Lab4 - Simluating Evolution.docx

Jessica Pollack CLUSTER 70B: Evolution of the Cosmos and Life WEEK 4 LABORATORY/DISCUSSION SIMULATING MECHANISMS OF EVOLUTION Objectives: • Simulate genetic drift, population bottlenecks, and founder effects • Demonstrate how changing the population size and starting allele frequencies change the effects of genetic drift • Calculate a Punnett Square for a cross and show the difference between phenotype and genotype. • Design an experiment to test the effects of various parameters on allele frequencies Key Concepts: • Evolution can be simulated in a computer • Genetic drift is a change in gene frequency in a population through random processes • Changes in starting parameters, such as population size, can have large effects on gene frequencies • Bottlenecks and founder effects are changes in gene frequencies due to random sampling Studying evolution ‘in the wild’ can take a long time, because most organisms have long generation times, so seeing the effects of changes in a population can take decades, if not centuries, to see. Working on small organisms, with short generation times, like bacteria or fruit flies, in a lab can alleviate some of this, but keeping them alive in a lab setting poses its own problems. Recently, computing power has gotten to the point that we can simulate evolution in silico , i.e., in a computer environment, and simple models can be run in a web browser. You’re going to play with some of these sorts of simulations in a lab to study of population genetics. Recall that random genetic drift is the accumulation of random changes in allele proportions in populations over generations. It is the effect of sampling error in the sense that an individual’s genotype is essentially a ‘random draw’ from the gene pool of the previous generation. Thus, over time the proportion of an allele can ‘drift’ up or down over time. It is important to note that this is not due to any advantage or disadvantage associated with the allele (selection). Just as statistical error is greater with small sample sizes, genetic drift particularly affects small populations. A ‘bottleneck’ occurs if a naturally large population is temporarily reduced to a few individuals, and ‘founder effects’ are when new populations are founded by a few individuals. The second exercise will quantify two of these effects in a simulated population. In the final exercise, you’ll be designing your own experiment with a simulated population. Part 1: Evolution: Population Genetics: Genetic Drift Go to: http://virtualbiologylab.org/population-genetics/ , scroll to the bottom to “ Model 3 – Random Genetic Drift” and click on ‘ Launch Model ’ This model is an adaptation of the classic experiment conducted by Peter Buri (1956), which documented genetic drift in laboratory populations of Drosophila. In this model, there are two alleles for a gene coding for eye color: the dominant wild type allele (+) codes for red eye color, while the recessive mutant allele ( bw ) codes for brown eye color. In the model, ten vials (populations) of flies are held at a constant population size and the proportions of the bw allele are tracked over generations.

Parts of this lab are modified from Virtual Biology Lab (2010) Run the simulation twice with default settings, and make sure the “ stop when fixed ” button is ON. (You may want to adjust the speed at the top to make the simulation run faster.) Each colored line in the graph on the right, “Allele Proportion”, represents the proportion of the bw allele in a population. For each simulation trials, in the table below take note of: ● How many populations are fixed for the bw allele (that is, those populations show a ‘Prop. bw’ of 1) ● In which populations (i.e., population #1-10) the bw allele became fixed. at the top of the pop-up bubble ● Determine in which generation the first population became fixed (for either bw or non- bw ). This is the “Time to Fix.” on the far right ● Determine the generation where 50% of the populations are fixed. ● Determine the generation where all the populations are fixed. This will be the last generation of your simulation (located below the graph) Repeat the above but change the population size to 20 then 80 (you can change this in a green box toward the bottom of the window). Remember to do this twice for each population size. 1. What was the effect of increasing population size on time to fixation? Explain your results. It took a lot longer to become fixed in a bigger population. This is because the more individuals that you have, it is less likely that any given allele will be lost due to chance. Trial Total # of populations fixed for bw Which populations are fixed for bw ? Generation where the first population is fixed for bw or non-bw Generation where five of the populations are fixed bw or non-bw Generation where all populations are fixed bw or non-bw N=10 1 4 4, 5, 6, 10 8 25 37 2 7 2, 3, 4, 5, 8, 9, 10 13 66 66 N=20 1 5 2, 5, 6, 7, 10 23 129 129 2 6 2, 3, 5, 6, 8, 9 18 75 75 N=80 1 3 1, 7, 8 44 133 292 2 2 1, 2 9 27 70 2. Since the simulation starts with 50% of the bw allele, what is the average number of populations that would become fixed for this allele? Explain your answer. Fifty percent of the population will become fixed. This is because genetic drift is random, by chance red or brown will occur. 2

Parts of this lab are modified from Virtual Biology Lab (2010) 5. Do they stay the same each time you hit reset? No they do not. 6. Do you think this influences the performance of the simulation or the conclusions that can be drawn from the results generated by the simulation? Why or why not? No, I don’t think this has a big enough influence on the performance of the simulation because the change in allele frequency does not change enough to affect the outcome. To start, record the initial mainland allele frequencies on the table below. Make sure “ Stop-at-Fixation” is OFF (unchecked). Change the ‘carrying capacity’ at the top left (labeled as ‘Mainland-K’) to ‘500’. This sets the initial population size, and also the maximum population size. Then create a bottleneck on the population – change the bottleneck size to ‘10’ (below the ‘Mainland-K’ setting at the top left) and click “ bottleneck ”. This takes a random sample of the original population of 10 individuals. Now click “ Go ” TWICE, which will start and immediately stop the simulation so you can record the allele frequencies in the population immediately after the bottleneck (you can do this by hovering over the lines at the left-hand side of the graph). Write the bottleneck allele frequencies on the table. Then click “ Go ” one more time to allow the population to grow until it reaches a steady state. Once the population on the mainland has reached carrying capacity (shown by the number under ‘N’ at the bottom of the simulation screen) for the first time , click “ Go ” again to stop the simulation and record the final allele frequencies. Repeat the simulation (you have to press ‘ Reset ’ between each run) with the following parameters and record all data. Initial Allele Frequencies Bottleneck Allele Frequencies Return to K Allele Frequencies Trial Mainland-K Bottleneck Y B R Y B R Y B R 1 500 10 0.106 0.472 0.422 0.216 0.543 0.241 0.017 0.503 0.48 2 500 10 0.095 0.44 0.465 0.183 0.175 0.642 0.029 0.468 0.503 1 500 20 0.104 0.481 0.415 0 0.707 0.293 0 0.847 0.153 2 500 20 0.092 0.416 0.462 0.087 0.402 0.421 0.099 0.411 0.441 1 500 50 0.099 0.435 0.466 0 0.613 0.387 0 0.465 0.535 2 500 50 0.108 0.473 0.419 0.103 0.545 0.353 0.145 0.719 0.136 1 500 100 0.102 0.496 0.402 0.097 0.432 0.471 0.89 0.451 0.460 2 500 100 0.09 0.459 0.451 0.095 0.379 0.527 0.229 0.321 0.45 7. How many phenotypes are present in the mainland population before and after the bottleneck? The phenotypes are determined by 3 alleles with incomplete dominance. Therefore, there should be 6 phenotypes because of the blending of these alleles. 8. What is the effect of bottlenecks size on the loss of genetic diversity? Bottlenecks affect the loss of genetic diversity because they can lead to bacterial genome evolution, which reduces the size of genetic diversity in a population. 4

Parts of this lab are modified from Virtual Biology Lab (2010) 9. What affects the likelihood that an allele will reach fixation in a population that has undergone a bottleneck? In a population that has undergone a bottleneck, a large population will become smaller and genetic drift’s strength will increase. This can change the random division of alleles which could also potentially lead to the loss of certain alleles and the point at which these alleles become fixed will also be affected. Island Colonization Start with the Mainland-K at 750, and for Island 1, set ‘Isle-1-K’ at 100. Set the Founding N for Island 1 based on the information in the table below. Remember to click “reset” after making any changes to settings. Before running the simulation, make sure to write down the starting mainland allele frequencies. Then click “ colonize 1 ” on Island 1, and click “ Go ” twice to start and stop the simulation so you can take down the initial allele frequencies of the founding population on island 1. Click “ Go ” again. Allow the simulation to run for 100 generations and then click “ Go ” again to stop the simulation. Record the allele frequencies for the island population after 50 generations. Run the simulation again according to complete your data collection. Source Population (Mainland) Allele Frequencies Initial Colonized Island 1 Allele Frequencies Island 1 Allele Frequencies After 50 Generations on the Colonized Island Run Island 1 Founding Pop Size Y B R Y B R Y B R I s l a n d 1 1 25 0.087 0.455 0.458 0.093 0.462 0.445 0 0.593 0.407 2 20 0.109 0.443 0.448 0.101 0.455 0.444 0.021 0.506 0.473 3 15 0.102 0.43 0.468 0.095 0.455 0.45 0 0.446 0.554 4 10 0.086 0.469 0.445 0.099 0.452 0.449 0.253 0.364 0.383 Island Colonization Questions: 10. In your own words, describe the differences/similarities between a bottleneck, and the founder effect. How does the data you collected support your explanation? A bottleneck and the founder effect are both examples of genetic drift where there is a change in allele frequencies when the population size is decreased. The bottleneck effect is when large numbers of the population die, and the founder effect is when a small number of the population moves to a less populated area. The data collected supports my explanation because as the population colonized on the island, the population size and diversity decreased. Part 3: Evolution: Population Genetics: Fishbowl 5

Parts of this lab are modified from Virtual Biology Lab (2010) 12. Did the population stay at HW equilibrium? Explain your results. No, the population did not stay at HW equilibrium. In the first and last trial it was almost in HW equilibrium, and the allele frequencies were almost the same from generation to generation. The second trial; however, was nowhere near as close to equilibrium as the other trials. In this case, the dominant allele declined and the recessive allele increased. Exercise: Design your own Fishbowl Experiment Using your understanding of the mechanisms of evolution and the simulation, design an experiment that violates one of the remaining assumptions above. Describe what variable you would change and your hypothesis . Run three trials for the control (in HW equilibrium) and the experimental variable. You may use your results from question 12 above as your control trials. Present your results in a table or a figure, then briefly explain the results. Experimental (i.e., independent) variable: Assortative mating - 1 Hypothesis: The dominant allele will take over and the recessive allele will be lost as individuals choose mates with certain genotypes. Table and/or Figure of Results: Initial Allele Frequencies Final Allele Frequencies Trial R r R r 1 0.45 0.55 1.02 0 2 0.53 0.47 1.08 0 3 0.58 0.42 1.17 0 Explanation of results: When the fish decided to mate only with fish with certain genotypes, the dominant allele succeeded while the recessive allele declined. This led to a small population with only one type of fish. As the generations increased, the population size decreased. Does your data support or refute your hypothesis? My data does support my hypothesis . 7