Detecting gene–gene interactions that underlie human diseases

Cordell, Heather J

doi:10.1038/nrg2579

Cited by 1,350Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsBioinformatics and Genomic Networks

Detecting gene–gene interactions that underlie human diseases

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Cordell, Heather J·2009·Nature Reviews Genetics

DOI 10.1038/nrg2579PMC PMC2872761

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Abstract

Following the identification of several disease-associated polymorphisms by genome-wide association (GWA) analysis, interest is now focusing on the detection of effects that, owing to their interaction with other genetic or environmental factors, might not be identified by using standard single-locus tests. In addition to increasing the power to detect associations, it is hoped that detecting interactions between loci will allow us to elucidate the biological and biochemical pathways that underpin disease. Here I provide a critical survey of the methods and related software packages currently used to detect the interactions between genetic loci that contribute to human genetic disease. I also discuss the difficulties in determining the biological relevance of statistical interactions.

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Many genetic and epigenetic factors, including multiple rare single nucleotide variants, copy number variations, and other structural variation, likely contribute to common complex diseases.
“It is now widely believed that many genetic and epigenetic factors are likely to contribute to common complex diseases, including multiple rare SNVs (defined by convention as those that have frequencies < 1%), copy number variations (CNVs), and other forms of structural variation. 3–12”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [9]
Interaction effects involving genetic or environmental factors are likely to be important in association studies involving rare variants.
“In addition, just as interaction effects involving either genetic or environmental factors must be considered in standard GWA studies9, they are also likely to be important in association studies involving rare variants.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [9]

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Authors: Cordell, H. J.
Affiliations: Centre for Life (GB)
Corresponding author: Cordell, Heather J
ORCID: Cordell, Heather J
Venue: Nature Reviews Genetics
Publisher: Springer Science and Business Media LLC
Published: 2009-06
Volume: 10
Issue: 6
Pages: 392-404
ISSN: 1471-0056 (ISSN-L)
1471-0064
DOI: 10.1038/nrg2579
PMC: PMC2872761
OpenAlex: W2063575312
Cited by: 1,350
References: 129
Citations by year: 2026: 18 · 2025: 33 · 2024: 51 · 2023: 31 · 2022: 52 · 2021: 73 · 2020: 59 · 2019: 71 · 2018: 70 · 2017: 79 · 2016: 95 · 2015: 88 · 2014: 122 · 2013: 121 · 2012: 142
FWCI: 11.81
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature Reviews Genetics (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Newcastle University ePrints (Newcastle Univesity)
Funders: Wellcome Trust (074524)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: gene-gene interaction, epistasis, genetic association studies, complex diseases, genome-wide association study, biological pathways, statistical software
MeSH terms: Epistasis, Genetic, Genes, Humans, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genome-Wide Association Study

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Detecting gene–gene interactions that underlie human diseases

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