学术讲座公告---Association Studies and Bioinformatics Strategies for Complex Trait Analysis

  • 发布时间:[2017-09-19] 来源:[学院] 点击量:[754]

报告题目:
Association Studies and Bioinformatics Strategies for Complex Trait Analysis 
报告人:张文超
报告时间:9月21日上午10点
报告地点:303会议室
报告主题简介:
As of today, the predominant thinking in biology research is that multiple genes interact with environmental variables at transcriptome, proteome, metabolome, and other ‘ome’ levels to produce observable phenotypes. Therefore, interactions among genes and between genes and environment contribute significantly to the phenotypic variation of complex traits and may be possible explanations for missing heritability. However, most of the existing bioinformatics method and tools for genotype to phenotype association mapping is centered on the linear mixed models, and only each individual gene’ additive effect is considered. The interactions among genes (GxG) and gene with environment (GxE) are seldom considered, herein, the traditional association tool only can explain a very small portion of the phenotypic variance, less than 40% for analyzing most complex traits. In this report, we described two novel bioinformatics tools, PEPIS and PATOWAS. PEPIS is based on a polygenic linear mixed model, which can explain more than 80% of the phenotypic variance, and is a great progress when compared with other traditional association tools. PATOWAS extend the genome wide association (GWA) into a more general ome-wide, including not only  genome wide associative (WS) transcriptome-wide association(TWS) and metabolome-wide associations (MWA), is really an integrative ome-wide association tool, which can explain close 100% the phenotypic variance for TWS an MWS. The completely considering the GWS, TWS and MWS for the same data can provide us a panoramic bioinformatics insight.        
报告人简介:
张文超,分别于2001,2004年获得吉林大学测控技术与仪器,通信与信息系统学士和硕士学位,2008年获得中科院电子学研究所通信与信息系统博士学位。2007-2010年在Analogix 半导体公司任职算法工程师。2010年到2012年在北卡大学夏洛特分校从事生物信息方面的博士后研究。 2012年至今,在Noble Foundation 和Noble Research Institute 任职生物信息分析师(Bioinformatics Research Analyst)。目前主要从事生物信息和计算生物方面的研究工作,集中在以质谱数据为核心的计算蛋白组学和计算代谢组学,以遗传统计为核心的基因型到表现型的关联分析,并取得了多项研究成果。近年来在分子生物学报(Molecular Biology) 分析化学(Analyst Chemistry),Plos Computational Biology, BMC Bioinformatics等国际期刊上发表10多篇学术论文,根据Google Scholar累计引用目前已达150多次。另外开发3个生物信息学软件,并申请多项专利。


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