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Integrative statistical approaches for the analysis of whole-genome sequencing data

编辑:wfy 时间:2019年05月05日 访问次数:564

题目:Integrative statistical approaches for the analysis of whole-genome sequencing data

报告人: Iuliana Ionita-Laza (Columbia University)

时间:2019.05.24(周五)下午 4:00

地点:紫金港校区管理学院行政楼14楼1417报告厅

摘要:

Continuous advances in massively parallel sequencing technologies make large whole-genome sequencing studies increasingly feasible. The analysis of such data is challenging due to the large number of rare variants in noncoding regions of the genome, our limited understanding of their functional effects, and the lack of natural units for testing. In this talk I will describe some of our work to address these challenges. In particular, I will discuss unsupervised and semi-supervised approaches to predict cell type/tissue specific regulatory function for variants in noncoding regions. I will also discuss sequence-based association tests for noncoding regions that are able to integrate a large number of functional predictions for improved power to identify the signals in noncoding regions. Throughout the talk I will show applications to several datasets.

欢迎广大师生踊跃参加!

联系人:张立新(stazlx@zju.edu.cn

              浙江大学数据科学研究中心、浙江大学数学科学学院统计学研究所 

报告人简介:

Iuliana Ionita-Laza, PhD, focuses her research in the area of statistical genetics and, in particular, the development of statistical and computational methods for problems arising in human genetics. She is currently working on the optimal design of genetic variation discovery studies using the new sequencing technologies, methods for the analysis of copy-number variation data, methods to test for the effect of rare variants in complex traits, and two-stage designs in genome-wide association studies. On the applied side, Dr. Ionita-Laza is actively involved in a study investigating the effect of copy-number variants in childhood asthma as well as genetic studies on bipolar disorder and lung cancer.