QstFstComp: Compare Qst to Fst
In kjgilbert/QstFstComp: Qst Fst comparisons with unbalanced half-sib designs

Description Usage Arguments Author(s) References Examples

Compares the QST of a single phenotypic trait to the mean FST of series of marker loci. It calculates the distribution of QST - FST under a model assuming neutrality of both the phenotypic trait and the genetic markers from which FST is estimated. Returns the simulated estimates of Qst - Fst under neutrality following the procedure described in Gilbert and Whitlock (2014) and Whitlock & Guillaume (2009). Also returns the simulated estimates of Fst and Qst used to compute the null distribution.

1 2	QstFstComp(fst.dat, qst.dat, numpops, nsim = 1000, AFLP = FALSE, breeding.design, dam.offspring.relatedness = 0.25, output = "concise")

`fst.dat`	A data frame with the first column indicating population of origin and the following columns representing genotypes at loci; see the README https://github.com/kjgilbert/QstFstComp/blob/master/README.md for further description. If using AFLPs, this is a data frame of q-hat values, with pops in columns, loci in rows and the corresponding q-hat variances in the following columns, and `AFLP=TRUE` must be designated.
`qst.dat`	the input table of the breeding data For `breeding.design = "half.sib.sire"`: qst.dat should have four columns in this order: population, sire, dam, and the trait value of the individual. Each population, sire, and dam should have unique names or numbers. For `breeding.design = "half.sib.dam"`: qst.dat should have three columns in this order: population, dam, and the trait value of the individual. Each population and dam should have unique names or numbers.
`numpops`	number of populations in the sample
`nsim`	number of simulation replicates to perform to create the null distributions and bootstraps @param AFLP whether or not to use AFLP data @param breeding.design the breeding design used when collecting the trait data There are two options for breeding design: "half.sib.sire" is a half sib design with dam nested within sire nested within population which works for either balanced or unbalanced sampling designs "half.sib.dam" is a half sib design with dam nested within population which works for either balanced or unbalanced sampling designs @param dam.offspring.relatedness relatedness between offspring in the dam model, default is 1/4, i.e. half-sib @param output whether to output full, concise, or without writing out vector of resampled Q-F values, see details below @return Returns either a concise list of a subset of results or a full list with all possible results. Both output options write the vector of Qst-Fst values to a text file unless "_nowrite" is appended to the option, e.g. "concise_nowrite" returns only the concise output without writing output to a text file. Concise list returns (default) the calculated difference between Qst and Fst with 95% critical values, one- and two- tailed p-values for this difference, the Fst value estimated from the data with 95% confidence intervals, the Qst value estimated from the data with 95% confidence intervals, and the additive genetic variance for the trait with 95% confidence intervals Full list returns the calculated difference between Qst and Fst with 95% critical values, one- and two- tailed p-values for this difference, a list of all Qst-Fst values for plotting the distribution of Qst-Fst, the Fst value estimated from the data with 95% confidence intervals, the resampled Fst as calculated from bootstrapping across simulations, with standard deviation and 95% confidence intervals, a list of all resampled Fst values for plotting the distribution of Fst, the Qst value estimated from the data with 95% confidence intervals, the resampled neutral Qst as calculated from bootstrapping across simulations, with standard deviation and 95% critical values, a list of all resampled Qst values for plotting the distribution of the neutral Qst, the ANOVA table for the calculated means squared, n coefficients, and degrees of freedom, the additive genetic variance for the trait with 95% confidence intervals, and the coefficient of additive genetic variance for the trait with 95% confidence intervals Be mindful of the fact that with a small number of loci, bootstrapped confidence intervals of Fst can be less accurate.

Kimberly J Gilbert & Michael C Whitlock

Gilbert KJ and MC Whitlock (2015) Qst Fst comparisons with unbalanced half-sib designs. Molecular Ecology Resources, 15(2), 262-267.

Whitlock MC and F Guillaume (2009) Testing for spatially divergent selection: Comparing Qst to Fst. Genetics, 183:1055-1063.

## using balanced half-sib sire trait data and biallelic marker data 
data(hssire) # trait data
data(biallelic) # marker data
QstFstComp(biallelic, hssire, numpops=15, nsim=100, breeding.design="half.sib.sire", output="full")

data(hsdam)
data(aflp)
QstFstComp(aflp, hsdam, numpops=15, nsim=100, AFLP=TRUE, breeding.design="half.sib.dam", output="concise")