You only need to write lines of code for each question. When answering questions that ask you to identify or interpret something, the length of your response doesn’t matter. For example, if the answer is just ‘yes,’ ‘no,’ or a number, you can just give that answer without adding anything else.
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We will go through comparable code and concepts in the live learning session. If you run into trouble, start by using the help help() function in R, to get information about the datasets and function in question. The internet is also a great resource when coding (though note that no outside searches are required by the assignment!). If you do incorporate code from the internet, please cite the source within your code (providing a URL is sufficient).
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Please bring questions that you cannot work out on your own to office hours, work periods or share with your peers on Slack. We will work with you through the issue.
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You will need to install PLINK and run the analyses. Please follow the OS-specific setup guide in SETUP.md. PLINK is a free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner.
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Question 1: Data inspection
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Before fitting any models, it is essential to understand the data. Use R or bash code to answer the following questions about the gwa.qc.A1.fam, gwa.qc.A1.bim, and gwa.qc.A1.bed files, available at the following Google Drive link: https://drive.google.com/drive/folders/11meVqGCY5yAyI1fh-fAlMEXQt0VmRGuz?usp=drive_link. Please download all three files from this link and place them in 02_activities/data/.
The variable type of the response variable is continuous, as indicated by the values in the sixth column of the .fam file, which represent the phenotype values for each sample.
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Read the .bim file. How many SNPs does the dataset contain?
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wc-l ./02_activities/data/gwa.qc.A1.bim
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101083 ./02_activities/data/gwa.qc.A1.bim
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We have 101083 SNPs in the dataset.
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Question 2: Allele Frequency Estimation
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Load the genotype matrix for SNPs rs1861, rs3813199, rs3128342, and rs11804831 using additive coding. What are the allele frequencies (AFs) for these four SNPs?
#CHROM ID REF ALT PROVISIONAL_REF? ALT_FREQS OBS_CT
+ <int> <char> <char> <char> <char> <num> <int>
+1: 1 rs3813199 G A Y 0.0569126 7942
+2: 1 rs11804831 T C Y 0.1543410 7924
+3: 1 rs3128342 C A Y 0.3051210 7928
+4: 1 rs1861 C A Y 0.0539859 7928
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I am not fully sure about the question, but from the table we can see that the alternative allele frequencies (AFs) for the four SNPs are as follows: rs3813199: AF = 0.0569 rs11804831: AF = 0.1543 rs3128342: AF = 0.3051 rs1861: AF = 0.0540 If you are asking for the reference allele frequencies, you can calculate them as 1 - AF.
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What are the minor allele frequencies (MAFs) for these four SNPs?
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As we can see the alternative allele frequencies (AFs) for the four SNPs are all less than 0.5, so the minor allele frequencies (MAFs) for these four SNPs are the same as their AFs: rs3813199: AF = 0.0569 rs11804831: AF = 0.1543 rs3128342: AF = 0.3051 rs1861: AF = 0.0540
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Question 3: Hardy–Weinberg Equilibrium (HWE) Test
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Conduct the Hardy–Weinberg Equilibrium (HWE) test for all SNPs in the .bim file. Then, load the file containing the HWE p-value results and display the first few rows of the resulting data frame.
Warning: Removed 36 rows containing missing values or values outside the scale range
+(`geom_point()`).
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Which model fits better? Justify your answer.
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The additive model fits better than the recessive model. This is because the additive model has a higher adjusted R-squared value (0.08901) compared to the recessive model (0.08559 ), indicating that the additive model explains more variance in the phenotype. ### Criteria
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Criteria
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Complete
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Incomplete
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Data Inspection
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Correct sample/SNP counts and variable type identified.
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Missing or incorrect counts or variable type.
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Allele Frequency Estimation
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Correct allele and minor allele frequencies computed.
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Frequencies missing or wrong.
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Hardy–Weinberg Equilibrium Test
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Correct PLINK command and p-value extraction in R.
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PLINK command or extraction incorrect/missing.
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Genetic Association Test
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Correct regressions, plots, coding, and interpretation.
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Regression, plots, or interpretation missing/incomplete.
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Submission Information
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📌 Please review our Assignment Submission Guide for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.
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Note:
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If you like, you may collaborate with others in the cohort. If you choose to do so, please indicate with whom you have worked with in your pull request by tagging their GitHub username. Separate submissions are required.
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Submission Parameters
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Submission Due Date: 11:59 PM – 16/03/2026
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Branch name for your repo should be: assignment-1
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What to submit for this assignment:
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Populate this Quarto document (assignment_1.qmd).
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Render the document with Quarto: quarto render assignment_1.qmd.
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Submit both assignment_1.qmd and the rendered HTML file assignment_1.html in your pull request.
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What the pull request link should look like for this assignment: https://github.com/<your_github_username>/gen_data/pull/<pr_id>
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Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support team review your submission easily.
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Checklist:
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Created a branch with the correct naming convention.
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Ensured that the repository is public.
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Reviewed the PR description guidelines and adhered to them.
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Verified that the link is accessible in a private browser window.
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Confirmed that both assignment_1.qmd and assignment_1.html are included in the pull request.
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If you encounter any difficulties or have questions, please don’t hesitate to reach out to our team via our Slack help channel. Our technical facilitators and learning support team are here to help you navigate any challenges.
