Molecular clocks on the other hand are specific sequences of  DNA ,  RNA  or  proteins  (amino acids) that are used to determine at molecular level the similarities and differences among species, to find out the timeline of divergence, [ 6 ]  and to trace back the common ancestor of species based on the  mutation rates  and sequence changes being accumulated in those specific sequences. [ 6 ]  The primary driver of evolution is the mutation or changes in genes and accounting for those changes over time determines the approximate genetic distance between species. These specific molecular clocks are fairly  conserved  across a range of species and have a constant rate of mutation like a clock and are calibrated based on evolutionary events (fossil records). For example, gene for alpha-globin (constituent of hemoglobin) mutates at a rate of 0.56 per base pair per billion years. [ 6 ]  The molecular clock can fill those gaps created by missing fossil records.


In the genome of an  organism , each  gene  is located at a specific place called the  locus  for that gene. Allelic variations at these loci cause phenotypic variation within species (e.g. hair colour, eye colour). However, most alleles do not have an observable impact on the phenotype. Within a population new alleles generated by mutation either die out or spread throughout the population. When a population is split into different isolated populations (by either geographical or ecological factors), mutations that occur after the split will be present only in the isolated population. Random fluctuation of allele frequencies also produces genetic differentiation between populations. This process is known as  genetic drift . By examining the differences between  allele frequencies  between the populations and computing genetic distance, we can estimate how long ago the two populations were separated. [ 7 ]


Let’s suppose a sequence of DNA or a hypothetical gene that has mutation rate of one  base  per 10 million years. Using this sequence of DNA, the divergence of two different species or genetic distance between two different species can be determined by counting the number of base pair differences among them. For example, in Figure 2 a difference of 4 bases in the hypothetical sequence among those two species would indicate that they diverged 40 million years ago, and their common ancestor would have lived at least 20 million years ago before their divergence. Based on molecular clock, the equation below can be used to calculate the time since divergence. [ 8 ]


Number of mutation ÷ Mutation per year (rate of mutation) = time since divergence


Recent advancement in  sequencing technology  and the availability of comprehensive  genomic databases  and  bioinformatics tools  that are capable of storing and processing colossal amount of data generated by the advanced sequencing technology has tremendously improved  evolutionary studies  and the understanding of evolutionary relationships among species. [ 9 ] [ 10 ]


Different  biomolecular markers  such DNA, RNA and  amino acid  sequences (protein) can be used for determining the genetic distance. [ 11 ] [ 12 ]


The selection criteria [ 13 ]  of appropriate biomarker for genetic distance entails the following three steps:


The choice of variability depends on the intended outcome. For example, very high level of variability is recommended for  demographic studies  and  parentage analyses , medium to high variability for comparing distinct populations, and moderate to very low variability is recommended for phylogenetic studies. [ 13 ]  The genomic localization and  ploidy  of the marker is also an important factor. For example, the  gene copy number  is inversely proportional to the robustness with  haploid  genome ( mitochondrial DNA ) more prone to  genetic drift  than diploid genome ( nuclear DNA ).


The choice and examples of molecular markers for evolutionary biology studies. [ 13 ]


loci = ( Multilocus )


genotype


Evolutionary forces  such as mutation, genetic drift,  natural selection , and  gene flow  drive the process of evolution and genetic diversity. All these forces play significant role in genetic distance within and among species. [ 19 ]


Different statistical measures exist that aim to quantify genetic deviation between populations or species. By utilizing assumptions gained from experimental analysis of evolutionary forces, a model that more accurately suits a given experiment can be selected to study a genetic group. Additionally, comparing how well different metrics model certain population features such as isolation can identify metrics that are more suited for understanding newly studied groups [ 20 ]  The most commonly used genetic distance metrics are Nei's genetic distance, [ 7 ]  Cavalli-Sforza and Edwards measure, [ 21 ]  and Reynolds, Weir and Cockerham's genetic distance. [ 22 ]


One of the most basic and straight forward distance measures is  Jukes-Cantor distance . This measure is constructed based on the assumption that no insertions or deletions occurred, all substitutions are independent, and that each nucleotide change is equally likely. With these presumptions, we can obtain the following equation: [ 23 ]


where

 d

 A
 B

 {\displaystyle d_{AB}}

  is the Jukes-Cantor distance between two sequences A, and B, and

 f

 A
 B

 {\displaystyle f_{AB}}

  being the dissimilarity between the two sequences.


In 1972,  Masatoshi Nei  published what came to be known as Nei's standard genetic distance. This distance has the nice property that if the rate of genetic change (amino acid substitution) is constant per year or generation then Nei's standard genetic distance ( D ) increases in proportion to divergence time. This measure assumes that genetic differences are caused by  mutation  and  genetic drift . [ 7 ]


This distance can also be expressed in terms of the arithmetic mean of gene identity. Let

 j

 X

 {\displaystyle j_{X}}

  be the probability for the two members of population

 X

 {\displaystyle X}

  having the same allele at a particular locus and

 j

 Y

 {\displaystyle j_{Y}}

  be the corresponding probability in population

 Y

 {\displaystyle Y}

 . Also, let

 j

 X
 Y

 {\displaystyle j_{XY}}

  be the probability for a member of

 X

 {\displaystyle X}

  and a member of

 Y

 {\displaystyle Y}

  having the same allele. Now let

 J

 X

 {\displaystyle J_{X}}

 ,

 J

 Y

 {\displaystyle J_{Y}}

  and

 J

 X
 Y

 {\displaystyle J_{XY}}

  represent the  arithmetic mean  of

 j

 X

 {\displaystyle j_{X}}

 ,

 j

 Y

 {\displaystyle j_{Y}}

  and

 j

 X
 Y

 {\displaystyle j_{XY}}

  over all loci, respectively. In other words,


where

 L

 {\displaystyle L}

  is the total number of loci examined. [ 24 ]


Nei's standard distance can then be written as [ 7 ]


In 1967  Luigi Luca Cavalli-Sforza  and  A. W. F. Edwards  published this measure. It assumes that genetic differences arise due to  genetic drift  only. One major advantage of this measure is that the populations are represented in a hypersphere, the scale of which is one unit per gene substitution. The  chord distance  in the hyperdimensional sphere is given by [ 2 ] [ 21 ]


Some authors drop the factor

 2
 π

 {\displaystyle {\frac {2}{\pi }}}

  to simplify the formula at the cost of losing the property that the scale is one unit per gene substitution.


In 1983, this measure was published by John Reynolds,  Bruce Weir  and  C. Clark Cockerham .
This measure assumes that genetic differentiation occurs only by  genetic drift  without mutations. It estimates the  coancestry coefficient

 Θ

 {\displaystyle \Theta }

  which provides a measure of the genetic divergence by: [ 22 ]


The Kimura two parameter model (K2P) was developed in 1980 by Japanese biologist Motoo Kimura.  It is compatible with the  neutral theory  of evolution, which was also developed by the same author.  As depicted in Figure 4, this measure of genetic distance accounts for the type of mutation occurring, namely whether it is a  transition  (i.e. purine to purine or pyrimidine to pyrimidine) or a  transversion  (i.e. purine to pyrimidine or vice versa).  With this information, the following formula can be derived:


where P is

 n

 1

 n

 {\displaystyle {\frac {n_{1}}{n}}}

  and Q is

 n

 2

 n

 {\displaystyle {\frac {n_{2}}{n}}}

 , with

 n

 1

 {\displaystyle n_{1}}

  being the number of transition type conversions,

 n

 2

 {\displaystyle n_{2}}

  being the number of transversion type conversions, and

 n

 {\displaystyle n}

  being the number of nucleotides sites compared. [ 25 ]


It is worth noting when transition and transversion type substitutions have an equal chance of occurring, and

 P

 {\displaystyle P}

  is assumed to equal

 Q
 2

 {\displaystyle {\frac {Q}{2}}}

 , then the above formula can be reduced down to the Jukes Cantor model.  In practice however,

 P

 {\displaystyle P}

  is typically larger than

 Q

 {\displaystyle Q}

 . [ 25 ]


It has been shown that while K2P works well in classifying distantly-related species, it is not always the best choice for comparing closely-related species.  In these cases, it may be better to use p-distance instead. [ 26 ]


The Kimura three parameter (K3P) model was first published in 1981.  This measure assumes three rates of substitution when nucleotides mutate, which can be seen in Figure 5.  There is one rate for  transition  type mutations, one rate for  transversion  type mutations to corresponding bases (e.g. G to C; transversion type 1 in the figure), and one rate for  transversion  type mutations to non-corresponding bases (e.g. G to T; transversion type 2 in the figure).


With these rates of substitution, the following formula can be derived:


where

 P

 {\displaystyle P}

  is the probability of a transition type mutation,

 Q

 {\displaystyle Q}

  is the probability of a transversion type mutation to a corresponding base, and

 R

 {\displaystyle R}

  is the probability of a transversion type mutation to a non-corresponding base.  When

 Q

 {\displaystyle Q}

  and

 R

 {\displaystyle R}

  are assumed to be equal, this reduces down to the Kimura 2 parameter distance. [ 27 ]


Many other measures of genetic distance have been proposed with varying success.


Nei's  D A  distance was created by Masatoshi Nei, a Japanese-American biologist in 1983. This distance assumes that genetic differences arise due to  mutation  and  genetic drift , but this distance measure is known to give more reliable population trees than other distances particularly for microsatellite DNA data. This method is not ideal in cases where natural selection plays a significant role in a populations genetics. [ 28 ] [ 29 ]


D

 A

 {\displaystyle D_{A}}

 : Nei's DA distance, the genetic distance between populations X and Y


ℓ

 {\displaystyle \ell }

 : A locus or gene studied with

 ∑

 ℓ

 {\displaystyle \sum _{\ell }}

   being the sum of loci or genes


X

 u

 {\displaystyle X_{u}}

  and

 Y

 u

 {\displaystyle Y_{u}}

 : The frequencies of allele u in populations X and Y, respectively


L: The total number of loci examined


Euclidean distance is a formula brought about from Euclid's  Elements, a 13 book set detailing the foundation of all euclidean mathematics. The foundational principles outlined in these works is used not only in euclidean spaces but expanded upon by Issac Newton and Gottfried Leibniz in isolated pursuits to create calculus. [ 31 ]   The euclidean distance formula is  used to convey, as simply as possible, the genetic dissimilarity between populations, with a larger distance indicating greater dissimilarity. [ 32 ]  As seen in figure 6, this method can be visualized in a graphical manner, this is due to the work of René Descartes who created the fundamental principle of analytic geometry, or the cartesian coordinate system. In an interesting example of historical repetitions, René Descartes was not the only one who discovered the fundamental principle of analytical geometry, this principle was as discovered in an isolated pursuit by Pierre de Fermat who left his work unpublished. [ 33 ] [ 34 ]


D

 E
 U

 {\displaystyle D_{EU}}

 : Euclidean genetic distance between populations X and Y


X

 u

 {\displaystyle X_{u}}

  and

 Y

 u

 {\displaystyle Y_{u}}

 : Allele frequencies at locus u in populations X and Y, respectively


It was specifically developed for microsatellite markers and is based on the  stepwise-mutation model  (SMM). The Goldstein distance formula is modeled in such a way that expected value will increase linearly with time, this property is maintained even when the assumptions of single-step mutations and symmetrical mutation rate are violated. Goldstein distance is derived from the average square distance model, of which Goldstein was also a contributor. [ 35 ]


This calculation represents the minimum amount of  codon  differences for each  locus . [ 36 ]  The measurement is based on the assumption that genetic differences arise due to  mutation  and  genetic drift . [ 37 ]


D

 m

 =

 J

 X

 +

 J

 Y

 2

 −

 J

 X
 Y

 {\displaystyle D_{m}={\frac {J_{X}+J_{Y}}{2}}-J_{XY}}


D

 m

 {\textstyle D_{m}}

 : Minimum amount of codon difference per locus


J

 X

 {\displaystyle J_{X}}

  and

 J

 Y

 {\displaystyle J_{Y}}

 : Average probability of two members of the X population having the same allele


J

 X
 Y

 {\displaystyle J_{XY}}

 : Average probability of members of the X and Y populations having the same allele


Similar to Euclidean distance, Czekanowski distance involves calculated the distance between points of allele frequency that are graphed on an axis created by . However, Czekanowski assumes a direct path is not available and sums the sides of the triangle formed by the data points instead of finding the hypotenuse. This formula is nicknamed the Manhattan distance because its methodology is similar to the nature of the New York City burrow. Manhattan is mainly built on a grid system requiring resentence to only make 90 degree turns during travel, which parallels the thinking of the formula.


D

 C
 z

 =

 1
 2

 |

 X

 u

 −

 Y

 u

 |

 {\displaystyle D_{Cz}={\frac {1}{2}}|X_{u}-Y_{u}|}


|

 X

 u

 −

 Y

 u

 |

 =

 |

 P

 X
 x

 −

 P

 Y
 x

 |

 +

 |

 P

 X
 y

 −

 P

 Y
 y

 |

 {\displaystyle |X_{u}-Y_{u}|=|P_{Xx}-P_{Yx}|+|P_{Xy}-P_{Yy}|}


P

 X
 x

 {\displaystyle P_{Xx}}

  and

 P

 Y
 x

 {\displaystyle P_{Yx}}

 : X-axis value of the frequency of an allele for X and Y populations


P

 X
 y

 {\displaystyle P_{Xy}}

  and

 P

 Y
 y

 {\displaystyle P_{Yy}}

 : Y-axis value of the frequency of an allele for X and Y populations


Similar to Czekanowski distance, Roger's distance involves calculating the distance between points of allele frequency. However, this method takes the direct distance between the points.


D

 R

 =

 1
 L

 ∑

 u

 (

 X

 u

 −

 Y

 u

 )

 2

 2

 {\displaystyle D_{R}={\frac {1}{L}}{\sqrt {\frac {\sum \limits _{u}(X_{u}-Y_{u})^{2}}{2}}}}

 [ 38 ]


L

 {\displaystyle L}

 : Total number of microsatallite loci examined


While these formulas are easy and quick calculations to make, the information that is provided gives limited information. The results of these formulas do not account for the potential effects of the number of codon changes between populations, or separation time between populations. [ 39 ]


A commonly used measure of genetic distance is the  fixation index  (F ST ) which varies between 0 and 1. A value of 0 indicates that two populations are genetically identical (minimal or no genetic diversity between the two populations) whereas a value of 1 indicates that two populations are genetically different (maximum genetic diversity between the two populations). No mutation is assumed. Large populations between which there is much migration, for example, tend to be little differentiated whereas small populations between which there is little migration tend to be greatly differentiated. F ST  is a convenient measure of this differentiation, and as a result F ST  and related statistics are among the most widely used descriptive statistics in population and evolutionary genetics. But F ST  is more than a descriptive statistic and measure of genetic differentiation. F ST  is directly related to the  Variance  in allele frequency among populations and conversely to the degree of resemblance among individuals within populations. If F ST  is small, it means that allele frequencies within each population are very similar; if it is large, it means that allele frequencies are very different.


Genetic diversity  is the total number of  genetic  characteristics in the genetic makeup of a species. It ranges widely, from the number of species to differences within  species , and can be correlated to the span of survival for a species. [ 1 ]  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. [ 2 ]


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 .


A study conducted by the  National Science Foundation  in 2007 found that genetic diversity (within-species diversity) and  biodiversity  are dependent upon each other —  i.e. that diversity within a species is necessary to maintain diversity among species, and vice versa. According to the lead researcher in the study, Dr. Richard Lankau, "If any one type is removed from the system, the cycle can break down, and the community becomes dominated by a single species." [ 3 ]   Genotypic  and  phenotypic  diversity have been found in all species at the  protein ,  DNA , and  organismal  levels; in nature, this diversity is nonrandom, heavily structured, and correlated with environmental variation and  stress . [ 4 ]


The interdependence between genetic and species diversity is delicate. Changes in species diversity lead to changes in the environment, leading to  adaptation  of the remaining species. Changes in genetic diversity, such as in loss of species, leads to a loss of  biological diversity . [ 2 ]  Loss of genetic diversity in domestic animal populations has also been studied and attributed to the extension of markets and  economic globalization . [ 5 ] [ 6 ]


Neutral genetic diversity consists of  genes  that do not increase  fitness  and are not responsible for adaptability. [ 7 ]   Natural selection  does not act on these neutral genes. [ 7 ]  Adaptive genetic diversity consists of genes that increase fitness and are responsible for adaptability to changes in the environment. [ 7 ]  Adaptive genes are responsible for ecological, morphological, and behavioral traits. [ 8 ]  Natural selection acts on adaptive genes which allows the organisms to evolve. [ 7 ]  The rate of  evolution  on adaptive genes is greater than on neutral genes due to the influence of selection. [ 8 ]  However, it has been difficult to identify alleles for adaptive genes and thus adaptive genetic diversity is most often measured indirectly. [ 7 ]  For example,  heritability  can be measured as

 h

 2

 =

 V

 A

 /

 V

 P

 {\displaystyle h^{2}=V_{A}/V_{P}}

  or  adaptive population differentiation  can be measured as

 Q

 S
 T

 =

 V

 G

 /

 (

 V

 G

 +
 2

 V

 A

 )

 {\displaystyle Q_{ST}=V_{G}/(V_{G}+2V_{A})}

 . [ 7 ]  It may be possible to identify adaptive genes through  genome-wide association studies  by analyzing genomic data at the population level. [ 9 ]


Identifying adaptive genetic diversity is important for  conservation  because the adaptive potential of a species may dictate whether it survives or becomes  extinct , especially as the  climate changes . [ 7 ] [ 10 ]  This is magnified by a lack of understanding whether low neutral genetic diversity is correlated with high  genetic drift  and high  mutation load . [ 10 ]  In a review of current research, Teixeira and Huber (2021), discovered some species, such as those in the genus  Arabidopsis , appear to have high adaptive potential despite suffering from low genetic diversity overall due to severe  bottlenecks . [ 10 ]  Therefore species with low neutral genetic diversity may possess high adaptive genetic diversity, but since it is difficult to identify adaptive genes, a measurement of overall genetic diversity is important for planning conservation efforts and a species that has experienced a rapid decline in genetic diversity may be highly susceptible to extinction. [ 10 ] [ 9 ]


Variation in the populations gene pool allows  natural selection  to act upon traits that allow the population to adapt to changing environments. Selection for or against a trait can occur with changing environment – resulting in an increase in genetic diversity (if a new  mutation  is selected for and maintained) or a decrease in genetic diversity (if a disadvantageous allele is selected against). [ 11 ]  Hence, genetic diversity plays an important role in the survival and adaptability of a species. [ 12 ]  The capability of the population to adapt to the changing environment will depend on the presence of the necessary genetic diversity. [ 13 ] [ 14 ]  The more genetic diversity a population has, the more likelihood the population will be able to adapt and survive. Conversely, the vulnerability of a population to changes, such as climate change or novel  diseases  will increase with reduction in genetic diversity. [ 15 ]  For example, the inability of koalas to adapt to fight  Chlamydia  and the  koala retrovirus  (KoRV) has been linked to the koala's low genetic diversity. [ 16 ]  This low genetic diversity also has geneticists concerned for the koalas' ability to adapt to climate change and human-induced environmental changes in the future. [ 16 ]


Large populations are more likely to maintain genetic material and thus generally have higher genetic diversity. [ 11 ]  Small populations are more likely to experience the loss of diversity over time by random chance, which is an example of  genetic drift . When an  allele  (variant of a gene) drifts to fixation, the other allele at the same locus is lost, resulting in a loss in genetic diversity. [ 17 ]  In small population sizes,  inbreeding , or  mating  between individuals with similar genetic makeup, is more likely to occur, thus perpetuating more common alleles to the point of fixation, thus decreasing genetic diversity. [ 18 ]  Concerns about genetic diversity are therefore especially important with large mammals due to their small population size and high levels of human-caused population effects. [16]


A  genetic bottleneck  can occur when a population goes through a period of low number of individuals, resulting in a rapid decrease in genetic diversity. Even with an increase in population size, the genetic diversity often continues to be low if the entire species began with a small population, since beneficial mutations (see below) are rare, and the gene pool is limited by the small starting population. [ 19 ]  This is an important consideration in the area of  conservation genetics , when working toward a rescued population or species that is genetically healthy.


Random  mutations  consistently generate  genetic variation . [ 11 ]  A mutation will increase genetic diversity in the short term, as a new gene is introduced to the gene pool. However, the persistence of this gene is dependent of drift and selection (see above). Most new mutations either have a neutral or negative effect on fitness, while some have a positive effect. [ 11 ]  A beneficial mutation is more likely to persist and thus have a long-term positive effect on genetic diversity. Mutation rates differ across the genome, and larger populations have greater mutation rates. [ 11 ]  In smaller populations a mutation is less likely to persist because it is more likely to be eliminated by drift. [ 11 ]


Gene flow , often by migration, is the movement of genetic material (for example by pollen in the wind, or the migration of a bird). Gene flow can introduce novel alleles to a population. These alleles can be integrated into the population, thus increasing genetic diversity. [ 20 ]


For example, an  insecticide -resistant mutation arose in  Anopheles gambiae  African mosquitoes. Migration of some  A. gambiae  mosquitoes to a population of  Anopheles coluzziin  mosquitoes resulted in a transfer of the beneficial resistance gene from one species to the other. The genetic diversity was increased in  A. gambiae  by mutation and in  A. coluzziin  by gene flow. [ 21 ]


When humans initially started farming, they used  selective breeding  to pass on desirable traits of the crops while omitting the undesirable ones. Selective breeding leads to  monocultures : entire farms of nearly genetically identical plants. Little to no genetic diversity makes crops extremely susceptible to widespread disease; bacteria morph and change constantly and when a disease-causing bacterium changes to attack a specific genetic variation, it can easily wipe out vast quantities of the species. If the genetic variation that the bacterium is best at attacking happens to be that which humans have selectively bred to use for harvest, the entire crop will be wiped out. [ 22 ]


The nineteenth-century  Great Famine  in Ireland was caused in part by a lack of biodiversity. Since new potato plants do not come as a result of reproduction, but rather from pieces of the parent plant, no genetic diversity is developed, and the entire crop is essentially a clone of one potato, it is especially susceptible to an epidemic. In the 1840s, much of Ireland's population depended on potatoes for food. They planted namely the "lumper" variety of potato, which was susceptible to a rot-causing  oomycete  called  Phytophthora infestans . [ 23 ]  The fungus destroyed the vast majority of the potato crop, and left one million people to starve to death.


Genetic diversity in agriculture does not only relate to disease, but also  herbivores . Similarly, to the above example, monoculture agriculture selects for traits that are uniform throughout the plot. If this  genotype  is susceptible to certain herbivores, this could result in the loss of a large portion of the crop. [ 24 ] [ 25 ]  One way farmers get around this is through  inter-cropping . By planting rows of unrelated, or genetically distinct crops as barriers between herbivores and their preferred host plant, the farmer effectively reduces the ability of the herbivore to spread throughout the entire plot. [ 26 ] [ 27 ] [ 28 ]


The genetic diversity of livestock species permits  animal husbandry  in a range of environments and with a range of different objectives. It provides the raw material for  selective breeding  programmes and allows livestock populations to adapt as environmental conditions change. [ 29 ]


Livestock biodiversity  can be lost as a result of  breed  extinctions and other forms of  genetic erosion . As of June 2014, among the 8,774 breeds recorded in the Domestic Animal Diversity Information System ( DAD-IS ), operated by the Food and Agriculture Organization of the United Nations ( FAO ), 17 percent were classified as being at risk of extinction and 7 percent already extinct. [ 29 ]  There is now a Global Plan of Action for Animal Genetic Resources that was developed under the auspices of the Commission on Genetic Resources for Food and Agriculture in 2007, that provides a framework and guidelines for the management of animal genetic resources.


Awareness of the importance of maintaining animal genetic resources has increased over time. FAO has published two reports on  the state of the world's animal genetic resources for food and agriculture , which cover detailed analyses of our global livestock diversity and ability to manage and conserve them.


High genetic diversity in viruses must be considered when designing vaccinations. High genetic diversity results in difficulty in designing targeted vaccines, and allows for viruses to quickly evolve to resist vaccination lethality. For example, malaria vaccinations are impacted by high levels of genetic diversity in the protein antigens. [ 30 ]  In addition,  HIV-1  genetic diversity limits the use of currently available viral load and resistance tests. [ 31 ]


Coronavirus  populations have considerable evolutionary diversity due to  mutation  and  homologous recombination . [ 32 ]   For example, the sequencing of 86 SARS-CoV-2 coronavirus samples obtained from infected patients revealed 93 mutations indicating the presence of considerable genetic diversity. [ 33 ]   Replication of the coronavirus  RNA genome  is catalyzed by an  RNA-dependent RNA polymerase .  During replication this polymerase may undergo template switching, a form of homologous recombination. [ 34 ]   This process which also generates genetic diversity appears to be an adaptation for coping with RNA genome damage. [ 35 ]


The natural world has several ways of preserving or increasing genetic diversity. Among oceanic  plankton ,  viruses  aid in the genetic shifting process. Ocean viruses, which infect the plankton, carry genes of other organisms in addition to their own. When a virus containing the genes of one cell infects another, the genetic makeup of the latter changes. This constant shift of genetic makeup helps to maintain a healthy population of plankton despite complex and unpredictable environmental changes. [ 36 ]


Cheetahs  are a  threatened species . Low genetic diversity and resulting poor sperm quality has made breeding and survivorship difficult for cheetahs. Moreover, only about 5% of cheetahs survive to adulthood. [ 37 ]  However, it has been recently discovered that female cheetahs can mate with more than one male per litter of cubs. They undergo induced ovulation, which means that a new egg is produced every time a female mates. By mating with multiple males, the mother increases the genetic diversity within a single litter of cubs. [ 38 ]


Attempts to increase the viability of a species by increasing genetic diversity is called genetic rescue. For example, eight panthers from  Texas  were introduced to the  Florida  panther population, which was declining and suffering from inbreeding depression. Genetic variation was thus increased and resulted in a significant increase in population growth of the Florida Panther. [ 39 ]  Creating or maintaining high genetic diversity is an important consideration in species rescue efforts, in order to ensure the longevity of a population.


Genetic diversity of a population can be assessed by some simple measures.


Furthermore, stochastic simulation software is commonly used to predict the future of a population given measures such as allele frequency and population size. [ 41 ]


Genetic diversity can also be measured. The various recorded ways of measuring genetic diversity include: [ 42 ]


Genetic load  is the difference between the  fitness  of an average  genotype  in a  population  and the fitness of some reference genotype, which may be either the best present in a  population , or may be the theoretically  optimal  genotype. The average individual taken from a population with a low genetic load will generally, when  grown in the same conditions , have more surviving offspring than the average individual from a population with a high genetic load. [ 1 ] [ 2 ]  Genetic load can also be seen as reduced fitness at the population level compared to what the population would have if all individuals had the reference high-fitness genotype. [ 3 ]  High genetic load may put a population in danger of  extinction .


Consider n genotypes

 A

 1

 ,
 …
 ,

 A

 n

 {\displaystyle \mathbf {A} _{1},\dots ,\mathbf {A} _{n}}

 , which have the  fitnesses

 w

 1

 ,
 …
 ,

 w

 n

 {\displaystyle w_{1},\dots ,w_{n}}

  and  frequencies

 p

 1

 ,
 …
 ,

 p

 n

 {\displaystyle p_{1},\dots ,p_{n}}

 , respectively. Ignoring  frequency-dependent selection , the genetic load

 L

 {\displaystyle L}

  may be calculated as:


where

 w

 max

 {\displaystyle w_{\max }}

  is either some theoretical optimum, or the  maximum  fitness observed in the population. In calculating the genetic load,

 w

 1

 …

 w

 n

 {\displaystyle w_{1}\dots w_{n}}

  must be actually found in at least a single copy in the population, and

 w
 ¯

 {\displaystyle {\bar {w}}}

  is the  average   fitness  calculated as the mean of all the fitnesses  weighted  by their corresponding frequencies:


where the

 i

 t
 h

 {\displaystyle i^{\mathrm {th} }}

  genotype is

 A

 i

 {\displaystyle \mathbf {A} _{i}}

  and has the fitness and frequency

 w

 i

 {\displaystyle w_{i}}

  and

 p

 i

 {\displaystyle p_{i}}

  respectively.


One problem with calculating genetic load is that it is difficult to evaluate either the theoretically optimal genotype, or the maximally fit genotype actually present in the population. [ 4 ]  This is not a problem within  mathematical models  of genetic load, or for empirical studies that compare the relative value of genetic load in one setting to genetic load in another.


Deleterious mutation  load is the main contributing factor to genetic load overall. [ 5 ]  The Haldane-Muller theorem of  mutation–selection balance  says that the load depends only on the deleterious  mutation rate  and not on the  selection coefficient . [ 6 ]  Specifically, relative to an ideal genotype of fitness 1, the mean population fitness is

 exp

 (
 −
 U
 )

 {\displaystyle \exp(-U)}

  where U is the total deleterious mutation rate summed over many independent sites. The intuition for the lack of dependence on the selection coefficient is that while a mutation with stronger effects does more harm per generation, its harm is felt for fewer generations.


A slightly deleterious mutation may not stay in mutation–selection balance but may instead become  fixed  by  genetic drift  when its  selection coefficient  is less than one divided by the  effective population size . [ 7 ]  In asexual populations, the  stochastic  accumulation of mutation load is called  Muller's ratchet , and occurs in the absence of beneficial mutations, when after the most-fit genotype has been lost, it cannot be regained by  genetic recombination . Deterministic accumulation of mutation load occurs in asexuals when the deleterious mutation rate exceeds one per replication. [ 8 ]  Sexually reproducing species are expected to have lower genetic loads. [ 9 ]  This is one hypothesis for the  evolutionary advantage of sexual reproduction . Purging of deleterious mutations in sexual populations is facilitated by  synergistic epistasis  among deleterious mutations. [ 10 ]


High load can lead to a  small population size , which in turn increases the accumulation of mutation load, culminating in  extinction  via  mutational meltdown . [ 11 ] [ 12 ]


The accumulation of deleterious mutations in humans has been of concern to many geneticists, including  Hermann Joseph Muller , [ 13 ]   James F. Crow , [ 10 ]   Alexey Kondrashov , [ 14 ]   W. D. Hamilton , [ 15 ]  and  Michael Lynch . [ 16 ]


In sufficiently genetically loaded populations, new beneficial mutations create fitter genotypes than those previously present in the population. When load is calculated as the difference between the fittest genotype present and the average, this creates a  substitutional load . The difference between the theoretical maximum (which may not actually be present) and the average is known as the "lag load". [ 17 ]   Motoo Kimura 's original argument for the  neutral theory of molecular evolution  was that if most differences between species were adaptive, this would exceed the speed limit to adaptation set by the substitutional load. [ 18 ]  However, Kimura's argument confused the lag load with the substitutional load, using the former when it is the latter that in fact sets the maximal rate of evolution by natural selection. [ 19 ]


More recent "travelling wave" models of rapid adaptation derive a term called the "lead" that is equivalent to the substitutional load, and find that it is a critical determinant of the rate of adaptive evolution. [ 20 ] [ 21 ]


Inbreeding increases  homozygosity . In the short run, an increase in inbreeding increases the probability with which offspring get two copies of a recessive deleterious alleles, lowering fitnesses via  inbreeding depression . [ 22 ]  In a species that habitually inbreeds, e.g. through  self-fertilization , a proportion of recessive deleterious alleles can be  purged . [ 23 ] [ 24 ]


Likewise, in a small population of humans practicing  endogamy , deleterious alleles can either overwhelm the population's gene pool, causing it to become extinct, or alternately, make it fitter. [ 25 ]


Combinations of alleles that have evolved to work well together may not work when recombined with a different suite of coevolved alleles, leading to  outbreeding depression . Segregation load occurs in the presence of  overdominance , i.e. when heterozygotes are more fit than either homozygote. In such a case, the heterozygous genotype gets broken down by Mendelian  segregation , resulting in the production of homozygous offspring. Therefore, there is segregation load as not all individuals have the theoretical optimum genotype. Recombination load arises through unfavorable combinations across multiple loci that appear when favorable  linkage disequilibria  are broken down. [ 26 ]  Recombination load can also arise by combining deleterious alleles subject to  synergistic epistasis , i.e. whose damage in combination is greater than that predicted from considering them in isolation. [ 27 ]   Evidence was reviewed indicating that meiosis reduces  recombination  load, thus providing a selective advantage of  sexual reproduction . [ 28 ]


Migration load is hypothesized to occur when maladapted non-native organisms enter a new environment. [ 29 ]


On one hand, beneficial genes from migrants can increase the fitness of local populations. [ 30 ]  On the other hand, migration may reduce the fitness of local populations by introducing maladptive alleles. This is hypothesized to occur when the migration rate is "much greater" than the selection coefficient. [ 30 ]


Migration load may occur by reducing the fitness of local organisms, or through natural selection imposed on the newcomers, such as by being eliminated by local predators. [ 31 ] [ 32 ]  Most studies have only found evidence for this theory in the form of selection against immigrant populations, however, one study found evidence for increased mutational burden in recipient populations, as well. [ 33 ]


Genetic variation in populations can be analyzed and quantified by the frequency of  alleles . Two fundamental calculations are central to  population genetics :  allele frequencies  and genotype frequencies. [ 1 ]   Genotype frequency  in a population is the number of individuals with a given  genotype  divided by the total number of individuals in the population. [ 2 ]
In  population genetics , the  genotype frequency  is the frequency or proportion (i.e., 0 <  f  < 1) of genotypes in a population.


Although allele and genotype frequencies are related, it is important to clearly distinguish them.


Genotype frequency  may also be used in the future (for "genomic profiling") to predict someone's having a disease [ 3 ]   or even a birth defect. [ 4 ]  It can also be used to determine ethnic diversity.


Genotype frequencies may be represented by a  De Finetti diagram .


As an example, consider a population of 100 four-o-'clock plants ( Mirabilis jalapa ) with the following genotypes:


When calculating an allele frequency for a  diploid  species, remember that  homozygous  individuals have two copies of an allele, whereas  heterozygotes  have only one. In our example, each of the 42 pink-flowered heterozygotes has one copy of the  a  allele, and each of the 9 white-flowered homozygotes has two copies. Therefore, the allele frequency for  a  (the white color allele) equals


This result tells us that the allele frequency of  a  is 0.3. In other words, 30% of the alleles for this gene in the population are the  a  allele.


Compare genotype frequency:
let's now calculate the genotype frequency of  aa  homozygotes (white-flowered plants).


Allele and genotype frequencies always sum to one (100%).


The  Hardy–Weinberg law  describes the relationship between allele and genotype frequencies when a population is not evolving. Let's examine the Hardy–Weinberg equation using the population of four-o'clock plants that we considered above:

if the allele  A  frequency is denoted by the symbol  p  and the allele  a  frequency denoted by  q , then  p+q=1 .
For example, if  p =0.7, then  q  must be 0.3. In other words, if the allele frequency of  A  equals 70%, the remaining 30% of the alleles must be  a , because together they equal 100%. [ 5 ]


For a  gene  that exists in two alleles, the Hardy–Weinberg equation states that  ( p 2 ) + (2 pq ) + ( q 2 ) = 1 .
If we apply this equation to our flower color gene, then


If  p =0.7 and  q =0.3, then


This result tells us that, if the allele frequency of  A  is 70% and the allele frequency of  a  is 30%, the expected genotype frequency of  AA  is 49%,  Aa  is 42%, and  aa  is 9%. [ 6 ]


In biology,  mother's curse  is an evolutionary effect that males inherit deleterious  mitochondrial  genome (mtDNA)  mutations  from their mother, while those mutations are beneficial, neutral or less deleterious to females.


As mtDNA is usually maternally inherited, mtDNA mutations deleterious to males but beneficial, neutral or less deleterious to females are not subjected to be selected against, which results in a sex-biased selective sieve. [ 1 ]  Therefore, male-specific deleterious mtDNA mutations could be maintained and reach a high frequency in  populations , decreasing males'  fitness  and population viability. In addition, the effect of mtDNA mutations on fitness has a threshold effect, i.e. only when the number of mutation reaches the threshold, mtDNA mutations will decrease individual fitness. [ 2 ]


Males are more susceptible to mtDNA defects, not only because of lack of selection for mtDNA on males but also due to sperm's higher energy requirements for motility. [ 3 ]  There are evidence showing mtDNA mutations are more likely to affect males. In humans,  Leber's hereditary optic neuropathy  (LHON) is caused by one or several point mutations on mtDNA and LHON affects more males than females. [ 4 ]  In mice, a deletion on mtDNA causes oligospermia and asthenozoospermia, resulting in infertility. [ 5 ]  Taken together, mtDNA mutations pose a greater threat on males than on females.


Mother's curse predicts that mtDNA mutations pose a greater threat on males and male-specific detrimental mutations in mtDNA could be maintained and reach a high frequency. Several researches support these predictions. In humans, a mtDNA haplogroup that exhibits reduction in sperm mobility reaches a frequency of 20%. [ 2 ]  A 2017 study found the mother's curse preserving a mutation that causes  Leber's hereditary optic neuropathy  in a population of French Canadians for over 290 years. [ 6 ]


In  Drosophila melanogaster , mtDNA polymorphism mainly affects nuclear gene expression in males but not in females and those genes are predominantly male-biased. [ 7 ]  Moreover, Camus e t al. [ 1 ] [ 8 ]  constructed 13  D. melanogaster  lines with isogenic nuclear genome and different mtDNA haplotypes. They demonstrated that mtDNA polymorphism is responsible for male aging, while there is no significant effect on female longevity.  Smith  et al. [ 3 ]  analyzed two different haplotype of mtDNA in hares and found that males of those two haplogroups show variation in their reproductive success. In addition, the mitochondrial genome is associated with sperm viability and length in seed beetles ( Callosobruchus maculatus ). [ 9 ]


If mtDNA mutations deleterious to male fitness could not be selected against, they would reach a high frequency despite the high fitness cost for males. Eventually, detrimental mutations would be fixed and lead to species extinction. However, we have not observed extinction in spite of high  mutation load  of mtDNA. So there must be ways that species could decrease the effects of male-specific deleterious mtDNA mutations. [ 2 ]


Mitochondria play a pivotal role in eukaryotic respiration. Because of maternal inheritance, mtDNA has no selection in males. Instead, mutations only deleterious to males could be maintained and reach a higher frequency by selection or genetic drift in females. As a consequence, asymmetric effects of mtDNA mutations result in sexual conflict. On the other hand, to alleviate the effect of mother's curse, interaction between mtDNA and nuclear genes promotes  coevolution  of mitochondrial and nuclear genomes.


In  evolutionary   genetics ,  Muller's ratchet  (named after  Hermann Joseph Muller , by analogy with a  ratchet effect ) is a process which, in the absence of  recombination  (especially in an  asexual   population ), results in an accumulation of irreversible deleterious mutations. [ 1 ] [ 2 ]  This happens because in the absence of recombination, and assuming reverse mutations are rare, offspring bear at least as much mutational load as their parents. [ 2 ]  Muller proposed this mechanism as one reason why  sexual reproduction  may be  favored  over  asexual reproduction , as sexual organisms benefit from recombination and consequent elimination of deleterious mutations. The negative effect of accumulating irreversible deleterious mutations may not be prevalent in organisms which, while they reproduce asexually, also undergo other forms of recombination. This effect has also been observed in those regions of the genomes of sexual organisms that do not undergo recombination.


Although Muller discussed the advantages of sexual reproduction in his 1932 talk, it does not contain the word "ratchet". Muller first introduced the term "ratchet" in his 1964 paper, [ 2 ]  and the phrase "Muller's ratchet" was coined by  Joe Felsenstein  in his 1974 paper, "The Evolutionary Advantage of Recombination". [ 3 ]


Asexual reproduction compels genomes to be inherited as indivisible blocks so that once the least mutated genomes in an asexual population begin to carry at least one deleterious mutation, no genomes with fewer such mutations can be expected to be found in future generations (except as a result of  back mutation ). This results in an eventual accumulation of mutations known as  genetic load . In theory, the genetic load carried by asexual populations eventually becomes so great that the population goes extinct. [ 4 ]  Also, laboratory experiments have confirmed the existence of the ratchet and the consequent extinction of populations in many organisms (under intense drift and when recombinations are not allowed) including RNA viruses, bacteria, and eukaryotes. [ 5 ] [ 6 ] [ 7 ]  In sexual populations, the process of genetic recombination allows the genomes of the  offspring  to be different from the genomes of the parents. In particular, progeny (offspring) genomes with fewer mutations can be generated from more highly mutated parental genomes by putting together mutation-free portions of parental chromosomes. Also,  purifying selection , to some extent, unburdens a loaded population when recombination results in different combinations of mutations. [ 2 ]


Among  protists  and  prokaryotes , a plethora of supposedly asexual organisms exists. More and more are being shown to exchange genetic information through a variety of mechanisms. In contrast, the genomes of  mitochondria  and  chloroplasts  do not recombine and would undergo Muller's ratchet were they not as small as they are (see Birdsell and Wills [pp. 93–95]). [ 8 ]  Indeed, the probability that the least mutated genomes in an asexual population end up carrying at least one (additional) mutation depends heavily on the genomic mutation rate and this increases more or less linearly with the size of the genome (more accurately, with the number of base pairs present in active genes). However, reductions in genome size, especially in parasites and symbionts, can also be caused by direct selection to get rid of genes that have become unnecessary. Therefore, a smaller genome is not a sure indication of the action of Muller's ratchet. [ 9 ]


In sexually reproducing organisms, nonrecombining chromosomes or chromosomal regions such as the mammalian  Y chromosome  (with the exception of multicopy sequences which do engage intrachromosomal recombination and gene conversion [ 4 ] ) should also be subject to the effects of Muller's ratchet. Such nonrecombining sequences tend to shrink and evolve quickly. However, this fast evolution might also be due to these sequences' inability to repair DNA damage via template-assisted repair, which is equivalent to an increase in the mutation rate for these sequences. Ascribing cases of genome shrinkage or fast evolution to Muller's ratchet alone is not easy.


Muller's ratchet relies on  genetic drift , and turns faster in smaller populations because in such populations deleterious mutations have a better chance of fixation. Therefore, it sets the limits to the maximum size of asexual genomes and to the long-term evolutionary continuity of asexual lineages. [ 4 ]  However, some asexual lineages are thought to be quite ancient;  Bdelloid  rotifers, for example, appear to have been asexual for nearly 40 million years. [ 10 ]  However, rotifers were found to possess a substantial number of foreign genes from possible  horizontal gene transfer  events. [ 11 ]  Furthermore, a vertebrate fish,  Poecilia formosa , seems to defy the ratchet effect, having existed for 500,000 generations. This has been explained by maintenance of genomic diversity through parental  introgression  and a high level of heterozygosity resulting from the hybrid origin of this species. [ 12 ]


In 1978, John Haigh used a  Wright–Fisher model  to analyze the effect of Muller's ratchet in an asexual population. [ 13 ]  If the ratchet is operating the fittest class (least loaded individuals) is small and prone to extinction by the effect of genetic drift. In his paper Haigh derives the equation that calculates the frequency of individuals carrying

 k

 {\displaystyle k}

  mutations for the population with stationary distribution:


n

 k

 =

 N

 e

 −
 θ

 θ

 k

 k
 !

 {\displaystyle n_{k}\ =\ {\frac {Ne^{-\theta }\theta ^{k}}{k!}}}


θ
 =

 λ
 s

 {\displaystyle \theta =\ {\frac {\lambda }{s}}}


where,

 n

 k

 {\displaystyle n_{k}\ }

  is the number of individual carrying

 k

 {\displaystyle k}

  mutations,

 N

 {\displaystyle N}

  is the population size,

 λ

 {\displaystyle \lambda }

  is the mutation rate and

 s

 {\displaystyle s}

  is the selection coefficient.


Thus, the frequency of the individuals of the fittest class (

 k
 =
 0

 {\displaystyle k=0}

 ) is:


n

 0

 =
 N

 e

 −

 θ

 {\displaystyle {n_{0}}\ =N\ \ e^{-\ \theta }}


In an asexual population which suffers from ratchet the frequency of fittest individuals would be small, and go extinct after few generations. [ 13 ]  This is called a click of the ratchet. Following each click, the rate of accumulation of deleterious mutation would increase, and ultimately results in the extinction of the population.


It has been argued that  recombination  was an evolutionary development as ancient as  life  on Earth. [ 14 ]   Early  RNA replicators  capable of recombination may have been the ancestral sexual source  from which  asexual  lineages could periodically emerge. [ 14 ]   Recombination in the early sexual lineages may have provided a means for coping with genome damage. [ 15 ]   Muller's ratchet under such ancient conditions would likely have impeded the evolutionary persistence of the asexual lineages that were unable to undergo recombination. [ 14 ]


Since deleterious mutations are harmful by definition, accumulation of them would result in loss of individuals and a smaller population size. Small populations are more susceptible to the ratchet effect and more deleterious mutations would be fixed as a result of genetic drift. This creates a positive feedback loop which accelerates extinction of small asexual populations. This phenomenon has been called  mutational meltdown . [ 16 ]   It appears that mutational meltdown due to  Muller’s ratchet  can be avoided by a little bit of sex as in the common  apomictic  asexual flowering plant  Ranunculus auricomus . [ 17 ]


Pleiotropy  (from  Greek   πλείων   pleion , 'more', and  τρόπος   tropos , 'way') occurs when one  gene  influences two or more seemingly unrelated  phenotypic traits . Such a gene that exhibits multiple phenotypic expression is called a pleiotropic gene.  Mutation  in a pleiotropic gene may have an effect on several traits simultaneously, due to the gene coding for a product used by a myriad of cells or different targets that have the same signaling function.


Pleiotropy can arise from several distinct but potentially overlapping mechanisms, such as  gene  pleiotropy,  developmental  pleiotropy, and selectional pleiotropy. Gene pleiotropy occurs when a gene product interacts with multiple other  proteins  or catalyzes multiple reactions. Developmental pleiotropy occurs when  mutations  have multiple effects on the resulting  phenotype . Selectional pleiotropy occurs when the resulting phenotype has many effects on  fitness  (depending on factors such as age and sex). [ 1 ]


An example of pleiotropy is  phenylketonuria , an inherited disorder that affects the level of  phenylalanine , an  amino acid  that can be obtained from food, in the human body. Phenylketonuria causes this amino acid to increase in amount in the body, which can be very dangerous. The disease is caused by a defect in a single gene on  chromosome  12 that codes for enzyme  phenylalanine hydroxylase , that affects multiple systems, such as the nervous and  integumentary system . [ 2 ]


Pleiotropic gene action can limit the rate of multivariate evolution when  natural selection ,  sexual selection  or  artificial selection  on one trait favors one allele, while selection on other traits favors a different allele. Some gene evolution is harmful to an organism.  Genetic correlations  and responses to selection most often exemplify pleiotropy.


Pleiotropic traits had been previously recognized in the scientific community but had not been experimented on until  Gregor Mendel 's 1866 pea plant experiment. Mendel recognized that certain pea plant traits (seed coat color, flower color, and axial spots) seemed to be inherited together; [ 3 ]  however, their  correlation  to a single gene has never been proven. The term "pleiotropie" was first coined by  Ludwig Plate  in his  Festschrift , which was published in 1910. [ 3 ]  He originally defined pleiotropy as occurring when "several characteristics are dependent upon ... [inheritance]; these characteristics will then always appear together and may thus appear correlated". [ 4 ]  This definition is still used today.


After Plate's definition,  Hans Gruneberg  was the first to study the  mechanisms  of pleiotropy. [ 3 ]  In 1938 Gruneberg published an article dividing pleiotropy into two distinct types: "genuine" and "spurious" pleiotropy. "Genuine" pleiotropy is when two distinct primary products arise from one  locus . "Spurious" pleiotropy, on the other hand, is either when one primary product is utilized in different ways or when one primary product initiates a cascade of events with different  phenotypic  consequences. Gruneberg came to these distinctions after experimenting on rats with skeletal  mutations . He recognized that "spurious" pleiotropy was present in the mutation, while "genuine" pleiotropy was not, thus partially invalidating his own original  theory . [ 5 ]  Through subsequent  research , it has been established that Gruneberg's definition of "spurious" pleiotropy is what we now identify simply as "pleiotropy". [ 3 ]


In 1941 American geneticists  George Beadle  and  Edward Tatum  further invalidated Gruneberg's definition of "genuine" pleiotropy, advocating instead for the  "one gene-one enzyme" hypothesis  that was originally introduced by French biologist  Lucien Cuénot  in 1903. [ 3 ] [ 6 ]  This hypothesis shifted future research regarding pleiotropy towards how a single gene can produce various phenotypes.


In the mid-1950s  Richard Goldschmidt  and  Ernst Hadorn , through separate individual research, reinforced the faultiness of "genuine" pleiotropy. A few years later, Hadorn partitioned pleiotropy into a "mosaic" model (which states that one locus directly affects two phenotypic traits) and a "relational" model (which is analogous to "spurious" pleiotropy). These terms are no longer in use but have contributed to the current understanding of pleiotropy. [ 3 ]


By accepting the one gene-one enzyme hypothesis, scientists instead focused on how uncoupled phenotypic traits can be affected by  genetic recombination  and mutations, applying it to  populations  and  evolution . [ 3 ]  This view of pleiotropy, "universal pleiotropy", defined as locus mutations being capable of affecting essentially all traits, was first implied by  Ronald Fisher 's  Geometric Model  in 1930. This mathematical model illustrates how evolutionary  fitness  depends on the independence of phenotypic variation from random changes (that is, mutations). It theorizes that an increasing phenotypic independence corresponds to a decrease in the likelihood that a given mutation will result in an increase in fitness. [ 7 ]  Expanding on Fisher's work,  Sewall Wright  provided more evidence in his 1968 book  Evolution and the Genetics of Populations: Genetic and Biometric Foundations  by using molecular genetics to support the idea of "universal pleiotropy". The concepts of these various studies on evolution have seeded numerous other research projects relating to individual fitness. [ 1 ]


In 1957 evolutionary biologist  George C. Williams  theorized that antagonistic effects will be exhibited during an organism's  life cycle  if it is closely linked and pleiotropic.  Natural selection  favors genes that are more beneficial prior to  reproduction  than after (leading to an increase in  reproductive success ). Knowing this, Williams argued that if only close  linkage  was present, then beneficial traits will occur both before and after reproduction due to natural selection. This, however, is not observed in nature, and thus  antagonistic pleiotropy  contributes to the slow deterioration with age ( senescence ). [ 8 ]


Pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. The underlying mechanism is genes that code for a product that is either used by various cells or has a cascade-like signaling function that affects various targets.


Most genetic traits are polygenic in nature: controlled by many genetic variants, each of small effect. These genetic variants can reside in protein coding or non-coding regions of the genome. In this context pleiotropy refers to the influence that a specific genetic variant, e.g., a  single nucleotide polymorphism  or SNP, has on two or more distinct traits.


Genome-wide association studies  (GWAS) and machine learning analysis of large genomic datasets have led to the construction of SNP based  polygenic predictors  for human traits such as height, bone density, and many disease risks. Similar predictors exist for plant and animal species and are used in agricultural breeding.


One measure of pleiotropy is the fraction of genetic variance that is common between two distinct complex human traits: e.g., height vs bone density, breast cancer vs heart attack risk, or diabetes vs hypothyroidism risk. This has been calculated for hundreds of pairs of traits, with results shown in the Table. In most cases examined the genomic regions controlling each trait are largely disjoint, with only modest overlap.


Thus, at least for complex human traits so far examined, pleiotropy is limited in extent.


One basic model of pleiotropy's origin describes a single gene  locus  to the expression of a certain trait. The locus affects the expressed trait only through changing the expression of other loci. Over time, that locus would affect two traits by interacting with a second locus.  Directional selection  for both traits during the same time period would increase the positive correlation between the traits, while selection on only one trait would decrease the positive correlation between the two traits. Eventually, traits that underwent directional selection simultaneously were linked by a single gene, resulting in pleiotropy.


The "pleiotropy-barrier" model proposes a logistic growth pattern for the increase of pleiotropy over time. This model differentiates between the levels of pleiotropy in evolutionarily younger and older genes subjected to natural selection. It suggests a higher potential for phenotypic innovation in evolutionarily newer genes due to their lower levels of pleiotropy.


Other more complex models compensate for some of the basic model's oversights, such as multiple traits or assumptions about how the loci affect the traits. They also propose the idea that pleiotropy increases the  phenotypic variation  of both traits since a single mutation on a gene would have twice the effect. [ 9 ]


Pleiotropy can have an effect on the evolutionary rate of  genes  and  allele frequencies . Traditionally, models of pleiotropy have predicted that evolutionary rate of genes is related negatively with pleiotropy – as the number of traits of an organism increases, the evolutionary rates of genes in the organism's population decrease. [ 10 ]  This relationship has not been clearly found in  empirical studies  for a long time. [ 11 ] [ 12 ]  However, a study based on human disease genes revealed the evidence of lower evolutionary rate in genes with higher pleiotropy.


In mating, for many animals the signals and receptors of sexual communication may have evolved simultaneously as the expression of a single gene, instead of the result of selection on two independent genes, one that affects the signaling trait and one that affects the  receptor  trait. [ 13 ]  In such a case, pleiotropy would facilitate mating and survival. However, pleiotropy can act negatively as well. A study on seed beetles found that  intralocus sexual conflict  arises when selection for certain alleles of a gene that are beneficial for one sex causes expression of potentially harmful traits by the same gene in the other sex, especially if the gene is located on an  autosomal chromosome . [ 14 ]


Pleiotropic genes act as an arbitrating force in  speciation . William R. Rice and Ellen E. Hostert (1993) conclude that the observed  prezygotic  isolation in their studies is a product of pleiotropy's balancing role in indirect selection. By imitating the traits of all-infertile  hybridized  species, they noticed that the fertilization of eggs was prevented in all eight of their separate studies, a likely effect of pleiotropic genes on speciation. [ 15 ]  Likewise, pleiotropic gene's  stabilizing selection  allows for the allele frequency to be altered. [ 16 ]


Studies on  fungal   evolutionary genomics  have shown pleiotropic traits that simultaneously affect  adaptation  and  reproductive isolation , converting adaptations directly to  speciation . A particularly telling case of this effect is host specificity in pathogenic  ascomycetes  and specifically, in  venturia , the fungus responsible for  apple scab . These  parasitic  fungi each adapts to a host, and are only able to mate within a shared host after obtaining resources. [ 17 ]  Since a single toxin gene or  virulence  allele can grant the ability to colonize the host, adaptation and  reproductive isolation  are instantly facilitated, and in turn, pleiotropically causes adaptive speciation. The studies on fungal evolutionary genomics will further elucidate the earliest stages of divergence as a result of gene flow, and provide insight into pleiotropically induced adaptive divergence in other  eukaryotes . [ 17 ]


Sometimes, a pleiotropic gene may be both harmful and beneficial to an organism, which is referred to as  antagonistic pleiotropy . This may occur when the trait is beneficial for the organism's early life, but not its late life. Such "trade-offs" are possible since  natural selection  affects traits expressed earlier in life, when most organisms are most fertile, more than traits expressed later in life. [ 18 ]


This idea is central to the  antagonistic pleiotropy hypothesis , which was first developed by  G.   C. Williams  in 1957. Williams suggested that some genes responsible for increased fitness in the younger, fertile organism contribute to decreased fitness later in life, which may give an evolutionary explanation for  senescence . An example is the  p53  gene, which suppresses  cancer  but also suppresses  stem cells , which replenish worn-out tissue. [ 13 ]


Unfortunately, the process of antagonistic pleiotropy may result in an altered evolutionary path with delayed  adaptation , in addition to effectively cutting the overall benefit of any  alleles  by roughly half. However, antagonistic pleiotropy also lends greater evolutionary "staying power" to genes controlling beneficial traits, since an organism with a mutation to those genes would have a decreased chance of successfully reproducing, as multiple traits would be affected, potentially for the worse. [ 19 ]


Sickle cell anemia  is a classic example of the mixed benefit given by the staying power of pleiotropic genes, as the mutation to Hb-S provides the fitness benefit of  malaria  resistance to  heterozygotes  as  sickle cell trait , while  homozygotes  have significantly lowered life expectancy—what is known as " heterozygote advantage ". Since both of these states are linked to the same mutated gene, large populations today are susceptible to sickle cell despite it being a fitness-impairing genetic disorder. [ 20 ]


Albinism is the mutation of the  TYR gene , also termed tyrosinase. This mutation causes the most common form of albinism. The mutation alters the production of  melanin , thereby affecting melanin-related and other dependent traits throughout the organism. Melanin is a substance made by the body that is used to absorb light and provides coloration to the skin. Indications of albinism are the absence of color in an organism's eyes, hair, and skin, due to the lack of melanin. Some forms of albinism are also known to have symptoms that manifest themselves through rapid-eye movement, light sensitivity, and  strabismus . [ 21 ]


Pleiotropy in genes has been linked between certain  psychiatric disorders  as well. Deletion in the  22q11.2  region of  chromosome 22  has been associated with  schizophrenia  and  autism . [ 22 ]  Schizophrenia and autism are linked to the same gene deletion but manifest very differently from each other. The resulting phenotype depends on the stage of life at which the individual develops the disorder. Childhood manifestation of the gene deletion is typically associated with autism, while adolescent and later expression of the gene deletion often manifests in schizophrenia or other psychotic disorders. [ 23 ]  Though the disorders are linked by genetics, there is no increased risk found for adult schizophrenia in patients who experienced autism in childhood. [ 24 ]


A 2013 study also genetically linked five psychiatric disorders, including schizophrenia and autism. The link was a  single nucleotide polymorphism  of two genes involved in  calcium channel signaling  with  neurons . One of these genes,  CACNA1C , has been found to influence  cognition . It has been associated with autism, as well as linked in studies to schizophrenia and  bipolar disorder . [ 25 ]  These particular studies show clustering of these diseases within patients themselves or families. [ 26 ]  The estimated  heritability  of schizophrenia is 70% to 90%, [ 27 ]  therefore the pleiotropy of genes is crucial since it causes an increased risk for certain psychotic disorders and can aid psychiatric diagnosis.


A common example of pleiotropy is the human disease  phenylketonuria  (PKU). This disease causes  mental retardation  and reduced  hair  and  skin pigmentation , and can be caused by any of a large number of mutations in the single gene on chromosome 12 that codes for the  enzyme   phenylalanine hydroxylase , which converts the  amino acid   phenylalanine  to  tyrosine . Depending on the mutation involved, this conversion is reduced or ceases entirely. Unconverted phenylalanine builds up in the bloodstream and can lead to levels that are toxic to the developing nervous system of newborn and infant children. The most dangerous form of this is called classic PKU, which is common in infants. The baby seems normal at first but actually incurs permanent intellectual disability. This can cause symptoms such as mental retardation, abnormal gait and posture, and delayed growth. Because tyrosine is used by the body to make  melanin  (a component of the pigment found in the hair and skin), failure to convert normal levels of phenylalanine to tyrosine can lead to fair hair and skin. [ 2 ]
The frequency of this disease varies greatly. Specifically, in the United States, PKU is found at a rate of nearly 1 in 10,000 births. Due to newborn screening, doctors are able to detect PKU in a baby sooner. This allows them to start treatment early, preventing the baby from suffering from the severe effects of PKU. PKU is caused by a mutation in the PAH gene, whose role is to instruct the body on how to make phenylalanine hydroxylase. Phenylalanine hydroxylase is what converts the phenylalanine, taken in through diet, into other things that the body can use. The mutation often decreases the effectiveness or rate at which the hydroxylase breaks down the phenylalanine. This is what causes the phenylalanine to build up in the body. [ 28 ]


Sickle cell anemia is a genetic disease that causes deformed red blood cells with a rigid, crescent shape instead of the normal flexible, round shape. [ 29 ]  It is caused by a change in one nucleotide, a  point mutation [ 30 ]  in the  HBB gene . The HBB gene encodes information to make the beta-globin subunit of  hemoglobin , which is the protein red blood cells use to carry oxygen throughout the body. Sickle cell anemia occurs when the HBB gene mutation causes both beta-globin subunits of hemoglobin to change into hemoglobin   S (HbS). [ 31 ]


Sickle cell anemia is a pleiotropic disease because the expression of a single mutated HBB gene produces numerous consequences throughout the body. The mutated hemoglobin forms polymers and clumps together causing the deoxygenated sickle red blood cells to assume the disfigured sickle shape. [ 32 ]  As a result, the cells are inflexible and cannot easily flow through blood vessels, increasing the risk of  blood clots  and possibly depriving vital organs of oxygen. [ 31 ]  Some complications associated with sickle cell anemia include pain, damaged organs,  strokes ,  high blood pressure , and loss of vision. Sickle red blood cells also have a shortened lifespan and die prematurely. [ 33 ]


Marfan syndrome (MFS) is an  autosomal   dominant disorder  which affects 1 in 5–10,000 people. [ 34 ]  MFS arises from a mutation in the  FBN1  gene, which encodes for the  glycoprotein  fibrillin-1, a major constituent of extracellular  microfibrils  which form  connective tissues . [ 34 ]   Over 1,000 different mutations in FBN1 have been found to result in abnormal function of fibrillin, which consequently relates to connective tissues elongating progressively and weakening. Because these fibers are found in tissues throughout the body, mutations in this gene can have a widespread effect on certain systems, including the  skeletal ,  cardiovascular , and  nervous system , as well as the eyes and lungs. [ 34 ]


Without medical intervention, prognosis of Marfan syndrome can range from moderate to life-threatening, with 90% of known causes of death in diagnosed patients relating to cardiovascular complications and  congestive cardiac failure .  Other characteristics of MFS include an increased arm span and decreased upper to lower body ratio. [ 34 ]


A gene recently discovered in laboratory  house mice , termed "mini-muscle", causes, when mutated, a 50% reduction in hindlimb muscle mass as its primary effect (the phenotypic effect by which it was originally identified). [ 9 ]  In addition to smaller hindlimb muscle mass, the mutant mice exhibit lower heart rates during physical activity, and a higher endurance. Mini Muscle Mice also exhibit larger kidneys and livers. All of these morphological deviations influence the behavior and  metabolism  of the mouse. For example, mice with the Mini Muscle mutation were observed to have a higher per-gram aerobic capacity. [ 35 ]  The mini-muscle allele shows a  mendelian recessive  behavior. [ 10 ]  The mutation is a single nucleotide polymorphism ( SNP ) in an  intron  of the  myosin  heavy polypeptide   4 gene. [ 36 ]


DNA repair  pathways that repair damage to cellular DNA use many different proteins.  These proteins often have other functions in addition to DNA repair. [ 37 ]  In humans, defects in some of these multifunctional proteins can cause widely differing clinical phenotypes. [ 37 ]   As an example, mutations in the  XPB  gene that encodes the largest subunit of the basal  Transcription factor II H  have several pleiotropic effects.  XPB mutations are known to be deficient in  nucleotide excision repair  of DNA and in the quite separate process of gene  transcription . [ 37 ]    In humans,  XPB   mutations  can give rise to the cancer-prone disorder  xeroderma pigmentosum  or the noncancer-prone multisystem disorder  trichothiodystrophy . Another example in humans is the  ERCC6  gene, which encodes a protein that mediates DNA repair,  transcription , and other cellular processes throughout the body. [ 38 ]  Mutations in  ERCC6  are associated with disorders of the eye ( retinal dystrophy ), heart (cardiac  arrhythmias ), and immune system (lymphocyte  immunodeficiency ). [ 39 ]


Chickens exhibit various traits affected by pleiotropic genes. Some chickens exhibit  frizzle feather trait , where their feathers all curl outward and upward rather than lying flat against the body. Frizzle feather was found to stem from a deletion in the genomic region coding for α-Keratin. This gene seems to pleiotropically lead to other abnormalities like increased  metabolism , higher food consumption, accelerated heart rate, and delayed sexual maturity. [ 40 ]


Domesticated chickens underwent a rapid selection process that led to unrelated phenotypes having high correlations, suggesting pleiotropic, or at least close linkage, effects between comb mass and  physiological  structures related to  reproductive  abilities. Both males and females with larger combs have higher bone density and strength, which allows females to deposit more  calcium  into eggshells. This linkage is further evidenced by the fact that two of the genes,  HAO1  and BMP2, affecting medullary bone (the part of the bone that transfers calcium into developing eggshells) are located at the same locus as the gene affecting comb mass. HAO1 and BMP2 also display pleiotropic effects with commonly desired domestic chicken behavior; those chickens who express higher levels of these two genes in bone tissue produce more eggs and display less  egg incubation  behavior. [ 41 ]


The  thrifty gene hypothesis , or  Gianfranco's hypothesis [ citation needed ]  is an attempt by  geneticist   James V. Neel  to explain why certain populations and subpopulations in the modern day are prone to  diabetes mellitus type 2 . He proposed the hypothesis in 1962 to resolve a fundamental problem: diabetes is clearly a very harmful medical condition, yet it is quite common, and it was already evident to Neel that it likely had a strong genetic basis. The problem is to understand how disease with a likely genetic component and with such negative effects may have been favoured by the process of natural selection. Neel suggested the resolution to this problem is that genes which predispose to diabetes (called 'thrifty genes') were historically advantageous, but they became detrimental in the modern world. In his words they were "rendered detrimental by 'progress'". Neel's primary interest was in diabetes, but the idea was soon expanded to encompass  obesity  as well. Thrifty genes are genes which enable individuals to efficiently collect and process food to deposit fat during periods of food abundance in order to provide for periods of food shortage (feast and famine). [ citation needed ]


According to the hypothesis, the 'thrifty'  genotype  would have been advantageous for  hunter-gatherer  populations, especially  child-bearing  women, because it would allow them to fatten more quickly during times of abundance. Fatter individuals carrying the thrifty genes would thus better survive times of food scarcity. However, in modern societies with a constant abundance of food, this genotype effectively prepares individuals for a famine that never comes. The result of this mismatch between the  environment in which the brain evolved  and the environment of today is widespread chronic obesity and related health problems like diabetes.


The hypothesis has received various criticisms and several modified or alternative hypotheses have been proposed.


James Neel, a professor of  Human Genetics  at the  University of Michigan Medical School , proposed the "thrifty genotype" hypothesis in 1962 in his paper "Diabetes Mellitus: A 'Thrifty' Genotype Rendered Detrimental by 'Progress'?" Neel intended the paper to provoke further contemplation and research on the possible evolutionary and genetic causes of diabetes among populations that had only recently come into regular contact with  Westerners . [ 1 ]


The genetic paradox Neel sought to address was this: diabetes conferred a significant reproductive (and thus evolutionary) disadvantage to anyone who had it; yet the populations Neel studied had diabetes in such high frequencies that a  genetic predisposition  to develop diabetes seemed plausible. Neel sought to unravel the mystery of why genes that promote diabetes had not been naturally-selected out of the population's  gene pool . [ 2 ]


Neel proposed that a genetic predisposition to develop diabetes was adaptive to the feast and famine cycles of paleolithic human existence, allowing humans to fatten rapidly and profoundly during times of feast in order that they might better survive during times of famine. This would have been advantageous then but not in the current environment. [ 3 ]


The hypothesis was proposed before there was a clear distinction between the different types of diabetes. Neel later stated that the hypothesis applied to  non-insulin-dependent diabetes mellitus . In its original form the theory more specifically stated that diabetes may be due to a rapid insulin response which would prevent loss of glucose from the urine. Furthermore, it made use of a then popular theory which was later disproven. This argued that specific insulin antagonists were released in response to insulin with this causing diabetes. [ 4 ]


In the decades following the publications of his first paper on the "thrifty genotype" hypothesis, Neel researched the frequency of diabetes and (increasingly) obesity in a number of other populations and sought out observations that might disprove or discount his "thrifty gene" hypothesis. [ citation needed ]


Neel's further investigations cast doubt on the "thrifty genotype" hypothesis. If a propensity to develop diabetes were an evolutionary adaptation, then diabetes would have been a disease of long standing in those populations currently experiencing a high frequency of diabetes. However, Neel found no evidence of diabetes among these populations earlier in the century. [ 5 ]  And when he tested younger members of these populations for  glucose intolerance  - which might have indicated a predisposition for diabetes - he found none. [ 6 ]


In 1989, Neel published a review of his further research based on the "thrifty genotype" hypothesis and in the Introduction noted the following: "The data on which that (rather soft) hypothesis was based has now largely collapsed." However, Neel argued that "...the concept of a "thrifty genotype" remains as viable as when first advanced...".  He went on to advance that the thrifty genotype concept be thought of in the context of a "compromised" genotype that affects several other metabolically-related diseases. [ 7 ]


Neel in a 1998 review described an expanded form of the original hypothesis, diabetes being caused by "thrifty genes" adapted specifically for intermittent starvation, to a more complex theory of several related diseases such as diabetes, obesity, and hypertension (see also  metabolic syndrome ) being caused by physiological systems adapted for an older environment being pushed beyond their limits by environmental changes. Thus, one possible remedy for these diseases is changing diet and exercise activity to more closely reflect that of the ancestral environment. [ 8 ]


The thrifty genotype hypothesis has been used to explain high, and rapidly escalating, levels of obesity and diabetes among groups newly introduced to  western diets  and environments, from South  Pacific Islanders , [ 9 ]  to  Sub Saharan Africans , [ 10 ]  to  Native Americans  in the  Southwestern United States , [ 11 ]  to  Inuit . [ 12 ]


The original "thrifty gene" hypothesis argued that  famines  were common and severe enough to select for thrifty gene in the 2.5 million years of human  paleolithic  history. This assumption is contradicted by some  anthropological  evidence. [ 13 ] [ 14 ] [ 15 ] [ 16 ]  Many of the populations that later developed high rates of obesity and diabetes appeared to have no discernible history of famine or starvation (for example, Pacific Islanders whose "tropical-equatorial islands had luxuriant vegetation all year round and were surrounded by lukewarm waters full of fish."). [ 14 ] [ 15 ]  However, this implies that the period after which humans migrated out of Africa would have provided sufficient time to reverse any pre-existing famine-adapted alleles, for which there is little to no evidence. One criticism of the 'thrifty gene' idea is that it predicts that modern hunter gatherers should get fat in the periods between famines. Data on the  body mass index  of hunter-gatherer and subsistence agriculturalists show that between famines they do not deposit large fat stores. [ 16 ]  However, genes that promote only limited fat deposition in the context of pre-industrialized lifestyles and diets may promote excessive fat deposition and obesity when caloric intake is increased and expenditure is decreased beyond the range of the environments these genes evolved in (a gene x environment interaction).


As a response to such criticisms, a modified "thrifty" gene hypothesis is that the famines and seasonal shortages of food that occurred only during the agricultural period may have exerted enough pressure to select for "thrifty" genes. [ 17 ]


The thrifty phenotype hypothesis arose from challenges posed to the thrifty gene hypothesis.  The thrifty phenotype hypothesis theorizes that instead of arising genetically, the "thrifty factors" developed as a direct result of the environment within the womb during development.  The development of insulin resistance is theorized to be directly related to the body "predicting" a life of starvation for the developing fetus. [ 18 ]


Hence, one of the main causes of type 2 diabetes has been attributed to poor fetal and infant growth and the subsequent development of the metabolic syndrome. Since the hypothesis was proposed, many studies worldwide have confirmed the initial epidemiological evidence. Although the relationship with insulin resistance is clear at all ages studied, the relation of insulin secretion is less clear. The relative contribution of genes and environment to these relationships remains a matter of debate. [ 19 ]


Other relevant observations arose from metabolism researchers who note that for practically every other species on earth,  fat metabolism  is well regulated [ 20 ]  and that "most wild animals are in fact very lean" and that they remain lean "even when adequate food is supplied."


In response to the criticisms of the original thrifty genotype theory, several new ideas have been proposed for explaining the evolutionary bases of obesity and related diseases. [ citation needed ]


The "thrifty epigenomic hypothesis" is a combination of the thrifty phenotype and thrifty genotype hypotheses. While it argues that there is an ancient,  canalized  (genetically coded) physiological system for being "thrifty", the hypothesis argues that an individual's disease risk is primarily determined by epigenetic events. Subtle, epigenetic modifications at many genomic loci ( gene regulatory networks ) alter the shape of the canal in response to environmental influences and thereby establish a predisposition for complex diseases such as  metabolic syndrome . There may be epigenetic inheritance of disease risk. [ 21 ]


Watve and  Yajnik  suggested that changing  insulin resistance  mediates two phenotypic transitions: a transition in reproductive strategy from "r" (large number of offspring with smaller investment in each) to "K" (smaller number of offspring with greater investment in each) (see  r/K selection theory ); and a switch from a lifestyle dependent upon muscular strength to one dependent on brain power ("soldier to diplomat"). Because the environmental conditions that would facilitate each transition are heavily overlapping, the scientists surmise, a common switch could have evolved for the two transitions. [ 18 ]


The main problem with this idea is the timing at which the transition is presumed to have happened, and how this would then translate into the genetic predisposition to type 2 diabetes and obesity [ citation needed ] . For example, the decline in reproductive investment in human societies (the so-called r to K shift) has occurred far too recently to have been caused by a change in genetics.


Sellayah and colleagues have postulated an 'Out of Africa' theory to explain the evolutionary origins of obesity. The theory cites diverse ethnic based differences in obesity susceptibility in western civilizations to contend that, neither the thrifty or drifty gene hypotheses can explain the demographics of the modern obesity crisis. Although the arguments against these patterns arising due to 'drift' are unclear. Sellayah et al. argue that ethnic groups whose ancestors were adapted to hot climates have low metabolic rates due to lack of thermogenic capacity, whereas those groups whose ancestors were cold-adapted were endowed with greater thermogenic capacity and higher metabolic rates. Sellayah and colleagues provide evidence of thermogenic capacity, metabolic rates and obesity prevalence in various indigenous populations in support of their argument. [ 22 ]  Contrasting this analysis however a study of the spatial distribution of obesity across the mainland USA showed that once the effects of poverty and race were accounted for there was no association between ambient temperature and obesity rates. [ 23 ]


The most highly cited alternative to the thrifty gene hypothesis is the  drifty gene hypothesis  proposed by the British biologist  John Speakman . This idea differs fundamentally from all the other ideas in that it does not propose any selective advantage for the obese state, either now or in the past. The main feature of this hypothesis is that the current pattern of obesity does not suggest that obesity has been under strong positive selection for a protracted period of time. It is argued instead that the obesity comes about because of  genetic drift  in the genes controlling the upper limit on our body fatness. Such drift may have started because around 2 million years ago ancestral humans effectively removed the risk from predators, which was probably a key factor selecting against fatness. The drifty gene hypothesis was presented as part of a presidential debate at the 2007 Obesity Society meeting in New Orleans, with the counter-arguments favouring the thrifty gene presented by British nutritionist Andrew Prentice. The main thrust of Prentice's argument against the drifty gene idea is that Speakman's critique of the thrifty gene hypothesis ignores the huge impact that famines have on fertility. It is argued by Prentice that famine may actually have only been a force driving evolution of thrifty genes for the past 15,000 years or so (since the invention of agriculture), but because famines exert effects on both survival and fertility the selection pressure may have been sufficient even over such a short timescale to generate a pressure for "thrifty" genes. These alternative arguments were published in two back-to-back papers in the  International Journal of Obesity  in November 2008. [ 17 ] [ 24 ]


Prentice et al. [ 17 ]  predicted that the emerging molecular genetics field would ultimately provide a way to test between the adaptive 'thrifty gene' idea and the non-adaptive 'drifty gene' idea because it would be possible to find signatures of positive selection in the human genome, at genes that are linked to both obesity and type 2 diabetes, if the 'thrifty gene' hypothesis is correct. Two comprehensive studies have been performed seeking such signatures of selection. Ayub et al. (2014) [ 25 ]  searched for signatures of positive selection at 65 genes linked to type 2 diabetes, and Wang and Speakman (2016) [ 26 ]  searched for signatures of selection at 115 genes linked to obesity. In both cases there was no evidence for such selection signatures at a higher rate than in random genes selected for matched GC content and recombination rate. These two papers provide strong evidence against the thrifty gene idea, and indeed against any adaptive explanation which relies on selection during our recent evolutionary history, but rather provide strong support the 'drifty gene' interpretation.


Many attempts have been made to search for one or more genes contributing to thrift. Modern tools of  genome wide association studies  have revealed many genes with small effects associated with obesity or type 2 diabetes but all of them together explain only between 1.4 and 10% of population variance. [ 27 ] [ 28 ]  This leaves a large gap between the pregenomic and emerging genomic estimates of heritability of obesity and Type 2 diabetes, sometimes called the " missing heritability problem ." The reasons for this discrepancy are not completely understood. A likely possibility is that the missing heritability is explained by rare variants of large effect that are found only in limited populations. These would be impossible to detect by standard whole genome sequencing approaches even with hundreds of thousands of participants. The extreme endpoint of this distribution are the so-called 'monogenic' obesities where most of the impact on body weight can be tied to a mutation in a single gene that runs in a single family. The classic example of such a genetic effect is the presence of mutations in the leptin gene. [ 29 ]


An important unanswered question is whether such rare variants exist because of chance mutations, population founder events and maintenance by processes such as drift, or whether there is any selective advantage involved in their maintenance and spread. An example of such a rare variant effect was recently discovered among Samoan islanders. [ 30 ]  Among the islanders the variant is extremely common, but in other populations it is extremely rare or absent. The variant predisposes to obesity but strangely is protective against type 2 diabetes. Based on cell studies it was suggested the variant may protect individuals against periods of 'famine' and there is also evidence that it has been under positive selection. The most likely scenario then is that this rare variant was established in the islanders by a founder effect among a small initial colonising population, and was able to spread because of a selective advantage it conferred within that small group. Hence, in small populations under particular environmental conditions it may be feasible that the 'thrifty gene' idea is correct. It remains to be seen if rare variants that fill the gap in the missing heritability estimates are also 'thrifty genes' or if they are rare chance events sustained by drift, as implicated for the common variants currently linked to obesity and type 2 diabetes. [ 25 ] [ 26 ]


Unequal crossing over  is a type of gene duplication or deletion event that deletes a sequence in one strand and replaces it with a duplication from its sister  chromatid  in  mitosis  or from its homologous chromosome during  meiosis .  It is a type of  chromosomal crossover  between homologous sequences that are not paired precisely. Normally genes are responsible for occurrence of crossing over. It exchanges sequences of different links between chromosomes.  Along with  gene conversion , it is believed to be the main  driver for the generation of  gene duplications  and is a source of mutation in the genome. [ 1 ]


During  meiosis , the duplicated chromosomes ( chromatids ) in eukaryotic organisms are attached to each other in the  centromere  region and are thus paired. The maternal and paternal chromosomes then align alongside each other.  During this time, recombination can take place via crossing over of sections of the paternal and maternal chromatids and leads to reciprocal recombination or non-reciprocal recombination. [ 1 ]   Unequal crossing over requires a measure of similarity between the sequences for misalignment to occur.  The more similarity within the sequences, the more likely unequal crossing over will occur. [ 1 ]   One of the sequences is thus lost and replaced with the duplication of another sequence.


When two sequences are misaligned, unequal crossing over may create a tandem repeat on one chromosome and a deletion on the other.  The rate of unequal crossing over will increase with the number of repeated sequences around the duplication.  This is because these repeated sequences will pair together, allowing for the mismatch in the cross over point to occur. [ 2 ]


Unequal crossing over is the process most responsible for creating regional gene duplications in the genome. [ 1 ]   Repeated rounds of unequal crossing over cause the homogenization of the two sequences.  With the increase in the duplicates, unequal crossing over can lead to dosage imbalance in the genome and can be highly deleterious. [ 1 ] [ 2 ]


In unequal crossing over, there can be large sequence exchanges between the chromosomes.  Compared with gene conversion, which can only transfer a maximum of 1,500 base pairs, unequal crossing over in yeast rDNA genes has been found to transfer about 20,000 base pairs in a single crossover event [ 1 ] [ 3 ]  Unequal crossover can be followed by the  concerted evolution  of  duplicated sequences.


It has been suggested that longer intron found between two beta-globin genes are a response to deleterious selection from unequal crossing over in the beta-globin genes. [ 1 ] [ 4 ]   Comparisons between alpha-globin, which does not have long introns, and beta-globin genes show that alpha-globin have 50 times higher concerted evolution.


When unequal crossing over creates a  gene duplication , the duplicate has 4  evolutionary  fates. This is due to the fact that  purifying selection  acting on a duplicated copy is not very strong.  Now that there is a redundant copy,  neutral mutations  can act on the duplicate.  Most commonly the neutral mutations will continue until the duplicate becomes a  pseudogene .  If the duplicate copy increases the dosage effect of the gene product, then the duplicate may be retained as a redundant copy.   Neofunctionalization  is also a possibility: the duplicated copy acquires a mutation that gives it a different function than its ancestor.  If both copies acquire mutations, it is possible that a  subfunctional event  occurs.  This happens when both of the duplicated sequences have a more specialized function than the ancestral copy [ 5 ]


Gene duplications are the main reason for the increase of genome size, and as unequal crossing over is the main mechanism for gene duplication, unequal crossing over contributes to genome size evolution is the most common regional duplication event that increases the size of the genome.


When viewing the genome of a eukaryote, a striking observation is the large amount of tandem, repetitive DNA sequences that make up a large portion of the genome.  For example, over 50% of the  Dipodmys ordii  genome is made up of three specific repeats.  Drosophila virilis  has three sequences that make up 40% of the genome, and 35% of the  Absidia glauca  is repetitive DNA sequences. [ 1 ]   These short sequences have no selection pressure acting on them and the frequency of the repeats can be changed by unequal crossing over. [ 6 ]


Biopiracy  (also known as  scientific colonialism   [ citation needed ] ) is the unauthorized appropriation of knowledge and  genetic resources  of farming and indigenous communities by individuals or institutions seeking exclusive  monopoly  control through  patents  or intellectual property. [ 1 ]  While  bioprospecting  is the act of exploring natural resources for undiscovered chemical compounds with medicinal or anti-microbial properties, commercial success from bioprospecting leads to the company's attempt at protecting their  intellectual property  rights on indigenous medicinal plants, seeds, genetic resources, and  traditional medicines . [ 2 ]