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Gene Flow And Genetic Drift

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Last Updated: 02 July 2021

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In addition to natural selection, allelic frequencies in the population can change over time by mutations, gene flow, and genetic drift. Genetic variation can be generated in population, for instance, these beetles, simply by random mutations. Harmful mutations in the DNA of organisms are quickly eliminated from population by natural selection, while beneficial ones spread. Additionally, genes from outside the population can contribute to genetic variation through immigration of new individuals. When beetles from two populations regularly exchange individuals, two gene pools will eventually become more similar. Last, if the population size decreases due to some random event, such as storm, allelic frequencies will likely change dramatically, simply due to the smaller number of remaining alleles in the population. This change is referred to as Genetic drift.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Genetic Drift

In addition to flowing from one population to another, like plastic grocery sack in wind, alleles can change frequency within their own population. This is lovingly referred to as genetic drift. Genetic drift occurs thanks to random sampling within the population that leads to changes in allele frequency. And yeah, by sampling we mean passing on of genes the old fashioned way: via reproduction. There's typically a good range of diversity for alleles within the population, but any given allele might only exist in the DNA of a limited number of individuals. If those individuals reproduce more or less successfully by sheer happenstance, then we get a genetic drift: those alleles become suddenly more or less prominent in the gene pool. This effect is amplified when there are fewer individuals carrying genes for specific trait. For example, if only a handful of individuals carry a gene for trait, and randomly none of them ever reproduce, that gene disappears. Conversely, if they all manage to reproduce, and for some random reason, individuals without trait don't reproduce, that trait suddenly becomes much more prevalent. That's genetic drift for you, nature's version of roulette for passing on alleles. There are also two mechanisms for sudden, usually drastic, genetic drift: bottleneck effect and the founder effect. The Bottleneck effect describes genetic drift that occurs when population goes through traumatic event where a random majority of the population doesn't make it. We know. This sounds awful. It's like one of those crazy scenes on National Geographic where a sudden flood takes out bunch of lizards sunbathing on river rocks. They should have moved faster. As you can imagine, this makes a big difference in the resulting gene pool, and the end result will depend, in part, on chance events. As example, maybe most lizards have long tongues excellent for catching fast flies, but that deluge leaves only a few Mylie Cyrus - like lizards as surviving individuals. Now all their lizardy descendants will have a lower frequency of long - tongue alleles. Must have been some event. The Reds didn't even make it out other side. In bottleneck event, population goes through events where only a few random individuals survive. Here, marbles represent survivors, and the color of marble represents alleles for traits in the population. Let's imagine that it's gene that cod for either red, orange, or green skin. Because why not? These alleles had particular distribution in parent population, but after some serious stuff goes down that destroys most of the population, random surviving alleles may not be equally represented in the next generation. In this example, most surviving individuals carry orange allele, and none of reds survive. This alter allelic frequency in the next generation. Hopefully that red trait isn't very useful Founder effect describes a type of genetic drift that seems pretty similar to the bottleneck effect.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

1. Introduction

Mycosphaerella graminicola cause Septoria tritici leaf blotch on wheat. Mcdonald and colleagues used restriction fragment length polymorphism markers to determine the genetic structure of this pathogen worldwide and found that all populations collected from different geographic locations had similar frequencies of common alleles except populations collected from Australia and Mexico. Australian and Mexican populations had significantly lower gene diversity, fewer alleles at each locus, fix alleles at several RFLP loci, and gene frequencies were significantly different from populations at other locations. In Australia, this is probably due to the founder effect, whereby only a relatively small number of individuals arrive on this continent with the introduction of modern agriculture. Patzcuaro, Mexico population was sampled from breeding nursery used by CIMMYT to screen for resistance to this pathogen. This nursery is located far away from wheat production areas and was inoculated with a limited number of strains, presenting a clear example of genetic drift due to small founding population and continued geographical isolation. In contrast, Israeli population had the highest level of gene diversity, consistent with the hypothesis that the Middle East is the center of origin for this pathogen. Table 1. Effect of genetic drift on gene diversity at RFLP loci in Mycosphaerella graminicola populations from Oregon, Israel, Denmark, United Kingdom, Uruguay, Canada, Mexico, and Australia. Populations from Mexico and Australia show low gene diversity consistent with founder effects while the Israeli population shows highest gene diversity consistent with center of origin. An extreme example of genetic drift due to bottlenecking is the population of Phytophthora infestans pathogen that causes late blight of potatoes. It appears that the original global pandemic was caused by a single clone that escaped out of Mexico and into North America, was introduced into Europe and then was transported around the world as a result of human commerce. Stripe rust of wheat in Australia shows evidence of a single founder event. P. Striiformis was introduced into Australia in 1979. Only one race was found in 1979 - 1980, corresponding to race found in Europe, suggesting that Europe was the source of the introduction. Since original introduction, mutations have created new pathotypes in single introduced genetic background. Chestnut blight in North America also shows some characteristics of founder population as it has much less genetic diversity than populations in Asia. It appears that the center of diversity and possible center of origin is in Japan.


Measuring Genetic Drift

Genetic drift means change in gene pool strictly by chance fixation of alleles. The effects of genetic drift can be acute in small populations and for infrequently occurring alleles, which can suddenly increase in frequency in the population or be totally wiped out. Alleles thus fixed by chance may be neutralthat is, they may not confer any survival or reproductive advantage. Therefore, for small populations, genetic drift can result in significant change in gene frequency in a short period of time. Genetic drift can be caused by a number of chance phenomena, such as differential number of offspring left by different members of the population so that certain genes increase or decrease in number over generations independent of selection, sudden immigration or emigration of individuals in the population, changing gene frequency in the resulting population, or population bottleneck. Of these, population bottlenecks can cause radical change in allele frequencies in very short time. Population bottlenecks occur when populations suddenly shrink in size owing to random events, such as sudden death of individuals due to environmental catastrophe, habitat destruction, predation, or hunting. When a small number of surviving individuals give rise to a new population, there is a radical change in gene frequency in the resulting population, in which certain genes of the original population may radically increase in proportion while others may radically decrease or be wiped out completely, independently of selection. Additionally, resulting population contains a small fraction of the genetic diversity of the original population. Founder effect is a severe case of population bottlenecking and it happens when few individuals migrate out of the population to establish new subpopulation. Random genetic drift accompanies such founder effect, to severely reduce genetic variation that exist in the original population. In new population, founder effect can rapidly increase the frequency of allele whose frequency was very low in the original population. If allele is a disease - related allele, founder effect can lead to prevalence of disease in new population. An increase in specific diseases in the human population due to founder effect is seen in Old Order Amish of eastern Pennsylvania, 66 and in the Afrikaner population of South Africa. 67 current Amish population has descended from a small number of German immigrants who settled in the United States during the eighteenth century. The incidence of Ellis - van Creveld syndrome is many times more prevalent in this Amish population than in the American population in general. The origin of this disease can be traced back to one couple, Samuel King and his wife, who came to the area in 1744. A mutated gene that causes syndrome was passed along from the Kings and their offspring. The Amish population practices endogamy. Additionally, in this community, gene flow is centrifugal. That is, members may leave the community but outsiders do not join the communitytherefore,. There has been no introduction of exogenous genes into the Amish gene pool.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

2. Material and Methods

Gene flow IS an exchange of genes or genetic material between different populations within species. Extreme forms of hybridization involve gene flow between completely different species, which IS one of concerns with GMOs, wherein rDNA flow can lead to downstream implications, including changes in diversity and uneven competition. Risk assessments of gene flow are usually very limited in time and space. Large - scale studies of genetically modified crop plants, for example, are seldom studied epidemiologically. That IS, they are not studied on the same temporal or spatial scales as they actually grow. This greatly limits their usefulness in application, since processes at work may miss important synergistic, antagonistic, and chaotic outcomes, which can occur in agricultural and other ecosystems. For example, experiments do not allow much certainty in how genetic material may integrate, persist, and be disperse. Recall from discussions on risk assessment that public health and environmental risks involve very rare events. It IS not uncommon to attempt to predict the outcome of less than one event in a million. With inherently complex environmental systems, such as agricultural ecosystems, reliable methods are needed to address ecosystem response, uncertainty, variability, and change. Bayesian statistics have been particularly effective in forecasting pollutant scenarios and should be useful for predicting potential environmental impacts from GMO gene flow. For example, Bayesian methods have been used to pull together multiple gene flow studies. These analyses have shown that increasing isolation distance appears to be more effective to reducing GM - pollen dispersal than use of buffer zone, especially for small recipient fields. Expanding the width of the recipient field relative to pollen donor field can greatly reduce the average level of fertilization by foreign pollen within the recipient field. Results indicate that GM - pollination success decreases with isolation distance with width of non - GM field. Biochemodynamic processes described in Chapters 2 and 3 2 3 can form the basis for developing estimates of temporal and spatial extent of gene flow and other movement of genetic materials within and among ecosystems. As shown at top in Figure 8. 6, GMOs and genetic materials are distributed in the hydrosphere, atmosphere, and biosphere. Much of this transport begins with atmospheric dispersion, followed by movement in surface waters, infiltration into groundwater, and entry into the food chain. Dispersion models may be useful in extrapolating from smaller scales. One approach IS to start with smaller - scale studies, apply landscape characterization by overlaying satellite images, climate and agricultural data, and generate representative scenarios of GMO gene flows. Next, individual models are developed from biochemodynamic information. With this information, various simulations can be generated from which indicators of GE organism's gene flow can be extrapolated and indicators of dynamics of species can be estimated or predict. This approach can be used to analyze gene flow at landscape and regional level and to estimate changes in neighboring areas ' GMO content.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

4. Discussion

It has been claimed that urbanization leads to smaller, more fragmented populations that are subject to stronger genetic drift. If true, then we expect urban populations to exhibit less genetic diversity than non - urban populations. This pattern has been reported from a diverse array of native species, including mammals, birds, amphibians, reptiles, mollusks, insects, and plants. The effects of urbanization on genetic drift in introduced species that thrive in urban environments are less clear. Understanding how urbanization affects patterns of genetic diversity within populations can be important for discerning whether parallel clines in allele frequencies are likely to have arisen due to natural selection or genetic drift. This IS especially relevant in plants like T. Repens, in which production of HCN results from epistatic interaction between two loci, and fixation of non - functional allele at either locus causes the population to be fixed for acyanogenic phenotype. Using simulation modelling, Santangelo et al. Show that population bottlenecks and founder events preferentially result in fixation of acyanogenic genotypes and can lead to urban - rural clines with HCN frequency as strong as those reported here. Thus, if T. Repens invades cities from rural areas, which IS expected given the agricultural history of this plant, populations may evolve decreased frequency of HCN in cities due to the effects of genetic drift alone. Although we cannot examine this process directly, we can test whether urban populations show the expected pattern of decreased genetic diversity at neutral loci, which should occur if urban populations experience population bottlenecks or founder events. Our results show that urbanization does not lead to consistent loss of genetic diversity in T. Repens, and some measures of urbanization were associated with increased genetic diversity. There was no consistent effect of distance from urban centre on any measure of genetic diversity. Contrary to the pattern predicted for native species,% impervious surface was associated with increased allelic richness, indicating that urban populations contain a greater number of alleles per locus than non - urban populations. Greater impervious surface itself IS unlikely to be the causal factor underlying this relationship, but instead urban features that covary with impervious surface. For example, most urban and suburban habitats in Ontario have an abundance of mowed grass in which T. Repens thrives. By contrast, T. Repens IS often difficult to find in rural areas because it IS restricted to mowed areas and grazed pastures, which are not always abundant in rural Ontario. It does poorly on crop lands, roadsides with tall grass and abandoned agricultural land undergoing succession. Although the density of populations does not change as function of distance from urban centre, our qualitative observations suggest that the total abundance of T. Repens IS greater in cities than in equivalently sized non - Urban areas. Thus, contemporary Urban T. Repens populations could support greater diversity of alleles than non - Urban populations that tend to be more fragmented.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Model

Many other models of gene flow have been described in addition to the island model. Figure 7 shows examples of one - and two - dimensional stepping - stone models and more complex multidimensional models of gene flow. Each of these models represents permutation of the same scheme and can be adapted to the reality of agricultural or natural ecosystem under study. The end result of gene flow is to make populations become genetically similar. This is illustrated in Figure 8, which shows how quickly geographically separated populations converge on same allele frequency when 10% of each population is made up of immigrants from other populations.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Drift and fixation

Natural selection, genetic drift, and gene flow are mechanisms that cause changes in allele frequencies over time. When one or more of these forces are acting on the population, population violates Hardy - Weinberg assumptions, and evolution occurs. The Hardy - Weinberg Theorem thus provides a null model for the study of evolution, and the focus of population genetics is to understand the consequences of violating these assumptions. Natural selection occurs when individuals with certain genotypes are more likely than individuals with other genotypes to survive and reproduce, and thus to pass on their alleles to the next generation. As Charles Darwin argued in on Origin of Species, if the following conditions are meet, natural selection must occur: there is variation among individuals within the population in some trait. This variation is heritable. Variation in this trait is associated with variation in fitness. Directional selection leads to an increase over time in the frequency of favored allele. Consider three genotypes that vary in fitness, such that AA individuals produce, on average, more offspring than individuals of other genotypes. In this case, assuming that selective regime remains constant and that action of selection is only a violation of Hardy - Weinberg assumptions, alleles would become more common each generation and would eventually become fixed in the population. The rate at which an advantageous allele approaches fixation depends in part on dominance relationships among alleles at the locus in question. The initial increase in frequency of rare, advantageous, dominant allele is more rapid than that of rare, advantageous, recessive allele because rare alleles are found mostly in heterozygotes. New recessive mutation therefore can't be seen by natural selection until it reaches high enough frequency to start appearing in homozygotes. The new dominant mutation, however, is immediately visible to natural selection because its effect on fitness is seen in heterozygotes. Once advantageous alleles have reached high frequency, deleterious alleles are necessarily rare and thus mostly present in heterozygotes, such that final approach to fixation is more rapid for advantageous recessive than for advantageous dominant allele. As a consequence, natural selection is not as effective as one might naively expect it to be at eliminating deleterious recessive alleles from populations. Balancing selection, in contrast to directional selection, maintains genetic polymorphism in populations. For example, if heterozygotes at locus have higher fitness than homozygotes, natural selection will maintain multiple alleles at stable equilibrium frequencies. Stable polymorphism can also persist in the population if fitness associated with genotype decreases as that genotype increases in frequency. It is important to note that heterozygote disadvantage and positive frequency - dependent selection can also act at locus, but neither maintains multiple alleles in the population, and thus neither is a form of balancing selection. Genetic drift results from sampling error inherent in transmission of gametes by individuals in a finite population.


Measuring Genetic Drift

Genetic drift is change in allele frequencies in the population over time due to random sampling events. Although specific genetic consequences of genetic drift during give demographic bottleneck are unpredictable, overall effect of drift is to erode genetic diversity. Effective population size, or N e, is a measure of how sensitive a population is to genetic drift. N e is defined as the size of a hypothetical, theoretically ideal population that would experience the same level of inbreeding, loss of heterozygosity, and genetic drift per generation as the real population in question. Other factors besides census size of population will influence changes in allele frequencies over time; by excluding these factors, N e make it possible to evaluate and compare measurements of drift across species with very different life histories. There are different ways to empirically estimate N e over both short - and long - term time scales, but N e is virtually always smaller, and often much smaller, than the census size of the population. The Frankham review published estimates of N e / N for wildlife species, and found that N e averages only 10 - 11% of total census size. In large populations, it takes a long time to see the major effect of genetic drift on allele frequencies; genetic diversity represents a balance between mutation and natural selection. However, when N e s < 1, where s is selection coefficient describing difference in fitness between two alleles, drift can counter selection, and alleles will behave as if they are neutral. Thus, through this mechanism, small populations may show greater maladaptation than larger ones. By similar logic, mildly deleterious mutations will tend to accumulate in small populations, because selection is ineffective at removing them. This can lead to mutational meltdown: as deleterious mutations become fix, they drive down the population growth rate, making the population progressively more susceptible to fixation of future mutations.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Data accessibility

After filtering for read quality and presence of correct barcode and SbfI recognition site, total of 93 314 044 clean read pairs were generated across eight individuals, including in pair - end Illumina HiSeq lane. Of these, 69% were identified as PCR duplicates and were remove, leaving 29 357 186 read pairs from which we assembled a total of 126 264 unique consensus RAD contigs. Single - end sequencing yielded 494 418 159 clean sequence reads across 192 individuals. These were added to 55 262 507 clean forward reads from eight individuals, including in pair - end sequencing lane for a total of 549 680 666 clean reads from 200 individuals that were align to above 126 264 RAD contigs. After all quality filters, total of 4858 variable SNP loci were available for analysis when the gray fox outgroup was included and 5293 SNPs were available without gray foxes. Mean coverage per locus ranges from 5 - 40 and the number of loci per individual ranges from 2381 - 4854. We find exceptionally high genetic differentiation among island fox populations. Pairwise F values between most islands were extremely high, ranging from 0. 463 - 0. 963, and all values were statistically significant. F values were insensitive to the threshold used for the allowed level of missing genotypes, as revealed by the high correlation between pairwise F values calculated using our standard threshold of < 50% missing genotypes vs. More stringent threshold of < 20% missing genotypes. Pairwise Jost's D values were also significantly correlated with pairwise F values, although SNI was more similar to SCL with Jost's D than with F. Best - support value of K in our Structure analysis was K = 7 based on mean LnP and K = 2 based on K method. However, K = 2 was clearly underestimated based on our F, Jost's D, PCA, and Neighbor - net results. Interestingly, although K = 7 was best - support based on mean LnP, no individuals had any measurable portion of their genome assigned to the seventh cluster, meaning K = 6 effectively had the highest support. With K = 7, individuals were generally assigned to a single island. However, approximately 73% of genomes of individuals from SRI were assigned to SMI and approximately 27% to SCI, indicating SRI has an intermediate genetic relationship to SMI and SCI. Several individuals on SCA also had a small proportion of their genomes assigned to SCI. As expect, all island foxes are grouped by island in PCA and Neighbor - net tree. Removal of gray foxes does not change this result. 4b. Island fox populations group geographically, with two broad clusters representing northern island SMI, SRI, and SCI and southern island SCA, SCL, SNI populations. Island fox populations had low within - population genetic variation compared to mainland gray foxes in Table 4. This pattern was evident for all four measures of Genetic variation estimate observed heterozygosity, expect heterozygosity, allelic richness, nucleotide diversity, but was most pronounced for, which is based on invariant sites as well as SNPs whereas other three measures are only based on SNPs.


Discussion

Our analysis of over 5000 SNPs reveals that genetic drift is the dominant evolutionary force driving genetic differentiation among island fox populations. Three lines of evidence support this conclusion. First, genetic variation, particularly nucleotide diversity, was much lower in island fox populations than in their sister species, gray fox, or other species with published data Second, most island fox populations have low effective population sizes, and all have genetic signatures of historical bottlenecks. Third, significant negative relationship between pairwise F and measures of within population genetic variation suggests that strength of genetic drift determines degree of divergence. Nonetheless, we also uncovered evidence for adaptive divergence among island fox populations based on high F outlier tests, indicating that divergent selection may have contributed to divergence despite strong genetic drift. No loci were associated with variation in climate or diet. However, patterns of population similarity at high F outlier loci mirror patterns of morphological similarity, suggesting genetically - base, adaptive differences exist among populations, supporting subspecies designation. Alone, findings of adaptive divergence among island fox populations suggest that they should continue to be managed separately. However, extremely low genetic variation and N e found in some populations, particularly SNI, indicate that they are vulnerable to negative inbreeding effects and loss of genetic variation. These populations might therefore benefit from genetic rescue using individuals from another island. These opposing management optionsmanaging islands separately to maintain adaptive differences vs. Supplementing small, declining populations with individuals from another island to boost fitness through genetic rescuecreate management conundrum. We argue that this uncertainty could best be resolved by research to determine the severity of inbreeding depression, if any, and the potential benefits / costs of genetic rescue. Below, we discuss these and other results in more detail.


Genetic Drift and Evolutionary Theory

Genetic drift is at the core of the shifting - balance theory of evolution coined by Sewall Wright where it is part of a two - phase process of adaptation of subdivided population.S In the first phase, genetic drift causes each subdivision to undergo random walk in allele frequencies to explore new combinations of genes. In the second phase, new favorable combination of alleles is fixed in the subpopulation by natural selection and is exported to other demes by factors like migration between populations. Much of the basic theory of genetic drift was developed in the context of understanding the shifting - balance theory of evolution. Genetic drift also has a fundamental role in the neutral theory of molecular evolution proposed by population geneticist Motoo Kimura. In this theory, most of the genetic variation in DNA and protein sequences is explained by balance between mutation and genetic drift. Mutation slowly creates new allelic variation in DNA and proteins, and genetic drift slowly eliminates this variability, thereby achieving a steady state. The fundamental prediction of genetic drift theory is that the substitution rate in genes is constant, and equal to the mutation rate.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Sources

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

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