I posted notes on the Wahlund effect and *F*-statistics a while ago. I’ve now posted an R Shiny application to illustrate the difference between Nei’s *G _{ST}* and Weir and Cockerham’s

*F*. The application simulates a sample of 25 diploid genotypes from 10 different populations. The genotypes are a multinomial sample from genotype frequencies calculated from Hardy-Weinberg expectations within each population, given the population allele frequency. That’s

_{ST}**. The allele frequencies in each population are sampled from a Beta distribution with a mean of**

*statistical sampling**p*= 0.5 and a variance of

*F*(1-

_{ST}p*p*). That’s

**(or genetic sampling). Just as the individuals we sampled within each population are a**

*evolutionary sampling**sample*of all individuals we could have sampled, the populations we sampled are a

*sample*of all populations we could have sampled.

If you keep the parametric *F _{ST}* the same and just keep hitting “Go”, you’ll see that the genotype counts change every time. That’s the evolutionary sampling. You’ll find a link to the application on the lecture detail page, or you can link directly to the application on shinyapps.io.

As a reminder, if you’re interested in the source code for this or other R Shiny applications I develop for this course, they’ll all be available on Github.