1) Basics of R

1.1) Basic Steps

R can be accessed by clicking an icon or entering the command “R” at the system command line, also refered to as Terminal
This produces a console window or causes R to start up as an interactive program at the current terminal window
R works fundamentally by a question-and-answer model: enter a line with a command and press Enter
Then the program does something relevant

1.1) Basic Steps (continued)

In this course, we will the RStudio IDE and a file format called R Markdown
RStudio and R Markdown allow us to code using R, bash and Python
Another IDE:
- https://www.onlinegdb.com/online_c++_compiler

1.2) Library ISwR

This notebook used the book Introductory Statistics with R, by Peter Dalgaard as a reference
For the book, R library ISwR (Introductory Statistics with R) can be freely downloaded
All examples in the book used as reference should run provided that ISwR library is not only installed by also loaded into the current search path

1.2) Library ISwR (continued)

Library can be installed and loaded by typing the following command into an R chunk

install.packages("ISwR")
library("ISwR")

1.3) Plot Random Numbers

For a first impression of what R can do, let´s try plotting a graph
Need to insert the R chunk and use the plot function

1.3) Plot Random Numbers (continued)

plot(rnorm(1000))

1.4) A Potent Calculator

R can process simple and complex arithmetic expression and produce a result for the user

2 + 2

## [1] 4

R can also be used to do other standard calculations. Here is how to calculate e to the power of -2

exp(-2)

## [1] 0.1353353

1.4) A Potent Calculator (continued)

Other than the R chunks, these calculations can be made using the RStudio Console
In RStusio, when using the R Markdown format (.Rmd), we can insert R, Bash (terminal) and Python chunks
Proceed to illustrate that in the RStudio IDE

1.5) Assignments

Assignments are made based on the necessity to store the results of calculations and use these results in downstream processing steps in an entire algorithm or pipeline
Like other languages, R has symbolic variables: names that can be used to represent values
To assign the value 2 to variable x, we can enter the following command

x <- 2

The character <- is called the assignment operator

1.5) Assignments (continued)

There is no immediate visible result, but from now on, x has the value 2 and can be used in subsequent arithmetic operations
We can “ask” R which value is stores in the x variable

## [1] 2

1.6) Operations after assignment of variable

Below, our variable x, is used to perform other calculations

## [1] 2

Addition

x + x

## [1] 4

1.6) Operations after assignment of variable (continued)

Multiplication

5*x

## [1] 10

Potentiation

x^3

## [1] 8

1.7) Vectorized Arithmetic

It is not useful to use one number at a time to run statistics
One strenght of R is that it can handle entire data vectors as single objects
A data vector is an array of numbers and a vector variable can be constructed like this

weight <- c(60, 72, 57, 90, 95, 72)

## To look at the vector variable, just type its name again
weight

## [1] 60 72 57 90 95 72

1.7) Vectorized Arithmetic (continued)

You can do calculations with vectors just like ordinary numbers, so long as they have the same length
One exception to this rule that we will see will be when we use the mean of weigths of persons (represented by xbar)
In that case, the mean will be one single number, which will be subtracted from each sample value

1.7) Vectorized Arithmetic (continued)

Suppose the weight vector indicates the weight of men in kilograms
One simple formula to indicate whether a person is obese or not, is the body mass index (BMI)
BMI is calculated by dividing the person´s weight by the square of their height, in meters

1.7) Vectorized Arithmetic (continued)

Therefore, in R, we need to have a vector with the height values to calculate the bmi vector, containing the body-mass index for the individuals indicated in the weight vector

height <- c(1.75, 1.80, 1.65, 1.90, 1.74, 1.91)
bmi <- weight/height^2
bmi

## [1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630

1.8) Calculate the Mean of the variable weight

The mean is calculated by the sum of the observations divided by the total number of observations

= /n

1.8) Calculate the Mean of the variable weight (continued)

Let´s calculate the mean of the variable weight

sum(weight)

## [1] 446

sum(weight)/length(weight)

## [1] 74.33333

1.9) Calculate the Standard-Deviation of the variable weight

Standard-deviation can be calculated with the following equation

1.9) Calculate the Standard-Deviation of the variable weight (continued)

xbar, the mean of variable weight, can be calculated using the sum and length of variable weight

xbar <- sum(weight)/length(weight)
xbar

## [1] 74.33333

1.9) Calculate the Standard-Deviation of the variable weight (continued)

Now we can calculate the difference of each replicate in the weight variable and the mean of the weight variable, one by one

weight - xbar

## [1] -14.333333  -2.333333 -17.333333  15.666667  20.666667  -2.333333

1.9) Calculate the Standard-Deviation of the variable weight (continued)

Notice how R uses xbar, which has length one, to calculate the new weight - xbar data vector
xbar is recycled and subracted from every element in the weight variable (weight data vector)

1.10) Calculation of the Standard Deviation

Calculate the squared deviations

(weight - xbar)^2

## [1] 205.444444   5.444444 300.444444 245.444444 427.111111   5.444444

Calculate the sum of squared deviations

sum((weight - xbar)^2)

## [1] 1189.333

Calculate the standard deviation

sqrt(
  sum(
    (weight - xbar)^2/
      (length(weight)-1)
)
)

## [1] 15.42293

1.11) Standard Statistical Procedures

It is a standard medical practice to access whether a person is obese or not using validated scientific criteria
As a simple procedure to show this concept, let’s assume that an individual with a normal weight should have a BMI in the range 20-25
We want to know if our data deviates from the normal range of BMI
In R, this can be done using a statistical test called t-test

You do not need to understand what a t-test is, just remember that is is used to evaluate the distribution of sample values compared to the normal distribution
You can use a one-sample t-test to assess whether the six persons’ BMI can be assumed to have mean 22.5 given that they come from a normal distribution
You can do that using the function t.test

1.11) Standard Statistical Procedures: t-test

t.test(
  bmi,
  mu = 22.5
)

## 
##  One Sample t-test
## 
## data:  bmi
## t = 0.34488, df = 5, p-value = 0.7442
## alternative hypothesis: true mean is not equal to 22.5
## 95 percent confidence interval:
##  18.41734 27.84791
## sample estimates:
## mean of x 
##  23.13262

1.12) Plot Graphics

Let’s now plot a scatterplot of the height and weight of individuals

plot(height, weight)

1.12) Plot Graphics Modifying the plotting character

You will frequently want to modify drawing of your graphs in various ways
One way is usig the parameter “plotting character”, pch

plot(height, weight, pch =2)

1.13) Plot an expected Line for weights at BMI of 22.5

The term for weight at BMI of 22.5 is:

22.5*(height)^2

This term can be used to compute the line of expected weights at a BMI of 22.5 using the lines() function

1.13) Plot an expected Line for weights at BMI of 22.5 (continued)

As a note, we can’t use the lines() function without first creating a plot in R.

plot(height, weight, pch =2)
hh <- c(1.65, 1.70, 1.75, 1.80, 1.85, 1.90)
lines(hh, 22.5 * (hh)^2)

1.14) Vectors

The weight and height vectors are called numeric vectors
Besides numeric vectors, there are numeric and character vectors

In the next chunk, we access the iris data, and look at a summary of the dataset

data(iris)
summary(iris)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##

2.1) The iris data-frame

Another form to look at the iris data-frame is typing its name

iris

##     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
## 1            5.1         3.5          1.4         0.2     setosa
## 2            4.9         3.0          1.4         0.2     setosa
## 3            4.7         3.2          1.3         0.2     setosa
## 4            4.6         3.1          1.5         0.2     setosa
## 5            5.0         3.6          1.4         0.2     setosa
## 6            5.4         3.9          1.7         0.4     setosa
## 7            4.6         3.4          1.4         0.3     setosa
## 8            5.0         3.4          1.5         0.2     setosa
## 9            4.4         2.9          1.4         0.2     setosa
## 10           4.9         3.1          1.5         0.1     setosa
## 11           5.4         3.7          1.5         0.2     setosa
## 12           4.8         3.4          1.6         0.2     setosa
## 13           4.8         3.0          1.4         0.1     setosa
## 14           4.3         3.0          1.1         0.1     setosa
## 15           5.8         4.0          1.2         0.2     setosa
## 16           5.7         4.4          1.5         0.4     setosa
## 17           5.4         3.9          1.3         0.4     setosa
## 18           5.1         3.5          1.4         0.3     setosa
## 19           5.7         3.8          1.7         0.3     setosa
## 20           5.1         3.8          1.5         0.3     setosa
## 21           5.4         3.4          1.7         0.2     setosa
## 22           5.1         3.7          1.5         0.4     setosa
## 23           4.6         3.6          1.0         0.2     setosa
## 24           5.1         3.3          1.7         0.5     setosa
## 25           4.8         3.4          1.9         0.2     setosa
## 26           5.0         3.0          1.6         0.2     setosa
## 27           5.0         3.4          1.6         0.4     setosa
## 28           5.2         3.5          1.5         0.2     setosa
## 29           5.2         3.4          1.4         0.2     setosa
## 30           4.7         3.2          1.6         0.2     setosa
## 31           4.8         3.1          1.6         0.2     setosa
## 32           5.4         3.4          1.5         0.4     setosa
## 33           5.2         4.1          1.5         0.1     setosa
## 34           5.5         4.2          1.4         0.2     setosa
## 35           4.9         3.1          1.5         0.2     setosa
## 36           5.0         3.2          1.2         0.2     setosa
## 37           5.5         3.5          1.3         0.2     setosa
## 38           4.9         3.6          1.4         0.1     setosa
## 39           4.4         3.0          1.3         0.2     setosa
## 40           5.1         3.4          1.5         0.2     setosa
## 41           5.0         3.5          1.3         0.3     setosa
## 42           4.5         2.3          1.3         0.3     setosa
## 43           4.4         3.2          1.3         0.2     setosa
## 44           5.0         3.5          1.6         0.6     setosa
## 45           5.1         3.8          1.9         0.4     setosa
## 46           4.8         3.0          1.4         0.3     setosa
## 47           5.1         3.8          1.6         0.2     setosa
## 48           4.6         3.2          1.4         0.2     setosa
## 49           5.3         3.7          1.5         0.2     setosa
## 50           5.0         3.3          1.4         0.2     setosa
## 51           7.0         3.2          4.7         1.4 versicolor
## 52           6.4         3.2          4.5         1.5 versicolor
## 53           6.9         3.1          4.9         1.5 versicolor
## 54           5.5         2.3          4.0         1.3 versicolor
## 55           6.5         2.8          4.6         1.5 versicolor
## 56           5.7         2.8          4.5         1.3 versicolor
## 57           6.3         3.3          4.7         1.6 versicolor
## 58           4.9         2.4          3.3         1.0 versicolor
## 59           6.6         2.9          4.6         1.3 versicolor
## 60           5.2         2.7          3.9         1.4 versicolor
## 61           5.0         2.0          3.5         1.0 versicolor
## 62           5.9         3.0          4.2         1.5 versicolor
## 63           6.0         2.2          4.0         1.0 versicolor
## 64           6.1         2.9          4.7         1.4 versicolor
## 65           5.6         2.9          3.6         1.3 versicolor
## 66           6.7         3.1          4.4         1.4 versicolor
## 67           5.6         3.0          4.5         1.5 versicolor
## 68           5.8         2.7          4.1         1.0 versicolor
## 69           6.2         2.2          4.5         1.5 versicolor
## 70           5.6         2.5          3.9         1.1 versicolor
## 71           5.9         3.2          4.8         1.8 versicolor
## 72           6.1         2.8          4.0         1.3 versicolor
## 73           6.3         2.5          4.9         1.5 versicolor
## 74           6.1         2.8          4.7         1.2 versicolor
## 75           6.4         2.9          4.3         1.3 versicolor
## 76           6.6         3.0          4.4         1.4 versicolor
## 77           6.8         2.8          4.8         1.4 versicolor
## 78           6.7         3.0          5.0         1.7 versicolor
## 79           6.0         2.9          4.5         1.5 versicolor
## 80           5.7         2.6          3.5         1.0 versicolor
## 81           5.5         2.4          3.8         1.1 versicolor
## 82           5.5         2.4          3.7         1.0 versicolor
## 83           5.8         2.7          3.9         1.2 versicolor
## 84           6.0         2.7          5.1         1.6 versicolor
## 85           5.4         3.0          4.5         1.5 versicolor
## 86           6.0         3.4          4.5         1.6 versicolor
## 87           6.7         3.1          4.7         1.5 versicolor
## 88           6.3         2.3          4.4         1.3 versicolor
## 89           5.6         3.0          4.1         1.3 versicolor
## 90           5.5         2.5          4.0         1.3 versicolor
## 91           5.5         2.6          4.4         1.2 versicolor
## 92           6.1         3.0          4.6         1.4 versicolor
## 93           5.8         2.6          4.0         1.2 versicolor
## 94           5.0         2.3          3.3         1.0 versicolor
## 95           5.6         2.7          4.2         1.3 versicolor
## 96           5.7         3.0          4.2         1.2 versicolor
## 97           5.7         2.9          4.2         1.3 versicolor
## 98           6.2         2.9          4.3         1.3 versicolor
## 99           5.1         2.5          3.0         1.1 versicolor
## 100          5.7         2.8          4.1         1.3 versicolor
## 101          6.3         3.3          6.0         2.5  virginica
## 102          5.8         2.7          5.1         1.9  virginica
## 103          7.1         3.0          5.9         2.1  virginica
## 104          6.3         2.9          5.6         1.8  virginica
## 105          6.5         3.0          5.8         2.2  virginica
## 106          7.6         3.0          6.6         2.1  virginica
## 107          4.9         2.5          4.5         1.7  virginica
## 108          7.3         2.9          6.3         1.8  virginica
## 109          6.7         2.5          5.8         1.8  virginica
## 110          7.2         3.6          6.1         2.5  virginica
## 111          6.5         3.2          5.1         2.0  virginica
## 112          6.4         2.7          5.3         1.9  virginica
## 113          6.8         3.0          5.5         2.1  virginica
## 114          5.7         2.5          5.0         2.0  virginica
## 115          5.8         2.8          5.1         2.4  virginica
## 116          6.4         3.2          5.3         2.3  virginica
## 117          6.5         3.0          5.5         1.8  virginica
## 118          7.7         3.8          6.7         2.2  virginica
## 119          7.7         2.6          6.9         2.3  virginica
## 120          6.0         2.2          5.0         1.5  virginica
## 121          6.9         3.2          5.7         2.3  virginica
## 122          5.6         2.8          4.9         2.0  virginica
## 123          7.7         2.8          6.7         2.0  virginica
## 124          6.3         2.7          4.9         1.8  virginica
## 125          6.7         3.3          5.7         2.1  virginica
## 126          7.2         3.2          6.0         1.8  virginica
## 127          6.2         2.8          4.8         1.8  virginica
## 128          6.1         3.0          4.9         1.8  virginica
## 129          6.4         2.8          5.6         2.1  virginica
## 130          7.2         3.0          5.8         1.6  virginica
## 131          7.4         2.8          6.1         1.9  virginica
## 132          7.9         3.8          6.4         2.0  virginica
## 133          6.4         2.8          5.6         2.2  virginica
## 134          6.3         2.8          5.1         1.5  virginica
## 135          6.1         2.6          5.6         1.4  virginica
## 136          7.7         3.0          6.1         2.3  virginica
## 137          6.3         3.4          5.6         2.4  virginica
## 138          6.4         3.1          5.5         1.8  virginica
## 139          6.0         3.0          4.8         1.8  virginica
## 140          6.9         3.1          5.4         2.1  virginica
## 141          6.7         3.1          5.6         2.4  virginica
## 142          6.9         3.1          5.1         2.3  virginica
## 143          5.8         2.7          5.1         1.9  virginica
## 144          6.8         3.2          5.9         2.3  virginica
## 145          6.7         3.3          5.7         2.5  virginica
## 146          6.7         3.0          5.2         2.3  virginica
## 147          6.3         2.5          5.0         1.9  virginica
## 148          6.5         3.0          5.2         2.0  virginica
## 149          6.2         3.4          5.4         2.3  virginica
## 150          5.9         3.0          5.1         1.8  virginica

3.1) More Visualization of the iris dataset

We need to cover these basic R functions:

dim()
head()
View()
class()
str()

dim(iris)
head(iris)
View(iris)
class(iris)
str(iris)

3.2) Visualize iris dataset with basic R functions

head

head(iris, n = 10)

##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1           5.1         3.5          1.4         0.2  setosa
## 2           4.9         3.0          1.4         0.2  setosa
## 3           4.7         3.2          1.3         0.2  setosa
## 4           4.6         3.1          1.5         0.2  setosa
## 5           5.0         3.6          1.4         0.2  setosa
## 6           5.4         3.9          1.7         0.4  setosa
## 7           4.6         3.4          1.4         0.3  setosa
## 8           5.0         3.4          1.5         0.2  setosa
## 9           4.4         2.9          1.4         0.2  setosa
## 10          4.9         3.1          1.5         0.1  setosa

number of columns and rows

dim(iris)

## [1] 150   5

the class() function

class(iris)

## [1] "data.frame"

4) More Visualization

4.1) Scatterplot

plot(data=iris, iris$Sepal.Length, iris$Sepal.Width) ## R will complain about this command

## Warning in plot.window(...): "data" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "data" is not a graphical parameter

## Warning in axis(side = side, at = at, labels = labels, ...): "data" is not a
## graphical parameter

## Warning in axis(side = side, at = at, labels = labels, ...): "data" is not a
## graphical parameter

## Warning in box(...): "data" is not a graphical parameter

## Warning in title(...): "data" is not a graphical parameter

plot(iris$Sepal.Length, iris$Sepal.Width) ## According to the error, this was plotted using a different synthax

4.1) Scatterplot

Plot without warning and message

plot(data=iris, iris$Sepal.Length, iris$Sepal.Width) ## R will complain about this command

plot(iris$Sepal.Length, iris$Sepal.Width) ## According to the error, this was plotted using a different synthax

4.2) Boxplot

boxplot(data=iris, iris$Sepal.Length, iris$Sepal.Width)

5) Data Visualization with specialized libraries

In R, there are packages designed with the purpose of making good-looking graphics. This is the case with the ggplot2. The chunk below installs the ggplot2 library, loads the library into the R environment and then plots the data present in the iris data-frame.
You can uncomment the installation line if you need to install it

5.1) Ggplot2

#install.packages("ggplot2")
library(ggplot2)
ggplot(data=iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + geom_point(size=4)

In our activities to visualize human genomic data, we will use a library called qqman, to visualize the biological association, through a plot known as the Manhattan plot.

5.2) Visualizing GWAS

The simplest definition of a GWAS is the statistical or significant association between a phenotype (trait) and a genotype. This association can also be called biological association.
Information about association of SNPs with Huntington’s Disease can be found at the Chaves 2019 Huntington’s disease paper

5.3.1) Package installation

We need to install and load package qqman

## install.packages("qqman")
library(qqman)

##

## For example usage please run: vignette('qqman')

##

## Citation appreciated but not required:

## Turner, (2018). qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. Journal of Open Source Software, 3(25), 731, https://doi.org/10.21105/joss.00731.

##

5.3.1) Package installation

After, we load data-frame to be visualized
Exact location of text file in your system needs to be determined

GWAS_TABLE <- read.table("../data/chr4.txt", 
                         sep = " ",
                         header = T)

5.3.2) Get information about object with head()

Use head() function to inspect the data-frame just loaded

head(GWAS_TABLE, n = 10)

##    CHR         SNP     BP         P
## 1    4 chr4:128096 128096 0.0133500
## 2    4 chr4:516586 516586 0.0076260
## 3    4 chr4:523979 523979 0.0024960
## 4    4 chr4:527217 527217 0.0217400
## 5    4 chr4:566177 566177 0.0008988
## 6    4 chr4:578679 578679 0.0162100
## 7    4 chr4:578790 578790 0.0103700
## 8    4 chr4:579307 579307 0.0334600
## 9    4 chr4:580259 580259 0.0190400
## 10   4 chr4:585318 585318 0.0317600

5.3.3) Plotting GWAS data-frame

In this section we use functions plot() and boxplot() to visualize data-frame
In the y access, we see genomic coordinates and in the x access, p-valoues of the biological association
Note that depending on the synthax used for plotting, R may complain

Comments not turned off

plot(data=GWAS_TABLE, GWAS_TABLE$BP, GWAS_TABLE$P) ## R complains

## Warning in plot.window(...): "data" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "data" is not a graphical parameter

## Warning in axis(side = side, at = at, labels = labels, ...): "data" is not a
## graphical parameter

## Warning in axis(side = side, at = at, labels = labels, ...): "data" is not a
## graphical parameter

## Warning in box(...): "data" is not a graphical parameter

## Warning in title(...): "data" is not a graphical parameter

Turn off comments

plot(data=GWAS_TABLE, GWAS_TABLE$BP, GWAS_TABLE$P) ## R complains

5.3.4) Plotting GWAS data-frame

plot(GWAS_TABLE$BP, GWAS_TABLE$P) ## R does not complain

5.4) Boxplot

Boxplot

boxplot(data=GWAS_TABLE, GWAS_TABLE$BP, GWAS_TABLE$P)

5.5) Boxplot

Compare the chromosomal coordinates and the values of the p-values in the boxplot above
The chunk below allows one to ommit NA values in data-frame

GWAS_TABLE_Ommit <- na.omit(GWAS_TABLE)

A seguir, criamos uma variável que armazena as posições das SNPs a serem realçadas em verde no Manhattan plot. Estas SNPs são mutações biológica e estatisticamente associadas à Doença de Huntington no gene de uma sortilina, localizada em proximidade física ao gene da proteína huntingtina mutada, a qual é a causadora medeliana (segue as leis de segregação genética de Mendel) da Doença de Huntington.

5.6) Create vector to highlight genome coordinates in Manhattan plot

SNP_HIGHLIGHT <- c("chr4:3043512","chr4:3043513","chr4:3048207","chr4:3224216",
                   "chr4:3231772","chr4:3233844","chr4:3235081","chr4:3235084",
                   "chr4:3236881","chr4:3236883","chr4:3241845","chr4:3243804",
                   "chr4:3263138","chr4:3265130","chr4:3265710","chr4:3314646",
                   "chr4:3380088","chr4:3409359","chr4:3411110","chr4:3415336",
                   "chr4:3415378","chr4:3438643","chr4:3446091","chr4:3449886",
                   "chr4:3473066","chr4:3476809","chr4:3480439","chr4:3487151",
                   "chr4:3496058","chr4:3496110","chr4:3506933","chr4:3508752",
                   "chr4:3510957","chr4:3512690","chr4:3517746","chr4:3518190",
                   "chr4:3529671","chr4:3532327","chr4:3533066","chr4:3746133",
                   "chr4:3747842","chr4:3748134","chr4:3765305","chr4:3765336",
                   "chr4:3944253","chr4:3944752","chr4:3944888","chr4:3946166",
                   "chr4:3946175","chr4:3969218","chr4:4051294","chr4:4076788",
                   "chr4:4103104","chr4:4103105","chr4:4109198","chr4:4109210",
                   "chr4:4240627","chr4:4242705","chr4:4243668","chr4:4245210",
                   "chr4:4245510","chr4:4245513","chr4:4245591","chr4:4245926",
                   "chr4:4245929","chr4:4246109","chr4:4246433","chr4:4246453",
                   "chr4:4246457","chr4:4246497","chr4:4249414","chr4:4249415",
                   "chr4:4249484","chr4:4271623","chr4:4275306","chr4:4304749",
                   "chr4:4318931","chr4:4318970","chr4:4319564","chr4:4319728",
                   "chr4:4319750","chr4:4322078","chr4:4709657","chr4:4732282",
                   "chr4:4789635","chr4:4822960","chr4:4824890","chr4:4825092",
                   "chr4:4825180","chr4:4865316","chr4:4865321","chr4:5018702",
                   "chr4:5812778","chr4:5814082","chr4:5833660","chr4:5833899",
                   "chr4:5835541","chr4:5851205","chr4:5862752","chr4:5862938",
                   "chr4:5862943","chr4:5901873","chr4:5905499","chr4:5906287",
                   "chr4:6018891","chr4:6019046","chr4:6020190","chr4:6020367",
                   "chr4:6025638","chr4:6025656","chr4:6025766","chr4:6026058",
                   "chr4:6083488","chr4:6204935","chr4:6235553","chr4:6237142",
                   "chr4:6238466","chr4:6239906","chr4:6240929","chr4:6245022",
                   "chr4:6245618","chr4:6245732","chr4:6245915","chr4:6246075",
                   "chr4:6246373","chr4:6246959","chr4:6290594","chr4:6292020",
                   "chr4:6294095","chr4:6298375","chr4:6316092","chr4:6321396",
                   "chr4:6324647","chr4:6324785","chr4:6327669","chr4:6328354",
                   "chr4:6328507","chr4:6333130","chr4:6333559","chr4:6333669",
                   "chr4:6335966","chr4:6435341","chr4:6435486","chr4:6435926",
                   "chr4:6437191","chr4:6437197","chr4:6457121","chr4:6457131",
                   "chr4:6457132","chr4:6568390","chr4:6570032","chr4:6570768",
                   "chr4:6596360","chr4:6613252","chr4:6613462","chr4:6620991",
                   "chr4:6624771","chr4:6626154","chr4:6641969","chr4:6642090",
                   "chr4:6644466","chr4:6644467","chr4:6644468","chr4:6647889",
                   "chr4:6648300","chr4:6662665","chr4:6663319","chr4:6663715",
                   "chr4:6674554","chr4:6678553","chr4:6678599","chr4:6690535",
                   "chr4:6698664","chr4:6698667","chr4:6698706","chr4:6720572",
                   "chr4:6911679","chr4:6985889","chr4:6987394","chr4:7002344",
                   "chr4:7004495","chr4:7004506","chr4:7005196","chr4:7005199",
                   "chr4:7024077","chr4:7024398","chr4:7029430","chr4:7031064",
                   "chr4:7044357","chr4:7044380","chr4:7048842","chr4:7052115",
                   "chr4:7055253","chr4:7064243","chr4:7067765","chr4:7073187",
                   "chr4:7074027","chr4:7677967","chr4:7701947","chr4:7702795",
                   "chr4:7703505","chr4:7703807","chr4:7704795","chr4:7704818",
                   "chr4:7709703","chr4:7712150","chr4:7714490","chr4:7733843",
                   "chr4:7735162","chr4:7735164","chr4:7736103","chr4:7736112")

5.7) Biological Association Visualization: Manhattan Plot

Finally, we plot the Manhattanh plot graph

manhattan(GWAS_TABLE_Ommit, 
          highlight = SNP_HIGHLIGHT, 
          annotateTop = T, 
          annotatePval = 0.20, 
          genomewideline = -log10(0.0001))

6) References

7) Session Info

sessionInfo()

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] qqman_0.1.9   ggplot2_3.5.1
## 
## loaded via a namespace (and not attached):
##  [1] bslib_0.5.0       compiler_4.1.1    pillar_1.9.0      jquerylib_0.1.4  
##  [5] highr_0.11        tools_4.1.1       digest_0.6.36     jsonlite_1.8.8   
##  [9] evaluate_0.24.0   lifecycle_1.0.4   tibble_3.2.1      gtable_0.3.5     
## [13] pkgconfig_2.0.3   rlang_1.1.4       cli_3.6.3         rstudioapi_0.16.0
## [17] yaml_2.3.7        xfun_0.39         fastmap_1.2.0     withr_3.0.1      
## [21] dplyr_1.1.2       knitr_1.45        generics_0.1.3    sass_0.4.6       
## [25] vctrs_0.6.2       revealjs_0.9      grid_4.1.1        tidyselect_1.2.0 
## [29] calibrate_1.7.7   glue_1.7.0        R6_2.5.1          fansi_1.0.6      
## [33] rmarkdown_2.27    farver_2.1.2      magrittr_2.0.3    MASS_7.3-60.0.1  
## [37] scales_1.3.0      htmltools_0.5.5   colorspace_2.1-0  labeling_0.4.3   
## [41] utf8_1.2.4        munsell_0.5.1     cachem_1.1.0

Introduction to the R Language Syntax and the Command Line (Terminal)

Gepoliano Chaves, Ph. D.

August 19th, 2024