Assistant Professor and Endowed Chair (WGC) for Statistical Genomics
Ph.D. 1998, Michigan State University
Today geneticists use more high-throughput technologies in their research. Phenotypic data and genotypic data are even generated on daily basis. The accumulated big data not only provides vigorous opportunities, but also creates challenges for analyses. We have been using statistical genomics-based approaches to face these challenges. Ongoing research include 1) use of general linear model and random model to reduce both false positives and false negatives in gene discovery; 2) genomic approach to predict performance under varied environments; and 3) specific programming to integrate non-CPU (Central Processing Unit) for high performance computation. Our major goal is to develop innovative, cutting-edge statistical methods and computing tools to advance genomic research toward the sustainability of food production and healthcare management.
1) Wang Q, Tian F, Pan Y, Buckler ES, Zhang Z*: A SUPER Powerful Method for Genome Wide Association Study. PLoS One 2014, 9:e107684.
2) Li M, Liu X, Bradbury P, Yu J, Zhang Y-M, Todhunter RJ, Buckler ES, Zhang Z*: Enrichment of statistical power for genome-wide association studies. BMC Biol 2014, 12:73.
3) Yang Y, Wang Q, Chen Q, Liao R, Zhang X, Yang H, Zheng Y, Zhang Z*, Pan Y*: A new genotype imputation method with tolerance to high missing rate and rare variants. PLoS One 2014, 9. 31. Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z*: GAPIT: genome association and prediction integrated tool. Bioinformatics 2012, 28:2397–2399.
4) Zhang Z*, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM, Buckler ES: Mixed linear model approach adapted for genome-wide association studies. Nat Genet 2010, 42:355–360.
5) Zhang Z*, Buckler ES, Casstevens TM, Bradbury PJ: Software engineering the mixed model for genome-wide association studies on large samples. Br Bioinform 2009, 10:664–675.
6) Zhang Z, Todhunter RJ, Buckler ES, Van Vleck LD: Technical note: Use of marker-based relationships with multiple-trait derivative-free restricted maximal likelihood. J Anim Sci 2007, 85:881–885.
7) Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan D, Guo Y, Wang A, Wang L, Deng L, Li W, Lu Y, Weng Q, Liu K, Huang T, Zhou T, Jing Y, Lin Z, Buckler ES, Qian Q, Zhang QF, Li J, Han B: Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 2010, 42:961–967.
8) Edward S Buckler, James B Holland, Peter J Bradbury, Charlotte B Acharya, Patrick J Brown, Chris Browne, Elhan Ersoz, Sherry Flint-Garcia, Arturo Garcia, Jeffrey C Glaubitz, Major M Goodman, Carlos Harjes, Kate Guill, Dallas E Kroon, Sara Larsson, Nicholas K Lepak, Huihui Li, Sharon E Mitchell, Gael Pressoir, Jason A Peiffer, Marco Oropeza Rosas, Torbert R Rocheford, M Cinta Romay, Susan Romero, Stella Salvo, Hector Sanchez Villeda, H Sofia Da Silva, Qi Sun, Feng Tian, Narasimham Upadyayula, Doreen Ware, Heather Yates, Jianming Yu, Zhiwu Zhang, Stephen Kresovich, Michael D McMullen: The genetic architecture of maize flowering time. Science (80- ) 2009, 325:714–718.
9) Zhang Z, Bradbury PJ, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES: TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 2007, 23:2633–2635.
10) RL Quaas, and Z Zhang. Multiple-breed genetic evaluation in the US beef cattle context: methodology. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil, 13-18 August, 2006, Pages 24-12.