Statistical Test for Genetic Pleiotropy with Response Data Missing at Random

Genetic pleiotropy, where a single gene influences multiple phenotypic traits, is critical for understanding genetic functions and disease mechanisms. However, many methods for detecting pleiotropy overlook the issue of missing data, common in biological studies. In this paper, we assume the response is missing at random (MaR), which is commonly used in statistics analysis. The inverse probability weighting (IPW) method is used for parameter estimation and an integrated decision procedure is applied for genetic pleiotropy test. Simulation studies demonstrate the method's efficacy, and applications to real data illustrates its practical utility.