Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer

Slideshow

Dr. Yuehua Cui

 Dr. Yuehua Cui
Dr. Yuehua Cui
Michigan State University
Oct_29.pdf (221.23 KB)

Kernel-based genome-wide gene-set association test

With the radical breakthrough in biotechnology, high throughput genomic data are routinely generated. These data present unprecedented opportunities in disentangling the genetic secret of complex diseases, while also present daunting challenges in statistical modeling and inference. In the last few years, we have witnessed significant advancement in statistical methodology development for genetic/genomic data analysis, among which kernel-based methods have becoming increasingly popular, owing to the advantage in handling nonlinear relationship. In this talk, I will introduce some background and present a kernel-based association test focusing on a univariate disease trait to assess gene set association. Then I will extend the method to a multi-trait analysis framework to understand potential pleiotropic genetic effect (i.e., one gene affects multiple traits) from a gene-set analysis perspective. Examples of synthetic and real data analyses will be shown to demonstrate the utility of the methods.

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.