Association Tests For Modified Group Stratification Effects Based On Household Data

Exploring associations between complex traits and genetic variants plays an important role in biostatistics and many approaches have been developed in this area.Moreover,a large amount of data were collected from familial and longitudinal studies in recent years.The major challenges to analyze this type of data are population stratification and the correlation within the family.However,most of the current methods are based on data of independent individuals and can not handle the population stratification effect well.In this paper,we present a semi-parametric strategy(called PC-GEE)based on family data to detect associations,in which we use principal component(PC)analysis to control the population stratification and apply the generalized estimation equation(GEE)to handle the correlated structure within the family.We use extensive simulation experiments to evaluate the performance of the PC-GEE method.Simulation results demonstrate that the PCGEE can well control the population stratification and it is the most powerful one in almost all scenarios.Besides,we apply the proposed method to analyze Genetic Analysis Workshop 17 mini-exome data,and the results show that the PC-GEE method also has a good performance.We have made these methods available within an implementation of the software program R.