Association Analysis Of Gene Variation Based On Adaptive Combination Of Cauchy Variables

Most of the common diseases in humans are complex genetic diseases.In recent years,scientists have also been working to explore the mechanism behind complex human diseases through genetic analysis methods and control the diseases through preventive methods.Multiple research results have shown,rare variants in genes can explain the heritability of complex human diseases.Due to the extremely low frequency of rare variants in genomic regions,association analysis based on gene sets has become a popular analysis tool.This method is used for analysis the association of rare variants in genomic regions with complex diseases.However,when only a small proportion of variants are causal,combining the association signals of multiple markers within a genomic region may cause noise due to the inclusion of non-causal variants.Besides,the existing set-based methods are sensitive to the genetic architecture.Therefore,we extend the aggregated Cauchy association test(ACAT)and propose two adaptive Cauchy-variable combination methods.These two methods adaptively combine Cauchy variables transformed from the p-value of a single variant test through data.Among them,the first method selects the p value of the optimal number of variant sites according to the data,and can remove variants with larger p-values.The second method adaptively selects the best threshold from a set of thresholds based on data to truncate noises.In addition,when the genotype and phenotype data of the original data are not available,these two methods can use summary statistics obtained from open access database.Extensive simulation studies and Genetic Analysis Workshop 19 real data analysis show that these two methods are more powerful than the other comparative methods when only a small proportion of variants are causal.And they are robust to the varied genetic architecture.