title: Bayesian B-spline mapping for dynamic quantitative traits.
abstract: Owing to their ability and flexibility to describe individual gene expression at different time points, random regression (RR) analyses have become a popular procedure for the genetic analysis of dynamic traits whose phenotypes are collected over time. Specifically, when modelling the dynamic patterns of gene expressions in the RR framework, B-splines have been proved successful as an alternative to orthogonal polynomials. In the so-called Bayesian B-spline quantitative trait locus (QTL) mapping, B-splines are used to characterize the patterns of QTL effects and individual-specific time-dependent environmental errors over time, and the Bayesian shrinkage estimation method is employed to estimate model parameters. Extensive simulations demonstrate that (1) in terms of statistical power, Bayesian B-spline mapping outperforms the interval mapping based on the maximum likelihood; (2) for the simulated dataset with complicated growth curve simulated by B-splines, Legendre polynomial-based Bayesian mapping is not capable of identifying the designed QTLs accurately, even when higher-order Legendre polynomials are considered and (3) for the simulated dataset using Legendre polynomials, the Bayesian B-spline mapping can find the same QTLs as those identified by Legendre polynomial analysis. All simulation results support the necessity and flexibility of B-spline in Bayesian mapping of dynamic traits. The proposed method is also applied to a real dataset, where QTLs controlling the growth trajectory of stem diameters in Populus are located.
authors.affiliation: Department of Gastroenterology, Tumor Hospital of Harbin Medical University, Harbin 150086, People's Republic of China.
authors.forename: Jun
authors.initials: J
authors.lastname: Xing
authors.forename: Jiahan
authors.initials: J
authors.lastname: Li
authors.forename: Runqing
authors.initials: R
authors.lastname: Yang
authors.forename: Xiaojing
authors.initials: X
authors.lastname: Zhou
authors.forename: Shizhong
authors.initials: S
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D010641:Phenotype; D018547:Plant Stems / Q000235:genetics / Q000254:growth & development; D032107:Populus / Q000254:growth & development; D040641:Quantitative Trait Loci / Q000235:genetics*; D012044:Regression Analysis; D015203:Reproducibility of Results