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• W2892379449 abstract "Many quantitative traits of fundamental importance to agricultural, evolutionary, and biomedical genetic research can better be described as dynamic processes. Understanding the genetic control of such dynamic or longitudinal traits (such as growth curves, HIV dynamics and drug response) has been a long-standing challenge because of their intrinsic developmental complexity. More recently, a general statistical framework, called functional mapping, has been proposed to map quantitative trait loci (QTLs) that regulate the developmental pattern and process of dynamic traits. Functional mapping has proven to be biologically relevant because it is incorporated by fundamental biological principles to test the genetic and developmental mechanisms for trait changes based on tractable mathematical functions. Original functional mapping was derived within the maximum likelihood (ML) context and implemented with the EM algorithms. Although ML-based functional mapping has many favorable statistical properties for parameter estimation, it has quickly become limited in capacity when a high-dimensional longitudinal problem, as commonly seen in systems biology, is encountered. In my research, I derive a general functional mapping framework for QTL mapping of dynamic traits within the Bayesian paradigm. The Markov Chain Monte Carlo (MCMC) techniques were implemented for functional mapping to estimate biologically and statistically sensible parameters that model the structures of time-dependent genetic effects and covariances. The Bayesian approach is useful to handle difficulties in constructing confidence intervals as well as the identify ability problem, enhancing the statistical inference of functional mapping. By comparing the Bayes factors from separate models, the actual number of QTLs that are involved in the dynamic variation of a trait can be estimated. The model framework was extended to estimate the effects of epistatic interactions between different QTLs on dynamic traits in various developmental stages. I have undertaken extensive simulation studies to investigate the statistical behavior of the new statistical model and used a real example for the F2 mice to validate model utilization. Bayesian-based functional mapping via MCMC algorithms estimates parameters that determine the shape and function of a particular biological process, thus providing a flexible platform to test biologically meaningful hypotheses regarding the complex relationships between gene actions or interactions and developmental processes. ( en )" @default.
• W2892379449 created "2018-09-27" @default.
• W2892379449 creator A5030054597 @default.
• W2892379449 date "2007-01-01" @default.
• W2892379449 modified "2023-09-27" @default.
• W2892379449 title "Bayesian Functional Mapping of Complex Dynamic Traits" @default.
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