Statistical Methods for Integrative Analysis of Multi-Omics Data

Friday July 20, 2pm, Carslaw 829

Hongyuan Cao
University of Missouri, Department of Statistics

Statistical Methods for Integrative Analysis of Multi-Omics Data

Genome-wise complex trait analysis (GCTA) was developed and applied to heritability analyses on complex traits and more recently extended to mental disorders. However, besides the intensive computation, previous literature also limits the scope to univariate phenotype, which ignores mutually informative but partially independent pieces of information provided in other phenotypes. Our goal is to use such auxiliary information to improve power. We show that the proposed method leads to a large power increase, while controlling the false discovery rate, both empirically and theoretically. Extensive simulations demonstrate the advantage of the proposed method over several state-of-the-art methods. We illustration our methods on dataset from a schizophrenia study.

Dr. Cao is an assistant professor of statistics at University of Missouri-Columbia. She got her Ph.D. in statistics from UNC-Chapel Hill in 2010. She published over 20 papers among which several are in top statistics journals, such as Biometrika, Journal of the American Statistical Association and Journal of The Royal Statistical Society, Series B. She serves as an associate editor of Biometrics. Her research interests include high dimensional and large scale statistical analysis, survival analysis, longitudinal data analysis and bioinformatics.