Title（问题）：Graph-based interaction association mapping in genome-wide studies
Speaker（报告人）：Professor Yu Zhang
Department of Statistics, The Pennsylvania State University,USA
Time（时间）：2011年5月26日（周四）下昼2:00 — 3:00
Abstract（摘要）：Genome-wide association studies are becoming increasingly important given the advance in high-throughput genotyping and sequencing technologies. In addition to detecting marginal associations of individual markers, it is also of interests to identify multi-marker associations and gene-gene interactions. Mapping from an astronomical number of possible interactions in the genome-scale is a daunting task both computationally and statistically. For high-density markers, it is further needed to account for marker dependence so as to significantly improve the mapping resolution and reduce the analytical complexity. We introduce a graph-based Bayesian model for large-scale interaction association mapping. Com-pared with existing algorithms and our BEAM models, the new method has two major improvements. 1) We construct disease interaction graphs to identify multiple complex gene-gene interactions. Compared with saturated interaction models, graph models offer great flexibility that can significantly increase the power of interaction mapping. The inferred graphs also provide detailed interaction structures between disease associated markers. 2) We design probabilistic models to account for the complex dependence between high-density markers. Without accounting for marker dependence, hundreds of similar interactions due to correlation with the same disease mutations will be detected, which will substantially increase the computational burden but offer little extra information. We will use simulation and real data examples to demonstrate the performance of the new method compared with existing approaches. Our method can be further adapted to genome-wide regulatory data sets measured at the individual-level, for which interaction mapping will be much more interesting and informative.
