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New strategies for identifying gene-gene interactions in hypertension

Open access
Moore, Jason H, Williams, Scott M·2002·Annals of Medicine

Abstract

Essential hypertension is a common disease that has a complex multifactorial etiology. For this reason, it is not surprising that studies of the effects of single genes on hypertension have often failed to replicate the original findings. We propose, as a working hypothesis, that the failure to replicate some single locus results is because the impact of single alleles on the risk of hypertension is dependent on genetic variations at other loci (i.e. gene-gene interactions) and on environmental factors (i.e. gene-environment interactions). Thus, studies that do not consider the appropriate genetic and/or environmental contexts may not identify important susceptibility loci. The identification and characterization of such gene-gene and gene-environment interactions have been limited by a lack of powerful statistical methods and/or a lack of large enough sample sizes. Here, we review the general problem of identifying gene-gene interactions and describe several traditional and several newer methods that are being used to assess complex genetic interactions in essential hypertension.

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Metadata

Authors
Moore, J. H., & Williams, S. M.
Venue
Annals of Medicine
Published
2002-01
Volume
34
Issue
2
Pages
88-95
ISSN
  • 0785-3890 (ISSN-L)
  • 1365-2060
OpenAlex
W2104605543
Cited by
393
References
4
FWCI
10.84
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: New strategies for identifying gene-gene interactions in hypertension. https://app.cochat.ai/papers/10.1080/07853890252953473
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Moore, J. H., & Williams, S. M. (2002). New strategies for identifying gene-gene interactions in hypertension. Annals of Medicine. https://doi.org/10.1080/07853890252953473

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.