├── NAMESPACE ├── man ├── MRAID_CPP.Rd └── MRAID.Rd ├── src ├── Makevars ├── Makevars.win ├── RcppExports.cpp └── MRAID_CPP.cpp ├── R ├── RcppExports.R └── MRAID.R ├── example ├── examplecode.txt ├── Zscore_1.txt ├── Zscore_2.txt └── Sigma1sin.txt ├── DESCRIPTION └── README.md /NAMESPACE: -------------------------------------------------------------------------------- 1 | useDynLib(MRAID, .registration=TRUE) 2 | importFrom(Rcpp, evalCpp) 3 | exportPattern("^[[:alpha:]]+") 4 | -------------------------------------------------------------------------------- /man/MRAID_CPP.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/RcppExports.R 3 | \name{MRAID_CPP} 4 | \alias{MRAID_CPP} 5 | \title{MRAID} 6 | \usage{ 7 | MRAID_CPP( 8 | betaxin, 9 | betayin, 10 | Sigma1sin, 11 | Sigma2sin, 12 | samplen1, 13 | samplen2, 14 | Gibbsnumberin, 15 | burninproportion, 16 | initial_betain, 17 | pi_beta_shape_in, 18 | pi_beta_scale_in, 19 | pi_c_shape_in, 20 | pi_c_scale_in, 21 | pi_1_shape_in, 22 | pi_1_scale_in, 23 | pi_0_shape_in, 24 | pi_0_scale_in, 25 | maxvarin, 26 | alphain 27 | ) 28 | } 29 | \description{ 30 | MRAID 31 | } 32 | -------------------------------------------------------------------------------- /src/Makevars: -------------------------------------------------------------------------------- 1 | 2 | ## With R 3.1.0 or later, you can uncomment the following line to tell R to 3 | ## enable compilation with C++11 (where available) 4 | ## 5 | ## Also, OpenMP support in Armadillo prefers C++11 support. However, for wider 6 | ## availability of the package we do not yet enforce this here. It is however 7 | ## recommended for client packages to set it. 8 | ## 9 | ## And with R 3.4.0, and RcppArmadillo 0.7.960.*, we turn C++11 on as OpenMP 10 | ## support within Armadillo prefers / requires it 11 | CXX_STD = CXX11 12 | 13 | PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) 14 | PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) 15 | -------------------------------------------------------------------------------- /src/Makevars.win: -------------------------------------------------------------------------------- 1 | 2 | ## With R 3.1.0 or later, you can uncomment the following line to tell R to 3 | ## enable compilation with C++11 (where available) 4 | ## 5 | ## Also, OpenMP support in Armadillo prefers C++11 support. However, for wider 6 | ## availability of the package we do not yet enforce this here. It is however 7 | ## recommended for client packages to set it. 8 | ## 9 | ## And with R 3.4.0, and RcppArmadillo 0.7.960.*, we turn C++11 on as OpenMP 10 | ## support within Armadillo prefers / requires it 11 | CXX_STD = CXX11 12 | 13 | PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) 14 | PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) 15 | -------------------------------------------------------------------------------- /R/RcppExports.R: -------------------------------------------------------------------------------- 1 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand 2 | # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 3 | 4 | #' MRAID 5 | #' 6 | #' @export 7 | MRAID_CPP <- function(betaxin, betayin, Sigma1sin, Sigma2sin, samplen1, samplen2, Gibbsnumberin, burninproportion, initial_betain, pi_beta_shape_in, pi_beta_scale_in, pi_c_shape_in, pi_c_scale_in, pi_1_shape_in, pi_1_scale_in, pi_0_shape_in, pi_0_scale_in, maxvarin, alphain) { 8 | .Call(`_MRAID_MRAID_CPP`, betaxin, betayin, Sigma1sin, Sigma2sin, samplen1, samplen2, Gibbsnumberin, burninproportion, initial_betain, pi_beta_shape_in, pi_beta_scale_in, pi_c_shape_in, pi_c_scale_in, pi_1_shape_in, pi_1_scale_in, pi_0_shape_in, pi_0_scale_in, maxvarin, alphain) 9 | } 10 | 11 | -------------------------------------------------------------------------------- /example/examplecode.txt: -------------------------------------------------------------------------------- 1 | #Sumamry data with 100 candidate correlated SNPs, n1=30000, n2=30000, 2 | library(MRAID) 3 | 4 | #load the Zscore vector for the exposure 5 | x<-read.table("Zscore_1.txt") 6 | Zscore_1<-as.vector(x[[1]]) 7 | 8 | #load the Zscore vector for the outcome 9 | y<-read.table("Zscore_2.txt") 10 | Zscore_2<-as.vector(y[[1]]) 11 | 12 | #load the LD matrix in exposure GWAS data 13 | zx<-read.table("Sigma1sin.txt") 14 | Sigma1sin<-as.matrix(zx) 15 | 16 | #load the LD matrix in outcome GWAS data 17 | zy<-read.table("Sigma2sin.txt") 18 | Sigma2sin<-as.matrix(zy) 19 | 20 | #load the sample size 21 | samplen1=30000 22 | samplen2=30000 23 | 24 | #run MRAID 25 | result<-MRAID(Zscore_1, Zscore_2, Sigma1sin, Sigma2sin, samplen1, samplen2, Gibbsnumber=1000,burninproportion=0.2,pi_beta_shape=0.5, 26 | pi_beta_scale=4.5,pi_c_shape=0.5,pi_c_scale=9.5,pi_1_shape=0.5,pi_1_scale=1.5,pi_0_shape=0.05,pi_0_scale=9.95) 27 | 28 | #result -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: MRAID 2 | Type: Package 3 | Title: MRAID(Mendelian Randomization with Automated Instrument 4 | Determination) 5 | Version: 1.0 6 | Date: 2021-06-05 7 | Author: Zhongshang Yuan and Lu Liu 8 | Maintainer: Zhongshang Yuan 9 | Description: MRAID is an R package for efficient statistical inference of two-sample Mendelian Randomization. MRAID takes GWAS summary statistics as inputs to estimate causal effects of one trait on another. MRAID is able to model an initial set of candidate SNP instruments that are in high LD with each other and perform automated instrument selection to identify suitable SNPs to serve as instrumental variables. MRAID simultaneously accounts for both uncorrelated and correlated horizontal pleiotropy, relies on a scalable sampling-based inference algorithm to perform numerical integration, circumventing the difficulty in likelihood function, leading to calibrated p-values that enable reasonably large-scale exposure screening. 10 | License: GPL-3 11 | Imports: Rcpp (>= 1.0.6) 12 | LinkingTo: Rcpp, RcppArmadillo, RcppDist 13 | RoxygenNote: 7.1.1 14 | NeedsCompilation: yes 15 | Packaged: 2021-06-05 01:07:04 UTC; liulu 16 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MRAID 2 | 3 | MRAID(Mendelian Randomization with Automated Instrument Determination),is an R package for efficient statistical inference of two-sample Mendelian Randomization. MRAID takes GWAS summary statistics as inputs to estimate causal effects of one trait on another. MRAID is able to model an initial set of candidate SNP instruments that are in high LD with each other and perform automated instrument selection to identify suitable SNPs to serve as instrumental variables. MRAID simultaneously accounts for both uncorrelated and correlated horizontal pleiotropy, relies on a scalable sampling-based inference algorithm to perform numerical integration, circumventing the difficulty in likelihood function, leading to calibrated p-values that enable reasonably large-scale exposure screening. 4 | 5 | This approach is described in, 6 | > Yuan Z, Liu L, Guo P, Yan R, Xue F, Zhou X. [Likelihood-based Mendelian randomization analysis with automated instrument selection and horizontal pleiotropic modeling](https://xzlab.org/papers/2022_Yuan_etal_SA.pdf). Science Advances 8, eabl5744. 7 | 8 | # Installation 9 | It is easy to install the development version of MRAID package using the 'devtools' package. 10 | 11 | ``` 12 | # install.packages("devtools") 13 | library(devtools) 14 | install_github("yuanzhongshang/MRAID") 15 | ``` 16 | # Usage 17 | The main function in the package is MRAID, you can find the instructions by '?MRAID'. 18 | ``` 19 | library(MRAID) 20 | 21 | ?MRAID 22 | ``` 23 | 24 | # Example 25 | One simple example to use the package can be found at https://github.com/yuanzhongshang/MRAID/tree/master/example 26 | 27 | # Development 28 | This R package is developed by Zhongshang Yuan and Lu Liu. 29 | -------------------------------------------------------------------------------- /example/Zscore_1.txt: -------------------------------------------------------------------------------- 1 | "x" 2 | "1" 12.3007640811616 3 | "2" 13.0155343565996 4 | "3" 7.20981590654004 5 | "4" 9.50501661989846 6 | "5" 8.45140321627226 7 | "6" 6.39772218577067 8 | "7" 8.74103612702631 9 | "8" 5.68610596068957 10 | "9" 8.49696574623466 11 | "10" 5.55853495897943 12 | "11" 5.55853495897943 13 | "12" 5.53521180848738 14 | "13" 5.49483509844158 15 | "14" 5.45866459573998 16 | "15" 6.05732492163597 17 | "16" 6.06151286474462 18 | "17" 6.05757731572311 19 | "18" 6.02631484394979 20 | "19" 6.03325622527556 21 | "20" 6.00418458528389 22 | "21" 6.4224240176092 23 | "22" 6.42843510430295 24 | "23" 6.46020810752527 25 | "24" 6.4701627592558 26 | "25" 6.45240929871123 27 | "26" 6.35429151515727 28 | "27" 6.4393515964247 29 | "28" 6.39112157968057 30 | "29" 5.90396336333804 31 | "30" 5.90508408297839 32 | "31" 5.94919294517477 33 | "32" 5.93415583618375 34 | "33" 5.8809054301975 35 | "34" 5.9566920725283 36 | "35" 6.03238399919426 37 | "36" 6.03238399919426 38 | "37" 6.06047316876714 39 | "38" 6.08613123171828 40 | "39" 5.99504417523731 41 | "40" 6.06577852620256 42 | "41" 6.2571658443346 43 | "42" 6.36465108037266 44 | "43" 6.36465108037266 45 | "44" 6.2998616390844 46 | "45" 6.2998616390844 47 | "46" 6.2998616390844 48 | "47" 6.2998616390844 49 | "48" 6.29613073473846 50 | "49" 5.64401521420806 51 | "50" 6.61401864625851 52 | "51" 6.52993169686443 53 | "52" 6.5518102300096 54 | "53" 5.69447267750459 55 | "54" 5.71097000288996 56 | "55" 5.71082862538913 57 | "56" 5.62458777322352 58 | "57" 5.6223398120051 59 | "58" 5.6223398120051 60 | "59" 7.39152407297886 61 | "60" 5.63277045803952 62 | "61" 5.63277045803952 63 | "62" 6.6366904227619 64 | "63" 8.93179841157407 65 | "64" 8.92023956665625 66 | "65" 8.8648495418734 67 | "66" 8.86638432891286 68 | "67" 8.90210518302067 69 | "68" 6.62994643983891 70 | "69" 8.97552547461288 71 | "70" 8.9582335516503 72 | "71" 7.40281157142507 73 | "72" 8.9582335516503 74 | "73" 5.70968712441086 75 | "74" 6.31483998832416 76 | "75" 8.95212941052673 77 | "76" 8.95239106447281 78 | "77" 8.96085444923941 79 | "78" 6.62247808934161 80 | "79" 8.92380586584626 81 | "80" 8.95865843594174 82 | "81" 8.95678228197543 83 | "82" 6.6733534386282 84 | "83" 5.71514830231925 85 | "84" 6.47424168044434 86 | "85" 6.6733534386282 87 | "86" 5.55038403301897 88 | "87" 6.71517115743689 89 | "88" 6.73276970783746 90 | "89" 6.71997291423365 91 | "90" 5.53041728629797 92 | "91" 5.57642467287172 93 | "92" 6.53630120202851 94 | "93" 5.60993542764034 95 | "94" 6.69494291005589 96 | "95" 5.61225231252673 97 | "96" 5.60422458618369 98 | "97" 5.62073658651099 99 | "98" 6.68663518992735 100 | "99" 6.45257475293146 101 | "100" 6.55355518153561 102 | -------------------------------------------------------------------------------- /example/Zscore_2.txt: -------------------------------------------------------------------------------- 1 | "x" 2 | "1" 1.23654997936635 3 | "2" 1.2000109853741 4 | "3" 1.37694013879096 5 | "4" 0.652969050966092 6 | "5" 0.540665115603461 7 | "6" 0.835483897448862 8 | "7" 1.62566356447441 9 | "8" 1.2179346066904 10 | "9" 0.597521158150172 11 | "10" 0.162344922098952 12 | "11" 0.177556188595376 13 | "12" 0.173402386193619 14 | "13" 0.110977490880113 15 | "14" 0.339170885325386 16 | "15" 0.306134670248526 17 | "16" 0.313275155496149 18 | "17" 0.197058607497716 19 | "18" 0.251774661773661 20 | "19" 0.21867614532692 21 | "20" 0.250089528462368 22 | "21" 0.117315325488914 23 | "22" 0.111783926638019 24 | "23" 0.143766614352246 25 | "24" 0.111783926638019 26 | "25" 0.0708606028912026 27 | "26" 0.158402603794519 28 | "27" 0.106593274447511 29 | "28" 0.23549350670707 30 | "29" -0.556060171527803 31 | "30" -0.567863022551997 32 | "31" -0.262208724455497 33 | "32" -0.220423778234558 34 | "33" -0.149635198279412 35 | "34" -0.158834497973518 36 | "35" -0.102431780114884 37 | "36" -0.0940176327695628 38 | "37" -0.121208003959177 39 | "38" -0.140804703296962 40 | "39" -0.0301574134958394 41 | "40" -0.0293294800478858 42 | "41" 0.0133579975752419 43 | "42" 0.0222600219396292 44 | "43" 0.0211025535882495 45 | "44" -0.0153114099115863 46 | "45" -0.0153114099115863 47 | "46" -0.0153114099115863 48 | "47" -0.0153114099115863 49 | "48" -0.0153114099115863 50 | "49" 0.238977162811249 51 | "50" -0.195065068982085 52 | "51" -0.343188791022793 53 | "52" -0.236261464332279 54 | "53" 1.20575117112605 55 | "54" 1.20653338773373 56 | "55" 1.20653338773373 57 | "56" 1.0455979894985 58 | "57" 1.0455979894985 59 | "58" 1.0455979894985 60 | "59" 0.0890364789832191 61 | "60" 1.05731840858135 62 | "61" 1.0455979894985 63 | "62" -0.397805252975995 64 | "63" 0.592189296171968 65 | "64" 0.611241155131378 66 | "65" 0.605728306593382 67 | "66" 0.599180921625841 68 | "67" 0.632411346495931 69 | "68" -0.267626527651116 70 | "69" 0.608386519170623 71 | "70" 0.615802828144256 72 | "71" 0.0571148414910243 73 | "72" 0.630065515733199 74 | "73" 1.16434882245808 75 | "74" -0.131312732989087 76 | "75" 0.639478460929715 77 | "76" 0.638101145174938 78 | "77" 0.622750506641557 79 | "78" -0.293370735022469 80 | "79" 0.628657359913338 81 | "80" 0.62687768695971 82 | "81" 0.615442285167115 83 | "82" -0.254430548798927 84 | "83" 1.14473193483474 85 | "84" -0.534169688891517 86 | "85" -0.250702720316407 87 | "86" 1.21206581699591 88 | "87" -0.247168134099522 89 | "88" -0.241812684715444 90 | "89" -0.321466035518769 91 | "90" 1.19667339687514 92 | "91" 1.16615281900335 93 | "92" -0.329154876189738 94 | "93" 1.22080261858269 95 | "94" -0.263574208942294 96 | "95" 1.23213142293383 97 | "96" 1.27378613411338 98 | "97" 1.25344398889194 99 | "98" -0.177819333988492 100 | "99" -0.325154416375683 101 | "100" -0.195765821366979 102 | -------------------------------------------------------------------------------- /man/MRAID.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/MRAID.R 3 | \name{MRAID} 4 | \alias{MRAID} 5 | \title{The function of MRAID method two sample Mendelian Randomization} 6 | \usage{ 7 | MRAID( 8 | Zscore_1, 9 | Zscore_2, 10 | Sigma1sin, 11 | Sigma2sin, 12 | samplen1, 13 | samplen2, 14 | Gibbsnumber = 1000, 15 | burninproportion = 0.2, 16 | pi_beta_shape = 0.5, 17 | pi_beta_scale = 4.5, 18 | pi_c_shape = 0.5, 19 | pi_c_scale = 9.5, 20 | pi_1_shape = 0.5, 21 | pi_1_scale = 1.5, 22 | pi_0_shape = 0.05, 23 | pi_0_scale = 9.95 24 | ) 25 | } 26 | \arguments{ 27 | \item{Zscore_1}{the Zscore vector of the SNP effect size vector for the exposure} 28 | 29 | \item{Zscore_2}{the Zscore vector of the SNP effect size vector for the outcome} 30 | 31 | \item{Sigma1sin}{the LD matrix for the SNPs in the exposure GWAS data} 32 | 33 | \item{Sigma2sin}{the LD matrix for the SNPs in the outcome GWAS data,both Sigma2sin and sigma1sin are often from the same reference panel} 34 | 35 | \item{samplen1}{the sample size of exposure GWAS} 36 | 37 | \item{samplen2}{the sample size of outcome GWAS} 38 | 39 | \item{Gibbsnumber}{the number of Gibbs sampling iterations with the default to be 1000} 40 | 41 | \item{burninproportion}{the proportion to burn in from Gibbs sampling iterations, with default to be 0.2} 42 | 43 | \item{pi_beta_shape}{the prior shape paramter for pi_beta with the default to be 0.5} 44 | 45 | \item{pi_beta_scale}{the prior scale paramter for pi_beta with the default to be 4.5} 46 | 47 | \item{pi_c_shape}{the prior shape paramter for pi_c with the default to be 0.5} 48 | 49 | \item{pi_c_scale}{the prior shape paramter for pi_c with the default to be 9.5} 50 | 51 | \item{pi_1_shape}{the prior shape paramter for pi_1 with the default to be 0.5} 52 | 53 | \item{pi_1_scale}{the prior scale paramter for pi_1 with the default to be 1.5} 54 | 55 | \item{pi_0_shape}{the prior shape paramter for pi_0 with the default to be 0.05} 56 | 57 | \item{pi_0_scale}{the prior scale paramter for pi_0 with the default to be 9.95} 58 | } 59 | \value{ 60 | A list of estimated parameters including the p values for the causal effect test 61 | \item{causal_effect}{The estimate of causal effect} 62 | \item{causal_pvalue}{The p value for the causal effect} 63 | \item{correlated_pleiotropy_effect}{The estimate of correlated pleiotropy effect} 64 | \item{sigmaeta}{The variance estimate for the uncorrelated pleiotropy effect} 65 | \item{sigmabeta}{The variance estimate for the SNP effect sizes on the exposure} 66 | \item{sigma_error_1}{The variance estimate of the error in exposure GWAS model} 67 | \item{sigma_error_2}{The variance estimate of the error in outcome GWAS model} 68 | } 69 | \description{ 70 | MRAID is able to model an initial set of candidate SNP instruments that are in high LD with each other and perform automated instrument selection to identify suitable SNPs to serve as instrumental variables. MRAID simultaneously accounts for both uncorrelated and correlated horizontal pleiotropy, relies on a scalable sampling-based inference algorithm to perform numerical integration, circumventing the difficulty in likelihood function 71 | } 72 | -------------------------------------------------------------------------------- /src/RcppExports.cpp: -------------------------------------------------------------------------------- 1 | // Generated by using Rcpp::compileAttributes() -> do not edit by hand 2 | // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 3 | 4 | #include 5 | #include 6 | 7 | using namespace Rcpp; 8 | 9 | #ifdef RCPP_USE_GLOBAL_ROSTREAM 10 | Rcpp::Rostream& Rcpp::Rcout = Rcpp::Rcpp_cout_get(); 11 | Rcpp::Rostream& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get(); 12 | #endif 13 | 14 | // MRAID_CPP 15 | SEXP MRAID_CPP(SEXP betaxin, SEXP betayin, SEXP Sigma1sin, SEXP Sigma2sin, SEXP samplen1, SEXP samplen2, SEXP Gibbsnumberin, SEXP burninproportion, SEXP initial_betain, SEXP pi_beta_shape_in, SEXP pi_beta_scale_in, SEXP pi_c_shape_in, SEXP pi_c_scale_in, SEXP pi_1_shape_in, SEXP pi_1_scale_in, SEXP pi_0_shape_in, SEXP pi_0_scale_in, SEXP maxvarin, SEXP alphain); 16 | RcppExport SEXP _MRAID_MRAID_CPP(SEXP betaxinSEXP, SEXP betayinSEXP, SEXP Sigma1sinSEXP, SEXP Sigma2sinSEXP, SEXP samplen1SEXP, SEXP samplen2SEXP, SEXP GibbsnumberinSEXP, SEXP burninproportionSEXP, SEXP initial_betainSEXP, SEXP pi_beta_shape_inSEXP, SEXP pi_beta_scale_inSEXP, SEXP pi_c_shape_inSEXP, SEXP pi_c_scale_inSEXP, SEXP pi_1_shape_inSEXP, SEXP pi_1_scale_inSEXP, SEXP pi_0_shape_inSEXP, SEXP pi_0_scale_inSEXP, SEXP maxvarinSEXP, SEXP alphainSEXP) { 17 | BEGIN_RCPP 18 | Rcpp::RObject rcpp_result_gen; 19 | Rcpp::RNGScope rcpp_rngScope_gen; 20 | Rcpp::traits::input_parameter< SEXP >::type betaxin(betaxinSEXP); 21 | Rcpp::traits::input_parameter< SEXP >::type betayin(betayinSEXP); 22 | Rcpp::traits::input_parameter< SEXP >::type Sigma1sin(Sigma1sinSEXP); 23 | Rcpp::traits::input_parameter< SEXP >::type Sigma2sin(Sigma2sinSEXP); 24 | Rcpp::traits::input_parameter< SEXP >::type samplen1(samplen1SEXP); 25 | Rcpp::traits::input_parameter< SEXP >::type samplen2(samplen2SEXP); 26 | Rcpp::traits::input_parameter< SEXP >::type Gibbsnumberin(GibbsnumberinSEXP); 27 | Rcpp::traits::input_parameter< SEXP >::type burninproportion(burninproportionSEXP); 28 | Rcpp::traits::input_parameter< SEXP >::type initial_betain(initial_betainSEXP); 29 | Rcpp::traits::input_parameter< SEXP >::type pi_beta_shape_in(pi_beta_shape_inSEXP); 30 | Rcpp::traits::input_parameter< SEXP >::type pi_beta_scale_in(pi_beta_scale_inSEXP); 31 | Rcpp::traits::input_parameter< SEXP >::type pi_c_shape_in(pi_c_shape_inSEXP); 32 | Rcpp::traits::input_parameter< SEXP >::type pi_c_scale_in(pi_c_scale_inSEXP); 33 | Rcpp::traits::input_parameter< SEXP >::type pi_1_shape_in(pi_1_shape_inSEXP); 34 | Rcpp::traits::input_parameter< SEXP >::type pi_1_scale_in(pi_1_scale_inSEXP); 35 | Rcpp::traits::input_parameter< SEXP >::type pi_0_shape_in(pi_0_shape_inSEXP); 36 | Rcpp::traits::input_parameter< SEXP >::type pi_0_scale_in(pi_0_scale_inSEXP); 37 | Rcpp::traits::input_parameter< SEXP >::type maxvarin(maxvarinSEXP); 38 | Rcpp::traits::input_parameter< SEXP >::type alphain(alphainSEXP); 39 | rcpp_result_gen = Rcpp::wrap(MRAID_CPP(betaxin, betayin, Sigma1sin, Sigma2sin, samplen1, samplen2, Gibbsnumberin, burninproportion, initial_betain, pi_beta_shape_in, pi_beta_scale_in, pi_c_shape_in, pi_c_scale_in, pi_1_shape_in, pi_1_scale_in, pi_0_shape_in, pi_0_scale_in, maxvarin, alphain)); 40 | return rcpp_result_gen; 41 | END_RCPP 42 | } 43 | 44 | static const R_CallMethodDef CallEntries[] = { 45 | {"_MRAID_MRAID_CPP", (DL_FUNC) &_MRAID_MRAID_CPP, 19}, 46 | {NULL, NULL, 0} 47 | }; 48 | 49 | RcppExport void R_init_MRAID(DllInfo *dll) { 50 | R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); 51 | R_useDynamicSymbols(dll, FALSE); 52 | } 53 | -------------------------------------------------------------------------------- /R/MRAID.R: -------------------------------------------------------------------------------- 1 | #' @title The function of MRAID method two sample Mendelian Randomization 2 | #' @description MRAID is able to model an initial set of candidate SNP instruments that are in high LD with each other and perform automated instrument selection to identify suitable SNPs to serve as instrumental variables. MRAID simultaneously accounts for both uncorrelated and correlated horizontal pleiotropy, relies on a scalable sampling-based inference algorithm to perform numerical integration, circumventing the difficulty in likelihood function 3 | #' @param Zscore_1 the Zscore vector of the SNP effect size vector for the exposure 4 | #' @param Zscore_2 the Zscore vector of the SNP effect size vector for the outcome 5 | #' @param Sigma1sin the LD matrix for the SNPs in the exposure GWAS data 6 | #' @param Sigma2sin the LD matrix for the SNPs in the outcome GWAS data,both Sigma2sin and sigma1sin are often from the same reference panel 7 | #' @param samplen1 the sample size of exposure GWAS 8 | #' @param samplen2 the sample size of outcome GWAS 9 | #' @param Gibbsnumber the number of Gibbs sampling iterations with the default to be 1000 10 | #' @param burninproportion the proportion to burn in from Gibbs sampling iterations, with default to be 0.2 11 | #' @param pi_beta_shape the prior shape paramter for pi_beta with the default to be 0.5 12 | #' @param pi_beta_scale the prior scale paramter for pi_beta with the default to be 4.5 13 | #' @param pi_c_shape the prior shape paramter for pi_c with the default to be 0.5 14 | #' @param pi_c_scale the prior shape paramter for pi_c with the default to be 9.5 15 | #' @param pi_1_shape the prior shape paramter for pi_1 with the default to be 0.5 16 | #' @param pi_1_scale the prior scale paramter for pi_1 with the default to be 1.5 17 | #' @param pi_0_shape the prior shape paramter for pi_0 with the default to be 0.05 18 | #' @param pi_0_scale the prior scale paramter for pi_0 with the default to be 9.95 19 | #' @return A list of estimated parameters including the p values for the causal effect test 20 | #' \item{causal_effect}{The estimate of causal effect} 21 | #' \item{causal_pvalue}{The p value for the causal effect} 22 | #' \item{correlated_pleiotropy_effect}{The estimate of correlated pleiotropy effect} 23 | #' \item{sigmaeta}{The variance estimate for the uncorrelated pleiotropy effect} 24 | #' \item{sigmabeta}{The variance estimate for the SNP effect sizes on the exposure} 25 | #' \item{sigma_error_1}{The variance estimate of the error in exposure GWAS model} 26 | #' \item{sigma_error_2}{The variance estimate of the error in outcome GWAS model} 27 | 28 | MRAID<-function(Zscore_1, Zscore_2, Sigma1sin, Sigma2sin, samplen1, samplen2, Gibbsnumber=1000,burninproportion=0.2,pi_beta_shape=0.5, 29 | pi_beta_scale=4.5,pi_c_shape=0.5,pi_c_scale=9.5,pi_1_shape=0.5,pi_1_scale=1.5,pi_0_shape=0.05,pi_0_scale=9.95){ 30 | betaxin<-Zscore_1/sqrt(samplen1-1) 31 | betayin<-Zscore_2/sqrt(samplen2-1) 32 | initial_betain<-rep(0,length(betaxin)) 33 | 34 | dat21 <- Sigma2sin 35 | diag(dat21) <- 0 36 | dat21[abs(dat21)>0.25] <- NA 37 | colnames(dat21) <- c(1:ncol(dat21)) 38 | rownames(dat21) <- c(1:nrow(dat21)) 39 | datt <- dat21 40 | while (sum(is.na(datt))!=0) { 41 | num <- NULL 42 | for (j in 1:ncol(datt)) { 43 | num1 <- sum(is.na(datt[,j])) 44 | num <- cbind(num,num1) 45 | } 46 | num2 <- which.max(num) 47 | datt <- datt[-num2,-num2] 48 | } 49 | datt <- as.matrix(datt) 50 | id <- colnames(datt) 51 | id <- as.numeric(id) 52 | b_out <- betayin[id] 53 | b_exp <- betaxin[id] 54 | se_out <- rep(1/sqrt(samplen2-1),length(b_out)) 55 | fit <- summary(lm(b_out ~ -1 +b_exp, weights = 1/se_out^2)) 56 | alpha_ivw <- fit$coefficients[1,1] 57 | 58 | maxvarin<-1000 59 | re=MRAID_CPP(betaxin,betayin,Sigma1sin,Sigma2sin,samplen1,samplen2,Gibbsnumberin=Gibbsnumber,burninproportion=burninproportion,initial_betain=initial_betain, 60 | pi_beta_shape_in=pi_beta_shape,pi_beta_scale_in=pi_beta_scale,pi_c_shape_in=pi_c_shape,pi_c_scale_in=pi_c_scale,pi_1_shape_in=pi_1_shape,pi_1_scale_in=pi_1_scale, 61 | pi_0_shape_in=pi_0_shape,pi_0_scale_in=pi_0_scale,maxvarin,alphain=alpha_ivw) 62 | 63 | pvalue<-2*(1-pnorm(abs(re$alpha/re$sd))) 64 | result=list() 65 | result$causal_effect=re$alpha 66 | result$causal_pvalue=pvalue 67 | result$correlated_pleiotropy_effect=re$rho 68 | result$sigmabeta=re$sigmabeta 69 | result$sigmaeta=re$sigmaeta 70 | result$sigma_error_1=re$sigma2x 71 | result$sigma_error_2=re$sigma2y 72 | 73 | if (result$causal_effect==0){ 74 | maxvarin=0 75 | re=MRAID_CPP(betaxin,betayin,Sigma1sin,Sigma2sin,samplen1,samplen2,Gibbsnumberin=Gibbsnumber,burninproportion=burninproportion,initial_betain=initial_betain, 76 | pi_beta_shape_in=pi_beta_shape,pi_beta_scale_in=pi_beta_scale,pi_c_shape_in=pi_c_shape,pi_c_scale_in=pi_c_scale,pi_1_shape_in=pi_1_shape,pi_1_scale_in=pi_1_scale, 77 | pi_0_shape_in=pi_0_shape,pi_0_scale_in=pi_0_scale,maxvarin,alphain=alpha_ivw) 78 | 79 | pvalue<-2*(1-pnorm(abs(re$alpha/re$sd))) 80 | result=list() 81 | result$causal_effect=re$alpha 82 | result$causal_pvalue=pvalue 83 | result$correlated_pleiotropy_effect=re$rho 84 | result$sigmabeta=re$sigmabeta 85 | result$sigmaeta=re$sigmaeta 86 | result$sigma_error_1=re$sigma2x 87 | result$sigma_error_2=re$sigma2y 88 | } 89 | 90 | return(result) 91 | } 92 | 93 | 94 | 95 | 96 | 97 | 98 | -------------------------------------------------------------------------------- /src/MRAID_CPP.cpp: -------------------------------------------------------------------------------- 1 | //library('Rcpp') 2 | #include 3 | // [[Rcpp::depends(RcppArmadillo, RcppDist)]] 4 | 5 | #include 6 | 7 | #include 8 | #include 9 | #include 10 | #include 11 | #include 12 | #include 13 | #include 14 | #include 15 | 16 | // Enable C++11 via this plugin (Rcpp 0.10.3 or later) 17 | // [[Rcpp::plugins(cpp11)]] 18 | 19 | using namespace Rcpp; 20 | using namespace arma; 21 | using namespace std; 22 | 23 | 24 | 25 | 26 | 27 | 28 | //*******************************************************************// 29 | // MAIN FUNC // 30 | //*******************************************************************// 31 | //' MRAID 32 | //' 33 | //' @export 34 | // [[Rcpp::export]] 35 | 36 | SEXP MRAID_CPP(SEXP betaxin, SEXP betayin, SEXP Sigma1sin, SEXP Sigma2sin, SEXP samplen1, SEXP samplen2, SEXP Gibbsnumberin, SEXP burninproportion, SEXP initial_betain, SEXP pi_beta_shape_in, SEXP pi_beta_scale_in, 37 | SEXP pi_c_shape_in, SEXP pi_c_scale_in, SEXP pi_1_shape_in, SEXP pi_1_scale_in,SEXP pi_0_shape_in, SEXP pi_0_scale_in, SEXP maxvarin, SEXP alphain){// * 38 | try{ 39 | const int Gibbs_number = Rcpp::as(Gibbsnumberin); 40 | const double burnin_p = Rcpp::as(burninproportion); 41 | const double maxvar = Rcpp::as(maxvarin); 42 | const int n1 = Rcpp::as(samplen1); 43 | const int n2 = Rcpp::as(samplen2); 44 | const double lamda_beta1 = Rcpp::as(pi_beta_shape_in); 45 | const double lamda_beta2 = Rcpp::as(pi_beta_scale_in); 46 | const double lamda_c1 = Rcpp::as(pi_c_shape_in); 47 | const double lamda_c2 = Rcpp::as(pi_c_scale_in); 48 | const double lamda_21 = Rcpp::as(pi_1_shape_in); 49 | const double lamda_22 = Rcpp::as(pi_1_scale_in); 50 | const double lamda_31 = Rcpp::as(pi_0_shape_in); 51 | const double lamda_32 = Rcpp::as(pi_0_scale_in); 52 | const double alpha0 = as(alphain); 53 | const arma::vec betax = as(betaxin); 54 | const arma::vec betay = as(betayin); 55 | const arma::mat Sigma1s = as(Sigma1sin); 56 | const arma::mat Sigma2s = as(Sigma2sin); 57 | const arma::vec initial_beta = as(initial_betain); 58 | 59 | //const double sigma2y = Rcpp::as(sigma2yin); 60 | //const double sigma2z = Rcpp::as(sigma2zin); 61 | //const double sigma2beta = Rcpp::as(sigma2betain); 62 | //const double alpha_input = Rcpp::as(alphainputin); 63 | int p1 = Sigma1s.n_cols, p2 = Sigma2s.n_cols; 64 | if (p1 != p2){ 65 | perror("The dimensions of x1 and x2 are not matched"); 66 | } 67 | int p = p1; 68 | 69 | vec betaxh=betax, betayh=betay; 70 | 71 | mat Sigma1 = Sigma1s* (n1-1); 72 | mat Sigma2 = Sigma2s* (n2-1); 73 | //double sigma0_square = 1.0; 74 | double sigmaz1 = 2.0/p; 75 | double pi = 0.1; 76 | //pi means pi_beta 77 | double pi_2 = 0.05; 78 | //pi_2 mean pi_c 79 | double sigma2y = 0.9; 80 | double y_norm = n1-1; 81 | double z_norm = n2-1; 82 | double post_sigma2y_shape = 0.5*n1-1; 83 | double post_sigma2z_shape = 0.5*n2-1; 84 | double pi_gamma_21 = 0.25; 85 | //pi_1 86 | double pi_gamma_31 = 0.005; 87 | //pi_0 88 | double sigma2z = 0.95; 89 | double sigma2gamma_1 = 1.0/p; 90 | double rho=0.1414; 91 | //double sigma2gamma_2 = 1.0/p; 92 | 93 | double alpha = alpha0; 94 | 95 | vec latent_beta = initial_beta; 96 | 97 | vec pleiotropy_gamma = zeros(p); 98 | 99 | mat Identity = eye(p,p); 100 | 101 | // int Gibbs_number = 1000; 102 | int burnin = ceil(burnin_p*Gibbs_number); 103 | vec sample_alpha(Gibbs_number); 104 | vec sample_pi(Gibbs_number); 105 | vec sample_pi_2(Gibbs_number); 106 | vec sample_sigma2y(Gibbs_number); 107 | //sigma_x in the first eqution 108 | vec sample_sigma2z(Gibbs_number); 109 | //sigma_y in the second equation 110 | vec sample_sigmaz1(Gibbs_number); 111 | //sigma_beta2 112 | vec sample_sigma2gamma_1(Gibbs_number); 113 | //vec sample_sigma2gamma_2(Gibbs_number); 114 | vec sample_rho(Gibbs_number); 115 | //vec sample_sigma2gamma_2(Gibbs_number); 116 | vec sample_pi_gamma_21(Gibbs_number); 117 | //pi_1 in the model 118 | vec sample_pi_gamma_31(Gibbs_number); 119 | //pi_0 in the model 120 | double U_beta_1,U_gamma_1; 121 | vec latent_gamma = zeros(p); 122 | vec latent_gamma_pleiotropy = zeros(p); 123 | vec I = zeros(p); 124 | double part_1,part_2,Probability_gamma,randu_number; 125 | double part_1_pleiotropy,part_2_pleiotropy,Probability_gamma_pleiotropy,randu_number_pleiotropy; 126 | double part_1_I,part_2_I,Probability_I_indicator,randu_number_I; 127 | double post_sigma2y_scale; 128 | double post_sigma2z_scale; 129 | double post_sigmaz1_scale; 130 | double post_sigmaz1_shape; 131 | double post_sigma2gamma_1_scale; 132 | //double post_sigma2gamma_2_scale; 133 | double post_sigma2gamma_1_shape; 134 | //double post_sigma2gamma_2_shape; 135 | double sigmaz1_prior_shape=p/10+1; 136 | double sigmaz1_prior_scale=0.2; 137 | double sigma2gamma_1_prior_shape=p/5.0+1; 138 | double sigma2gamma_1_prior_scale=0.2; 139 | //double sigma2gamma_2_prior_shape=p/5.0+1; 140 | //double sigma2gamma_2_prior_scale=0.2; 141 | 142 | mat x2left = Sigma2 - (n2-1)*Identity; 143 | mat x1left = Sigma1 - (n1-1)*Identity; 144 | 145 | for(int m=0; m maxvar){ 212 | double sample_rho_variance = 1.0/(as_scalar((I % latent_beta).t()* Sigma2 * (I % latent_beta))/sigma2z); 213 | 214 | double sample_rho_mean = (as_scalar((I % latent_beta).t()* betayh*(n2-1)-(I % latent_beta).t()*Sigma2*pleiotropy_gamma - (I % latent_beta).t()*Sigma2*latent_beta*alpha)/sigma2z)*sample_rho_variance; 215 | 216 | sample_rho(m) = as_scalar(randn(1)*sqrt(sample_rho_variance)+ sample_rho_mean); 217 | 218 | rho=sample_rho(m); 219 | } else { 220 | sample_rho(m) = 0; 221 | rho=sample_rho(m); 222 | } 223 | 224 | 225 | double indicator_alpha = (as_scalar(latent_beta.t()* Sigma2 * latent_beta)/sigma2z); 226 | 227 | if (indicator_alpha>maxvar){ 228 | double sample_alpha_variance = 1.0/(as_scalar(latent_beta.t()* Sigma2 * latent_beta)/sigma2z); 229 | 230 | double sample_alpha_mean = (as_scalar(latent_beta.t()* betayh*(n2-1)-latent_beta.t()*Sigma2*pleiotropy_gamma-latent_beta.t()*Sigma2*(latent_beta % I)*rho)/sigma2z)*sample_alpha_variance; 231 | 232 | sample_alpha(m) = as_scalar(randn(1)*sqrt(sample_alpha_variance)+ sample_alpha_mean); 233 | 234 | }else{ 235 | sample_alpha(m) = 0; 236 | } 237 | 238 | double shape1 = sum(latent_gamma)+lamda_beta1; 239 | 240 | double shape2 = sum(1.0-latent_gamma)+lamda_beta2; 241 | 242 | sample_pi(m)= r_4beta(shape1,shape2,0,1); 243 | 244 | pi = sample_pi(m); 245 | 246 | double shape1_pleiotropy_21 = sum(latent_gamma % latent_gamma_pleiotropy)+lamda_21; 247 | 248 | double shape2_pleiotropy_21 = sum(latent_gamma % (1.0-latent_gamma_pleiotropy))+lamda_22; 249 | 250 | sample_pi_gamma_21(m)= r_4beta(shape1_pleiotropy_21,shape2_pleiotropy_21,0,1); 251 | 252 | pi_gamma_21 = sample_pi_gamma_21(m); 253 | 254 | double shape1_pleiotropy_31 = sum((1-latent_gamma) % latent_gamma_pleiotropy)+lamda_31; 255 | 256 | double shape2_pleiotropy_31 = sum((1-latent_gamma) % (1.0-latent_gamma_pleiotropy))+lamda_32; 257 | 258 | sample_pi_gamma_31(m)= r_4beta(shape1_pleiotropy_31,shape2_pleiotropy_31,0,1); 259 | 260 | pi_gamma_31 = sample_pi_gamma_31(m); 261 | 262 | alpha=sample_alpha(m); 263 | 264 | double shape1_pi_2 = sum(latent_gamma % I)+lamda_c1; 265 | 266 | double shape2_pi_2 = sum(latent_gamma % (1.0-I))+lamda_c2; 267 | 268 | sample_pi_2(m)= r_4beta(shape1_pi_2,shape2_pi_2,0,1); 269 | 270 | pi_2 = sample_pi_2(m); 271 | 272 | post_sigma2y_scale = 2/(y_norm + as_scalar(latent_beta.t()* Sigma1 * latent_beta) - 2 * as_scalar(latent_beta.t()* betaxh*(n1-1))); 273 | 274 | post_sigma2y_scale=abs(post_sigma2y_scale); 275 | 276 | sample_sigma2y(m) = 1.0/as_scalar(randg(1, distr_param(post_sigma2y_shape,post_sigma2y_scale))); 277 | 278 | sigma2y = sample_sigma2y(m); 279 | 280 | post_sigma2z_scale = 2/(z_norm + as_scalar(latent_beta.t()* Sigma2 * latent_beta)*alpha*alpha + as_scalar(pleiotropy_gamma.t()* Sigma2 * pleiotropy_gamma)+ 281 | 282 | as_scalar((latent_beta % I).t()* Sigma2 * (latent_beta % I))*rho*rho + 2*alpha*rho*as_scalar(latent_beta.t()*Sigma2*(latent_beta % I)) + 283 | 284 | 2*rho*as_scalar(pleiotropy_gamma.t()*Sigma2*(latent_beta % I)) + 285 | 286 | 2*alpha*as_scalar(pleiotropy_gamma.t()*Sigma2*latent_beta)- 2*rho*as_scalar((latent_beta % I).t()* betayh*(n2-1))- 2*as_scalar(pleiotropy_gamma.t()* betayh*(n2-1)) - 2 * alpha * as_scalar(latent_beta.t()* betayh*(n2-1))); 287 | 288 | post_sigma2z_scale = abs(post_sigma2z_scale); 289 | 290 | sample_sigma2z(m) = 1.0/as_scalar(randg(1, distr_param(post_sigma2z_shape,post_sigma2z_scale))); 291 | 292 | sigma2z = sample_sigma2z(m); 293 | 294 | post_sigmaz1_scale = 2/(as_scalar(sum(latent_gamma % latent_beta % latent_beta)+2*sigmaz1_prior_scale)); 295 | 296 | post_sigmaz1_shape = 0.5*sum(latent_gamma)+ sigmaz1_prior_shape; 297 | 298 | post_sigmaz1_scale = abs(post_sigmaz1_scale); 299 | 300 | sample_sigmaz1(m) = 1.0/as_scalar(randg(1, distr_param(post_sigmaz1_shape,post_sigmaz1_scale))); 301 | 302 | sigmaz1 = sample_sigmaz1(m); 303 | 304 | post_sigma2gamma_1_scale = 2/(as_scalar(sum(latent_gamma_pleiotropy % pleiotropy_gamma % pleiotropy_gamma)+2*sigma2gamma_1_prior_scale)); 305 | 306 | post_sigma2gamma_1_shape = 0.5*sum(latent_gamma_pleiotropy)+ sigma2gamma_1_prior_shape; 307 | 308 | post_sigma2gamma_1_scale = abs(post_sigma2gamma_1_scale); 309 | 310 | sample_sigma2gamma_1(m) = 1.0/as_scalar(randg(1, distr_param(post_sigma2gamma_1_shape,post_sigma2gamma_1_scale))); 311 | 312 | sigma2gamma_1 = sample_sigma2gamma_1(m); 313 | 314 | } 315 | 316 | 317 | double alpha_estimate = mean(sample_alpha.subvec((burnin-1),(Gibbs_number-1))); 318 | 319 | double alpha_sd = stddev(sample_alpha.subvec((burnin-1),(Gibbs_number-1))); 320 | 321 | double rho_estimate = mean(sample_rho.subvec((burnin-1),(Gibbs_number-1))); 322 | 323 | double sigma2y_estimate = mean(sample_sigma2y.subvec((burnin-1),(Gibbs_number-1))); 324 | 325 | double sigma2z_estimate = mean(sample_sigma2z.subvec((burnin-1),(Gibbs_number-1))); 326 | 327 | double sigmaz1_estimate = mean(sample_sigmaz1.subvec((burnin-1),(Gibbs_number-1))); 328 | 329 | double sigma2gamma_1_estimate = mean(sample_sigma2gamma_1.subvec((burnin-1),(Gibbs_number-1))); 330 | 331 | 332 | return List::create(Rcpp::Named("alpha") = alpha_estimate, 333 | // Rcpp::Named("sample_alpha") = sample_alpha, 334 | // Rcpp::Named("sample_pi") = sample_pi, 335 | // Rcpp::Named("sample_pi_gamma_31") = sample_pi_gamma_31, 336 | //Rcpp::Named("sample_pi_gamma_21") = sample_pi_gamma_21, 337 | //Rcpp::Named("sample_pi_2") = sample_pi_2, 338 | //Rcpp::Named("sample_rho") = sample_rho, 339 | Rcpp::Named("rho") = rho_estimate, 340 | Rcpp::Named("sigma2x") = sigma2y_estimate, 341 | Rcpp::Named("sigma2y") = sigma2z_estimate, 342 | Rcpp::Named("sigmabeta") = sigmaz1_estimate, 343 | Rcpp::Named("sigmaeta") = sigma2gamma_1_estimate, 344 | Rcpp::Named("sd") = alpha_sd); 345 | //Rcpp::Named("sigmaX") = sigma2y, 346 | //Rcpp::Named("sigmaY") = sigma2z, 347 | //Rcpp::Named("sigmabeta") = sigma2beta, 348 | //Rcpp::Named("loglik_seq") = loglik_out, 349 | //Rcpp::Named("loglik") = loglik_max, 350 | //Rcpp::Named("iteration") = Iteration-1); 351 | } catch( std::exception &ex ) { 352 | forward_exception_to_r( ex ); 353 | } catch(...) { 354 | ::Rf_error( "C++ exception (unknown reason)..." ); 355 | } 356 | return R_NilValue; 357 | }// end func 358 | -------------------------------------------------------------------------------- /example/Sigma1sin.txt: -------------------------------------------------------------------------------- 1 | "rs6674631" "rs151078149" "rs10915621" "rs12063965" "rs79791497" "rs12118185" "rs145614101" "rs12044914" "rs116275323" "rs10799172" "rs10799173" "rs6681359" "rs4484964" "rs4345839" "rs4584438" "rs4543848" "rs9439510" "rs4462197" "rs10753353" "rs4314925" "rs12741116" 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