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  • 统计与管理学院2017年学术报告第49

     

    【主  题】Adaptive False Discovery Rate regression with application in integrative analysis of large-scale genomic data

    【报告人】杨灿, 助教授

    Hong Kong Baptist University

    【时  间】 2017年07月04日(星期二)10:30-11:30

    【地  点】 上海财经大学统计与管理学院大楼1316室

    【摘  要】To address scientific questions, we often design experiments and collect data from experiments. Conventionally, we often focus on the data set at hand and improve analysis results by refining models. The rising of Big Data may change the way of doing research – What if combining our data at hand with other existing information that hides in the Big Data Mountain?

    In this talk, we consider a large-scale testing problem in genomic data analysis. 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, e.g., epigenome and transcriptome. There is great demanding of effective statistical approaches to integrate genomic annotations with the results from genome-wide association studies (GWAS). To explore genetic architecture of human complex phenotypes, rather than only relying on GWAS, we introduce Adaptive False Discovery Rate (AdaFDR) regression to integrate genomic annotations with GWAS. For a given phenotype, not only AdaFDR increase the power of mapping its risk variants, but also adaptively incorporates relevant annotations for prioritization of genetic risk variants, allowing nonlinear effects among these annotations, such as interaction effects between genomic features. The developed algorithm is scalable to genome-wide analysis. Using AdaFDR, we performed integrative analysis of genome-wide association studies on human complex phenotypes and genome-wide annotation resources, e.g., Roadmap epigenome. The analysis results revealed interesting regulatory patterns of risk variants, offering new biological insights on genetic architectures of complex phenotypes.

    【嘉宾简介】Dr. Yang Can obtained his PhD at HKUST and he is now an assistant Professor at HKBU. His research interests include statistical genomics, bioinformatics, and machine learning. He is particularly interested in developing computationally efficient and statistically rigorous methods to address the challenging problems in the areas of statistical genomics, machine learning and etc. He has made contributions in development of statistical theory, methodology and algorithm, as well as scientific discovery. His research papers have appeared in a number of high-impact journals, including American Journal of Human Genetics, Annals of Statistics, Bioinformatics, IEEE Transactions on Pattern Analysis and Machine Intelligence, PLoS Genetics, and Proceedings of the National Academy of Sciences.

    【邀请人】 黄涛