A semiparametric Bayesian approach for joint modeling of longitudinal trait and event time

Inference on the whole biological system is the recent focus in bioscience. Different biomarkers, although seem to function separately, can actually control some event(s) of interest simultaneously. This fundamental biological principle has motivated the researchers for developing joint models which can explain the biological system efficiently. Because of the advanced biotechnology, huge amount of biological information can be easily obtained in current years. Hence dimension reduction is one of the major issues in current biological research. In this article, we propose a Bayesian semiparametric approach of jointly modeling observed longitudinal trait and event-time data. A sure independence screening procedure based on the distance correlation and a modified version of Bayesian Lasso are used for dimension reduction. Traditional Cox proportional hazards model is used for modeling the event-time. Our proposed model is used for detecting marker genes controlling the biomass and first flowering time of soybean plants. Simulation studies are performed for assessing the practical usefulness of the proposed model. Proposed model can be used for the joint analysis of traits and diseases for humans, animals and plants.