Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer.
One of the greatest challenges facing human geneticists is the identification and characterization of susceptibility genes for common complex multifactorial human diseases. This challenge is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes and with environmental exposures. We introduce multifactor-dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus information, to improve the identification of polymorphism combinations associated with disease risk. The MDR method is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model-free (i.e., it assumes no particular inheritance model), and is directly applicable to case-control and discordant-sib-pair studies. Using simulated case-control data, we demonstrate that MDR has reasonable power to identify interactions among two or more loci in relatively small samples. When it was applied to a sporadic breast cancer case-control data set, in the absence of any statistically significant independent main effects, MDR identified a statistically significant high-order interaction among four polymorphisms from three different estrogen-metabolism genes. To our knowledge, this is the first report of a four-locus interaction associated with a common complex multifactorial disease.
Ritchie
M D
MD
Program in Human Genetics, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, TN 37232, USA.
Hahn
L W
LW
Roodi
N
N
Bailey
L R
LR
Dupont
W D
WD
Parl
F F
FF
Moore
J H
JH
D000483:Alleles; D001189:Aryl Hydrocarbon Hydroxylases; D001943:Breast Neoplasms / Q000201:enzymology* / Q000235:genetics* / Q000378:metabolism; D016022:Case-Control Studies; D002874:Chromosome Mapping / Q000379:methods* / Q000706:statistics & numerical data; D003198:Computer Simulation; D019363:Cytochrome P-450 CYP1A1 / Q000235:genetics; D065633:Cytochrome P-450 CYP1B1; D003577:Cytochrome P-450 Enzyme System / Q000235:genetics; D004967:Estrogens / Q000378:metabolism*; D005260:Female; D005787:Gene Frequency / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005838:Genotype; D006801:Humans; D016555:Matched-Pair Analysis; D008780:Methyltransferases / Q000235:genetics; D020125:Mutation, Missense / Q000235:genetics; D011110:Polymorphism, Genetic / Q000235:genetics; D015203:Reproducibility of Results; D018401:Sample Size; D018709:Statistics, Nonparametric; D044465:White People / Q000235:genetics
D004967:Estrogens; D003577:Cytochrome P-450 Enzyme System; D001189:Aryl Hydrocarbon Hydroxylases; C585496:CYP1B1 protein, human; D019363:Cytochrome P-450 CYP1A1; D065633:Cytochrome P-450 CYP1B1; D008780:Methyltransferases; C019608:caffeate O-methyltransferase