Simulation of quantitative trait (QT) values follows from a simple linear model of the additive effect of mutations. A mutation contributes to the change in mean of QT value by a specific amount. For protective variants, mutations decrease QT; for detrimental variants, mutations increase QT. Under this model both analytic and empirical power and sample size calculations are available for quantitative traits analysis methods.
spower LNR -h
Model specific options are documented in details below. You should find the rest of the options otherwise documented.
These options specify the effect of quantitative trait loci in the region of interest, modeled by the shift in mean QT value due to these mutations. The unit of mean shift is the standard deviation. For example
-a 0.1 means a detrimental mutation increase the mean value of the QT by \(0.1\sigma\).
-a is QT shift due to detrimental rare variants. When used by itself, all detrimental rare variants will be assigned a fixed effect size as specified. With the
-A option, they together model “variable effects” with
-a being the minimum effect size and
-A being the maximum effect size for detrimental rare variants. In variable effects model the maximum effect will be assigned to the variant having smallest MAF, and the minimum effect to the one having largest MAF. Values in between are interpolated based on these specified max. and min. values. Similarly
-B are for fixed and variable effects of protective variants, which decrease the mean QT as oppose to increasing it.
-d are effects for common detrimental and protective variants respectively. No variable effects model for common variants is available for this model.