Kui Zhang

Kui Zhang


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  • Dave House Endowed Professor, Mathematical Sciences
  • PhD, Probability and Statistics, Beijing University
  • BS, Probability and Statistics, Beijing University


Dr. Kui Zhang holds the Dave House Endowed Professorship in Statistics, Data Mining and Data Analytics at Michigan Tech. He obtained his PhD in probability and statistics from Beijing University in 1999 and joined the Department of Mathematical Sciences at Michigan Technological University as a full professor in August of 2015. Dr. Zhang did his postdoctoral training in statistical genetics in the Department of Epidemiology and Public Health at Yale University, School of Medicine, from 1999 to 2001 and in Program of Molecular and Computational Biology of Department of Biological Sciences at University of Southern California from 2001 to 2003. He was a faculty member in the Department of Biostatistics at the University of Alabama at Birmingham from 2003 to 2015. Dr. Zhang has published more than 80 papers in peer reviewed journals and his research is currently supported by NIH.

Dr. Zhang’s methodological research interests focus on the development of novel statistical methods and efficient computational and bioinformatics tools to address scientific problems in biomedical fields, especially in statistical genetics and genomics. His research topics include developing new methods and designing novel algorithms for mapping complex disease genes with population and family data, developing statistical methods for haplotype analysis, developing methods for the analysis of next generation sequencing data, developing statistical methods for rare variant association, and developing methods for the analysis of gene expression including microarray and RNA-Seq data and other types of biological data.

Dr. Zhang’s collaborative research interests are to apply powerful and innovative statistical and computational methods to address problems from genetic studies and other types of studies including but not limited to biomedical research. He has been actively involved in many applied studies during his research career. He has collaborated with scientists in their studies through three ways. First, he has been actively involved as a co-investigator in their grant applications. The duties include setting appropriate study designs, calculating sample sizes and power, adapting available methods and developing new methods for data analysis, and writing up statistical analysis. Second, he has provided the statistical support for their studies using available methods and software packages. Third, he has developed new methods for their studies when there is no available method or available methods cannot provide reliable results. Currently, Dr. Zhang is involved in several genetic and non-genetic studies, and he is actively seeking new collaborations with researchers at Michigan Tech.

Links of Interest

Areas of Expertise

  • Statistical Genetics and Genomics
  • Bioinformatics
  • Biostatistics
  • Applied Statistics

Recent Publications

  • Zhang K, Deng M, Chen T, Waterman MS, Sun F. A dynamic programming algorithm for haplotype block partitioning. Proc Natl Acad Sci U S A. 2002 May 28;99(11):7335-9. PubMed PMID: 12032283; PubMed Central PMCID: PMC124231.
  • Zhang K, Calabrese P, Nordborg M, Sun F. Haplotype block structure and its applications to association studies: power and study designs. Am J Hum Genet. 2002 Dec;71(6):1386-94. PubMed PMID: 12439824; PubMed Central PMCID: PMC378580.
  • Zhang K, Qin ZS, Liu JS, Chen T, Waterman MS, Sun F. Haplotype block partitioning and tag SNP selection using genotype data and their applications to association studies. Genome Res. 2004 May;14(5):908-16. PubMed PMID: 15078859; PubMed Central PMCID: PMC479119.
  • Zhang K, Qin Z, Chen T, Liu JS, Waterman MS, Sun F. HapBlock: haplotype block partitioning and tag SNP selection software using a set of dynamic programming algorithms. Bioinformatics. 2005 Jan 1;21(1):131-4. PubMed PMID: 15333454.
  • Zhang K, Sun F, Zhao H. HAPLORE: a program for haplotype reconstruction in general pedigrees without recombination. Bioinformatics. 2005 Jan 1;21(1):90-103. PubMed PMID: 15231536.
  • Zhang K, Zhao H. A comparison of several methods for haplotype frequency estimation and haplotype reconstruction for tightly linked markers from general pedigrees. Genet Epidemiol. 2006 Jul;30(5):423-37. PubMed PMID: 16685719.
  • Yoo YJ, Tang J, Kaslow RA, Zhang K. Haplotype inference for present-absent genotype data using previously identified haplotypes and haplotype patterns. Bioinformatics. 2007 Sep 15;23(18):2399-406. PubMed PMID: 17644820.
  • Wu J, Chen GB, Zhi D, Liu N, Zhang K. A hidden Markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patterns. Front Genet. 2014;5:267. PubMed PMID: 25161663; PubMed Central PMCID: PMC4129397.
  • Zhang K, Zhao H. Assessing reliability of gene clusters from gene expression data. Funct Integr Genomics. 2000 Nov;1(3):156-73. PubMed PMID: 11793234.
  • Deng M, Zhang K, Mehta S, Chen T, Sun F. Prediction of protein function using protein-protein interaction data. J Comput Biol. 2003;10(6):947-60. PubMed PMID: 14980019.
  • Gao L, Fang Z, Zhang K, Zhi D, Cui X. Length bias correction for RNA-seq data in gene set analyses. Bioinformatics. 2011 Mar 1;27(5):662-9. PubMed PMID: 21252076; PubMed Central PMCID: PMC3042188.
  • Zhi D, Wu J, Liu N, Zhang K. Genotype calling from next-generation sequencing data using haplotype information of reads. Bioinformatics. 2012 Apr 1;28(7):938-46. PubMed PMID: 22285565; PubMed Central PMCID: PMC3493122.
  • Zhang K, Zhi D. Joint haplotype phasing and genotype calling of multiple individuals using haplotype informative reads. Bioinformatics. 2013 Oct 1;29(19):2427-34. PubMed PMID: 23943637; PubMed Central PMCID: PMC3777110.
  • Yan Q, Tiwari HK, Yi N, Gao G, Zhang K, Lin WY, Lou XY, Cui X, Liu N. A Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model. Hum Hered. 2015;79(2):60-8. PubMed PMID: 25791389.
  • Zhi D, Liu N, Zhang K. On the design and analysis of next-generation sequencing genotyping for a cohort with haplotype-informative reads. Methods. 2015 Jun;79-80:41-6. PubMed PMID: 25644447; NIHMSID: NIHMS662048; PubMed Central PMCID: PMC4437872.
  • Ma Y, Zhao J, Wong JS, Ma L, Li W, Fu G, Xu W, Zhang K, Kittles RA, Li Y, Song Q. Accurate inference of local phased ancestry of modern admixed populations. Sci Rep. 2014 Jul 23;4:5800. PubMed PMID: 25052506; PubMed Central PMCID: PMC4107375.
  • Xing D, Qasem SA, Owusu K, Zhang K, Siegal GP, Wei S. Changing prognostic factors in osteosarcoma: analysis of 381 cases from two institutions. Hum Pathol. 2014 Aug;45(8):1688-96. PubMed PMID: 24931466.
  • Daigle GT, Jolly PE, Chamot EA, Ehiri J, Zhang K, Khan E, Sou S. System-level factors as predictors of adherence to clinical appointment schedules in antiretroviral therapy in Cambodia. AIDS Care. 2015;27(7):836-43. PubMed PMID: 25803006.
  • Aissani B, Zhang K, Wiener H. Follow-up to genome-wide linkage and admixture mapping studies implicates components of the extracellular matrix in susceptibility to and size of uterine fibroids. Fertil Steril. 2015 Feb;103(2):528-34.e13. PubMed PMID: 25455875; NIHMSID: NIHMS638476; PubMed Central PMCID: PMC4314358.

Recent Funding

  • 2014/09/10-2017/06/30
    R01 HG008115-02, National Human Genome Research Institute (NHGRI)
    Zhang, Kui (PI)
    Next-Generation Bioinformatics for Next-Generation Sequencing
  • 2006/07/01-2012/06/30
    R01 GM074913-05, National Institute of General Medical Sciences (NIGMS)
    Zhang, Kui (PI)
    Haplotype Analysis in Linkage Disequilibrium Mapping
    To develop association methods based on haplotypes for mapping genes that are responsible for complex human diseases.
    Role: PI
  • 2007/09/27-2008/08/31
    R13 HG004593-01, National Human Genome Research Institute (NHGRI)
    Zhang, Kui (PI)
    Haplotype analysis of population and pedigree data in association studies
    To organize a scientific meeting to discuss the haplotype analysis in association studies.
    Role: PI