Methodological issues in detecting gene-gene interactions in breast cancer susceptibility: a population-based study in Ontario

Briollais, Laurent; Wang, Yuanyuan; Rajendram, Isaac; Onay, Venus; Shi, Ellen; Knight, Julia A.; Özçelik, Hilmi

doi:10.1186/1741-7015-5-22

Cited by 41 publications

(28 citation statements)

References 68 publications

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“…Rare haplotypes with an allele frequency of less than 1% were combined in a group to be analyzed. We applied logistic regression models (LRMs) to perform two-way interaction analyses between two SNPs for each gene (Briollais et al, 2007). For each of four age/gender groups, we screened the most promising pairs of SNPs that yielded P ＜ 0.01 for the interaction likelihood ratio test (i.e.…”

Section: Discussionmentioning

confidence: 99%

Candidate gene polymorphisms for diabetes mellitus, cardiovascular disease and cancer are associated with longevity in Koreans

Park

Choi

et al. 2009

Exp Mol Med

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Abbreviations: ABCC8, ATP-binding cassette, sub-family C (CFTR/ MRP), member 8; ACE, angiotensin I converting enzyme (peptidyldipeptidase A) 1; ADCY2, adenylate cyclase 2 (brain); ALDH2, aldehyde dehydrogenase 2 family (mitochondrial); APOE, apolipoprotein E; CDK10, cyclin-dependent kinase 10; CETP, cholesteryl ester transfer protein, plasma; CI, confidence interval; CR, call rate; CVD, cardiovascular disease; DM, diabetes mellitus; HWE, Hardy-Weinberg equilibrium; IL12RB2, interleukin 12 receptor, beta 2; LD, linkage disequilibrium; LRM, logistic regression model; LYN, V-yes-1 Yamaguchi sarcoma viral related oncogene homolog; MAF, minor allele frequency; MGP, matrix Gla protein; MTTP, microsomal triglyceride transfer protein; OR, odds ratio; PAX4, paired box 4; PCSK1, convertase subtilisin/kexin type 1; PON1, paraoxonase 1; PPP1R14C, protein phosphatase 1, regulatory (inhibitor) subunit 14C; PPP1R1A, protein phosphatase 1, regulatory (inhibitor) subunit 1A; SNP, single nucleotide polymorphism; TNFSF11, tumor necrosis factor (ligand) superfamily, member 11 AbstractLong-lived people may have a unique genetic makeup that makes them more resistant than the general population to prevalent age-related diseases; however, not much is known about genes involved in the longevity. To identify susceptibility variants controlling longevity, we performed a high-throughput candidate gene study using 137 Koreans over 90 yr old and 213 young healthy Koreans. We evaluated 463 informative markers located in 176 candidate genes mostly for diabetes mellitus, cardiovascular disease and cancer under five genetic models. We estimated the odds ratios for each allele, genotype, haplotype, and gene-gene interaction using logistic regression analysis. Associations between 13 genes and longevity were detected at a P-value less than 0.01. Particularly, the rs671 (A) allele of the aldehyde dehydrogenase 2 family (mitochondrial) (ALDH2) gene was associated with longevity only in men (OR 2.11, P = 0.008). Four genes, proprotein convertase subtilisin/kexin type 1 (PCSK1, P = 0.008), epidermal growth factor receptor (EGFR, P = 0.003), paired box 4 (PAX4, P = 0.008), and V-yes-1 Yamaguchi sarcoma viral related oncogene homolog (LYN, P = 0.002) consistently yielded statistical evidence for association with longevity. The findings of the current study may provide a starting point for future studies to unravel genetic factors controlling longevity in Koreans.

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Section: Discussionmentioning

confidence: 99%

Candidate gene polymorphisms for diabetes mellitus, cardiovascular disease and cancer are associated with longevity in Koreans

Park

Choi

et al. 2009

Exp Mol Med

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“…However, the multiple testing problem generated by inclusion of many different genetic variations and/or environmental factors together in the statistical test is a challenge. Luckily, a variety of statistical approaches have been developed to overcome this problem, such as logistic regression and nonparametric multifactor dimensionality reduction methods [Briollais et al, 2007;Heidema et al, 2006]. These methods are characterized by certain strengths and weaknesses, and utilization of combinations of these methods may be a better choice in gene-gene and gene-environment interaction studies [Briollais et al, 2007;Heidema et al, 2006].…”

Section: Gene-gene and Gene-environment Interactions In Cancermentioning

confidence: 99%

Genetic variations as cancer prognostic markers: review and update

Savas

Liu

2009

Hum. Mutat.

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Cancer molecular epidemiology traditionally studies the relationship between genetic variations and cancer risk. However, recent studies have also focused on disease outcomes. The application and design of disease outcome studies have been an extension of disease risk assessment. Yet there are a number of unique considerations important in outcome assessments. We review how genetic approaches used for disease susceptibility, such as candidate gene and genome-wide association study (GWAS) approaches, can be adapted carefully to systematically identify cancer prognostic and predictive alleles. We discuss the interrelatedness among the disease susceptibility, treatment response, and prognosis at the genetic level and focus on how the emerging technologies and approaches can uniquely benefit the genetic prognosis studies.

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“…In logistic regression, unaccounted variance is reduced because of the net result of all terms in the model. Logistic regression requires explicit model specification, repeated re-fitting Modeling of environmental and genetic interactions with AMBROSIA P Chanda et al and is adversely affected by multi-collinearity with SNPs that are in high LD (Briollais et al, 2007). The KWII, in contrast, does not contain information regarding lower order terms, and the AMBROSIA model is assembled with non-overlapping terms.…”

Section: Discussionmentioning

confidence: 99%

Modeling of environmental and genetic interactions with AMBROSIA, an information-theoretic model synthesis method

2011

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To develop a model synthesis method for parsimoniously modeling gene-environmental interactions (GEI) associated with clinical outcomes and phenotypes. The AMBROSIA model synthesis approach utilizes the k-way interaction information (KWII), an information-theoretic metric capable of identifying variable combinations associated with GEI. For model synthesis, AMBROSIA considers relevance of combinations to the phenotype, it precludes entry of combinations with redundant information, and penalizes for unjustifiable complexity; each step is KWII based. The performance and power of AMBROSIA were evaluated with simulations and Genetic Association Workshop 15 (GAW15) data sets of rheumatoid arthritis (RA).AMBROSIA identified parsimonious models in data sets containing multiple interactions with linkage disequilibrium present. For the GAW15 data set containing 9187 singlenucleotide polymorphisms, the parsimonious AMBROSIA model identified nine RA-associated combinations with power 490%. AMBROSIA was compared with multifactor dimensionality reduction across several diverse models and had satisfactory power. Software source code is available from http://www.cse.buffalo.edu/DBGROUP/bioinformatics/ resources.html. AMBROSIA is a promising method for GEI model synthesis.

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Methodological issues in detecting gene-gene interactions in breast cancer susceptibility: a population-based study in Ontario

Cited by 41 publications

References 68 publications

Candidate gene polymorphisms for diabetes mellitus, cardiovascular disease and cancer are associated with longevity in Koreans

Candidate gene polymorphisms for diabetes mellitus, cardiovascular disease and cancer are associated with longevity in Koreans

Genetic variations as cancer prognostic markers: review and update

Modeling of environmental and genetic interactions with AMBROSIA, an information-theoretic model synthesis method

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