A Statistical Framework for Genome-wide Scanning and Testing of Imprinted Quantitative Trait Loci.
From: Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA. cui@stt.msu.edu
Journal of theoretical biology
- Publish Date: Jan 2007
- ISSN: 0022-5193
- Volume: 244
- Issue: 1
- Pages: 115-26
- Medium: Print
- Language: English
- Citation (JAMA): Cui Yuehua, et al. A Statistical Framework for Genome-wide Scanning and Testing of Imprinted Quantitative Trait Loci.. J. Theor. Biol. Jan 2007;244:115-26
Abstract
Non-equivalent expression of alleles at a locus results in genomic imprinting. In this article, a statistical framework for genome-wide scanning and testing of imprinted quantitative trait loci (iQTL) underlying complex traits is developed based on experimental crosses of inbred line species in backcross populations. The joint likelihood function is composed of four component likelihood functions with each of them derived from one of four backcross families. The proposed approach models genomic imprinting effect as a probability measure with which one can test the degree of imprinting. Simulation results show that the model is robust for identifying iQTL with various degree of imprinting ranging from no imprinting, partial imprinting to complete imprinting. Under various simulation scenarios, the proposed model shows consistent parameter estimation with reasonable precision and high power in testing iQTL. When a QTL shows Mendelian effect, the proposed model also outperforms traditional Mendelian model. Extension to incorporate maternal effect is also given. The developed model, built within the maximum likelihood framework and implemented with the EM algorithm, provides a quantitative framework for testing and estimating iQTL involved in the genetic control of complex traits.
Mesh Headings (Keywords): Algorithms, Animals, Crosses, Genetic, Genome, Genomic Imprinting, Genotype, Models, Genetic, Models, Statistical, Phenotype, Quantitative Trait Loci
Check for Full Text / PubMed Unique Identifier (PMID): 16959270
This abstract is part of PubMed, a service of the U.S. National Library of Medicine. PubMed includes more than 17 million citations from MEDLINE and other life science journals for biomedical articles. See Copyright and Disclaimers.
Linked medical terms appearing on this page are added by Healia to help readers find more information and are not part of the original PubMed document.
The data herein was last updated on July 8th, 2008 and may not reflect the most current and accurate data available from NLM.