Contingency Chi-Square Calculations & Nematode Report – Science & Math’s Assignment Help

Assignment Task


You will be given information on how to approach writing the nematode report
You will learn how to do contingency chi-square calculations

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Nematode Report Instructions
The report that you are to write for this experiment  (excluding references, tables, figures) and is to be written individually.  Please ensure that you are familiar with the definition of and the penalties for plagiarism. Collaboration on report writing will be deemed as plagiarism.

1. Title that is descriptive without being overly long 
2. Introduction: 

Must address:
Effective population size (Ne)
a. what is it?
b. what are the consequences, in terms of genetic drift, of reductions in Ne for
i. genetic diversity (loss/fixation of alleles) within populations
ii. increase in divergence between populations

c. what are the other major determinants of genetic diversity? In particular, what are the effects of population bottlenecks and allele starting frequencies?

Nematodes (Caenorhabditis elegans)
a. what are they… very brief statement about life history
b. why are they useful for this experiment (things like ease of culture, timing of development etc.)

The final part of the introduction should be a clear and concise statement of the aim(s) of the experiment in your own words AND a brief hypothesis (e.g. That variation in population size and/or genotype starting frequency will influence direction and magnitude of genetic change in populations). Simply quoting the manual or saying “This experiment aims to illustrate evolution” will not suffice.

3. Methods section. 
All that is required is “Refer to GEN2EGE practical notes”. 

4. Results section (see LMS for actual experimental results):

The Results section must contain the following elements:

  • A statistical analysis of the expected and observed ratios of each phenotype for each of the populations using a X 2 (chi-square) test of significance for each population 
  • Note that for every statistical test you use, you should record ALL of 
  1. (a) the null hypothesis (may not be the same for every test)
  2. (b) the test used, 
  3. (c) the test statistic X2 
  4. (d) degrees of freedom 
  5. (e) p value estimated from the value of X2 and
  6. (f) is the null hypothesis accepted or rejected?

Simulation data that explore the impact of population size (one of your worm experiment variables), and genotype ratios at Generation 0 (the start of the experiment: either 50:50 or 90:10) (also one of your worm experiment variables) under a range of fitness differences (is selection) (no difference i.e. A1A1 = 1 A1A2 = 1 A2A2 = 1), small difference (for example A1A1 = 0.9 A1A2 = 1 A2A2 = 1) and large difference (for example A1A1 = 0.2 A1A2 = 1 A2A2 = 1) for large or small population size and number of generations

  • are your results comparable to drift alone or do you need to look at selection simulations as well? 
  • If you need to look at selection, compare strengths of selection (remembering that 1 is the fittest a genotype can be, and 0 means that genotype will not survive at all)
  • You can also use the simulated data from weeks 2 and/or 3 for comparison – how long on average did it take for an allele to be lost from a population under the different experimental conditions? On average, was the loss of an allele the predominant outcome or were the ratios of loss/fixation about even? 
  • o How do your data compare?

The keys to a good Results section are to be selective about the data you display (do not simply dump all the simulations into your Results), and to be concise in your description of the most important features of the data. Note however that you need to have two elements in your Results section: tables and/or figures that illustrate the data and a verbal description of the main features of those data that refers explicitly to the relevant table and/or figure that displays those data. If you are unsure, check the Results sections of some the papers covered in the oral presentations.

• Do not confuse the statistical null hypothesis that is tested using the X 2 statistic (the statistical expectation) and the population genetic hypotheses that you may make (eg that, according to population genetic theory, selection is weak in small populations and may be overwhelmed by genetic drift in those populations, or that population bottlenecks promote drift and the loss of rare alleles). Note also that it is the observed data that are to be described and analysed statistically while simulated data provides illustration of the theoretical framework that you will use in your Discussion.

5. Discussion section that addresses the following:

What is the evidence that drift (or selection) has occurred?

Which variable (starting allele frequency and/or population size) seems to be more important in determining the extent of drift? (think about the results from the simulation exercises and about sampling theory)?

Is the drift strong or weak? And is it the same in all 4 populations?

  • if the observed experimental data differ significantly (in the statistical sense) from the expected values that you calculated using the initial allele frequency, is the deviation related to the population size and the severity of the bottleneck? For example, consider whether the “3 founder” worm populations deviated more, or less, from the statistical null hypothesis than the “30 founder” worm populations.
  • Think about sampling theory and population size. A simple way of looking at this is to ask how likely it is that the minor allele in the 1: 9 genotype ratio 3 and 30 worm replicate populations disappears completely, then comparing this to your expectation of “fixation” frequency in the number of 1:1 genotype ratio populations. 

Was there any evidence of selection occurring during the experiment?  If so, was it obvious in all populations? Only in the 30 worm populations? Sensitive to genotype starting frequency?

The last part of the Discussion must bring it back to the Aims and the population genetic hypothesis (NOT the statistical hypothesis) stated in the Introduction. Were your data consistent with the hypothesis? If so, how? If not, what alternative hypothesis could you propose that is consistent with the data? And, to really impress, you could suggest what the next experiment(s) might be and why.

6. References, listed alphabetically by first author’s last name 

7. Supplementary data – raw data tables if you need to refer to them directly, and additional simulation files to which you refer. Do not simply dump everything you think might be relevant into a supplement. Be selective.

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