Population genetics

Population genetics is a subfield of genetics that deals with genetic differences within and between populations, and is a part of evolutionary biology. Studies in this branch of biology examine such phenomena as adaptation, speciation, and population structure.[1]

Population genetics was a vital ingredient in the emergence of the modern evolutionary synthesis. Its primary founders were Sewall Wright, J. B. S. Haldane and Ronald Fisher, who also laid the foundations for the related discipline of quantitative genetics. Traditionally a highly mathematical discipline, modern population genetics encompasses theoretical, lab, and field work. Population genetic models are used both for statistical inference from DNA sequence data and for proof/disproof of concept.[2]

What sets population genetics apart today from newer, more phenotypic approaches to modelling evolution, such as evolutionary game theory and adaptive dynamics, is its emphasis on genetic phenomena as dominance, epistasis, and the degree to which genetic recombination breaks up linkage disequilibrium. This makes it appropriate for comparison to population genomics data.

History

Population genetics began as a reconciliation of Mendelian inheritance and biostatistics models. Natural selection will only cause evolution if there is enough genetic variation in a population. Before the discovery of Mendelian genetics, one common hypothesis was blending inheritance. But with blending inheritance, genetic variance would be rapidly lost, making evolution by natural or sexual selection implausible. The Hardy–Weinberg principle provides the solution to how variation is maintained in a population with Mendelian inheritance. According to this principle, the frequencies of alleles (variations in a gene) will remain constant in the absence of selection, mutation, migration and genetic drift.[3]

Biston.betularia.7200
The typical white-bodied form of the peppered moth.
Biston.betularia.f.carbonaria.7209
Industrial melanism: the black-bodied form of the peppered moth appeared in polluted areas.

The next key step was the work of the British biologist and statistician Ronald Fisher. In a series of papers starting in 1918 and culminating in his 1930 book The Genetical Theory of Natural Selection, Fisher showed that the continuous variation measured by the biometricians could be produced by the combined action of many discrete genes, and that natural selection could change allele frequencies in a population, resulting in evolution. In a series of papers beginning in 1924, another British geneticist, J.B.S. Haldane, worked out the mathematics of allele frequency change at a single gene locus under a broad range of conditions. Haldane also applied statistical analysis to real-world examples of natural selection, such as peppered moth evolution and industrial melanism, and showed that selection coefficients could be larger than Fisher assumed, leading to more rapid adaptive evolution as a camouflage strategy following increased pollution.[4][5]

The American biologist Sewall Wright, who had a background in animal breeding experiments, focused on combinations of interacting genes, and the effects of inbreeding on small, relatively isolated populations that exhibited genetic drift. In 1932 Wright introduced the concept of an adaptive landscape and argued that genetic drift and inbreeding could drive a small, isolated sub-population away from an adaptive peak, allowing natural selection to drive it towards different adaptive peaks.

The work of Fisher, Haldane and Wright founded the discipline of population genetics. This integrated natural selection with Mendelian genetics, which was the critical first step in developing a unified theory of how evolution worked.[4][5] John Maynard Smith was Haldane's pupil, whilst W.D. Hamilton was heavily influenced by the writings of Fisher. The American George R. Price worked with both Hamilton and Maynard Smith. American Richard Lewontin and Japanese Motoo Kimura were heavily influenced by Wright.

Modern synthesis

The mathematics of population genetics were originally developed as the beginning of the modern synthesis. Authors such as Beatty[6] have asserted that population genetics defines the core of the modern synthesis. For the first few decades of the 20th century, most field naturalists continued to believe that Lamarckism and orthogenesis provided the best explanation for the complexity they observed in the living world.[7] During the modern synthesis, these ideas were purged, and only evolutionary causes that could be expressed in the mathematical framework of population genetics were retained.[8] Consensus was reached as to which evolutionary factors might influence evolution, but not as to the relative importance of the various factors.[8]

Theodosius Dobzhansky, a postdoctoral worker in T. H. Morgan's lab, had been influenced by the work on genetic diversity by Russian geneticists such as Sergei Chetverikov. He helped to bridge the divide between the foundations of microevolution developed by the population geneticists and the patterns of macroevolution observed by field biologists, with his 1937 book Genetics and the Origin of Species. Dobzhansky examined the genetic diversity of wild populations and showed that, contrary to the assumptions of the population geneticists, these populations had large amounts of genetic diversity, with marked differences between sub-populations. The book also took the highly mathematical work of the population geneticists and put it into a more accessible form. Many more biologists were influenced by population genetics via Dobzhansky than were able to read the highly mathematical works in the original.[9]

In Great Britain E.B. Ford, the pioneer of ecological genetics, continued throughout the 1930s and 1940s to empirically demonstrate the power of selection due to ecological factors including the ability to maintain genetic diversity through genetic polymorphisms such as human blood types. Ford's work, in collaboration with Fisher, contributed to a shift in emphasis during the course of the modern synthesis towards natural selection as the dominant force.[4][5][10][11]

Neutral theory and origin-fixation dynamics

The original, modern synthesis view of population genetics assumes that mutations provide ample raw material, and focuses only on the change in frequency of alleles within populations.[12] The main processes influencing allele frequencies are natural selection, genetic drift, gene flow and recurrent mutation. Fisher and Wright had some fundamental disagreements about the relative roles of selection and drift.[13]

The availability of molecular data on all genetic differences led to the neutral theory of molecular evolution. In this view, many mutations are deleterious and so never observed, and most of the remainder are neutral, i.e. are not under selection. With the fate of each neutral mutation left to chance (genetic drift), the direction of evolutionary change is driven by which mutations occur, and so cannot be captured by models of change in the frequency of (existing) alleles alone.[12][14]

The origin-fixation view of population genetics generalizes this approach beyond strictly neutral mutations, and sees the rate at which a particular change happens as the product of the mutation rate and the fixation probability.[12]

Four processes

Selection

Natural selection, which includes sexual selection, is the fact that some traits make it more likely for an organism to survive and reproduce. Population genetics describes natural selection by defining fitness as a propensity or probability of survival and reproduction in a particular environment. The fitness is normally given by the symbol w=1-s where s is the selection coefficient. Natural selection acts on phenotypes, so population genetic models assume relatively simple relationships to predict the phenotype and hence fitness from the allele at one or a small number of loci. In this way, natural selection converts differences in the fitness of individuals with different phenotypes into changes in allele frequency in a population over successive generations.

Before the advent of population genetics, many biologists doubted that small differences in fitness were sufficient to make a large difference to evolution.[9] Population geneticists addressed this concern in part by comparing selection to genetic drift. Selection can overcome genetic drift when s is greater than 1 divided by the effective population size. When this criterion is met, the probability that a new advantageous mutant becomes fixed is approximately equal to 2s.[15][16] The time until fixation of such an allele depends little on genetic drift, and is approximately proportional to log(sN)/s.[17]

Dominance

Dominance means that the phenotypic and/or fitness effect of one allele at a locus depends on which allele is present in the second copy for that locus. Consider three genotypes at one locus, with the following fitness values[18]

- Genotype: A1A1 A1A2 A2A2 - Relative fitness: 1 1-hs 1-s

s is the selection coefficient and h is the dominance coefficient. The value of h yields the following information:

- h=0 A1 dominant, A2 recessive - h=1 A2 dominant, A1 recessive - 0<h<1 incomplete dominance - h<0 overdominance - h>1 Underdominance

Epistasis

Synergistic versus antagonistic epistasis
The logarithm of fitness as a function of the number of deleterious mutations. Synergistic epistasis is represented by the red line - each subsequent deleterious mutation has a larger proportionate effect on the organism's fitness. Antagonistic epistasis is in blue. The black line shows the non-epistatic case, where fitness is the product of the contributions from each of its loci.

Epistasis means that the phenotypic and/or fitness effect of an allele at one locus depends on which alleles are present at other loci. Selection does not act on a single locus, but on a phenotype that arises through development from a complete genotype.[19] However, many population genetics models of sexual species are "single locus" models, where the fitness of an individual is calculated as the product of the contributions from each of its loci—effectively assuming no epistasis.

In fact, the genotype to fitness landscape is more complex. Population genetics must either model this complexity in detail, or capture it by some simpler average rule. Empirically, beneficial mutations tend to have a smaller fitness benefit when added to a genetic background that already has high fitness: this is known as diminishing returns epistasis.[20] When deleterious mutations also have a smaller fitness effect on high fitness backgrounds, this is known as "synergistic epistasis". However, the effect of deleterious mutations tends on average to be very close to multiplicative, or can even show the opposite pattern, known as "antagonistic epistasis".[21]

Synergistic epistasis is central to some theories of the purging of mutation load[22] and to the evolution of sexual reproduction.

Mutation

Drosophila melanogaster - side (aka)
Drosophila melanogaster

Mutation is the ultimate source of genetic variation in the form of new alleles. In addition, mutation may influence the direction of evolution when there is mutation bias, i.e. different probabilities for different mutations to occur. For example, recurrent mutation that tends to be in the opposite direction to selection can lead to mutation-selection balance. At the molecular level, if mutation from G to A happens more often than mutation from A to G, then genotypes with A will tend to evolve.[23] Different insertion vs. deletion mutation biases in different taxa can lead to the evolution of different genome sizes.[24][25] Developmental or mutational biases have also been observed in morphological evolution.[26][27] For example, according to the phenotype-first theory of evolution, mutations can eventually cause the genetic assimilation of traits that were previously induced by the environment.[28][29]

Mutation bias effects are superimposed on other processes. If selection would favor either one out of two mutations, but there is no extra advantage to having both, then the mutation that occurs the most frequently is the one that is most likely to become fixed in a population.[30][31]

Mutation can have no effect, alter the product of a gene, or prevent the gene from functioning. Studies in the fly Drosophila melanogaster suggest that if a mutation changes a protein produced by a gene, this will probably be harmful, with about 70 percent of these mutations having damaging effects, and the remainder being either neutral or weakly beneficial.[32] Most loss of function mutations are selected against. But when selection is weak, mutation bias towards loss of function can affect evolution.[33] For example, pigments are no longer useful when animals live in the darkness of caves, and tend to be lost.[34] This kind of loss of function can occur because of mutation bias, and/or because the function had a cost, and once the benefit of the function disappeared, natural selection leads to the loss. Loss of sporulation ability in a bacterium during laboratory evolution appears to have been caused by mutation bias, rather than natural selection against the cost of maintaining sporulation ability.[35] When there is no selection for loss of function, the speed at which loss evolves depends more on the mutation rate than it does on the effective population size,[36] indicating that it is driven more by mutation bias than by genetic drift.

Mutations can involve large sections of DNA becoming duplicated, usually through genetic recombination.[37] This leads to copy-number variation within a population. Duplications are a major source of raw material for evolving new genes.[38] Other types of mutation occasionally create new genes from previously noncoding DNA.[39][40]

Genetic drift

Genetic drift is a change in allele frequencies caused by random sampling.[41] That is, the alleles in the offspring are a random sample of those in the parents.[42] Genetic drift may cause gene variants to disappear completely, and thereby reduce genetic variability. In contrast to natural selection, which makes gene variants more common or less common depending on their reproductive success,[43] the changes due to genetic drift are not driven by environmental or adaptive pressures, and are equally likely to make an allele more common as less common.

The effect of genetic drift is larger for alleles present in few copies than when an allele is present in many copies. The population genetics of genetic drift are described using either branching processes or a diffusion equation describing changes in allele frequency.[44] These approaches are usually applied to the Wright-Fisher and Moran models of population genetics. Assuming genetic drift is the only evolutionary force acting on an allele, after t generations in many replicated populations, starting with allele frequencies of p and q, the variance in allele frequency across those populations is

[45]

Ronald Fisher held the view that genetic drift plays at the most a minor role in evolution, and this remained the dominant view for several decades. No population genetics perspective have ever given genetic drift a central role by itself, but some have made genetic drift important in combination with another non-selective force. The shifting balance theory of Sewall Wright held that the combination of population structure and genetic drift was important. Motoo Kimura's neutral theory of molecular evolution claims that most genetic differences within and between populations are caused by the combination of neutral mutations and genetic drift.[46]

The role of genetic drift by means of sampling error in evolution has been criticized by John H Gillespie[47] and Will Provine,[48] who argue that selection on linked sites is a more important stochastic force, doing the work traditionally ascribed to genetic drift by means of sampling error. The mathematical properties of genetic draft are different from those of genetic drift.[49] The direction of the random change in allele frequency is autocorrelated across generations.[41]

Gene flow

Gene flow final
Gene flow is the transfer of alleles from one population to another population through immigration of individuals. In this example, one of the birds from population A immigrates to population B, which has fewer of the dominant alleles, and through mating incorporates its alleles into the other population.
Greatwall large
The Great Wall of China is an obstacle to gene flow of some terrestrial species.

Because of physical barriers to migration, along with the limited tendency for individuals to move or spread (vagility), and tendency to remain or come back to natal place (philopatry), natural populations rarely all interbreed as may be assumed in theoretical random models (panmixy).[50] There is usually a geographic range within which individuals are more closely related to one another than those randomly selected from the general population. This is described as the extent to which a population is genetically structured.[51] Genetic structuring can be caused by migration due to historical climate change, species range expansion or current availability of habitat. Gene flow is hindered by mountain ranges, oceans and deserts or even man-made structures such as the Great Wall of China, which has hindered the flow of plant genes.[52]

Gene flow is the exchange of genes between populations or species, breaking down the structure. Examples of gene flow within a species include the migration and then breeding of organisms, or the exchange of pollen. Gene transfer between species includes the formation of hybrid organisms and horizontal gene transfer. Population genetic models can be used to identify which populations show significant genetic isolation from one another, and to reconstruct their history.[53]

Subjecting a population to isolation leads to inbreeding depression. Migration into a population can introduce new genetic variants,[54] potentially contributing to evolutionary rescue. If a significant proportion of individuals or gametes migrate, it can also change allele frequencies, e.g. giving rise to migration load.[55]

In the presence of gene flow, other barriers to hybridization between two diverging populations of an outcrossing species are required for the populations to become new species.

Horizontal gene transfer

Tree Of Life (with horizontal gene transfer)
Current tree of life showing vertical and horizontal gene transfers.

Horizontal gene transfer is the transfer of genetic material from one organism to another organism that is not its offspring; this is most common among prokaryotes.[56] In medicine, this contributes to the spread of antibiotic resistance, as when one bacteria acquires resistance genes it can rapidly transfer them to other species.[57] Horizontal transfer of genes from bacteria to eukaryotes such as the yeast Saccharomyces cerevisiae and the adzuki bean beetle Callosobruchus chinensis may also have occurred.[58][59] An example of larger-scale transfers are the eukaryotic bdelloid rotifers, which appear to have received a range of genes from bacteria, fungi, and plants.[60] Viruses can also carry DNA between organisms, allowing transfer of genes even across biological domains.[61] Large-scale gene transfer has also occurred between the ancestors of eukaryotic cells and prokaryotes, during the acquisition of chloroplasts and mitochondria.[62]

Linkage

If all genes are in linkage equilibrium, the effect of an allele at one locus can be averaged across the gene pool at other loci. In reality, one allele is frequently found in linkage disequilibrium with genes at other loci, especially with genes located nearby on the same chromosome. Recombination breaks up this linkage disequilibrium too slowly to avoid genetic hitchhiking, where an allele at one locus rises to high frequency because it is linked to an allele under selection at a nearby locus. Linkage also slows down the rate of adaptation, even in sexual populations.[63][64][65] The effect of linkage disequilibrium in slowing down the rate of adaptive evolution arises from a combination of the Hill–Robertson effect (delays in bringing beneficial mutations together) and background selection (delays in separating beneficial mutations from deleterious hitchhikers).

Linkage is a problem for population genetic models that treat one gene locus at a time. It can, however, be exploited as a method for detecting the action of natural selection via selective sweeps.

In the extreme case of an asexual population, linkage is complete, and population genetic equations can be derived and solved in terms of a travelling wave of genotype frequencies along a simple fitness landscape.[66] Most microbes, such as bacteria, are asexual. The population genetics of their adaptation have two contrasting regimes. When the product of the beneficial mutation rate and population size is small, asexual populations follow a "successional regime" of origin-fixation dynamics, with adaptation rate strongly dependent on this product. When the product is much larger, asexual populations follow a "concurrent mutations" regime with adaptation rate less dependent on the product, characterized by clonal interference and the appearance of a new beneficial mutation before the last one has fixed.

Applications

Explaining levels of genetic variation

Neutral theory predicts that the level of nucleotide diversity in a population will be proportional to the product of the population size and the neutral mutation rate. The fact that levels of genetic diversity vary much less than population sizes do is known as the "paradox of variation".[67] While high levels of genetic diversity were one of the original arguments in favor of neutral theory, the paradox of variation has been one of the strongest arguments against neutral theory.

It is clear that levels of genetic diversity vary greatly within a species as a function of local recombination rate, due to both genetic hitchhiking and background selection. Most current solutions to the paradox of variation invoke some level of selection at linked sites.[68] For example, one analysis suggests that larger populations have more selective sweeps, which remove more neutral genetic diversity.[69] A negative correlation between mutation rate and population size may also contribute.[70]

Life history affects genetic diversity more than population history does, e.g. r-strategists have more genetic diversity.[68]

Detecting selection

Population genetics models are used to infer which genes are undergoing selection. One common approach is to look for regions of high linkage disequilibrium and low genetic variance along the chromosome, to detect recent selective sweeps.

A second common approach is the McDonald–Kreitman test. The McDonald–Kreitman test compares the amount of variation within a species (polymorphism) to the divergence between species (substitutions) at two types of sites, one assumed to be neutral. Typically, synonymous sites are assumed to be neutral.[71] Genes undergoing positive selection have an excess of divergent sites relative to polymorphic sites. The test can also be use to obtain a genome-wide estimate of the proportion of substitutions that are fixed by positive selection, α.[72][73] According to the neutral theory of molecular evolution, this number should be near zero. High numbers have therefore been interpreted as a genome-wide falsification of neutral theory.[74]

Demographic inference

The simplest test for population structure in a sexually reproducing, diploid species, is to see whether genotype frequencies follow Hardy-Weinberg proportions as a function of allele frequencies. For example, in the simplest case of a single locus with two alleles denoted A and a at frequencies p and q, random mating predicts freq(AA) = p2 for the AA homozygotes, freq(aa) = q2 for the aa homozygotes, and freq(Aa) = 2pq for the heterozygotes. In the absence of population structure, Hardy-Weinberg proportions are reached within 1-2 generations of random mating. More typically, there is an excess of homozygotes, indicative of population structure. The extent of this excess can be quantified as the inbreeding coefficient, F.

Individuals can be clustered into K subpopulations[75][76]. The degree of population structure can then be calculated using FST, which is a measure of the proportion of genetic variance that can be explained by population structure. Genetic population structure can then be related to geographic structure, and genetic admixture can be detected.

Coalescent theory relates genetic diversity in a sample to demographic history of the population from which it was taken. It normally assumes neutrality, and so sequences from more neutrally-evolving portions of genomes are therefore selected for such analyses. It can be used to infer the relationships between species (phylogenetics), as well as the population structure, demographic history (e.g. population bottlenecks, population growth), biological dispersal, source-sink dynamics[77] and introgression within a species.

Another approach to demographic inference relies on the allele frequency spectrum.[78]

Evolution of genetic systems

By assuming that there are loci that control the genetic system itself, population genetic models are created to describe the evolution of dominance and other forms of robustness, the evolution of sexual reproduction and recombination rates, the evolution of mutation rates, the evolution of evolutionary capacitors, the evolution of costly signalling traits, the evolution of ageing, and the evolution of co-operation. For example, most mutations are deleterious, so the optimal mutation rate for a species may be a trade-off between the damage from a high deleterious mutation rate and the metabolic costs of maintaining systems to reduce the mutation rate, such as DNA repair enzymes.[79]

One important aspect of such models is that selection is only strong enough to purge deleterious mutations and hence overpower mutational bias towards degradation if the selection coefficient s is greater than the inverse of the effective population size. This is known as the drift barrier and is related to the nearly neutral theory of molecular evolution. Drift barrier theory predicts that species with large effective population sizes will have highly streamlined, efficient genetic systems, while those with small population sizes will have bloated and complex genomes containing for example introns and transposable elements.[80] However, somewhat paradoxically, species with large population sizes might be so tolerant to the consequences of certain types of errors that they evolve higher error rates, e.g. in transcription and translation, than small populations.[81]

See also

References

  1. ^ "Population genetics - Latest research and news | Nature". www.nature.com. Retrieved 2018-01-29.
  2. ^ Servedio, Maria R.; Brandvain, Yaniv; Dhole, Sumit; Fitzpatrick, Courtney L.; Goldberg, Emma E.; Stern, Caitlin A.; Van Cleve, Jeremy; Yeh, D. Justin (9 December 2014). "Not Just a Theory—The Utility of Mathematical Models in Evolutionary Biology". PLoS Biology. 12 (12): e1002017. doi:10.1371/journal.pbio.1002017. PMC 4260780. PMID 25489940.
  3. ^ Ewens W.J. (2004). Mathematical Population Genetics (2nd Edition). Springer-Verlag, New York. ISBN 978-0-387-20191-7.
  4. ^ a b c Bowler, Peter J. (2003). Evolution : the history of an idea (3rd ed.). Berkeley: University of California Press. pp. 325–339. ISBN 978-0-520-23693-6.
  5. ^ a b c Larson, Edward J. (2004). Evolution : the remarkable history of a scientific theory (Modern Library ed.). New York: Modern Library. pp. 221–243. ISBN 978-0-679-64288-6.
  6. ^ Beatty, John (1986). "The Synthesis and the Synthetic Theory". Integrating Scientific Disciplines. Science and Philosophy. 2. Springer Netherlands. pp. 125–135. doi:10.1007/978-94-010-9435-1_7. ISBN 9789024733422.
  7. ^ Mayr, edited by Ernst; Mayer, William B. Provine ; with a new preface by Ernst (1998). The Evolutionary synthesis : perspectives on the unification of biology ([New ed]. ed.). Cambridge, Massachusetts: Harvard University Press. pp. 295–298. ISBN 9780674272262.CS1 maint: Extra text: authors list (link)
  8. ^ a b Provine, W. B. (1988). "Progress in evolution and meaning in life". Evolutionary progress. University of Chicago Press. pp. 49–79.
  9. ^ a b Provine, William B. (1978). "The role of mathematical population geneticists in the evolutionary synthesis of the 1930s and 1940s". Studies of the History of Biology. 2: 167–192.
  10. ^ Mayr, Ernst (1988). Toward a New Philosophy of Biology: Observations of an Evolutionist. Cambridge, Massachusetts: Belknap Press of Harvard University Press. p. 402. ISBN 978-0-674-89665-9.
  11. ^ Mayr, edited by Ernst; Mayer, William B. Provine ; with a new preface by Ernst (1998). The Evolutionary synthesis : perspectives on the unification of biology ([New ed]. ed.). Cambridge, Massachusetts: Harvard University Press. pp. 338–341. ISBN 9780674272262.CS1 maint: Extra text: authors list (link)
  12. ^ a b c McCandlish, David M.; Stoltzfus, Arlin (September 2014). "Modeling Evolution Using the Probability of Fixation: History and Implications". The Quarterly Review of Biology. 89 (3): 225–252. doi:10.1086/677571.
  13. ^ Wright and Fisher on Inbreeding and Random Drift by James F. Crow, published in Genetics Published 2010
  14. ^ Casillas, Sònia; Barbadilla, Antonio (2017). "Molecular Population Genetics". Genetics. 205 (3): 1003–1035. doi:10.1534/genetics.116.196493. PMC 5340319. PMID 28270526.
  15. ^ JBS Haldane (1927). "A Mathematical Theory of Natural and Artificial Selection, Part V: Selection and Mutation". Mathematical Proceedings of the Cambridge Philosophical Society. 23 (7): 838–844. Bibcode:1927PCPS...23..838H. doi:10.1017/S0305004100015644.
  16. ^ Orr, H. A. (2010). "The population genetics of beneficial mutations". Philosophical Transactions of the Royal Society B: Biological Sciences. 365 (1544): 1195–1201. doi:10.1098/rstb.2009.0282. PMC 2871816. PMID 20308094.
  17. ^ Hermisson J, Pennings PS; Pennings (2005). "Soft sweeps: molecular population genetics of adaptation from standing genetic variation". Genetics. 169 (4): 2335–2352. doi:10.1534/genetics.104.036947. PMC 1449620. PMID 15716498.
  18. ^ Gillespie, John (2004). Population Genetics: A Concise Guide (2nd ed.). Johns Hopkins University Press. ISBN 978-0-8018-8008-7.
  19. ^ Miko, I. (2008). "Epistasis: Gene interaction and phenotype effects". Nature Education. 1 (1): 197.
  20. ^ Berger, D.; Postma, E. (13 October 2014). "Biased Estimates of Diminishing-Returns Epistasis? Empirical Evidence Revisited". Genetics. 198 (4): 1417–1420. doi:10.1534/genetics.114.169870. PMC 4256761. PMID 25313131.
  21. ^ Kouyos, Roger D.; Silander, Olin K.; Bonhoeffer, Sebastian (June 2007). "Epistasis between deleterious mutations and the evolution of recombination". Trends in Ecology & Evolution. 22 (6): 308–315. doi:10.1016/j.tree.2007.02.014. PMID 17337087.
  22. ^ Crow, JF (5 August 1997). "The high spontaneous mutation rate: is it a health risk?". Proceedings of the National Academy of Sciences of the United States of America. 94 (16): 8380–8386. Bibcode:1997PNAS...94.8380C. doi:10.1073/pnas.94.16.8380. PMC 33757. PMID 9237985.
  23. ^ Smith N.G.C., Webster M.T., Ellegren, H.; Webster; Ellegren (2002). "Deterministic Mutation Rate Variation in the Human Genome". Genome Research. 12 (9): 1350–1356. doi:10.1101/gr.220502. PMC 186654. PMID 12213772.CS1 maint: Multiple names: authors list (link)
  24. ^ Petrov DA, Sangster TA, Johnston JS, Hartl DL, Shaw KL; Sangster; Johnston; Hartl; Shaw (2000). "Evidence for DNA loss as a determinant of genome size". Science. 287 (5455): 1060–1062. Bibcode:2000Sci...287.1060P. doi:10.1126/science.287.5455.1060. PMID 10669421.CS1 maint: Multiple names: authors list (link)
  25. ^ Petrov DA (2002). "DNA loss and evolution of genome size in Drosophila". Genetica. 115 (1): 81–91. doi:10.1023/A:1016076215168. PMID 12188050.
  26. ^ Kiontke K, Barriere A, Kolotuev I, Podbilewicz B, Sommer R, Fitch DHA, Felix MA; Barrière; Kolotuev; Podbilewicz; Sommer; Fitch; Félix (2007). "Trends, stasis, and drift in the evolution of nematode vulva development". Current Biology. 17 (22): 1925–1937. doi:10.1016/j.cub.2007.10.061. PMID 18024125.CS1 maint: Multiple names: authors list (link)
  27. ^ Braendle C, Baer CF, Felix MA; Baer; Félix (2010). Barsh, Gregory S (ed.). "Bias and Evolution of the Mutationally Accessible Phenotypic Space in a Developmental System". PLoS Genetics. 6 (3): e1000877. doi:10.1371/journal.pgen.1000877. PMC 2837400. PMID 20300655.CS1 maint: Multiple names: authors list (link)
  28. ^ Palmer, RA (2004). "Symmetry breaking and the evolution of development". Science. 306 (5697): 828–833. Bibcode:2004Sci...306..828P. CiteSeerX 10.1.1.631.4256. doi:10.1126/science.1103707. PMID 15514148.
  29. ^ West-Eberhard, M-J. (2003). Developmental plasticity and evolution. New York: Oxford University Press. ISBN 978-0-19-512235-0.
  30. ^ Stoltzfus, A & Yampolsky, L.Y. (2009). "Climbing Mount Probable: Mutation as a Cause of Nonrandomness in Evolution". J Hered. 100 (5): 637–647. doi:10.1093/jhered/esp048. PMID 19625453.
  31. ^ Yampolsky, L.Y. & Stoltzfus, A (2001). "Bias in the introduction of variation as an orienting factor in evolution". Evol Dev. 3 (2): 73–83. doi:10.1046/j.1525-142x.2001.003002073.x. PMID 11341676.
  32. ^ Sawyer SA, Parsch J, Zhang Z, Hartl DL; Parsch; Zhang; Hartl (2007). "Prevalence of positive selection among nearly neutral amino acid replacements in Drosophila". Proc. Natl. Acad. Sci. U.S.A. 104 (16): 6504–10. Bibcode:2007PNAS..104.6504S. doi:10.1073/pnas.0701572104. PMC 1871816. PMID 17409186.CS1 maint: Multiple names: authors list (link)
  33. ^ Haldane, JBS (1933). "The Part Played by Recurrent Mutation in Evolution". American Naturalist. 67 (708): 5–19. doi:10.1086/280465. JSTOR 2457127.
  34. ^ Protas, Meredith; Conrad, M; Gross, JB; Tabin, C; Borowsky, R (2007). "Regressive evolution in the Mexican cave tetra, Astyanax mexicanus". Current Biology. 17 (5): 452–454. doi:10.1016/j.cub.2007.01.051. PMC 2570642. PMID 17306543.
  35. ^ Maughan H, Masel J, Birky WC, Nicholson WL; Masel; Birky Jr; Nicholson (2007). "The roles of mutation accumulation and selection in loss of sporulation in experimental populations of Bacillus subtilis". Genetics. 177 (2): 937–948. doi:10.1534/genetics.107.075663. PMC 2034656. PMID 17720926.CS1 maint: Multiple names: authors list (link)
  36. ^ Masel J, King OD, Maughan H; King; Maughan (2007). "The loss of adaptive plasticity during long periods of environmental stasis". American Naturalist. 169 (1): 38–46. doi:10.1086/510212. PMC 1766558. PMID 17206583.CS1 maint: Multiple names: authors list (link)
  37. ^ Hastings, P J; Lupski, JR; Rosenberg, SM; Ira, G (2009). "Mechanisms of change in gene copy number". Nature Reviews Genetics. 10 (8): 551–564. doi:10.1038/nrg2593. PMC 2864001. PMID 19597530.
  38. ^ Long M, Betrán E, Thornton K, Wang W; Betrán; Thornton; Wang (November 2003). "The origin of new genes: glimpses from the young and old". Nat. Rev. Genet. 4 (11): 865–75. doi:10.1038/nrg1204. PMID 14634634.CS1 maint: Multiple names: authors list (link)
  39. ^ Liu N, Okamura K, Tyler DM; Okamura; Tyler; Phillips; Chung; Lai (2008). "The evolution and functional diversification of animal microRNA genes". Cell Res. 18 (10): 985–96. doi:10.1038/cr.2008.278. PMC 2712117. PMID 18711447.CS1 maint: Multiple names: authors list (link)
  40. ^ McLysaght, Aoife; Hurst, Laurence D. (25 July 2016). "Open questions in the study of de novo genes: what, how and why". Nature Reviews Genetics. 17 (9): 567–578. doi:10.1038/nrg.2016.78. PMID 27452112.
  41. ^ a b Masel, J. (2011). "Genetic drift". Current Biology. 21 (20): R837–R838. doi:10.1016/j.cub.2011.08.007. PMID 22032182.
  42. ^ Futuyma, Douglas (1998). Evolutionary Biology. Sinauer Associates. p. Glossary. ISBN 978-0-87893-189-7.
  43. ^ Avers, Charlotte (1989). "Process and Pattern in Evolution". Oxford University Press.
  44. ^ Wahl L.M. (2011). "Fixation when N and s Vary: Classic Approaches Give Elegant New Results". Genetics. 188 (4): 783–785. doi:10.1534/genetics.111.131748. PMC 3176088. PMID 21828279.
  45. ^ Nicholas H. Barton; Derek E. G. Briggs; Jonathan A. Eisen; David B. Goldstein; Nipam H. Patel (2007). Evolution. Cold Spring Harbor Laboratory Press. p. 417. ISBN 978-0-87969-684-9.
  46. ^ Futuyma, Douglas (1998). Evolutionary Biology. Sinauer Associates. p. 320. ISBN 978-0-87893-189-7.
  47. ^ Gillespie, J.H. (2000). "Genetic Drift in an Infinite Population: The Pseudohitchhiking Model". Genetics. 155 (2): 909–919. PMC 1461093. PMID 10835409.
  48. ^ Provine, William B. The "Random Genetic Drift" Fallacy. CreateSpace.
  49. ^ Neher, Richard A.; Shraiman, Boris I. (August 2011). "Genetic Draft and Quasi-Neutrality in Large Facultatively Sexual Populations". Genetics. 188 (4): 975–996. doi:10.1534/genetics.111.128876. ISSN 0016-6731. PMC 3176096. PMID 21625002.
  50. ^ Buston, P. M.; Pilkington, J. G.; et al. (2007). "Are clownfish groups composed of close relatives? An analysis of microsatellite DNA vraiation in Amphiprion percula". Molecular Ecology. 12 (3): 733–742. doi:10.1046/j.1365-294X.2003.01762.x. PMID 12675828.
  51. ^ Repaci, V.; Stow, A.J.; Briscoe, D.A. (2007). "Fine-scale genetic structure, co-founding and multiple mating in the Australian allodapine bee (Ramphocinclus brachyurus)". Journal of Zoology. 270 (4): 687–691. doi:10.1111/j.1469-7998.2006.00191.x.
  52. ^ Su, H.; Qu, L.; He, K., Zhang, Z.; Wang, J.; Chen, Z.; Gu, H.; Qu; He; Zhang; Wang; Chen; Gu (2003). "The Great Wall of China: a physical barrier to gene flow?". Heredity. 90 (3): 212–9. doi:10.1038/sj.hdy.6800237. PMID 12634804.CS1 maint: Multiple names: authors list (link)
  53. ^ Gravel, S., S. (2012). "Population Genetics Models of Local Ancestry". Genetics. 1202 (2): 607–619. arXiv:1202.4811. Bibcode:2012arXiv1202.4811G. doi:10.1534/genetics.112.139808. PMC 3374321. PMID 22491189.
  54. ^ Morjan, C.; Rieseberg, L.; Rieseberg (2004). "How species evolve collectively: implications of gene flow and selection for the spread of advantageous alleles". Mol. Ecol. 13 (6): 1341–56. doi:10.1111/j.1365-294X.2004.02164.x. PMC 2600545. PMID 15140081.CS1 maint: Multiple names: authors list (link)
  55. ^ Bolnick, Daniel I.; Nosil, Patrik (September 2007). "Natural Selection in Populations Subject to a Migration Load". Evolution. 61 (9): 2229–2243. doi:10.1111/j.1558-5646.2007.00179.x. PMID 17767592.
  56. ^ Boucher, Y.; Douady, C.J.; Papke, R.T.; Walsh, D.A.; Boudreau, M.E.; Nesbo, C.L.; Case, R.J.; Doolittle, W.F.; Douady; Papke; Walsh; Boudreau; Nesbø; Case; Doolittle (2003). "Lateral gene transfer and the origins of prokaryotic groups". Annu Rev Genet. 37: 283–328. doi:10.1146/annurev.genet.37.050503.084247. PMID 14616063.CS1 maint: Multiple names: authors list (link)
  57. ^ Walsh T (2006). "Combinatorial genetic evolution of multiresistance". Curr. Opin. Microbiol. 9 (5): 476–82. doi:10.1016/j.mib.2006.08.009. PMID 16942901.
  58. ^ Kondo N, Nikoh N, Ijichi N, Shimada M, Fukatsu T; Nikoh; Ijichi; Shimada; Fukatsu (2002). "Genome fragment of Wolbachia endosymbiont transferred to X chromosome of host insect". PNAS. 99 (22): 14280–5. Bibcode:2002PNAS...9914280K. doi:10.1073/pnas.222228199. PMC 137875. PMID 12386340.CS1 maint: Multiple names: authors list (link)
  59. ^ Sprague G (1991). "Genetic exchange between kingdoms". Curr. Opin. Genet. Dev. 1 (4): 530–3. doi:10.1016/S0959-437X(05)80203-5. PMID 1822285.
  60. ^ Gladyshev EA, Meselson M, Arkhipova IR; Meselson; Arkhipova (May 2008). "Massive horizontal gene transfer in bdelloid rotifers". Science. 320 (5880): 1210–3. Bibcode:2008Sci...320.1210G. doi:10.1126/science.1156407. PMID 18511688.CS1 maint: Multiple names: authors list (link)
  61. ^ Baldo A, McClure M; McClure (1 September 1999). "Evolution and horizontal transfer of dUTPase-encoding genes in viruses and their hosts". J. Virol. 73 (9): 7710–21. PMC 104298. PMID 10438861.
  62. ^ Poole A, Penny D; Penny (2007). "Evaluating hypotheses for the origin of eukaryotes". BioEssays. 29 (1): 74–84. doi:10.1002/bies.20516. PMID 17187354.
  63. ^ Weissman, D. B.; Hallatschek, O. (15 January 2014). "The Rate of Adaptation in Large Sexual Populations with Linear Chromosomes". Genetics. 196 (4): 1167–1183. doi:10.1534/genetics.113.160705. PMC 3982688. PMID 24429280.
  64. ^ Weissman, Daniel B.; Barton, Nicholas H.; McVean, Gil (7 June 2012). "Limits to the Rate of Adaptive Substitution in Sexual Populations". PLoS Genetics. 8 (6): e1002740. doi:10.1371/journal.pgen.1002740. PMC 3369949. PMID 22685419.
  65. ^ Neher, R. A.; Shraiman, B. I.; Fisher, D. S. (30 November 2009). "Rate of Adaptation in Large Sexual Populations". Genetics. 184 (2): 467–481. arXiv:1108.3464. doi:10.1534/genetics.109.109009. PMC 2828726. PMID 19948891.
  66. ^ Michael M. Desai, Daniel S. Fisher; Fisher (2007). "Beneficial Mutation Selection Balance and the Effect of Linkage on Positive Selection". Genetics. 176 (3): 1759–1798. doi:10.1534/genetics.106.067678. PMC 1931526. PMID 17483432.
  67. ^ Lewontin, [by] R. C. (1973). The genetic basis of evolutionary change ([4th printing.] ed.). New York: Columbia University Press. ISBN 978-0231033923.
  68. ^ a b Ellegren, Hans; Galtier, Nicolas (6 June 2016). "Determinants of genetic diversity". Nature Reviews Genetics. 17 (7): 422–433. doi:10.1038/nrg.2016.58. PMID 27265362.
  69. ^ Corbett-Detig, Russell B.; Hartl, Daniel L.; Sackton, Timothy B.; Barton, Nick H. (10 April 2015). "Natural Selection Constrains Neutral Diversity across A Wide Range of Species". PLOS Biology. 13 (4): e1002112. doi:10.1371/journal.pbio.1002112. PMC 4393120. PMID 25859758.
  70. ^ Sung, W.; Ackerman, M. S.; Miller, S. F.; Doak, T. G.; Lynch, M. (17 October 2012). "Drift-barrier hypothesis and mutation-rate evolution" (PDF). Proceedings of the National Academy of Sciences. 109 (45): 18488–18492. Bibcode:2012PNAS..10918488S. doi:10.1073/pnas.1216223109. PMC 3494944. PMID 23077252.
  71. ^ Charlesworth, J. Eyre-Walker (2008). "The McDonald–Kreitman Test and Slightly Deleterious Mutations". Molecular Biology and Evolution. 25 (6): 1007–1015. doi:10.1093/molbev/msn005. PMID 18195052.
  72. ^ Eyre-Walker, A (2006). "The genomic rate of adaptive evolution" (PDF). Trends in Ecology and Evolution. 21 (10): 569–575. doi:10.1016/j.tree.2006.06.015. PMID 16820244.
  73. ^ Smith, N. G. C.; Eyre-Walker, A. (2002). "Adaptive protein evolution in Drosophila". Nature. 415 (6875): 1022–1024. doi:10.1038/4151022a. PMID 11875568.
  74. ^ Hahn, M.W. (2008). "Toward a selection theory of molecular evolution". Evolution. 62 (2): 255–265. doi:10.1111/j.1558-5646.2007.00308.x. PMID 18302709.
  75. ^ Pritchard, J K; Stephens, M; Donnelly, P (June 2000). "Inference of population structure using multilocus genotype data". Genetics. 155 (2): 945–959. ISSN 0016-6731. PMC 1461096. PMID 10835412.
  76. ^ Verity, Robert; Nichols, Richard A. (August 2016). "Estimating the Number of Subpopulations (K) in Structured Populations". Genetics. 203 (4): 1827–1839. doi:10.1534/genetics.115.180992. ISSN 0016-6731. PMC 4981280. PMID 27317680.
  77. ^ Manlik, Oliver; Chabanne, Delphine; Daniel, Claire; Bejder, Lars; Allen, Simon J.; Sherwin, William B. (13 November 2018). "Demography and genetics suggest reversal of dolphin source-sink dynamics, with implications for conservation". Marine Mammal Science. 35 (3): 732–759. doi:10.1111/mms.12555.
  78. ^ Gutenkunst, Ryan N.; Hernandez, Ryan D.; Williamson, Scott H.; Bustamante, Carlos D.; McVean, Gil (23 October 2009). "Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data". PLoS Genetics. 5 (10): e1000695. doi:10.1371/journal.pgen.1000695. PMC 2760211. PMID 19851460.
  79. ^ Sniegowski P, Gerrish P, Johnson T, Shaver A; Gerrish; Johnson; Shaver (2000). "The evolution of mutation rates: separating causes from consequences". BioEssays. 22 (12): 1057–1066. doi:10.1002/1521-1878(200012)22:12<1057::AID-BIES3>3.0.CO;2-W. PMID 11084621.CS1 maint: Multiple names: authors list (link)
  80. ^ Lynch, Michael, John S. Conery; Conery (2003). "The origins of genome complexity". Science. 302 (5649): 1401–1404. Bibcode:2003Sci...302.1401L. CiteSeerX 10.1.1.135.974. doi:10.1126/science.1089370. PMID 14631042.CS1 maint: Multiple names: authors list (link)
  81. ^ Rajon, E.; Masel, J. (3 January 2011). "Evolution of molecular error rates and the consequences for evolvability". Proceedings of the National Academy of Sciences. 108 (3): 1082–1087. Bibcode:2011PNAS..108.1082R. doi:10.1073/pnas.1012918108. PMC 3024668. PMID 21199946.

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Allele frequency

Allele frequency, or gene frequency, is the relative frequency of an allele (variant of a gene) at a particular locus in a population, expressed as a fraction or percentage. Specifically, it is the fraction of all chromosomes in the population that carry that allele. Microevolution is the change in allele frequencies that occurs over time within a population.

Given the following:

a particular locus on a chromosome and a given allele at that locus

a population of N individuals with ploidy n, i.e. an individual carries n copies of each chromosome in their somatic cells (e.g. two chromosomes in the cells of diploid species)

the allele exists in i chromosomes in the populationthen the allele frequency is the fraction of all the occurrences i of that allele and the total number of chromosome copies across the population, i/(nN).

The allele frequency is distinct from the genotype frequency, although they are related, and allele frequencies can be calculated from genotype frequencies.In population genetics, allele frequencies are used to describe the amount of variation at a particular locus or across multiple loci. When considering the ensemble of allele frequencies for a large number of distinct loci, their distribution is called the allele frequency spectrum.

Balding–Nichols model

In population genetics, the Balding–Nichols model is a statistical description of the allele frequencies in the components of a sub-divided population. With background allele frequency p the allele frequencies, in sub-populations separated by Wright's FST F, are distributed according to independent draws from

where B is the Beta distribution. This distribution has mean p and variance Fp(1 – p).

The model is due to David Balding and Richard Nichols and is widely used in the forensic analysis of DNA profiles and in population models for genetic epidemiology.


Cline (biology)

In biology, a cline (from the Greek “klinein”, meaning “to lean”) is a measurable gradient in a single character (or biological trait) of a species across its geographical range. First coined by Julian Huxley in 1938, the “character” of the cline referred to is usually genetic (e.g allele frequency, blood type), or phenotypic (e.g. body size, skin pigmentation). Clines can show smooth, continuous gradation in a character, or they may show more abrupt changes in the trait from one geographic region to the next.A cline refers to a spatial gradient in a specific, singular trait, rather than a gradient in a population as a whole. A single population can therefore theoretically have as many clines as it has traits. Additionally, Huxley recognised that these multiple independent clines may not act in concordance with each other. For example, it has been observed that in Australia, birds generally become smaller the further towards the north of the country they are found. In contrast, the intensity of their plumage colouration follows a different geographical trajectory, being most vibrant where humidity is highest and becoming less vibrant further into the arid centre of the country.

Because of this, clines were defined by Huxley as being an “auxiliary taxonomic principle”; that is, clinal variation in a species is not awarded taxonomic recognition in the way subspecies or species are.While the terms “ecotype” and “cline” are sometimes used interchangeably, they do in fact differ in that “ecotype” refers to a population which differs from other populations in a number of characters, rather than the single character that varies amongst populations in a cline.

Ecological genetics

Ecological genetics is the study of genetics in natural populations.

This contrasts with classical genetics, which works mostly on crosses between laboratory strains, and DNA sequence analysis, which studies genes at the molecular level.

Research in this field is on traits of ecological significance—that is, traits related to fitness, which affect an organism's survival and reproduction. Examples might be: flowering time, drought tolerance, polymorphism, mimicry, avoidance of attacks by predators.

Research usually involves a mixture of field and laboratory studies. Samples of natural populations may be taken back to the laboratory for their genetic variation to be analysed. Changes in the populations at different times and places will be noted, and the pattern of mortality in these populations will be studied. Research is often done on insects and other organisms that have short generation times.

Endogamy

Endogamy is the practice of marrying within a specific social group, caste or ethnic group, rejecting those from others as unsuitable for marriage or other close personal relationships.

Endogamy is common in many cultures and ethnic groups. Several religious and ethnic religious groups are traditionally more endogamous, although sometimes with the added dimension of requiring marital religious conversion. This permits an exogamous marriage, as the convert, by accepting the partner's religion, becomes accepted within the endogamous rules. Endogamy, as distinct from consanguinity, may result in transmission of genetic disorders, the so-called founder effect, within the relatively closed community.

Ewens's sampling formula

In population genetics, Ewens's sampling formula, describes the probabilities associated with counts of how many different alleles are observed a given number of times in the sample.

ExPASy

ExPASy is a bioinformatics resource portal operated by the SIB Swiss Institute of Bioinformatics and in particular the SIB Web Team. It is an extensible and integrative portal accessing many scientific resources, databases and software tools in different areas of life sciences. Scientists can access a wide range of resources in many different domains, such as proteomics, genomics, phylogenetics/evolution, systems biology, population genetics, and transcriptomics. The individual resources (databases, web-based and downloadable software tools) are hosted in a decentralised way by different groups of the SIB Swiss Institute of Bioinformatics and partner institutions. Specifically, a single web portal provides a common entry point to a wide range of resources developed and operated by many different SIB groups and external institutions. The portal features a search function across selected resources. Internally, the availability and usage of resources are monitored. The portal is aimed for both expert users and for people who are not familiar with a specific domain in life sciences: in particular, the new web interface provides visual guidance for newcomers to ExPASy.

Exogamy

Exogamy is the social norm of marrying outside one's social group. The group defines the scope and extent of exogamy, and the rules and enforcement mechanisms that ensure its continuity. One form of exogamy is dual exogamy, in which two groups engage in continual wife exchange.In social science, exogamy is viewed as a combination of two related aspects: biological and cultural. Biological exogamy is marriage of nonblood-related beings, regulated by forms of incest law. Cultural exogamy is marrying outside a specific cultural group; the opposite being endogamy, marriage within a social group.

Fitness (biology)

Fitness (often denoted or ω in population genetics models) is the quantitative representation of natural and sexual selection within evolutionary biology. It can be defined either with respect to a genotype or to a phenotype in a given environment. In either case, it describes individual reproductive success and is equal to the average contribution to the gene pool of the next generation that is made by individuals of the specified genotype or phenotype. The fitness of a genotype is manifested through its phenotype, which is also affected by the developmental environment. The fitness of a given phenotype can also be different in different selective environments.

With asexual reproduction, it is sufficient to assign fitnesses to genotypes. With sexual reproduction, genotypes are scrambled every generation. In this case, fitness values can be assigned to alleles by averaging over possible genetic backgrounds. Natural selection tends to make alleles with higher fitness more common over time, resulting in Darwinian evolution.

The term "Darwinian fitness" can be used to make clear the distinction with physical fitness. Fitness does not include a measure of survival or life-span; Herbert Spencer's well-known phrase "survival of the fittest" should be interpreted as: "Survival of the form (phenotypic or genotypic) that will leave the most copies of itself in successive generations."

Inclusive fitness differs from individual fitness by including the ability of an allele in one individual to promote the survival and/or reproduction of other individuals that share that allele, in preference to individuals with a different allele. One mechanism of inclusive fitness is kin selection.

Gene pool

The gene pool is the set of all genes, or genetic information, in any population, usually of a particular species.

Genetic divergence

Genetic divergence is the process in which two or more populations of an ancestral species accumulate independent genetic changes (mutations) through time, often after the populations have become reproductively isolated for some period of time. In some cases, subpopulations living in ecologically distinct peripheral environments can exhibit genetic divergence from the remainder of a population, especially where the range of a population is very large (see parapatric speciation). The genetic differences among divergent populations can involve silent mutations (that have no effect on the phenotype) or give rise to significant morphological and/or physiological changes. Genetic divergence will always accompany reproductive isolation, either due to novel adaptations via selection and/or due to genetic drift, and is the principal mechanism underlying speciation.On a molecular genetics level, genetic divergence is due to changes in a small number of genes in a species, resulting in speciation. However, researchers argue that it is unlikely that divergence is a result of a significant, single, dominant mutation in a genetic locus because if that were so, the individual with that mutation would have zero fitness. Consequently, they could not reproduce and pass the mutation on to further generations. Hence, it is more likely that divergence, and subsequently reproductive isolation, are the outcomes of multiple small mutations over evolutionary time.

Genetic diversity

Genetic diversity is the total number of genetic characteristics in the genetic makeup of a species. It is distinguished from genetic variability, which describes the tendency of genetic characteristics to vary.

Genetic diversity serves as a way for populations to adapt to changing environments. With more variation, it is more likely that some individuals in a population will possess variations of alleles that are suited for the environment. Those individuals are more likely to survive to produce offspring bearing that allele. The population will continue for more generations because of the success of these individuals.The academic field of population genetics includes several hypotheses and theories regarding genetic diversity. The neutral theory of evolution proposes that diversity is the result of the accumulation of neutral substitutions. Diversifying selection is the hypothesis that two subpopulations of a species live in different environments that select for different alleles at a particular locus. This may occur, for instance, if a species has a large range relative to the mobility of individuals within it. Frequency-dependent selection is the hypothesis that as alleles become more common, they become more vulnerable. This occurs in host–pathogen interactions, where a high frequency of a defensive allele among the host means that it is more likely that a pathogen will spread if it is able to overcome that allele.

Genetic history of the British Isles

The genetic history of the British Isles is the subject of research within the larger field of human population genetics. It has developed in parallel with DNA testing technologies capable of identifying genetic similarities and differences between populations. The conclusions of population genetics regarding the British Isles in turn draw upon and contribute to the larger field of understanding the history of humanity in the British Isles generally, complementing work in linguistics, archeology, history and genealogy.

Research concerning the most important routes of migration into the British Isles is the subject of debate. Apart from the most obvious route across the narrowest point of the English Channel into Kent, other routes may have been important over the millennia, including a land bridge in the Mesolithic period, and also maritime connections along the Atlantic coasts.

The periods of the most important migrations are contested. The Neolithic introduction of farming technologies from Europe is frequently proposed as a period of major population change in the British Isles. Such technology could either have been learned by locals from a small number of immigrants or by colonists who significantly changed the population. Analysis of nuclear DNA from Mesolithic and Neolithic individuals suggests that the best model is of near-total replacement of the British Mesolithic population by seaborne neolithic Europeans whose ancestry came mostly from Anatolia (about 75%) and European hunter-gatherers (about 25%).Other potentially important historical periods of migration which have been subject to consideration in this field include the introduction of Celtic languages and technologies (during the Bronze and Iron Ages), the Roman era, the period of Gaelic influx, the period of Anglo-Saxon influx, the Viking era, the Norman invasion of 1066 and the era of the European wars of religion. There are also many potential eras of movement between different parts of the British Isles.

Genetic variation

Genetic variation describes the difference in DNA among individuals. There are multiple sources of genetic variation, including Mutation and Genetic recombination.

Hypergamy

Hypergamy (colloquially referred to as "marrying up" or "gold-digging", occasionally referred to as "higher-gamy") is a term used in social science for the act or practice of a person marrying a spouse of higher caste or social status than themselves.

The antonym "hypogamy" refers to the inverse: marrying a person of lower social class or status (colloquially "marrying down"). Both terms were coined in the Indian subcontinent in the 19th century while translating classical Hindu law books, which used the Sanskrit terms anuloma and pratiloma, respectively, for the two concepts.

Mahram

A mahram is an unmarriageable kin with whom marriage or sexual intercourse would be considered haram, illegal in Islam, or people from whom purdah is not obligatory or legal escorts of a woman during journey longer than a day and night, 24 hours.

Population

In biology, a population is all the organisms of the same group or species, which live in a particular geographical area, and have the capability of interbreeding. The area of a sexual population is the area where inter-breeding is potentially possible between any pair within the area, and where the probability of interbreeding is greater than the probability of cross-breeding with individuals from other areas.In sociology, population refers to a collection of humans. Demography is a social science which entails the statistical study of human populations.

Population in simpler terms is the number of people in a city or town, region, country or world; population is usually determined by a process called census (a process of collecting, analyzing, compiling and publishing data).

Population biology

Population biology is an interdisciplinary field combining the areas of ecology and evolutionary biology. Population biology draws on tools from mathematics, statistics, genomics, genetics, and systematics. Population biologists study allele frequency changes (evolution) within populations of the same species (population genetics), and interactions between populations of different species (ecology).

Population size

In population genetics and population ecology, population size (usually denoted N) is the number of individual organisms in a population. Population size is directly associated with amount of genetic drift, and is the underlying cause of effects like population bottlenecks and the founder effect. Genetic drift is the major source of decrease of genetic diversity within populations which drives fixation and can potentially lead to speciation events.

Population genetics
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Selection
Effects of selection
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Genetic drift
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Genetic history by region
Population genetics by group
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