Genetic drift occurs in a population Which of the following statements might be true

Introduction

Genetic drift, also known as the ‘Sewall Wright effect’, is one of four factors (next to mutation, gene flow, and natural selection) causing a gene pool to change over time. Genetic drift is the random variation in allele frequencies between generations due to sampling error in finite populations. As an example consider a single locus with two alleles, A and a, with equal frequencies p=0.5 and q=0.5, in a population of 10 diploid parents that interbreed and produce 10 offspring. Although the probability of drawing either allele a or A is 0.5, it is likely that a random sampling of 20 alleles from the available allele pool will yield a slightly different allele frequency in the offspring. For example, allele frequencies in the second generation might have shifted to p=0.4 and q=0.6, purely due to chance effects. Continuing with these second-generation allele frequencies, and after a second random sampling of 20 alleles, in the third generation deviations from the initial 0.5 frequencies will become even more likely. Note that this process of genetic drift shows that the Hardy–Weinberg equilibrium, predicting constant allele frequencies over time, does not hold in finite populations. Genetic drift is also nondirectional and as likely to decrease as to increase the frequency of one particular allele. Although genetic drift is an evolutionary process (because allele frequencies are changing), it does not directly change the degree of adaptation of an individual or a population.

III.G. Genetic Drift

Genetic drift involves the loss of alleles from a population by chance. Random fluctuations in allele frequencies in small populations reduce genetic variation, leading to increased homozygosity and loss of evolutionary adaptability to change.

The rate at which alleles are lost from a sexually reproducing population by genetic drift can be predicted. Sewall Wright (1969) developed the basic theoretical model in 1931 and showed analytically how the rate varies with population size. Actually, it is not the census size (N) that is important but rather the genetic effective population size (Ne). This parameter takes into account the fact that closely related individuals will share alleles by common descent. Monozygotic twins are genetically identical and therefore should be counted as one individual rather than two. Sibs share half their genes with each other and half with each of their parents and are therefore not equivalent to two genetically unrelated individuals. The genetic effective number of individuals in a population is therefore almost always less than the number of individuals counted by an ecologist. Ne can, under some breeding systems, be one or two orders of magnitude less than N. Consider, for example, the number of adults in a sexually reproducing population: In a monogamous species the census count of adults is useful, but in a harem species only 1 of the 10 males may be contributing to Ne. Ne can be variously defined in terms of unequal sex ratios among breeders, fluctuations in population size over several generations, and variance in family size (Lande and Barrowclough, 1987).

Wright (1969) defined the variance effective population size (Ne) as the number of individuals in an ideal population that would experience genetic drift at the same rate as the actual population. Ne can be defined and estimated in various ways using temporal ecological data, DNA sequences, and various methods of estimating migration rate. Some methods of estimation have theoretical value but little operational utility—it is almost impossible to determine the values that some algorithms require. Nevertheless, by estimating Ne one can assess the effects of different population management strategies. Unequal numbers of males and females, increased variance in family size, and temporal fluctuations in N all cause Ne to be much less than the census size, N. In many endangered populations Ne is only 10–30, and at such levels genetic variation becomes significant for a population's viability.

The Neutral Theory

These simple models of genetic drift can readily be applied to the study of molecular evolution and variation, assuming selective neutrality at the loci in question. Neutral theory allows for the possibility that many mutations are subject to purifying selection and are rapidly eliminated from the population (see Fixation Probabilities); it is claimed, however, that the fate of most mutations that are not removed by purifying selection is determined by drift rather than by selection (Kimura, 1983). This theory constitutes a useful null hypothesis, which can be tested against data on molecular evolution and variation by means of predictions of several different types.

III.C.1. The Neutral Theory

These simple models of genetic drift can readily be applied to the study of molecular evolution and variation, assuming selective neutrality at the loci in question. Neutral theory allows for the possibility that many mutations are subject to purifying selection and are rapidly eliminated from the population (see Section IV,D,3); it is claimed that the fate of the bulk of the mutations that are not removed by purifying selection is determined by drift rather than selection (Kimura, 1983). This theory thus constitutes a useful null hypothesis, which can be tested against data on molecular evolution and variation by means of predictions of several types.

Finite Population Size

Finite population sizes lead to genetic drift, which is widely recognized as a major evolutionary force by itself. The presence of genetic drift invalidates the stability of the allele frequencies (postulate 2). On the other hand, genetic drift only affects allele frequencies from one generation to the next and is therefore not visible when looking at a single generation. As a result, genetic drift does not affect the expected genotype frequencies (postulate 1). However, population size does have an actual effect on the genotype proportions that is unrelated to drift. Rather, population size affects HWE because of the non-random mating occurring in finite populations. Truly random mating is unlikely in small populations, especially in species with two separate sexes or when self-fertilization is not possible. Such non-random mating leads to an excess of heterozygotes and therefore to negative values of FIS. The expected effect on FIS is proportional to the inverse of the effective population size (Balloux, 2004; Waples, 2015):

EFIS=−1/2Ne+1

For most values of the effective population size, this effect is negligibly small. Even when Ne equals 100 individuals—small enough to make genetic drift very strong—the expected value of FIS is only about − 0.005; this means that in practice the effect of population size will be virtually undetectable in tests of HWE.

Genetic Distance

When two populations are genetically isolated, both mutation and genetic drift lead to differentiation in the allele frequencies at selectively neutral loci. As the amount of time that two populations are separated increases, the difference in allele frequencies between them should also increase, until each population is completely fixed for separate alleles. Therefore, calculation of genetic distance (D) between two populations provides a relative estimate of the time elapsed since these populations have existed as a single panmictic unit. Small estimations of distance among completely isolated populations indicate that they have only been separated for a short period of time. Alternatively, in the absence of isolation, small values of genetic distance may indicate population structure (i.e., subpopulations in which there is random mating, but between which there is a reduced amount of gene flow).

The explanation

Sewell Wright (1931) emphasized the important role of random genetic drift in the evolutionary process. He suggested that in moderate-size populations drift would act to change allele frequencies in a direction that might not be favored by natural selection but could allow the population to explore the fitness landscape and possibly evolve to a higher fitness plateau. Fisher was not sympathetic to this view and the data collected by E. B. Ford allowed a direct comparison of the impact of selection and drift on allele frequency variation.

Ford had worked with a single locus wing color polymorphism in the moth Panaxia dominula. From 1941 to 1946, collections made at Oxford, UK provided yearly estimates of the frequency of the medionigra allele. The adult moths were mostly active during the month of July for 12–23 days. During this time, individual moths would be marked and then the frequency of recaptures would be recorded. These data allowed Fisher and Ford to estimate the size of the adult population.

Fisher realized that the estimated frequency of the medionigra allele would vary due to two different sampling processes; the finite breeding population (e.g. drift) and the number of adults sampled to estimate the medionigra allele frequency. Fisher then devised a statistical test to see if the allele frequency variation over a six year sampling period was about what was expected from drift and allele frequency estimation or if it was greater than this, as might be expected if selection was acting on this polymorphisms but changing direction over time.

The estimates of population size were crude, so Fisher and Ford (1947) set the effective population size to be constant and at the lowest number seen over their six-year sample. They found that the observed variation was significantly greater than expected by drift and sampling alone. Fisher and Ford (1947) then concluded “Thus our analysis, the first in which the relative parts played by random survival and selection in a wild population can be tested, does not support the view that chance fluctuations in gene-ratios, such as may occur in very small isolated populations, can be of any significance in evolution.” The last part of this statement is a little strong given that at best this study was on a single population. Nevertheless, this work showed the power of combining evolutionary theory, statistics, and detailed field observation in an evolutionary study.

Thirty years later, as many more datasets of allele frequency time-series in natural populations became available, Schaffer et al. (1977) published an improved version of Fisher and Ford's test. However, both Schaffer et al. (1977) and Fisher and Ford (1947) only had estimates of the census population size. Mueller et al. (1985) made actual estimates of effective population size from demographic and census data of butterfly populations and used computer simulations to derive drift and sampling-only expectations.

B. Demographic Exchangeability

Since genetic exchangeability can counter the subdividing effects of natural selection and genetic drift for sexually reproducing populations, it is crucial to know what kinds of mechanisms prevent or promote subdivision of asexual populations. Another explanation is required for the cohesion of asexual taxa, and this explanation is termed demographic exchangeability. The fundamental niche is defined by the genetic tolerances of individuals to some set of ecological conditions. If two individual members of an asexual population occupy the same fundamental niche, then as individuals they are demographically exchangeable. Thus, complete demographic exchangeability occurs when all individuals in a population display exactly the same range of tolerances for all relevant ecological variables. Another way of thinking about this is in the historical perspective of ancestor–descendant relationships. Consider that in a hypothetical sexual population with complete genetic exchangeability, any single individual organism could become the ancestor to all members of the population at some point in the future. In an asexual population with complete demographic exchangeability, the same result could occur. Any one individual could become the ancestor to all members of the future population. Demographic exchangeability is an important mechanism for maintaining cohesion among asexual taxa.

To better understand how demographic exchangeability works, we can compare it with genetic exchangeability in sexual taxa. Suppose a mutation arises in an individual of a sexual population, which decreases the individual’s ability to mate with some subset of the members of its population. This means it would no longer have complete genetic exchangeability. This is obviously a weakening of the cohesion to the population for this individual and any of its descendants. Now further suppose that an asexual individual has a mutation that changes its tolerances to the ecological factors that define its fundamental niche. It will then pass on these changes to its descendants. Given that the parameters of their fundamental niche are no longer completely demographically exchangeable, their cohesion to the overall population has consequently been diminished. Templeton’s[20] concept of demographic exchangeability readily allows the incorporation of microevolutionary forces other than gene flow. Random genetic drift and natural selection are forces that operate as readily on asexual as sexual reproductive systems, and they act as cohesive forces for asexual species.

The cohesion, evolutionary, and phylogenetic concepts each have in common their recognition of the importance of cohesion and ancestor–descendant relationships. Templeton, the author of the cohesion concept, criticized the evolutionary concept as dealing with the manifestation of cohesion and not its mechanics. Templeton has greatly illuminated the mechanisms of cohesion, but his cohesion concept is really contained within the framework of the evolutionary concept.

A good intellectual benchmark to start from, or return to, when considering any aspect of species or speciation is Darwin’s basic proposition that new species originate by the splitting of preexisting species. Under this premise we accept the idea that species are new lineages, but we are not constrained in any way on how to identify them. This leads us to an ontological species concept rather than an operational species concept. Arguably, the evolutionary species concept is the best available concept that can be used to identify species level units of biodiversity because this concept is consistent with what is known about naturally occurring units that are the result of evolutionary history[12]. Nevertheless, the cohesion concept articulated by Templeton[20] complements the evolutionary species concept by identifying the mechanisms that are responsible for the speciation process. Templeton’s cohesion mechanisms[20] deserve careful consideration because they play a critical role in the process of speciation and generation of biodiversity. Students of invertebrate zoology are encouraged to carefully read and consider Templeton’s paper[20] because it is especially relevant to the diverse reproductive strategies of aquatic species of invertebrates.

Culture Maintenance and Size

Unruh et al. (1983) believed that the best way to retain heterozygosity and prevent genetic drift in laboratory cultures is to maintain relatively large population sizes (>100) in the laboratory. After studying heterozygosity and effective size in laboratory populations of the aphidiid Aphidius ervi Haliday, Unruh et al. (1983) warned that genetic drift and loss of heterozygosity are more severe than would be expected from the number of individuals used to maintain cultures. Discussed were factors that make effective population sizes much smaller than would be apparent, including fluctuations between generations, haplodiploidy, sex linkage, highly variable progeny production by individual females, and highly skewed sex ratios.

Omwega and Overholt (1996) found that the effective population sizes for two geographic laboratory colonies of the braconid Cotesia flavipes Cameron from Pakistan were estimated at 3.4% and 9.0% of the number of breeding females used to continue the colonies. Approximately 1000 breeding females were used to perpetuate each colony during the first 12 laboratory generations, and during this time there was actually a slight increase in heterozygosity at the MDH (malate dehydrogenase) genetic locus in each colony. However, during the next 10 generations, only 500 females were used per generation, and there was a rapid decrease in genetic variation, as measured by electrophoresis. Therefore, using 1000 mated females to perpetuate the C. flavipes colonies maintained greater genetic variation than using 500 females.

Genetic Variability and Molecular Evolution

Molecular epidemiological analysis of the available hantavirus genome sequence indicates that hantavirus genetic diversity is largely driven by genetic drift, that is, accumulation of point mutations and nucleotide insertions/deletions that are fixed on the genome due to stochastic processes. The percentage identities for the nucleotide sequences of S and M segments between viruses with the same genotype are similar, suggesting that the two segments have evolved in parallel.

Many RNA viruses have highly mutable and genetically diverse genomes, largely due to their high replication rates and error-prone polymerases. Populations of closely related viruses with similar genomes found in a single host are described as quasispecies. The formation of quasispecies populations in a single rodent reservoir is linked to rapid viral evolution. Recent studies demonstrate the presence of viral quasispecies in rodents infected with TUL or PUU hantavirus. The ability of PUUV to accumulate changes in the NCR of the S segment during passage in cell culture and the capacity of HNTV to escape antibody neutralization in vitro also demonstrate the adaptability and plasticity of hantaviruses.

Genetic diversity in hantaviruses is also influenced by genetic shift, which occurs through both reassortment of genome RNA segments and recombination. Reassortant strains of two closely related hantaviruses have been found in nature. The exchange of gene segments seems to be nonrandom and occurs more frequently in M segments than in S or L segments. In vitro studies have demonstrated the simulation of genetic reassortment between two related strains of SNV. Such studies demonstrate a markedly decreased probability of reassortment between members of different species. These findings suggested that genetic distance and the frequency of formation of viable virus through reassortment are inversely correlated. This may be due to specific structural and functional properties of the viral genomes and their replication. Additionally, genetic recombination appears to be involved in the evolution of TULV; however, the role of recombination in hantavirus evolution requires further study.

The specific features enabling a given hantavirus to cause renal rather than pulmonary symptoms, or subclinical/mild rather than severe manifestations, are not completely understood. A single amino acid substitution in the HTNV glycoprotein may substantially alter the virulence of the virus in newborn mice. PUUV variants passaged in colonized bank voles and in cultured Vero E6 cells had reduced infectivity in wild rodents. Nucleotide-sequence comparisons of the wild-type and Vero E6-adapted variants revealed only one amino acid substitution in the L protein, and silent mutations in L and in the 3′ NCR of S segment. More extensive studies involving other viruses will be needed to establish a direct correlation between genomic changes and viral phenotype.

In summary, several possible mechanisms, generating the genetic diversity observed among RNA viruses, could explain the emergence of new strains from old viruses. These mechanisms may thereby underlie new disease patterns and promote expansion into new hosts.