Gene Association Detection Analysis Based On Local Linear Regression Method

The development of next-generation sequencing technology has provided us with great convenience in genetic association studies and many effective analysis methods were proposed continuously.However,population stratification is still a major issue in current genetic association studies.Many existing methods have been developed to remove the bias due to population stratification for common variant association studies,but such methods may be not effective for rare variant,which will lead to power reduction.Therefore,in this paper,we develop a principal component analysis strategy based on local linear regression(PC-LLR)method to eliminate population stratification effect in both rare variant and common variant association studies.This method mainly summarizes the ancestral population information through principal components,and uses local linear regression to eliminate the population stratification effect brought by ancestral population information.Simulation results indicate that the new PC-LLR can eliminate population stratification effect well.It has correct the type I error rates in all cases and higher power in the most cases,while most existing methods have inflated type I error rates at least in some cases.We also demonstrate that the PC-LLR is more effective to eliminate population stratification effect through applying the PC-LLR to the whole-exome sequencing data set from genetic analysis workshop 19(GAW19).