A Novel Association Test For Rare Variant With Covariates Based On Algebraic Statistics

The study of genetic variants detected to be associated with human complex diseases has been widely concerned by scholars in many fields.Recently,with the continuous innovation of the next generation sequencing technology,people have been able to detect a large number of rare variants in human genome,which promotes the development of research on the identification of rare variants related to complex human diseases.Considering that rare mutation sites may play an important role in complex diseases,people have been trying to proposing a more effective statistical method for rare variants association test.Due to the extremely low frequency of minor alleles(MAFs)of rare mutations,the performance of traditional statistical methods has been greatly challenged.At present,contingency table analysis is a common test method in correlation analysis using dichotomous traits as response variables.However,the observation numbers of most cells in contingency table are very close or equal to 0s because of the extremely low mutation rates of rare variants,which leads to the common contingency table analysis can not effectively detect the correlation between rare variations and traits.In this paper,we propose a new algebraic statistical method which is based on the independence model of 2×J×K contingency tables and a generalization of Fisher's exact test,and use Markov Chain Monte Carlo method to sample.We consider not only the low mutation rate of rare mutation,but also the covariate factors such as gender and age,which improves the power of correlation test between complex diseases and rare mutations.Simulation results show that our proposed method is better than the existing methods in most case.Even if the correlation between variables is heterogeneous,our method still has good robustness.