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Standing genetic variation and compensatory evolution in transgenic organisms: a growth-enhanced salmon simulation.

Ahrens RN, Devlin RH - Transgenic Res. (2010)

Bottom Line: In the present study, using a growth hormone transgenic salmon example, we have modeled selection of modifier loci (single and multiple) in the presence of a transgene and have found that accounting for genetic background can significantly affect the persistence of transgenes in populations, potentially reducing or reversing a "Trojan gene" effect.Further, with the presence of a transgene in a population, genetic backgrounds were found to shift in non-transgenic individuals as well, an effect expected to direct phenotypes away from naturally selected optima.The present model has revealed the importance of understanding effects of selection for background genetics on the evolution of phenotypes in populations harbouring transgenes.

View Article: PubMed Central - PubMed

Affiliation: Fisheries Centre, University of British Columbia, 2202 Main Mall, Vancouver, BC, V6T 1Z4, Canada. ahrens@zoology.ubc.ca

ABSTRACT
Genetically modified strains usually are generated within defined genetic backgrounds to minimize variation for the engineered characteristic in order to facilitate basic research investigations or for commercial application. However, interactions between transgenes and genetic background have been documented in both model and commercial agricultural species, indicating that allelic variation at transgene-modifying loci are not uncommon in genomes. Engineered organisms that have the potential to allow entry of transgenes into natural populations may cause changes to ecosystems via the interaction of their specific phenotypes with ecosystem components and services. A transgene introgressing through natural populations is likely to encounter a range of natural genetic variation (among individuals or sub-populations) that could result in changes in phenotype, concomitant with effects on fitness and ecosystem consequences that differ from that seen in the progenitor transgenic strain. In the present study, using a growth hormone transgenic salmon example, we have modeled selection of modifier loci (single and multiple) in the presence of a transgene and have found that accounting for genetic background can significantly affect the persistence of transgenes in populations, potentially reducing or reversing a "Trojan gene" effect. Influences from altered life history characteristics (e.g., developmental timing, age of maturation) and compensatory demographic/ecosystem controls (e.g., density dependence) also were found to have a strong influence on transgene effects. Further, with the presence of a transgene in a population, genetic backgrounds were found to shift in non-transgenic individuals as well, an effect expected to direct phenotypes away from naturally selected optima. The present model has revealed the importance of understanding effects of selection for background genetics on the evolution of phenotypes in populations harbouring transgenes.

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Equilibrium spawning population relative to the pure wild-type equilibrium population size (, black contour lines) and transgene frequency (AT) (colour shaded contours) as a function of relative transgenic age-1 viability (VT) and transgenic male mating advantage (mT) assuming no effect of background genetics on the transgene. Viability and mating advantage were scaled relative to wild type. Darkest blue shading indicates fixation of the transgene while dark orange indicates the transgene is lost. 90% of transgenic juveniles were assumed to smolt after 1 year in this scenario, while only 10% of wild juveniles smolted after 1 year. Relative improvement in juvenile survival at low density was set low (Ω = 2)
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Fig1: Equilibrium spawning population relative to the pure wild-type equilibrium population size (, black contour lines) and transgene frequency (AT) (colour shaded contours) as a function of relative transgenic age-1 viability (VT) and transgenic male mating advantage (mT) assuming no effect of background genetics on the transgene. Viability and mating advantage were scaled relative to wild type. Darkest blue shading indicates fixation of the transgene while dark orange indicates the transgene is lost. 90% of transgenic juveniles were assumed to smolt after 1 year in this scenario, while only 10% of wild juveniles smolted after 1 year. Relative improvement in juvenile survival at low density was set low (Ω = 2)

Mentions: Base simulations with the model (without action of modifiers) yielded results similar to those previously described (Davis and Fulford 1999; Davis et al. 1999; Hedrick 2001; Maclean and Laight 2000; Muir and Howard 1999, 2001; Muir and Howard 2002). The model also effectively approximated salmon population dynamics, responding as expected to variation in survival, age of maturation, reproductive fitness parameters, and life history variation. The scenario depicted in Fig. 1 shows that when fitness components with opposing effects (enhanced male mating success, coupled with reduced survival as first year juveniles and in the smolt to adult interval) are combined, the transgene is able to invade the population over a wide range of conditions, reaching either fixation or a stable equilibrium. The presence of the transgene also can reduce mean population fitness and result in lower equilibrium population size () compared to a wild-type () population (, i.e., a ‘Trojan gene’ effect). Relative change in population size (Eq. 8) depends on juvenile survival rate reduction for individuals possessing the transgene (VT) as well as the strength of density-dependent compensatory changes in juvenile survival (Ω; Fig. 2a). Expected lifetime fecundity of GH-transgenic () and wild-type () individuals, survival during the second year of freshwater residence, and the proportion of individuals smolting after 1 year of freshwater residence (εW or εT), also can strongly influence changes in allele frequency and population size. It is important to note that in simulations where a high proportion of transgenic individuals matured at only 2 years of age and the juvenile survival reduction associated with the transgene was weaker (i.e., 80% of wild-type), equilibrium population size increased even when male mating advantage was only twofold (solid grey line Fig. 2a). When viability effects increased (i.e. 50% of wild type) at the same mating advantage, population size did not increase and smaller reductions in population size were predicted relative to the more extreme scenario (VT = 0.3 and mT = 5). Reduction in mean generation time of transgenic individuals caused transgene frequencies and relative equilibrium population size to increase, even when juvenile viability was reduced. Increased juvenile survival compensation (Ω) reduced the relative decline in population size. Under scenarios where the transgene reached fixation, relative change in population size approached an asymptote described by Eq. 9. Although compensatory changes in survival in response to reduced population density prevents population extinction under a ‘Trojan gene’ scenario, strong reductions in population size are still possible when density dependent effects on juvenile survival (Ω) are weak. Equation 10 defines the lower bound on Ω (Fig. 2b). Below these values of Ω, under a ‘Trojan gene’ scenario, population extinction occurred. It is important to note that as the viability of transgenic individuals declines, the critical bound on Ω increases geometrically.Fig. 1


Standing genetic variation and compensatory evolution in transgenic organisms: a growth-enhanced salmon simulation.

Ahrens RN, Devlin RH - Transgenic Res. (2010)

Equilibrium spawning population relative to the pure wild-type equilibrium population size (, black contour lines) and transgene frequency (AT) (colour shaded contours) as a function of relative transgenic age-1 viability (VT) and transgenic male mating advantage (mT) assuming no effect of background genetics on the transgene. Viability and mating advantage were scaled relative to wild type. Darkest blue shading indicates fixation of the transgene while dark orange indicates the transgene is lost. 90% of transgenic juveniles were assumed to smolt after 1 year in this scenario, while only 10% of wild juveniles smolted after 1 year. Relative improvement in juvenile survival at low density was set low (Ω = 2)
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3090570&req=5

Fig1: Equilibrium spawning population relative to the pure wild-type equilibrium population size (, black contour lines) and transgene frequency (AT) (colour shaded contours) as a function of relative transgenic age-1 viability (VT) and transgenic male mating advantage (mT) assuming no effect of background genetics on the transgene. Viability and mating advantage were scaled relative to wild type. Darkest blue shading indicates fixation of the transgene while dark orange indicates the transgene is lost. 90% of transgenic juveniles were assumed to smolt after 1 year in this scenario, while only 10% of wild juveniles smolted after 1 year. Relative improvement in juvenile survival at low density was set low (Ω = 2)
Mentions: Base simulations with the model (without action of modifiers) yielded results similar to those previously described (Davis and Fulford 1999; Davis et al. 1999; Hedrick 2001; Maclean and Laight 2000; Muir and Howard 1999, 2001; Muir and Howard 2002). The model also effectively approximated salmon population dynamics, responding as expected to variation in survival, age of maturation, reproductive fitness parameters, and life history variation. The scenario depicted in Fig. 1 shows that when fitness components with opposing effects (enhanced male mating success, coupled with reduced survival as first year juveniles and in the smolt to adult interval) are combined, the transgene is able to invade the population over a wide range of conditions, reaching either fixation or a stable equilibrium. The presence of the transgene also can reduce mean population fitness and result in lower equilibrium population size () compared to a wild-type () population (, i.e., a ‘Trojan gene’ effect). Relative change in population size (Eq. 8) depends on juvenile survival rate reduction for individuals possessing the transgene (VT) as well as the strength of density-dependent compensatory changes in juvenile survival (Ω; Fig. 2a). Expected lifetime fecundity of GH-transgenic () and wild-type () individuals, survival during the second year of freshwater residence, and the proportion of individuals smolting after 1 year of freshwater residence (εW or εT), also can strongly influence changes in allele frequency and population size. It is important to note that in simulations where a high proportion of transgenic individuals matured at only 2 years of age and the juvenile survival reduction associated with the transgene was weaker (i.e., 80% of wild-type), equilibrium population size increased even when male mating advantage was only twofold (solid grey line Fig. 2a). When viability effects increased (i.e. 50% of wild type) at the same mating advantage, population size did not increase and smaller reductions in population size were predicted relative to the more extreme scenario (VT = 0.3 and mT = 5). Reduction in mean generation time of transgenic individuals caused transgene frequencies and relative equilibrium population size to increase, even when juvenile viability was reduced. Increased juvenile survival compensation (Ω) reduced the relative decline in population size. Under scenarios where the transgene reached fixation, relative change in population size approached an asymptote described by Eq. 9. Although compensatory changes in survival in response to reduced population density prevents population extinction under a ‘Trojan gene’ scenario, strong reductions in population size are still possible when density dependent effects on juvenile survival (Ω) are weak. Equation 10 defines the lower bound on Ω (Fig. 2b). Below these values of Ω, under a ‘Trojan gene’ scenario, population extinction occurred. It is important to note that as the viability of transgenic individuals declines, the critical bound on Ω increases geometrically.Fig. 1

Bottom Line: In the present study, using a growth hormone transgenic salmon example, we have modeled selection of modifier loci (single and multiple) in the presence of a transgene and have found that accounting for genetic background can significantly affect the persistence of transgenes in populations, potentially reducing or reversing a "Trojan gene" effect.Further, with the presence of a transgene in a population, genetic backgrounds were found to shift in non-transgenic individuals as well, an effect expected to direct phenotypes away from naturally selected optima.The present model has revealed the importance of understanding effects of selection for background genetics on the evolution of phenotypes in populations harbouring transgenes.

View Article: PubMed Central - PubMed

Affiliation: Fisheries Centre, University of British Columbia, 2202 Main Mall, Vancouver, BC, V6T 1Z4, Canada. ahrens@zoology.ubc.ca

ABSTRACT
Genetically modified strains usually are generated within defined genetic backgrounds to minimize variation for the engineered characteristic in order to facilitate basic research investigations or for commercial application. However, interactions between transgenes and genetic background have been documented in both model and commercial agricultural species, indicating that allelic variation at transgene-modifying loci are not uncommon in genomes. Engineered organisms that have the potential to allow entry of transgenes into natural populations may cause changes to ecosystems via the interaction of their specific phenotypes with ecosystem components and services. A transgene introgressing through natural populations is likely to encounter a range of natural genetic variation (among individuals or sub-populations) that could result in changes in phenotype, concomitant with effects on fitness and ecosystem consequences that differ from that seen in the progenitor transgenic strain. In the present study, using a growth hormone transgenic salmon example, we have modeled selection of modifier loci (single and multiple) in the presence of a transgene and have found that accounting for genetic background can significantly affect the persistence of transgenes in populations, potentially reducing or reversing a "Trojan gene" effect. Influences from altered life history characteristics (e.g., developmental timing, age of maturation) and compensatory demographic/ecosystem controls (e.g., density dependence) also were found to have a strong influence on transgene effects. Further, with the presence of a transgene in a population, genetic backgrounds were found to shift in non-transgenic individuals as well, an effect expected to direct phenotypes away from naturally selected optima. The present model has revealed the importance of understanding effects of selection for background genetics on the evolution of phenotypes in populations harbouring transgenes.

Show MeSH
Related in: MedlinePlus