Genetic pleiotropy occurs when a single gene influences two or more seemingly unrelated phenotypic traits.In order to better understand a gene in multiple ways,it is significant to detect pleiotropy and understand its causes.However,most current statistical methods to discover pleiotropy mainly test the null hypothesis that none of the traits is associated with a variant,which depar-tures from the null to test just one associated trait or k associated traits.Schaid and Tong,et al.(2016) first proposed a sequential testing framework to analyze pleiotropy based on a linear model and a multivariate normal distribution.In this paper,we extend the linear model to Box-Cox transformation model and proposed a new decision method.It improves the efficiency of hypothesis test and controls the Type I error.We then apply the method using economic data to multivari-ate sectoral employments in response to governmental expenditures and provide a quantitative assessment and some insights of different impacts from economic policy.