What type of genetic drift that follows the colonization of a new habitat?

Epidemiology

Breast cancer is the most common cancer in women and the second most common cancer overall, accounting for 11.6% of new cancer diagnoses worldwide in 2018. It has long been established that reproductive and menstrual histories are risk factors. Parous women, especially multiparous, are at lower risk of developing breast cancer than nulliparous women. Furthermore, the younger the age at first pregnancy and the later the age at menarche, the lower the risk of breast cancer. Breastfeeding, regular exercise, and reduced alcohol intake also appear to have a role in decreasing breast cancer risk.

The incidence of breast cancer varies greatly between different populations, with age-standardized incidence rates highest in women in Australia and New Zealand (94.2 per 100,000) and Western Europe (92.6 per 100,000). Incidence rates up to 3.6 times lower are seen in women from the Middle Africa (27.9 per 100,000) and South-Central Asia (25.9 per 100,000). Although these differences could be attributed to genetic differences between these population groups, study of immigrant populations moving from an area with a low incidence to one with a high incidence has shown that the risk of developing breast cancer rises with time to that of the native population, supporting the view that non-genetic factors are highly significant. Some of this changing risk may be accounted for byepigenetic factors.

It has long been recognizes that people from lower socioeconomic groups have an increased risk of developing gastric cancer. Specific dietary irritants, such as salts and preservatives, or potential environmental agents, such as nitrates, have been suggested as possible carcinogens. Gastric cancer also shows variations in incidence in different populations, with age-standardized incidence rates almost four times higher in Eastern Asia compared with Western Europe. Migration studies have shown that the risk of gastric cancer for immigrants from high-risk populations does not fall to that of the native low-risk population until two to three generations later. It has been suggested that this could be attributed to exposure to environmental factors at an early critical age, for example, early infection withHelicobacter pylori, causing chronic gastric inflammation and associated with a fivefold to sixfold increased gastric cancer risk.

Family and Twin Studies

The Fate of a Newly Arisen Mutation in a Large Population

We first examine the impact of genetic drift on the evolutionary fate of a newly arisen mutation. Let A symbolize the group of all the old alleles at an autosomal locus, and let a be a newly arisen mutation at this locus that is initially present in only a single individual with the new genotype Aa. We initially regard this individual as a self-compatible, random-mating hermaphrodite (Hardy's assumptions for the Hardy–Weinberg law) with normal meiosis and no subsequent mutations producing new a alleles; that is, the a allele is unique in its mutational origin. The first step in the survival of this new mutant allele is to be passed on to a gamete during meiosis, whose pgf has already been given in Eq. (4.1). The chances for a surviving to the next generation also depend upon how many offspring the initial carrier, Aa, has. Suppose that the initial Aa carrier has n offspring. Then, the pgf for the total number of a alleles this individual passes on to the next generation is:

(4.5)h(z|n)=∏j=1ngj(z)=[g(z)]n

where gj(z) is the pgf for the meiotic event associated with offspring j. Eq. (4.5) reflects the fact that all meioses are independent events with the same pgf, g(z). The problem with Eq. (4.5) is that it assumes that we know n, the number of offspring born to the initial Aa individual that, in this simple model, survive to adulthood in the next generation. At this point we encounter another level of sampling that can contribute to genetic drift at the population level—not all individuals in general will have exactly the same number of surviving offspring even if the environment is constant and every offspring has the same probability of surviving. The random sampling of the number of surviving offspring produced by an individual can also be described by a series of probabilities, say pn, that represent the probability of having n surviving offspring. Eq. (4.5) is the conditional pgf given n, but now we can define the unconditional pgf as

(4.6)h(z)=∑n=0∞pnh(z|n)=∑n=0∞pn[g(z)]n

Note that if we define a new dummy variable t = g(z), then Eq. (4.6) becomes the pgf for the random variable n, the number of surviving offspring produced by an individual. Hence, the pgf h(g(z)) incorporates the effects of sampling meiotic events and sampling the number of surviving offspring on describing the total number of a alleles that survive into the next generation. For example, let us assume that n is from a Poisson distribution, a commonly used distribution for family size in idealized populations, as mentioned in Chapter 3. The pgf for a Poisson distribution is ek(t−1) where k is the mean number of surviving offspring of an Aa individual and t is the dummy variable. In this special case of Eq. (4.6), the pgf for the number of a alleles in the next generation is

(4.7)ek[g(z)−1]=ek[12+12z−1]=ek2[z−1]

To find the probability of survival, it is easier to first find the probability of loss; that is, the probability that there are 0 copies of a in the next generation. Recall that this is found simply by setting the dummy variable to 0 to yield the probability of loss of the a allele in the next generation as e−k/2. If the total population size is approximately stable and the a allele is neutral (that is, it has no effect on the probabilities for the number of offspring), each of the individuals, including Aa, in this idealized population has an average of k = 2 offspring, and e−1 = 0.367879. Note that over a third of all new neutral mutants are lost by the very first generation after mutation just by the sampling processes that contribute to genetic drift. The probability of surviving just a single generation is 1-Probability(loss) = 0.632121.

To find the probability of surviving for just two generations, assume that n copies survived into the first generation. Because mating is at random and if we further assume the population is very large, these copies will almost certainly all be in Aa genotypes as the frequency of a is extremely rare (recall the Hardy–Weinberg law). Under these assumptions, each of the n copies of a that are in Aa individuals will also produce a random number of a copies in the next generation as described by the pgf given in Eq. (4.6); that is h(z). Because there are n carriers of a in the first generation, the total pgf for the second generation given n is [h(z)]n. However, n itself is a random variable described by pgf h(t), and we need to incorporate this fact to get the unconditional pgf for the second generation. Exactly like the derivation of Eq. (4.6), the unconditional pgf for the number of a alleles in the second generation is h(h(z)); that is, the dummy variable for the second generation is the pgf from the first generation. For the Poisson case, the pgf for the second generation is

(4.8)ek2[ek2[z−1]−1]

Setting z = 0 and k = 2, Eq. (4.8) yields the probability of loss by the second generation to be 0.531464, so the probability of surviving for two generations is 0.468536. Thus, by just two generations, more than half of all new mutant alleles are lost by genetic drift. The recursion used to generate Eq. (4.8) can be repeated multiple times to obtain the pgf's of later generations (Schaffer, 1970). For example, the pgf for the third generation is h(h(h(z))). Table 4.1 shows the probabilities of loss of the mutant allele for the first 10 generations in our idealized population. As can be seen, very few mutants survive even just 10 generations of genetic drift.

Table 4.1. The Probabilities of a New Mutant Surviving Over the First Ten Generations After Its Occurrence as a Function of the Average Number of Offspring Produced by Individuals in the Population

Generationk = 2k = 310.6321210.77687020.4685360.68817230.3740820.64379840.3120800.61928350.2680770.60502160.2351510.59648170.2095480.58807780.1890500.58609390.1722550.584860100.1582350.584092

The ultimate probability of survival (ups) can be found by solving the equation h(z) = z for 0 ≤ z ≤ 1, and an approximation to this solution that incorporates the impact of meiosis (Eq. 4.1) is (modified from Schaffer, 1970, which only deals with the haploid case):

(4.9)ups≈k−2k+v

where v is the variance in the number of offspring. For the Poisson case, k = v, as mentioned in Chapter 3. Also, if k = 2 as in our example of a neutral allele in a stable population, ups = 0. This of course, is an approximation, and we will see later that the actual probability of survival in our assumed large population is extremely small in a large population but greater than 0.

Humans are unique among the large-bodied vertebrates in that we have had sustained population growth for at least the last 10,000 years with the beginning of agriculture (Coventry et al., 2010). To consider a growing population, Table 4.1 also presents the survival probabilities for a population in which the average number of surviving offspring per individual is 3. As can be seen, the probability of survival is consistently larger under population growth. Moreover, the approximate ultimate probability of survival is (from Eq. (4.9) with k = v = 3) 0.1667.

Up to now we have assumed that all individuals in the population have the same average number of offspring. However, other than the assumptions that the total population size is large and capable of indefinite growth, the k in our model of offspring number only refers to the average number of offspring by bearers of the new mutant a. Suppose the overall average number of offspring in the growing population were four, then an average size of just three offspring would mean extremely strong natural selection against the Aa individuals bearing the new, mutant allele (a 25% reduction in number of expected offspring in the next generation). As will be shown in Chapter 9, strong selection against a dominant mutant such as a would result in its rapid elimination when genetic drift and population growth are ignored. As Table 4.1 and the ups of 0.1667 show, even a strongly deleterious dominant allele can persist in the human gene pool. A recessive deleterious allele is even more sheltered against the effects of natural selection (Chapter 9), so such recessive deleterious alleles will have an even higher probability of persistence in the human gene pool. Indeed, deep sequencing studies reveal that humans have many more rare variants that appear deleterious over that expected in a constant-sized population (Coventry et al., 2010). Recall also from Chapter 3 the large number of rare variants that individual humans carry that are loss-of-function mutations or otherwise predicted to be deleterious (Gudbjartsson et al., 2015). The accumulation of deleterious mutations in the gene pool is sometimes called the mutational load, and humans have a uniquely high mutational load (Lynch, 2010). The concept of mutational load was first introduced by Muller (1950), who won the Nobel Prize for his work demonstrating that radiation can increase the mutation rate. Muller was concerned about an increase in radiation levels due to nuclear testing and the threat of nuclear war increasing the mutational load in humans, and Lynch was concerned with mutation rates and relaxed selection. However, population growth, and therefore indirectly agriculture, has played a much more important role in increasing the mutational load in humans. Demography and genetic drift are major evolutionary forces that have strongly shaped the unique nature of the human gene pool with its vast excess of rare, deleterious variants.

Modifiers of Recombination and Genetic Drift in Large Populations

Genetic drift acts in all populations, and so the stochastic effects of finite population size can play a role in large populations as well. Under Hill–Robertson interference (discussed above), genetic linkage is seen to increase the amount of genetic drift near a selected locus, thus reducing the effective population size for the locus when either a beneficial mutation arises or in the presence of purifying selection against a deleterious allele. Keightley and Otto (2006) contrasted the probability of fixation for an allele modifying recombination with a neutral allele, and showed that purifying selection against repeated deleterious mutations provided an advantage to modifier alleles, causing them to fix with a higher probability. Surprisingly, this effect increased with increasing population size.

To understand this somewhat counter-intuitive result, we note that recombination frees the focal locus from Hill–Robertson interference, allowing deleterious mutations to be purged by selection. A larger number of polymorphic loci increases the opportunity for Hill–Robertson interference, which increases the advantage seen for recombination. Larger populations (where genetic drift is overall weaker) will maintain greater polymorphism, and thus see on average a greater amount of Hill–Robertson interference, and a larger advantage to recombination. The Keightley–Otto model gives a truly synthetic treatment of the role of negative disequilibrium where both selection and drift determine how selection on a new mutation affects the fate of other loci, and recombination frees loci from these shared fates.

Genetic Drift

Genetic drift is the change in allele frequencies in a population over time due to random sampling events (e.g., differences among individuals in survival or fecundity that are unrelated to their phenotype/genotype). Although the specific genetic consequences of genetic drift during a given demographic bottleneck are unpredictable, the overall effect of drift is to erode genetic diversity.

Effective population size, or Ne, is a measure of how sensitive a population is to genetic drift. Ne is defined as the size of a hypothetical, theoretically ideal population that would experience the same level of inbreeding, loss of heterozygosity, and genetic drift per generation as the real population in question (Kimura and Crow, 1963). Other factors besides the census size of a population will influence the change in allele frequencies over time (e.g., an uneven sex ratio, past fluctuations in population size, nonrandom variation in family size); by excluding these factors, Ne makes it possible to evaluate and compare measurements of drift across species with very different life histories. There are different ways to empirically estimate Ne over both short- and long-term time scales (see review by Hare et al., 2011), but Ne is virtually always smaller, and often much smaller, than the census size of a population. Frankham (1995) reviewed published estimates of Ne/N for wildlife species, and found that Ne averaged only 10–11% of total census size.

In large (unthreatened) populations, it takes a long time to see a major effect of genetic drift on allele frequencies; genetic diversity represents a balance between mutation and natural selection. However, when Nes<<1, where s is the selection coefficient describing the difference in fitness between two alleles, drift can counter selection, and the alleles will behave as if they are neutral (Wright, 1931). Thus through this mechanism, small populations may show greater maladaptation (i.e., mismatch between environment and mean phenotype) than larger ones. By similar logic, mildly deleterious mutations will tend to accumulate in small populations, because selection is ineffective at removing them. This can lead to ‘mutational meltdown’: as deleterious mutations become fixed, they drive down population growth rate (and size), making the population progressively more susceptible to fixation of future mutations (Lynch et al., 1995).

Genetic Drift and the Founder Effect

Genetic drift is the change in frequencies of alleles in a population due to chance. If a population is small then chance could determine whether a neutral allele becomes extinct or increases in frequency to fixation. If a population is very small then such random genetic drift could determine the fate of an allele even in the presence of moderately strong natural selection. In nature, however, it may be unusual for a population to stay small long enough for drift to occur – the population could become extinct, grow, or merge with another population. Tendencies to genetic drift will be opposed by gene flow. Hence if a fungus is abundant and widespread with copious spores capable of long distance dispersal, gene flow is likely to counteract any tendency to genetic drift. There is evidence for this in the cosmopolitan and abundant fungi Neurospora crassa, Puccinia graminis f. sp. tritici, and Schizophyllum commune.

There are, however, ways in which random events could determine the genetic structure of a population and the course of microevolution. One or a few individuals will not cover the genetic diversity in the population; many alleles present in the whole population will be absent from such a small set of individuals. A small set of individuals could occur as the result of a catastrophe almost destroying a population or by the dispersal of one or a few individuals to a new environment. The population resulting from such a founder effect will be genetically different from the one from which it originated. Many fungi live in environments that are highly favourable but transient, and will hence be liable to colonisation from one or a few spores when they arise, and population crashes when they disappear. Founder effects are likely to occur with such fungi and, if the fungi are not highly abundant, may not subsequently be overwhelmed by gene flow.

Australia provides lots of examples of single founder events: Puccinia striiformis – cause of yellow (stripe) rust of wheat – was introduced into Australia in 1979 (p. 263), as a single race from Europe, but mutations have now resulted in new pathotypes which differ from those in Europe. Similarly, Cryphonectria parasitica – cause of chestnut blight (pp. 287–289) – in North America has much less genetic diversity than in Asia, probably reflecting a founder effect. The dry-rot fungus, Serpula lacrymans, originated in northeast Asia, where it has most genetic variations. However, there is very little genetic variation in the founder populations across the globe (Figure 4.11). In some areas the indoor genetic populations are unique (e.g. Japan), representing a single founder event, whereas elsewhere (e.g. Australia), there is slightly more variation representing founder events from Japan and from Europe.

2.4.4 Genetic Drift

Genetic drift (also called random genetic drift) means a change in the gene pool strictly by chance fixation of alleles. The effects of genetic drift can be acute in small populations and for infrequently occurring alleles, which can suddenly increase in frequency in the population or be totally wiped out. The alleles thus fixed by chance (genetic sampling error) may be neutral—that is, they may not confer any survival or reproductive advantage. Therefore, for small populations, genetic drift can result in a significant change in gene frequency in a short period of time.

Genetic drift can be caused by a number of chance phenomena, such as differential number of offspring left by different members of a population so that certain genes increase or decrease in number over generations independent of selection, sudden immigration or emigration of individuals in a population changing gene frequency in the resulting population, or population bottleneck. Of these, population bottleneck can cause a radical change in allele frequencies in a very short time. A population bottleneck occurs when a population suddenly shrinks in size owing to random events, such as sudden death of individuals due to environmental catastrophe, habitat destruction, predation, or hunting. When the small number of surviving individuals gives rise to a new population, there is a radical change in the gene frequency in the resulting population, in which certain genes (including rare alleles) of the original population may radically increase in proportion while others may radically decrease or be wiped out completely, independently of selection. Additionally, the resulting population contains a small fraction of the genetic diversity of the original population. The founder effect is a severe case of population bottleneck and happens when a few individuals migrate out of a population to establish a new subpopulation. Random genetic drift accompanies such founder effect, to severely reduce the genetic variation that exists in the original population. In the new population, the founder effect can rapidly increase the frequency of an allele whose frequency was very low in the original population. If the allele is a disease-related allele, the founder effect can lead to the prevalence of the disease in the new population. An increase in a specific disease in a human population due to the founder effect is seen in the Old Order Amish of eastern Pennsylvania,66 and in the Afrikaner population of South Africa.67

The current Amish population has descended from a small number of German immigrants who settled in the United States during the eighteenth century. The incidence of Ellis–van Creveld syndrome (a form of dwarfism with polydactyly, abnormalities of the nails and teeth, and heart problems) is many times more prevalent in this Amish population than in the American population in general. The origin of this disease can be traced back to one couple, Samuel King and his wife, who came to the area in 1744. The mutated gene that causes the syndrome was passed along from the Kings and their offspring. The Amish population practices endogamy (individuals tend to mate within their own subgroup). Additionally, in this community the gene flow is centrifugal—that is, members may leave the community but outsiders do not join the community—therefore, there has been no introduction of exogenous genes into the Amish gene pool. As a result, the frequency of the disease gene has rapidly increased over generations.

Another example of founder effect comes from the Afrikaner population of South Africa, which is mainly descended from one group of European (mainly Dutch, but also German and French) immigrants that landed there in 1652. The present-day Afrikaner population has a very high prevalence of Huntington’s disease; over 200 affected individuals in more than 50 supposedly unrelated families have been found to be ancestrally related through a common progenitor in the seventeenth century. Thus, the root of the disease can be traced back over 14 generations to a common progenitor who supposedly carried the gene for Huntington’s disease. Huntington’s disease is an autosomal dominant disease caused by triplet (CAG) repeat expansion in the gene (and the mRNA), containing 40 to>100 CAG triplets. The onset and severity of the disease is directly correlated with the number of repeats.

What are the types of genetic drifts?

Which drift type can eventually result in a new species?

What is genetic drift a the formation of new species?

What are three examples of genetic drift?