IMRP

MR_pleio is a R program to perform Iterative Memdelian Randomization and Pleiotropy analysis (IMRP, Zhu et al. An iterative approach to detect pleiotropy and perform Mendelian

Randomization analysis using GWAS summary statistics", Bioinformatics, 2020). To call the program, the summary statistics of instrumental variables for the exposure and outcome

need to be combined into a single data set, with matched reference and effect alleles. We suggested to standardize the summary statistics before performing IMRP, although this is

not a necessary step. We provide an example of MR analysis of HDL on CAD analyzed in the manuscript. The HLD data was downloaded from http://csg.sph.umich.edu/abecasis/

public/lipids2013/; CAD data was downloaded from http://www.cardiogramplusc4d.org/data-downloads/. If you have any questions, please contact Xiaofeng Zhu (xxz10@case.edu).

To run the program, type

library(devtools) install_github("XiaofengZhuCase/IMRP") library("IMRP")

The combined HDL and CAD data is named HDLCAD1.csv with each columns representing "SNP","Chr","Position","A1","A2","HDL_beta","HDL_se","HDL_N", "CAD_freq","CAD_beta","CAD_se".

Here HDL is a continuous trait and CAD is a binary trait. This approach is the same as suggested in the software MRmix (Qi and Chatterjee, Nat Commun 2019)

Standardizing summary statistics

HDLCAD1$x1=HDLCAD1$HDL_beta/HDLCAD1$HDL_se/sqrt(HDLCAD1$HDL_N) HDLCAD1$x2=HDLCAD1$CAD_betasqrt(2HDLCAD1$CAD_freq*(1-HDLCAD1$CAD_freq)) HDLCAD1$x1_se=1/sqrt(HDLCAD1$HDL_N) HDLCAD1$x2_se=HDLCAD1$CAD_sesqrt(2HDLCAD1$CAD_freq*(1-HDLCAD1$CAD_freq))

to call MR_pleio,

HDLCAD1_out=MR_pleio("x2","x1","x2_se","x1_se",as.data.frame(HDLCAD1),SignifThreshold=0.05,rho=0.03, method="IVW")

In above function, 0.05 is the threshold to define outliers in pleiotropic variants. This value can be changed depending on that more or less potential pleiiotropic variants will be excluded.

rho=0.03 is the correlation coefficient estimated using the genome wide summary statistics of HDL and CAD.

output

HDLCAD1_out $CausalEstimate Beta -0.07497658 ### causal effect estimate

$SdCausalEstimate SE ### standard error of causal effect estimate 0.02682728

$Causal_p ### p value for testing causal effect=0 Beta 0.005193358

$SNPPvalue ### P-values of testing for pleiotropic effects of each instrumental variables [1] 7.540308e-01 1.213973e-01 1.765340e-02 7.072475e-01 1.371316e-01 [6] 1.614926e-01 4.872331e-01 4.480403e-05 1.724450e-04 4.013943e-01 [11] 8.098048e-01 5.771981e-01 1.376007e-02 6.629121e-01 3.385389e-01 [16] 8.395957e-02 9.293667e-02 8.796184e-01 6.673686e-01 6.327465e-01 [21] 6.923009e-01 5.909383e-22 6.007826e-01 1.087855e-05 6.387741e-01 [26] 8.385293e-01 6.110703e-01 8.234167e-02 1.686317e-02 2.583850e-01 [31] 4.460922e-01 3.500133e-01 4.801952e-04 7.470540e-04 8.292815e-03 [36] 7.654568e-03 4.717011e-01 1.604432e-01 1.487996e-01 1.520034e-02 [41] 4.610298e-01 7.022568e-01 6.825129e-03 2.505968e-08 5.600326e-01 [46] 1.813336e-01 4.364929e-01 3.351774e-01 5.669551e-01 1.036621e-01 [51] 3.854098e-03 3.962321e-01 8.243925e-01 2.062745e-02 1.742970e-02 [56] 3.078318e-03 6.822560e-06 9.713822e-01 3.447723e-01 4.875966e-01 [61] 6.659414e-02 2.844937e-01 4.063577e-01 2.046420e-01 3.032017e-02 [66] 3.145641e-02 6.247301e-01 9.638166e-01 8.065951e-01 8.263397e-02 [71] 3.664172e-01 7.131909e-01 9.402118e-01 6.119039e-01 6.127570e-01 [76] 5.716073e-01 5.651548e-02 8.898345e-01 7.170033e-02 1.803775e-01 [81] 2.607218e-02 7.432054e-03 6.443119e-01 4.776510e-05 5.723056e-01 [86] 5.056964e-01 5.830633e-01 9.254104e-04 8.278921e-02 7.677411e-03 [91] 8.723933e-01 7.311049e-01 8.262254e-01 7.928084e-01 3.879858e-01 [96] 9.880206e-01 1.752683e-03 1.509789e-02 9.984555e-03 3.057486e-03 [101] 1.711400e-01 2.661010e-01 3.290598e-01 4.517062e-03 1.185573e-02 [106] 6.981939e-01 1.150645e-01 7.323114e-01 2.170176e-01 9.441846e-01 [111] 5.357276e-01 3.590136e-01 3.993563e-04 8.469080e-03 6.381885e-03 [116] 1.837256e-09 1.260571e-07 3.778442e-01 1.708251e-01 2.067299e-01 [121] 1.443810e-03 2.713742e-02 2.776027e-01 3.117851e-02 6.351858e-01 [126] 2.614562e-01 7.124180e-03 2.271693e-01 8.221943e-02 3.328679e-02 [131] 4.093434e-01 8.268081e-01 2.037193e-02 5.976885e-03 8.657520e-02 [136] 1.010016e-01 2.227204e-01 7.530752e-01 6.208741e-04 5.392791e-01 [141] 6.976097e-01 4.913947e-05 8.697273e-01

$PleioOutlier #### pleiotropic variants outliers with P<0.05 [1] 3 8 9 13 22 24 29 33 34 35 36 40 43 44 51 54 55 56 57 [20] 65 66 81 82 84 88 90 97 98 99 100 104 105 113 114 115 116 117 121 [39] 122 124 127 130 133 134 139 142

$GlobalPvalue_pre #### global P value for testing if tere are any pleiotropic variants among the instrumental variables. [1] 1.393238e-65

$GlobalPvalue_aft ### global P value for testing if there are still pleiotropic variants after excluding pleiotropic variants. [1] 0.6019805

$chisquare ### chisqure statistics for testing if tere are any pleiotropic variants among the instrumental variables, following a chisqure distribution with df=number of the instrumental variables-1. [1] 647.4692