Dataset Information

Detecting pathway-based gene-gene and gene-environment interactions in pancreatic cancer.

ABSTRACT: Data mining and data reduction methods to detect interactions in epidemiologic data are being developed and tested. In these analyses, multifactor dimensionality reduction, focused interaction testing framework, and traditional logistic regression models were used to identify potential interactions with up to three factors. These techniques were used in a population-based case-control study of pancreatic cancer from the San Francisco Bay Area (308 cases, 964 controls). From 7 biochemical pathways, along with tobacco smoking, 26 polymorphisms in 20 genes were included in these analyses. Combinations of genetic markers and cigarette smoking were identified as potential risk factors for pancreatic cancer, including genes in base excision repair (OGG1), nucleotide excision repair (XPD, XPA, XPC), and double-strand break repair (XRCC3). XPD.751, XPD.312, and cigarette smoking were the best single-factor predictors of pancreatic cancer risk, whereas XRCC3.241*smoking and OGG1.326*XPC.PAT were the best two-factor predictors. There was some evidence for a three-factor combination of OGG1.326*XPD.751*smoking, but the covariate-adjusted relative-risk estimates lacked precision. Multifactor dimensionality reduction and focused interaction testing framework showed little concordance, whereas logistic regression allowed for covariate adjustment and model confirmation. Our data suggest that multiple common alleles from DNA repair pathways in combination with cigarette smoking may increase the risk for pancreatic cancer, and that multiple approaches to data screening and analysis are necessary to identify potentially new risk factor combinations.

SUBMITTER: Duell EJ

PROVIDER: S-EPMC4410856 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature

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Detecting pathway-based gene-gene and gene-environment interactions in pancreatic cancer.

Duell Eric J EJ Bracci Paige M PM Moore Jason H JH Burk Robert D RD Kelsey Karl T KT Holly Elizabeth A EA

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20080601 6

Data mining and data reduction methods to detect interactions in epidemiologic data are being developed and tested. In these analyses, multifactor dimensionality reduction, focused interaction testing framework, and traditional logistic regression models were used to identify potential interactions with up to three factors. These techniques were used in a population-based case-control study of pancreatic cancer from the San Francisco Bay Area (308 cases, 964 controls). From 7 biochemical pathway ...[more]

PMID: 18559563

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Project description:BackgroundThe rapid advance in large-scale SNP-chip technologies offers us great opportunities in elucidating the genetic basis of complex diseases. Methods for large-scale interactions analysis have been under development from several sources. Due to several difficult issues (e.g., sparseness of data in high dimensions and low replication or validation rate), development of fast, powerful and robust methods for detecting various forms of gene-gene interactions continues to be a challenging task.Methodology/principal findingsIn this article, we have developed an evolution-based method to search for genome-wide epistasis in a case-control design. From an evolutionary perspective, we view that human diseases originate from ancient mutations and consider that the underlying genetic variants play a role in differentiating human population into the healthy and the diseased. Based on this concept, traditional evolutionary measure, fixation index (Fst) for two unlinked loci, which measures the genetic distance between populations, should be able to reveal the responsible genetic interplays for disease traits. To validate our proposal, we first investigated the theoretical distribution of Fst by using extensive simulations. Then, we explored its power for detecting gene-gene interactions via SNP markers, and compared it with the conventional Pearson Chi-square test, mutual information based test and linkage disequilibrium based test under several disease models. The proposed evolution-based method outperformed these compared methods in dominant and additive models, no matter what the disease allele frequencies were. However, its performance was relatively poor in a recessive model. Finally, we applied the proposed evolution-based method to analysis of a published dataset. Our results showed that the P value of the Fst -based statistic is smaller than those obtained by the LD-based statistic or Poisson regression models.Conclusions/significanceWith rapidly growing large-scale genetic association studies, the proposed evolution-based method can be a promising tool in the identification of epistatic effects.

Dataset Information

Detecting pathway-based gene-gene and gene-environment interactions in pancreatic cancer.

Publications

Detecting pathway-based gene-gene and gene-environment interactions in pancreatic cancer.

Similar Datasets

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