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Modified versions of Bayesian Information Criterion for genome-wide association studies

Author

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  • Frommlet, Florian
  • Ruhaltinger, Felix
  • Twaróg, Piotr
  • Bogdan, Małgorzata

Abstract

For the vast majority of genome-wide association studies (GWAS) statistical analysis was performed by testing markers individually. Elementary statistical considerations clearly show that in the case of complex traits an approach based on multiple regression or generalized linear models is preferable to testing single markers. A model selection approach to GWAS can be based on modifications of the Bayesian Information Criterion (BIC), where some search strategies are necessary to deal with a huge number of potential models. Comprehensive simulations based on real SNP data confirm that model selection has larger power to detect causal SNPs in complex models than single-marker tests. Furthermore, testing single markers leads to substantial problems with proper ranking of causal SNPs and tends to detect a certain number of false positive SNPs, which are not linked to any of the causal mutations. This behavior of single-marker tests is typical in GWAS for complex traits and can be explained by an aggregated influence of many small random sample correlations between genotypes of the SNP under investigation and other causal SNPs. These findings might at least partially explain problems with low power and nonreplicability of results in GWAS. A real data analysis illustrates advantages of model selection in practice, where publicly available gene expression data as traits for individuals from the HapMap project are reanalyzed.

Suggested Citation

  • Frommlet, Florian & Ruhaltinger, Felix & Twaróg, Piotr & Bogdan, Małgorzata, 2012. "Modified versions of Bayesian Information Criterion for genome-wide association studies," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1038-1051.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:5:p:1038-1051
    DOI: 10.1016/j.csda.2011.05.005
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    References listed on IDEAS

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    1. Jiahua Chen & Zehua Chen, 2008. "Extended Bayesian information criteria for model selection with large model spaces," Biometrika, Biometrika Trust, vol. 95(3), pages 759-771.
    2. Florian Frommlet, 2010. "Tag SNP selection based on clustering according to dominant sets found using replicator dynamics," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(1), pages 65-83, April.
    3. Erhardt Vinzenz & Bogdan Malgorzata & Czado Claudia, 2010. "Locating Multiple Interacting Quantitative Trait Loci with the Zero-Inflated Generalized Poisson Regression," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-27, June.
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    4. Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 1-40, March.

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