摘要
[ Objective] The objective of this study was to evaluate the genetic diversity and characterization of special maize population consisting of 135 Fl fami- lies. [ Method ] In this study, association analysis was conducted in 135 F1 families derived from two maize landraces, and the efficiency of this method was evalua- ted through simulation. [ Result] Association analysis with different kinds of families showed that large population size and robust phenotypic data were required for association mapping. For all the phenotypic traits, the model controlling beth population structure and relative kinship （ Q ＋ K） performed better than the model controlling relative kinship （K）, and similarly to the model controlling population structure （Q）. Across 100 simulation runs in QULINE, the average power of QTL detection for the two models were 88.64% and 83.64% respectively, and the number of false QTL was reduced from 399 with GLM model to 199 with K mod- el. Our simulation results suggested that these F1 families can be used for association analysis, and the power of the QTL detection was related to the maximum al- lele frequency （MAF）and the phenotypic variation （PVE） explained by QTL. [ Conclusion] The results from this study suggest that association analysis using the F1 families is an effective approach to study maize landraces for discovering elite genes which we are interested in from these special populations.
[ Objective] The objective of this study was to evaluate the genetic diversity and characterization of special maize population consisting of 135 Fl fami- lies. [ Method ] In this study, association analysis was conducted in 135 F1 families derived from two maize landraces, and the efficiency of this method was evalua- ted through simulation. [ Result] Association analysis with different kinds of families showed that large population size and robust phenotypic data were required for association mapping. For all the phenotypic traits, the model controlling beth population structure and relative kinship （ Q ＋ K） performed better than the model controlling relative kinship （K）, and similarly to the model controlling population structure （Q）. Across 100 simulation runs in QULINE, the average power of QTL detection for the two models were 88.64% and 83.64% respectively, and the number of false QTL was reduced from 399 with GLM model to 199 with K mod- el. Our simulation results suggested that these F1 families can be used for association analysis, and the power of the QTL detection was related to the maximum al- lele frequency （MAF）and the phenotypic variation （PVE） explained by QTL. [ Conclusion] The results from this study suggest that association analysis using the F1 families is an effective approach to study maize landraces for discovering elite genes which we are interested in from these special populations.
基金
Surpported by the Key Program of Department of Education of Sichuan Province,China(12ZB097)
