Using Semi-parametric Tests Between Unrelated Individuals Of Quantitative Traits And Candidate Genes Associated Test

Recently,various statistical methods robust to population stratification were proposed for association designs studies, unrelated individuals to identify associations between candidate markers and traits of interest(both qualitative and quantitative).Because although genetic association studies using unrelated individuals may be bias caused by population stratification, alternative methods that are robust to population stratification such as family-based association designs may be less powerful. Here, we proposed a semiparametric test for association(SPTA). SPTA controls for population stratification through a set of genomic markers by first deriving a genetic background variable for each sampled individuals through his/her genotypes at a series of independent markers, and then modeling the relationship between trait values, genotypic scores at the candidate marker, and genetic background variables through a semiparametric model. The simulation results suggest that our procedure has a correct type I error rate in the presence of population stratifacation and is more powerful than statistical association tests for family-based association designs in all the cases considered.