Office: Petty 105
Starting year at UNCG: 2018
Office Hours: Face to Face: MW 11:00am-12:00pm or by appointment
Degree(s): Ph.D. in Statistics, Pennsylvania State University (2011)
Member of the Research Group(s): Statistics
- Sun J., Oualkacha K., Greenwood C., Lakhal-Chaieb L. (2019). Multivariate association test for rare variants controlling for cryptic and family relatedness. Canadian Journal of Statistics, 47(1), p.90-107. DOI: 10.1002/cjs.11475
- Sun J., Oualkacha K., Forgetta V., Zheng H.F., Richards J.B., Evans D.S., Orwoll E., and Greenwood C. (2018). Exome-wide rare variant analyses of two bone mineral density phenotypes: the challenges of analyzing rare genetic variation. Scientific Reports, 8, Article number: 220. DOI: 10.1038/s41598-017-18385-9
- Wang C., Sun J., Guillaume B., Ge T., Hibar D.P., Greenwood C., Qiu A., and Alzheimer’s Disease Neuroimaging Initiative (2017). A Set-Based Mixed Effect Model for Gene-Environment Interaction and Its Application to Neuroimaging Phenotypes. Frontiers in Neuroscience, 11:191.
- Sun J., Bhatnagar S., Oualkacha K., Ciampi A., and Greenwood C. (2016). Joint Analysis of Multiple Blood Pressure Phenotypes in GAW19 Data by Using a Multivariate Rare-Variant Association Test. BMC Proceeding, 10(Suppl 7):14.
- Sun J., Oualkacha K., Forgetta V., Zheng H., Richards B., Ciampi A., Greenwood C., and the UK10K Consortium (2016). A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects. European Journal of Human Genetics, 24(9), p.1344-1351.
- Sun J., Zheng Y., and Hsu L. (2013). A Unified Mixed Effects Model for Rare Variant Association in Sequencing Studies. Genetic Epidemiology, 37(4), p.334-344.
- Lindsay B., Yi G., and Sun J. (2011). Issues and Strategies in the Selection of Composite Likelihoods. Statistica Sinica, 21(1), p.71-106.
Dr. Jianping Sun received her Ph.D. in Statistics from Pennsylvania State University in 2011, and then had two postdoc experiences at Fred Hutchinson Cancer Research Center in Seattle and McGill University in Canada, respectively. Her research interests include both statistical methodology and applied research in high-dimensional complex genomic data. Her main methodology focuses are hierarchical modeling, multivariate analysis, and composite likelihood on complex data. She also has rich experiences in statistical genetics, especially in rare-variate association study, gene by environment interaction, next generation sequencing data, and analysis of gene expression and DNA methylation data.