Improving the power of sib pair quantitative trait loci detection by phenotype winsorization.
OBJECTIVE
In sib pair studies, quantitative trait loci (QTL) identification may be adversely affected by non-normality in the phenotypic distribution, particularly when subjects falling in the tails of the distribution bias the trait mean or variance. We evaluated the robustness and power of reducing the influence of subjects with extreme phenotypic values by Winsorizing non-normal distributions in three versions of Haseman-Elston regression-based methods of QTL linkage analysis.


METHODS
Data were simulated for normal and non-normal distributions. Phenotypic values that correspond to cutoff points at the omega and 1 - omega percentiles of the distribution were identified, and phenotypic values falling outside the boundaries of the omega and 1 - omega cutoff points were replaced by the omega and 1 - omega values, respectively. One million replications were performed for the three tests of linkage for Winsorized and non-Winsorized data.


RESULTS
Winsorization reduced conservatism in the tails of the empirical type I error rate for the vast majority of the tests of linkage, increased the power of QTL detection in non-normal data and created a slight negative bias in symmetrical phenotypic distributions.


CONCLUSIONS
Winsorizing can improve the power of QTL detection with certain non-normal distributions but can also introduce bias into the estimate of the QTL effect.
Fernández
José R
JR
Department of Nutrition Sciences, Division of Physiology and Metabolism, The University of Alabama at Birmingham, 35294-3360, USA. fernandj@shrp.uab.edu
Etzel
Carol
C
Beasley
T Mark
TM
Shete
Sanjay
S
Amos
Christopher I
CI
Allison
David B
DB
D020022:Genetic Predisposition to Disease; D005821:Genetic Techniques; D008957:Models, Genetic; D010641:Phenotype; D019655:Quantitative Trait, Heritable; D013223:Statistics as Topic