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  1. Article ; Online: Saddlepoint approximations to score test statistics in logistic regression for analyzing genome-wide association studies.

    Johnsen, Pål V / Bakke, Øyvind / Bjørnland, Thea / DeWan, Andrew Thomas / Langaas, Mette

    Statistics in medicine

    2023  Volume 42, Issue 16, Page(s) 2746–2759

    Abstract: We investigate saddlepoint approximations of tail probabilities of the score test statistic in logistic regression for genome-wide association studies. The inaccuracy in the normal approximation of the score test statistic increases with increasing ... ...

    Abstract We investigate saddlepoint approximations of tail probabilities of the score test statistic in logistic regression for genome-wide association studies. The inaccuracy in the normal approximation of the score test statistic increases with increasing imbalance in the response and with decreasing minor allele counts. Applying saddlepoint approximation methods greatly improve the accuracy, even far out in the tails of the distribution. By using exact results for a simple logistic regression model, as well as simulations for models with nuisance parameters, we compare double saddlepoint methods for computing two-sided
    MeSH term(s) Logistic Models ; Genome-Wide Association Study/methods ; Phenotype ; Probability ; Polymorphism, Single Nucleotide
    Language English
    Publishing date 2023-04-24
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.9746
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: A new method for exploring gene-gene and gene-environment interactions in GWAS with tree ensemble methods and SHAP values.

    Johnsen, Pål V / Riemer-Sørensen, Signe / DeWan, Andrew Thomas / Cahill, Megan E / Langaas, Mette

    BMC bioinformatics

    2021  Volume 22, Issue 1, Page(s) 230

    Abstract: Background: The identification of gene-gene and gene-environment interactions in genome-wide association studies is challenging due to the unknown nature of the interactions and the overwhelmingly large number of possible combinations. Parametric ... ...

    Abstract Background: The identification of gene-gene and gene-environment interactions in genome-wide association studies is challenging due to the unknown nature of the interactions and the overwhelmingly large number of possible combinations. Parametric regression models are suitable to look for prespecified interactions. Nonparametric models such as tree ensemble models, with the ability to detect any unspecified interaction, have previously been difficult to interpret. However, with the development of methods for model explainability, it is now possible to interpret tree ensemble models efficiently and with a strong theoretical basis.
    Results: We propose a tree ensemble- and SHAP-based method for identifying as well as interpreting potential gene-gene and gene-environment interactions on large-scale biobank data. A set of independent cross-validation runs are used to implicitly investigate the whole genome. We apply and evaluate the method using data from the UK Biobank with obesity as the phenotype. The results are in line with previous research on obesity as we identify top SNPs previously associated with obesity. We further demonstrate how to interpret and visualize interaction candidates.
    Conclusions: The new method identifies interaction candidates otherwise not detected with parametric regression models. However, further research is needed to evaluate the uncertainties of these candidates. The method can be applied to large-scale biobanks with high-dimensional data.
    MeSH term(s) Algorithms ; Gene-Environment Interaction ; Genome-Wide Association Study ; Polymorphism, Single Nucleotide ; Trees
    Language English
    Publishing date 2021-05-04
    Publishing country England
    Document type Journal Article
    ZDB-ID 2041484-5
    ISSN 1471-2105 ; 1471-2105
    ISSN (online) 1471-2105
    ISSN 1471-2105
    DOI 10.1186/s12859-021-04041-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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