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Statistical Applications in Genetics and Molecular Biology

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Volume 16, Issue 3


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Mixture model-based association analysis with case-control data in genome wide association studies

Fadhaa Ali
  • Department of Statistics, College of Administration and Economics, University of Baghdad, Baghdad, Iraq
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Jian Zhang
  • Corresponding author
  • School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, Kent CT2 7NF, UK
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Published Online: 2017-07-19 | DOI: https://doi.org/10.1515/sagmb-2016-0022


Multilocus haplotype analysis of candidate variants with genome wide association studies (GWAS) data may provide evidence of association with disease, even when the individual loci themselves do not. Unfortunately, when a large number of candidate variants are investigated, identifying risk haplotypes can be very difficult. To meet the challenge, a number of approaches have been put forward in recent years. However, most of them are not directly linked to the disease-penetrances of haplotypes and thus may not be efficient. To fill this gap, we propose a mixture model-based approach for detecting risk haplotypes. Under the mixture model, haplotypes are clustered directly according to their estimated disease penetrances. A theoretical justification of the above model is provided. Furthermore, we introduce a hypothesis test for haplotype inheritance patterns which underpin this model. The performance of the proposed approach is evaluated by simulations and real data analysis. The results show that the proposed approach outperforms an existing multiple testing method.

This article offers supplementary material which is provided at the end of the article.

Keywords: genome wide association studies; haplotype mixture model; odds ratios; testing for inheritance patterns


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About the article

Published Online: 2017-07-19

Published in Print: 2017-07-26

Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 16, Issue 3, Pages 173–187, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2016-0022.

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