Profile

Education

PhD     Statistical Genetics, Michigan State University, 1998
PhD     Animal Breeding and Genetics, Northeast Agricultural University, China, 1991
MS       Animal Breeding and Genetics, Jilin Agricultural University, China, 1988
BS        Animal Science, Jilin Agricultural University, China, 1982

Research

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.

Teaching

CROP SCI 545: Statistical Genomics. This graduate student course mainly cover 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 an elective course for Bioinformatics Certificate. Recommended preparation: MBIOS 578.

Selected Publications

1. Li, L., X. Zheng, J. Wang, X. Zhang, X. He, L. Xiong, S. Song, J. Su, Y. Diao, Z. Yuan, Z. Zhang*, and Z. Hu*. Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids. Nature Communication, 2023.
2. Tang, Z., M. Wang, M. Schirrmann, K-H Dammer, X. Li, R. Brueggeman, S. Sankaran, A.H. Carter, M.O. Pumphrey, Y. Hu*, X. Chen*, and Z. Zhang*. Affordable High Throughput Field Detection of Wheat Stripe Rust Using Deep Learning with Semi-Automated Image Labeling. Computers and Electronics in Agriculture, 2023.
3. Tang, Z., Y. Hu*, and Z. Zhang. ROOSTER: An image labeler and classifier through interactive recurrent annotation. F1000Research, 2023.
4. Hu, Y., S.M. Sjoberg, C.J. Chen, A.L. Hauvermale, C.F. Morris, S.R. Delwiche, A.E. Cannon, C.M. Steber*, and Z. Zhang*. As the number falls, alternatives to the Hagberg–Perten falling number method: A review. Comprehensive Reviews in Food Science and Food Safety, 2022.
5. Wang, J. and Z. Zhang. GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction. Genomics, Proteomics & Bioinformatics, 2021.
6. Hu, Y., and Z. Zhang*. 2021. GridFree: a python package of imageanalysis for interactive grain counting and measuring. Plant, Physiology.
7. Tibbs Cortes, L. , Z. Zhang, and J.Yu. 2021. Status and prospects of genome‐wide association studies in plants. The Plant Genome, DOI: 10.1002/tpg2.20077.
8. Yin, L., H. Zhang, Z. Tang, J. Xu, D. Yin, Z. 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.
9. Tang, Z., A. Parajuli, C. J. Chen, Y. Hu, S. Revolinski, C. Augusto Medina, S. Lin, and Z. 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.
10. Huang, W., P. Zheng, Z. Cui, Z. Li, Y. Gao, H. Yu, Y. Tang, X. Yuan, and Z. Zhang. 2020. MMAP: A Cloud Computing Platform for Mining the Maximum Accuracy of Predicting Phenotypes from Genotypes. Bioinformatics.
11. Chen, C.J. and Z. Zhang. 2020. GRID: A Python Package for Field Plot Phenotyping Using Aerial Images. Remote Sensing.
12. Huang, M., Liu, X., Y. Zhou, R.M. Summers, and Z. Zhang*. 2019. BLINK: A Package for Next Level of Genome Wide Association Studies with Both Individuals and Markers in the Millions. GigaScience.
13. Chen, C., and Z. Zhang*. 2018. iPat: Intelligent Prediction and Association Tool for Genomic Research. Bioinformatics 34(11): 1925-1927.
14. Wang, J., Z. Zhou, Zhe Zhang, H. Li, D. Liu, Q. Zhang, P.J. Bradbury, E.S. Buckler, and Z. Zhang*. 2018. Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits. Heredity.
15. Dong, H., R. Wang, Y. Yuan, J. Anderson, M.O. Pumphrey, Z. 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.
16. Liu, X., M. Huang, B. Fan, E.S. Buckler, and Z. 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.
17. 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 Z. Zhang*. 2016. GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction. The Plant Genome 9(2) 1-9.
18. Zhou, Y., M.I. Vales, A. Wang, and Z. Zhang*. 2016. Systematic bias of correlation coefficient may explain negative accuracy of genomic prediction. Briefings in Bioinformatics.
19. Wang, Q., F. Tian, Y. Pan, E.S. Buckler, and Z. Zhang*. 2014. A SUPER Powerful Method for Genome Wide Association Study. PLoS One 9:e107684.
20. Li, M., X. Liu, P. Bradbury, J. Yu, Y-M Zhang, R.J. Todhunter, E.S. Buckler, and Z. Zhang*. 2014. Enrichment of Statistical Power for Genome-wide Association Studies. BMC Biol 12:73.
21. Yang, Y., Q. Wang, Q. Chen, R. Liao, X. Zhang, H. Yang, Y. Zheng, Z. Zhang*, and Y. Pan*. 2014. A New Genotype Imputation Method with Tolerance to High Missing Rate and Rate Variants. PloS One 9 (6), e101025.
22. 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.
23. Zhang, Z.*, S. 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.
24. Zhang, Z., J. 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.
25. Zhang, Z., P.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.