Session 01 - Exercises

Last updated: 2022-07-25

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Before you begin:

Make sure that R is installed on your computer
For this lab, we will use a few R libraries:

require(data.table)
require(dplyr)
require(tidyr)
require(ggplot2)

The R template to do the exercises is here.

Note: if on the online server, set your working directory to your home directory using in R

setwd("home/<username>/")

The data files are in the folder /data/SISG2022M15/data/.

Case-Control Association Testing

Introduction

We will be using the LHON dataset covered in the lecture notes for this portion of the exercises. The LHON dataset is from a case-control study and includes both phenotype and genotype data for a candidate gene.

Let’s first load the LHON data file into the R session. You can read the file directly from the web (if you are connected to the web) using the following command:

LHON.df <- fread("https://raw.githubusercontent.com/joellembatchou/SISG2022_Association_Mapping/master/data/LHON.txt", header=TRUE)

Alternatively, you can save the file to your computer and read it into R from the directory where the file is located:

LHON.df <- fread("LHON.txt", header=TRUE)

Helpful suggestions for R

There are many ways to obtain summary information for a dataset. Here are some short examples:

Get information on number of rows/columns as well as variable types

df %>% str

Get counts for a specific variable in the table

df %>% count(Variable1)
# cross tabulation for two variables
df %>% group_by(Variable1) %>% count(Variable2)

Alternatively you could have run

df %>% select(Variable1) %>% table
# cross tabulation for two variables
df %>% select(Variable1, Variable2) %>% table

Functions like as.numeric() and factor() will be useful to convert between numeric and categorical variables.
For any R function you don’t know the input syntax, you can get that information using ?<function_name>

Exercises

Here are some things to look at:

Examine the variables in the dataset:

How many observations?
How many cases/controls?
What is the distribution of the genotypes across cases/controls?
What about for allele types?

Perform a logistic regression analysis for this data with CC as the reference genotype using the glm() function. (Hint: make sure to convert the phenotype to a binary 0/1 variable and specify family = binomial(link = "logit") in the glm call)
Obtain odds ratios and confidence intervals for the CT and TT genotypes relative to the CC reference genotype. Interpret.
Is there evidence of differences in odds of being a case for the CT and TT genotypes (compared to CC)?

Extra: 5. Perform the logistic regression analysis with the additive genotype coding. Obtain odds ratios and confidence intervals. Is there evidence of an association? How does it compare with the 2-parameter model?

Association Testing with Quantitative Traits

Introduction

We will be using the Blood Pressure dataset for this portion of the exercises. This dataset contains diastolic and systolic blood pressure measurements for 1000 individuals, and genotype data at 11 SNPs in a candidate gene for blood pressure. Covariates such as gender (sex) and body mass index (bmi) are included as well.

Let’s first load the file into R. You can read the file directly from the web (if you are connected to the web) using the following command:

BP.df <- fread("https://raw.githubusercontent.com/joellembatchou/SISG2022_Association_Mapping/master/data/bpdata.csv", header=TRUE)

Alternatively, you can save the file to your computer and read it into R from the directory where the file is located:

BP.df <- fread("bpdata.csv", header=TRUE)

Exercises

Here are some things to try:

Perform a linear regression of systolic blood pressure (sbp) on SNP3 using the lm() function. Compare the estimates, confidence intervals and p-values you get using:

additive (linear) model
dominant model
recessive model
2 parameter model

(Hint: for each case, first add a new column to the data frame, containing the ‘predictor’ variable you need. Then do the regression using lm())

Provide a plot illustrating the relationship between sbp and the three genotypes at SNP3.

For question 3 and 4 below, R also has a ‘formula’ syntax, frequently used when specifying regression models with many predictors. To regress an outcome y on several covariates, the syntax is:

outcome ~ covariate1 + covariate2 + covariate3

Now redo the linear regression analysis of sbp from question 1 for the additive model, but this time adjust for sex and bmi. Do the results change?
What proportion of the heritability of sbp is explained by all 11 SNPs combined? (contrast categorical coding vs additive coding for the genotypes)

sessionInfo()

R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.0

loaded via a namespace (and not attached):
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 [9] getPass_0.2-2    digest_0.6.29    jsonlite_1.8.0   evaluate_0.15   
[13] tibble_3.1.7     lifecycle_1.0.1  pkgconfig_2.0.3  rlang_1.0.3     
[17] cli_3.3.0        rstudioapi_0.13  yaml_2.3.5       xfun_0.31       
[21] fastmap_1.1.0    httr_1.4.3       stringr_1.4.0    knitr_1.39      
[25] sass_0.4.1       fs_1.5.2         vctrs_0.4.1      rprojroot_2.0.3 
[29] glue_1.6.2       R6_2.5.1         processx_3.7.0   fansi_1.0.3     
[33] rmarkdown_2.14   callr_3.7.0      magrittr_2.0.3   whisker_0.4     
[37] ps_1.7.1         promises_1.2.0.1 htmltools_0.5.2  ellipsis_0.3.2  
[41] httpuv_1.6.5     utf8_1.2.2       stringi_1.7.6    crayon_1.5.1