Chapter 16.2 Lecture Notes PDF

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Dr. Lambrecht...

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 Biology Learning Objectives: ● Explain how the Hardy-Weinberg equilibrium works and how it relates to information in populations. ● Evaluate the application of Hardy-Weinberg equilibrium equation to information and evolution. ● Understand how application of Hardy-Weinberg equation can be used to determine if a population is evolving. Context: Populations of sexually reproducing organisms contain genetic information that can be used to determine whether evolution has occurred. Major Themes: Heritable information provides for continuity of life; imperfect information transfer produces variation. Bottom Line: The Hardy-Weinberg theorem can be used to determine whether a population is in or out of Hardy-Weinberg equilibrium. _________________________________________________________________________________________ __________________________________________________________________________________________

Chapter 16.2: Using Population Genetics Info to Predict Evolution ➢ Mendel’s two laws of inheritance ○ The law of segregation, which states that paired chromosomes move to opposite nuclei during formation of gametes, and the law of independent assortment, which states that non-homologous chromosomes migrate independently of each other. ➢ In Section 16.1, we used the law of independent assortment to describe how non-homologous chromosomes migrate without regard to each other during meiosis. ○ This then affects the variation and genetic information in a population of organisms ➢ In this section, you will examine the information content in the genes of a population to determine whether the population is evolving at a genetic locus. ➢ The MN blood group in humans is determined by two alleles at one locus on chromosome 4. ○ Expression of the gene leads to production of a glycoprotein that presents on the surface of red blood cells. ■ These glycoproteins are used by the immune system to distinguish self from non-self ➢ MM individuals present one glycoprotein, NN present another, and MN individuals present both, so a simple blood test can determine phenotype and genotype simultaneously. ➢ A team of scientists led by Ian Fontanilla collected data on the MN locus in several populations of humans in the Philippines. ○ The populations that they selected were distributed along a north-south gradient and across different islands ○ They collected blood from over 500 individuals, each of which was asked to fill out a questionnaire to assess information on place of birth, ethnic identity, and migration of individuals from one region to another.

The observed genotype frequencies are depicted in Figure 16.9 by the bars labeled MM, MN and NN. The populations in the Figure 16.9 are arranged from northernmost to southernmost, left to right.

➢ You can use the genotype frequencies to determine allele frequencies in a population. ○ To find the frequency of the M allele in the Isabela population, use the fact that each of the MM individuals has two M alleles and each of the heterozygotes has one. ○ In Figure 16.9, the frequency of the MM genotype is 0.73 and the frequency of the MN genotype is 0.12. ○ Suppose there are 100 individuals in the population. ■ Then there are 100 x 0.73 = 73 MM individuals and 100 x 0.12 = 12 MN individuals, for a total of (73 x 2) + (12 x 1) = 158 M alleles. ■ Because there are 100 x 2 alleles, the frequency of M alleles is 158/200 = 0.79. ● You can find the frequency of N in a similar way, counting (15 x 2) + (12 x 1) = 42 N alleles out of 200, for a frequency of 0.21. ➢ A different hypothetical population size leads to the same allele frequencies because the population size always cancels out in the allele frequency calculation. ○ Therefore, you can use the MM genotype frequency, denoted fMM, and the MN genotype frequency, fMN, rather than the number of MM and MN individuals, to calculate allele frequencies. ○ The frequency of M alleles (denoted p ) is fMM  + fMN  /2 = 0.73 + 0.12/2 = 0.79. ○ Similarly, the frequency of N alleles (denoted q ) is fNN + fMN/2 = 0.15 + 0.12/2 = 0.21. ■ The sum of allele frequencies, p  + q, is always 1, which is a good way to check your calculations. ○ The allele frequencies for each of the eight populations are given in Figure 16.10. ➢ To see if the populations were undergoing evolution at the MN locus, the investigators determined whether the genotype frequencies had been staying relatively constant, a concept called Hardy-Weinberg equilibrium.

○ To understand the Hardy-Weinberg theorem and how it can be applied, you need to use the genetics and basic probability rules that you learned in Chapter 3.

➢ Table 16.2 shows all the possible pairings that can occur in a large population, and the percentage of offspring from each pairing that are predicted to be a given genotype. ○ Note that several of the pairings are the same, representing males of one genotype mating with females of another as well as the reverse pairing.

➢ Table 16.3 organizes this same information by genotype, and lists all the possible pairings that can produce that genotype and with what probability. ○ Bio-Math Exploration 16.2 explains the calculations in columns 3 to 6 of Table 16.3, and derives a simpler formula for the predicted percentages of each offspring genotype. ■ If the predicted proportion of each offspring genotype is the same as the proportion of each parental genotype, then the population is said to be in Hardy-Weinberg equilibrium.

➢ If a population is not in Hardy-Weinberg equilibrium, as in Isabela, the conditions of the equilibrium are not being met and evolution is occurring. ○ We can use the information in the actual genotype and allele frequencies to determine whether a population is in Hardy-Weinberg equilibrium. ○ If it is not, we might be able to speculate on why not. ○ For instance, in a plant population with fewer heterozygotes than expected, we might predict that selfing of flowers is occurring, or that heterozygotes are selected against for some reason. ■ In other words, we can use information to predict evolutionary processes, integrating two of our Big Ideas. ➢ You should keep in mind that once the conditions are met (i.e., evolutionary mechanisms are no longer acting on the population), the population eventually comes to equilibrium, but that any population that is not currently in equilibrium either had or has some mechanism acting on it. ○ These mechanisms include natural selection, genetic drift due to small population size, gene flow, mutation, or possibly non-random mating. ○ Initial frequencies may change but converge on a stable equilibrium that does not change over time when the assumptions of no selection, mutation or gene flow, large population size, and random mating hold.

➢ Figure 16.11 shows the Hardy-Weinberg equilibrium genotype frequencies for the MN genetic locus in the eight populations in the Philippines. ○ For each population in Figure 16.11, the observed genotype frequencies from Figure 16.9 and the predicted genotype frequencies are represented by two shades of the same color. ■ Within a population, if the bars of a similar color are the same height (as in Palawan), then the population is judged to be in Hardy-Weinberg equilibrium.

➢ Several populations appear to be in Hardy-Weinberg equilibrium, including Manila, which is a large city with much immigration. ○ Even if the observed and equilibrial genotype frequencies are not exactly equal (as is true even for Manila, Pangasinan, and Davao del Sur), keep in mind that there are always sampling errors and random events, such as genetic drift, that can affect allele and genotype frequencies. ○ Theoretical Hardy-Weinberg equilibrium conditions include an infinitely large population, which we will never have in reality. ■ Small errors will lead to slight differences between observed and Hardy-Weinberg equilibrium genotype frequencies. ○ You may have correctly concluded that four of the eight populations were in or very close to Hardy-Weinberg equilibrium and no evolution was occurring. ➢ There appears to be no connection between north-south latitude and whether a population is in or out of equilibrium. ○ However, isolated populations and/or populations with very low migration are less likely to be in equilibrium. ■ For instance, Butuan had the lowest rate of migration and a large population of indigenous people that have not integrated socially with people of other regions.

○ Isabela is also composed of a high proportion of a particular ethnic group and the high migration found for that city turned out to be migrations from nearby towns all with similar ethnicity. ■ Those two towns were not close together, yet had similar allele frequencies of M and N alleles, frequencies that were much different from all the other towns and cities. ● These two towns also appeared to not be in Hardy-Weinberg equilibrium, and isolation of small population can cause that through the process of genetic drift. ■ In addition, non-random mating, in this case inbreeding, may contribute to lack of equilibrium. ➢ Two other towns also appeared to have genotype frequencies that were not in Hardy-Weinberg equilibrium ○ One of them, Camarines Sur, had the highest migration rate. ○ High migration and consequent mating of migrants with the local population can cause departures from Hardy-Weinberg equilibrium through the process of gene flow. ■ For instance, a high proportion of MM individuals coming into the population could alter the observed ratio of MM and that predicted under Hardy-Weinberg for the measured allele frequencies. ○ Cebu also appeared to be out of Hardy-Weinberg equilibrium, perhaps due to isolation on an island. ○ Palawan is also isolated on an island, but had a migration rate twice that of Cebu, and here it appears that gene flow is perhaps counteracting the effect of genetic drift. ➢ You have learned how scientists can use the information in observed genotype frequencies to determine allele frequencies and predicted Hardy-Weinberg genotype frequencies. ○ Although statistical tests exist to determine the probability that a population is in or out of Hardy-Weinberg equilibrium, it is enough for us here to simply compare observed and predicted frequencies. ○ The point is that we can use the information content in genetic loci, as well as other information about populations, such as isolation and migration, to speculate on evolutionary mechanisms that may or may not be acting on such populations....