, Volume 130, Issue 3, pp 227–239 | Cite as

A statistical model for dissecting genomic imprinting through genetic mapping

  • Yuehua Cui
  • James M. Cheverud
  • Rongling Wu
Original Paper


As a result of nonequivalent genetic contribution of maternal and paternal genomes to offsprings, genomic imprinting or called parent-of-origin effect, has been broadly identified in plants, animals and humans. Its role in shaping organism’s development has been unanimously recognized. However, statistical methods for identifying imprinted quantitative trait loci (iQTL) and estimating the imprinted effect have not been well developed. In this article, we propose an efficient statistical procedure for genomewide estimating and testing the effects of significant iQTL underlying the quantitative variation of interested traits. The developed model can be applied to two different genetic cross designs, backcross and F2 families derived from inbred lines. The proposed procedure is built within the maximum likelihood framework and implemented with the EM algorithm. Extensive simulation studies show that the proposed model is well performed in a variety of situations. To demonstrate the usefulness of the proposed approach, we apply the model to a published data in an F2 family derived from LG/S and SM/S mouse stains. Two partially maternal imprinting iQTL are identified which regulate the growth of body weight. Our approach provides a testable framework for identifying and estimating iQTL involved in the genetic control of complex traits.


EM algorithm Genomic Imprinting Inbred Lines Maximum likelihood Quantitative trait loci 



We thank the anonymous referees and the editor for their valuable comments on the manuscript. This research was supported by a start-up fund from Michigan State University.


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Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  1. 1.Department of Statistics and ProbabilityMichigan State UniversityEast LansingUSA
  2. 2.Department of Anatomy and NeurobiologyWashington University Medical SchoolSt. LouisUSA
  3. 3.Department of StatisticsUniversity of FloridaGainesvilleUSA

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