These authors contributed equally to this work.
Modelling pollen-mediated gene flow in rice: risk assessment and management of transgene escape
Article first published online: 3 FEB 2010
© 2010 The Authors. Journal compilation © 2010 Blackwell Publishing Ltd
Plant Biotechnology Journal
Volume 8, Issue 4, pages 452–464, May 2010
How to Cite
Rong, J., Song, Z., De Jong, T. J., Zhang, X., Sun, S., Xu, X., Xia, H., Liu, B. and Lu, B.-R. (2010), Modelling pollen-mediated gene flow in rice: risk assessment and management of transgene escape. Plant Biotechnology Journal, 8: 452–464. doi: 10.1111/j.1467-7652.2009.00488.x
- Issue published online: 6 APR 2010
- Article first published online: 3 FEB 2010
- Received 1 May 2009; revised 14 October 2009; accepted 15 October 2009.
- gene flow model;
- isolation distance;
- outcrossing rate;
- pollen competition;
- pollen dispersal;
- transgene escape
Fast development and commercialization of genetically modified plants have aroused concerns of transgene escape and its environmental consequences. A model that can effectively predict pollen-mediated gene flow (PMGF) is essential for assessing and managing risks from transgene escape. A pollen-trap method was used to measure the wind-borne pollen dispersal in cultivated rice and common wild rice, and effects of relative humidity, temperature and wind speed on pollen dispersal were estimated. A PMGF model was constructed based on the pollen dispersal pattern in rice, taking outcrossing rates of recipients and cross-compatibility between rice and its wild relatives into consideration. Published rice gene flow data were used to validate the model. Pollen density decreased in a simple exponential pattern with distances to the rice field. High relative humidity reduced pollen dispersal distances. Model simulation showed an increased PMGF frequency with the increase of pollen source size (the area of a rice field), but this effect levelled off with a large pollen-source size. Cross-compatibility is essential when modelling PMGF from rice to its wild relatives. The model fits the data well, including PMGF from rice to its wild relatives. Therefore, it can be used to predict PMGF in rice under diverse conditions (e.g. different outcrossing rates and cross-compatibilities), facilitating the determination of isolation distances to minimize transgene escape. The PMGF model may be extended to other wind-pollinated plant species such as wheat and barley.