Information

Evolutionary elimination of recessive gene

Evolutionary elimination of recessive gene


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

As recessive gene is suppressed, why over millions of year of evolution have not wiped them away completely, why don't they just go extinct, as people with those genes go extinct?


Simply put, there may not be enough selective pressure to force those alleles out of the population. For example, take a hypothetical gene for eye color. The dominant allele gives brown eyes, the recessive blue, due to the lack of expression of a certain pigment-processing enzyme. So, two heterozygous (brown-eyed) organisms are crossed:

| A | a | -------------- A | AA | Aa | -------------- a | Aa | aa | --------------

which results in 75% brown-eyed offspring, and 25% blue-eyed offspring. Unless there is some very strong factor in the environment that has a negative selective pressure on blue eyes, the recessive allele will remain in the population.

OK, that's for an allele with no negative pressure acting on it. What about a disease allele, for example one that has a point or other mutation inactivating an essential enzyme, such as iduronate-2-sulfatase (I2S), a key enzyme involved in the breakdown of waste in the lysosome. It is part of a group of diseases known as lysosomal storage disorders, and is the causative agent of Hunter Syndrome.

People who have the so-called "wild-type" allele(s) of I2S have normal function, even if they are heterozygous. However, if two heterozygotes produce offspring (or if a heterozygote produces offspring with a homozygous recessive individual), then there is a chance of a child with two recessive or defective forms of the enzyme, and they are much more likely to develop Hunter Syndrome (almost no genetic disease is 100% penetrant). If untreated, this disease can have many quite horrible effects (especially given that it usually appears in childhood). The disease is also progressive (it gets worse the older you get), and while it in itself does not typically cause death, patients have (sometimes severely) limited lifetimes.

So why haven't the mutant, disease-causing forms of I2S been "weeded out" by natural selection? In a single word: heterozygosity. Studies (I don't have a reference, unfortunately) have shown that supplying a Hunter patient with as little as 10% of the typical amount of active enzyme in an unaffected person is enough to resolve the acute symptoms of the disease, and begin reversing many of the long-term effects. All things being equal, one would expect that a heterozygote carrying one functioning allele of I2S and one mutant allele would express about 50% of the amount of enzyme that a completely unaffected individual does, and this is more than enough to prevent the onset of disease. There are likely other molecular mechanisms involved to express the functional version even more, so their levels may be even higher than 50%.

So, while there is certainly a strong negative selection pressure on people carrying two copies of a mutated allele (or two different mutations, there are actually many of them) that prevent most or all expression of active protein, that same selection pressure does not apply to heterozygotes, and so the mutant alleles remain present in the population.

I strongly recommend reading through the fairly short but definitely thorough course Understanding Evolution from the University of California, Berkeley. It may explain some of the concepts I've mentioned more clearly.

Note: I once worked for Shire, which, among other things, specializes in rare diseases including lysosomal storage disorders. I specifically worked with their Hunter Syndrome therapeutic, known as Elaprase. Other companies are also working on various therapeutics.

This is in no way an endorsement of the company or its products, it's just where I learned all of this stuff.


In humans, cystic fibrosis is an inherited disease due to an autosomal recessive gene located on chromosome #7. In the most common defective allele, three base pairs are deleted and a single phenylalanine is missing. Affected individuals carry two of the recessive alleles for the disease (genotype ff) and, as a result, form extremely thick mucus in their respiratory systems and elsewhere in the body. Their lungs are susceptible to frequent infections while the disease is progressive and eventually fatal. Usually the victims die in their teens or early twenties, and so do not reproduce. Among whites, one person in 20 is a carrier, who is heterozygous (Ff) for cystic fibrosis.

Severe natural selection has operated on the gene pool for cystic fibrosis (f) and normal Alleles (F) over the centuries. The affected individuals do not reproduce and do not pass on their genes. New cases arise only when both parents are heterozygous or through new mutations in a "normal" parent.

In the following exercise, you will compare the effects of natural selection alone and natural selection plus negative eugenics on the frequency of the f allele in a model system. Among the children of marriages between heterozygous carriers of the f allele and genetically normal individuals, the frequency of the f allele should be 25%, the same as in the parents. We will use that frequency at the start of both this and the following experiments.


Youreka Science

Youreka Science was created by Florie Mar, PhD, while she was a cancer researcher at UCSF. While teaching 5th graders about the structure of a cell, Mar realized the importance of incorporating scientific findings into classroom in an easy-to-understand way. From that she started creating whiteboard drawings that explained recent papers in the scientific literature… Continue Reading


Population Evolution

The mechanisms of inheritance, or genetics, were not understood at the time Charles Darwin and Alfred Russel Wallace were developing their idea of natural selection. This lack of understanding was a stumbling block to understanding many aspects of evolution. In fact, the predominant (and incorrect) genetic theory of the time, blending inheritance, made it difficult to understand how natural selection might operate. Darwin and Wallace were unaware of the genetics work by Austrian monk Gregor Mendel, which was published in 1866, not long after the publication of Darwin’s book On the Origin of Species. Mendel’s work was rediscovered in the early twentieth century at which time geneticists were rapidly coming to an understanding of the basics of inheritance. Over the next few decades, genetics and evolution were integrated into what became known as the modern synthesis—the coherent understanding of the relationship between natural selection and genetics that took shape by the 1940s and is generally accepted today. In sum, the modern synthesis describes how evolutionary processes, such as natural selection, can affect a population’s genetic makeup, and, in turn, how this can result in the gradual evolution of populations and species. The theory also connects this change of a population over time, called microevolution, with the processes that gave rise to new species and higher taxonomic groups with widely divergent characters, called macroevolution.

Evolution and Flu Vaccines

Every fall, the media starts reporting on flu vaccinations and potential outbreaks. Scientists, health experts, and institutions determine recommendations for different parts of the population, predict optimal production and inoculation schedules, create vaccines, and set up clinics to provide inoculations. You may think of the annual flu shot as a lot of media hype, an important health protection, or just a briefly uncomfortable prick in your arm. But do you think of it in terms of evolution?

The media hype of annual flu shots is scientifically grounded in our understanding of evolution. Each year, scientists across the globe strive to predict the flu strains that they anticipate being most widespread and harmful in the coming year. This knowledge is based on how flu strains have evolved over time and over the past few flu seasons. Scientists then work to create the most effective vaccine to combat those selected strains. Hundreds of millions of doses are produced in a short period in order to provide vaccinations to key populations at the optimal time.

Because viruses, like the flu, evolve very quickly (especially in evolutionary time), this poses quite a challenge. Viruses mutate and replicate at a fast rate, so the vaccine developed to protect against last year’s flu strain may not provide the protection needed against the coming year’s strain. The evolution of these viruses means continued adaptions to ensure survival, including adaptations to survive previous vaccines.

Population Genetics

Recall that a gene for a particular character may have several alleles, or variants, that code for different traits associated with that character. For example, in the ABO blood type system in humans, three alleles determine the particular blood-type protein on the surface of red blood cells. Each individual in a population of diploid organisms can only carry two alleles for a particular gene, but more than two may be present in the individuals that make up the population. Mendel followed alleles as they were inherited from parent to offspring. In the early twentieth century, biologists in a field of study known as population genetics began to study how selective forces change a population through changes in allele and genotypic frequencies.

The allele frequency (or gene frequency) is the rate at which a specific allele appears within a population. Until now we have discussed evolution as a change in the characteristics of a population of organisms, but behind that phenotypic change is genetic change. In population genetics, the term evolution is defined as a change in the frequency of an allele in a population. Using the ABO blood type system as an example, the frequency of one of the alleles, I A , is the number of copies of that allele divided by all the copies of the ABO gene in the population. For example, a study in Jordan found a frequency of I A to be 26.1 percent ( Hanania, Hassawi, & Irshaid, 2007). The I B and I 0 alleles made up 13.4 percent and 60.5 percent of the alleles respectively, and all of the frequencies added up to 100 percent. A change in this frequency over time would constitute evolution in the population.

The allele frequency within a given population can change depending on environmental factors therefore, certain alleles become more widespread than others during the process of natural selection. Natural selection can alter the population’s genetic makeup for example, if a given allele confers a phenotype that allows an individual to better survive or have more offspring. Because many of those offspring will also carry the beneficial allele, and often the corresponding phenotype, they will have more offspring of their own that also carry the allele, thus, perpetuating the cycle. Over time, the allele will spread throughout the population. Some alleles will quickly become fixed in this way, meaning that every individual of the population will carry the allele, while detrimental mutations may be swiftly eliminated if derived from a dominant allele from the gene pool. The gene pool is the sum of all the alleles in a population.

Sometimes, allele frequencies within a population change randomly with no advantage to the population over existing allele frequencies. This phenomenon is called genetic drift. Natural selection and genetic drift usually occur simultaneously in populations and are not isolated events. It is hard to determine which process dominates because it is often nearly impossible to determine the cause of change in allele frequencies at each occurrence. An event that initiates an allele frequency change in an isolated part of the population, which is not typical of the original population, is called the founder effect . Natural selection, random drift, and founder effects can lead to significant changes in the genome of a population.

Hardy-Weinberg Principle of Equilibrium

In the early twentieth century, English mathematician Godfrey Hardy and German physician Wilhelm Weinberg stated the principle of equilibrium to describe the genetic makeup of a population. The theory, which later became known as the Hardy-Weinberg principle of equilibrium, states that a population’s allele and genotype frequencies are inherently stable— unless some kind of evolutionary force is acting upon the population, neither the allele nor the genotypic frequencies would change. The Hardy-Weinberg principle assumes conditions with no mutations, migration, emigration, or selective pressure for or against genotype, plus an infinite population. While no population can satisfy those conditions, the principle offers a useful model against which to compare real population changes.

Working under this theory, population geneticists represent different alleles as different variables in their mathematical models. The variable p represents the dominant allele in the population while the variable q represents the recessive allele. For example, when looking at Mendel’s peas, the variable p represents the frequency of y alleles that confer the color yellow and the variable q represents the frequency of y alleles that confer the color green. If these are the only two possible alleles for a given locus in the population, p + q = 1. In other words, all the p alleles and all the q alleles make up all of the alleles for that locus that are found in the population.

But what ultimately interests most biologists is not the frequencies of different alleles, but the frequencies of the resulting genotypes, known as the population’s genetic structure , from which scientists can surmise the distribution of phenotypes. If the phenotype is observed, only the genotype of the homozygous recessive alleles can be known the calculations provide an estimate of the remaining genotypes. Since each individual carries two alleles per gene, if the allele frequencies (p and q) are known, predicting the frequencies of these genotypes is a simple mathematical calculation to determine the probability of getting these genotypes if two alleles are drawn at random from the gene pool. So in the above scenario, an individual pea plant could be pp (YY), and thus produce yellow peas pq (Yy), also yellow or qq (yy), and thus producing green peas (Figure 1). In other words, the frequency of pp individuals is simply p 2 the frequency of pq individuals is 2pq and the frequency of qq individuals is q 2 . And, again, if p and q are the only two possible alleles for a given trait in the population, these genotype frequencies will sum to one: p 2 + 2pq + q 2 = 1.

Figure 1: When populations are in the Hardy-Weinberg equilibrium, the allelic frequency is stable from generation to generation and the distribution of alleles can be determined from the Hardy-Weinberg equation. If the allelic frequency measured in the field differs from the predicted value, scientists can make inferences about what evolutionary forces are at play. (credit: “Hardy-Weinberg equilibrium” by OpenStax is licensed under CC BY 4.0)

In plants, violet flower color (V) is dominant over white (v). If p = 0.8 and q = 0.2 in a population of 500 plants, how many individuals would you expect to be homozygous dominant (VV), heterozygous (Vv), and homozygous recessive (vv)? How many plants would you expect to have violet flowers, and how many would have white flowers?

In theory, if a population is at equilibrium—that is, there are no evolutionary forces acting upon it—generation after generation would have the same gene pool and genetic structure, and these equations would all hold true all of the time. Of course, even Hardy and Weinberg recognized that no natural population is immune to evolution. Populations in nature are constantly changing in genetic makeup due to drift, mutation, possibly migration, and selection. As a result, the only way to determine the exact distribution of phenotypes in a population is to go out and count them. But the Hardy-Weinberg principle gives scientists a mathematical baseline of a non-evolving population to which they can compare evolving populations and thereby infer what evolutionary forces might be at play. If the frequencies of alleles or genotypes deviate from the value expected from the Hardy-Weinberg equation, then the population is evolving.

Summary

The modern synthesis of evolutionary theory grew out of the cohesion of Darwin’s, Wallace’s, and Mendel’s thoughts on evolution and heredity, along with the more modern study of population genetics. It describes the evolution of populations and species, from small-scale changes among individuals to large-scale changes over paleontological time periods. To understand how organisms evolve, scientists can track populations’ allele frequencies over time. If they differ from generation to generation, scientists can conclude that the population is not in Hardy-Weinberg equilibrium, and is thus evolving.


Results

Experiment I

After the percentage survival data were arcsin-transformed, we performed a linear regression of the outbreeding-control means on the Wright’s f-value. The residuals from the regression were then regarded as environmental variances. Another regression analysis of experimental values on the residuals obtained above was performed and the residuals of the second regression were then regarded as providing data corrected for environmental effects ( 18 ).

By the regression analysis of the corrected values of early survival rates on f-value, both regression coefficients were not significant (Table 1). Namely, the early survival rate indicated no linear trend with the increase of f-value, showing no immediate effect of the mother–son inbreeding on the early survival of their progeny.

Experiment II

After correcting the data through the variance of the outbreeding controls (as mentioned above), we performed ANCOVA analysis with f-value as a covariate for all lineages. There were no significant differences between the slopes of regression lines of lineages ( Fig. 3), showing no interaction between f-value and lineage difference (Table 2). We found significant differences both in f-values and in lineages as shown in Table 2.

Parental inbreeding effect on the fecundity (fertilized oviposition) of the progeny females in Experiment II. Number of eggs laid for 10 days was corrected by environmental variance observed in outbreeding control. Corrected mean (circle or box) with ±SD and linear regression line of each lineage are given. The scale of the abscissa is extended three times from f = 0.5 to 1.0.

The regression analysis for the number of eggs laid by the females of each lineage on f-value showed that there were significant lines (having negative slopes) for the three lineages ( Fig. 3 and Table 3) even by the use of the sequential Bonferroni’s test.

Experiment III

Whether or not inbreeding in a parental generation affects the unfertilized oviposition of progeny females was tested. We corrected the data of experiment III in the same way as in experiments I and II.

There were no significant differences between lineages, nor in f-value, whereas there was a significant interaction between f-value and lineage by ANCOVA (P < 0.018 in Table 4). Such a significant difference among slopes of the regression lines forced us to analyse the lineage data separately.

The regression analysis performed for each lineage showed that the number of eggs of lineage L2 significantly decreased with increase of f-value (P < 0.05 by the sequential Bonferroni’s test in Table 5). Although we could not say that there was an effect of inbreeding on the unfertilized oviposition in this population because of the significant interaction between lineage and generation in ANCOVA , it should be noted that one lineage (L2) apparently suffered an inbreeding effect on its uninseminated oviposition ( Fig. 4).

Parental inbreeding effect on the unfertilized oviposition of the progeny females in Experiment III. Number of eggs laid for 10 days was adjusted by environmental variance observed in outbreeding control. Corrected mean (circle or box) with ±SD and linear regression line of each lineage are given. The scale of the abscissa is extended three times from f = 0.5 to 1.0.

It was important to note that the L2 lineage also had a reduction in the fertilized oviposition in experiment II, and that the decrease in the number of eggs/10 days was not a result of the early death of ovipositing females in both experiments II and III.

Experiment IV

After correcting the arcsin-transformed data through the variance of the outbreeding control (mentioned above), we performed ANCOVA with f-value as a covariate for whole regression lines in Fig. 5. There was no significant difference among slopes, showing no interaction between f-value and lineage difference. Even after eliminating the interaction effect from the analysis, we did not find any significant differences in f-values nor in lineages (Table 6). The regression analysis of the early survival rates of the progeny on f-value also did not show any significant regression lines (by the sequential Bonferroni’s test) in this experiment ( Fig. 5 and Table 7).

Experiment V

In experiment II, as mentioned before, females produced from inbred parents were mated with the Mc strain males. Therefore, we should consider the possibility that outbreeding after continuous inbreeding directly affected the oviposition of females, especially in later generations. ANOVA testing (error d.f.=105) revealed that the interaction of lineage × breeding-type was not significant (d.f.=5, f=0.95, P > 0.45). There was no difference between breeding types (after eliminating interaction, sib-mating vs. outbreeding: d.f.=1, f=3.08, P > 0.08), whereas there was a significant difference between lineages (d.f.=5, f=6.32, P < 0.0001). These showed that outbreeding had no significant effect on the oviposition rate of females from inbred lineages, while there was a big difference in oviposition rate between lineages (at the F6 generation). Scheffe’s post hoc test between lineages revealed that the oviposition rate of L2 was significantly different from the other five lineages at P = 0.03.

Experiment VI

Lastly, we observed the genetic characteristics of the inbreeding depression observed in experiments II and III. The lineage L2 decreased in fecundity in experiments II and III with successive inbreeding, while no such decrease was found in L5 ( Figs 3 and 4, Tables 3 and 5). Thus, cross experiments between these two lineages were performed at about the 14th generation after establishment of the inbreeding lineages.

Nineteen to 29 female offspring produced by six parental females of each cross type were lumped and their oviposition observed for 10 days. In both the fertilized (mated with Mc males) and unfertilized females ( Fig. 6), there were significant differences in the 10-day oviposition rate between the cross types of parents by ANOVA (the former: error d.f.=98, d.f.=3, f=8.24, P < 0.001, and the latter: error d.f.=101, d.f.=3 f=13.421, P < 0.0001). The differences were apparently due to the lower oviposition rate in L2C2 (for L2C2 vs. L5C5, L2C2 vs. L2C5 and L2C2 vs. L5C2, P < 0.01, <0.0002 and <0.0008 in fertilized oviposition, and for all combinations, P < 0.0001 in unfertilized oviposition by Scheffe’s post hoc test for multiple comparisons, respectively), showing that heterosis concurred in L2C5 and L5C2. This suggested that the L2 lineage carried some genetically deleterious factors.

In order to further confirm the genetic characteristics, backcross experiments (for the combination of the crosses, see Fig. 7) were carried out in the same manner as those of the interlineage cross. In this experiment, only the unfertilized oviposition of progeny females was observed. There was a significant difference in the 10-day oviposition rate between the cross types of parents by ANOVA (error d.f.=202, d.f.=5, f=16.93, P < 0.0001). As seen in Fig. 7, L2C5B2 and L5C2B2 females produced by the backcross of L2 males laid a medium number of eggs between those laid by L2C2C2 (depressed control) and L5C5C5 (normal control), while L2C5B5 and L5C2B5 laid similar numbers of eggs to the normal controls. The number of eggs laid by the L2C5B2 was significantly different from those laid by L2C5B5, L5C2B5, L2C2C2 and L5C5C5 (P < 0.017, P < 0.026, P < 0.023 and P < 0.013 by Scheffe’s post hoc test, respectively). Furthermore, the number of eggs laid by the L5C2B2 was significantly different from that of L2C2C2 (P < 0.0007). However, the numbers of eggs laid by L2C5B5 and L5C2B5 females did not differ from that of L5C5C5 (both at P > 0.90), but differed significantly from that of L2C2C2 (both at P < 0.0001).

These results showed that the recessive genes supposed in the L2 lineage had been heterozygous in the F1 progeny of L2C5 and L5C2 parents and were segregated in L2C5B2 and L5C2B2 females by the backcrosses.

L1, L3, L5 and L6 strains used in this study are still being maintained in our laboratory mainly by sib-mating, though L2 is now extinct due to sterility.


The Impact of Evolutionary Driving Forces on Human Complex Diseases: A Population Genetics Approach

Investigating the molecular evolution of human genome has paved the way to understand genetic adaptation of humans to the environmental changes and corresponding complex diseases. In this review, we discussed the historical origin of genetic diversity among human populations, the evolutionary driving forces that can affect genetic diversity among populations, and the effects of human movement into new environments and gene flow on population genetic diversity. Furthermore, we presented the role of natural selection on genetic diversity and complex diseases. Then we reviewed the disadvantageous consequences of historical selection events in modern time and their relation to the development of complex diseases. In addition, we discussed the effect of consanguinity on the incidence of complex diseases in human populations. Finally, we presented the latest information about the role of ancient genes acquired from interbreeding with ancient hominids in the development of complex diseases.

1. Introduction

Geneticists have made significant progress in understanding the genetics behind many human diseases. These accomplishments include monogenic disease such as Huntington’s disease. On the other hand, the discovery of genetic determinants for complex diseases such as diabetes, Crohn’s disease, ischemic heart disease, stroke and some types of cancer (e.g., lung, colon, prostate, and breast), schizophrenia, and bipolar disorder is still poorly understood [1, 2]. However, release of the complete human genome sequence in 2001 has improved our understanding of the patterns of human genome diversity and its linkage to human complex diseases in the last decade [3, 4]. In order to study genetic diversity of the human genome at population level, the HapMap project was initiated to investigate the genetic differences on both inter- and intrapopulation levels. This was made possible by the introduction of advanced technologies such as Chip-based genotyping and next-generation sequencing techniques [5–7]. All these efforts have led to a vast amount of population genetic information. For instance, allele frequencies and levels of genetic association information for 3.5 million single nucleotide polymorphisms (SNPs), allele frequencies of approximately 15 million SNPs, 1 million short insertions and deletions, and 20000 structural variants are now available [5–8]. This huge amount of genetic variation data has been used in many Genome Wide Association Studies (GWAS) on various human diseases. According to National Human Genome Research Institute, the number of published GWAS studies till May 28, 2014, is 1921 [9] focusing on different human traits, such as height (522), and diseases, such as diabetes (251), breast cancer (191), lung cancer (35), coronary heart disease (150), and hypertension (39). GWAS have generated vast amount of information that increased our understanding of the genetic basis of many complex diseases by identifying genetic variants associated with the disease and its distribution in different populations. The availability of this information facilitates deeper understanding of complex diseases in both population genetics and evolutionary context.

2. Origin of Genetic Diversity in Human Populations

There are several factors that determine the amount of inter- and intragenetic diversity in human populations, which in turn is reflected in different phenotypes, including healthy and diseased phenotypes. These include mutation rates and recombination events that create and reorganize genetic diversity on the molecular level. Moreover, other factors are capable of changing the population size such as migration rates in or out of the population and birth and death rates. In addition, cultural behavior of human populations, such as selective or directed marriages or consanguinity, is also capable of effecting allelic frequencies within populations [10–14].

Generally, genetic, historical, and archeological evidences supported the Out-of-Africa hypothesis, which emphasized the elevated diversity of the original African population [15–18]. On the other hand, other evidences suggest much more multifaceted scenario in which early human populations have interbred with ancient hominids such as Neanderthals and Denisovans that lead to 1–6% contribution in modern Eurasian genomes and Melanesian genomes [19–21].

3. Evolutionary Driving Forces Effecting Genetic Diversity

It is well known that the main driving forces of evolution in any population are mutation, natural selection, genetic drift, and gene flow. The ability of these driving forces to perform their role is dependent on the amount of genetic diversity within and among populations. Genetic diversity among populations rises from mutations in genetic material, reshuffling of genes through sexual reproduction, and migration of individuals among populations (gene flow) [22]. The effect of the evolutionary driving forces on genetic diversity and evolution depends on the amount of genetic variations that already exist in a population. The amount of genetic variation within a given population remains constant in the absence of selection, mutation, migration, and genetic drift [23].

4. New Environment Effect of Genetic Diversity

The migration of human populations to new and different geographical habitats with different environmental challenges such as new climate, food varieties, and exotic pathogens acted as selective pressure on human populations that lead to adaptive changes in population genetic makeup to cope with these new challenges in order to achieve the golden goal of survival [24]. This selective pressure “natural selection” leads to the increase of frequency of favored genetic makeups and the elimination of deleterious genetic makeups that fail to adapt with the new environmental challenges [25]. This in turn may lead to the reduction of genetic diversity. Thus, natural selective events have shaped the present genetic diversity of the existing populations and consequently genetic variants involved in many diseases in both direct and indirect fashion [26–30].

5. Genetic Differentiation among Human Populations and the Role of Gene Flow

Genetic differentiation among human populations is significantly influenced by geographical isolation due to the accumulation of local allele frequency differences [31]. It was Wright in 1943 that first introduced the theory of Isolation By Distance (IBD) which describes the accumulation of local genetic differences under the assumption of local spatial dispersal [32]. According to IBD theory, pairwise measures of genetic differentiation are expected to increase with increasing geographical separation. This was proven in human populations on global, continental, and regional scales [33–35]. Physical barriers such as mountain chains, deserts, and large water bodies can limit gene flow among populations. Limited migration of individuals or groups among population can have an effect on genetic diversity leading to genetic differentiation among these populations and leads to the adaptive evolution in isolation. For example, the Sahara barrier causes the north to south (N–S) major orientation of genetic differentiation among the inhabitants of Africa [31]. Another significant geographic barrier, which has been suggested as an obstacle for gene flow, is the Himalaya mountain range resulting in the east to west (E–W) pattern of Asiatic genetic differentiation despite the fact that many problems with human populations sampling around the mountain were documented [31, 36–38]. It is well known that the rate of genetic differentiation differs according to orientations in Africa, Asia, and Europe, but not in the Americas [31] which can partially be justified by the presence of physical barriers that limited gene flow in certain directions in these continents. Thus, lack of significant physical barriers justifies that lack of directional genetic differentiation in the two Americas.

It was found that when comparing two nearby populations, Europe was found to be the continent with the smallest genetic differentiation, in relation to geographic distances measured using

-statistics (FST) (FST = 5 × 10 −4 ) followed by Asia (FST = 9 × 10 −3 ), Africa (FST = 1.7 × 10 −2 ), and America (FST = 2.6 × 10 −2 ). Generally, the genetic differentiation among two European populations separated by a thousand km is at least one order of magnitude lesser than in African, American, or Asiatic populations [31].

6. Natural Selection: The Most Significant Evolutionary Driving Force

Negative selection, also called purifying selection, is the most well-known form of natural selection [39]. Negative selection removes disadvantageous alleles or mutations from the population gene pool and reduces their frequencies in the population with a reduction rate corresponding to their biological effect. Thus, we should expect that lethal, nonsynonymous, or nonsense mutations will be eliminated from the population gene pool faster than synonymous mutations. On the other hand, less deleterious mutations that have milder effect on the correct expression of a gene can be found in a lower frequency in the population. The resulting change of genetic diversity in the population gene pool is low since negative selection effect on these mutations is mild. Another form of natural selection is positive selection, also called Darwinian selection, in which natural selection favors genetic mutations that are advantageous for the fitness or the survival of individuals. Positive selection will increase the frequencies of such variants in the population gene pool [25, 40]. The increase of the frequencies of variants will affect the genetic diversity in the population directly and indirectly by increasing the frequencies of genetically linked variants through genetic draft or genetic hitchhiking process [41, 42]. For example, several data indicate that the 503F variant of OCTN1 gene has increased in frequency due to recent positive selection and that disease-causing variants in linkage disequilibrium with 503F have hitchhiked to relatively high frequency, thus forming the inflammatory bowel disease 5 (IBD5) risk haplotype. Moreover, association results and expression data support IRF1, which is nearby of 503F hitchhiking variants, as a strong candidate for Crohn’s disease causation [43]. This may justify the observation that IBD5, which is a 250 kb haplotype on chromosome 5, is associated with an increased risk of Crohn’s disease in European population [44–46]. On the other hand, other genetic variants that are not linked with the positively selected variants will be eliminated resulting in reduction of genetic diversity in a process called selective sweep. For instance, evidences for positive selection at the GPX1 locus (3p21) and recent selective sweep in the vicinity of the locus were observed in Asian populations [47]. GPX1 locus is a selenoprotein gene characterized by the integration of selenium into the primary sequence as the amino acid selenocysteine. Selenoproteins have antioxidant properties, and thus interindividual differences in selenoprotein expression or activity could encompass an effect on risk for a range of complex diseases, cancers, neurodegenerative disorders, and diabetes complications [48–51]. Information about selective sweep of GPX1 gene can illustrate the role of selenoprotein genetic variants in the etiology of various human complex diseases [52–55]. An additional form of natural selection is the balancing selection, in which several alleles may coexist at a given locus if they are advantageous either individually or together [56, 57]. Balancing selection is favored when heterozygote genotype has a higher relative fitness than homozygote genotype. Crohn’s disease and ulcerative colitis are examples of balancing selection mediated evolution, which have been shown to be evolved in response to pathogen-driven balancing selection [58]. Based on “hygiene hypothesis,” the lack of exposure to parasites in modern settings resulted in immune imbalances, augmenting susceptibility to the development of autoimmune and allergic conditions. Population genetics analysis showed that five interleukin (IL) genes, including IL7R and IL18RAP, have been a target of balancing selection, a selection process that maintains genetic variability within a population. Fumagalli et al. showed that six risk alleles for inflammatory bowel disease (IBD) or celiac disease are significantly correlated with micropathogen richness validating the hygiene hypothesis for IBD and provide a large set of putative targets for susceptibility to helminthes infections [58].

7. Detecting the Effects of Natural Selection

All mentioned above forms of selection create characteristic molecular fingerprint also called selection signature. These selection signatures could be in the form of differences in rate of nucleotide diversity, allele frequency spectra, haplotype diversity, or genetic differentiation within or among population genomes [59]. As mentioned above, the most famous method of detecting natural selection signature is FST which is depending on the level of genetic differentiation among populations who experienced diverse forms of selection pressures because of many reasons, such as geographical isolation and environmental or nutritional conditions [60, 61]. Thus geographical isolation along with varying selection forces should increase the degree of differentiation among human populations resulting in an increase in FST value at the locus under selection [62].

8. Natural Selection Signature on Complex Diseases in Human Populations

Natural selection signatures have been detected on many complex diseases (Table 1). Among the complex diseases showing clear signatures of natural selection among human populations is blood pressure. Genetic differentiation analysis (FST) of blood pressure associated single nucleotide polymorphism (SNP) analysis showed accelerated differentiation among the four studied European subpopulations, namely, Utah Residents with Northern and Western European ancestry (CEU), British in England and Scotland (GBR), Toscani in Italia (TSI), and Finnish in Finland (FIN), with FST (EUR)

value = 0.0022 and 0.0054, respectively, for systolic blood pressure (SBP) and diastolic blood pressure (DBP).

At the individual SNP level, a nonsynonymous SNP (rs3184504) in SH2B3 gene that is associated with blood pressure showed significant differentiation between European and non-European populations with FST value = 0.0042 and branch length value = 0.0088. It was found that the allele (T) was rare in African and Asian populations with

and 0.01, respectively, while it has a high minor allele frequency of

in the European population [63]. Moreover, genome wide association (GWA) SNPs associated with systemic lupus erythematosus (SLE) showed the most significant collective molecular selection signatures among all studied inflammatory and autoimmune disorders. The 29 SLE SNPs were significant for global genetic differentiation among human populations with FST value of 0.008 and branch length analyses value of 0.0072. Most of the observed genetic differentiation in SLE associated SNPs allele frequencies differences was driven by differences between African and European populations with FST AFR-EUR value of 0.0028 or the Eurasia split in the branch length analysis value of 0.001. For instance, a risk SNP (rs6705628) identified in Asian samples had a low allele frequency in Europeans of 0.01 but high allele frequency in Africans of 0.36 and Asians of 0.19 [63, 64]. In addition, the population genetics analysis of type 2 diabetes (T2D) suggested marginally increased differentiation of T2D SNPs among global populations with FST (ALL) value of 0.0354, which was likely attributed to the Eurasia split from Africa. At the individual T2D SNP level, the rs8042680 in PRC1 gene showed the most significant selection signal. This SNP has a high derived protective allele frequency in European but is rare in African and absent in Asian populations [63, 64]. An additional complex disease that showed selection signature is coronary heart disease (CHD). The population genetics analysis of CHD associated SNPs showed a marginal increase of genetic differentiation between African and European populations with FST (African-European (AFR-EUR)) value of 0.034. The individual CHD SNP showing the most significant selection signal was rs599839 in PSRC1 gene, which was also significantly associated with low-density lipoprotein (LDL) [63, 65, 66].

Furthermore, several genetic differentiation analyses of GWA studies of SNPs associated with different types of cancers, such as breast, prostate, and colorectal cancers were performed. The most significant collective evidence of global population differentiations was observed in the 34 SNPs associated with prostate cancer with a global FST value of 0.017 or total branch length value of 0.01. Majority of the observed differentiation was mapped to the African lineage in the maximum likelihood (ML) branch length analysis value (AFR) of 0.0002. The most two significant SNPs (rs1465618 and rs103294) are located in THADA gene and near LILRA3 gene, respectively. Moreover, multiple SNPs (rs7590268, rs6732426, rs13429458, rs17030845, rs12478601, rs7578597, and rs10495903) in the THADA gene have been reported to be associated with a variety of complex traits or diseases such as cleft palate [67, 68], hair morphology [69], polycystic ovary syndrome [70, 71], platelet counts [72], type 2 diabetes [73], IBD, and Crohn’s disease [74, 75]. This gene has also been reported as a gene under selection [30, 63, 76, 77]. In addition, a sign of high differentiation of colorectal cancer SNPs was detected among the three Asian populations, namely, Han Chinese in Beijing (CHB), Southern Han Chinese (CHS), and Japanese in Tokyo (JPT) with FST (ASN) value of 0.0006. In addition, the significant colorectal cancer SNP rs4925386 in LAMA5 gene has higher derived allele frequency in Africans, but relatively low frequencies in Asians and Europeans.

9. Natural Selection and Cancer

Even though [78] Peto et al. in 1975 suggested a paradox that advocated that large animals might have developed some mechanisms to resist cancer in a counterselection process [79], very few studies have investigated the effect of selection on the evolution of cancer-related genes. An example for cancer-related genes under negative selection is breast cancer 1, early onset gene (BRCA1) [39, 80]. Not only is this gene strongly associated with female breast cancer but its mutations have been reported as risk factor for several other types of cancers including male breast cancer, fallopian tube cancer, and pancreatic cancer [81–86]. On the other hand, signature of positive selection was identified on the TRPV6 gene, which is aggressiveness of prostate cancer among European-Americans. Additionally, TRPV6 gene has experienced positive selection in non-African populations, resulting in several nonsynonymous codon differences among individuals of different genetic backgrounds [87, 88]. Moreover UGT2B4 gene, associated with increased risk of breast cancer in Nigerians and African Americans, shows molecular signatures of recent positive selection or balancing selection [89]. Furthermore, signature of positive selection was identified on the PPP2R5E gene, which is involved in the negative regulation of cell growth and division. PPP2R5E gene encodes a regulatory subunit of the tumor suppressing protein phosphatase 2A and resides in a naturally selected genomic region in the Caucasian population of the HapMap [90]. This observed positive selection favors the Caucasian population making them less susceptible for soft tissue sarcoma. Scrutinizing molecular signatures of selection of this gene can lead to the identification of disease susceptibility variants. This information shows that cancer disease and its related genes were under the forces of evolution and natural selection throughout the evolutionary history and these evolutionary forces worked differently in different human populations.

10. Detrimental Consequences of Historical Selection Events in Modern Time

It was suggested that prehistoric selection events that may favored some genetic variants in ancient lifestyles, such as hunter-gatherers lifestyle, are not advantageous any longer. On the contrary, these positively selected genetic variants have become disadvantageous in modern societies with modern lifestyles. Many complex diseases, such as diabetes, obesity, hypertension, inflammatory or autoimmune diseases, allergies, and cancers, may have been by-products of these disadvantageous prehistoric selection events that are not fit with modern and more sedentary lifestyles. An excellent example is the thrifty gene hypothesis and the evolution and increased incidence of diabetes in modern populations. The thrifty gene hypothesis was first suggested by Neel, who suggested that diabetes predisposition genotypes in modern times were advantageous genotypes historically [91]. These positively selected genotypes that favored the storage of large quantities of body fat and slower metabolic rates were advantageous in the nomadic hunter-gatherers lifestyle and expected famine incidences. However, the change in the lifestyle to more sedentary type and the increase of available food resources lead to high rates of obesity and increased the risk of developing type II diabetes in individuals carrying these genotypes at present. Several studies supporting thrifty genotype hypothesis showed that the rapid change to modern lifestyle has led to high risk of diabetes and high levels of obesity in studied populations such as Native Americans of the United States and Tongans of the Pacific populations [92, 93]. Nominal evidence for positive selection at 14 loci of the diabetes susceptibility in samples of African, European, and East Asian ancestry was found only when using locus-by-locus analysis [94]. However, the debate about the validity of thrifty gene hypothesis is still ongoing.

Additional examples of detrimental health costs of historical natural selection leading to nowadays complex diseases are inflammatory and autoimmune diseases, such as type 1 diabetes, inflammatory bowel disease, Crohn’s disease, celiac disease, and rheumatoid arthritis. This can be justified by the hygiene hypothesis especially in North European populations [95]. The “hygiene hypothesis” was first proposed by Strachan [96]. The major concept of hygiene hypothesis is that coevolution with some pathogenic agents is protecting humans from a large spectrum of immune-related disorders. Historically, a strong and intensified immune response was the best way to survive in pathogen-rich environments thus, it was under strong positive selection, despite the fact that the same pathogens are still present but advancement in hygienic care and the use of antibiotics and vaccination, in the modern societies, lead to the reduction of pathogen-driven selection pressures. This reduction of selection pressures led to the conversion of the intensified immune response from being advantageous for human survival to be a health burden through inflammatory and autoimmune diseases [95, 97]. There is an increase of prevalence of autoimmune diseases in both developed and developing countries compared to third world countries. For example, type 1 diabetes has become a serious public health problem in some European countries, especially Finland [98]. In addition, incidences of inflammatory bowel diseases, Crohn’s disease or ulcerative colitis, and primary biliary cirrhosis are also rising. Similarly, Africans living in the United States and Asians living in the United Kingdom in these days exhibited a higher risk of developing allergic inflammatory diseases and asthma compared with the general population in these countries [99–101]. Genetic and ethnic backgrounds of these populations were found to have higher impact on the prevalence of asthma compared to environmental effects [102, 103]. Evolutionary justification of the above-mentioned examples is that, in these populations, alleles conferring high risk for inflammatory and autoimmune diseases were under strong selective pressure in the past and in different environmental conditions [104] and that inflammatory and autoimmune disorders observed nowadays are the by-products of past selection against infectious diseases [97].

11. Consanguinity and Complex Diseases

As we mentioned above, cultural behavior of human populations, such as directed marriages or consanguinity, is capable of effecting allelic frequencies and genetic diversity within populations. Complex diseases can be affected by consanguinity when they are controlled by multiple rare genes and transmitted in an autosomal recessive manner [105]. Unfortunately, little is known about the effects of consanguinity on the complex diseases despite its great importance to global health. It is worth mentioning that consanguineous marriage is a common tradition in many populations in North Africa, Middle East, West Asia, and South India [105, 106]. Highly consanguineous populations, especially those with relatively small effective population sizes, provide an uncomplicated route for identifying recessively inherited genes for complex diseases such as identifying multiple loci for Alzheimer disease in an Arab population [107]. Moreover, some studies showed increased incidence of complex diseases among consanguineous marriage offspring. For example, minimal but significant increase of schizophrenia incidence among progeny of cousin marriages among Bedouin Arabs was observed [108]. In addition, higher rate of ischemic stroke was observed among religiously isolated inbreeding population in Netherlands compared to the general population [109]. In addition, global high rate of consanguinity may have a special impact on a polygenic disease like diabetes mellitus, especially type 2 diabetes. Anokute, in a study of 210 cases of diabetes in the central region of Saudi Arabia, found that familial aggregation compared to nonaggregation yielded an odds ratio of 6 : 2, respectively, which suggests a casual association with diabetes that needs to be further explored in future studies [110]. These findings do not extend to other populations in the same region such as Palestinians and Bahrainis where there is no increase in prevalence of type 2 diabetes in consanguine marriages [111, 112]. A study by Bener et al., 2005, which was done in Qatar showed that diabetes was significantly common among the consanguineous marriages of the first-degree relatives compared with the control group (33.1% versus 24.6%) (OR = 1.59 95% CI = 1.11–2.29

) [113]. In another study done in Qatar by Bener et al., 2007, to determine the extent and nature of consanguinity in the Qatari population and its effects on common adult diseases, the rate of consanguinity in the present generation was 51% with a coefficient of inbreeding of 0.023724 [114]. The consanguinity rate and coefficient of inbreeding in the current generation were significantly higher than the maternal rate (51% versus 40.3% and 0.023724 versus 0.016410), respectively. All types of consanguineous marriages were higher in this generation, particularly first cousins (26.7 versus 21.4% paternal and 23.1% maternal) and double first cousins (4.3 versus 2.9% paternal and 0.8% maternal). The current generation of consanguineous parents had a slightly higher risk for most diseases such as cancer, mental disorders, heart diseases, gastrointestinal disorders, hypertension, hearing deficiency, and diabetes mellitus. All the reported diseases were more frequent in consanguineous marriages. Gosadi investigated the potential effect of consanguinity on type 2 diabetes susceptibility in Saudi population [115]. He suggested that consanguinity might increase the risk of type 2 diabetes by earlier development of the disease and by strengthening possible genetic effect on fasting blood glucose (FBG). Contradictory results have been obtained from association studies on breast cancer in consanguineous populations for BRCA1 and BRCA2 genes [116, 117]. Though, valuable information about the genetic background of complex diseases can be obtained from consanguineous populations if cultural, religious, and political bias concerning consanguineous marriage are circumvented.

12. Ancient Genes and Complex Diseases

Neanderthals, ancient hominids, and modern humans have coexisted for thousands of years and interbred outside of Africa especially in Europe and Asia [17]. This leads to the presence of several Neanderthals ancient genes in current European and Asian genomes (approximately 1–4%), while no Neanderthals ancient genes were observed among current African populations [19, 118]. Moreover, it was found that Neanderthal component in non-African modern human was more related to the Mezmaiskaya Neanderthal (Caucasus) than to the Altai Neanderthal (Siberia) or the Vindija Neanderthals [118]. In addition, several studies showed a higher Neanderthal admixture in East Asians when compared to Europeans [12, 119–121]. It was found that genes affecting keratin were found to have been introgressed from Neanderthals into East Asian and European humans, suggesting Neanderthals donated both morphological adaptation genes modern humans to cope with the new environments outside of Africa [120, 121].

Moreover, recent studies showed that the increased rates of type 2 diabetes in Europeans and Asians compared to Africans are due to interbreeding with ancient Neanderthals. It was found that many genes associated with complex diseases such as systemic lupus erythematosus, primary biliary cirrhosis, Crohn’s disease, and diabetes mellitus type 2 have been introgressed from Neanderthals into non-African modern humans [121]. Though some beneficial genes such as immune-related genes are donated from Neanderthal to non-African modern humans. For example, HLA-C

0702, found in Neanderthals, is common in modern Europeans and Asians but is rarely seen in Africans [122].

13. Conclusion

Population genetics and molecular evolution studies have paved the way to gain better understanding of genetic adaptation of human in order to cope with environmental and lifestyle changes. Understanding the effect of evolutionary driving forces on human complex traits, such as natural selection, facilitated our ability to understand the relationship between genetic diversity, adaptive phenotypes, and complex disease. Huge amount of population genetics data for different human populations is available and waiting to be investigated deeply integrating both population genetics and molecular evolution contexts. Molecular signatures of genetic variations such as single nucleotide polymorphism, copy number variation, and genomic structural variations should be investigated and linked with human adaptation, the changing environment, and complex diseases. In addition large scale investigations about changes in lifestyles and the development of complex diseases are needed, especially in the Arabian Gulf area where drastic lifestyle changes accrued after the petroleum discovery. Integrating information about population genetics, molecular evolution, environmental changes, epidemiology, and social and cultural studies is an immediate need. These multidisciplinary efforts can elucidate the relationship between molecular evolution concept and complex diseases and improve our understanding of the evolutionary mechanisms in disease susceptibility, resistance, or progression, in turn facilitating disease prevention, diagnosis, and treatment.

Competing Interests

The authors declare that they have no competing interests.

Acknowledgments

The authors would like to thank the Scientific Publishing Department in Diabetes Strategic Research Center for their help in preparing this work. This study was supported by the Diabetes Strategic Research Center, King Saudi University, Kingdom of Saudi Arabia.

References

  1. N. Risch and K. Merikangas, “The future of genetic studies of complex human diseases,” Science, vol. 273, no. 5281, pp. 1516–1517, 1996. View at: Publisher Site | Google Scholar
  2. National Institutes of Health, Genetics of Common, Complex Disease, NIH Fact Sheets, National Institutes of Health, Bethesda, Md, USA, 2010, https://report.nih.gov/nihfactsheets/ViewFactSheet.aspx?csid=42.
  3. E. S. Lander, L. M. Linton, B. Birren et al. et al., “Initial sequencing and analysis of the human genome,” Nature, vol. 409, no. 6822, pp. 860–921, 2001. View at: Publisher Site | Google Scholar
  4. J. C. Venter, M. D. Adams, E. W. Myers et al. et al., “The sequence of the human genome,” Science, vol. 291, no. 5507, pp. 1304–1351, 2001. View at: Publisher Site | Google Scholar
  5. The International HapMap Consortium, “A haplotype map of the human genome,” Nature, vol. 437, pp. 1299–1320, 2005. View at: Publisher Site | Google Scholar
  6. K. A. Frazer, D. G. Ballinger, D. R. Cox et al., “A second generation human Haplotype map of over 3.1 million SNPs,” Nature, vol. 449, no. 7164, pp. 851–861, 2007. View at: Google Scholar
  7. D. M. Altshuler, R. A. Gibbs, L. Peltonen et al., “Integrating common and rare genetic variation in diverse human populations,” Nature, vol. 467, no. 7311, pp. 52–58, 2010. View at: Publisher Site | Google Scholar
  8. G. R. Abecasis, A. L. D. Auton, M. A. Brooks et al., “An integrated map of genetic variation from 1, 092 human genomes,” Nature, pp. 491–56, 2012. View at: Google Scholar
  9. National Human Genome Research Institute, Catalog of published GWA studies, http://www.genome.gov/gwastudies/.
  10. A.-H. Salem, F. M. Badr, M. F. Gaballah, and S. Paabo, “The genetics of traditional living: Y-chromosomal and mitochondrial lineages in the Sinai Peninsula,” American Journal of Human Genetics, vol. 59, no. 3, pp. 741–743, 1996. View at: Google Scholar
  11. M. T. Seielstad, E. Minch, and L. L. Cavalli-Sforza, “Genetic evidence for a higher female migration rate in humans,” Nature Genetics, vol. 20, no. 3, pp. 278–280, 1998. View at: Publisher Site | Google Scholar
  12. R. Chaix, F. Austerlitz, T. Khegay et al., “The genetic or mythical ancestry of descent groups: lessons from the Y chromosome,” American Journal of Human Genetics, vol. 75, no. 6, pp. 1113–1116, 2004. View at: Publisher Site | Google Scholar
  13. R. Chaix, L. Quintana-Murci, T. Hegay et al., “From social to genetic structures in central Asia,” Current Biology, vol. 17, no. 1, pp. 43–48, 2007. View at: Publisher Site | Google Scholar
  14. J. A. Wilder, Z. Mobasher, and M. F. Hammer, “Genetic evidence for unequal effective population sizes of human females and males,” Molecular Biology and Evolution, vol. 21, no. 11, pp. 2047–2057, 2004. View at: Publisher Site | Google Scholar
  15. L. Quintana-Murci, O. Semino, H.-J. Bandelt, G. Passarino, K. McElreavey, and A. S. Santachiara-Benerecetti, “Genetic evidence of an early exit of Homo sapiens sapiens from Africa through eastern Africa,” Nature Genetics, vol. 23, no. 4, pp. 437–441, 1999. View at: Publisher Site | Google Scholar
  16. V. Macaulay, C. Hill, A. Achilli et al., “Single, rapid coastal settlement of Asia revealed by analysis of complete mitochondrial genomes,” Science, vol. 308, no. 5724, pp. 1034–1036, 2005. View at: Publisher Site | Google Scholar
  17. P. Mellars, “A new radiocarbon revolution and the dispersal of modern humans in Eurasia,” Nature, vol. 439, no. 7079, pp. 931–935, 2006. View at: Publisher Site | Google Scholar
  18. G. Laval, E. Patin, L. B. Barreiro, and L. Q. Murci, “Formulating a historical and demographic model of recent human evolution based on resequencing data from noncoding regions,” PLoS ONE, vol. 5, no. 4, Article ID e10284, 2010. View at: Publisher Site | Google Scholar
  19. R. E. Green, J. Krause, A. W. Briggs et al., “A draft sequence of the neandertal genome,” Science, vol. 328, no. 5979, pp. 710–722, 2010. View at: Publisher Site | Google Scholar
  20. D. Reich, R. E. Green, M. Kircher et al., “Genetic history of an archaic hominin group from Denisova Cave in Siberia,” Nature, vol. 468, no. 7327, pp. 1053–1060, 2010. View at: Publisher Site | Google Scholar
  21. L. Abi-Rached, M. J. Jobin, S. Kulkarni et al., “The shaping of modern human immune systems by multiregional admixture with archaic humans,” Science, vol. 334, no. 6052, pp. 89–94, 2011. View at: Publisher Site | Google Scholar
  22. R. K. Butlin and T. Tregenza, “Levels of genetic polymorphism: marker loci versus quantitative traits,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 353, no. 1366, pp. 187–198, 1998. View at: Publisher Site | Google Scholar
  23. W. J. Ewens, Mathematical Population Genetics, Springer, New York, NY, USA, 2nd edition, 2004.
  24. J. Novembre and A. Di Rienzo, “Spatial patterns of variation due to natural selection in humans,” Nature Reviews Genetics, vol. 10, no. 11, pp. 745–755, 2009. View at: Publisher Site | Google Scholar
  25. P. C. Sabeti, S. F. Schaffner, B. Fry et al., “Positive natural selection in the human lineage,” Science, vol. 312, no. 5780, pp. 1614–1620, 2006. View at: Publisher Site | Google Scholar
  26. P. C. Sabeti, P. Varilly, B. Fry et al., “Genome-wide detection and characterization of positive selection in human populations,” Nature, vol. 449, pp. 913–918, 2007. View at: Publisher Site | Google Scholar
  27. J. M. Akey, M. A. Eberle, M. J. Rieder et al., “Population history and natural selection shape patterns of genetic variation in 132 genes,” PLoS Biology, vol. 2, no. 10, 2004. View at: Publisher Site | Google Scholar
  28. B. F. Voight, S. Kudaravalli, X. Wen, and J. K. Pritchard, “A map of recent positive selection in the human genome,” PLoS Biology, vol. 4, article e72, 2006. View at: Publisher Site | Google Scholar
  29. R. Blekhman, O. Man, L. Herrmann et al., “Natural selection on genes that underlie human disease susceptibility,” Current Biology, vol. 18, no. 12, pp. 883–889, 2008. View at: Publisher Site | Google Scholar
  30. J. K. Pickrell, G. Coop, J. Novembre et al., “Signals of recent positive selection in a worldwide sample of human populations,” Genome Research, vol. 19, no. 5, pp. 826–837, 2009. View at: Publisher Site | Google Scholar
  31. F. Jay, P. Sj཭in, M. Jakobsson, and M. G. B. Blum, “Anisotropic isolation by distance: the main orientations of human genetic differentiation,” Molecular Biology and Evolution, vol. 30, no. 3, pp. 513–525, 2013. View at: Publisher Site | Google Scholar
  32. M. Slatkin, “Isolation by distance in equilibrium and non-equilibrium populations,” Evolution, vol. 47, no. 1, pp. 264–279, 1993. View at: Publisher Site | Google Scholar
  33. S. Ramachandran, O. Deshpande, C. C. Roseman, N. A. Rosenberg, M. W. Feldman, and L. L. Cavalli-Sforza, “Support from the relationship of genetic and geographic in human populations for a serial founder effect originating in Africa,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 44, pp. 15942–15947, 2005. View at: Publisher Site | Google Scholar
  34. O. Lao, T. T. Lu, M. Nothnagel et al., “Correlation between genetic and geographic structure in Europe,” Current Biology, vol. 18, pp. 1241–1248, 2008. View at: Google Scholar
  35. E. Salmela, T. Lappalainen, I. Fransson et al., “Genome-wide analysis of single nucleotide polymorphisms uncovers population structure in Northern Europe,” PLoS ONE, vol. 3, no. 10, Article ID e3519, 2008. View at: Publisher Site | Google Scholar
  36. N. A. Rosenberg, J. K. Pritchard, J. L. Weber et al., “Genetic structure of human populations,” Science, vol. 298, no. 5602, pp. 2381–2385, 2002. View at: Publisher Site | Google Scholar
  37. T. Gayden, A. M. Cadenas, M. Regueiro et al., “The Himalayas as a directional barrier to gene flow,” American Journal of Human Genetics, vol. 80, no. 5, pp. 884–894, 2007. View at: Publisher Site | Google Scholar
  38. C. Wang, S. Zöllner, and N. A. Rosenberg, “A quantitative comparison of the similarity between genes and geography in worldwide human populations,” PLoS Genetics, vol. 8, no. 8, Article ID e1002886, 2012. View at: Publisher Site | Google Scholar
  39. C. D. Bustamante, A. Fledel-Alon, S. Williamson et al., “Natural selection on protein-coding genes in the human genome,” Nature, vol. 437, no. 7062, pp. 1153–1157, 2005. View at: Publisher Site | Google Scholar
  40. R. Nielsen, C. Bustamante, A. G. Clark et al., “A scan for positively selected genes in the genomes of humans and chimpanzees,” PLoS Biology, vol. 3, article e170, 2005. View at: Publisher Site | Google Scholar
  41. J. M. Smith and J. Haigh, “The hitch-hiking effect of a favourable gene,” Genetical Research, vol. 23, no. 1, pp. 23–35, 1974. View at: Publisher Site | Google Scholar
  42. N. L. Kaplan, R. R. Hudson, and C. H. Langley, “The “hitchhiking effect” revisited,” Genetics, vol. 123, no. 4, pp. 887–899, 1989. View at: Google Scholar
  43. C. D. Huff, D. J. Witherspoon, Y. Zhang et al., “Crohn's disease and genetic hitchhiking at IBD5,” Molecular Biology and Evolution, vol. 29, no. 1, pp. 101–111, 2012. View at: Publisher Site | Google Scholar
  44. J. D. Rioux, M. S. Silverberg, M. J. Daly et al., “Genomewide search in Canadian families with inflammatory bowel disease reveals two novel susceptibility loci,” American Journal of Human Genetics, vol. 66, no. 6, pp. 1863–1870, 2000. View at: Publisher Site | Google Scholar
  45. J. D. Rioux, M. J. Daly, M. S. Silverberg et al., “Genetic variation in the 5q31 cytokine gene cluster confers susceptibility to Crohn disease,” Nature Genetics, vol. 29, no. 2, pp. 223–228, 2001. View at: Publisher Site | Google Scholar
  46. P. R. Burton, D. G. Clayton, L. R. Cardon et al., “Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls,” Nature, vol. 447, pp. 661–678, 2007. View at: Google Scholar
  47. C. B. Foster, K. Aswath, S. J. Chanock, H. F. McKay, and U. Peters, “Polymorphism analysis of six selenoprotein genes: Support for a selective sweep at the glutathione peroxidase 1 locus (3p21) in Asian populations,” BMC Cell Biology, vol. 7, article 56, 2006. View at: Publisher Site | Google Scholar
  48. A. J. Duffield-Lillico, B. L. Dalkin, M. E. Reid et al., “Selenium supplementation, baseline plasma selenium status and incidence of prostate cancer: an analysis of the complete treatment period of the Nutritional Prevention of Cancer Trial,” BJU International, vol. 91, no. 7, pp. 608–612, 2003. View at: Publisher Site | Google Scholar
  49. M. A. Beck, J. Handy, and O. A. Levander, “Host nutritional status: the neglected virulence factor,” Trends in Microbiology, vol. 12, no. 9, pp. 417–423, 2004. View at: Publisher Site | Google Scholar
  50. G. J. Beckett and J. R. Arthur, “Selenium and endocrine systems,” Journal of Endocrinology, vol. 184, no. 3, pp. 455–465, 2005. View at: Publisher Site | Google Scholar
  51. U. Peters, N. Chatterjee, T. R. Church et al., “High serum selenium and reduced risk of advanced colorectal adenoma in a colorectal cancer early detection program,” Cancer Epidemiology Biomarkers and Prevention, vol. 15, no. 2, pp. 315–320, 2006. View at: Publisher Site | Google Scholar
  52. J. Ahn, M. D. Gammon, R. M. Santella et al., “No association between glutathione peroxidase Pro198Leu polymorphism and breast cancer risk,” Cancer Epidemiology Biomarkers and Prevention, vol. 14, no. 10, pp. 2459–2461, 2005. View at: Publisher Site | Google Scholar
  53. A. Aydin, Z. Arsova-Sarafinovska, A. Sayal et al., “Oxidative stress and antioxidant status in non-metastatic prostate cancer and benign prostatic hyperplasia,” Clinical Biochemistry, vol. 39, no. 2, pp. 176–179, 2006. View at: Publisher Site | Google Scholar
  54. H. Dursun, M. Bilici, A. Uyanik, N. Okcu, and M. Akyüz, “Antioxidant enzyme activities and lipid peroxidation levels in erythrocytes of patients with oesophageal and gastric cancer,” Journal of International Medical Research, vol. 34, no. 2, pp. 193–199, 2006. View at: Publisher Site | Google Scholar
  55. G. Ravn-Haren, A. Olsen, A. Tjønneland et al., “Associations between GPX1 Pro198Leu polymorphism, erythrocyte GPX activity, alcohol consumption and breast cancer risk in a prospective cohort study,” Carcinogenesis, vol. 27, no. 4, pp. 820–825, 2006. View at: Publisher Site | Google Scholar
  56. D. Charlesworth, “Balancing selection and its effects on sequences in nearby genome regions,” PLoS Genetics, vol. 2, no. 4, article e64, 2006. View at: Publisher Site | Google Scholar
  57. L. D. Hurst, “Fundamental concepts in genetics: genetics and the understanding of selection,” Nature Reviews Genetics, vol. 10, no. 2, pp. 83–93, 2009. View at: Publisher Site | Google Scholar
  58. M. Fumagalli, U. Pozzoli, R. Cagliani et al., “Parasites represent a major selective force for interleukin genes and shape the genetic predisposition to autoimmune conditions,” Journal of Experimental Medicine, vol. 206, no. 6, pp. 1395–1408, 2009. View at: Publisher Site | Google Scholar
  59. E. Vasseur and L. Quintana-Murci, “The impact of natural selection on health and disease: uses of the population genetics approach in humans,” Evolutionary Applications, vol. 6, no. 4, pp. 596–607, 2013. View at: Publisher Site | Google Scholar
  60. L. Excoffier, P. E. Smouse, and J. M. Quattro, “Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data,” Genetics, vol. 131, no. 2, pp. 479–491, 1992. View at: Google Scholar
  61. B. S. Weir and W. G. Hill, “Estimating F-statistics,” Annual Review of Genetics, vol. 36, pp. 721–750, 2002. View at: Publisher Site | Google Scholar
  62. M. Bamshad and S. P. Wooding, “Signatures of natural selection in the human genome,” Nature Reviews Genetics, vol. 4, no. 2, pp. 99–111, 2003. View at: Publisher Site | Google Scholar
  63. G. Zhang, L. J. Muglia, R. Chakraborty, J. M. Akey, and S. M. Williams, “Signatures of natural selection on genetic variants affecting complex human traits,” Applied and Translational Genomics, vol. 2, no. 1, pp. 77–93, 2013. View at: Publisher Site | Google Scholar
  64. W. Yang, H. Tang, Y. Zhang et al., “Meta-analysis followed by replication identifies loci in or near CDKN1B, TET3, CD80, DRAM1, and ARID5B as associated with systemic lupus erythematosus in Asians,” American Journal of Human Genetics, vol. 92, no. 1, pp. 41–51, 2013. View at: Publisher Site | Google Scholar
  65. M. S. Sandhu, D. M. Waterworth, S. L. Debenham et al., “LDL-cholesterol concentrations: a genome-wide association study,” The Lancet, vol. 371, no. 9611, pp. 483–491, 2008. View at: Publisher Site | Google Scholar
  66. C. J. Willer, S. Sanna, A. U. Jackson et al., “Newly identified loci that influence lipid concentrations and risk of coronary artery disease,” Nature Genetics, vol. 40, no. 2, pp. 161–169, 2008. View at: Publisher Site | Google Scholar
  67. K. U. Ludwig, E. Mangold, S. Herms et al., “Genome-wide meta-analyses of nonsyndromic cleft lip with or without cleft palate identify six new risk loci,” Nature Genetics, vol. 44, no. 9, pp. 968–971, 2012. View at: Publisher Site | Google Scholar
  68. E. Mangold, K. U. Ludwig, S. Birnbaum et al., “Genome-wide association study identifies two susceptibility loci for nonsyndromic cleft lip with or without cleft palate,” Nature Genetics, vol. 42, no. 1, pp. 24–26, 2010. View at: Publisher Site | Google Scholar
  69. S. E. Medland, D. R. Nyholt, J. N. Painter et al., “Common Variants in the Trichohyalin Gene Are Associated with Straight Hair in Europeans,” American Journal of Human Genetics, vol. 85, no. 5, pp. 750–755, 2009. View at: Publisher Site | Google Scholar
  70. Z.-J. Chen, H. Zhao, L. He et al., “Genome-wide association study identifies susceptibility loci for polycystic ovary syndrome on chromosome 2p16.3, 2p21 and 9q33.3,” Nature Genetics, vol. 43, no. 1, pp. 55–59, 2011. View at: Publisher Site | Google Scholar
  71. Y. Shi, H. Zhao, Y. Shi et al., “Genome-wide association study identifies eight new risk loci for polycystic ovary syndrome,” Nature Genetics, vol. 44, no. 9, pp. 1020–1025, 2012. View at: Publisher Site | Google Scholar
  72. C. Gieger, A. Radhakrishnan, A. Cvejic et al., “New gene functions in megakaryopoiesis and platelet formation,” Nature, vol. 480, no. 7376, pp. 201–208, 2011. View at: Publisher Site | Google Scholar
  73. E. Zeggini, L. J. Scott, R. Saxena, and B. F. Voight, “Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes,” Nature Genetics, vol. 40, no. 5, pp. 638–645, 2008. View at: Publisher Site | Google Scholar
  74. A. Franke, D. P. B. McGovern, J. C. Barrett et al., “Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci,” Nature Genetics, vol. 42, pp. 1118–1125, 2010. View at: Publisher Site | Google Scholar
  75. L. Jostins, S. Ripke, R. K. Weersma et al., “Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease,” Nature, vol. 491, no. 7422, pp. 119–124, 2012. View at: Publisher Site | Google Scholar
  76. K. Ding and I. J. Kullo, “Geographic differences in allele frequencies of susceptibility SNPs for cardiovascular disease,” BMC Medical Genetics, vol. 12, article 55, 2011. View at: Publisher Site | Google Scholar
  77. Y. C. Klimentidis, M. Abrams, J. Wang, J. R. Fernandez, and D. B. Allison, “Natural selection at genomic regions associated with obesity and type-2 diabetes: East Asians and sub-Saharan Africans exhibit high levels of differentiation at type-2 diabetes regions,” Human Genetics, vol. 129, no. 4, pp. 407–418, 2011. View at: Publisher Site | Google Scholar
  78. R. Peto, F. J. C. Roe, P. N. Lee, L. Levy, and J. Clack, “Cancer and ageing in mice and men,” British Journal of Cancer, vol. 32, no. 4, pp. 411–426, 1975. View at: Publisher Site | Google Scholar
  79. A. F. Caulin and C. C. Maley, “Peto's Paradox: evolution's prescription for cancer prevention,” Trends in Ecology & Evolution, vol. 26, no. 4, pp. 175–182, 2011. View at: Publisher Site | Google Scholar
  80. S. Pavard and C. J. E. Metcalf, “Negative selection on BRCA1 susceptibility alleles sheds light on the population genetics of late-onset diseases and aging theory,” PLoS ONE, vol. 2, no. 11, Article ID e1206, 2007. View at: Publisher Site | Google Scholar
  81. T. I. Andersen, A.-L. Borresen, and P. Moller, “A common BRCA1 mutation in Norwegian breast and ovarian cancer families?” The American Journal of Human Genetics, vol. 59, no. 2, pp. 486–487, 1996. View at: Google Scholar
  82. W. H. Lee and T. G. Boyer, “BRCA1 and BRCA2 in breast cancer,” The Lancet, vol. 358, p. S5, 2001. View at: Publisher Site | Google Scholar
  83. O. Dໞz, A. Osorio, M. Durán et al., “Analysis of BRCA1 and BRCA2 genes in Spanish breast/ovarian cancer patients: a high proportion of mutations unique to Spain and evidence of founder effects,” Human Mutation, vol. 22, no. 4, pp. 301–312, 2003. View at: Publisher Site | Google Scholar
  84. I. Cass, C. Holschneider, N. Datta, D. Barbuto, A. E. Walts, and B. Y. Karlan, “BRCA-mutation-associated fallopian tube carcinoma: a distinct clinical phenotype?” Obstetrics and Gynecology, vol. 106, no. 6, pp. 1327–1334, 2005. View at: Publisher Site | Google Scholar
  85. Y. C. Tai, S. Domchek, G. Parmigiani, and S. Chen, “Breast cancer risk among male BRCA1 and BRCA2 mutation carriers,” Journal of the National Cancer Institute, vol. 99, no. 23, pp. 1811–1814, 2007. View at: Publisher Site | Google Scholar
  86. W. Al-Sukhni, H. Rothenmund, A. Eppel Borgida et al., “Germline BRCA1 mutations predispose to pancreatic adenocarcinoma,” Human Genetics, vol. 124, no. 3, pp. 271–278, 2008. View at: Publisher Site | Google Scholar
  87. J. E. Stajich and M. W. Hahn, “Disentangling the effects of demography and selection in human history,” Molecular Biology and Evolution, vol. 22, no. 1, pp. 63–73, 2005. View at: Publisher Site | Google Scholar
  88. T. Paiss, S. Wörner, F. Kurtz et al., “Linkage of aggressive prostate cancer to chromosome 7q31-33 in German prostate cancer families,” European Journal of Human Genetics, vol. 11, no. 1, pp. 17–22, 2003. View at: Publisher Site | Google Scholar
  89. C. Sun, D. Huo, C. Southard et al., “A signature of balancing selection in the region upstream to the human UGT2B4 gene and implications for breast cancer risk,” Human Genetics, vol. 130, no. 6, pp. 767–775, 2011. View at: Publisher Site | Google Scholar
  90. L. F. Grochola, A. Vazquez, E. E. Bond et al., “Recent natural selection identifies a genetic variant in a regulatory subunit of protein phosphatase 2A that associates with altered cancer risk and survival,” Clinical Cancer Research, vol. 15, no. 19, pp. 6301–6308, 2009. View at: Publisher Site | Google Scholar
  91. J. V. Neel, “Diabetes mellitus: a ‘thrifty’ genotype rendered detrimental by ‘progress’?” The American Journal of Human Genetics, vol. 14, pp. 353–362, 1962. View at: Google Scholar
  92. B. Joffe and P. Zimmet, “The thrifty genotype in type 2 diabetes: an unfinished symphony moving to its finale?” Endocrine, vol. 9, no. 2, pp. 139–141, 1998. View at: Publisher Site | Google Scholar
  93. S. Myles, R. A. Lea, J. Ohashi et al., “Testing the thrifty gene hypothesis: the Gly482Ser variant in PPARGC1A is associated with BMI in Tongans,” BMC Medical Genetics, vol. 12, article 10, 2011. View at: Publisher Site | Google Scholar
  94. Q. Ayub, L. Moutsianas, Y. Chen et al., “Revisiting the thrifty gene hypothesis via 65 loci associated with susceptibility to type 2 diabetes,” American Journal of Human Genetics, vol. 94, no. 2, pp. 176–185, 2014. View at: Publisher Site | Google Scholar
  95. H. Okada, C. Kuhn, H. Feillet, and J.-F. Bach, “The 'hygiene hypothesis' for autoimmune and allergic diseases: an update,” Clinical and Experimental Immunology, vol. 160, no. 1, pp. 1–9, 2010. View at: Publisher Site | Google Scholar
  96. D. P. Strachan, “Hay fever, hygiene, and household size,” British Medical Journal, vol. 299, no. 6710, pp. 1259–1260, 1989. View at: Publisher Site | Google Scholar
  97. M. Sironi and M. Clerici, “The hygiene hypothesis: an evolutionary perspective,” Microbes and Infection, vol. 12, no. 6, pp. 421–427, 2010. View at: Publisher Site | Google Scholar
  98. V. Harjutsalo, L. Sjrg, and J. Tuomilehto, “Time trends in the incidence of type 1 diabetes in Finnish children: a cohort study,” The Lancet, vol. 371, no. 9626, pp. 1777–1782, 2008. View at: Publisher Site | Google Scholar
  99. S. J. Gillam, B. Jarman, P. White, and R. Law, “Ethnic differences in consultation rates in urban general practice,” British Medical Journal, vol. 299, no. 6705, pp. 953–957, 1989. View at: Publisher Site | Google Scholar
  100. D. R. Gold, A. Rotnitzky, A. I. Damokosh et al., “Race and gender differences in respiratory illness prevalence and their relationship to environmental exposures in children 7 to 14 years of age,” American Review of Respiratory Disease, vol. 148, no. 1, pp. 10–18, 1993. View at: Publisher Site | Google Scholar
  101. J. Von Behren, R. Kreutzer, and D. Smith, “Asthma hospitalization trends in California, 1983�,” Journal of Asthma, vol. 36, no. 7, pp. 575–582, 1999. View at: Publisher Site | Google Scholar
  102. M. S. Gilthorpe, R. Lay-Yee, R. C. Wilson, S. Walters, R. K. Griffiths, and R. Bedi, “Variations in hospitalization rates for asthma among black and minority ethnic communities,” Respiratory Medicine, vol. 92, no. 4, pp. 642–648, 1998. View at: Publisher Site | Google Scholar
  103. J. E. Miller, “The effects of race/ethnicity and income on early childhood asthma prevalence and health care use,” American Journal of Public Health, vol. 90, no. 3, pp. 428–430, 2000. View at: Publisher Site | Google Scholar
  104. L. B. Barreiro and L. Quintana-Murci, “From evolutionary genetics to human immunology: how selection shapes host defence genes,” Nature Reviews Genetics, vol. 11, no. 1, pp. 17–30, 2010. View at: Publisher Site | Google Scholar
  105. A. H. Bittles and M. L. Black, “Consanguinity, human evolution, and complex diseases,” Proceedings of the National Academy of Sciences of the United States of America, vol. 107, supplement 1, pp. 1779–1786, 2010. View at: Publisher Site | Google Scholar
  106. A. H. Bittles, The Global Prevalence of Consanguinity, 2010, http://www.consang.net/.
  107. L. A. Farrer, A. Bowirrat, R. P. Friedland, K. Waraska, A. D. Korczyn, and C. T. Baldwin, “Identification of multiple loci for Alzheimer disease in a consanguineous Israeli-Arab community,” Human Molecular Genetics, vol. 12, no. 4, pp. 415–422, 2003. View at: Publisher Site | Google Scholar
  108. M. Dobrusin, D. Weitzman, J. Levine et al., “The rate of consanguineous marriages among parents of schizophrenic patients in the Arab Bedouin population in Southern Israel,” World Journal of Biological Psychiatry, vol. 10, no. 4, pp. 334–336, 2009. View at: Publisher Site | Google Scholar
  109. M. J. E. Van Rijn, A. J. C. Slooter, A. F. C. Schut et al., “Familial aggregation, the PDE4D gene, and ischemic stroke in a genetically isolated population,” Neurology, vol. 65, no. 8, pp. 1203–1209, 2005. View at: Publisher Site | Google Scholar
  110. C. C. Anokute, “Suspected synergism between consanguinity and familial aggregation in type 2 diabetes mellitus in Saudi Arabia,” Journal of the Royal Society of Health, vol. 112, no. 4, pp. 167–169, 1992. View at: Publisher Site | Google Scholar
  111. F. Al-Mahroos and P. M. McKeigue, “High prevalence of diabetes in Bahrainis: associations with ethnicity and raised plasma cholesterol,” Diabetes Care, vol. 21, no. 6, pp. 936–942, 1998. View at: Publisher Site | Google Scholar
  112. L. Jaber, T. Shohat, J. I. Rotter, and M. Shohat, “Consanguinity and common adult diseases in Israeli Arab communities,” American Journal of Medical Genetics, vol. 70, no. 4, pp. 346–348, 1997. View at: Publisher Site | Google Scholar
  113. A. Bener, M. Zirie, and A. Al-Rikabi, “Genetics, obesity, and environmental risk factors associated with type 2 diabetes,” Croatian Medical Journal, vol. 46, no. 2, pp. 302–307, 2005. View at: Google Scholar
  114. A. Bener, R. Hussain, and A. S. Teebi, “Consanguineous marriages and their effects on common adult diseases: studies from an endogamous population,” Medical Principles and Practice, vol. 16, no. 4, pp. 262–267, 2007. View at: Publisher Site | Google Scholar
  115. I. M. Gosadi, Investigating the potential effect of consanguinity on type 2 diabetes susceptibility in a Saudi population [Ph.D. thesis], University of Sheffield, Sheffield, UK, 2013.
  116. A. Liede, I. A. Malik, Z. Aziz, P. de los Rios, E. Kwan, and S. A. Narod, “Contribution of BRCA1 and BRCA2 mutations to breast and ovarian cancer in Pakistan,” American Journal of Human Genetics, vol. 71, no. 3, pp. 595–606, 2002. View at: Publisher Site | Google Scholar
  117. A. Bener, H. R. E. Ayoubi, A. I. Ali, A. Al-Kubaisi, and H. Al-Sulaiti, “Does consanguinity lead to decreased incidence of breast cancer?” Cancer Epidemiology, vol. 34, no. 4, pp. 413–418, 2010. View at: Publisher Site | Google Scholar
  118. K. Prﳾr, F. Racimo, N. Patterson et al., “The complete genome sequence of a Neanderthal from the Altai Mountains,” Nature, vol. 505, no. 7481, pp. 43–49, 2014. View at: Publisher Site | Google Scholar
  119. M. Meyer, M. Kircher, M.-T. Gansauge et al., “A high-coverage genome sequence from an archaic denisovan individual,” Science, vol. 338, no. 6104, pp. 222–226, 2012. View at: Publisher Site | Google Scholar
  120. S. Sankararaman, S. Mallick, M. Dannemann et al., “The genomic landscape of Neanderthal ancestry in present-day humans,” Nature, vol. 507, no. 7492, pp. 354–357, 2014. View at: Publisher Site | Google Scholar
  121. B. Vernot and J. M. Akey, “Resurrecting surviving Neandertal lineages from modern human genomes,” Science, vol. 343, no. 6174, pp. 1017–1021, 2014. View at: Publisher Site | Google Scholar
  122. P. Parham, P. J. Norman, L. Abi-Rached, and L. A. Guethlein, “Human-specific evolution of killer cell immunoglobulin-like receptor recognition of major histocompatibility complex class I molecules,” Philosophical Transactions of the Royal Society B, vol. 367, no. 1590, pp. 800–811, 2012. View at: Publisher Site | Google Scholar
  123. E. Ahlqvist, T. S. Ahluwalia, and L. Groop, “Genetics of type 2 diabetes,” Clinical Chemistry, vol. 57, no. 2, pp. 241–254, 2011. View at: Publisher Site | Google Scholar

Copyright

Copyright © 2016 Amr T. M. Saeb and Dhekra Al-Naqeb. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Heritability of resistance

An important factor underlying the refuge strategy is that the dominance of resistance is reduced by increasing the dose of Bt toxins. When the concentration of a Bt toxin is low, some heterozygous (rs) individuals typically survive exposure to a Bt toxin, but when the concentration is high, only rr can survive ( Tabashnik et al. 2004 Crespo et al. 2009 ). Accordingly, Bt toxin genes inserted in transgenic plants were modified to produce high concentrations of Bt toxins, and genetically transformed plants with high levels of Bt toxins were selected to produce commercial transgenic cultivars ( Showalter et al. 2009 ).

Even when r alleles are present in insect populations in Bt fields, movement of insects from refuges to Bt fields can significantly reduce the heritability of resistance. Resistance to crops that produce high concentrations of Bt toxins is recessive in highly susceptible pests, although this is not necessarily the case in pests less susceptible to Bt ( Tabashnik et al. 2008a, 2009a ). For refuges to be effective, the abundant susceptible insects produced on non-Bt host plants must mate with the rare resistant pests surviving on Bt crops. In such cases, when resistance is recessive, most hybrid offspring produced by resistant pests are killed by Bt crops. This reduces the heritability of resistance and delays the evolution of resistance ( Gould 1998 Tabashnik and Carrière 2009 ). With time, however, movement of rr individuals from Bt fields to refuges can increase the frequency of r alleles in refuges ( Sisterson et al. 2004 ), especially when fitness costs are absent ( Carrière and Tabashnik 2001 Gould et al. 2006 ). Ultimately, some individuals with these r alleles will move back from refuges to Bt fields, which increases the heritability of resistance and accelerates the evolution of resistance ( Comins 1977 Caprio and Tabashnik 1992 Sisterson et al. 2004 ).

The ‘pyramid’ strategy for delaying pest resistance is based on use of crops producing two or more distinct Bt toxins targeting individual pests. The pyramid strategy is expected to be most effective when resistance to each Bt toxin is recessive, fitness costs and refuges are present, and selection with any one of the Bt toxins does not cause cross-resistance to the others ( Roush 1998 Zhao et al. 2005 Gould et al. 2006 ). Cross-resistance to Bt occurs when a genetically-based decrease in susceptibility to one toxin decreases susceptibility to other toxins ( Tabashnik et al. 2009a,b ). When resistance is recessive to both toxins in a pyramid, pests that bear two r alleles for survival to one toxin will nonetheless be killed unless they also bear two r alleles for survival to the second toxin. Thus when r alleles are rare, the only genotype with high survival on a crop that produces two or more Bt toxins is expected to be extremely rare. Accordingly, the refuge strategy is more effective for reducing the heritability of resistance when crops produce two or more Bt toxins than when crops produce a single Bt toxin ( Gould 1998 Roush 1998 ).

Concentrations of Bt toxins in Bt corn and cotton typically decline as the growing season progresses, but seasonal changes in toxin concentration can vary among toxins and cultivars ( Dutton et al. 2004 Nguyen and Ja 2009 Showalter et al. 2009 ). For example, in Bt cotton, Cry1Ac concentration usually decreases when plants start producing flowers and bolls ( Showalter et al. 2009 ), while Cry2Ab concentration tends to spike in mid-season before declining ( Adamczyk et al. 2001 ). Levels of a Bt vegetative insecticidal protein, Vip3A, are relatively stable throughout the season, although cotton plants producing Vip3A still lose some of their activity against H. armigera during mid-season ( Llewellyn et al. 2007 ). The reasons for the seasonal reduction in Bt concentration remain unclear, but could be related to mRNA instability, declining promoter activity, reduced nitrogen metabolism, lower overall protein production, and toxin interactions ( Showalter et al. 2009 ).

The seasonal decline in toxin concentrations may increase the dominance of resistance and accelerate the evolution of resistance, especially in pests less susceptible to Bt toxins. For example, resistance to Cry1Ac cotton was recessive in H. armigera fed Bt cotton in the 5–6 leaf stage ( Bird and Akhurst 2004 ), but became partially dominant on cotton in the 15 leaf stage, which had concentrations of Cry1Ac 75% lower than in 4-week-old plants ( Bird and Akhurst 2005 ). However, a seasonal decline in toxin concentrations does not always increase the dominance of resistance. When Diatraea saccharalis larvae fed on each of seven commercial Cry1Ab corn cultivars in 2005 and 2006 ( Wu et al. 2007 ), average survival for each insect genotype was lower on vegetative corn than on older, reproductive corn (2005: 0.5% vs. 1.5% for ss, 1.4% vs. 3.4% for rs and 9.6% vs. 24.5% for rr 2006: 0.0% vs. 2.7% for ss, 3.5% vs. 6.8% for rs, and 14.3% vs. 18.1% for rr). Nevertheless, in 2005, the average dominance of resistance (h = [survival of rs − survival of ss]/[survival of rr − survival of ss]) was slightly higher on vegetative corn (0.099) than on reproductive corn (0.083). In 2006, the average dominance of resistance was higher than in 2005, and it was slightly higher on reproductive (0.27) than on vegetative (0.24) corn. Thus, the higher survival on reproductive corn relative to vegetative corn, which presumably reflects lower Cry1Ab concentration in reproductive corn ( Wu et al. 2007 ), did not produce consistent or large increases in the dominance of resistance.

Seasonal declines in Bt toxin concentrations could also reduce success of the pyramid strategy. Mahon and Olsen (2009) measured seasonal changes in survival of a H. armigera strain highly resistant to Cry2Ab on cotton producing Cry1Ac and Cry2Ab. Survival of rr individuals was respectively 0, 2.5, and 8.5% on field-grown cotton in the pre-square, early square and fruiting stages, while survival of ss was 0, 0, and 1.6%. Survival of rs on pre-square and fruiting cotton was respectively 0 and 1.7% and did not differ significantly from ss (survival of rs on early square cotton was not measured), showing that resistance remained recessive on the different cotton stages. Mahon and Olsen (2009) did not measure the change in concentrations of Cry1Ac and Cry2Ab in cotton plants but proposed that increased survival of the Cry2Ab-resistant insects was likely due to a decline in the concentration of Cry1Ac. As the oldest cotton was tested soon after fruiting, it is also possible that survival of rr individuals and the dominance of resistance could increase further on older cotton, or on cultivars where the concentration of the Bt toxins decline faster than in the experimental cultivar used. Accordingly, the seasonal decline in the concentration of one toxin in a pyramid (here Cry1Ac) could invalidate the fundamental assumption of the pyramid strategy (i.e., the killing of insects resistant to one toxin by another toxin), and thus accelerate evolution of resistance. It is noteworthy that H. zea, a pest in which seasonal changes in survival on Bt crops have been reported ( Storer et al. 2003 ), has rapidly evolved resistance in the field to Cry1Ac and Cry2Ab produced by pyramided Bt cotton ( Tabashnik et al. 2009a ). There is a need to better evaluate and consider the consequences of seasonal declines in the concentrations of Bt toxins on the evolution of resistance to Bt crops (Brévault et al., unpublished data).


The Rest of Chapter 23-The Evolution of Populations

Practice Questions:

1) Most copies of harmful recessive alleles in a population are carried by individuals that are A) heterozygous for the allele. B) polymorphic. C) haploid. D) homozygous for the allele. E) afflicted with the disorder caused by the allele.

2) All of the following are criteria for maintaining a Hardy-Weinberg equilibrium involving two alleles except A) gene flow from other populations must be zero. B) there should be no natural selection. C) the frequency of all genotypes must be equal. D) matings must be random. E) populations must be large.

3) What is the most reasonable conclusion that can be drawn from the fact that the frequency of the recessive trait (aa) has not changed over time? A) There has been a high rate of mutation of allele A to allele a. B) The population is undergoing genetic drift. C) There has been sexual selection favoring allele a. D) The two phenotypes are about equally adaptive under laboratory conditions. E) The genotype AA is lethal.

4) What effect do sexual processes (meiosis and fertilization) have on the allelic frequencies in a population? A) They tend to increase the frequencies of deleterious alleles and decrease the frequencies of advantageous ones. B) They tend to selectively combine favorable alleles into the same zygote but do not change allelic frequencies. C) They tend to reduce the frequencies of deleterious alleles and increase the frequencies of advantageous ones. D) They tend to increase the frequency of new alleles and decrease the frequency of old ones.

5) Which of the following is the unit of evolution? In other words, which of the following can evolve in the Darwinian sense? A) species B) gene C) chromosome D) individual E) population


Nonrandom Mating

One of the cornerstones of the Hardy-Weinberg equilibrium is that mating in the population must be random. If individuals (usually females) are choosy in their selection of mates, the gene frequencies may become altered. Darwin called this sexual selection.

Nonrandom mating seems to be quite common. Breeding territories, courtship displays, "pecking orders" can all lead to it. In each case certain individuals do not get to make their proportionate contribution to the next generation.

Assortative mating

Humans seldom mate at random preferring phenotypes like themselves (e.g., size, age, ethnicity). This is called assortative mating. (Drawing by Koren © 1977 The New Yorker Magazine, Inc.)

Marriage between close relatives is a special case of assortative mating. The closer the kinship, the more alleles shared and the greater the degree of inbreeding. Inbreeding can alter the gene pool. This is because it predisposes to homozygosity. Potentially harmful recessive alleles — invisible in the parents — become exposed to the forces of natural selection in the children.

It turns out that many species — plants as well as animals — have mechanisms be which they avoid inbreeding. Examples:

  • Link to discussion of self-incompatibility in plants.
  • Male mice use olfactory cues to discriminate against close relatives when selecting mates. The preference is learned in infancy — an example of imprinting. The distinguishing odors are
    • controlled by the MHC alleles of the mice
    • detected by the vomeronasal organ (VNO).

    Evidence for secondary-variant genetic burden and non-random distribution across biological modules in a recessive ciliopathy

    The influence of genetic background on driver mutations is well established however, the mechanisms by which the background interacts with Mendelian loci remain unclear. We performed a systematic secondary-variant burden analysis of two independent cohorts of patients with Bardet-Biedl syndrome (BBS) with known recessive biallelic pathogenic mutations in one of 17 BBS genes for each individual. We observed a significant enrichment of trans-acting rare nonsynonymous secondary variants in patients with BBS compared with either population controls or a cohort of individuals with a non-BBS diagnosis and recessive variants in the same gene set. Strikingly, we found a significant over-representation of secondary alleles in chaperonin-encoding genes-a finding corroborated by the observation of epistatic interactions involving this complex in vivo. These data indicate a complex genetic architecture for BBS that informs the biological properties of disease modules and presents a model for secondary-variant burden analysis in recessive disorders.


    Watch the video: Hardy-Weinberg Equilibrium (December 2022).