Statistical Approaches to Mapping QTL of Dynamic Traits

Quantitative traits whose phenotypic values change with time or other quantitative factor are called dynamic quantitative traits. Genetic analyses of dynamic traits are usually conducted in one of two ways. One is to treat phenotypic values collected at different time points as repeated measurements of the same trait, which are analyzed in the framework of multivariate theory. Alternatively, a growth curve may be fit to the phenotypes at multiple time points and inference can be made through the parameters of the growth trajectories. The latter has been used in QTL mapping for developmental traits and resulted in an appearance of the functional mapping strategy. Aiming at the disadvantages of functional mapping strategy, we propose to replace the nonlinear and non-additive model biological meaningful by the orthogonal polynomial or B-Spline model to fit dynamic curves with arbitrary shape and analyze arbitrary complicated data, and the constant residual covariance matrix by the alterable one calculated by using auto-correlation function to deal with discrepancies in measurement schedule of phenotype among progenies. A novel RRM mapping strategy was developed for mapping QTL of dynamic traits, which performs higher detecting efficiency than functional mapping, especially for detection of multiple QTL, has been proved by our simulations and data analysis. Finally, a simplified and effective mapping strategy was further discussed by integrating functional mapping and RRM mapping strategies.