A Bayesian Random Walk Approach for Mapping Dynamic Quantitative Trait - Karacaören, Burak (Journal of Applied Nonlinear Dynamics, 2026)
Analyzing longitudinal observations with an appropriate methodology is important for detecting significant single nucleotide polymorphisms (SNPs) for complex traits in genome wide association studies (GWAS). Here we assumed that genomic signal over time could be traced by a Bayesian random walk- Kalman filter model in state space form to obtain longitudinal residuals. The Kalman filter removes the requirement for collecting the whole data set before making estimations, thus estimates are immediately available. For example, with the functional mapping approach, whole sets of observations must to be collected in advance. This may include months (if not years) of waiting time depending on the nature of the phenotypes and the experimental design. Whereas, because of the longitudinal residuals used in association mapping, biological reasoning can be easily applied given that the signal is genuine. The advantages of our method are demonstrated by both simulated and real datasets.