  Genomic imprinting has been thought to play an important role in seed
development in flowering plants. Seed in a flowering plant normally contains
diploid embryo and triploid endosperm. Empirical studies have shown that some
economically important endosperm traits are genetically controlled by imprinted
genes. However, the exact number and location of the imprinted genes are
largely unknown due to the lack of efficient statistical mapping methods. Here
we propose a general statistical variance components framework by utilizing the
natural information of sex-specific allelic sharing among sibpairs in line
crosses, to map imprinted quantitative trait loci (iQTL) underlying endosperm
traits. We propose a new variance components partition method considering the
unique characteristic of the triploid endosperm genome, and develop a
restricted maximum likelihood estimation method in an interval scan for
estimating and testing genome-wide iQTL effects. Cytoplasmic maternal effect
which is thought to have primary influences on yield and grain quality is also
considered when testing for genomic imprinting. Extension to multiple iQTL
analysis is proposed. Asymptotic distribution of the likelihood ratio test for
testing the variance components under irregular conditions are studied. Both
simulation study and real data analysis indicate good performance and
powerfulness of the developed approach.
