Adjusting for Treatment Effects in Studies of Quantitative Traits

TL;DR

This paper evaluates methods to correct for treatment effects in studies of quantitative traits, demonstrating how ignoring treatment can reduce statistical power and proposing correction techniques in simulated QTL mapping scenarios.

Contribution

It introduces and compares correction methods for treatment effects in quantitative trait studies, including a novel approach using mean subtraction, and assesses their effectiveness through simulations.

Findings

Correcting for treatment effects improves statistical power in QTL mapping.

Subtracting estimated treatment effects enhances detection accuracy.

Non-normal trait distributions require alternative analysis methods like Kruskal-Wallis.

Abstract

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.