Microarray experiments to estimate heterosis
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Date
2007-12-03T15:06:28Z
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Abstract
The genetic causes for heterosis, i.e., the increased performance of a hybrid
plant compared to the parental mean, may be assessed via microarrays.
This thesis addresses design and analysis issues of cDNA-microarray experiments
with regard to the estimation of heterosis. Standard microarray
designs like the loop design or common reference design are not optimal
when estimating heterosis. An optimality criterion is devised and two approaches
to obtain a suitable design are shown: a rather intuitive one and
an approach using simulated annealing. Data transformations are crucial
before analysing microarray data. However, transformations may conceal
interesting expression patterns. It is shown using a Box-Cox transformation
that significance of a heterotic effect is largely influenced by the transformation
parameter. Transformation of the linear predictor in a generalized
linear model has a similar effect and heterotic effects may—at least
partially—be removed by the transformation. For the estimation of linear
contrasts between genotypes, a linear mixed model for each gene is fitted
to the expression values. To improve variance estimates one may benefit
from other genes’ information. Therefore, an empirical Bayes approach is
developed that is capable of including more than one variance component
in the model.
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Keywords
Microarray, Mixed model, Design, Box-Cox Transformation, Simulated annealing