A new statistical screening approach for finding pharmacokinetics-related genes in genome-wide studies.
Biomedical researchers usually test the null hypothesis that there is no difference of the population mean of pharmacokinetics (PK) parameters between genotypes by the Kruskal-Wallis test. Although a monotone increasing pattern with a number of alleles is expected for PK-related genes, the Kruskal-Wallis test does not consider a monotonic response pattern. For detecting such patterns in clinical and toxicological trials, a maximum contrast method has been proposed. We show how that method can be used with pharmacogenomics data to a develop test of association. Further, using simulation studies, we compare the power of the modified maximum contrast method to those of the maximum contrast method and the Kruskal-Wallis test. On the basis of the results of those studies, we suggest rules of thumb for which statistics to use in a given situation. An application of all three methods to an actual genome-wide pharmacogenomics study illustrates the practical relevance of our discussion.
Sato
Y
Y
Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
Laird
N M
NM
Nagashima
K
K
Kato
R
R
Hamano
T
T
Yafune
A
A
Kaniwa
N
N
Saito
Y
Y
Sugiyama
E
E
Kim
S-R
SR
Furuse
J
J
Ishii
H
H
Ueno
H
H
Okusaka
T
T
Saijo
N
N
Sawada
J-I
JI
Yoshida
T
T
D003198:Computer Simulation; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D010597:Pharmacogenetics / Q000706:statistics & numerical data*; D010599:Pharmacokinetics; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide