Dear all,
I have an analysis of a data set that I am hoping to get some input on. I have
an experiment where I score a phenotype for a number of iso-genic lines. Two of
the lines are reference iso-lines representative of the two extreme phenotypes
(high and low). Both of these lines are on a common genetic background except
for their second chromosomes. The effect on the phenotype thus resides on the
second chromosomes. This has been formally established. Then I have eight
different iso-lines that were scored for the same phenotype, four of which were
scored similar to the high phenotype and four which were scored similar to the
low phenotype. These eight lines all differ in their genetic background
including their second chromosomes. Next, I have these same eight lines put on
a common background (the same background as the high and low reference lines)
and consequently only differ by their second chromosomes where I expect the
effect on the phenotype resides. I wish to model the interaction o
f genetic background and second chromosomes on the phenotype and at the same
time using the two reference lines with high and low scores as benchmarks for
high and low scoring phenotypes. So, each line is compared to itself with and
without common background, but also to the reference lines.
First, I thought about constructing a glm with line and background as factors
and then running a post hoc analysis to see the direction of effects within and
between lines. This generally works. However, I am thinking this is a messy
approach and I am not sure I am achieving the idea of using the reference lines
as benchmarks for phenotype scores. Any thoughts and ideas are most welcome.
Thanks in advance
Allan Edelsparre
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