Module 2 - ACE2/TMPRSS2 Variants and Hardy-Weinberg Equilibrium
Becca DeCamp & Christopher Schmitt
September 21, 2020
Introduction to Hardy-Weinberg Equilibrium (using Human Genome Data)
###Homework for Lab 2: DUE Friday, October 2nd
Readings:
- Chen J. The Hardy-Weinberg Principle and Its Applications in Modern Population Genetics. Frontiers in Biology 5(4): 348-53.
- Benetti E, et al. 2020. ACE2 gene variants may underlie interindividual variability and susceptibility to COVID-19 in the Italian population. European Journal of Human Genetics
- Maekawa et al. 2014. TMPRSS2 Met160Val polymorphism: Significant association with sporadic prostate cancer, but not with latend prostate cancer in Japanses men. Int J Urology 21(12): 1234-1238
- Wigginton JE, Cutler DJ, Abecasis GR. 2005. A note on exact tests of Hardy-Weinberg equilibrium. Am J Hum Genet 76: 887-893.
We have already discussed Hardy-Weinberg Equilibrium in class, but we haven’t really linked it to population genetics yet. Today’s lab will focus on the uses of Hardy-Weinberg Equilibrium in modern population genetics as we use R to look at Hardy-Weinberg Equilibrium in our assigned populations.
Hardy-Weinberg Equilibrium in Population Genetics
In modern population genetics studies, Hardy-Weinberg Equilibrium has a slightly different meaning than what we have talked about so far in class. In class, we learned that a population that is in true Hardy-Weinberg Equilibrium has to fit five conditions:
- the population must be extremely large (no genetic drift)
- it must be isolated from other populations (no gene flow)
- there must be no mutations
- there must be random mating (no assortative mating or sexual selection)
- there must be no natural selection
Obviously, holding a population of any species to this standard is pretty much impossible, so we will use Hardy-Weinberg differently to understand real populations (like humans) in our own population genetics study.
You will often see Hardy-Weinberg Equilibrium being used in modern population genetics to simply measure whether the observed genotype frequencies of a specific SNP or microsatellite are the same as the estimated genotype frequencies. Population geneticists will often use this measure of deviance as a “starting point,” so to speak, for their studies. They use Hardy-Weinberg as their starting point for a few reasons: to check the genotype quality of their data, to get a general sense of the amount of allelic variability and selection happening at specific loci, or, most commonly, as an assumption about the frequency of disease causing genotypes in their population. The assigned Benetti et al. (2020) and Maekawa et al. (2014) papers use Hardy-Weinberg in the latter manner, as a check to make sure there was no selection happening at their loci of interest.
In order to determine how much the observed genotype deviates from the expected genotype, we’ve been using a Chi-Squared Goodness-of-Fit test, the output of which is a P-value that will tell you how much the observed genotype frequency deviates from the expected frequency. However, we learned from the Wigginton reading that the Chi-Squared test has a tendency to give false positives, meaning it will tell us something is significantly different when it’s actually not; this is also called a Type I Error). In their paper, they propose a new mathematical method of calculating the “True” Hardy-Weinberg value, which fixes the problem of false positives. We will not need to get into the specifics of the equation to calculate the “True” Hardy-Weinberg Equilibrium, but you will be calculating your population’s “True” Hardy-Weinberg Equilibrium as well as using a traditional Chi-Squared test.
Now that we have learned a little more about the applications of Hardy-Weinberg Equilibrium in modern population genetics, let’s get to the module activities!
Learning Outcomes
Use the SCC and R coding language to observe and understand population differences in ACE2 variation, as well as vcftools to understand differences in TMPRSS2 variation.
Calculate Hardy-Weinberg Equilibrium for all ACE2 OR TMPRSS2 SNPs in individual populations using a traditional Chi-Squared test.
Perform a check on all SNPs not in Hardy-Weinberg Equilibrium by calculating “True” Hardy-Weinberg with the built-in Shiny App.
Research the consequence types of these SNPs in order to better understand how these SNPs might affect the function of the gene itself, and also how they might affect subsequent phenotypes.
Calculate the “True” Hardy-Weinberg Equilibrium using the Shiny App for EITHER: ACE2 SNP 2074192, an exonic SNP that has known phenotypic consequences (according to Fan et al., 2011), and determine what the Hardy-Weinberg Equilibrium of that SNP says about the population.
OR
TMPRSS2 SNP rs12329760, an exonic SNP that has known phenotypic consequences (according to Maekawa et al., 2014), and determine what the Hardy-Weinberg Equilibrium of that SNP says about the population.
- Use vcftools to calculate “True” Hardy-Weinberg Equilibrium for every SNP in the TMPRSS2 gene region.
IMPORTANT NOTE: Some students will find that their ACE2 gene region will not work for the HWE exercises. This is due to a lack of variation data in their specific population (in other words, all individuals have the same allelic variants at all ACE2 loci). If you find that’s the case (e.g., your HWE table is empty!), then run the following exercises using TMPRSS2.
Step 1: Accessing R Studio and your Data
The first thing we need to do is access R Studio within the SCC environment. This is a little different from just logging in to the SCC. To use R Studio, we actually have to log in to the SCC On Demand interface:
Use this link to log in to SCC On Demand using your Kerberos password.
Once logged in, navigate to the Interactive Apps menu item.
Within that menu item, choose the RStudio Server
When that launches, your screen should look like this:
Click on the Launch button.
It may take a moment, but once your SCC-based R Studio session is ready, you’ll see this on your screen:
Click on Connect to RStudio Server. Once your R Studio interface launches, it will look like this:
The pane on the left is your console, which is where you will enter code to run. The pane on the upper right is your (environment), which is where anything running, or any datasets or items running in R will be shown. Finally, the pane on the lower right is your working directory. It should be your SCC home space when you first log in.
If you want to change your working directory to your anth333 project space, you may nagivate there by clicking on the ... button on the upper right side of the pane and entering your project space name:
Finally, once you’ve navigated to the correct folder, you’ll want to set this as your working directory in R by clicking on the More menu item in the pane, and cliking on Set As Working Directory:
Conveniently enough, you can also access your space SCC via a Terminal window in this R Studio session using the Terminal tab next to your console. Please note that you may need to set your proper working directory in the Terminal, as well, using the cd command (e.g., cd /project/anth333/caschmit).
Step 2: Calculating Hardy-Weinberg Equilibrium Using Chi-Squared
Now that we are in R Studio and have our data in place in our working directory (from Lab 1), we can get started. The first thing we need to do here is install two packages.
In R space, packages are essentially bundles of functions that R users have written to perform analyses that do not come with the base program. In this class we will be using packages that were designed specifically for population genetics analysis. To install these packages, run these two lines of code in the console (this may take a few minutes!):
install.packages("vcfR")
install.packages("pegas")We only need to install these packages once, but we need to load them in to our workspace every time we open the program. To do this, use the following code:
library(vcfR)
library(pegas)Now that we have all our packages loaded, we can get to the analysis! In the “files” tab in the bottom left quadrant, you should see the VCF files you downloaded in your folder (give you put them there properly).
Try these exercises first with your ACE2 VCF file.
For the YRI population, however, there is no variation in the ACE2 gene region. Because of this, the demonstration data here will have to be done with TMPRSS2. If you find that you also have no variation within ACE2 in your population, please also use TMPRSS2 for the remainder of the module.
We will need to load our VCF file into our R workspace much like we did with our packages. Unlike the packages, however, we need to give a name to the file. To make things simple, it would be a good idea to name this file in accordance with the acronym of your population. I will use the YRI population as an example. Use one of the two options below to enter your VCF data into the R space:
#If your file is zipped (e.g., ends in '.vcf.gz') then use this code:
TMPRSS2_YRI <- read.vcfR("TMPRSS2_YRI.vcf.gz", verbose = TRUE)## Scanning file to determine attributes.
## File attributes:
## meta lines: 131
## header_line: 132
## variant count: 2246
## column count: 117
##
Meta line 131 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
## Character matrix gt rows: 2246
## Character matrix gt cols: 117
## skip: 0
## nrows: 2246
## row_num: 0
##
Processed variant 1000
Processed variant 2000
Processed variant: 2246
## All variants processed#If your file is unzipped (e.g., ends in just '.vcf') then use this code:
TMPRSS2_YRI <- read.vcfR("TMPRSS2_YRI.vcf", verbose = TRUE)This will give you a file in your workspace called a vcfR file. Most genetics-based packages have their own way of formatting and working with genetic data, and this is a format R can use. The next line of code will seem a little counterintuitive, because we have to change the format of the file again to work with functions in the pegas package. Doing this will not delete the original file (as long as you name the file something different), so we can use the vcfR file again in later analyses. The type of file we have to convert to for pegas is called a genind format, so we will do that here:
pegas.TMPRSS2_YRI <- vcfR2genind(TMPRSS2_YRI, sep = "[|/]")Finally, now that we have our data in the format that we need, we can do our Hardy-Weinberg tests. This function will test whether all of your population’s SNPs in the coding region of ACE2 or TMPRSS2 are in Hardy-Weinberg Equilibrium. As an example, I will run the funtion with my pegas.TMPRSS2_YRI file. Remember to save your results by assigning them a name:
HWE <- hw.test(pegas.TMPRSS2_YRI, B = 0) #B MUST equal 0 here, changing this value will affect the result!
HWE #Running the name of your results file will print the results## chi^2 df Pr(chi^2 >)
## rs575760704 0.000000e+00 0 1.000000e+00
## rs544440280 2.336398e-03 1 9.614482e-01
## rs564273360 0.000000e+00 0 1.000000e+00
## rs559811756 9.433138e-03 1 9.226276e-01
## rs553400003 0.000000e+00 0 1.000000e+00
## rs560183935 0.000000e+00 0 1.000000e+00
## rs181162134 0.000000e+00 0 1.000000e+00
## rs462471 2.466520e+00 1 1.162952e-01
## rs76000363 1.494810e+00 1 2.214717e-01
## rs143680939 1.494810e+00 1 2.214717e-01
## rs456142 2.466520e+00 1 1.162952e-01
## rs551045243 0.000000e+00 0 1.000000e+00
## rs571050655 0.000000e+00 0 1.000000e+00
## rs539639766 0.000000e+00 0 1.000000e+00
## rs560898303 0.000000e+00 0 1.000000e+00
## rs185078457 0.000000e+00 0 1.000000e+00
## rs112657409 7.881619e-01 1 3.746565e-01
## rs2838038 2.545009e-01 1 6.139235e-01
## rs555769183 0.000000e+00 0 1.000000e+00
## rs462574 3.266541e+00 1 7.070658e-02
## rs456298 2.466520e+00 1 1.162952e-01
## rs564403172 0.000000e+00 0 1.000000e+00
## rs142444476 2.336398e-03 1 9.614482e-01
## rs540046047 0.000000e+00 0 1.000000e+00
## rs560408119 0.000000e+00 0 1.000000e+00
## rs117652812 0.000000e+00 0 1.000000e+00
## rs548592256 0.000000e+00 0 1.000000e+00
## rs140532244 0.000000e+00 0 1.000000e+00
## rs531183955 0.000000e+00 0 1.000000e+00
## rs551106657 0.000000e+00 0 1.000000e+00
## rs17001042 5.439334e-01 1 4.608072e-01
## rs11910678 2.809573e+00 1 9.370329e-02
## rs189546747 0.000000e+00 0 1.000000e+00
## rs567175997 0.000000e+00 0 1.000000e+00
## rs183418223 9.433138e-03 1 9.226276e-01
## rs556079659 0.000000e+00 0 1.000000e+00
## rs569303172 0.000000e+00 0 1.000000e+00
## rs538240292 0.000000e+00 0 1.000000e+00
## rs558272207 0.000000e+00 0 1.000000e+00
## rs578005531 0.000000e+00 0 1.000000e+00
## rs187742976 0.000000e+00 0 1.000000e+00
## rs377348479 0.000000e+00 0 1.000000e+00
## rs573615979 2.142432e-02 1 8.836289e-01
## rs542538936 0.000000e+00 0 1.000000e+00
## rs75317729 9.433138e-03 1 9.226276e-01
## rs77675406 2.545009e-01 1 6.139235e-01
## rs544635611 0.000000e+00 0 1.000000e+00
## rs542458473 0.000000e+00 0 1.000000e+00
## rs12627374 0.000000e+00 0 1.000000e+00
## rs547186777 0.000000e+00 0 1.000000e+00
## rs567103273 0.000000e+00 0 1.000000e+00
## rs372405355 0.000000e+00 0 1.000000e+00
## rs549745562 0.000000e+00 0 1.000000e+00
## rs569561312 0.000000e+00 0 1.000000e+00
## rs538252674 0.000000e+00 0 1.000000e+00
## rs557822084 0.000000e+00 0 1.000000e+00
## rs191697229 2.336398e-03 1 9.614482e-01
## rs150014829 0.000000e+00 0 1.000000e+00
## rs62217525 0.000000e+00 0 1.000000e+00
## rs146564124 0.000000e+00 0 1.000000e+00
## rs542402063 0.000000e+00 0 1.000000e+00
## rs77996454 2.545009e-01 1 6.139235e-01
## rs149695119 5.883901e-02 1 8.083404e-01
## rs141230106 0.000000e+00 0 1.000000e+00
## rs200164183 0.000000e+00 0 1.000000e+00
## rs564691729 0.000000e+00 0 1.000000e+00
## rs527261679 0.000000e+00 0 1.000000e+00
## rs370347248 0.000000e+00 0 1.000000e+00
## rs374666536 0.000000e+00 0 1.000000e+00
## rs142194573 0.000000e+00 0 1.000000e+00
## rs118028230 0.000000e+00 0 1.000000e+00
## rs73905370 2.545009e-01 1 6.139235e-01
## rs569577322 0.000000e+00 0 1.000000e+00
## rs569583276 2.336398e-03 1 9.614482e-01
## rs55896064 2.545009e-01 1 6.139235e-01
## rs571587918 0.000000e+00 0 1.000000e+00
## rs534192391 0.000000e+00 0 1.000000e+00
## rs547523412 0.000000e+00 0 1.000000e+00
## rs57474639 3.737024e-01 1 5.409930e-01
## rs79971314 6.629730e-01 1 4.155123e-01
## rs555911696 9.433138e-03 1 9.226276e-01
## rs73372161 3.419130e-03 1 9.533716e-01
## rs538588854 0.000000e+00 0 1.000000e+00
## rs558791980 0.000000e+00 0 1.000000e+00
## rs571993620 0.000000e+00 0 1.000000e+00
## rs61459778 7.626580e-03 1 9.304090e-01
## rs145024812 9.433138e-03 1 9.226276e-01
## rs574538362 0.000000e+00 0 1.000000e+00
## rs138812485 0.000000e+00 0 1.000000e+00
## rs377591443 0.000000e+00 0 1.000000e+00
## rs531920590 0.000000e+00 0 1.000000e+00
## rs551817968 0.000000e+00 0 1.000000e+00
## rs566012796 0.000000e+00 0 1.000000e+00
## rs527693716 0.000000e+00 0 1.000000e+00
## rs148910500 0.000000e+00 0 1.000000e+00
## rs115975538 1.004612e+00 1 3.161970e-01
## rs536311940 0.000000e+00 0 1.000000e+00
## rs118133613 0.000000e+00 0 1.000000e+00
## rs143595083 9.433138e-03 1 9.226276e-01
## rs148038688 9.433138e-03 1 9.226276e-01
## rs546913068 0.000000e+00 0 1.000000e+00
## rs572265353 0.000000e+00 0 1.000000e+00
## rs534326511 0.000000e+00 0 1.000000e+00
## rs73905371 3.737024e-01 1 5.409930e-01
## rs73372163 1.088502e-02 1 9.169064e-01
## rs543578752 0.000000e+00 0 1.000000e+00
## rs563352095 0.000000e+00 0 1.000000e+00
## rs73372166 1.088502e-02 1 9.169064e-01
## rs113562865 1.887624e-01 1 6.639487e-01
## rs455281 7.305144e-01 1 3.927165e-01
## rs457909 0.000000e+00 0 1.000000e+00
## rs547401659 0.000000e+00 0 1.000000e+00
## rs461194 0.000000e+00 0 1.000000e+00
## rs73372168 1.211511e-01 1 7.277894e-01
## rs188235654 0.000000e+00 0 1.000000e+00
## rs570126729 0.000000e+00 0 1.000000e+00
## rs532216261 0.000000e+00 0 1.000000e+00
## rs552303675 0.000000e+00 0 1.000000e+00
## rs548979551 0.000000e+00 0 1.000000e+00
## rs192955773 9.433138e-03 1 9.226276e-01
## rs554890599 0.000000e+00 0 1.000000e+00
## rs540024706 0.000000e+00 0 1.000000e+00
## rs138765307 0.000000e+00 0 1.000000e+00
## rs556787864 0.000000e+00 0 1.000000e+00
## rs200395836 0.000000e+00 0 1.000000e+00
## rs183984610 0.000000e+00 0 1.000000e+00
## rs142750000 0.000000e+00 0 1.000000e+00
## rs572530227 0.000000e+00 0 1.000000e+00
## rs541351488 0.000000e+00 0 1.000000e+00
## rs374377315 0.000000e+00 0 1.000000e+00
## rs147359020 0.000000e+00 0 1.000000e+00
## rs186826600 0.000000e+00 0 1.000000e+00
## rs563633617 0.000000e+00 0 1.000000e+00
## rs532610867 0.000000e+00 0 1.000000e+00
## rs552105586 0.000000e+00 0 1.000000e+00
## rs565903681 0.000000e+00 0 1.000000e+00
## rs139015396 9.433138e-03 1 9.226276e-01
## rs548552823 0.000000e+00 0 1.000000e+00
## rs149855493 9.433138e-03 1 9.226276e-01
## rs546295824 0.000000e+00 0 1.000000e+00
## rs144823388 9.433138e-03 1 9.226276e-01
## rs570467504 0.000000e+00 0 1.000000e+00
## rs191394761 0.000000e+00 0 1.000000e+00
## rs148561341 0.000000e+00 0 1.000000e+00
## rs572351113 0.000000e+00 0 1.000000e+00
## rs76315847 3.844785e-02 1 8.445466e-01
## rs554852594 2.336398e-03 1 9.614482e-01
## rs185018051 0.000000e+00 0 1.000000e+00
## rs144359794 0.000000e+00 0 1.000000e+00
## rs146605032 0.000000e+00 0 1.000000e+00
## rs743542 5.216761e-04 1 9.817777e-01
## rs190618812 0.000000e+00 0 1.000000e+00
## rs181872728 0.000000e+00 0 1.000000e+00
## rs528337775 0.000000e+00 0 1.000000e+00
## rs548354821 0.000000e+00 0 1.000000e+00
## rs561982326 0.000000e+00 0 1.000000e+00
## rs113438095 0.000000e+00 0 1.000000e+00
## rs148988435 0.000000e+00 0 1.000000e+00
## rs570454392 0.000000e+00 0 1.000000e+00
## rs371012018 0.000000e+00 0 1.000000e+00
## rs185312677 2.336398e-03 1 9.614482e-01
## rs73372170 3.324100e-02 1 8.553307e-01
## rs534892317 0.000000e+00 0 1.000000e+00
## rs462321 3.803969e+00 1 5.113126e-02
## rs462326 2.989617e+00 1 8.380000e-02
## rs141323355 0.000000e+00 0 1.000000e+00
## rs143672898 0.000000e+00 0 1.000000e+00
## rs577217013 0.000000e+00 0 1.000000e+00
## rs545870269 0.000000e+00 0 1.000000e+00
## rs146797606 9.433138e-03 1 9.226276e-01
## rs573343651 0.000000e+00 0 1.000000e+00
## rs542060428 0.000000e+00 0 1.000000e+00
## rs565902164 0.000000e+00 0 1.000000e+00
## rs140428704 0.000000e+00 0 1.000000e+00
## rs530701233 0.000000e+00 0 1.000000e+00
## rs7283324 2.729747e+00 1 9.849486e-02
## rs462448 0.000000e+00 0 1.000000e+00
## rs532950488 0.000000e+00 0 1.000000e+00
## rs546232086 0.000000e+00 0 1.000000e+00
## rs575517837 0.000000e+00 0 1.000000e+00
## rs189793246 0.000000e+00 0 1.000000e+00
## rs548653178 0.000000e+00 0 1.000000e+00
## rs568346732 0.000000e+00 0 1.000000e+00
## rs150382508 9.433138e-03 1 9.226276e-01
## rs115774576 9.433138e-03 1 9.226276e-01
## rs376074355 0.000000e+00 0 1.000000e+00
## rs114549926 9.433138e-03 1 9.226276e-01
## rs181461974 2.336398e-03 1 9.614482e-01
## rs376403654 0.000000e+00 0 1.000000e+00
## rs542368768 0.000000e+00 0 1.000000e+00
## rs138064996 0.000000e+00 0 1.000000e+00
## rs7279603 1.185306e+00 1 2.762784e-01
## rs544036587 0.000000e+00 0 1.000000e+00
## rs67617179 4.089941e-01 1 5.224805e-01
## rs7278739 4.295801e+00 1 3.820660e-02
## rs533050705 0.000000e+00 0 1.000000e+00
## rs539858204 0.000000e+00 0 1.000000e+00
## rs7278627 1.887624e-01 1 6.639487e-01
## rs149109132 9.433138e-03 1 9.226276e-01
## rs548718178 0.000000e+00 0 1.000000e+00
## rs568605816 0.000000e+00 0 1.000000e+00
## rs531148133 0.000000e+00 0 1.000000e+00
## rs185416185 0.000000e+00 0 1.000000e+00
## rs571137450 0.000000e+00 0 1.000000e+00
## rs539777782 0.000000e+00 0 1.000000e+00
## rs146052428 2.142432e-02 1 8.836289e-01
## rs566918037 0.000000e+00 0 1.000000e+00
## rs370663590 0.000000e+00 0 1.000000e+00
## rs2070793 1.185306e+00 1 2.762784e-01
## rs117898838 2.336398e-03 1 9.614482e-01
## rs544417878 0.000000e+00 0 1.000000e+00
## rs2070792 1.185306e+00 1 2.762784e-01
## rs577753477 0.000000e+00 0 1.000000e+00
## rs540165785 0.000000e+00 0 1.000000e+00
## rs2070791 2.336398e-03 1 9.614482e-01
## rs2070790 4.047374e+00 1 4.424011e-02
## rs142769034 0.000000e+00 0 1.000000e+00
## rs147368850 0.000000e+00 0 1.000000e+00
## rs111572592 0.000000e+00 0 1.000000e+00
## rs2070789 2.729747e+00 1 9.849486e-02
## rs147986574 0.000000e+00 0 1.000000e+00
## rs141603473 1.211511e-01 1 7.277894e-01
## rs547356228 0.000000e+00 0 1.000000e+00
## rs542946711 0.000000e+00 0 1.000000e+00
## rs113564116 0.000000e+00 0 1.000000e+00
## rs555771010 0.000000e+00 0 1.000000e+00
## rs2070788 2.325824e+00 1 1.272428e-01
## rs537989379 0.000000e+00 0 1.000000e+00
## rs150907799 0.000000e+00 0 1.000000e+00
## rs139432971 2.336398e-03 1 9.614482e-01
## rs189832305 0.000000e+00 0 1.000000e+00
## rs9974589 1.454682e+00 1 2.277781e-01
## rs2070787 4.047374e+00 1 4.424011e-02
## rs542228195 0.000000e+00 0 1.000000e+00
## rs2070786 4.227761e+00 1 3.976788e-02
## rs575968857 0.000000e+00 0 1.000000e+00
## rs544822322 0.000000e+00 0 1.000000e+00
## rs564981706 0.000000e+00 0 1.000000e+00
## rs143060022 0.000000e+00 0 1.000000e+00
## rs547168890 0.000000e+00 0 1.000000e+00
## rs560788815 0.000000e+00 0 1.000000e+00
## rs369619813 0.000000e+00 0 1.000000e+00
## rs78503214 8.816327e-02 1 7.665253e-01
## rs569186960 0.000000e+00 0 1.000000e+00
## rs147233451 0.000000e+00 0 1.000000e+00
## rs146132415 0.000000e+00 0 1.000000e+00
## rs61735794 0.000000e+00 0 1.000000e+00
## rs140121827 0.000000e+00 0 1.000000e+00
## rs143712818 9.433138e-03 1 9.226276e-01
## rs112132031 3.419130e-03 1 9.533716e-01
## rs573482152 0.000000e+00 0 1.000000e+00
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## rs188585610 0.000000e+00 0 1.000000e+00
## rs575910538 0.000000e+00 0 1.000000e+00
## rs73231921 0.000000e+00 0 1.000000e+00
## rs372655953 0.000000e+00 0 1.000000e+00
## rs575531458 0.000000e+00 0 1.000000e+00
## rs544239350 2.336398e-03 1 9.614482e-01
## rs564489865 0.000000e+00 0 1.000000e+00
## rs571196173 0.000000e+00 0 1.000000e+00
## rs547090650 0.000000e+00 0 1.000000e+00
## rs144815871 5.187727e-01 1 4.713657e-01
## rs369745916 0.000000e+00 0 1.000000e+00
## rs566009716 0.000000e+00 0 1.000000e+00
## rs549150397 0.000000e+00 0 1.000000e+00
## rs148563236 2.142432e-02 1 8.836289e-01
## rs568717616 0.000000e+00 0 1.000000e+00
## rs554701196 0.000000e+00 0 1.000000e+00
## rs551426680 0.000000e+00 0 1.000000e+00
## rs181040610 0.000000e+00 0 1.000000e+00
## rs533521056 0.000000e+00 0 1.000000e+00
## rs553841651 0.000000e+00 0 1.000000e+00
## rs142946068 0.000000e+00 0 1.000000e+00
## rs151083361 0.000000e+00 0 1.000000e+00
## rs139808880 0.000000e+00 0 1.000000e+00
## rs114296721 2.142432e-02 1 8.836289e-01
## rs884166 1.738793e+00 1 1.872918e-01
## rs59484614 2.024095e-01 1 6.527830e-01
## rs564596145 0.000000e+00 0 1.000000e+00
## rs143657279 0.000000e+00 0 1.000000e+00
## rs185165758 0.000000e+00 0 1.000000e+00
## rs555672759 2.336398e-03 1 9.614482e-01
## rs146793180 2.336398e-03 1 9.614482e-01
## rs535215353 0.000000e+00 0 1.000000e+00
## rs562652985 0.000000e+00 0 1.000000e+00
## rs113504625 0.000000e+00 0 1.000000e+00
## rs551672207 0.000000e+00 0 1.000000e+00
## rs188011118 2.336398e-03 1 9.614482e-01
## rs533786198 0.000000e+00 0 1.000000e+00
## rs547075987 0.000000e+00 0 1.000000e+00
## rs117076712 0.000000e+00 0 1.000000e+00
## rs4327307 1.481481e-01 1 7.003114e-01
## rs555567995 0.000000e+00 0 1.000000e+00
## rs569130560 0.000000e+00 0 1.000000e+00
## rs538012773 0.000000e+00 0 1.000000e+00
## rs558434142 0.000000e+00 0 1.000000e+00
## rs182251131 0.000000e+00 0 1.000000e+00
## rs540668517 0.000000e+00 0 1.000000e+00
## rs374623858 0.000000e+00 0 1.000000e+00
## rs145568947 2.336398e-03 1 9.614482e-01
## rs187279637 0.000000e+00 0 1.000000e+00
## rs145134740 0.000000e+00 0 1.000000e+00
## rs1041449 1.075746e-01 1 7.429225e-01
## rs373907767 0.000000e+00 0 1.000000e+00
## rs377062920 0.000000e+00 0 1.000000e+00
## rs149110927 3.844785e-02 1 8.445466e-01
## rs547402085 0.000000e+00 0 1.000000e+00
## rs191773870 0.000000e+00 0 1.000000e+00
## rs373706792 0.000000e+00 0 1.000000e+00
## rs116326720 9.433138e-03 1 9.226276e-01
## rs569370190 0.000000e+00 0 1.000000e+00
## rs538435670 0.000000e+00 0 1.000000e+00
## rs558064990 2.336398e-03 1 9.614482e-01
## rs560247965 0.000000e+00 0 1.000000e+00
## rs572217056 0.000000e+00 0 1.000000e+00
## rs554479236 0.000000e+00 0 1.000000e+00
## rs143146719 3.844785e-02 1 8.445466e-01
## rs543002921 0.000000e+00 0 1.000000e+00
## rs148242543 0.000000e+00 0 1.000000e+00
## rs112245925 0.000000e+00 0 1.000000e+00
## rs181783732 0.000000e+00 0 1.000000e+00
## rs564918415 0.000000e+00 0 1.000000e+00
## rs527609581 0.000000e+00 0 1.000000e+00
## rs71318571 0.000000e+00 0 1.000000e+00
## rs560813074 2.336398e-03 1 9.614482e-01
## rs146950418 0.000000e+00 0 1.000000e+00
## rs137913263 0.000000e+00 0 1.000000e+00
## rs569458711 2.336398e-03 1 9.614482e-01
## rs143380592 2.336398e-03 1 9.614482e-01
## rs73903411 8.065844e-01 1 3.691320e-01
## rs374097887 0.000000e+00 0 1.000000e+00
## rs185923975 0.000000e+00 0 1.000000e+00
## rs77667933 0.000000e+00 0 1.000000e+00
## rs567914620 0.000000e+00 0 1.000000e+00
## rs536694562 0.000000e+00 0 1.000000e+00
## rs556589974 0.000000e+00 0 1.000000e+00
## rs576517652 0.000000e+00 0 1.000000e+00
## rs150507723 0.000000e+00 0 1.000000e+00
## rs558980572 0.000000e+00 0 1.000000e+00
## rs75579202 2.545009e-01 1 6.139235e-01
## rs374915335 0.000000e+00 0 1.000000e+00
## rs541176607 0.000000e+00 0 1.000000e+00
## rs4334565 2.161690e+00 1 1.414890e-01
## rs574387604 0.000000e+00 0 1.000000e+00
## rs572013450 2.336398e-03 1 9.614482e-01
## rs182567491 2.142432e-02 1 8.836289e-01
## rs713399 6.912000e-01 1 4.057565e-01
## rs76728412 1.312079e-02 1 9.088050e-01
## rs190956901 0.000000e+00 0 1.000000e+00
## rs184096964 0.000000e+00 0 1.000000e+00
## rs139423940 0.000000e+00 0 1.000000e+00
## rs193020956 1.211511e-01 1 7.277894e-01
## rs149635596 0.000000e+00 0 1.000000e+00
## rs371526198 0.000000e+00 0 1.000000e+00
## rs713400 0.000000e+00 0 1.000000e+00
## rs185299923 9.433138e-03 1 9.226276e-01You can save a text file version of this table using the following code:
write.table(HWE,file="TMPRSS2_YRI.HWE.txt")Now, you’ll have a saved copy of the results of your HWE analysis of all the variants in ACE2 or TMPRSS2 for your population in your workspace!
If you’ve got the text file saved, you can close R Studio and complete the rest of this exercise using the analysis results in the text file. You can look at it in the SCC using either gedit:
gedit TMPRSS2_YRI.HWE.txt &Or in the terminal space itself using the less command (don’t forget, to leave the less output, press ‘q’):
less TMPRSS2_YRI.HWE.txtNow, on the right of your table is a list of P-values. Look over your P-values to see if any of them are significant (p < 0.05). If any of your P-values are significant, this means that that SNP is not in Hardy-Weinberg Equilibrium!
Here’s some easy R code to filter out JUST those SNPs that are significant:
library(tidyverse)
HWE.sig <-
HWE %>%
as_tibble(HWE) %>%
mutate(snp = rownames(HWE)) %>%
rename(chi2 = 'chi^2', pval = 'Pr(chi^2 >)') %>%
select(snp,chi2,df,pval) %>%
filter(pval <= 0.05)
HWE.sig## # A tibble: 13 x 4
## snp chi2 df pval
## <chr> <dbl> <dbl> <dbl>
## 1 rs7278739 4.30 1 3.82e- 2
## 2 rs2070790 4.05 1 4.42e- 2
## 3 rs2070787 4.05 1 4.42e- 2
## 4 rs2070786 4.23 1 3.98e- 2
## 5 rs28524972 4.54 1 3.32e- 2
## 6 rs144192191 5.17 1 2.30e- 2
## 7 rs141400887 4.63 1 3.14e- 2
## 8 rs9983252 4.12 1 4.23e- 2
## 9 rs10154090 4.32 1 3.77e- 2
## 10 rs9305745 5.06 1 2.45e- 2
## 11 rs114027875 47.3 1 6.01e-12
## 12 rs7282202 8.18 1 4.25e- 3
## 13 rs57197209 11.4 1 7.41e- 4In this case the YRI population has 13 SNPs out of HWE in TMPRSS2. Record the SNP ID numbers that have significant P-values for your population, because they will be important for what we do next.
Hopefully, some of you have found that there are SNPs in your population that are not in Hardy-Weinberg Equilibrium. That’s exciting! But, think back to the Wigginton paper. There is a large Type 1 Error rate for Hardy-Weinberg tests performed using a Chi-Squared test. To account for this, we will test our significant SNPs for Type 1 Error using the Shiny App calculator Becca De Camp created for us. Here’s how we will do that:
Step 3: Finding the “True” Hardy-Weinberg Equilibrium
Here, you will be interacting with a user interface on this webpage that was created to calculate “true” Hardy-Weinberg Equilibrium P-values for one SNP at a time. The input for this UI will be the genotype frequencies from your population (i.e. the number of people from the population with the heterozygous genotype, the number of people with one homozygous genotype, etc.). You will find this information on Ensembl by doing the following:
Make a shortlist of FOUR SNP ID numbers of the SNPs that you found not to be in Hardy-Weinberg Equilibrium. When making this list, check the degrees of freedom, column. This UI will only accept SNPs with only two alleles, or one degree of freedom. Therefore, even though there are SNPs with more than two alleles that are not in Hardy-Weinberg Equilibrium, please ignore them for now. If you do not have four SNPs that meet these criteria, fill in your list with SNPs at random. Ultimately, EVERYONE should be looking at FOUR SNPs each.
Go to the ACE2 or TMPRSS2 gene page on Ensembl and click on the Variant table tab so you get this familiar page:
In the search bar at the top right of the variant table, type in the SNP ID number for one of your SNPs that’s not in Hardy-Weinberg Equilibrium and search it. I will use the ACE2 SNP rs2074192 to demonstrate this.
When you have found your SNP in the table, you should first make note of the consequence type of the SNP. For example, rs2074192 is an intronic variant. How do you think the consequence type will affect the phenotypic outcome of this SNP?
Next, click on the SNP ID to get to the SNP page. From there, click on the Population Genetics tab to get the population genetics table.
Find the Genotype Frequency column in this table:
Find your population in the table. Record the genotype frequency count numbers (heterozygous, homozygous 1 and homozygous 2). These will be your input to the UI below. Using this method, find these genotype frequencies for each SNP you want to test.
NOTE: before you’ve entered any data into the UI, it will show a result error (“Error: missing value where TRUE/FALSE needed”). This just means there’s nothing entered yet. Once you’ve entered your genotype counts, that error message will be replaced by the calculated “True” Hardy-Weinberg Equilibrium P-value for your SNP.
Record your results, and make sure to record the SNP ID of each SNP you are testing. The last thing we’ll do is think about what the calculations you did today mean.
Step 4: What Do Your Results Mean?
Think about the results you produced today in the context of your population. Here are some guiding questions to help you:
If your population had SNPs that were not in Hardy-Weinberg Equilibrium, what kind of consequence did the SNP(s) have? How would that consequence type affect the phenotype?
Does a SNP not being in Hardy-Weinberg Equilibrium mean that evolutionary forces are acting on it? How might evolutionary forces be acting on ACE2 if a SNP is not in Hardy-Weinberg Equilibrium?
PRO-TIP: HWE Using VCF Tools
You can actually run the same analysis we just did using a single line of code in vcftools, thanks to the hardy option.
If you’d like to try, load the vcftools module in the SCC:
module load vcftoolsNow, enter the following code. This is essentially telling vcftools to run the “true” HWE test on each variant site, and to give the output as a file called “TMPRSS2_YRI.hwe”:
vcftools --gzvcf TMPRSS2_YRI.vcf.gz --hardy --out TMPRSS2_YRIOnce it’s run, you can read the output file using the less command:
less TMPRSS2_YRI.hwe
Notice that the output is giving us both more and less information than our analysis in R Studio. Rather than the variant name, we’re given just the variant position (‘POS’) on which chromosome (‘CHR’). We’re also, however, given the observed (‘OBS(HOM1/HET/HOM2)’) and expected (‘E(HOM1/HET/HOM2)’) counts of individuals under “true” HWE with each genotype, along with the “true” HWE Chi-Squared (‘ChiSq_HWE’) and P-values (‘P_HWE’). It’s also giving us P-values regarding whether there is a significant deficit (‘P_HET_DEFICIT’) or excess (‘P_HET_EXCESS’) in heterozygotes, which can tell us a bit more about why the population is out of Hardy-Weinberg equilibrium (we’ll learn more about this in class).
To see only the significant SNPs for this output, we just need to import the file into R:
vcftools.HWE<-read.table("TMPRSS2_YRI.hwe",header=TRUE)
head(vcftools.HWE)## CHR POS OBS.HOM1.HET.HOM2. E.HOM1.HET.HOM2. ChiSq_HWE P_HWE
## 1 21 41464332 108/0/0 108.00/0.00/0.00 NaN 1
## 2 21 41464372 107/1/0 107.00/1.00/0.00 0.002336398 1
## 3 21 41464375 108/0/0 108.00/0.00/0.00 NaN 1
## 4 21 41464414 106/2/0 106.01/1.98/0.01 0.009433138 1
## 5 21 41464462 108/0/0 108.00/0.00/0.00 NaN 1
## 6 21 41464497 108/0/0 108.00/0.00/0.00 NaN 1
## P_HET_DEFICIT P_HET_EXCESS
## 1 1 1.0000000
## 2 1 1.0000000
## 3 1 1.0000000
## 4 1 0.9953488
## 5 1 1.0000000
## 6 1 1.0000000And then filter by P_HWE, which represents the HWE p-value:
library(tidyverse)
vcftools.HWE.sig <-
vcftools.HWE %>%
as_tibble(vcftools.HWE) %>%
select(CHR,POS,ChiSq_HWE,P_HWE) %>%
filter(P_HWE <= 0.05)
vcftools.HWE.sig## # A tibble: 10 x 4
## CHR POS ChiSq_HWE P_HWE
## <int> <int> <dbl> <dbl>
## 1 21 41469311 4.30 0.0429
## 2 21 41470276 4.23 0.0476
## 3 21 41470844 4.54 0.0417
## 4 21 41473914 5.17 0.0397
## 5 21 41475153 4.63 0.0395
## 6 21 41491796 4.32 0.0469
## 7 21 41492147 5.06 0.0285
## 8 21 41500731 47.3 0.0140
## 9 21 41518025 8.18 0.00526
## 10 21 41518039 11.4 0.00107