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Unformatted text preview: You also can increase the number horizontal lines in the output window by clicking on options then finer grid. It is possible to zoom in any region of the display window by selecting the region of interest with the left mouse button (drag the selection rectangle around the region you want to zoom in). Right–clicking anywhere on the graph will reset the zoom to 100%. Ask you TA for more information about the output window if necessary. 79 Lab4 ‐ Microevolution II‐ Genetic drift: In the Model menu, select Mendelian Genetics then Genetic Drift. The input window will appear. Make sure the Monte Carlo tab is selected. By default, you can enter values for four parameters: population size (N), initial frequency (p), number of loci and number of generations. Simulation 1‐1: First, let’s simulate a population of 250 individuals and observe what will happen to an allele which frequency is initially 0.5. In the input window enter these parameters: N=250, p=0.5 and number of loci=6. The number of loci set to 6 allows us to observe 6 simulated populations at a time, since all loci have the same parameters. Set the Runtime to 300 generations. Click on “View” at the top of the input window to open the output window. A new window appears, showing a graphic representation of the variation in allele frequency over time (expressed in generation number). You will notice that a certain number of coloured broken lines, each representing a locus (i.e. a population in this case) may have reached the value 0 or 1. This phenomenon is called allele fixation. If an allele frequency reaches 0, the allele is lost in the population. On the other hand, if the frequency reaches 1 it is said to be fixed. Answer Questions 1‐2 Run the simulation again (in the same conditions) by pressing the “Iterate” button in the output window. Answer Question 3‐6 Simulation 1‐2 Now let’s go back to the input window and reduce the population size to N=100 (don’t change the other parameters). Run the simulation. Answer Questions 7‐8 To test your hypothesis, run the simulation 5 times with 6 loci each time and write down the number of alleles that have been either lost or fixed after 100 generations with the following population size: N=25, 75 and 150. You will use a quick statistical test to help you to compare your results for each population size: the 95% confidence interval. The 95% interval tells us that we can be 95% confident that the average is located between its lower and upper limits. Thus if the 95% intervals limits of two averages don’t overlap, it is a good 80 Lab4 ‐ Microevolution indication the averages are actually different. Use your calculator and/or Excel to calculate the 95% CI: 1‐ Calculate the average number of fixed allele ( m ). 2‐ Calculate the standard deviation (SD) of the mean by choosing the STDEV function in excel (your TA will demonstrate how to at the beginning of the session). 3‐ Calculate the standard error (SE) using this formula: SD
where SD represents the standard deviation and n the SE sample size (number of measurements). n
4‐ The 95% confidence interval is centered on the mean value and has two limits: Upper limit L1= m + 2 x SE Lower limit L2= m – 2 x SE Enter your results in table 5 of the questionnaire. Answer Question 9. Simulation 1‐3 Set the population size to N=10 and run the simulation for 50 generations. Try different values of p (initial frequency) and run the simulation several times for each value tested. Count the number of fixed alleles, and the number of lost alleles. Answer Question 10. III‐ Drift and selection In previous simulations, we observed how genetic drift can affect a population by changing allele frequencies. We will see now what is happening if we introduce a new parameter in our simulation: selection. Selection is controlled in the simulation by assigning fitness values to the different genotypes. In the real world, both selection and genetic drift act simultaneously. The goal of this exercise is to observe the effect of selection on allele frequency and the combined effect of both genetic drift and selection. Close the previous window(s) and now select Model>Mendelian Genetics>Drift and Selection. Simulation 2‐1 In the input window, enter the following parameters: Set the fitness values to wAA=0.8, wAa=1 , waa=0.8, population size to N=500. Start with an initial frequency p=0.5 and run the simulation for 100 generations. Answer Question 11 81 Lab4 ‐ Microevolution Simulation 2‐2 Change the population size to N=250, then to N=50 Answer question 12. Simulation 2‐3 Now let’s give a selective advantage to one of the genotypes and set fitness values to wAA=1, wAa=1 , waa=0.9 in a population of 200 individuals and an initial frequency p=0.1. Answer Question 13 and 14 Simulation 4 Change the population size to N=25 and run the simulation several times (at least 20 times). Answer Question 15 and 16 This exercise (part II) was modified from a laboratory proposed by Dr J.Brown at Grinnel College in 1998. Don’t forget to hand in your questionnaire to your TA before you leave the class 82...
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This note was uploaded on 02/04/2014 for the course BIO 1130 taught by Professor Fenwick during the Fall '08 term at University of Ottawa.
 Fall '08
 FENWICK

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