Applying $G_{st}$ and $\theta$

If we return to the data that motivated this discussion, these are the results we get from analyses of the $GOT-1$ data from Isotoma petraea (Table 1).

Table 2: Comparison of Wright's $F$-statistics when ignoring sampling effects with Nei's $G_{ST}$ and Weir and Cockerham's $\theta$.

Method	$F_{is}$	$F_{st}$	$F_{it}$
Direct	0.1372	0.2143	0.3221
Nei	0.3092	0.2395	0.4746
Weir & Cockerham	0.5398	0.0387	0.5577

But first a note on how you'll see statistics like this reported in the literature. It can get a little confusing, because of the different symbols that are used. Sometimes you'll see $F_{is}$, $F_{st}$, and $F_{it}$. Sometimes you'll see $f$, $\theta$, and $F$. And it will seem as if they're referring to similar things. That's because they are. They're really just different symbols for the same thing (see Table 3).

Table 3: Equivalent notations often encountered in descriptions of population genetic structure.

Notation
$F_{it}$	$F$
$F_{is}$	$f$
$F_{st}$	$\theta$

Strictly speaking the symbols in Table 3 are the parameters, i.e., values in the population that we try to estimate. We should put hats over any values estimated from data to indicate that they are estimates of the parameters, not the parameters themselves. But we're usually a bit sloppy, and everyone know that we're presenting estimates, so we usually leave off the hats.