Mendelian randomization (MR) analysis uses a genetic variant as an instrumental variable to estimate and test for the causative effect of an exposure variable on an outcome (Ference, et al. 2012). MRkit is used to perform MR analysis to reveal the relationship between gene expression level (exposure) and quantitative phenotype (outcome). Details about the calculation can be found here. In brief, it involves calculating a 2-step least squares (2SLS) estimate of the effect of
git clone https://github.com/bingochenbin/MRkit.git
The MRkit only contains two command line tools, VCFgt2num_converter.py and MR_analysis.R, which have the following requirements:
python >= 3.8
R >= 4.1
In addition, the following R packages should be installed in your R environment:
data.table
foreach
iterators
doParallel
optparse
Note: All the programs are designed for parallel computation. Typing name of any program followed by
-hto show the help for the program.
Caution: The sample IDs in the input datasets must be identical and in the same order, and the missing values must be represented as "NA" values. In addition, all input data must be tab-delimited and have a header line.
Caution: The SNP genotyping data should only contain biallelic SNPs.
To prepare the SNP genotyping data for input, you need to first extract the GT information (genotype, encoded as allele values separated by either / or |) from your VCF file:
# Extract the GT from your VCF file using bcftools software
bcftools query -H -f '%CHROM\t%POS\t%ID\t%REF\t%ALT[\t%TGT]\n' /path/to/your/vcf \
| sed -r -e '1 s@:GT@@g;1 s@\[[0-9]+\]@@g;1 s@# @@' \
| bgzip > SNPgeno.GT.vcf.gz
# Get the *.bim file corresponding to the VCF file using plink software
plink --vcf /path/to/your/vcf --make-just-bim -out SNPgeno --allow-extra-chrThen, you need to convert the genotypes in the SNPgeno.GT.vcf.gz file to numeric codes using the VCFgt2num_converter.py script. Specifically, the fifth column of the *.bim file indicates the minor allele, which is coded as 1, while the sixth column indicates the major allele, which is coded as 0. The numeric code corresponding to a genotype is the sum of these values.
python3 /python/to/MRkit/scripts/VCFgt2num_converter.py\
-o SNPgenp_MR.txt\
-v SNPgeno.GT.vcf.gz\
-b SNPgeno.bimFinally, the above SNPgenp_MR.txt contains the input SNP genotyping data for performing MR analysis, and it looks like the following:
CHROM POS ID REF ALT WMG001A WMG002A WMG003A WMG004A WMG005A WMG006A WMG007A WMG008A WMG009A WMG010A WMG011A WMG012A WMG013A WMG014A WMG015A WMG016A WMG017A WMG018A WMG019A WMG020A WMG021A WMG022A WMG023A WMG024A WMG025A WMG026A WMG027A WMG028A WMG029A WMG030A WMG031A WMG032A WMG033A WMG034A WMG035A WMG036A WMG037A WMG038A WMG039A WMG040A WMG041A WMG042A WMG043A WMG044A WMG045A WMG046A WMG047A WMG048A WMG049A WMG050A WMG051A WMG052A WMG053A WMG054A WMG055A WMG056A WMG057A WMG058A WMG059A WMG060A WMG061A WMG062A WMG063A WMG064A WMG065A WMG066A WMG067A WMG068A WMG069A WMG070A WMG071A WMG072A WMG073A WMG074A WMG075A WMG076A WMG077A WMG078A WMG079A WMG080A WMG081A WMG082A WMG083A WMG084A WMG085A WMG086A WMG087A WMG088A WMG089A WMG090A WMG091A WMG092A WMG093A WMG094A WMG095A WMG096A WMG097A WMG098A WMG099A WMG100A WMG101A WMG102A WMG103A WMG104A WMG105A WMG106A WMG107A WMG108A WMG109A WMG110A WMG111A WMG112A WMG113A WMG114A WMG115A WMG116A WMG117A WMG118A WMG119A WMG120A WMG121A WMG122A WMG123A WMG124A WMG125A
chr1A 44293 chr1A_44293 G C 0 0 0 0 0 1 0 0 NA 0 0 0 0 0 0 0 0 NA 0 NA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 1 0 0
chr1A 47377 chr1A_47377 C T 2 0 0 0 0 0 0 0 NA 0 0 1 0 0 0 0 0 NA 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 NA 0 0 0 0 2 0 0 0 0 0 0 0 NA 0 0 0 0 NA 0 0 0 0 0
chr1A 59771 chr1A_59771 G A 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 NA 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 NA 1 0 1 0 0 NA 0 0 NA NA 0 0 0 0 0 1 0 NA 0 0 0 0 NA 0 0 0 0 0 0 1 0 0 0 NA 0 0 0 0 0 0 0 0 0
chr1A 59775 chr1A_59775 C T 0 1 0 0 1 0 0 0 NA 0 0 0 0 0 0 0 0 NA 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 NA 1 0 1 0 0 NA 0 0 NA NA 0 0 0 0 0 1 0 NA 0 0 0 0 NA 0 0 0 0 0 0 1 0 0 0 NA 0 0 0 0 0 0 0 0 0
chr1A 91617 chr1A_91617 G A 0 0 0 0 2 0 2 0 NA 0 2 0 0 2 2 0 0 NA 0 0 2 0 0 0 0 2 2 NA 0 0 0 0 0 0 0 0 2 0 0 0 NA 0 0 2 2 NA 2 0 0 2 0 0 2 0 0
chr1A 94403 chr1A_94403 C T 0 0 0 0 0 0 0 0 NA 0 0 0 2 0 0 0 2 NA 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 2 0 0 2 2 2 NA 2 0 0 0 2 2 0 0 0
chr1A 107209 chr1A_107209 C T 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 NA 0 1 0 0 2 2 2 2 2 1 0 2 0 2 0 0 0 2 1 0 0 1 NA 0 2 2 0 0 0 0 0 0 0 2 2 2 0 1 0 0 0 0 2 2 0 NA 0 0 2 2 0 2 0 2 0 2 1 2 0 2 1 0 0 NA 0 0 0 0 0
chr1A 133475 chr1A_133475 C T 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 NA 0 0 0 0 0 0 0 0 0
chr1A 146430 chr1A_146430 C A 0 0 0 0 2 0 2 0 NA 0 2 0 0 2 2 NA 0 NA 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0 0 NA 0 0 2 2 NA 2 0 0 2 0 0 2 0 0
The eQTL data must contain four columns: column 1 should be the Gene ID, column 2 should be the associated eQTL with the Gene, column 3 should represent the SNP most significantly associated with the Gene in the eQTL, and column 4 should include the P-value corresponding to the SNP. Here is the example of the eQTLs data:
GeneID eQTL SNP Pvalue
TraesCS1A02G000900 chr1A:1157892-1237436 chr1A_1176830 1.1521e-09
TraesCS1A02G000900 chr1A:1338726-1347248 chr1A_1346169 3.058955e-09
TraesCS1A02G000900 chr1A:2849218-2849335 chr1A_2849335 1.335856e-07
TraesCS1A02G000900 chr1A:3117209-3262121 chr1A_3262110 1.891741e-09
TraesCS1A02G000900 chr1A:3380025-3451632 chr1A_3388319 4.752083e-10
TraesCS1A02G000900 chr1A:3677521-3845279 chr1A_3761363 2.08425e-08
TraesCS1A02G002300 chr1A:505505-523633 chr1A_522091 6.325763e-18
TraesCS1A02G002300 chr1A:699531-800903 chr1A_799864 4.331337e-18
TraesCS1A02G002300 chr1A:1144542-1347913 chr1A_1170182 3.972977e-17
By considering the SNP with the strongest association signal within an interval as an eQTL, the eQTLs data can be similar to the following:
GeneID eQTL SNP Pvalue
TraesCS1A02G000900 chr1A_1176830 chr1A_1176830 1.1521e-09
TraesCS1A02G000900 chr1A_1346169 chr1A_1346169 3.058955e-09
TraesCS1A02G000900 chr1A_2849335 chr1A_2849335 1.335856e-07
TraesCS1A02G000900 chr1A_3262110 chr1A_3262110 1.891741e-09
TraesCS1A02G000900 chr1A_3388319 chr1A_3388319 4.752083e-10
TraesCS1A02G000900 chr1A_3761363 chr1A_3761363 2.08425e-08
TraesCS1A02G002300 chr1A_522091 chr1A_522091 6.325763e-18
TraesCS1A02G002300 chr1A_799864 chr1A_799864 4.331337e-18
TraesCS1A02G002300 chr1A_1170182 chr1A_1170182 3.972977e-17
The gene expression levels data consists of rows and columns representing samples and genes, respectively. Each row corresponds to a sample, and each column corresponds to a gene. The first column contains sample IDs, and the remaining columns contain expression levels for each gene. The values in each cell indicate the expression level of the corresponding gene for the respective sample. Here is an example of the gene expression levels data:
SampleID TraesCS1A02G000900 TraesCS1A02G002300 TraesCS1A02G005500 TraesCS1A02G005600 TraesCS1A02G005700
WMG001A -0.272017061710358 0.743909537792206 0.194734156131744 -0.107295274734497 0.372792392969131
WMG002A 2.04824256896973 0.620612382888794 0.757254421710968 0.0532063841819763 0.894102334976196
WMG003A -1.4418785572052 0.157659411430359 0.516654014587402 0.460377097129822 0.940327048301697
WMG004A -0.149225533008575 -0.128475815057755 0.132039785385132 -0.28729373216629 0.68341463804245
WMG005A -1.23140954971313 0.283700793981552 -0.530603528022766 -0.0574047863483429 0.256372392177582
WMG006A -1.18248176574707 1.38488328456879 -0.199847996234894 0.326062798500061 0.780471563339233
WMG007A -0.330554485321045 0.0447497069835663 -0.0657132938504219 -0.539554834365845 0.150831282138824
WMG008A -0.462489426136017 0.230420559644699 0.780251502990723 0.3719222843647 1.0348916053772
WMG009A -1.07727098464966 -0.680115222930908 0.573799729347229 0.287048280239105 0.476123720407486
The phenotypic data contains two columns, in which each row represents a single sample, and the first column contains the sample ID, the second column contains the corresponding phenotypic value for that sample. Here is an example of the phenotypic data:
SampleID Value
WMG001A 15.97
WMG002A NA
WMG003A NA
WMG004A NA
WMG005A 15.63
WMG006A 6.67
WMG007A 28.89
WMG008A 34.44
WMG009A 6.67
After you have already prepared the input datasets as requested, you can use the script MR_analysis.R to perform MR analysis. If this is your first time using the script, type its name followed by -h to display help information.
cmd> Rscript MR_analysis.R -h
Loading required package: parallel
Usage: MR_analysis.R [-h] -G FILENAME -E FILENAME -P FILENAME -Q FILENAME -d DIRECTORY -p outprefix [-n INT]
Perform the MR analysis to reveal the relationship between gene expression levels and the phenotype.
Note: Please see the https://github.com/bingochenbin/MRkit for details.
Options:
-G FILENAME, --geno_input=FILENAME
The SNP genotyping data file <required>
-E FILENAME, --expr_input=FILENAME
The gene expression levels data file <required>
-P FILENAME, --pheno_input=FILENAME
The phenotype data file <required>
-Q FILENAME, --eqtl_input=FILENAME
The eQTLs data file <required>
-d DIRECTORY, --outdir=DIRECTORY
Path to outdir <required>
-p STR, --outprefix=STR
Prefix to output filename <required>
-n INT, --cpu=INT
Number of cpu cores to use [default: 1]
-h, --help
Show this help message and exit
Then, you can use it to perform MR analysis similar to the following example:
Rscript /path/to/MRkit/scripts/MR_analysis.R -G SNPgeno_MR.txt -E MR_expr.txt -P MR_pheno.txt -Q MR_eQTLs.txt -d ./ -p MR_result_testAfter performing MR analysis, the main output file will be outprefix.MR.txt. This file contains seven columns: column 1 is the Gene ID, columns 2-4 are the SNP and its physical position on the genome, which is significantly associated with the Gene, column 5 is the estimated effect size of the gene expression level on the phenotype free of confounding from non-genetic factors, column 6 is the value of the test statistic
GeneID TopSNP CHROM POS Effect TMR Pvalue FDR
TraesCS1A02G000900 chr1A_3388319 chr1A 3388319 4.13926110466306 0.931073513546659 0.334584128542825 0.448391951424399
TraesCS1A02G002300 chr1A_799864 chr1A 799864 -4.91637110673723 4.19837301887306 0.0404627826004724 0.121388347801417
TraesCS1A02G005500 chr1A_3116428 chr1A 3116428 -7.13784650537219 0.574713503382365 0.448391951424399 0.448391951424399
Change the directory to the example and run the run_MRanalysis.sh to perform MR analysis using the example data. This will enable you to verify that the scripts run correctly and also allow you to observe the input and output datasets.
cd example && nohup bash run_MRanalysis.sh > run_MRanalysis.out
MRkit is released under Apache 2.0. The latest source code is freely available at github.
Please don't hesitate to leave a message at github Issues if you encounter any bugs or issues. We will try our best to deal with all issues as soon as possible. In addition, if any suggestions are available, feel free to contact: Bin Chen [email protected].
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Ference, B. A., Yoo, W., Alesh, I., Mahajan, N., Mirowska, K. K., Mewada, A., Kahn, J., Afonso, L., Williams, K. A., Sr, & Flack, J. M. (2012). Effect of long-term exposure to lower low-density lipoprotein cholesterol beginning early in life on the risk of coronary heart disease: a Mendelian randomization analysis. Journal of the American College of Cardiology, 60(25), 2631–2639. https://doi.org/10.1016/j.jacc.2012.09.017
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Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M. R., Powell, J. E., Montgomery, G. W., Goddard, M. E., Wray, N. R., Visscher, P. M., & Yang, J. (2016). Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nature genetics, 48(5), 481–487. https://doi.org/10.1038/ng.3538
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Liu, S., Li, C., Wang, H., Wang, S., Yang, S., Liu, X., Yan, J., Li, B., Beatty, M., Zastrow-Hayes, G., Song, S., & Qin, F. (2020). Mapping regulatory variants controlling gene expression in drought response and tolerance in maize. Genome biology, 21(1), 163. https://doi.org/10.1186/s13059-020-02069-1
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Pierce, B. L., Ahsan, H., & Vanderweele, T. J. (2011). Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. International journal of epidemiology, 40(3), 740–752. https://doi.org/10.1093/ije/dyq151