Score-based test in high-dimensional quantile regression for longitudinal data with application to a glomerular filtration rate data

Motivated by a genome-wide association study on the glomerular filtration rate, we develop a new robust test for longitudinal data to detect the effects of biomarkers in high-dimensional quantile regression, in the presence of prespecified control variables. The test is based on the sum of score-type statistics deduced from conditional quantile regression. The test statistic is constructed in a working-independent manner, but the calibration reflects the intrinsic within-subject correlation. Therefore, the test takes advantage of the feature of longitudinal data and provides more information than those based on only one measurement for each subject. Asymptotic properties of the proposed test statistic are established under both the null and local alternative hypotheses. Simulation studies show that the proposed test can control the family-wise error rate well, while providing competitive power. The proposed method is applied to the motivating glomerular filtration rate data to test the overall significance of a large number of candidate single-nucleotide polymorphisms that are possibly associated with the Type 1 diabetes, conditioning on the patients' demographics.