Reviewing the consequences of genetic purging on the success of rescue programs

Abstract

Genetic rescue is increasingly considered a promising and underused conservation strategy to reduce inbreeding depression and restore genetic diversity in endangered populations, but the empirical evidence supporting its application is limited to a few generations. Here we discuss on the light of theory the role of inbreeding depression arising from partially recessive deleterious mutations and of genetic purging as main determinants of the medium to long-term success of rescue programs. This role depends on two main predictions: (1) The inbreeding load hidden in populations with a long stable demography increases with the effective population size; and (2) After a population shrinks, purging tends to remove its (partially) recessive deleterious alleles, a process that is slower but more efficient for large populations than for small ones. We also carry out computer simulations to investigate the impact of genetic purging on the medium to long term success of genetic rescue programs. For some scenarios, it is found that hybrid vigor followed by purging will lead to sustained successful rescue. However, there may be specific situations where the recipient population is so small that it cannot purge the inbreeding load introduced by migrants, which would lead to increased fitness inbreeding depression and extinction risk in the medium to long term. In such cases, the risk is expected to be higher if migrants came from a large non-purged population with high inbreeding load, particularly after the accumulation of the stochastic effects ascribed to repeated occasional migration events. Therefore, under the specific deleterious recessive mutation model considered, we conclude that additional caution should be taken in rescue programs. Unless the endangered population harbors some distinctive genetic singularity whose conservation is a main concern, restoration by continuous stable gene flow should be considered, whenever feasible, as it reduces the extinction risk compared to repeated occasional migration and can also allow recolonization events.

Genetic load: genomic estimates and applications in non-model animals

“…These components capture the current loss in fitness of the population, as well as the predicted future fitness loss caused by deleterious mutations that may lower the population's long-term viability 17 . Such information is crucial for correct management decisions, including genetic rescue, assisted gene flow, population supplementation and reintroduction programs (see 10,63,64,109,110 ). Conservation geneticists tend to prioritize maximizing genetic diversity rather than minimizing the genetic load 111 , but with better data and understanding of the genetic load, a more balanced approach is likely to improve conservation outcomes 112 .…”

Using computational simulations to quantify genetic load and predict extinction risk

“…1). Given the importance of the DFE for simulations of deleterious variation, as well as recent debate over DFE parameters (68–70), below we provide a more detailed review of this topic.…”

“…Recently, these sequence-based DFE estimates have been criticized for not properly estimating the high fraction of strongly deleterious mutations estimated by experimental studies (68–70). Specifically, Kardos et al (69) and Pérez-Pereira et al (68) both argued that DFEs with a high fraction of strongly deleterious variation and dominance distributions that are far less recessive are better supported by experimental studies.…”

“…Recently, these sequence-based DFE estimates have been criticized for not properly estimating the high fraction of strongly deleterious mutations estimated by experimental studies (68–70). Specifically, Kardos et al (69) and Pérez-Pereira et al (68) both argued that DFEs with a high fraction of strongly deleterious variation and dominance distributions that are far less recessive are better supported by experimental studies. The model proposed by Kardos et al (69) assumes a mean s = -0.05 augmented with an additional 5% of mutations being recessive lethal, implying ∼67% of new mutations being strongly deleterious (here defined as s < -0.01), whereas the Pérez-Pereira et al (68) model assumes a mean s = -0.2, implying ∼71% of new mutations are strongly deleterious (Fig.…”

“…However, if experimental-based studies are presumed to estimate the full DFE for nonsynonymous mutations, they should nevertheless make predictions that are consistent with patterns of nonsynonymous variation in genomic sequencing datasets. To investigate whether the Pérez-Pereira et al (68) and Kardos et al (69) models are supported by such information, we ran simulations under a human demographic model (67) where we compared the predicted nonsynonymous SFS from to the empirical SFS from the 1000G dataset for humans (88). We find that both models exhibit poor agreement with the empirical 1000G SFS, predicting nonsynonymous SFS that are greatly shifted towards rare mutations (Fig.…”