Dr. Zhiwu Zhang

Zhiwu Zhang

Associate Professor of Distinguished Professorship for Statistical Genomics (Washington Grain Commission)

105 Johnson Hall
PO Box 646420
Pullman WA 99164-6420 USA
Phone (Office): 509-335-2899
Phone (Lab): 509-335-4551
Fax: 509-335-8674
Email: zhiwu.zhang@wsu.edu

Curriculum Vitae (pdf)

Zhiwu Zhang Lab Website

Google Scholar

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. Huang, M., Liu, X., Y. Zhou, R.M. Summers, and Z. Zhang*. 2018. BLINK: A Package for Next Level of Genome Wide Association Studies with Both Individuals and Markers in the Millions. GigaScience.
  2. Chen, C., and Z. Zhang*. 2018. iPat: Intelligent Prediction and Association Tool for Genomic Research. Bioinformatics 34(11): 1925-1927.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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 StudiesBMC Biol 12:73.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.

News Articles

Images from space could help farmers grow better wheat varieties

By Seth Truscott
College of Agricultural, Human, and Natural Resource Sciences

Scientist solving inbreeding barrier to more sustainable, nutritious hay

By Seth Truscott
College of Agricultural, Human, and Natural Resource Sciences

$3M USDA grant to WSU researchers for next generation variety development and workforce training

PI: Arron Cater
Co-PIs: Kimberly Campbell, Kate Evans,Scot Hulbert, Rebecca McGee, Kevin Murphy, Michael Pumphrey, Sindhuja Sankaran, Deven See, and Zhiwu Zhang

NSF grant aims to use big data to improve crops

By Tina Hilding, communications director, Voiland College of Engineering and Architecture, 509-335-5095, thilding@wsu.edu

Award on developing non-food grade Brassica biofuel feedstock cultivars

Agency: DOE

Investigators: Jack Brown, Jim B. Davis, Aaron Esser, Kurt Schroeder, Fangming Xiao, Zhiwu Zhang

$400K grant to help solve preharvest wheat sprouting

Agency: USDA

Investigators: Arron Carter, Camille Steber, and Zhiwu Zhang

Juggling thousands of balls

By Scott A. Yates, Director of Communication at Washington Grain Commission

‘Cyber breeder’ improves wheat varieties

By Matthew Weaver at Capital Press


PDF Accessibility

If you require an alternative format for any of the content provided on this website, please contact:

Samantha Crow
Administrative Assistant
509-677-3671
samantha.crow@wsu.edu