Dr. Zhiwu Zhang

Education

Ph.D.     Statistical Genetics, Michigan State University, 1998
Ph.D.     Animal Breeding and Genetics, Northeast Agricultural University, China, 1991
M.S.       Animal Breeding and Genetics, Jilin Agricultural University, China, 1988
B.S.        Animal Science, Jilin Agricultural University, China, 1982

Research

Genetic improvements are environmentally friendly and sustainable solutions to enhance food production. Prior to gene editing, the conventional methods rely on either selection pressure by phenotyping a large number of individuals, or selection accuracy by mapping the responsible genes or using genomic selection. My research interests include: 1) high throughput phenotyping by using hyperspectral images and remote sensing images from satellites and drones; 2) the development of innovative statistical methods and computing tools for gene mapping and genomic selection; 3) integration of high throughput genotyping, statistics, and machine learning to solve problems in plant breeding.

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. Hu, Y., and Z. Zhang*. 2021. GridFree: a python package of imageanalysis for interactive grain counting and measuring. Plant, Physiology.
2. 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.
3. 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.
4. 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.
5. 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.
6. Chen, C.J. and Z. 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 Z. 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 Z. 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 Z. 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, 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.
11. 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.
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 Z. 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 Z. 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 Z. 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 Z. Zhang*. 2014. Enrichment of Statistical Power for Genome-wide Association Studies. BMC Biol 12:73.
16. 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.
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.*, 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.
19. 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.
20. 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.