Zhiwu Zhang


Assistant Professor/Scientist

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 zzlab.net

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

The researchers at the Zhiwu Zhang Lab focuses on the developments of statistical methods and computing tools to advance biomedical researches toward improved healthcare management and sustainable food production.  His research has resulted in the creations of multiple statistical methods and software packages. For example, the enhancement of the Mixed Linear Model (MLM) for Genome-Wide Association Studies (GWAS) into the Compressed MLM (CMLM), which increases statistical power by 5~10%, and reduces computing time from weeks to hours. he has led he implementations of the CMLM method into two commonly used software packages,TASSEL and GAPIT. These software packages have received hundreds of citations and are used by thousands of researchers worldwide.

Over the last ten years, Dr. Zhang has published over 40 papers in leading journals, including Science, Nature Genetics, Plant Cell, and Bioinformatics. He has served as reviewers for multiple journals, such as Nature Genetics, Nature Methods, PNAS, and PLoS Genetics. In 2012, Dr. Zhang received recognition award from the Nature Publication Group for his contribution to referencing papers.

Top 10 Selected Publications (see Google Scholar for complete publications)
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.

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 Genomoe-wide Association Studies. BMC Biol 12:73.

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.

Zhang, Z.*, E. 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.

Zhang, Z.*, E.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.

Zhang, Z., R.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.

Huang, X., T. Sang, Q. Zhao, X. Wei, Q. Feng, Y. Zhao, C. Li, C. Zhu, T. Lu, Z. Zhang, M. Li, D. Fan, Y. Guo, A. Wang, L. Wang, L. Deng, W. Li, Y. Lu, Q. Weng, K. Liu, T. Huang, T. Zhou, Y. Jing, W. Li, L. Zhang, E.S. Buckler, Q. Qian, Q. Zhang, J. Li, and B. Han. 2010. Genome-wide Association Studies of 14 Agronomic Traits in Rice Landraces, Nature Genetics (42) 961–967.

Buckler, E.S., J.B. Holland, P.J. Bradbury, C.B. Acharya, P.J. Brown, C. Browne, E. Ersoz, S. Flint-Garcia, A. Garcia, J.C. Glaubitz, M.M. Goodman, C. Harjes, K. Guill, D.E. Kroon, S. Larsson, N.K. Lepak, H. Li, S.E. Mitchell, G. Pressoir, J.A. Peiffer, M.O. Rosas, T.R. Rocheford, M.C. Romay, S. Romero, S. Salvo, H. Sanchez Villeda, H.S. da Silva, Q. Sun, F. Tian, N. Upadyayula, D. Ware, H. Yates, J. Yu, Z. Zhang, S. Kresovich, and M.D. McMullen. 2009. The Genetic Architecture of Maize Flowering Time. Science, (325) 714-8.

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.

Quaas, R.L., and Z. Zhang. 2006. Multiple-breed Genetic Evaluation in the US Beef Cattle Context: Methodology. Proceedings ofhte 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil, 13-18 August 2006, Pages 24-12.