in the framework of neural networks. Our major goal is to develop innovative, cutting-edge statistical methods and computing tools to advance genomic and phenomic research toward the sustainability of food production and healthcare management.


CROP SCI 545: Statistical Genomics. This graduate student course mainly covers GWAS (Genome Wide Association Study) and GS (Genomic Selection). Typically offered in spring semester. Cross listed as ANIM SCI 545, BIOLOGY 545, HORT 545, and PL P 545. This is one of the elective courses for Bioinformatics Certificate.  Prerequisites: Statistical inference, General linear model, mixed linear model, Bayesian theory, computer programming in R, genetics, or permission by instructor.

Selected Publications

  1. Hu, Y., and  Zhang*. 2021. GridFree: a python package of imageanalysis for interactive grain counting and measuringPlant, Physiology.
  2. Tibbs Cortes, L.,  Zhang, and J.Yu. 2021. Status and prospects of genome‐wide association studies in plants.The Plant Genome, DOI: 10.1002/tpg2.20077.
  3. Yin, L., H. Zhang, Z. Tang, J. Xu, D. Yin,  Zhang, X. Yuan, M. Zhu, S. Zhao, X. Li, and X. Liu. 2021. rMVP: A Memory-efficient, Visualization-enhanced, and Parallel-accelerated tool for Genome-Wide Association Study. Genomics, Phenomics & Bioinformatics.
  4. Tang, Z., A. Parajuli, C. J. Chen, Y. Hu, S. Revolinski, C. Augusto Medina, S. Lin, and  Zhang*. 2021. Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation.Scientific Reports, doi: 10.1038/s41598-021-82797-x.
  5. Huang, W., P. Zheng, Z. Cui, Z. Li, Y. Gao, H. Yu, Y. Tang, X. Yuan, and  Zhang.2020. MMAP: A Cloud Computing Platform for Mining the Maximum Accuracy of Predicting Phenotypes from Genotypes. Bioinformatics.
  6. Chen, C.J. and  Zhang.2020. GRID: A Python Package for Field Plot Phenotyping Using Aerial Images. Remote Sensing.
  7. Huang, M., Liu, X., Y. Zhou, R.M. Summers, and  Zhang*.2019. BLINK: A Package for Next Level of Genome Wide Association Studies with Both Individuals and Markers in the Millions. GigaScience.
  8. Chen, C., and  Zhang*. 2018. iPat: Intelligent Prediction and Association Tool for Genomic Research.Bioinformatics 34(11): 1925-1927.
  9. Wang, J., Z. Zhou, Zhe Zhang, H. Li, D. Liu, Q. Zhang, P.J. Bradbury, E.S. Buckler, and  Zhang*. 2018. Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits. Heredity.
  10. Dong, H., R. Wang, Y. Yuan, J. Anderson, M.O. Pumphrey,  Zhang*,and J. Chen*. 2018. Evaluation of the Potential for Genomic Selection to Improve Spring Wheat Resistance to Fusarium Head Blight in the Pacific Northwest. Frontiers in Plant Science.
  11. Liu, X., M. Huang, B. Fan, E.S. Buckler, and  Zhang*. 2016. Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies.PLoS Genetics, DOI: 10.1371/journal.pgen.1005767.
  12. Tang, Y., X. Liu, J. Wang, M. Li, Q. Wang, F. Tian, Z. Su, Y. Pan, D. Liu, A.E. Lipka, E.S. Buckler, and  Zhang*.2016. GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction. The Plant Genome 9(2) 1-9.
  13. Zhou, Y., M.I. Vales, A. Wang, and  Zhang*.2016. Systematic bias of correlation coefficient may explain negative accuracy of genomic prediction. Briefings in Bioinformatics.
  14. Wang, Q., F. Tian, Y. Pan, E.S. Buckler, and Zhang*.2014. A SUPER Powerful Method for Genome Wide Association Study. PLoS One 9:e107684.
  15. Li, M., X. Liu, P. Bradbury, J. Yu, Y-M Zhang, R.J. Todhunter, E.S. Buckler, and Zhang*.2014. Enrichment of Statistical Power for Genome-wide Association StudiesBMC Biol 12:73.
  16. Yang, Y., Q. Wang, Q. Chen, R. Liao, X. Zhang, H. Yang, Y. Zheng, Zhang*,and Y. Pan*. 2014. A New Genotype Imputation Method with Tolerance to High Missing Rate and Rate Variants. PloS One 9 (6), e101025.
  17. Zhang, Z.*,Ersoz, C.Q. Lai, R.J. Todhunter, H.K. Tiwari, M.A. Gore, P.J. Bradbury, J. Yu, D.K. Arnett, J.M. Ordovas, and E.S. Buckler. 2010. Mixed Linear Model Approach Adapted for Genome-Wide Association Studies. Nature Genetics 42: 355-360.
  18. Zhang, Z.*,  Buckler, T.M. Casstevens, and P.J. Bradbury. 2009. Software Engineering the Mixed Model for Genome-wide Association Studies on Large Samples.Briefings in Bioinformatics 10(6):664-675.
  19. Zhang, Z.,  Todhunter, E.S. Buckler, and L.D. Van Vleck*. 2007. Technical Note: Use of Marker-based Relationships with Multiple-trait Derivative-free Restricted Maximal Likelihood.J Anim Sci, 85: 881-885.
  20. Zhang, Z.,J. Bradbury*, D.E. Kroon, T.M. Casstevens, Y. Ram-doss, and E.S. Buckler. 2007. TASSEL: Software for Association Mapping of Complex Traits in Diverse Samples.Bioinformatics 20: 2839-2840.