测序数据罕见遗传变异关联性分析方法评价

Evaluation of Rare Variant Association Testing for Sequencing Data

目的评价常见罕见变异统计分析方法的统计学性质,包括一类错误和检验效能,从而为分析罕见遗传变异资料的统计方法选择提供理论依据。方法通过计算机模拟试验的方法,对SKAT(sequence kernel association test)和FPCA(functional principal component analysis)等方法从一类错误和检验效能等方面进行统计学性质评价。结果SKAT方法在各种检验水准下,第一类错误率均控制在检验水准附近,且其检验效能均高于其它方法,其中SKATO(optimal unified association test)的检验效能最高。结论在罕见遗传变异统计方法中,SKATO是一种效能较高的罕见变异资料的关联分析方法。

Objective To evaluate the statistical properties of the existing statistical methods for rare variants,including type I error and power,and provide the theoretical basis for the practitioners to select the real data analysis methodsproperly.Methods Simulations were conducted to calculate the type I errorrates and the power of sequence kernel association test(SKAT)and functional principal component analysis(FPCA).Results The type one error rate of SKATs were controlled at all significant levels and more powerful than other methods.Conclusion SKATO is one of the powerful methods among the methods for rare variants association study.