A statistical test for detecting parent-of-origin effects when parental information is missing
Sacco Chiara (),
Viroli Cinzia and
Additional contact information
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
Statistical Applications in Genetics and Molecular Biology, 2017, vol. 16, issue 4, 275-289
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.
Keywords: finite mixtures; first-type error; twin data (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
For access to full text, subscription to the journal or payment for the individual article is required.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:16:y:2017:i:4:p:275-289:n:4
Ordering information: This journal article can be ordered from
Access Statistics for this article
Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf
More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().