Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer.

@article{Ritchie2001MultifactordimensionalityRR,
  title={Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer.},
  author={Marylyn DeRiggi Ritchie and Lance W. Hahn and Nady Roodi and L. R. Bailey and William D. Dupont and Fritz F Parl and J. H. Moore},
  journal={American journal of human genetics},
  year={2001},
  volume={69 1},
  pages={
          138-47
        }
}
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… 

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References

SHOWING 1-10 OF 52 REFERENCES

A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation.

The combinatorial partitioning method (CPM) is presented that examines multiple genes, each containing multiple variable loci, to identify partitions of multilocus genotypes that predict interindividual variation in quantitative trait levels and finds that many combinations of loci are involved in sets of genotypic partitions that predict triglyceride variability and that the most predictive sets show nonadditivity.

An association between the allele coding for a low activity variant of catechol-O-methyltransferase and the risk for breast cancer.

The findings suggest that the allele encoding low activity COMT may be an important contributor to the postmenopausal development of breast cancer in certain women.

Catechol-O-methyltransferase and breast cancer risk.

The results suggest that catechol-O-methyltransferase genotype is not related to breast cancer risk and RRs for COMT did not differ among African-American and white women and there was not strong modification of RR estimates by menopausal status, body mass index, physical activity or other covariates.

A Complete Enumeration and Classification of Two-Locus Disease Models

The marginal penetrance tables at both loci, the expected joint identity-by-descent (IBD) probabilities, and the correlation between marginal IBD probabilities at the two loci are studied.

Gene deletion of glutathione S-transferase theta: correlation with induced genetic damage and potential role in endogenous mutagenesis.

  • J. WienckeS. PembleB. KettererK. Kelsey
  • Biology
    Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
  • 1995
A close correlation of the DEB sensitivity trait with the novel polymorphism in GSTT1 is reported, which indicates that substrates for this isozyme are encountered commonly in the environment or are endogenous in nature.

Genetics of CYP1A1: coamplification of specific alleles by polymerase chain reaction and association with breast cancer.

  • T. RebbeckE. RosvoldD. DugganJ. ZhangK. Buetow
  • Biology
    Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
  • 1994
A polymerase chain reaction-based assay is introduced to measure allelic variability in exon 7 of the CYP1A1 gene and the Mendelian segregation of this polymorphism is confirmed in four multigeneration Centre d'Etude du Polymorphisme Humain families.

Association of cytochrome P450 1B1 (CYP1B1) polymorphism with steroid receptor status in breast cancer.

The data suggest that the CYP1B1 polymorphisms in exon 3 are not associated with increased breast cancer risk but that the m1 polymorphism may be functionally important for steroid receptor expression in breast cancer of Caucasian patients.

Genetic polymorphisms in catechol-O-methyltransferase, menopausal status, and breast cancer risk.

The association of risk with at least one low-activity COMT(Met) allele was strongest among the heaviest premenopausal women and among the leanest post menopausal women, suggesting that COMT, mediated by body mass index, may be playing differential roles in human breast carcinogenesis, dependent upon menopausal status.

Cytochrome P4501A1 and glutathione S-transferase (M1) genetic polymorphisms and postmenopausal breast cancer risk.

DNA analyses suggested no increased breast cancer risk with the null GSTM1 genotype, although there was some indication that the null genotype was associated with risk among the youngest postmenopausal women, and statistical power to detect an effect may be limited by small numbers, and larger sample sizes would be required.

Influence of exogenous estrogens, proliferative breast disease, and other variables on breast cancer risk

There was no significant association between breast cancer risk and birth control pills, cigarette smoking, or alcohol consumption, and a previous history of benign breast disease does not contraindicate replacement estrogen therapy.
...