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Cited by 18Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic diversity and population structure

Finding Associations in Dense Genetic Maps: A Genetic Algorithm Approach

Clark, Taane G., De Iorio, Maria, Griffiths, Robert C., Farrall, Martin·2005·Human Heredity
DOI 10.1159/000088845
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Abstract

Large-scale association studies hold promise for discovering the genetic basis of common human disease. These studies will consist of a large number of individuals, as well as large number of genetic markers, such as single nucleotide polymorphisms (SNPs). The potential size of the data and the resulting model space require the development of efficient methodology to unravel associations between phenotypes and SNPs in dense genetic maps. Our approach uses a genetic algorithm (GA) to construct logic trees consisting of Boolean expressions involving strings or blocks of SNPs. These blocks or nodes of the logic trees consist of SNPs in high linkage disequilibrium (LD), that is, SNPs that are highly correlated with each other due to evolutionary processes. At each generation of our GA, a population of logic tree models is modified using selection, cross-over and mutation moves. Logic trees are selected for the next generation using a fitness function based on the marginal likelihood in a Bayesian regression frame-work. Mutation and cross-over moves use LD measures to pro pose changes to the trees, and facilitate the movement through the model space. We demonstrate our method and the flexibility of logic tree structure with variable nodal lengths on simulated data from a coalescent model, as well as data from a candidate gene study of quantitative genetic variation.

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  • Ridge regression and LASSO have been considered in genetic association analysis contexts, and other methods have been proposed.

    “Two such techniques, ridge regression74 and the LASSO75,76 have been considered in genetic association analysis contexts, and other methods have also been proposed as well.77–80, 81”

    From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [77]

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Authors
Clark, T. G., De Iorio, M., Griffiths, R. C., & Farrall, M.
Affiliations
  • Imperial College London (GB)
  • University of Oxford (GB)
ORCID
Venue
Human Heredity
Publisher
S. Karger AG
Published
2005
Volume
60
Issue
2
Pages
97-108
ISSN
  • 0001-5652 (ISSN-L)
  • 1423-0062
DOI
10.1159/000088845
OpenAlex
W2094879249
Cited by
18
References
30
Citations by year
2014: 1
FWCI
1.08
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Human Heredity (publishedVersion)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
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 › Genetics › Genetic diversity and population structure
Keywords
genetic association studies, single nucleotide polymorphisms, genetic algorithm, logic tree, Boolean formulas, linkage disequilibrium, Bayesian regression, marginal likelihood, model space exploration, candidate gene association, quantitative genetic variation, coalescent models
MeSH terms
Algorithms, Computer Simulation, Genetic Markers, Humans, Hypertension, Peptidyl-Dipeptidase A, Models, Genetic, Nigeria, Linkage Disequilibrium, Polymorphism, Single Nucleotide

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