Academic journal article Genetics

Mapping Quantitative Trait Loci for Longitudinal Traits in Line Crosses

Academic journal article Genetics

Mapping Quantitative Trait Loci for Longitudinal Traits in Line Crosses

Article excerpt

ABSTRACT

Quantitative traits whose phenotypic values change over time are called longitudinal traits. Genetic analyses of longitudinal traits can be conducted using any of the following approaches: (1) treating the phenotypic values at different time points as repeated measurements of the same trait and analyzing the trait under the repeated measurements framework, (2) treating the phenotypes measured from different time points as different traits and analyzing the traits jointly on the basis of the theory of multivariate analysis, and (3) fitting a growth curve to the phenotypic values across time points and analyzing the fitted parameters of the growth trajectory under the theory of multivariate analysis. The third approach has been used in QTL mapping for longitudinal traits by fitting the data to a logistic growth trajectory. This approach applies only to the particular S-shaped growth process. In practice, a longitudinal trait may show a trajectory of any shape. We demonstrate that one can describe a longitudinal trait with orthogonal polynomials, which are sufficiently general for fitting any shaped curve. We develop a mixed-model methodology for QTL mapping of longitudinal traits and a maximum-likelihood method for parameter estimation and statistical tests. The expectation-maximization (EM) algorithm is applied to search for the maximum-likelihood estimates of parameters. The method is verified with simulated data and demonstrated with experimental data from a pseudobackcross family of Populus (poplar) trees.

(ProQuest Information and Learning: ... denotes formulae omitted.)

THE genetic variance of a quantitative trait is controlled by the segregation of many genes, each with a small effect. Small genetic effects are collectively called the polygenic effects. In the infinitesimal model, only polygenic effects exist. An alternative to this classical definition of genetic variance of a quantitative trait is the theory that the genetic variances of many quantitative traits are actually controlled by the segregation of one or more major genes plus numerous small genetic effects. Using the latter definition, the phenotype of interest still shows a continuous distribution because of the joint contribution from the polygenic effects and random environmental variant. Such a model is called the oligogenic model, which can be tested using segregation analysis (ELSTON and STEWART 1971; MORTON and MACLEAN 1974). In fact, the oligogenic model is the basis for QTL mapping because QTL analysis with the current statistical methods and sample sizes (n< 1000) can detect only genes withmajor ormoderate effects (e.g., HALEY and KNOTT 1992; ZENG 1994; KAO et al. 1999).

When a trait is measured repeatedly over time, it is called a time-dependent trait or longitudinal trait. Three approaches are currently available for analyzing longitudinal traits. The first approach is to treat the phenotypic values at different time points as repeated measurements of the same trait and analyze the trait under the repeated measurements framework. The second approach is to treat the phenotypes measured from different time points as different traits and analyze the traits jointly on the basis of the theory of multivariate analysis (WU et al. 1999). The third approach is to fit a growth curve to the phenotypic values across different time points and analyze the fitted parameters of the growth trajectory under the theory of multivariate analysis in a much reduced dimension (WU et al. 2002). The method of fitting a growth trajectory is considered to be the optimal one because it treats phenotypes measured over time as different traits, but takes into account the correlation generated by the ordered time points.

Abundant molecular markers are now available for QTL mapping. Many QTL mapping studies have focused on traits measured at a single time point (e.g., HALEY and KNOTT 1992; ZENG 1994). Methods for single-trait QTL mapping cannot be directly adopted for longitudinal traits because phenotypes measured at different time points may be controlled by different sets of genes (NUZHDIN and PASYUKOVA 1997; VERHAEGEN et al. …

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