Human Genetics, Genome Institute of Singapore, 60 Biopolis Street, #02-01 Genome, Singapore 138672, Singapore.
BMC Bioinformatics. 2014 Jan 22;15:24. doi: 10.1186/1471-2105-15-24.
The interest of the scientific community in investigating the impact of rare variants on complex traits has stimulated the development of novel statistical methodologies for association studies. The fact that many of the recently proposed methods for association studies suffer from low power to identify a genetic association motivates the incorporation of prior knowledge into statistical tests.
In this article we propose a methodology to incorporate prior information into the region-based score test. Within our framework prior information is used to partition variants within a region into several groups, following which asymptotically independent group statistics are constructed and then combined into a global test statistic. Under the null hypothesis the distribution of our test statistic has lower degrees of freedom compared with those of the region-based score statistic. Theoretical power comparison, population genetics simulations and results from analysis of the GAW17 sequencing data set suggest that under some scenarios our method may perform as well as or outperform the score test and other competing methods.
An approach which uses prior information to improve the power of the region-based score test is proposed. Theoretical power comparison, population genetics simulations and the results of GAW17 data analysis showed that for some scenarios power of our method is on the level with or higher than those of the score test and other methods.
科学界对研究稀有变异对复杂性状影响的兴趣，激发了用于关联研究的新型统计方法的发展。由于最近提出的许多用于关联研究的方法在识别遗传关联方面的功效较低，这促使我们将先验知识纳入统计检验中。
本文提出了一种将先验信息纳入基于区域评分检验的方法。在我们的框架中，先验信息用于将区域内的变异分为几个组，然后构建渐近独立的组统计量，再将它们组合成一个全局检验统计量。在零假设下，与基于区域的评分统计量相比，我们的检验统计量的分布具有较低的自由度。理论功效比较、群体遗传学模拟以及 GAW17 测序数据集分析的结果表明，在某些情况下，我们的方法可能与评分检验和其他竞争方法一样有效或更有效。
提出了一种利用先验信息来提高基于区域评分检验功效的方法。理论功效比较、群体遗传学模拟以及 GAW17 数据分析的结果表明，对于某些情况，我们的方法的功效与评分检验和其他方法相当或更高。
