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Joint Research Seminar (17 Jul 2015)

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Joint Research Seminar

Genomics Strategic Research Theme
Centre for Genomic Sciences

IPAC: A Flexible Statistical Approach to Integrating Pleitoropy and Annotation for Characterizing Functional Roles of Genetic Variants that Underlie Human Complex Phenotypes

By

Dr Can Yang

Assistant Professor
Department of Mathematics, Hong Kong Baptist University


17 July 2015 (Friday)
3:00 – 4:00pm


Seminar Room 2, G/F
The Hong Kong Jockey Club Building for Interdisciplinary Research

5 Sassoon Road, Pokfulam, Hong Kong

 
Abstract:

Recent international projects, such as the Encyclopedia of DNA Elements (ENCODE) project, the Roadmap project and the Genotype-Tissue Expression (GTEx) project, have generated vast amounts of genomic annotation data measured at the multiple layers, e.g., epigenome and transcriptome. On the other hand, increasing evidence suggests that seemly unrelated phenotypes can share common genetic factors, which is known as pleiotropy. A big challenge in integrative analysis is how to put pleiotropy and annotation into a unified model and automatically select most relevant genomic features from a potentially huge set of genomic features. In this talk, we introduce a flexible statistical approach, named IPAC, to integrating pleiotropy and annotation for characterizing functional roles of genetic variants that underlie human complex phenotypes. IPAC enabled us to automatically perform feature selection from a large number of annotated genomic features and naturally incorporate the selected features for prioritization of genetic risk variants. IPAC not only demonstrated a remarkably computational efficiency (e.g., it took about 2~3 minutes to handle millions of genetic variants and thousands of functional annotations), but also allowed rigorous statistical inference of the model parameters and false discovery rate control in risk variant prioritization. With the IPAC approach, we performed integrative analysis of genome-wide association studies on multiple complex human traits and genome-wide annotation resources, e.g., Roadmap epigenome. The analysis results revealed interesting regulatory patterns of risk variants. These findings undoubtedly deepen our understanding of genetic architectures of complex traits. This is a joint work with Dongjun Chung, Cong Li, Jin Liu, Xiang Wan, Qian Wang, Chao Yang, and Hongyu Zhao.

About the Speaker:

Dr. Yang obtained the PhD degree in electronic and computer engineering from the Hong Kong University of Science and Technology in 2011. He worked as a postdoctoral associate (2011-2012) and an associate research scientist (2012-2014) at Yale University, New Haven, Connecticut. Now he is working as an assistant professor at department of mathematics, Hong Kong Baptist University. Dr. Yang was the winner of the 2012 Hong Kong Young Scientist in Engineering Science, awarded by Hong Kong Institution of Science. His research interests include statistical genomics, bioinformatics, pattern recognition and machine learning. He is particularly interested in developing efficient computational and effective statistical methods to address the challenging problems in the areas of Biostatistics, Bioinformatics and pattern recognition, such as integrative genomic data analysis. His research papers have appeared in American Journal of Human Genetics, PLoS Genetics, IEEE Transactions on Pattern Analysis and Machine Intelligence, Bioinformatics and others. Recently he is working on characterizing the role of risk variants that underlie complex diseases.
 

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