I ran an experiment on amphibians with a fully-crossed breeding design to
identify sources of genetic and environmental variance, and
genotype-by-environment interactions, and now I am in the review process of
a journal to get my work published.

I used a linear mixed-effects model fit with REML to estimate the variance
components of sire, dam, treatment, and their interactions, and used
likelihood ratio tests to evaluate the significances of each component.
Sire, dam and all interaction effects are fit as random; treatment effects
are fixed. I understand, after reading many posts in this forum, that it is
not "correct" to get standard errors around the variance components of
random effects, but I have been asked to do so by reviewers of my
manuscript.
To determine the confidence levels around random effect variance
components, Douglas Bates has previously suggested creating a Markov chain
Monte Carlo sample of the parameters and evaluating the HPDintervals. I am
using the R package lme4, but have used the languageR package (pvals.fnc)
to get these intervals, as the mcmcsamp() and HPDinterval() functions were
not working for lme4 (or lme4a). However, I'm having difficulty
interpreting these intervals, which appear around the MCMC mean.

If anyone could please provide advice on this, including whether this is an
appropriate way to determine the confidence in variance components, and how
best to present these values, I would be forever grateful! If this is not
the appropriate forum to ask this question, I would appreciate being
directed somewhere more suitable.

Cheers,
Angie


For reference, my model code is:

LM=lmer(trait
~treatment+(1|sire)+(1|dam)+(1|sire:dam)+(1|sire:treatment)+(1|dam:treatment)+(1|sire:dam:treatment),data)

The covariance between paternal half-siblings (sire variance) corresponds
to the additive genetic variance. Covariance among maternal half-siblings
(dam variance) reveals the variation due to both additive genetic variance
and maternal environmental effects; thus, the difference between the sire
and dam components can be used to estimate maternal effects. The
sire-by-dam interaction variance estimates non-additive genetic variance
due to non-nuclear gene action and dominance. Interactions between genetic
and environmental factors (GEIs) indicate the extent to which the
phenotypic expression of genotypes depends on the environment.

-- 

*Angela Eads *(PhD candidate)
 School of Animal Biology, The University of Western Australia
Email: 20736466@student.uwa.edu.au*
*

	[[alternative HTML version deleted]]


