Linkage analysis with paramlink Session I: Introduction and pedigree drawing

Transcription

1 Linkage analysis with paramlink Session I: Introduction and pedigree drawing In this session we will introduce R, and in particular the package paramlink. This package provides a complete environment for linkage analysis in R, and we will use it in several sessions during the course. Acknowledging that R might be unfamiliar to many students, we aim to start nice and slowly. Thus we will not get as far as linkage and LOD scores today, but focus on these goals: ˆ Get R up and running with paramlink correctly installed. ˆ Practice some basic R commands. ˆ Use paramlink to create and play around with simple pedigrees. 1 Preparation 1.1 Create a course folder You should make a folder for all the files from this course. Unless you have strong objections please call the folder LINKAGE and place it directly in the root directory, e.g. C:\LINKAGE. (It will save a lot of time and energy if everyone uses the same name and location.) If you need help to do this, please ask a fellow student or one of us. 1.2 Download course material Go to and download all the files there. Place them in C:\LINKAGE. During the course additional files may be made available to you on this website. 1.3 R Hopefully you already have R version installed on your computer, with an R icon on your desktop. If not, go to and follow the instructions there to download and install R. Use default settings whenever asked. 1.4 Install paramlink Click on the R icon to open a new R session. If you are online, the following command will then install paramlink on your computer. (When asked to choose a mirror site, you can pick any country on the list, e.g. Norway.) > install.packages("paramlink") Once the package is installed, it will stay installed even if you close R and start a new session, so you won t have to repeat the above command. The only thing you have to do in each session (including this) is to load it into the memory, by typing > require(paramlink) 1

2 2 A crash course in R We will not spend much time here on basic use of R. Good resources are the pdf manuals available via the help-menu. But just to get going, let us start by practicing some basic arithmetic. Type in the commands and check that you agree with the output: > > 2 * > 4^2 > 1 / 7 The spacing in the commands above (and many other commands in this tutorial) is just for clarity - you don t need the extra spaces. Instead of working directly with numbers, it is often useful to work with variables. Try something like > x = 3 > y = 4 > 2 * x + y [1] 10 A cornerstone of R is the notion of a vector, which is simply a collection of several numbers (or other types of objects). For instance, the three numbers (1,3,5) taken together makes a vector. In R we denote this using the c command: > c(1,3,5) [1] We can store vectors as variables as before, and do arithmetics with vectors. Let s practice a bit! (Note: Any text after a #-sign is just a comment and not something you need to write.) > x = c(1,3,5) > 2 * x # multiplies each element by 2 > x + 1 # adds 1 to each element of x > y = c(10,20,30) # making a second vector > x + y # the vectors are added element by element > x * y # element-wise multiplication For vectors consisting of consecutive numbers, e.g. 3, 4, 5, 6, 7, there is a useful shortcut notation: > 3:7 [1] You can also combine the c function with the : -notation. > c(-4, 0:5, 20:15) [1] Extracting/removing elements from a vector is done using square brackets [ ]: > x = 11:20 > x[2] # extracting the second element > x[1:3] # extracting the three first elements > x[-3] # removing the third element An important thing to observe is that after all the above manipluation, the x vector is unchanged: > x [1]

3 This is very typical of the R language, and a common source of confusion for beginners. The point is that commands like x[-3] do not change the x vector itself, rather they return a new vector. This is then printed on the screen, but not stored anywhere. To permanently change x, we must feed the result back into x: > x = x[-3] > x [1] In addition to the basic arithmetical operations, R offers a large number of useful built-in functions. Just to mention a couple, length returns the total number of entries in a vector, and sum adds all the numbers of the vector: > length(x) [1] 9 > sum(x) [1] 142 Each function in R has a help page explaining what the function does. For instance, to access the help page of sum, write >?sum 3 Drawing pedigrees with paramlink We could easily spend days and weeks on the many finesses of basic R calculations. But in fact we already know enough R to move along to where the real fun starts for a geneticist: Creating and drawing pedigrees! For this we will need the paramlink package, so if you haven t done it already, make sure it is loaded: > require(paramlink) Also, let us change the working directory to where we keep the course files: > setwd("c:/linkage") There are two main ways of creating pedigrees in paramlink: Reading ped-files, or building from scratch in paramlink. Below we demonstrate both techniques for the pedigree shown in Figure Reading ped-files To work with pedigrees in paramlink (or any other software) we need to describe the pedigrees in a way that is interpretable by the computer. A common way of doing this is to write so-called ped-files. The format of such files is very specific, and easily understood once you get used to it. Take a look at the file toy_example.ped; this is the ped-file of the pedigree in Figure 1. If you open it in Notepad, Excel, or something similar, it should look like this (without the headings): FAMID ID FID MID SEX AFF A1 A A B A A A B A A A B B B B B B B 3

4 Figure 1: A pedigree with an autosomal dominant disease pattern The file has one row per individual, and the first 6 columns describe the pedigree: Family identifier, individual id, father s id, mother s id, sex (1=male, 2=female) and affection status (1=unaffected, 2=affected). Zeroes in the FID and MID columns means that the parents of the individual are not included in the pedigree (i.e. the individual is a founder). After the first 6, the remaining columns contain marker alleles. Since humans are diploid we need two columns per marker. In this case we see for example that individual 1 is heterozygous and has the A/B genotype, while individual 2 is homozygous A/A. To read the pedigree into R, we use the linkdat command (short for linkage data) and store the result in a variable called toy. (We could have chosen something else; almost any combination of letters and numbers can be used as a variable name in R.) > toy = linkdat("toy_example.ped") Family ID: 1. 8 individuals. 5 affected 3 non-affected. 2 nuclear subfamilies. 1 marker. It is a good habit to glance through the information printed during the reading process, at least to check that we agree on the number of individuals (8) and markers (1). If we print the toy variable (by typing toy), we see paramlink s representation of the ped-file: > toy ID FID MID SEX AFF V A/B A/A A/B A/A A/B B/B B/B B/B First of all, the numbers 1-8 shown to the left are not part of the ped-file, they are just row numbers which R by default prints with array-like structures. Secondly, observe that the FAMID column is removed. This is because paramlink usually deals with only one pedigree at a time, making this column redundant. Finally, to make it easier to read, the marker alleles are printed in a single column. To draw the pedigree, simply call the plot function. The result should pop up in a separate graphics window and look exactly like Figure 1: > plot(toy) 4

5 If you are disappointed that the pedigree doesn t show the genotypes, this is easy to fix. The following command plots the pedigree including the genotypes of marker number 1 (the only one in our case). Try it for yourself. > plot(toy, marker=1) 3.2 Building pedigrees from scratch The paramlink package contains several utility functions for creating pedigrees step-by-step. With a little practice this is a lot faster than writing ped-files by hand. To create the pedigree in Figure 1, we start with the nuclear family consisting of individuals 1-5. > toy2 = nuclearped(noffs=3, sex=c(2,2,1)) The argument noffs indicates the number of offspring, and the sex vector dictates their genders: female, female, male. You are encouraged to check the result by typing plot(toy2). Next, we add two children (by default: boys) to individual 5 using the addoffspring function: > toy2 = addoffspring(toy2, father=5, noffs=2) Since we don t say who the mother is, she is created as a new individual. Also, note again the typical syntax here: The addoffspring function does not change toy2, but creates a new object. Since we want to keep this object for further use, we must make sure to store in a variable. For convenience we used the same name toy2 for the modified pedigree, but this is not necessary. If you plot toy2 at this point, you ll see that the pedigree looks correct except for the affection statuses: Everyone is unaffected. Thus we need to swap the affection status (from 1 to 2) of individuals 1, 3, 5, 7 and 8: > toy2 = swapaff(toy2, c(1,3,5,7,8)) Plotting the result now should give a result exactly like Figure More pedigree functions In addition to nuclearped and addoffspring, there are many other functions in paramlink that facilitate pedigree building. Several of these are described on the help page of addoffspring. >?addoffspring Among the functions are swapsex (for changing gender of specified individuals), addparents, (include the parents of a single individual), removeindividuals (removes the specified individuals and their descendants) and relabel (gives new ID to some or all pedigree members). All of these functions follow the same basic syntax as above, and their use should be more or less self explanatory after a few examples. So let s play around for a bit with the toy2 pedigree! You should make a plot after each step to follow the development. > y = toy2 # making a copy, so that we can start again here if something goes wrong > y = addparents(y, id=6) # add parents to individual 6 > y = swapsex(y, 7:8) # make the children into girls > y = removeindividuals(y, 3:4) # remove 3 and 4 > y = relabel(y, old=c(1,2,9,10,5:8), new=1:8) # more sensible labelling The pedigree now looks like this: > plot(y) 5

6 Exercises Exercise 1. Create each of the pedigrees below from scratch in paramlink. Exercise 2. In addition to nuclearped, the functions cousinped and halfcousinped can also be good starting points for building pedigrees. For any positive integer k the command cousinped(k) creates a pedigree linking two k th cousins, while halfcousinped(k) produces two half cousins of k th degree. (A useful application of this is the case k = 0, which corresponds to half siblings.) a) Starting from scratch, use the cousinped and addoffspring functions to make the consanguineous pedigree on the left side below. b) Starting from scratch, use halfcousinped, removeindividuals and relabel to make the pedigree to the right. Hint: Start with halfcousinped using k=3. 6