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报告题目: A Novel Statistical Framework for Expression Quantitative Trait Loci Analysis in Multiple Human Tissues
时间: 4月12号上午10点
地点: 伊泰利大厦2号楼301
报告人: Gen Li, Assistant Professor at Columbia University
Abstract: Expression quantitative trait locus (eQTL) analyses identify genetic variants associated with the expression of a gene. Most up-to-date eQTL studies consider the connection between genetic variation and expression in a single tissue. Multi-tissue analyses have the potential to improve findings in a single tissue, and elucidate the genotypic basis of differences between tissues. In this talk, I will introduce a new statistical framework for multi-tissue eQTL analysis. The proposed method builds upon a hierarchical Bayesian model (MT-eQTL), which explicitly captures patterns of variation in the presence or absence of eQTL, as well as the heterogeneity of effect sizes across tissues. We also establish theoretical results for the proposed framework. The method is applied to the Genotype Tissue Expression (GTEx) data, where we obtain interpretable eQTL calling patterns and significant power gain by borrowing strength across tissues. Some challenges and ongoing work will also be discussed.
Dr. Li is Assistant professor in the Department of Biostatistics in Mailman School of Public Health at Columbia University. Dr. Li is devoted to developing new statistical learning methods for analyzing high dimensional biomedical data. He focuses on analyzing complex data with heterogeneous types that are collected from multiple sources. His methodological research interests include dimension reduction, predictive modeling, association analysis, and functional data analysis. He is also interested in genetics and bioinformatics. He is a consortium member of the NIH Common Fund program Genotype-Tissue Expression (GTEx) project, and contributes to the development of statistical methods for expression quantitative trait loci analysis in multiple tissues. He also has research interests in scientific domains including melanoma, microbiome, and chronic obstructive pulmonary disease.
公共卫生学院生物统计学教研室
2017-3-31
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