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Quantifying time-inhomogeneous stochastic introgression processes with hazard rates.

Atiyo Ghosh¹, Maria Conceição Serra, Patsy Haccou

¹Institute of Environmental Sciences (CML), Leiden University, P.O. Box 9518, Leiden 2300 RA, The Netherlands. ghosh@cml.leidenuniv.nl

Theoretical Population Biology

|December 20, 2011

Summary

This summary is machine-generated.

This study introduces a method to quantify introgression risk by calculating the hazard rate, considering time-varying gene flow and fitness bottlenecks. Findings reveal bottlenecks reduce risk but slow adaptation to changing gene flow.

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Area of Science:

Ecology
Genetics
Risk Assessment

Background:

Introgression, the transfer of genes between populations via hybridization and backcrossing, is a key concern for the spread of crop genes into wild relatives.
Assessing introgression risk requires quantitative measures that account for the inherent randomness of these processes.

Purpose of the Study:

To develop a methodology for calculating the hazard rate of introgression under time-varying gene flow from crops to wild populations.
To analyze the impact of fitness bottlenecks and different types of temporal variation (deterministic and random) on introgression risk.

Main Methods:

Development of a mathematical framework to calculate the hazard rate, representing the probability of gene escape per unit time.
Modeling scenarios with time-inhomogeneous gene flow, including periodic and random variations.
Inclusion of extended fitness bottlenecks for hybrid and backcrossed individuals.

Main Results:

Fitness bottlenecks were found to decrease the hazard rate, indicating reduced introgression risk.
Bottlenecks also attenuated and delayed the response of the hazard rate to fluctuations in gene flow.
Random variations in gene flow resulted in a lower hazard rate compared to deterministic variations.

Conclusions:

The developed methodology provides a quantitative tool for assessing introgression risk in dynamic environments.
Findings highlight the importance of considering fitness effects and temporal variability in gene flow for accurate risk assessment.
Results have implications for managing genetically modified crops and mitigating unintended gene flow.