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Genomic imprinting has been thought to play an important role in seeddevelopment in flowering plants. Seed in a flowering plant normally containsdiploid embryo and triploid endosperm. Empirical studies have shown that someeconomically important endosperm traits are genetically controlled by imprintedgenes. However, the exact number and location of the imprinted genes arelargely unknown due to the lack of efficient statistical mapping methods. Herewe propose a general statistical variance components framework by utilizing thenatural information of sex-specific allelic sharing among sibpairs in linecrosses, to map imprinted quantitative trait loci (iQTL) underlying endospermtraits. We propose a new variance components partition method considering theunique characteristic of the triploid endosperm genome, and develop arestricted maximum likelihood estimation method in an interval scan forestimating and testing genome-wide iQTL effects. Cytoplasmic maternal effectwhich is thought to have primary influences on yield and grain quality is alsoconsidered when testing for genomic imprinting. Extension to multiple iQTLanalysis is proposed. Asymptotic distribution of the likelihood ratio test fortesting the variance components under irregular conditions are studied. Bothsimulation study and real data analysis indicate good performance andpowerfulness of the developed approach.

A general statistical framework for dissecting parent-of-origin effects underlying endosperm traits in flowering plants