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A statistical test for detecting parent-of-origin effects when parental information is missing

Author

Listed:
  • Sacco Chiara

    (Department of Statistical Sciences “Paolo Fortunati”, University of Bologna, Via delle Belle Arti 41, Bologna 40126, Italy)

  • Viroli Cinzia

    (Department of Statistical Sciences “Paolo Fortunati”, University of Bologna, Via delle Belle Arti 41, Bologna 40126, Italy)

  • Falchi Mario

    (Department of Twin Research and Genetic Epidemiology, King’s College London, 3rd Floor, South Wing St Thomas’ Hospital, Westminster Bridge Rd, London SE1 7EH, UK)

Abstract

Genomic imprinting is an epigenetic mechanism that leads to differential contributions of maternal and paternal alleles to offspring gene expression in a parent-of-origin manner. We propose a novel test for detecting the parent-of-origin effects (POEs) in genome wide genotype data from related individuals (twins) when the parental origin cannot be inferred. The proposed method exploits a finite mixture of linear mixed models: the key idea is that in the case of POEs the population can be clustered in two different groups in which the reference allele is inherited by a different parent. A further advantage of this approach is the possibility to obtain an estimation of parental effect when the parental information is missing. We will also show that the approach is flexible enough to be applicable to the general scenario of independent data. The performance of the proposed test is evaluated through a wide simulation study. The method is finally applied to known imprinted genes of the MuTHER twin study data.

Suggested Citation

  • Sacco Chiara & Viroli Cinzia & Falchi Mario, 2017. "A statistical test for detecting parent-of-origin effects when parental information is missing," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(4), pages 275-289, September.
    • Handle: RePEc:bpj:sagmbi:v:16:y:2017:i:4:p:275-289:n:4
    DOI: 10.1515/sagmb-2017-0007
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    References listed on IDEAS

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    1. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    2. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    3. Bryan N Howie & Peter Donnelly & Jonathan Marchini, 2009. "A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 5(6), pages 1-15, June.
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