What type of selection increases genetic variation

While overdominance cannot occur for haploid pathogens such as bacteria or ascomycetous fungi, it is very important for the plant hosts. Overdominance is the basis of F1 hybrid crops such as maize and sorghum. After many generations of selection, an equilibrium will be achieved. The equilibrium allele frequency will be:. Overdominance maintains both alleles in the population to achieve the maximum overall fitness for a population Figure The equilibrium frequencies depend on the values of the selection coefficients, s and t, regardless of the initial allele frequencies.

The equilibrium at this point is stable. These equilibria are unstable and the equilibrium will be broken when allele frequencies deviate slightly from the equilibrium points 0 or 1 , whereupon the population moves toward the stable equilibrium point. This is an example of stabilizing selection. Fitness is a complicated concept. It is a measurement of the total output of viable progeny by an individual in its lifetime. If no offspring are produced, the fitness of an individual is zero.

Fitness increases with increasing life span also called viability and the number of progeny produced also called fecundity. As an example in plant pathology, the most fit pathogen genotype is the one that infects the most host plants in the shortest period of time and produces the most spores for pathogens that utilize asexual reproduction.

Populations of pathogens undergoing selection will constantly evolve to increase their overall level of fitness. The type of selection that operates will determine the direction of change in allele frequency to achieve the optimum overall fitness for the population Figure Figure The effects of selection on allele frequency in a one-locus, two allele fitness model.

In case 1, the A allele has a fitness advantage over the a allele. In case 2, the a allele has a fitness advantage over the A allele. According to Fisher's Fundamental Theorem of Natural Selection the mean fitness of a population always increases in a fluctuating environment. The change in fitness of a selected population will be proportional to the additive genetic variation for genes affecting fitness in the population. Therefore, populations will move to the nearest local optimum of allele frequencies that maximize fitness, which is not necessarily the global optimum.

As the amount of genetic variation in populations increases, the rate of change in fitness of the population increases proportionally. As a result, populations with the greatest genetic diversity have the greatest potential for evolution.

Natural selection is the driving force for boom and bust cycles and for the evolution of fungicide resistance in plant pathogens. Selection can occur on genes increasing and decreasing the frequencies of specific alleles or on genotypes increasing and decreasing the frequencies of specific clones. Both types of selection are well documented in pathogen populations.

As an example, selection on genes alleles occurs when mutants that lost an avirulence allele encounter a plant with a resistance gene. This process has been documented dozens of times for cereal rusts and mildews. Selection on genotypes occurs for Fusarium oxysporum formae speciales and probably explains the displacement of the "old" clone of Phytophthora infestans by the "new" clones of P.

A further example comes from the barley pathogen Rhynchosporium secalis. Figure 25 shows evidence of directional selection as the R. A final example comes from oat crown rust and stem rust, caused by Puccinia coronata and Puccinia graminis f. Following the development of Victoria blight caused by Helminthosporium victoriae which caused a devastating disease only on oat cultivars carrying a crown rust resistance gene originating from the oat cultivar Victoria, oat farmers rapidly shifted to new varieties carrying rust resistance derived from the oat cultivar Bond.

The corresponding pathogen populations rapidly shifted from virulence against the Victoria resistance genes toward virulence against the Bond resistance genes Figure The haploid model of natural selection is based on one locus with two alleles, and is applicable to haploid fungal pathogens such as ascomycetes , bacteria and viruses.

This model can be applied to selection for multilocus haplotypes as well as individual alleles at single loci. This model can be used to estimate rates of change for avirulence alleles sexual reproduction or for measuring competition among multilocus haplotypes under asexual reproduction.

If w 1 and w 2 are the fitnesses associated with alleles haplotypes A 1 and A 2 respectively, then we can predict the change in frequency of the A 2 allele haplotype after one generation of selection as follows:.

This formula represents the general selection formula that can be applied to any case of selection involving a haploid organism. This model is used to predict how fast allele or haplotype frequencies will change when an avirulent pathotype encounters a host population with the corresponding resistance allele.

Neither dominance nor overdominance is possible in this case. Selection quickly removes deleterious e. Though our prediction from selection models is that avirulence alleles should rapidly disappear after resistance genes are introduced into plant populations, many empirical studies indicate that populations of haploid pathogens are quite variable at avirulence loci.

Individual pathogen strains vary in other phenotypes as well as for avirulence genes. Three important questions to consider are:. Many plant pathologists such as Van der Plank considered these questions in the framework of the gene-for-gene interaction between plants and pathogens. They believed that "stabilizing selection" that we now know is really directional selection against unnecessary virulence alleles was one of the mechanisms maintaining genetic variation of haploid pathogens.

The basic idea of gene-for-gene interactions according to Van der Plank was that if the virulence allele loss of elicitor is not needed for the pathogen to infect the plant, and there is a fitness cost associated with losing the elicitor, then in the presence of the resistant host, selection will favor strains with the avirulence allele, and the level of virulence in the pathogen population will "stabilize.

Plant pathologists have tried to explain variation at the avirulence loci of pathogens for decades. The classic selection model in plant pathosystems is best exemplified by the models of K. Leonard and his colleagues Leonard and Czochor, Leonard, They explained why unnecessary virulence genes persist in natural and agricultural ecosystems. Coevolved host-virus associations are in red. B The genetic variance in log 2 viral load was estimated from the between family variance assuming that all genetic variance is additive.

Posterior probabilities for significantly different genetic variances are shown in grey see Supplementary file 1 and 2. To examine whether the genetic basis of resistance to coevolved and non-coevolved viruses was different we estimated the genetic correlations r g in their viral loads.

These represent the proportion of genetic variance in viral load between pairs of viruses that shares the same genetic causes. Similar results were obtained using the D. In the other species our estimates sometimes had wide credible intervals, but the genetic correlations between coevolved and non-coevolved viruses were mostly below 0.

Therefore if natural selection increases genetic variation in susceptibility to a natural pathogen, there is expected to be a smaller effect on non-coevolved viruses.

The resistant allele in each of these genes has arisen recently by mutation and been driven up in frequency by natural selection, presumably due to the presence of DMelSV in natural populations Bangham et al. Therefore, if these genetic variants confer resistance to DMelSV but not the other sigma viruses, then this may explain the differences in genetic variance that we observed.

To examine whether CHKov1 or p62 contributed to the differences in genetic variance we observed in D. The resistant allele of p62 was present at such a low frequency 1. To confirm these results we infected flies from 32 inbred D.

Each point is the viral load of a separate inbred fly line carrying the resistant Res or susceptible Sus allele of P62 or CHKov1. Horizontal bars are medians. To investigate how coevolution shapes the genetics of resistance, we mapped loci controlling resistance using a D.

This population samples genetic variation in a small number of genotypes from around the world the experiments above sampled many genotypes from a single location. It was founded by allowing two sets of 8 inbred founder lines to interbreed for 50 generations, then creating recombinant inbred lines RILs whose genomes are a fine-scale mosaic of the original founder genomes.

We used RILs from these populations, which have up to 15 alleles of each gene one founder line is shared between the two populations. We first estimated the genetic variance in viral load within our mapping population. The results recapitulated what we had found above in a natural population of flies — there was considerably more genetic variation in susceptibility to the coevolved virus than the non-coevolved viruses Figure 4A , filled circles. Therefore, our earlier result from a single population holds when sampling flies from across six continents, although the magnitude of the effect is considerably greater in this mapping population.

A The genetic variance in viral load within the mapping population filled circles. The open circles are estimates of the genetic variance after accounting for the effects of the QTL in panel C.

B QTL affecting viral load. C The effect of the seven QTL detected on the load of the three viruses. Only QTL that remained were significant following multiple regression with all the loci are shown. The coevolved virus is shown in red. To examine the genetic basis of virus resistance, we looked for associations between genotype and viral load across the genome Figure 4B.

The susceptible allele of CHKov1 was not present in the fly lines assayed. To examine the effect that the QTL have on viral load, we first split the founder alleles into a resistant class and a susceptible class see Materials and methods and then estimated the difference in viral load between the functionally distinct alleles.

Together, this modest number of loci with substantial effects on resistance explains most of the high genetic variance in resistance to the coevolved virus Figure 4A , filled versus open circles. We have found greater genetic variation in susceptibility to viruses that naturally infect Drosophila compared to viruses that do not, suggesting that selection by these pathogens has acted to increase the amount of genetic variation in susceptibility.

This effect was largely caused by a modest number of major-effect genes that explain over half of the genetic variance in resistance. As the genetic variants in the genes p62 ref 2 P and CHKov1 that confer resistance to DMelSV have been identified, this has previously allowed us to use patterns of DNA sequence variation to infer how selection has acted on resistance in D. In both these genes the resistant alleles have arisen relatively recently by mutation and natural selection has pushed them rapidly up in frequency, leaving a characteristic signature of elevated linkage disequilibrium and low genetic diversity around the variant causing resistance Bangham et al.

There is no indication of negative frequency dependent selection, and these polymorphisms appear to have arisen from partial selective sweeps Bangham et al. The most parsimonious explanation of these observations is that there has been directional selection favouring resistance alleles although this type of data cannot rule out negative frequency dependent selection as is predicted by models of coevolution.

At equilibrium, directional selection on a trait is not expected to affect its genetic variance relative to a population under mutation-drift balance; Hill, However, the genetic variance will transiently increase if the variants under selection are initially at low frequency Barton and Turelli, , as was the case for both p62 and CHKov1 Bangham et al.

A particular feature of pathogens is that the direction of selection is likely to continually change as new pathogens appear in populations or existing pathogens evolve to overcome host defences.

For example, in France and Germany in the s, DMelSV evolved to largely overcome the effects of the resistant allele of p62 Fleuriet and Periquet, ; Fleuriet and Sperlich, If selection by pathogens continually changes and resistance evolves from new mutations, then this may cause a sustained increase in genetic variance in susceptibility to infection.

A key question is whether the increased genetic variation that we see in coevolved Drosophila —sigma virus interactions will hold for coevolved pathogens more generally. Theory suggests that a critical factor determining levels of genetic variation is whether resistance is costly to evolve, as this can result in the maintenance of variation by negative frequency dependent selection Antonovics and Thrall, ; Boots et al.

In humans this has been proposed as an explanation of why there is less genetic variation in susceptibility to pathogens that are effectively controlled by the adaptive immune response, as these resistance mechanisms may be less costly Baker and Antonovics, However, it seems unlikely that virus resistance in Drosophila is costly, as experiments have failed to detect costs of DCV resistance Faria et al. Sigma viruses are also extreme host specialists, so evolutionary changes in resistance will tend to alter pathogen prevalence and so the strength of selection.

These epidemiological feedbacks are predicted to frequently increase genetic diversity Boots et al. However, in D. Therefore, it seems unlikely that our conclusions will be a quirk of the sigma virus system. In an analogous study of rust resistance in wild flax plants, sympatric putatively coevolved populations had fewer partial resistances than allopatric populations, suggesting more major gene effects, even though overall there was somewhat less genetic variation in susceptibility to sympatric fungal pathogens Antonovics et al.

Quantitative traits are typically controlled by a very large number of genetic variants, each of which tends to have a very small effect Shi et al.

However, susceptibility to sigma viruses has a simpler genetic basis, with seven polymorphisms explaining over half the genetic variance. This confirms our previous work in D. As these genetic variants mostly only affect the naturally occurring pathogen of D. One explanation for this observation is that most quantitative traits are under stabilising selection, so major effect variants will tend to be deleterious and removed by selection Gibson, In contrast, selection by pathogens likely changes through time and populations may be far from their optimal level of resistance.

Alternatively, the coevolution of hosts and parasites can favour discrete susceptible and resistant hosts Boots et al.

The simple genetics may also be driven by mutation—for many traits major-effect mutations that increase fitness may be extremely rare, but this may not be the case for virus resistance. For example a single loss of function mutation may prevent a virus binding to a host receptor or utilising other parts of the host cellular machinery, and so confer strong resistance.

Regardless of its causes, it may be common that susceptibility to infectious disease has a simple genetic basis.

In humans, Hill advocated the view that susceptibility to infectious disease is qualitatively different from other traits and has a much simpler genetic basis Hill, In Drosophila, resistance to DCV and parasitoid wasps both have a simple genetic basis Magwire et al.

In plants, major-effect polymorphisms in R genes are commonplace Hammond-Kosack and Jones, and in a plant-fungi system genotype-by-genotype interactions explain a larger proportion of the total variance in sympatric more coevolved associations Antonovics et al. In contrast, studies of bacterial resistance in Drosophila have typically used pathogens that are unlikely to have any history of coevolution, and have found a polygenic basis to resistance Bou Sleiman et al.

In these studies the polymorphism with the largest effect was found against the only natural D. A major source of emerging infectious disease is pathogens jumping into novel hosts where they have no co-evolutionary history Longdon et al.

Our results suggest that when a pathogen infects a novel host species, there may be far less genetic variation in susceptibility among individuals than is normally the case. Longer term, low levels of pre-standing genetic variation may slow down the rate at which the new host can evolve resistance to a new pathogen. In conclusion, we have demonstrated that selection by pathogens has increased the amount of genetic variation in host susceptibility. We find resistance has a simple underlying genetic architecture and is largely controlled by major effect resistance loci.

These infected lines were collected from the wild between — all from the UK, bar D. We set up a common garden experiment to measure genetic variation in susceptibility to natural and non-natural viruses across four host species. In a fractional factorial experiment each species was infected it with its own virus, as well as two viruses that do not infect that host species see Figure 1.

All fly stocks used see additional methods for stock details were tested for existing sigma virus infection using RT-PCR over two generations. For all species we collected flies from the wild and we used a full-sib mating design. The progeny of these crosses were infected by injecting them with 69 nl of the viruses intrathoracically and measuring viral RNA loads 15 days post infection, as in Longdon et al.

This time point was selected as RNA viral load tends to plateau from around day 15 post infection and there is no mortality from infection in this period. The specifics for each host species, including sample sizes, are detailed in the additional methods. We genotyped parents of each D. We genotyped parental flies using PCR assays that produce different sized products depending on whether flies carry resistant or susceptible alleles.

Information on these PCRs and primer sequences can be found in Supplementary file 4. We then calculated the number of resistance alleles in each family by summing the number of alleles from both mothers and fathers. We produced genotype information for of the families. Another resistance allele has been identified in the gene Ge-1 Cao et al.

However, this allele has been found to occur at a low frequency in wild populations. We genotyped parental flies from our experiment parental flies for some families could not be collected and found the resistant allele was not present, suggesting it is rare or absent. We further examined the effect of alleles known to affect susceptibility to DMelSV on all three viruses.

No lines in the panel were resistant for p62 and susceptible for CHKov1. Injections were carried out over 13 weeks. Each day of injection a mean of 47 unique lines range 20—60 and 51 replicate vials were injected with 1—3 different viruses.

In total we assayed DSPR lines lines had two biological replicates. Each replicate vial contained a mean of 12 flies range 1— In total, 15, flies were injected across both panels of DSPR lines. We injected lines with all three viruses, 38 with 2 viruses and 20 with one virus.

The order of injection of lines and of viruses, was randomised across injection days. In the mids, Charles Darwin famously described variation in the anatomy of finches from the Galapagos Islands. Alfred Russel Wallace noted the similarities and differences between nearby species and those separated by natural boundaries in the Amazon and Indonesia. Independently they came to the same conclusion: over generations, natural selection of inherited traits could give rise to new species.

Use the resources below to teach the theory of evolution in your classroom. Genes are units of hereditary information. A gene is a section of a long molecule called deoxyribonucleic acid DNA. Genetics is the study of genes and how traits are inherited—or passed down—from one generation to the next.

Join our community of educators and receive the latest information on National Geographic's resources for you and your students. Skip to content. Image genetic variation In many species, special genetic variations give animals a camouflaged appearance to blend in with their environment, like this Catalpa Sphinx moth Ceratomia catalpae which uses its textured wings to blend in with a tree's bark.

Photograph by J. Twitter Facebook Pinterest Google Classroom. Encyclopedic Entry Vocabulary. Media Credits The audio, illustrations, photos, and videos are credited beneath the media asset, except for promotional images, which generally link to another page that contains the media credit. Even if conservation efforts boost population growth, low heterozygosity is likely to persist, since bottlenecks periods of reduced population size have a more pronounced influence on Ne than periods of larger population size.

We have already seen that genetic drift leads to differentiation among demes within a metapopulation. If we assume a simple model in which individuals have equal probabilities of dispersing among all demes each of effective size N e within a metapopulation, then the migration rate m is the fraction of gene copies within a deme introduced via immigration per generation.

Natural selection can produce genetic variation among demes within a metapopulation if different selective pressures prevail in different demes. If N e is large enough to discount the effects of genetic drift, then we expect directional selection to fix the favored allele within a given focal deme.

However, the continual introduction, via gene flow, of alleles that are advantageous in other demes but deleterious in the focal deme, can counteract the effects of selection. In this scenario, the deleterious allele will remain at an intermediate equilibrium frequency that reflects the balance between gene flow and natural selection. The common conception of evolution focuses on change due to natural selection.

Natural selection is certainly an important mechanism of allele-frequency change, and it is the only mechanism that generates adaptation of organisms to their environments. Other mechanisms, however, can also change allele frequencies, often in ways that oppose the influence of selection. A nuanced understanding of evolution demands that we consider such mechanisms as genetic drift and gene flow, and that we recognize the error in assuming that selection will always drive populations toward the most well adapted state.

Carroll, S. Conservation Biology: Evolution in Action. Darwin, C. London, England: John Murray, Gillespie, J. Population Genetics: A Concise Guide , 2nd ed. Haldane, J. A mathematical theory of natural and artificial selection, Part I.

Transactions of the Cambridge Philosophical Society 23 , 19—41 Hedrick, P. Genetics of Populations, 3rd ed. The Hardy-Weinberg Principle. Evolution Introduction. Life History Evolution. Mutations Are the Raw Materials of Evolution. Speciation: The Origin of New Species.

Avian Egg Coloration and Visual Ecology. The Ecology of Avian Brood Parasitism. The Maintenance of Species Diversity.

Neutral Theory of Species Diversity. Population Genomics. Semelparity and Iteroparity. Geographic Mosaics of Coevolution.