[R-meta] comparing rma to lm

Juenger, Thomas E tjuenger @ending from @u@tin@utex@@@edu
Thu Sep 13 01:33:35 CEST 2018


I study plant quantitative genetics.  We use statistical analyses to map genes controlling plant traits.  I'm emailing to ask a few simple questions about meta-analyses using metafor in R.

My research program centers on how the effect of inheriting alternative alleles at a genetic locus is altered by environmental variation.  We call this gene-by-environment interaction (GxE).  We can study this phenomenon by growing our mapping populations in different environmental contexts - this can be different greenhouses, growth chambers, treatment applications or field sites in nature.  Ultimately we end up with an effect estimate (the mean difference between individuals carrying alternative alleles - we call this the "additive effect" in quantitative genetics) and standard error for each locus affecting a trait in each environmental context.  Our mapping approach often involves mixed models to test how the effect of alleles changes by condition.  However, we generally do not know the mechanistic driver or cause of the GxE and we imagine it can differ among the many loci influencing a particular trait of interest.

Our most recent experiment grows a mapping population at 10 different field locations.  We'd like to look for drivers of the GxE using a regression approach.  We started running simply lm models to ask how various climate factors affected the additive effect across the 10 experiments - things like latitude, temperature, rainfall.   A friend mentioned that it could be interesting to think about this as a meta-analysis problem, and include our error estimates when looking for covariates/moderators that drive the GxE.  The suggestion seems a good one given we have excellent data about the uncertainty of the effects.  We've just started looking at some basic analyses using rma.

My initial thought is that we would see less significant results when taking into account the uncertainty in the additive effect estimates.  However, we actually see the opposite.  In every case our rma models have more significant covariates/moderators than simple linear models.  I'm surprised by this and am trying to understand why this might be so.

Any thoughts or ideas - I have a feeling I'm missing something simple...

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