Linkage analysis with paramlink Appendix: Running MERLIN from paramlink

Transcription

1 Linkage analysis with paramlink Appendix: Running MERLIN from paramlink Magnus Dehli Vigeland 1 Introduction While multipoint analysis is not implemented in paramlink, a convenient wrapper for MERLIN (arguably the best available program for multipoint analysis) is provided. For this to function, MERLIN must be installed and properly pointed to in the Path environmental variable on your computer. The function used to call MERLIN from paramlink is called merlin. Its default action is to generate (and afterwards remove) files named merlin.ped, merlin.dat, merlin.map, merlin.freq and merlin.model and silently run the following command in an external terminal window: merlin -p merlin.ped -d merlin.dat -m merlin.map -f merlin.freq - -model merlin.model - -markernames If the analysis is successful, the function reads the s and processes them exactly as the lod function does for singlepoint scores. Thus all the utilities we used to examine singlepoint results, e.g. plot, summarize and lod.peaks work just as before. 2 Basic examples We will start by letting MERLIN compute the for the toy example we have been using. After running this example you will know if MERLIN is correctly installed and available from paramlink. > require(paramlink) > setwd("c:/linkage") > x = linkdat("toy_example.ped", model=1) In the above command, note that one can set the model already in the linkdat function, allowing you to skip the setmodel command afterwords. Now, let us ask MERLIN to compute the of the marker: > merlin(x) M1 theta=0: If MERLIN is not properly installed, you will instead get an error message saying: Error in system(command, intern = T) : merlin not found If you have downloaded and unzipped MERLIN, and you still get this message, it is probably because the merlin folder is not included in the Path environmental variable. Searching for editing the path environmental variable in windows 7 or something similar in Google should help you solve the problem. Before moving on, we compare MERLIN s result with paramlink s own lod function. Since there is only one marker in this case, there should be no difference between multipoint and singlepoint analysis. > lod(x) M1 theta=0: Apart from the MERLIN result being rounded to 3 decimal places, the results are reassuringly identical. 1

2 2.1 The large dominant pedigree We turn to the dominant.ped pedigree, which we load and associate with an AD model: > y = linkdat("dominant.ped", model=1) You may remember that MERLIN cannot handle pedigrees that are too large: The default limit is a bit size 1 of 24. If you run summary(y) you will see the bit size among the information printed on the screen. For this pedigree it equals 28, implying that it is too large for multipoint analysis 2. However, we can trim the pedigree and do an affected only analysis. The trimming is done by the trim function: > y_aff = trim(y, keep="affected") Removing individuals: 6, 7, 12, 13, 19, 20, 21, 22 and 23 You should plot the result and check that all the affected members are remaining. The resulting pedigree is small enough for MERLIN (a quick summary(y_aff) tells us that the bit size is down to 16), so multipoint s should be available to us through the merlin function. As above we compute the singlepoint scores as well for comparison. > single_lods = lod(y_aff) > multi_lods = merlin(y_aff) We plot the multipoint results (red) together with the singlepoint scores (dashed black): > plot(single_lods, lty=3) # lty=3 gives dashed line > par(new=t) # prepares R for a new plot in the same window > plot(multi_lods, col="red") # the 'col' argument specifies line color Position (cm) on chromosome 1 The plot shows several typical features. First of all, the peak of the multipoint curve is higher than the singlepoint peak, because of the relatively low information content of each SNP individually. Furthermore, the peak of the multipoint curve is much cleaner, having a well defined start and stop. To extract more detailed information, including the boundaries of the region, use lod.peaks: > lod.peaks(multi_lods, threshold=2) [[1]] CHR MARKER POS LOD M Inf -Inf 2 1 M M M M Inf -Inf 1 The bit size of a pedigree is defined as twice the number of individuals minus the number of founders. 2 This is not entirely true: It is possible to manually increase the bit size limit. 2

3 3 A real case with ped-, dat- and map-files Up to now, all our examples have consisted of a single ped-file without map information for the markers. In this section we show a more realistic example, namely the one we used in the MERLIN tutorial on Monday. To access the files, reset the working directory to the folder with the tutorial files: > setwd("c:/linkage/merlin") We load the ped-, dat- and map-files as follows: > z = linkdat(ped="hskr10.ped", dat="hskr10.dat", map="hskr10.map") Family ID: individuals. 6 affected 9 non-affected. 4 nuclear subfamilies. 380 markers. As usual we proceed to make a plot of the pedigree, to get an impression of what we are dealing with. Recall that available=true option plots the genotyped individuals in red. > plot(z, available=true) At the moment, paramlink does not recognize model files in MERLIN format (this feature may be added in future versions of paramlink). Hence we set the model ourselves, with the parameters found in the model-file Merlin.model > z = setmodel(z, chrom="autosomal", penetrances=c(0, 0.9, 1), dfreq=0.0001) Before doing the computations, let us remove markers with Mendelian errors: > z = mendeliancheck(z, remove=true) Note the use of remove=true here, causing the function to return a new pedigree object where the erroneous markers removed. Without this statement, mendeliancheck simply returns the indices of the erroneous markers. The removed markers are exactly those that were identified by PEDSTATS in the MERLIN tutorial. Now we call MERLIN to do the multipoint analysis. > m_lods = merlin(z) The curve looks like this: > plot(m_lods) 3

4 If you wonder why the curve looks a bit different from the pdf produced by MERLIN, this is just because paramlink by default does a cutoff around y = 1. If you want to change the range on the y-axis, this is controlled by the ylim argument in the plot function. For example, the following command plots the same graph as above, but includes y-values from 10 to 4. Also, we add a red horizontal line at 0. > plot(m_lods, ylim=c(-10,4)) > abline(h=0, col="red") Although the linkage peak is easy to spot on the grahp, it is hard to see exactly what the highest LOD score is. The summary function helps us: > summary(m_lods) Max : Achieved at marker(s): rs To get even more information about the peak, including its exact position, we use lod.peaks with a suitable threshold: > lod.peaks(m_lods, threshold=1) [[1]] CHR MARKER POS LOD rs rs rs rs

5 5 10 rs rs rs rs rs The output shows that the peak consists of 7 markers, and is located at cm cm (defined by the flanking markers). 4 Drawing several curves in the same plot The flexibility of R s plotting functionality makes it relatively painless to combine several LOD curves in a single plot. We use this here to investigate how the penetrance parameter f 1 affects the LOD in z family we looked at in the previous section. (This is very similar to what you did in Exercise 1 in the MERLIN session.) First, define three pedigree objects where f 1 equals 100%, 80% and 60% respectively. > z100 = setmodel(z, penetrances = c(0.0001, 1, 1)) > z80 = setmodel(z, penetrances = c(0.0001, 0.8, 1)) > z60 = setmodel(z, penetrances = c(0.0001, 0.6, 1)) Now compute multipoint s for each of the three: > m_lods100 = merlin(z100) > m_lods80 = merlin(z80) > m_lods60 = merlin(z60) The code below shows how to plot the three results together. We use different colors for the three curves, with a legend explaining the colors. Finally we put a horizontal line at y = 0. > plot(m_lods100, col="green") > par(new=true) # new plot in the same window > plot(m_lods80, col="red") > par(new=true) # new plot in the same window > plot(m_lods60, col="blue") > legend("topleft", c("f1=100%", "f1=80%", "f1=60%"), lwd=2, col = c("green", "red", "blue")) > abline(h=0) f1=100% f1=80% f1=60% The plot clearly shows the strange behaviour of the peak starting at 100 cm. For a truly linked region one would expect the to go down when the penetrance is reduced. The fact that it goes up instead may be explained in several ways. Firstly, the region in this case may not be linked to the disease at all. A peak of 1.4 could very well be purely coincidental. If - on the other hand - the peak is linked to the disease, then this effect typically arises if some of the unaffected family members are actually carriers of the disease allele. 5