Dataset Information

Candidate gene-environment interactions in breast cancer.

ABSTRACT: Gene-environment interactions have the potential to shed light on biological processes leading to disease, identify individuals for whom risk factors are most relevant, and improve the accuracy of epidemiological risk models. We review the progress that has been made in investigating gene-environment interactions in the field of breast cancer. Although several large-scale analyses have been carried out, only a few significant interactions have been reported. One of these, an interaction between CASP8-rs1045485 and alcohol consumption has been replicated, but others have not, including LSP1- rs3817198 and parity, and 1p11.2-rs11249433 and ever being parous. False positive interactions may arise if the gene and environment are correlated and the causal variant is less frequent than the tag SNP. We conclude that while much progress has been made in this area it is still too soon to tell whether gene-environment interactions will fulfil their promise. Before we can make this assessment we will need to replicate (or refute) the reported interactions, identify the causal variants that underlie tag-SNP associations and validate the next generation of epidemiological risk models.

SUBMITTER: Fletcher O

PROVIDER: S-EPMC4200144 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Candidate gene-environment interactions in breast cancer.

Fletcher Olivia O Dudbridge Frank F

BMC medicine 20141017

Gene-environment interactions have the potential to shed light on biological processes leading to disease, identify individuals for whom risk factors are most relevant, and improve the accuracy of epidemiological risk models. We review the progress that has been made in investigating gene-environment interactions in the field of breast cancer. Although several large-scale analyses have been carried out, only a few significant interactions have been reported. One of these, an interaction between ...[more]

PMID: 25604189

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Project description:BackgroundThe abuse of psychogenic drugs can lead to multiple health-related problems. Genetic and environmental vulnerabilities are factors in the emergence of substance use disorders. Empirical evidence regarding the gene-environment interaction in substance use is mixed. Summaries of the latest findings from a candidate gene approach will be useful for revealing the significance of particular gene contributions. Thus, we aim to identify different gene-environment interactions in patterns of substance use and investigate whether any effects trend notably across different genders and races.MethodsWe reviewed published studies, until March 1, 2022, on substance use for candidate gene-environment interaction. Basic demographics of the included studies, target genes, environmental factors, main findings, patterns of gene-environment interaction, and other relevant information were collected and summarized.ResultsAmong a total of 44 studies, 38 demonstrated at least one significant interaction effect. About 61.5% of studies on the 5-HTTLPR gene, 100% on the MAOA gene, 42.9% on the DRD2 gene, 50% on the DRD4 gene, 50% on the DAT gene, 80% on the CRHR1 gene, 100% on the OPRM1 gene, 100% on the GABRA1 gene, and 50% on the CHRNA gene had a significant gene-environment interaction effect. The diathesis-stress model represents a dominant interaction pattern (89.5%) in the studies with a significant interaction effect; the remaining significant effect on substance use is found in the differential susceptibility model. The social push and swing model were not reported in the included studies.ConclusionThe gene-environment interaction research on substance use behavior is methodologically multidimensional, which causes difficulty in conducting pooled analysis, or stated differently-making it hard to identify single sources of significant influence over maladaptive patterns of drug taking. In decreasing the heterogeneity and facilitating future pooled analysis, researchers must (1) replicate the existing studies with consistent study designs and measures, (2) conduct power calculations to report gene-environment correlations, (3) control for covariates, and (4) generate theory-based hypotheses with factorial based experiments when designing future studies.

Dataset Information

Candidate gene-environment interactions in breast cancer.

Publications

Candidate gene-environment interactions in breast cancer.

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