Dear MCMCglmm users,

I just started using MCMCglmm to calculate the heritability of
plant-associated bacterial populations, and am working through Jarrod’s
JStat paper, the vignettes, and manual. However, I am still uncertain if I
fit the right model to my data, and if this is a valid way to calculate
heritability in my case. I would love to hear your suggestions.

Here is more info about my data: from one farm, we collected 91 soil
samples from 27 different genotypes of corn planted in a randomized
complete block design. There are 3-5 soil samples per corn genotype, and
each soil sample was taken from different plant replicates of the same corn
genotype. My data table contains the counts for each kind of bacterium per
soil sample. For example, in one soil sample from one genotype of corn,
there are about 420 different kinds of bacteria; one kind of bacterium may
be present in one soil sample in several copies (a positive count), or has
a zero count either because it is not there, or because it is present in
very low abundance. The overall data table has many zeros as not all
bacteria are present in every sample. I don’t have any pedigree info for
the soil samples.

I am interested to know if the corn genotypes control their associating
bacterial populations, so I would like to use the counts of bacteria from
each corn genotype as a phenotype to calculate the heritability of the
corn-associated bacteria. I am using the following prior similar to
prior1.1.2 in the WAMWiki tutorial, as a weaker prior did not help with the
high autocorrelations in corn genotypes:

p.var = var(Data$sum) # sum is the total number of bacteria per soil sample

prior1 = list(G = list(G1 = list(V = matrix(p.var * 0.05), nu = 1)), R =
list(V = matrix(p.var * 0.95), nu = 1))

And I fit the following model to each kind of bacterium:

Model=MCMCglmm(bacteria_count~1, random=~corn genotype, prior=prior1,
data=Data,nitt=500000,thin=1000,burnin=15000) # bacteria_count is a vector
containing the counts of that kind of bacterium in each sample

The model runs smoothly; I’ve plotted the posterior distribution for the
variance components and ran the Heidelberg stationarity test to check for
convergence, and they all looked fine.

My first question is if my model makes sense, and if it answers how much
variation in the bacteria counts is explained by corn genotypes. Also, the
corns were planted in a randomized block design at different
coordinations/rows/subrows in the same farm, but I did not include location
in my model. Can I treat the samples as they were from the same site, and
ignore the detailed location info?

I also wonder if I can calculate heritability like this without any
pedigree info. I searched the literature, but I rarely see microbiologists
do it this way. For example, in Peiffer et al. (2013), the heritability of
the plant-associated bacterial populations was calculated using constrained
PCoA analyses.

Another concern is whether I should fit a repeated-measure model on my
data. Although 3-5 soil samples were collected from the same corn genotype,
they were not from the same plant. This is not the same scenario as the
gryphon example WAMWiki tutorial.

Also, because many bacteria counts are zero, do I need a zero-inflated
Poisson model?

I appreciate any comment with this analysis, and please forgive me if I am
asking some very basic or obvious questions.

Thanks a lot,

Zhao
-- 
Zhao Jin
Ph.D. Candidate
Field of Microbiology, Cornell University

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