Document Type
Article
Publication Date
2011
College/Unit
Eberly College of Arts and Sciences
Department/Program/Center
Statistics
Abstract
Genomic imprinting is a phenomenon in which the same allele is expressed differently, depending on its parental origin. Such a phenomenon, also called the parent-of-origin effect, has been recognized to play a pivotal role in embryological development and pathogenesis in many species. Here we propose a statistical design for detecting imprinted loci that control quantitative traits based on a random set of three-generation families from a natural population in humans. This design provides a pathway for characterizing the effects of imprinted genes on a complex trait or disease at different generations and testing transgenerational changes of imprinted effects. The design is integrated with population and cytogenetic principles of gene segregation and transmission from a previous generation to next. The implementation of the EM algorithm within the design framework leads to the estimation of genetic parameters that define imprinted effects. A simulation study is used to investigate the statistical properties of the model and validate its utilization. This new design, coupled with increasingly used genome-wide association studies, should have an immediate implication for studying the genetic architecture of complex traits in humans.
Digital Commons Citation
Li, Yao; Guo, Yunqian; Wang, Jianxin; Hou, Wei; CHang, Myron N.; Liao, Duanping; and Wu, Rongling, "A Statistical Design for Testing Transgenerational Genomic Imprinting in Natural Human Populations" (2011). Faculty & Staff Scholarship. 2736.
https://researchrepository.wvu.edu/faculty_publications/2736
Source Citation
Li Y, Guo Y, Wang J, Hou W, Chang MN, Liao D, et al. (2011) A Statistical Design for Testing Transgenerational Genomic Imprinting in Natural Human Populations. PLoS ONE 6(2): e16858. https://doi.org/10.1371/journal.pone.0016858
Comments
© 2011 Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.