关键词: 胚乳性状, 三倍体, 区间作图, 极大似然估计, EM算法

Abstract:

Endosperm traits are important in determining grain quality in cereals. Endosperm, which is derived from two polar nuclei fusing with one sperm, is a triploid tissue whose genetic constitution is more complex than common diploid tissue. Current statistical methods for mapping quantitative trait loci (QTL) under diploid genetic control have not been effective for dealing with endosperm traits because of the complexity of their triploid inheritance. Based on the quantitative genetic models for triploid endosperm traits and mixture distribution theory, a new maximum likelihood method implemented via expectation-maximization algorithm which included the exact and approximate EM algorithms for mapping of quantitative trait loci (QTL) underlying endosperm traits was proposed in this paper. The method used the DNA molecular marker genotypes of each plant in segregation population such as F2, backcross etc. and the plant average of several endosperms of each plant as data set to map QTL. Because the method considered the mixture distribution property of QTL genotypes within flanking marker genotypes, it was expected that the proposed method not only increase the statistical power of QTL mapping but also improved the accuracy and precision of QTL effect estimates. Efficiency and feasibility of the methods have been verified through Monte Carlo simulation studies. The means and standard deviations of the estimated QTL effects and locations as well as the empirical powers under different QTL heritabilities and sampling strategies were listed in Table 1. Results of simulation showed that: (1) Whether the exact EM algorithm or the approximate EM algorithm, the method may provide accurate estimates of both the QTL effects and locations with very high statistical power; (2) Though the exact EM algorithm can’t significantly increase the statistical power of QTL detecting and the accuracy of QTL locations compared with the approximate EM algorithm, it may apparently increase the precision of QTL locations. The exact EM algorithm had less standard deviation of the estimated QTL locations than the approximate EM algorithm under the investigated treatments. The next step of endosperm QTL mapping is to consider both the maternal diploid genotype and the triploid endosperm genotypes jointly.

Key words: Endosperm traits, Triploid, Interval mapping, Maximum likelihood estimation, EM algorithm