报告题目：Allele-specific, network-based genome-wide association studies 

　　报告人：Weixiong Zhang, Ph.D.Professor 

　　      Washington University in St. Louis, MO, USA 

　　时间：2016年4月15日（星期五）上午9:30—11:00 

　　地点：成都生物研究所综合楼二楼会议室 

　　报告摘要：

　　Hundreds of genetic markers have shown associations with various complex diseases, yet the ‘‘missing heritability’’ remains alarmingly elusive. Combinatorial interactions may account for a substantial portion of this missing heritability, but their discoveries have been impeded by computational complexity and genetic heterogeneity. We present BlocBuster, a novel systems-level approach that efficiently constructs genome-wide, allele-specific networks that accurately segregate homogenous combinations of genetic factors, tests the associations of these combinations with the given phenotype, and rigorously validates the results using a series of unbiased validation methods. BlocBuster employs a correlation measure that is customized for single nucleotide polymorphisms and returns a multi-faceted collection of values that captures genetic heterogeneity. We applied BlocBuster to analyze psoriasis, discovering a combinatorial pattern with an odds ratio of 3.64 and Bonferroni-corrected p-value of 5.01610^216. This pattern was replicated in independent data, reflecting robustness of the method. In addition to improving prediction of disease susceptibility and broadening our understanding of the pathogenesis underlying psoriasis, these results demonstrate BlocBuster’s potential for discovering combinatorial genetic associations within heterogeneous genome-wide data, thereby transcending the limiting ‘‘small effects’’ produced by individual markers examined in isolation.

　　该报告关于一个新的GWAS 方法以及在人类复杂性疾病方面的应用，内容虽然与植物无关，但该方法可以用到植物性状的研究中。

　　报告人简介：

　　Dr. Weixiong Zhang is a full professor of Computer Science and of Genetics at Washington University in St. Louis, MO, USA. He received his BS and MS in Computer Engineering from Tsinghua University and his MS and PhD in Computer Science from UCLA. His main research interests include computational biology and artificial intelligence. He has published more than 140 research papers in journals and peer-reviewed conferences in these areas. His research has been supported by NIH, NSF, USDA, DARPA, the Alzheimer’s Association and Monsanto Company. In recent years, he has been focusing on developing methods and tools for analyzing large scale biological data for transcriptome modeling, analyzing noncoding small RNA gene regulation, understanding genome-wide genotype-phenotype associations, as well as their applications to complex human diseases, such as Alzheimer’s disease and psoriasis, and plant stress tolerance in rice, cassava, soybean and Arabidopsis. He is currently a Deputy Editor of PLoS Computational Biology and an Associate Editor of Artificial Intelligence. More information of his research can be found at http://www.cse.wustl.edu/~zhang.

　　请相关领域的研究人员和学生积极参加！

　　中国科学院环境与应用微生物重点实验室

　　环境微生物四川省重点实验室

　　2016. 4. 13