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

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.

Reproductive performance of alternative male phenotypes of growth hormone transgenic Atlantic salmon (Salmo salar)

“…2008; Valosaari et al. 2008; Ahrens and Devlin 2010). Frequently, the model parameters consist of empirical measurements of fitness‐related life history traits such as growth, survival and reproductive probabilities, age at sexual maturity, female fecundity and male fertility (Muir and Howard 2002).…”

Population‐level effects of fitness costs associated with repressible female‐lethal transgene insertions in two pest insects

“…; Uchino et al. ; Ahrens and Devlin ). In RIDL insects, expression of the transgene sequence comprises the intended expression and off‐target expression.…”

Accuracy of nonmolecular identification of growth‐hormone‐transgenic coho salmon after simulated escape

“…Even if there is potential for learning, which may allow human observers to more accurately assign genotypes to naturalized phenotypes, the reliability of identifying the genotypes correctly, at present, appears too low. Over the longer term, and if transgenic fish would be capable of reproducing in the wild, distinguishing between naturalized phenotypes of wild-type and transgenic genotypes may become easier or more difficult, depending on selective forces acting on phenotype (Ahrens and Devlin 2011). In contrast, the high reliability and accuracy of PCR analyses (Rehbein et al 2002, Devlin et al 2004) for detecting transgenic animals will not change.…”