Optimal Utilization of Genomic Information for Dissecting Complex Traits

加州大学河滨分校获得了一笔赠款，用于开始研究新的统计方法的理论开发，以最大限度地利用已建立的基因组数据库，从基因上解剖复杂的特征。要使用的统计方法是贝叶斯方法，它将通过马尔科夫链蒙特卡罗(MCMC)算法实现。要研究的具体领域包括开发最佳统计方法和计算算法，以利用整个基因组的标记定位具有上位性效应(基因座之间的相互作用)的数量性状基因座(QTL)。这些方法与传统的基因组扫描方法明显不同，因为后者是一维搜索，往往导致基因检测能力较低，难以解释结果。这些问题可以通过使用单一的统一的多效应模型来避免。

The University of California at Riverside is awarded a grant to start research into the theoretical development of new statistical methods for optimal utilization of established genomic databases for genetically dissecting complex traits. The statistical method to be used is the Bayesian method, which will be implemented via the Markov chain Monte Carlo (MCMC) algorithm. Specific areas to be studied include development of optimal statistical methods and computational algorithms for mapping quantitative trait loci (QTL) with epistatic effects (interaction between loci) using markers of the entire genome. The methods are clearly in contrast to the conventional methods of genome scanning in that the latter are one-dimensional searches that often result in low power of gene detection and difficulty in interpreting the results. These problems can be avoided by using a single unified multiple effects model.