Literature DB >> 15611867

Oligogenic combinations associated with breast cancer risk in women under 53 years of age.

Christopher E Aston1, David A Ralph, Dominique P Lalo, Sharmila Manjeshwar, Bobby A Gramling, Daniele C DeFreese, Amy D West, Dannielle E Branam, Linda F Thompson, Melissa A Craft, Debra S Mitchell, Craig D Shimasaki, John J Mulvihill, Eldon R Jupe.   

Abstract

Common, but weakly penetrant, functional polymorphisms probably account for most of the genetic risk for breast cancer in the general population. Current polygenic risk models assume that component genes act independently. To test for potential gene-gene interactions, single nucleotide polymorphisms in ten genes with known or predicted roles in breast carcinogenesis were examined in a case-control study of 631 Caucasian women diagnosed with breast cancer under the age of 53 years and 1,504 controls under the age of 53 years. Association of breast cancer risk with individual genes and with two- and three-gene combinations was analyzed. Sixty-nine oligogenotypes from 37 distinct two- and three-gene combinations met stringent criteria for significance. Significant odds ratios (ORs) covered a 12-fold range: 0.5-5.9. Of the observed ORs, 17% differed significantly from the ORs predicted by a model of independent gene action, suggesting epistasis, i.e., that these genes interact to affect breast cancer risk in a manner not predictable from single gene effects. Exploration of the biological basis for these oligogenic interactions might reveal etiologic or therapeutic insights into breast cancer and other cancers.

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Year:  2004        PMID: 15611867     DOI: 10.1007/s00439-004-1206-7

Source DB:  PubMed          Journal:  Hum Genet        ISSN: 0340-6717            Impact factor:   4.132


  84 in total

1.  Long-term recreational physical activity and breast cancer in the National Health and Nutrition Examination Survey I epidemiologic follow-up study.

Authors:  R A Breslow; R Ballard-Barbash; K Munoz; B I Graubard
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2001-07       Impact factor: 4.254

2.  American Cancer Society guidelines for breast cancer screening: update 2003.

Authors:  Robert A Smith; Debbie Saslow; Kimberly Andrews Sawyer; Wylie Burke; Mary E Costanza; W Phil Evans; Roger S Foster; Edward Hendrick; Harmon J Eyre; Steven Sener
Journal:  CA Cancer J Clin       Date:  2003 May-Jun       Impact factor: 508.702

3.  High breast cancer incidence rates among California teachers: results from the California Teachers Study (United States).

Authors:  Leslie Bernstein; Mark Allen; Hoda Anton-Culver; Dennis Deapen; Pamela L Horn-Ross; David Peel; Richard Pinder; Peggy Reynolds; Jane Sullivan-Halley; Dee West; William Wright; Al Ziogas; Ronald K Ross
Journal:  Cancer Causes Control       Date:  2002-09       Impact factor: 2.506

4.  Cyclin D1 polymorphism and risk for squamous cell carcinoma of the head and neck: a case-control study.

Authors:  Y Zheng; H Shen; E M Sturgis; L E Wang; S A Eicher; S S Strom; M L Frazier; M R Spitz; Q Wei
Journal:  Carcinogenesis       Date:  2001-08       Impact factor: 4.944

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

Authors:  J A Lavigne; K J Helzlsouer; H Y Huang; P T Strickland; D A Bell; O Selmin; M A Watson; S Hoffman; G W Comstock; J D Yager
Journal:  Cancer Res       Date:  1997-12-15       Impact factor: 12.701

6.  Frequency of breast cancer attributable to BRCA1 in a population-based series of American women.

Authors:  B Newman; H Mu; L M Butler; R C Millikan; P G Moorman; M C King
Journal:  JAMA       Date:  1998-03-25       Impact factor: 56.272

7.  BRCA1 mutations and breast cancer in the general population: analyses in women before age 35 years and in women before age 45 years with first-degree family history.

Authors:  K E Malone; J R Daling; J D Thompson; C A O'Brien; L V Francisco; E A Ostrander
Journal:  JAMA       Date:  1998-03-25       Impact factor: 56.272

8.  Cyclin D1 gene polymorphism is associated with an increased risk of urinary bladder cancer.

Authors:  Lizhong Wang; Tomonori Habuchi; Takeshi Takahashi; Kenji Mitsumori; Toshiyuki Kamoto; Yoshiyuki Kakehi; Hideaki Kakinuma; Kazunari Sato; Akira Nakamura; Osamu Ogawa; Tetsuro Kato
Journal:  Carcinogenesis       Date:  2002-02       Impact factor: 4.944

9.  A comprehensive evaluation of family history and breast cancer risk. The Utah Population Database.

Authors:  M L Slattery; R A Kerber
Journal:  JAMA       Date:  1993-10-06       Impact factor: 56.272

Review 10.  Future possibilities in the prevention of breast cancer: role of genetic variation in breast cancer prevention.

Authors:  H S Feigelson; B E Henderson
Journal:  Breast Cancer Res       Date:  2000-05-24       Impact factor: 6.466

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  17 in total

Review 1.  A systematic analysis of disease-associated variants in the 3' regulatory regions of human protein-coding genes II: the importance of mRNA secondary structure in assessing the functionality of 3' UTR variants.

Authors:  Jian-Min Chen; Claude Férec; David N Cooper
Journal:  Hum Genet       Date:  2006-06-29       Impact factor: 4.132

2.  An ensemble learning approach jointly modeling main and interaction effects in genetic association studies.

Authors:  Zhaogong Zhang; Shuanglin Zhang; Man-Yu Wong; Nicholas J Wareham; Qiuying Sha
Journal:  Genet Epidemiol       Date:  2008-05       Impact factor: 2.135

3.  Powerful tests for detecting a gene effect in the presence of possible gene-gene interactions using garrote kernel machines.

Authors:  Arnab Maity; Xihong Lin
Journal:  Biometrics       Date:  2011-04-19       Impact factor: 2.571

4.  A testing framework for identifying susceptibility genes in the presence of epistasis.

Authors:  Joshua Millstein; David V Conti; Frank D Gilliland; W James Gauderman
Journal:  Am J Hum Genet       Date:  2005-11-11       Impact factor: 11.025

5.  Identification of interacting genes in genome-wide association studies using a model-based two-stage approach.

Authors:  Zhaogong Zhang; Adan Niu; Qiuying Sha
Journal:  Ann Hum Genet       Date:  2010-07-15       Impact factor: 1.670

6.  Polymorphisms in genes involved in sex hormone metabolism, estrogen plus progestin hormone therapy use, and risk of postmenopausal breast cancer.

Authors:  Brenda Diergaarde; John D Potter; Eldon R Jupe; Sharmila Manjeshwar; Craig D Shimasaki; Thomas W Pugh; Daniele C Defreese; Bobby A Gramling; Ilonka Evans; Emily White
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2008-07       Impact factor: 4.254

7.  Prediction of Colorectal Cancer Risk Using a Genetic Risk Score: The Korean Cancer Prevention Study-II (KCPS-II).

Authors:  Jaeseong Jo; Chung Mo Nam; Jae Woong Sull; Ji Eun Yun; Sang Yeun Kim; Sun Ju Lee; Yoon Nam Kim; Eun Jung Park; Heejin Kimm; Sun Ha Jee
Journal:  Genomics Inform       Date:  2012-09-28

8.  SNP-set analysis replicates acute lung injury genetic risk factors.

Authors:  Nuala J Meyer; Zhongyin John Daye; Melanie Rushefski; Richard Aplenc; Paul N Lanken; Michael G S Shashaty; Jason D Christie; Rui Feng
Journal:  BMC Med Genet       Date:  2012-06-28       Impact factor: 2.103

9.  Identifying gene interaction enrichment for gene expression data.

Authors:  Jigang Zhang; Jian Li; Hong-Wen Deng
Journal:  PLoS One       Date:  2009-11-30       Impact factor: 3.240

10.  Genome-wide association reveals three SNPs associated with sporadic amyotrophic lateral sclerosis through a two-locus analysis.

Authors:  Qiuying Sha; Zhaogong Zhang; Jennifer C Schymick; Bryan J Traynor; Shuanglin Zhang
Journal:  BMC Med Genet       Date:  2009-09-09       Impact factor: 2.103

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