BIOL206 - Prac 4 - \'Gen Al Ex\' Student Manual for HWE Simulations PDF

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Prac 4...

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Practical 3 Testing for HWE and examining the underlying assumptions with modern genetic data In this exercise we introduce you to real sets of microsatellite genetic data. As you will see the number of loci and alleles are large, consequently even asking whether a population is in Hardy-Weinberg Equilibrium can be a challenging exercise. Fortunately, some very useful software has been developed that make this and other analyses fairly straight forward. You are provided with two data sets, one generated from tissue/DNA collected from Grey Nurse Shark, the other from Cunningham’s skink. The data consist of microsatellite allele lengths at 5 and 10 loci for the grey nurse shark and Cunningham’s skink respectively. You will analyse these data with software named GenAIEx which was developed by Rod Peakall and Peter Smouse at ANU, Canberra. There are a multitude of softwares available, this one was chosen because of its familiar excel format and advanced applications for spatial genetics. In this exercise you will: 1/ Collect summary statistics of genetic data 2/ Ask whether the sample is in HWE 3/ Test some of the assumptions of HWE 4/ Obtain information on the dispersal behaviours of these animals that would be very difficult (& expensive) to obtain using non-genetic approaches

Instructions: 1/ Open up the Grey Reef Shark data file in excel 2/ Double click on the GenAIEx program, enable this macro and a tab “add-ins” should appear in the menu bar. Within this tab is a GenAlEx menu list. Summary Genetic Data First we will collect some summary statistics of the genetic data. The sort of information that is useful to know before large scale analysis includes the number of alleles present per locus and levels of observed and expected heterozygosity. The following steps should provide this information. Make sure you are on the worksheet named ‘Microsatellite Data’ or ‘D’. Under the GenAIEx menu, click on Frequency and then click OK for the allele frequency parameters. You will then be presented with a window for the co-dominant frequency options – make sure only the Het & Poly per pop option is ticked and press OK. A worksheet will be created with the summary genetic data. Observed heterozygosity (Ho) is your actual level of heterozygosity; Expected heterozygosity (He) is the level of heterozygosity expected under HWE. Q How would you test to see if Ho was significantly different from He?

At this point stop for further tutor discussion.

Do samples significantly deviate from HWE? Open up the worksheet named ‘Microsatellite Data’ or ‘D’. Under the GenAIEx menu, click on HWE and click OK for the HWE parameters. Then on the HWE options window, just click the summary option (and unclick the others). Press OK. A worksheet will be created where a ChiSquare test has been used to evaluate whether the data conform to HWE. Do the data conform to the assumptions of HWE?

Do you think the dispersal of grey reef shark is restricted or random throughout the region we have sampled? (Hint: assumption of random mating with HWE)

With low levels of dispersal across the area that you have sampled, you expect a high proportion of related individuals to be found in fairly close proximity to each other. Related individuals share alleles and consequently genetic data, especially microsatellite data, can provide a good estimate of the level of relatedness between individuals. If we have information on where individuals were sampled, we can test whether levels of relatedness decline with distance (this would be a clue that dispersal is not random across the area that you sampled). Write down your prediction of the relationship between geographic distance and genetic relatedness in grey reef shark?

Stop for tutor discussion.

Examining Dispersal Open the worksheet named ‘Genetic Distance’ or ‘GD’. This is a matrix containing a measure of the genetic relatedness between each pair of individuals. Under the GenAIEx menu, click on the ‘Spatial’ option, then click on single population. Under the data parameters menu, set the number of permutations and bootstraps to 999 each. Also make sure the ‘variable distance classes’ option is checked and the Geographic Distance worksheet is set to ‘Geographic Distance’ or ‘GGD’. The next window provides options for variable distance classes. The distances at which we want to analyse relatedness are as follows (maximum distances) of 0, 100, 500, 1000. Add each of these distances by typing the value and clicking Add Size. The unit of measurement is kilometres for grey nurse shark. Click OK and a graph of relatedness versus geographic distances is produced. The red dotted lines on this graph are the 95% confidence intervals – levels of relatedness above or below this line are significant. If your relatedness values fall within the dotted red lines data are not significantly different from random. Is there any evidence of restricted dispersal in Grey Nurse Shark?

Now we will follow the same steps to analyse the Cunningham’s skink data. So first collect some summary statistics (use exactly the same steps that you used with the grey nurse shark data): Make sure you are on the worksheet named ‘Microsatellite Data’ or ‘D’. Under the GenAIEx menu, click on Frequency and then click OK for the allele frequency parameters. You will then be presented with a window for the co-dominant frequency options – make sure only the Freq per Pop and Het & Poly per pop option is ticked and press OK. Two worksheets will be created with summary genetic data.

Describe the relationship between He and number of alleles

Now, test whether the loci significantly deviate from HWE expectations. Open up the worksheet named ‘Microsatellite Data’ or ‘D’. Under the GenAIEx menu, click on Disequil option, then HWE and click OK for the HWE parameters. Then on the HWE options window, just click the summary option (and unclick the others). Press OK. A worksheet will be created where a ChiSquare test has been used to evaluate whether the data conform to HWE. Are all loci in HWE?

Finally, test whether dispersal is random in the sample of Cunningham’s skink. Use the same approach that was used to characterise dispersal in Grey Nurse Shark. Set the distance classes to 0, 100, 500 and 1000. In this case the distances given are in meters. Open the worksheet named ‘Genetic Distance’ or ‘GD’. This is a matrix containing a measure of the genetic relatedness between each pair of individuals. Under the GenAIEx menu, click on the ‘Spatial’ option, then click on single population. Under the data parameters menu, set the number of permutations and bootstraps to 999 each. Also make sure the ‘variable distance classes’ option is checked and the Geographic Distance worksheet is set to ‘Geographic Distance’ or ‘GGD’. The next window provides options for variable distance classes. Add each of the distances (0, 100, 500, 1000) by typing the value and clicking Add Size. Click OK and a graph of relatedness versus geographic distances is produced. Describe the result and write down your conclusion of dispersal in Cunningham’s skink....