IMR Press / FBE / Volume 4 / Issue 7 / DOI: 10.2741/E558

Frontiers in Bioscience-Elite (FBE) is published by IMR Press from Volume 13 Issue 2 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on as a courtesy and upon agreement with Frontiers in Bioscience.


A bivariate variance components model for mapping iQTLs underlying endosperm traits

Show Less
1 Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
2 Department of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA
3 Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA
4 Center for Computational Biology, Beijing Forestry University, Beijing, People's Republic of China

*Author to whom correspondence should be addressed.


Front. Biosci. (Elite Ed) 2012, 4(7), 2464–2475;
Published: 1 June 2012

Genomic imprinting plays a pivotal role in early stage development in plants. Linkage analysis has been proven to be useful in mapping imprinted quantitative trait loci (iQTLs) underlying imprinting phenotypic traits in natural populations or experimental crosses. For correlated traits, studies have shown that multivariate genetic linkage analysis can improve QTL mapping power and precision, especially when a QTL has a pleiotropic effect on several traits. In addition, the joint analysis of multiple traits can test a number of biologically interesting hypotheses, such as pleiotropic effects vs close linkage. Motivated by a triploid maize endosperm dataset, we extended the variance components linkage analysis model incorporating imprinting effect proposed by Li and Cui (2010) to a bivariate trait modeling framework, aimed to improve the mapping precision and to identify pleiotropic imprinting effects. We proposed to partition the genetic variance of a QTL into sex-specific allelic variance components, to model and test the imprinting effect of an iQTL on two traits. Both simulation studies and real data analysis show the power and utility of the method.

Close linkage
Likelihood ratio test
Maximum likelihood
Pleiotropic effect
Back to top