I have been doing a lot of reading on quantitative genetics and the G- (and B-) matrix lately. I get the principle behind performing the analysis now but I am still not sure how to do it. I'd like to be able to try it out with some dummy/old data in R to see how it is done.
Let's pretend I have measured 5 traits of Drosophila (Wing length, lifespan, eye pigmentation, bristle number, and fitness). Could I construct a G-matrix for the population from this data and also get a B-matrix to measure the selection on the traits?
Aims of the post:
1) What information (trait measurements, fitness scores etc.) is needed to construct G- and B- matrices?
2) I would like to find print or online material that would guide me in actually implementing this method. It seems like there is an awful lot of papers saying that G-matrices are great, but no one actually says how to do them in real life...
Answer
To construct the G-matrix you need additive genetic variances and covariances for all traits, so you normally need results from breeding experiments (e.g. phenotypic midparent-offspring data), that you can do parent-offspring regressions on. Don't know any good online sources but see Balding et al. 2007 p. 534ff for some info. I've seen methods that claim that the G-matrix can be estimated directly from phenotypic data from unrelated individuals (e.g. Zintzaras 2011) or from genomic info e.g. SNPs (Vattikuti et al. 2013), but are not familiar with these and do not know how reliable they are.
A clear background on using mixed models to estimate the G-matrix, including examples/tutorials, can be found in Wilson et al (2009).
Or did you have something else/something more specific in mind?
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