Events

Jianping Sun

University of North Carolina at Greensboro
Colloquia

When

Date: Wednesday, September 29, 2021
Time: 4:00 pm - 5:00 pm
Location: Virtual through Zoom
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 genomic data. Her main method-ology focuses are hierarchical modeling, multivariate analysis, composite likelihood, and clinical trials. She also has rich experiences in statistical genetics, especially in rare-variate association study, gene by environment interaction, and analysis of gene expression data.

In genetic studies of complex diseases, multiple measures of related phenotypes are often collected. Jointly analyzing these phenotypes may improve power to detect sets of rare variants affecting multiple traits. In this work, we consider association testing between a set of rare variants and multiple phenotypes in family-based designs. We use a mixed linear model to express the correlations among the phenotypes and between related individuals. Given the many sources of correlations in this situation, deriving an appropriate test statistic is not straightforward. We derive a vector of score statistics, whose joint distribution is approximated using a copula. This allows us to have closed-form expressions for the p-values of several test statistics. A comprehensive simulation study and an application to Genetic Analysis Workshop 18 (GAW18) data highlight the gains associated with joint testing over univariate approaches, especially in the presence of pleiotropy or highly correlated phenotypes.