Genetic expression for an endosperm trait in seeds of cereal crops may be controlled simultaneously by the triploid endosperm genotypes and the diploid maternal genotypes. However, current statistical methods for mapp...Genetic expression for an endosperm trait in seeds of cereal crops may be controlled simultaneously by the triploid endosperm genotypes and the diploid maternal genotypes. However, current statistical methods for mapping quantitative trait loci (QTLs) underlying endosperm traits have not been effective in dealing with the putative maternal genetic effects. Combining the quantitative genetic model for diploid maternal traits with triploid endosperm traits, here we propose a new statistical method for mapping QTLs con- trolling endosperm traits with maternal genetic effects. This method applies the data set of both DNA molecular marker genotypes of each plant in segregation population and the quantitative observations of single endosperms in each plant to map QTL. The maximum likelihood method implemented via the expectation-maximization algorithm was used to the estimate parameters of a putative QTL. Since this method involves the maternal effect that may contribute to en- dosperm traits, it might be more congruent with the genetics of endosperm traits and more helpful to increasing the preci- sion of QTL mapping. The simulation results show the pro- posed method provides accurate estimates of the QTL effects and locations with high statistical power.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.39900080,30270724&30370758).
文摘Genetic expression for an endosperm trait in seeds of cereal crops may be controlled simultaneously by the triploid endosperm genotypes and the diploid maternal genotypes. However, current statistical methods for mapping quantitative trait loci (QTLs) underlying endosperm traits have not been effective in dealing with the putative maternal genetic effects. Combining the quantitative genetic model for diploid maternal traits with triploid endosperm traits, here we propose a new statistical method for mapping QTLs con- trolling endosperm traits with maternal genetic effects. This method applies the data set of both DNA molecular marker genotypes of each plant in segregation population and the quantitative observations of single endosperms in each plant to map QTL. The maximum likelihood method implemented via the expectation-maximization algorithm was used to the estimate parameters of a putative QTL. Since this method involves the maternal effect that may contribute to en- dosperm traits, it might be more congruent with the genetics of endosperm traits and more helpful to increasing the preci- sion of QTL mapping. The simulation results show the pro- posed method provides accurate estimates of the QTL effects and locations with high statistical power.
