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Additive genetic variance can be estimated in a number of ways, and is a key concept in evolutionary biology and quantitative genetics.

What are the typical methods (experimental designs) used to estimate this variance? How are they done?

I found papers such as this, which use paternal half sib breeding designs and the fit a model to estimate genetic variance and then multiply the values by 4 to estimate the G-matrix.

Of particular importance is the sire-level covariance matrix because, when multiplied by four, it provides an estimate of G.

I am interested in this because I am working on hemiclones and want to think about the differences, strengths, and weaknesses of classical methods compared to hemiclonal analysis.

*note, I mean to estimate the standing additive genetic variation within a population

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    $\begingroup$ I think for that you have to first establish the additive nature of the set of genes (i.e their independence). $\endgroup$ – WYSIWYG Dec 1 '14 at 14:06
  • $\begingroup$ The question is very much related to how we can estimate heritability and an answer will probably talk about regression of parents on offspring, identical twins, common garden and response to selection. The response to selection experiment intuitively seems to me is the most accurate and straightforward solution in order to measure the additive genetic variance. $\endgroup$ – Remi.b Dec 3 '14 at 3:39

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