  The knockoff-based multiple testing setup of Barber & Candes (2015) for
variable selection in multiple regression where sample size is as large as the
number of explanatory variables is considered. The method of Benjamini &
Hochberg (1995) based on ordinary least squares estimates of the regression
coefficients is adjusted to the setup, transforming it to a valid p-value based
false discovery rate controlling method not relying on any specific correlation
structure of the explanatory variables. Simulations and real data applications
show that our proposed method that is agnostic to {\pi}0, the proportion of
unimportant explanatory variables, and a data-adaptive version of it that uses
an estimate of {\pi}0 are powerful competitors of the false discovery rate
controlling method in Barber & Candes (2015).
