GWAS Exercises 3 - GWAS with a Quantiative Trait

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1 GWAS Exercises 3 - GWAS with a Quantiative Trait Peter Castaldi January 28, 2013 PLINK can also test for genetic associations with a quantitative trait (i.e. a continuous variable). In this exercise, we will be testing the effect of SNPs on the expression level of a gene transcript. This type of analysis is often called eqtl analysis. We will be using genotype and gene expression data from 90 cell lines obtained from HapMap CEU individuals. There is a package in R called GGtools that contains a number of functions and data structures for doing eqtl analysis. We won t review this R package in detail, but this was used to make many of the datasets that we will work with today. In this exercise we are going to test for association between SNPs on Chromosome 17 and mrna levels for the ORMDL3 gene. 1 Data Files Copy the genotype data and expression data for ORMDL3 to your directory with the following command: cp /cluster/tufts/cbicourse/gas/data/plink/ormdl3*. You should now have three files starting with ``ORMDL3'' in your directory. Take a peek at the MAP file by typing: head ORMDL3.map etc. The PED file is very large, you can try using the `head' command, but as you'll see, it's not very helpful. A unix command which shows how many rows in the file is: 1

2 wc -l ORMDL3.ped And how many columns: head -1 ORMDL3.ped awk '{print NF}' In the simplest case, all of the information needed for a GWAS is contained in the PED and MAP files. However, if you have multiple phenotypes or alternate phenotypes, it is often more convenient to have a separate file that contains your covariates and alternate phenotypes. The PLINK documentation refers to these as alternate phenotype and covariate files, but in practice you can use the same file for both. The first two columns of an alternate phenotype file need to be the Family ID and Individual ID columns, this is how PLINK merges the data in the PED file with the data in the phenotype file. ORMDL3 is a gene on Chromosome 17 that has been associated with susceptibility to asthma in multuiple GWAS. The PED file that you have contains genotype data for Chromosome 17 from 90 HapMap CEU samples. The phenotype file ORMDL3 Pheno.txt contains data on the expression of ORMDL3. 2 Examing Phenotype Distributions in R When doing association testing with a continuous variable, it is important to examine the distributional proporties of your phenotype. Phenotypes that are non-normally distributed can be susceptible to distorted analytic results from outlier values having an undue influence on the analysis. Start R by typing: module add R/ bsub -Ip -q int_public6 R The R code below reads in the alternate phenotype file, and displays the structure of the object with the str command. The ORMDL3 expression values are contained in the ORMDL3 variable. We examine the quantiles of this variable and plot the histogram of the variable distribution. > pheno <- read.table("ormdl3_pheno.txt", header = T, stringsasfactors = F) > str(pheno) 2

3 'data.frame': 90 obs. of 4 variables: $ FID : int $ IID : int $ ORMDL3: num $ male : int > quantile(pheno$ormdl3) 0% 25% 50% 75% 100% > pdf("ormdl3_histogram.pdf") > hist(pheno$ormdl3) > dev.off() null device 1 Take a look at your histogram by moving the file ORMDL3 Histogram.pdf to your desktop with WinScp. Remember that pdfs can t be opened directly from the WinScp window, you have to find them in Windows and click on it in the Windows environment (or open directly with Adobe, etc). The histogram should look like this: 3

4 Histogram of pheno$ormdl3 Frequency pheno$ormdl3 4

5 Run the following PLINK command to test all of the genotyped SNPs on Chromosome 17 for association with expression of ORMDL3 in this particular cell type. If you haven't already, type: module add plink/1.06 plink --file ORMDL3 --pheno ORMDL3_Pheno.txt --pheno-name ORMDL3 --assoc --out ORMDL3_Res Now let s read the results into R and do some basic interpretation, starting with generating a Q-Q plot. > res <- read.table("ormdl3_res.qassoc", stringsasfactors = F, + header = T) > library(snpstats) > pdf("ormdl3_qq.pdf") > qq.chisq(-2 * log(res$p), df = 2, pvals = TRUE, overdisp = TRUE) N omitted lambda > dev.off() null device 1 5

6 QQ plot Observed P value Expected Expected distribution: chi squared (2 df) 6

7 This is the QQ plot of your dreams. Probably the first thought should be that something is wrong, because there is such an excess of positive results. However, in this case, we know that there are strong signals between SNPs and the expression level of ORMDL3. A few questions: Question 1: You may have noticed that some numbers were spit out as you did the QQ plot. What is lambda and what is it supposed to represent? Question 2: Why is a QQ plot a good way to evaluate GWAS results? (Hint: What is the expected proportion of null to positive results in a large-scale genetic association analysis?) Question 3: The gray area on the QQ plot indicated the 95% confidence interval around the line of identity. Completely null results should be expected to mostly fall within the gray area. Why does the gray area increase as expected p-values become very low? Let s look at the top ten results from our analysis. First we have to order the results by p-value, then display the top ten results. > res <- res[order(res$p), ] > print(res[1:10, ]) CHR SNP BP NMISS BETA SE R2 T P rs e rs e rs e rs e rs e rs e rs e rs e rs e rs e-08 Move your results file and the ORMDL3.map to your desktop using WinScp. Open these files in the WGAViewer program. Click on 'File' Click on 'Open External Data File' 'Open PLINK Output' 7

8 Fill in the fields If you can get the program to load your data, you'll see a genome-wide representation of our results. The database plotter window can be an easy way to browse your results, though I personally don't like it very much. It does show that there is one large peak of low p-values. Any guess as to where this may be located? You can explore this pulling the genome coordinates from the browser and looking them up on the UCSC genome browser. Use the 2009 assembly. Here is what the results look like on locuszoom around the ORMDL3 gene. 8

9 Plotted SNPs 10 rs r log 10(p value) Recombination rate (cm/mb) ERBB2 IKZF3 GSDMB GSDMA CSF3 THRA MSL1 C17orf37 ZPBP2 ORMDL3 PSMD3 MED24 NR1D1 GRB7 SNORD Position on chr17 (Mb) 9

10 3 Other Stuff At this point, we ve covered how to do large-scale association analysis with both binary and continuous phenotypes in PLINK. Some additional tasks that you can try with this data. ˆ We saw a signal around ORMDL3. How could you determine whether there is one independent signal at that locus or multiple signals? ˆ Are there other significant hits for ORMDL3 expression on Chromsome 17? How would you decide? ˆ Do a case-control analysis testing for SNP associations on Chromosome 17 with gender. 10