Nianjun Liu

Epidemiology and Biostatistics
(812) 855-7506
PH C111

  • Yale University, PhD in Biostatistics, 2005
  • Yale University, M.Phil. in Biostatistics, 2003
  • Peking University, MS in Applied Mathematics, 1993
  • Peking University, BS in Computational Mathematics, 1990
Professional Positions
  • Aug 2016 - present, Professor (with tenure), Department of Epidemiology and Biostatistics, Indiana University Bloomington
  • Oct 2010 - Jul 2016, Associate Professor (with tenure), Department of Biostatistics, the University of Alabama at Birmingham
  • Jan 2010 - Jul 2016, Assistant Professor (secondary), Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, the University of Alabama at Birmingham
  • Aug 2015 - Jul 2016, Senior Scientist, Global Center for Craniofacial, Oral & Dental Disorders (GC-CODED), the University of Alabama at Birmingham
  • May 2016 - Jul 2016, Associate Professor (joint), Informatics Institute, the University of Alabama at Birmingham
  • Aug 2005  - Sep 2010, Assistant Professor, Department of Biostatistics, the University of Alabama at Birmingham
  • 2016: 2015 Best Paper Award in Statistical Genetics Research by The Science Unbound Foundation, USA
  • 2005: International Genetic Epidemiology Society Travel Award - 2016: 2015 Best Paper Award in Statistical Genetics Research by The Science Unbound Foundation, US
Research Interests

As an applied statistician, my general research interests include the development and use of statistical and computational methods to understand the complexity of biological systems, especially for human health and disease. I engage in interdisciplinary research through my methodological and collaborative studies. My methodological research mainly focuses on the development of novel statistical methods to address scientific questions in biomedicine and public health. I have developed statistical methods and implemented software packages for use in haplotype analysis, population structure inference and admixture mapping, bioinformatics, and genetic association analysis for data gathered from candidate gene, genome-wide association, and next-generation sequencing studies. My collaborative research is focused on the application of innovative and powerful analysis methods for studies in other fields such as genetics, molecular biology, public health, dentistry, and medicine, to decipher biological architecture and unveil disease mechanisms, and provide guidelines for use in clinical practice. My current research focuses on big data, including statistical genetics/genomics and bioinformatics, omics and imaging data, and personalized medicine. My research has been supported by several National Institutes of Health (NIH) and National Science Foundation (NSF) grants, on which I serve as the Principal Investigator (PI) or sub-contract PI.

Selected Publications
N. Liu, S.L. Sawyer, N. Mukherjee, A.J. Pakstis, J.R. Kidd, K.K. Kidd, A.J. Brookes, H. Zhao (2004) Haplotype Block Structures Show Significant Variation among Populations. Genetic Epidemiology 27: 385-400.

N. Liu, R. Bucala, H. Zhao (2009) Modeling Informatively Missing Genotypes in Haplotype Analysis. Communications in Statistics  Theory and Methods 38(18): 3445 - 3460. PMCID: PMC2801447.

N. Yi, N. Liu, D. Zhi, J. Li (2011) Hierarchical generalized linear models for multiple groups of rare and common variants: jointly estimating group and individual-variant effects. PLoS Genet 7(12): e1002382. PMCID: PMC3228815.

M.A. Perera, L.H. Cavallari, N.A. Limdi, E.R. Gamazon, A. Konkashbaev, R. Daneshjou, A. Pluzhnikov, D.C. Crawford, J. Wang, N. Liu et al. (2013) Genetic variants associated with warfarin dose in African-American individuals: a genome-wide association study. Lancet 382(9894):790-6. PMCID: PMC3759580.

W. Lin, N. Yi, X. Lou, D. Zhi, K. Zhang, G. Gao, H.K. Tiwari, N. Liu* (2013) Haplotype Kernel Association Test as a Powerful Method to Identify Chromosomal Regions Harboring Uncommon Causal Variants. Genetic Epidemiology 37(6):560-70. PMCID: PMC4116485.

Q. Yan, D. E. Weeks, J. C. Celedn, H. K. Tiwari, B. Li, X. Wang, W. Lin, X. Lou, G. Gao, W. Chen, and N. Liu* (2015) Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method. Genetics 201(4):1329-39. PMCID: PMC4676518. (Selected by the GENETICS Editors as one of the December 2015 Issue Highlights)

Q. Yan, D. E. Weeks, H. K. Tiwari, N. Yi, K. Zhang, G. Gao, W. Lin, X. Lou, W. Chen, and N. Liu* (2016) Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples. Human Heredity 80(3):126-138. PMCID: PMC4940283.