  A population-based study of a quantitative trait, e.g. Blood Pressure(BP) may
be seriously compromised when the trait is subject to the effects of a
treatment. Without appropriate corrections this can lead to considerable
reduction of statistical power. Here we demonestrate this in the scenario of
QTL mapping through Single-Marker Analysis. The data are simulated from a
normal mixtrure for different values of allele frequencies, separation between
normal populations and Linkage Disequilibrium, and several methods of
correction are compared to check which can best compensate for the loss of
power if treatment effects are ignored. In one of these methods, underlying BPs
are approximated by subtracting an estimate of mean value of medicine effect
from obsereved BPs in treated subjects. We domonestrate the efficacy of this
method throughout different choices of parameters. Finally to account for
quantitative traits that follow non-normal distributions, data are simulated
from lognormal mixtures similarly and Kruskal-Wallis test is used to obtain
estimates of powers for different methods of analysis.
