evolutionary rescue - senecio7の日記

Osmond MM, Otto SP & Martin G 2020 Genetic paths to evolutionary rescue and the distribution of fitness effects along them. Genetics 214:493-510.

in multi-step rescue, intermediate genotypes that themselves go extinct provide a "springboard" to rescue genotypes
our approach allows us to quantify how a race between evolution and extinction leads to a genetic basis of adaptation that is composed of fewer loci of larger effect
we hope this work brings awareness to the impact of demography on the genetic basis of adaptation

a recurrent observation, especially in experimental evolution with asexual microbes, is that the more novel the environment and the stronger the selection pressure, the more likely it is that adaptation primarily proceeds by fewer mutations of larger effect
i.e., that adaptation is oligogenic sensu Bell 2009
an extreme case is the evolution of drug resistance, which is often achieved by just one or two mutations

we therefore largely lack a theoretical framework for the genetic basis of evolutionary rescue that captures the arguably more realistic situation where an intermediate number of mutations are at play
the existence of a more complete framework could therefore provide valuable information for those investigating the genetic basis of drug resistance (e.g., the expected number and effect sizes of mutations) and would extend our understanding of the genetic basis of adaptation to cases of nonequilibrial demography (i.e., rapid evolution and "eco-evo" dynamics)

resistance often appears to arise by a single mutation
but not always
the fitness effect of rescue genotypes is more often large than small, creating a hump-shaped distribution of selection coefficients

we use Fisher's geometric model to describe adaptation following an abrupt environmental change that instigates population decline
here (1) the dynamics of each genotype depends on their absolute fitness (instead of only on their relative fitness)
(2) multiple mutations can segregate simultaneously (instead of assuming only sequential fixation), allowing multiple mutations to fix—and in our case, rescue—the population together as a single haplotype
i.e., stochastic tunnelling, Iwasa et al. 2004b
variation in absolute fitness, which allows population size to vary, can create feedbacks between demography and evolution
we also explore the possibility of rescue by mutant haplotypes containing more than one mutation
we ask:
(1) how many mutational steps is evolutionary rescue likely to take
(2) what is the expected distribution of fitness effects of the surviving genotypes and their component mutations?

we ignore environmental effects
the phenotype is the breeding value
we use the isotropic version of Fisher's geometric model
mutations (in addition to selection) are assumed to be uncorrelated across the scaled traits
each mutation affects all scaled phenotypes
we use the "classic" form of Fisher's geometric model (Harmand et al. 2017)
the probability density function of a mutant phenotype is multivariate normal, centered on the current phenotype, with variance λ in each dimension and no covariance
using a probability density function of mutant phenotypes implies a continuum-of-alleles (Kimura 1965)
phenotype is continuous and each mutation is unique
mutations are assumed to be additive in phenotype, which induces epistasis in fitness (as well as dominance under diploid selection), as fitness is a nonlinear function of phenotype
we assume asexual reproduction, i.e., no recombination, which is appropriate for many cases of antimicrobial drug resistance and experimental evolution, while recognizing the value of expanding this work to sexual populations
whether anisotropy can be reduced to isotropy with fewer dimensions in the case of evolutionary rescue, where the tails are essential, is unknown

when the mutation rate, U, is substantially less than a critical value, U_C = λn²/4, we are in a "strong selection, weak mutation" regime
essentially all mutations arise on a wild-type background (Martin and Roques 2016), consistent with the House of Cards approximation (Turelli 1984, 1985)
in this regime, rescue tends to occur by a single mutation of large effect
when U ≫ U_C, we are in a "weak selection, strong mutation" regime
many cosegregating mutations are present within each genome, creating a multivariate normal phenotypic distribution (Martin and Roques 2016), consistent with the Gaussian approximation (Kimura 1965; Lande 1980)
in this regime, rescue tends to occur by many mutations of small effect

our prediction, that rescue by more de novo mutations can be more likely than rescue by fewer, is novel
the general conclusion has been that, since the probability of rescue scales with U^k (where U is the mutation rate and k is the minimum number of mutations required for rescue), the probability of rescue declines with the number of mutations
when the probability of a beneficial mutation arising declines with its selective advantage, the probability of sampling once from the extreme tail of the DFE can be lower than sampling multiple mutations closer to the bulk of the DFE
rescue via multiple mutations can become the dominant path
rescue by multiple mutations may also be more likely with standing genetic variation, as small-effect intermediate mutations may segregate at higher frequencies than large-effect rescue mutations before the environmental change
this is especially true with recombination, where rescue genotypes can arise from segregating intermediate mutations without mutation (Uecker and Hermisson 2016)

we have investigated the genetic basis of evolutionary rescue in an asexual population that is initially genetically uniform
extending this work to allow for recombination and standing genetic variation at the time of environmental change—as expected for many natural populations—would be valuable
the effect of standing genetic variance on one-step rescue might be incorporated by a simple rescaling of N₀, to account for the additional mutants present in the standing variation
allowing these standing genetic variants to be springboards to multi-step rescue will help clarify the role of standing genetic variation on the genetic basis of rescue more generally
recombination can help combine such springboard mutations into rescue genotypes but will also break these combinations apart, as demonstrated in a two-locus two-allele model of rescue (Uecker and Hermisson 2016)
also left unexplored is the effect of density-dependent fitness
combining density-dependence and standing genetic variance is known to create complex dynamics in a one-locus two-allele model of rescue (Uecker et al. 2014)