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Cited by 51Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsBioinformatics and Genomic Networks

Gene-centric gene–gene interaction: a model-based kernel machine method

Open access
Li, Shaoyu, Cui, Yuehua·2012·The Annals of Applied Statistics
DOI 10.1214/12-aoas545
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Abstract

Much of the natural variation for a complex trait can be explained by variation in DNA sequence levels. As part of sequence variation, gene–gene interaction has been ubiquitously observed in nature, where its role in shaping the development of an organism has been broadly recognized. The identification of interactions between genetic factors has been progressively pursued via statistical or machine learning approaches. A large body of currently adopted methods, either parametrically or nonparametrically, predominantly focus on pairwise single marker interaction analysis. As genes are the functional units in living organisms, analysis by focusing on a gene as a system could potentially yield more biologically meaningful results. In this work, we conceptually propose a gene-centric framework for genome-wide gene–gene interaction detection. We treat each gene as a testing unit and derive a model-based kernel machine method for two-dimensional genome-wide scanning of gene–gene interactions. In addition to the biological advantage, our method is statistically appealing because it reduces the number of hypotheses tested in a genome-wide scan. Extensive simulation studies are conducted to evaluate the performance of the method. The utility of the method is further demonstrated with applications to two real data sets. Our method provides a conceptual framework for the identification of gene–gene interactions which could shed novel light on the etiology of complex diseases.

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  • An extension of KMM can incorporate multiple data modalities and their interactions in regression on outcomes with distributions in the exponential family.

    “Another extension of KMM can incorporate multiple data modalities and their interactions in regression on outcomes with distributions in the exponential family (Li and Cui 2012).”

    From Similarity-based multimodal regression (2024) · cites this paper as Li and Cui 2012

    Section 2.2 models two genes and their interaction, and the Discussion says the framework could be extended to other genomic features, such as pathways. The cited paper does not describe incorporating multiple data modalities or extending the regression to outcomes across the exponential family; its stated model uses normally distributed errors. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Authors
Li, S., & Cui, Y.
Affiliations
St. Jude Children's Research Hospital (US)
ORCID
Venue
The Annals of Applied Statistics
Publisher
Institute of Mathematical Statistics
Published
2012-09-01
Volume
6
Issue
3
Pages
1134-1161
ISSN
  • 1932-6157 (ISSN-L)
  • 1941-7330
DOI
10.1214/12-aoas545
OpenAlex
W2072986941
Cited by
51
References
57
Citations by year
2025: 1 · 2024: 1 · 2023: 5 · 2022: 2 · 2021: 5 · 2020: 6 · 2019: 3 · 2018: 6 · 2017: 2 · 2016: 8 · 2015: 2 · 2014: 3 · 2013: 6 · 2012: 1
FWCI
2.32
Type
journal article
Language
en
Open access
Locations
  • The Annals of Applied Statistics (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Project Euclid (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (0707031, 1121650)
  • U.S. Department of Health and Human Services
  • National Institutes of Health
  • National Institute of Child Health and Human Development
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development
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, kernel methods, genome-wide association study, complex traits, complex diseases

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