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| Content Provider | Springer Nature Link |
|---|---|
| Author | Hu, Zhiqiu Xu, Chenwu |
| Copyright Year | 2005 |
| Abstract | 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 controlling 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 endosperm traits, it might be more congruent with the genetics of endosperm traits and more helpful to increasing the precision of QTL mapping. The simulation results show the proposed method provides accurate estimates of the QTL effects and locations with high statistical power. |
| Starting Page | 1470 |
| Ending Page | 1476 |
| Page Count | 7 |
| File Format | |
| ISSN | 10016538 |
| Journal | Chinese Science Bulletin |
| Volume Number | 50 |
| Issue Number | 14 |
| e-ISSN | 18619541 |
| Language | English |
| Publisher | Science in China Press |
| Publisher Date | 2005-01-01 |
| Publisher Place | Beijing |
| Access Restriction | Subscribed |
| Subject Keyword | endosperm trait quantitative trait loci maternal effect maximum likelihood estimation expectation-maximization algorithm Science Life Sciences Physics Chemistry/Food Science Geosciences Engineering |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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