R/multivariable_mr.R
In MRCIEU/TwoSampleMR: Two Sample MR Functions and Interface to MR Base Database
Defines functions
mv_subset
mv_lasso_feature_selection
mv_ivw
mv_basic
mv_multiple
mv_residual
mv_harmonise_data
mv_extract_exposures_local
mv_extract_exposures
Documented in
mv_basic
mv_extract_exposures
mv_extract_exposures_local
mv_harmonise_data
mv_ivw
mv_lasso_feature_selection
mv_multiple
mv_residual
mv_subset
#' Extract exposure variables for multivariable MR
#'
#' Requires a list of IDs from available_outcomes. For each ID, it extracts instruments. Then, it gets the full list of all instruments and extracts those SNPs for every exposure. Finally, it keeps only the SNPs that are a) independent and b) present in all exposures, and harmonises them to be all on the same strand.
#'
#' @param id_exposure Array of IDs (e.g. c(299, 300, 302) for HDL, LDL, trigs)
#' @param clump_r2 The default is `0.01`.
#' @param clump_kb The default is `10000`.
#' @param harmonise_strictness See the `action` option of [harmonise_data()]. The default is `2`.
#' @param opengwas_jwt Used to authenticate protected endpoints. Login to <https://api.opengwas.io> to obtain a jwt. Provide the jwt string here, or store in .Renviron under the keyname OPENGWAS_JWT.
#' @param find_proxies Look for proxies? This slows everything down but is more accurate. The default is `TRUE`.
#' @param force_server Whether to search through pre-clumped dataset or to re-extract and clump directly from the server. The default is `FALSE`.
#' @param pval_threshold Instrument detection p-value threshold. Default = `5e-8`
#' @param pop Which 1000 genomes super population to use for clumping when using the server
#' @param plink_bin If `NULL` and `bfile` is not `NULL` then will detect packaged plink binary for specific OS. Otherwise specify path to plink binary. Default = `NULL`
#' @param bfile If this is provided then will use the API. Default = `NULL`
#'
#' @export
#' @return data frame in `exposure_dat` format
mv_extract_exposures <- function(id_exposure, clump_r2=0.001, clump_kb=10000, harmonise_strictness=2, opengwas_jwt=ieugwasr::get_opengwas_jwt(), find_proxies=TRUE, force_server=FALSE, pval_threshold=5e-8, pop="EUR", plink_bin=NULL, bfile=NULL)
{
stopifnot(length(id_exposure) > 1)
id_exposure <- ieugwasr::legacy_ids(id_exposure)
# Get best instruments for each exposure
exposure_dat <- extract_instruments(id_exposure, p1 = pval_threshold, r2 = clump_r2, kb=clump_kb, opengwas_jwt = opengwas_jwt, force_server=force_server)
temp <- exposure_dat
temp$id.exposure <- 1
temp <- temp[order(temp$pval.exposure, decreasing=FALSE), ]
temp <- subset(temp, !duplicated(SNP))
temp <- clump_data(temp, clump_p1=pval_threshold, clump_r2=clump_r2, clump_kb=clump_kb, pop=pop, plink_bin=plink_bin, bfile=bfile)
exposure_dat <- subset(exposure_dat, SNP %in% temp$SNP)
# Get effects of each instrument from each exposure
d1 <- extract_outcome_data(exposure_dat$SNP, id_exposure, opengwas_jwt = opengwas_jwt, proxies=find_proxies)
stopifnot(length(unique(d1$id)) == length(unique(id_exposure)))
d1 <- subset(d1, mr_keep.outcome)
d2 <- subset(d1, id.outcome != id_exposure[1])
d1 <- convert_outcome_to_exposure(subset(d1, id.outcome == id_exposure[1]))
# Harmonise against the first id
d <- harmonise_data(d1, d2, action=harmonise_strictness)
# Only keep SNPs that are present in all
tab <- table(d$SNP)
keepsnps <- names(tab)[tab == length(id_exposure)-1]
d <- subset(d, SNP %in% keepsnps)
# Reshape exposures
dh1 <- subset(d, id.outcome == id.outcome[1], select=c(SNP, exposure, id.exposure, effect_allele.exposure, other_allele.exposure, eaf.exposure, beta.exposure, se.exposure, pval.exposure))
dh2 <- subset(d, select=c(SNP, outcome, id.outcome, effect_allele.outcome, other_allele.outcome, eaf.outcome, beta.outcome, se.outcome, pval.outcome))
names(dh2) <- gsub("outcome", "exposure", names(dh2))
dh <- rbind(dh1, dh2)
return(dh)
}
#' Attempt to perform MVMR using local data
#'
#' Allows you to read in summary data from text files to format the multivariable exposure dataset.
#'
#' Note that you can provide an array of column names for each column, which is of length `filenames_exposure`
#'
#' @param filenames_exposure Filenames for each exposure dataset. Must have header with at least SNP column present. Following arguments are used for determining how to read the filename and clumping etc.
#' @param sep Specify delimeter in file. The default is space, i.e. `sep=" "`. If length is 1 it will use the same `sep` value for each exposure dataset. You can provide a vector of values, one for each exposure dataset, if the values are different across datasets. The same applies to all dataset-formatting options listed below.
#' @param phenotype_col Optional column name for the column with phenotype name corresponding the the SNP. If not present then will be created with the value `"Outcome"`. Default is `"Phenotype"`.
#' @param snp_col Required name of column with SNP rs IDs. The default is `"SNP"`.
#' @param beta_col Required for MR. Name of column with effect sizes. THe default is `"beta"`.
#' @param se_col Required for MR. Name of column with standard errors. The default is `"se"`.
#' @param eaf_col Required for MR. Name of column with effect allele frequency. The default is `"eaf"`.
#' @param effect_allele_col Required for MR. Name of column with effect allele. Must be "A", "C", "T" or "G". The default is `"effect_allele"`.
#' @param other_allele_col Required for MR. Name of column with non effect allele. Must be "A", "C", "T" or "G". The default is `"other_allele"`.
#' @param pval_col Required for enrichment tests. Name of column with p-value. The default is `"pval"`.
#' @param units_col Optional column name for units. The default is `"units"`.
#' @param ncase_col Optional column name for number of cases. The default is `"ncase"`.
#' @param ncontrol_col Optional column name for number of controls. The default is `"ncontrol"`.
#' @param samplesize_col Optional column name for sample size. The default is `"samplesize"`.
#' @param gene_col Optional column name for gene name. The default is `"gene"`.
#' @param id_col Optional column name to give the dataset an ID. Will be generated automatically if not provided for every trait / unit combination. The default is `"id"`.
#' @param min_pval Minimum allowed p-value. The default is `1e-200`.
#' @param log_pval The pval is -log10(P). The default is `FALSE`.
#' @param pval_threshold Default=`5e-8` for clumping
#' @param plink_bin If `NULL` and `bfile` is not `NULL` then will detect packaged plink binary for specific OS. Otherwise specify path to plink binary. Default = `NULL`
#' @param bfile If this is provided then will use the API. Default = `NULL`
#' @param clump_r2 Default=`0.001` for clumping
#' @param clump_kb Default=`10000` for clumping
#' @param pop Which 1000 genomes super population to use for clumping when using the server
#' @param harmonise_strictness See action argument in [harmonise_data()]. Default=`2`
#'
#' @export
#' @return List
mv_extract_exposures_local <- function(
filenames_exposure,
sep = " ",
phenotype_col = "Phenotype",
snp_col = "SNP",
beta_col = "beta",
se_col = "se",
eaf_col = "eaf",
effect_allele_col = "effect_allele",
other_allele_col = "other_allele",
pval_col = "pval",
units_col = "units",
ncase_col = "ncase",
ncontrol_col = "ncontrol",
samplesize_col = "samplesize",
gene_col = "gene",
id_col = "id",
min_pval = 1e-200,
log_pval = FALSE,
pval_threshold=5e-8,
plink_bin=NULL,
bfile=NULL,
clump_r2=0.001,
clump_kb=10000,
pop="EUR",
harmonise_strictness=2
) {
message("WARNING: Experimental function")
stopifnot(inherits(filenames_exposure, "character") | inherits(filenames_exposure, "list"))
if(inherits(filenames_exposure, "list")) {
stopifnot(all(sapply(filenames_exposure, function(x) inherits(x, "data.frame"))))
flag <- "data.frame"
} else {
flag <- "character"
}
n <- length(filenames_exposure)
if(length(sep) == 1) {sep <- rep(sep, n)}
if(length(phenotype_col) == 1) {phenotype_col <- rep(phenotype_col, n)}
if(length(snp_col) == 1) {snp_col <- rep(snp_col, n)}
if(length(beta_col) == 1) {beta_col <- rep(beta_col, n)}
if(length(se_col) == 1) {se_col <- rep(se_col, n)}
if(length(eaf_col) == 1) {eaf_col <- rep(eaf_col, n)}
if(length(effect_allele_col) == 1) {effect_allele_col <- rep(effect_allele_col, n)}
if(length(other_allele_col) == 1) {other_allele_col <- rep(other_allele_col, n)}
if(length(pval_col) == 1) {pval_col <- rep(pval_col, n)}
if(length(units_col) == 1) {units_col <- rep(units_col, n)}
if(length(ncase_col) == 1) {ncase_col <- rep(ncase_col, n)}
if(length(ncontrol_col) == 1) {ncontrol_col <- rep(ncontrol_col, n)}
if(length(samplesize_col) == 1) {samplesize_col <- rep(samplesize_col, n)}
if(length(gene_col) == 1) {gene_col <- rep(gene_col, n)}
if(length(id_col) == 1) {id_col <- rep(id_col, n)}
if(length(min_pval) == 1) {min_pval <- rep(min_pval, n)}
if(length(log_pval) == 1) {log_pval <- rep(log_pval, n)}
l_full <- list()
l_inst <- list()
for(i in seq_along(filenames_exposure))
{
if(flag == "character") {
l_full[[i]] <- read_outcome_data(filenames_exposure[i],
sep = sep[i],
phenotype_col = phenotype_col[i],
snp_col = snp_col[i],
beta_col = beta_col[i],
se_col = se_col[i],
eaf_col = eaf_col[i],
effect_allele_col = effect_allele_col[i],
other_allele_col = other_allele_col[i],
pval_col = pval_col[i],
units_col = units_col[i],
ncase_col = ncase_col[i],
ncontrol_col = ncontrol_col[i],
samplesize_col = samplesize_col[i],
gene_col = gene_col[i],
id_col = id_col[i],
min_pval = min_pval[i],
log_pval = log_pval[i]
)
} else {
l_full[[i]] <- format_data(filenames_exposure[[i]],
type="outcome",
phenotype_col = phenotype_col[i],
snp_col = snp_col[i],
beta_col = beta_col[i],
se_col = se_col[i],
eaf_col = eaf_col[i],
effect_allele_col = effect_allele_col[i],
other_allele_col = other_allele_col[i],
pval_col = pval_col[i],
units_col = units_col[i],
ncase_col = ncase_col[i],
ncontrol_col = ncontrol_col[i],
samplesize_col = samplesize_col[i],
gene_col = gene_col[i],
id_col = id_col[i],
min_pval = min_pval[i],
log_pval = log_pval[i]
)
}
if(l_full[[i]]$outcome[1] == "outcome") l_full[[i]]$outcome <- paste0("exposure", i)
l_inst[[i]] <- subset(l_full[[i]], pval.outcome < pval_threshold)
l_inst[[i]] <- subset(l_inst[[i]], !duplicated(SNP))
l_inst[[i]] <- convert_outcome_to_exposure(l_inst[[i]])
l_inst[[i]] <- subset(l_inst[[i]], pval.exposure < pval_threshold)
l_inst[[i]] <- clump_data(l_inst[[i]], clump_p1=pval_threshold, clump_r2=clump_r2, clump_kb=clump_kb, bfile=bfile, plink_bin=plink_bin, pop=pop)
message("Identified ", nrow(l_inst[[i]]), " hits for trait ", l_inst[[i]]$exposure[1])
}
exposure_dat <- dplyr::bind_rows(l_inst)
id_exposure <- unique(exposure_dat$id.exposure)
temp <- exposure_dat
temp$id.exposure <- 1
temp <- temp[order(temp$pval.exposure, decreasing=FALSE), ]
temp <- subset(temp, !duplicated(SNP))
temp <- clump_data(temp, clump_p1=pval_threshold, clump_r2=clump_r2, clump_kb=clump_kb, bfile=bfile, plink_bin=plink_bin, pop=pop)
exposure_dat <- subset(exposure_dat, SNP %in% temp$SNP)
message("Identified ", length(unique(temp$SNP)), " variants to include")
d1 <- lapply(l_full, function(x) {
subset(x, SNP %in% exposure_dat$SNP)
}) %>% dplyr::bind_rows()
stopifnot(length(unique(d1$id)) == length(unique(id_exposure)))
d1 <- subset(d1, mr_keep.outcome)
d2 <- subset(d1, id.outcome != id_exposure[1])
d1 <- convert_outcome_to_exposure(subset(d1, id.outcome == id_exposure[1]))
# Harmonise against the first id
d <- harmonise_data(d1, d2, action=harmonise_strictness)
# Only keep SNPs that are present in all
tab <- table(d$SNP)
keepsnps <- names(tab)[tab == length(id_exposure)-1]
d <- subset(d, SNP %in% keepsnps)
# Reshape exposures
dh1 <- subset(d, id.outcome == id.outcome[1], select=c(SNP, exposure, id.exposure, effect_allele.exposure, other_allele.exposure, eaf.exposure, beta.exposure, se.exposure, pval.exposure))
dh2 <- subset(d, select=c(SNP, outcome, id.outcome, effect_allele.outcome, other_allele.outcome, eaf.outcome, beta.outcome, se.outcome, pval.outcome))
names(dh2) <- gsub("outcome", "exposure", names(dh2))
dh <- rbind(dh1, dh2)
return(dh)
}
#' Harmonise exposure and outcome for multivariable MR
#'
#' @param exposure_dat Output from [mv_extract_exposures()].
#' @param outcome_dat Output from `extract_outcome_data(exposure_dat$SNP, id_output)`.
#' @param harmonise_strictness See the `action` option of [harmonise_data()]. The default is `2`.
#'
#' @export
#' @return List of vectors and matrices required for mv analysis.
#' \describe{
#' \item{exposure_beta}{a matrix of beta coefficients, in which rows correspond to SNPs and columns correspond to exposures.}
#' \item{exposure_se}{is the same as `exposure_beta`, but for standard errors.}
#' \item{exposure_pval}{the same as `exposure_beta`, but for p-values.}
#' \item{expname}{A data frame with two variables, `id.exposure` and `exposure` which are character strings.}
#' \item{outcome_beta}{an array of effects for the outcome, corresponding to the SNPs in `exposure_beta`.}
#' \item{outcome_se}{an array of standard errors for the outcome.}
#' \item{outcome_pval}{an array of p-values for the outcome.}
#' \item{outname}{A data frame with two variables, `id.outcome` and `outcome` which are character strings.}
#' }
#'
mv_harmonise_data <- function(exposure_dat, outcome_dat, harmonise_strictness=2)
{
stopifnot(all(c("SNP", "id.exposure", "exposure", "effect_allele.exposure", "beta.exposure", "se.exposure", "pval.exposure") %in% names(exposure_dat)))
nexp <- length(unique(exposure_dat$id.exposure))
stopifnot(nexp > 1)
tab <- table(exposure_dat$SNP)
keepsnp <- names(tab)[tab == nexp]
exposure_dat <- subset(exposure_dat, SNP %in% keepsnp)
exposure_mat <- reshape2::dcast(exposure_dat, SNP ~ id.exposure, value.var="beta.exposure")
# Get outcome data
dat <- harmonise_data(subset(exposure_dat, id.exposure == exposure_dat$id.exposure[1]), outcome_dat, action=harmonise_strictness)
dat <- subset(dat, mr_keep)
dat$SNP <- as.character(dat$SNP)
exposure_beta <- reshape2::dcast(exposure_dat, SNP ~ id.exposure, value.var="beta.exposure")
exposure_beta <- subset(exposure_beta, SNP %in% dat$SNP)
exposure_beta$SNP <- as.character(exposure_beta$SNP)
exposure_pval <- reshape2::dcast(exposure_dat, SNP ~ id.exposure, value.var="pval.exposure")
exposure_pval <- subset(exposure_pval, SNP %in% dat$SNP)
exposure_pval$SNP <- as.character(exposure_pval$SNP)
exposure_se <- reshape2::dcast(exposure_dat, SNP ~ id.exposure, value.var="se.exposure")
exposure_se <- subset(exposure_se, SNP %in% dat$SNP)
exposure_se$SNP <- as.character(exposure_se$SNP)
index <- match(exposure_beta$SNP, dat$SNP)
dat <- dat[index, ]
stopifnot(all(dat$SNP == exposure_beta$SNP))
exposure_beta <- as.matrix(exposure_beta[,-1])
exposure_pval <- as.matrix(exposure_pval[,-1])
exposure_se <- as.matrix(exposure_se[,-1])
rownames(exposure_beta) <- dat$SNP
rownames(exposure_pval) <- dat$SNP
rownames(exposure_se) <- dat$SNP
outcome_beta <- dat$beta.outcome
outcome_se <- dat$se.outcome
outcome_pval <- dat$pval.outcome
expname <- subset(exposure_dat, !duplicated(id.exposure), select=c(id.exposure, exposure))
outname <- subset(outcome_dat, !duplicated(id.outcome), select=c(id.outcome, outcome))
return(list(exposure_beta=exposure_beta, exposure_pval=exposure_pval, exposure_se=exposure_se, outcome_beta=outcome_beta, outcome_pval=outcome_pval, outcome_se=outcome_se, expname=expname, outname=outname))
}
#' Perform basic multivariable MR
#'
#' Performs initial multivariable MR analysis from Burgess et al 2015.
#' For each exposure the outcome is residualised for all the other exposures, then unweighted regression is applied.
#'
#' @param mvdat Output from [mv_harmonise_data()].
#' @param intercept Should the intercept by estimated (`TRUE`) or force line through the origin (`FALSE`, default).
#' @param instrument_specific Should the estimate for each exposure be obtained by using all instruments from all exposures (`FALSE`, default) or by using only the instruments specific to each exposure (`TRUE`).
#' @param pval_threshold P-value threshold to include instruments. The default is `5e-8`.
#' @param plots Create plots? The default is `FALSE`.
#'
#' @export
#' @return List of results
mv_residual <- function(mvdat, intercept=FALSE, instrument_specific=FALSE, pval_threshold=5e-8, plots=FALSE)
{
# This is a matrix of
beta.outcome <- mvdat$outcome_beta
beta.exposure <- mvdat$exposure_beta
pval.exposure <- mvdat$exposure_pval
nexp <- ncol(beta.exposure)
effs <- array(1:nexp)
se <- array(1:nexp)
pval <- array(1:nexp)
nsnp <- array(1:nexp)
marginal_outcome <- matrix(0, nrow(beta.exposure), ncol(beta.exposure))
p <- list()
nom <- colnames(beta.exposure)
nom2 <- mvdat$expname$exposure[match(nom, mvdat$expname$id.exposure)]
for (i in 1:nexp) {
# For this exposure, only keep SNPs that meet some p-value threshold
index <- pval.exposure[,i] < pval_threshold
# Get outcome effects adjusted for all effects on all other exposures
if(intercept)
{
if(instrument_specific)
{
marginal_outcome[index,i] <- stats::lm(beta.outcome[index] ~ beta.exposure[index, -c(i), drop=FALSE])$res
mod <- summary(stats::lm(marginal_outcome[index,i] ~ beta.exposure[index, i]))
} else {
marginal_outcome[,i] <- stats::lm(beta.outcome ~ beta.exposure[, -c(i), drop=FALSE])$res
mod <- summary(stats::lm(marginal_outcome[,i] ~ beta.exposure[,i]))
}
} else {
if(instrument_specific)
{
marginal_outcome[index,i] <- stats::lm(beta.outcome[index] ~ 0 + beta.exposure[index, -c(i), drop=FALSE])$res
mod <- summary(stats::lm(marginal_outcome[index,i] ~ 0 + beta.exposure[index, i]))
} else {
marginal_outcome[,i] <- stats::lm(beta.outcome ~ 0 + beta.exposure[, -c(i), drop=FALSE])$res
mod <- summary(stats::lm(marginal_outcome[,i] ~ 0 + beta.exposure[,i]))
}
}
if(sum(index) > (nexp + as.numeric(intercept)))
{
effs[i] <- mod$coef[as.numeric(intercept) + 1, 1]
se[i] <- mod$coef[as.numeric(intercept) + 1, 2]
} else {
effs[i] <- NA
se[i] <- NA
}
pval[i] <- 2 * stats::pnorm(abs(effs[i])/se[i], lower.tail = FALSE)
nsnp[i] <- sum(index)
# Make scatter plot
d <- data.frame(outcome=marginal_outcome[,i], exposure=beta.exposure[,i])
flip <- sign(d$exposure) == -1
d$outcome[flip] <- d$outcome[flip] * -1
d$exposure <- abs(d$exposure)
if(plots)
{
p[[i]] <- ggplot2::ggplot(d[index,], ggplot2::aes(x=exposure, y=outcome)) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept=0, slope=effs[i]) +
# ggplot2::stat_smooth(method="lm") +
ggplot2::labs(x=paste0("SNP effect on ", nom2[i]), y="Marginal SNP effect on outcome")
}
}
result <- data.frame(id.exposure = nom, id.outcome = mvdat$outname$id.outcome, outcome=mvdat$outname$outcome, nsnp = nsnp, b = effs, se = se, pval = pval, stringsAsFactors = FALSE)
result <- merge(mvdat$expname, result)
out <- list(
result=result,
marginal_outcome=marginal_outcome
)
if(plots) out$plots <- p
return(out)
}
#' Perform IVW multivariable MR
#'
#' Performs modified multivariable MR analysis.
#' For each exposure the instruments are selected then all exposures for those SNPs are regressed against the outcome together, weighting for the inverse variance of the outcome.
#'
#' @param mvdat Output from [mv_harmonise_data()].
#' @param intercept Should the intercept by estimated (`TRUE`) or force line through the origin (`FALSE`, default).
#' @param instrument_specific Should the estimate for each exposure be obtained by using all instruments from all exposures (`FALSE`, default) or by using only the instruments specific to each exposure (`TRUE`).
#' @param pval_threshold P-value threshold to include instruments. The default is `5e-8`.
#' @param plots Create plots? The default is `FALSE`.
#'
#' @export
#' @return List of results
mv_multiple <- function(mvdat, intercept=FALSE, instrument_specific=FALSE, pval_threshold=5e-8, plots=FALSE)
{
# This is a matrix of
beta.outcome <- mvdat$outcome_beta
beta.exposure <- mvdat$exposure_beta
pval.exposure <- mvdat$exposure_pval
w <- 1/mvdat$outcome_se^2
nexp <- ncol(beta.exposure)
effs <- array(1:nexp)
se <- array(1:nexp)
pval <- array(1:nexp)
nsnp <- array(1:nexp)
# marginal_outcome <- matrix(0, nrow(beta.exposure), ncol(beta.exposure))
p <- list()
nom <- colnames(beta.exposure)
nom2 <- mvdat$expname$exposure[match(nom, mvdat$expname$id.exposure)]
for (i in 1:nexp)
{
# For this exposure, only keep SNPs that meet some p-value threshold
index <- pval.exposure[,i] < pval_threshold
# # Get outcome effects adjusted for all effects on all other exposures
# marginal_outcome[,i] <- lm(beta.outcome ~ beta.exposure[, -c(i)])$res
# Get the effect of the exposure on the residuals of the outcome
if(!intercept)
{
if(instrument_specific)
{
mod <- summary(stats::lm(beta.outcome[index] ~ 0 + beta.exposure[index, ,drop=FALSE], weights=w[index]))
} else {
mod <- summary(stats::lm(beta.outcome ~ 0 + beta.exposure, weights=w))
}
} else {
if(instrument_specific)
{
mod <- summary(stats::lm(beta.outcome[index] ~ beta.exposure[index, ,drop=FALSE], weights=w[index]))
} else {
mod <- summary(stats::lm(beta.outcome ~ beta.exposure, weights=w))
}
}
if(instrument_specific && sum(index) <= (nexp + as.numeric(intercept)))
{
effs[i] <- NA
se[i] <- NA
} else {
effs[i] <- mod$coef[as.numeric(intercept) + i, 1]
se[i] <- mod$coef[as.numeric(intercept) + i, 2]
}
pval[i] <- 2 * stats::pnorm(abs(effs[i])/se[i], lower.tail = FALSE)
nsnp[i] <- sum(index)
# Make scatter plot
d <- data.frame(outcome=beta.outcome, exposure=beta.exposure[,i])
flip <- sign(d$exposure) == -1
d$outcome[flip] <- d$outcome[flip] * -1
d$exposure <- abs(d$exposure)
if(plots)
{
p[[i]] <- ggplot2::ggplot(d[index,], ggplot2::aes(x=exposure, y=outcome)) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept=0, slope=effs[i]) +
# ggplot2::stat_smooth(method="lm") +
ggplot2::labs(x=paste0("SNP effect on ", nom2[i]), y="Marginal SNP effect on outcome")
}
}
result <- data.frame(id.exposure = nom, id.outcome = mvdat$outname$id.outcome, outcome=mvdat$outname$outcome, nsnp = nsnp, b = effs, se = se, pval = pval, stringsAsFactors = FALSE)
result <- merge(mvdat$expname, result)
out <- list(
result=result
)
if(plots)
out$plots=p
return(out)
}
#' Perform basic multivariable MR
#'
#' Performs initial multivariable MR analysis from Burgess et al 2015.
#' For each exposure the outcome is residualised for all the other exposures, then unweighted regression is applied.
#'
#' @param mvdat Output from [mv_harmonise_data()].
#' @param pval_threshold P-value threshold to include instruments. The default is `5e-8`.
#'
#' @export
#' @return List of results
mv_basic <- function(mvdat, pval_threshold=5e-8)
{
# This is a matrix of
beta.outcome <- mvdat$outcome_beta
beta.exposure <- mvdat$exposure_beta
pval.exposure <- mvdat$exposure_pval
nexp <- ncol(beta.exposure)
effs <- array(1:nexp)
se <- array(1:nexp)
pval <- array(1:nexp)
nsnp <- array(1:nexp)
marginal_outcome <- matrix(0, nrow(beta.exposure), ncol(beta.exposure))
p <- list()
nom <- colnames(beta.exposure)
nom2 <- mvdat$expname$exposure[match(nom, mvdat$expname$id.exposure)]
for (i in 1:nexp) {
# For this exposure, only keep SNPs that meet some p-value threshold
index <- pval.exposure[,i] < pval_threshold
# Get outcome effects adjusted for all effects on all other exposures
marginal_outcome[,i] <- stats::lm(beta.outcome ~ beta.exposure[, -c(i)])$res
# Get the effect of the exposure on the residuals of the outcome
mod <- summary(stats::lm(marginal_outcome[index,i] ~ beta.exposure[index, i]))
effs[i] <- mod$coef[2, 1]
se[i] <- mod$coef[2, 2]
pval[i] <- 2 * stats::pnorm(abs(effs[i])/se[i], lower.tail = FALSE)
nsnp[i] <- sum(index)
# Make scatter plot
d <- data.frame(outcome=marginal_outcome[,i], exposure=beta.exposure[,i])
flip <- sign(d$exposure) == -1
d$outcome[flip] <- d$outcome[flip] * -1
d$exposure <- abs(d$exposure)
p[[i]] <- ggplot2::ggplot(d[index,], ggplot2::aes(x=exposure, y=outcome)) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept=0, slope=effs[i]) +
# ggplot2::stat_smooth(method="lm") +
ggplot2::labs(x=paste0("SNP effect on ", nom2[i]), y="Marginal SNP effect on outcome")
}
result <- data.frame(id.exposure = nom, id.outcome = mvdat$outname$id.outcome, outcome=mvdat$outname$outcome, nsnp = nsnp, b = effs, se = se, pval = pval, stringsAsFactors = FALSE)
result <- merge(mvdat$expname, result)
return(list(result=result, marginal_outcome=marginal_outcome, plots=p))
}
#' Perform IVW multivariable MR
#'
#' Performs modified multivariable MR analysis.
#' For each exposure the instruments are selected then all exposures for those SNPs are regressed against the outcome together, weighting for the inverse variance of the outcome.
#'
#' @param mvdat Output from [mv_harmonise_data()].
#' @param pval_threshold P-value threshold to include instruments. The default is `5e-8`.
#'
#' @export
#' @return List of results
mv_ivw <- function(mvdat, pval_threshold=5e-8)
{
# This is a matrix of
beta.outcome <- mvdat$outcome_beta
beta.exposure <- mvdat$exposure_beta
pval.exposure <- mvdat$exposure_pval
w <- 1/mvdat$outcome_se^2
nexp <- ncol(beta.exposure)
effs <- array(1:nexp)
se <- array(1:nexp)
pval <- array(1:nexp)
nsnp <- array(1:nexp)
# marginal_outcome <- matrix(0, nrow(beta.exposure), ncol(beta.exposure))
p <- list()
nom <- colnames(beta.exposure)
nom2 <- mvdat$expname$exposure[match(nom, mvdat$expname$id.exposure)]
for (i in 1:nexp) {
# For this exposure, only keep SNPs that meet some p-value threshold
index <- pval.exposure[,i] < pval_threshold
# # Get outcome effects adjusted for all effects on all other exposures
# marginal_outcome[,i] <- lm(beta.outcome ~ beta.exposure[, -c(i)])$res
# Get the effect of the exposure on the residuals of the outcome
mod <- summary(stats::lm(beta.outcome[index] ~ 0 + beta.exposure[index, ], weights=w[index]))
effs[i] <- mod$coef[i, 1]
se[i] <- mod$coef[i, 2]
pval[i] <- 2 * stats::pnorm(abs(effs[i])/se[i], lower.tail = FALSE)
nsnp[i] <- sum(index)
# Make scatter plot
d <- data.frame(outcome=beta.outcome, exposure=beta.exposure[,i])
flip <- sign(d$exposure) == -1
d$outcome[flip] <- d$outcome[flip] * -1
d$exposure <- abs(d$exposure)
p[[i]] <- ggplot2::ggplot(d[index,], ggplot2::aes(x=exposure, y=outcome)) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept=0, slope=effs[i]) +
# ggplot2::stat_smooth(method="lm") +
ggplot2::labs(x=paste0("SNP effect on ", nom2[i]), y="Marginal SNP effect on outcome")
}
result <- data.frame(id.exposure = nom, id.outcome = mvdat$outname$id.outcome, outcome=mvdat$outname$outcome, nsnp = nsnp, b = effs, se = se, pval = pval, stringsAsFactors = FALSE)
result <- merge(mvdat$expname, result)
return(list(result=result, plots=p))
}
#' Apply LASSO feature selection to mvdat object
#'
#' @param mvdat Output from [mv_harmonise_data()].
#'
#' @export
#' @return data frame of retained features
mv_lasso_feature_selection <- function(mvdat)
{
message("Performing feature selection")
b <- glmnet::cv.glmnet(x=mvdat$exposure_beta, y=mvdat$outcome_beta, weight=1/mvdat$outcome_se^2, intercept=0)
c <- glmnet::coef.glmnet(b, s = "lambda.min")
i <- !c[,1] == 0
d <- dplyr::tibble(exposure=rownames(c)[i], b=c[i,])
return(d)
}
#' Perform multivariable MR on subset of features
#'
#' The function proceeds as follows:
#' \enumerate{
#' \item Select features (by default this is done using LASSO feature selection).
#' \item Subset the mvdat to only retain relevant features and instruments.
#' \item Perform MVMR on remaining data.
#' }
#' @param mvdat Output from [mv_harmonise_data()].
#' @param features Dataframe of features to retain, must have column with name 'exposure' that has list of exposures to retain from mvdat. The default is `mvdat_lasso_feature_selection(mvdat)`.
#' @param intercept Should the intercept by estimated (`TRUE`) or force line through the origin (`FALSE`, the default).
#' @param instrument_specific Should the estimate for each exposure be obtained by using all instruments from all exposures (`FALSE`, default) or by using only the instruments specific to each exposure (`TRUE`).
#' @param pval_threshold P-value threshold to include instruments. The default is `5e-8`.
#' @param plots Create plots? The default is `FALSE`.
#'
#' @export
#' @return List of results
mv_subset <- function(mvdat, features=mv_lasso_feature_selection(mvdat), intercept=FALSE, instrument_specific=FALSE, pval_threshold=5e-8, plots=FALSE)
{
# Update mvdat object
mvdat$exposure_beta <- mvdat$exposure_beta[, features$exposure, drop=FALSE]
mvdat$exposure_se <- mvdat$exposure_se[, features$exposure, drop=FALSE]
mvdat$exposure_pval <- mvdat$exposure_pval[, features$exposure, drop=FALSE]
# Find relevant instruments
instruments <- apply(mvdat$exposure_pval, 1, function(x) any(x < pval_threshold))
stopifnot(sum(instruments) > nrow(features))
mvdat$exposure_beta <- mvdat$exposure_beta[instruments,,drop=FALSE]
mvdat$exposure_se <- mvdat$exposure_se[instruments,,drop=FALSE]
mvdat$exposure_pval <- mvdat$exposure_pval[instruments,,drop=FALSE]
mvdat$outcome_beta <- mvdat$outcome_beta[instruments]
mvdat$outcome_se <- mvdat$outcome_se[instruments]
mvdat$outcome_pval <- mvdat$outcome_pval[instruments]
mv_multiple(mvdat, intercept=intercept, instrument_specific=instrument_specific, pval_threshold=pval_threshold, plots=plots)
}
MRCIEU/TwoSampleMR documentation built on Sept. 28, 2024, 6:51 p.m.
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Defines functions mv_subset mv_lasso_feature_selection mv_ivw mv_basic mv_multiple mv_residual mv_harmonise_data mv_extract_exposures_local mv_extract_exposures
Documented in mv_basic mv_extract_exposures mv_extract_exposures_local mv_harmonise_data mv_ivw mv_lasso_feature_selection mv_multiple mv_residual mv_subset
#' Extract exposure variables for multivariable MR
#'
#' Requires a list of IDs from available_outcomes. For each ID, it extracts instruments. Then, it gets the full list of all instruments and extracts those SNPs for every exposure. Finally, it keeps only the SNPs that are a) independent and b) present in all exposures, and harmonises them to be all on the same strand.
#'
#' @param id_exposure Array of IDs (e.g. c(299, 300, 302) for HDL, LDL, trigs)
#' @param clump_r2 The default is `0.01`.
#' @param clump_kb The default is `10000`.
#' @param harmonise_strictness See the `action` option of [harmonise_data()]. The default is `2`.
#' @param opengwas_jwt Used to authenticate protected endpoints. Login to <https://api.opengwas.io> to obtain a jwt. Provide the jwt string here, or store in .Renviron under the keyname OPENGWAS_JWT.
#' @param find_proxies Look for proxies? This slows everything down but is more accurate. The default is `TRUE`.
#' @param force_server Whether to search through pre-clumped dataset or to re-extract and clump directly from the server. The default is `FALSE`.
#' @param pval_threshold Instrument detection p-value threshold. Default = `5e-8`
#' @param pop Which 1000 genomes super population to use for clumping when using the server
#' @param plink_bin If `NULL` and `bfile` is not `NULL` then will detect packaged plink binary for specific OS. Otherwise specify path to plink binary. Default = `NULL`
#' @param bfile If this is provided then will use the API. Default = `NULL`
#'
#' @export
#' @return data frame in `exposure_dat` format
mv_extract_exposures <- function(id_exposure, clump_r2=0.001, clump_kb=10000, harmonise_strictness=2, opengwas_jwt=ieugwasr::get_opengwas_jwt(), find_proxies=TRUE, force_server=FALSE, pval_threshold=5e-8, pop="EUR", plink_bin=NULL, bfile=NULL)
{
stopifnot(length(id_exposure) > 1)
id_exposure <- ieugwasr::legacy_ids(id_exposure)
# Get best instruments for each exposure
exposure_dat <- extract_instruments(id_exposure, p1 = pval_threshold, r2 = clump_r2, kb=clump_kb, opengwas_jwt = opengwas_jwt, force_server=force_server)
temp <- exposure_dat
temp$id.exposure <- 1
temp <- temp[order(temp$pval.exposure, decreasing=FALSE), ]
temp <- subset(temp, !duplicated(SNP))
temp <- clump_data(temp, clump_p1=pval_threshold, clump_r2=clump_r2, clump_kb=clump_kb, pop=pop, plink_bin=plink_bin, bfile=bfile)
exposure_dat <- subset(exposure_dat, SNP %in% temp$SNP)
# Get effects of each instrument from each exposure
d1 <- extract_outcome_data(exposure_dat$SNP, id_exposure, opengwas_jwt = opengwas_jwt, proxies=find_proxies)
stopifnot(length(unique(d1$id)) == length(unique(id_exposure)))
d1 <- subset(d1, mr_keep.outcome)
d2 <- subset(d1, id.outcome != id_exposure[1])
d1 <- convert_outcome_to_exposure(subset(d1, id.outcome == id_exposure[1]))
# Harmonise against the first id
d <- harmonise_data(d1, d2, action=harmonise_strictness)
# Only keep SNPs that are present in all
tab <- table(d$SNP)
keepsnps <- names(tab)[tab == length(id_exposure)-1]
d <- subset(d, SNP %in% keepsnps)
# Reshape exposures
dh1 <- subset(d, id.outcome == id.outcome[1], select=c(SNP, exposure, id.exposure, effect_allele.exposure, other_allele.exposure, eaf.exposure, beta.exposure, se.exposure, pval.exposure))
dh2 <- subset(d, select=c(SNP, outcome, id.outcome, effect_allele.outcome, other_allele.outcome, eaf.outcome, beta.outcome, se.outcome, pval.outcome))
names(dh2) <- gsub("outcome", "exposure", names(dh2))
dh <- rbind(dh1, dh2)
return(dh)
}
#' Attempt to perform MVMR using local data
#'
#' Allows you to read in summary data from text files to format the multivariable exposure dataset.
#'
#' Note that you can provide an array of column names for each column, which is of length `filenames_exposure`
#'
#' @param filenames_exposure Filenames for each exposure dataset. Must have header with at least SNP column present. Following arguments are used for determining how to read the filename and clumping etc.
#' @param sep Specify delimeter in file. The default is space, i.e. `sep=" "`. If length is 1 it will use the same `sep` value for each exposure dataset. You can provide a vector of values, one for each exposure dataset, if the values are different across datasets. The same applies to all dataset-formatting options listed below.
#' @param phenotype_col Optional column name for the column with phenotype name corresponding the the SNP. If not present then will be created with the value `"Outcome"`. Default is `"Phenotype"`.
#' @param snp_col Required name of column with SNP rs IDs. The default is `"SNP"`.
#' @param beta_col Required for MR. Name of column with effect sizes. THe default is `"beta"`.
#' @param se_col Required for MR. Name of column with standard errors. The default is `"se"`.
#' @param eaf_col Required for MR. Name of column with effect allele frequency. The default is `"eaf"`.
#' @param effect_allele_col Required for MR. Name of column with effect allele. Must be "A", "C", "T" or "G". The default is `"effect_allele"`.
#' @param other_allele_col Required for MR. Name of column with non effect allele. Must be "A", "C", "T" or "G". The default is `"other_allele"`.
#' @param pval_col Required for enrichment tests. Name of column with p-value. The default is `"pval"`.
#' @param units_col Optional column name for units. The default is `"units"`.
#' @param ncase_col Optional column name for number of cases. The default is `"ncase"`.
#' @param ncontrol_col Optional column name for number of controls. The default is `"ncontrol"`.
#' @param samplesize_col Optional column name for sample size. The default is `"samplesize"`.
#' @param gene_col Optional column name for gene name. The default is `"gene"`.
#' @param id_col Optional column name to give the dataset an ID. Will be generated automatically if not provided for every trait / unit combination. The default is `"id"`.
#' @param min_pval Minimum allowed p-value. The default is `1e-200`.
#' @param log_pval The pval is -log10(P). The default is `FALSE`.
#' @param pval_threshold Default=`5e-8` for clumping
#' @param plink_bin If `NULL` and `bfile` is not `NULL` then will detect packaged plink binary for specific OS. Otherwise specify path to plink binary. Default = `NULL`
#' @param bfile If this is provided then will use the API. Default = `NULL`
#' @param clump_r2 Default=`0.001` for clumping
#' @param clump_kb Default=`10000` for clumping
#' @param pop Which 1000 genomes super population to use for clumping when using the server
#' @param harmonise_strictness See action argument in [harmonise_data()]. Default=`2`
#'
#' @export
#' @return List
mv_extract_exposures_local <- function(
filenames_exposure,
sep = " ",
phenotype_col = "Phenotype",
snp_col = "SNP",
beta_col = "beta",
se_col = "se",
eaf_col = "eaf",
effect_allele_col = "effect_allele",
other_allele_col = "other_allele",
pval_col = "pval",
units_col = "units",
ncase_col = "ncase",
ncontrol_col = "ncontrol",
samplesize_col = "samplesize",
gene_col = "gene",
id_col = "id",
min_pval = 1e-200,
log_pval = FALSE,
pval_threshold=5e-8,
plink_bin=NULL,
bfile=NULL,
clump_r2=0.001,
clump_kb=10000,
pop="EUR",
harmonise_strictness=2
) {
message("WARNING: Experimental function")
stopifnot(inherits(filenames_exposure, "character") | inherits(filenames_exposure, "list"))
if(inherits(filenames_exposure, "list")) {
stopifnot(all(sapply(filenames_exposure, function(x) inherits(x, "data.frame"))))
flag <- "data.frame"
} else {
flag <- "character"
}
n <- length(filenames_exposure)
if(length(sep) == 1) {sep <- rep(sep, n)}
if(length(phenotype_col) == 1) {phenotype_col <- rep(phenotype_col, n)}
if(length(snp_col) == 1) {snp_col <- rep(snp_col, n)}
if(length(beta_col) == 1) {beta_col <- rep(beta_col, n)}
if(length(se_col) == 1) {se_col <- rep(se_col, n)}
if(length(eaf_col) == 1) {eaf_col <- rep(eaf_col, n)}
if(length(effect_allele_col) == 1) {effect_allele_col <- rep(effect_allele_col, n)}
if(length(other_allele_col) == 1) {other_allele_col <- rep(other_allele_col, n)}
if(length(pval_col) == 1) {pval_col <- rep(pval_col, n)}
if(length(units_col) == 1) {units_col <- rep(units_col, n)}
if(length(ncase_col) == 1) {ncase_col <- rep(ncase_col, n)}
if(length(ncontrol_col) == 1) {ncontrol_col <- rep(ncontrol_col, n)}
if(length(samplesize_col) == 1) {samplesize_col <- rep(samplesize_col, n)}
if(length(gene_col) == 1) {gene_col <- rep(gene_col, n)}
if(length(id_col) == 1) {id_col <- rep(id_col, n)}
if(length(min_pval) == 1) {min_pval <- rep(min_pval, n)}
if(length(log_pval) == 1) {log_pval <- rep(log_pval, n)}
l_full <- list()
l_inst <- list()
for(i in seq_along(filenames_exposure))
{
if(flag == "character") {
l_full[[i]] <- read_outcome_data(filenames_exposure[i],
sep = sep[i],
phenotype_col = phenotype_col[i],
snp_col = snp_col[i],
beta_col = beta_col[i],
se_col = se_col[i],
eaf_col = eaf_col[i],
effect_allele_col = effect_allele_col[i],
other_allele_col = other_allele_col[i],
pval_col = pval_col[i],
units_col = units_col[i],
ncase_col = ncase_col[i],
ncontrol_col = ncontrol_col[i],
samplesize_col = samplesize_col[i],
gene_col = gene_col[i],
id_col = id_col[i],
min_pval = min_pval[i],
log_pval = log_pval[i]
)
} else {
l_full[[i]] <- format_data(filenames_exposure[[i]],
type="outcome",
phenotype_col = phenotype_col[i],
snp_col = snp_col[i],
beta_col = beta_col[i],
se_col = se_col[i],
eaf_col = eaf_col[i],
effect_allele_col = effect_allele_col[i],
other_allele_col = other_allele_col[i],
pval_col = pval_col[i],
units_col = units_col[i],
ncase_col = ncase_col[i],
ncontrol_col = ncontrol_col[i],
samplesize_col = samplesize_col[i],
gene_col = gene_col[i],
id_col = id_col[i],
min_pval = min_pval[i],
log_pval = log_pval[i]
)
}
if(l_full[[i]]$outcome[1] == "outcome") l_full[[i]]$outcome <- paste0("exposure", i)
l_inst[[i]] <- subset(l_full[[i]], pval.outcome < pval_threshold)
l_inst[[i]] <- subset(l_inst[[i]], !duplicated(SNP))
l_inst[[i]] <- convert_outcome_to_exposure(l_inst[[i]])
l_inst[[i]] <- subset(l_inst[[i]], pval.exposure < pval_threshold)
l_inst[[i]] <- clump_data(l_inst[[i]], clump_p1=pval_threshold, clump_r2=clump_r2, clump_kb=clump_kb, bfile=bfile, plink_bin=plink_bin, pop=pop)
message("Identified ", nrow(l_inst[[i]]), " hits for trait ", l_inst[[i]]$exposure[1])
}
exposure_dat <- dplyr::bind_rows(l_inst)
id_exposure <- unique(exposure_dat$id.exposure)
temp <- exposure_dat
temp$id.exposure <- 1
temp <- temp[order(temp$pval.exposure, decreasing=FALSE), ]
temp <- subset(temp, !duplicated(SNP))
temp <- clump_data(temp, clump_p1=pval_threshold, clump_r2=clump_r2, clump_kb=clump_kb, bfile=bfile, plink_bin=plink_bin, pop=pop)
exposure_dat <- subset(exposure_dat, SNP %in% temp$SNP)
message("Identified ", length(unique(temp$SNP)), " variants to include")
d1 <- lapply(l_full, function(x) {
subset(x, SNP %in% exposure_dat$SNP)
}) %>% dplyr::bind_rows()
stopifnot(length(unique(d1$id)) == length(unique(id_exposure)))
d1 <- subset(d1, mr_keep.outcome)
d2 <- subset(d1, id.outcome != id_exposure[1])
d1 <- convert_outcome_to_exposure(subset(d1, id.outcome == id_exposure[1]))
# Harmonise against the first id
d <- harmonise_data(d1, d2, action=harmonise_strictness)
# Only keep SNPs that are present in all
tab <- table(d$SNP)
keepsnps <- names(tab)[tab == length(id_exposure)-1]
d <- subset(d, SNP %in% keepsnps)
# Reshape exposures
dh1 <- subset(d, id.outcome == id.outcome[1], select=c(SNP, exposure, id.exposure, effect_allele.exposure, other_allele.exposure, eaf.exposure, beta.exposure, se.exposure, pval.exposure))
dh2 <- subset(d, select=c(SNP, outcome, id.outcome, effect_allele.outcome, other_allele.outcome, eaf.outcome, beta.outcome, se.outcome, pval.outcome))
names(dh2) <- gsub("outcome", "exposure", names(dh2))
dh <- rbind(dh1, dh2)
return(dh)
}
#' Harmonise exposure and outcome for multivariable MR
#'
#' @param exposure_dat Output from [mv_extract_exposures()].
#' @param outcome_dat Output from `extract_outcome_data(exposure_dat$SNP, id_output)`.
#' @param harmonise_strictness See the `action` option of [harmonise_data()]. The default is `2`.
#'
#' @export
#' @return List of vectors and matrices required for mv analysis.
#' \describe{
#' \item{exposure_beta}{a matrix of beta coefficients, in which rows correspond to SNPs and columns correspond to exposures.}
#' \item{exposure_se}{is the same as `exposure_beta`, but for standard errors.}
#' \item{exposure_pval}{the same as `exposure_beta`, but for p-values.}
#' \item{expname}{A data frame with two variables, `id.exposure` and `exposure` which are character strings.}
#' \item{outcome_beta}{an array of effects for the outcome, corresponding to the SNPs in `exposure_beta`.}
#' \item{outcome_se}{an array of standard errors for the outcome.}
#' \item{outcome_pval}{an array of p-values for the outcome.}
#' \item{outname}{A data frame with two variables, `id.outcome` and `outcome` which are character strings.}
#' }
#'
mv_harmonise_data <- function(exposure_dat, outcome_dat, harmonise_strictness=2)
{
stopifnot(all(c("SNP", "id.exposure", "exposure", "effect_allele.exposure", "beta.exposure", "se.exposure", "pval.exposure") %in% names(exposure_dat)))
nexp <- length(unique(exposure_dat$id.exposure))
stopifnot(nexp > 1)
tab <- table(exposure_dat$SNP)
keepsnp <- names(tab)[tab == nexp]
exposure_dat <- subset(exposure_dat, SNP %in% keepsnp)
exposure_mat <- reshape2::dcast(exposure_dat, SNP ~ id.exposure, value.var="beta.exposure")
# Get outcome data
dat <- harmonise_data(subset(exposure_dat, id.exposure == exposure_dat$id.exposure[1]), outcome_dat, action=harmonise_strictness)
dat <- subset(dat, mr_keep)
dat$SNP <- as.character(dat$SNP)
exposure_beta <- reshape2::dcast(exposure_dat, SNP ~ id.exposure, value.var="beta.exposure")
exposure_beta <- subset(exposure_beta, SNP %in% dat$SNP)
exposure_beta$SNP <- as.character(exposure_beta$SNP)
exposure_pval <- reshape2::dcast(exposure_dat, SNP ~ id.exposure, value.var="pval.exposure")
exposure_pval <- subset(exposure_pval, SNP %in% dat$SNP)
exposure_pval$SNP <- as.character(exposure_pval$SNP)
exposure_se <- reshape2::dcast(exposure_dat, SNP ~ id.exposure, value.var="se.exposure")
exposure_se <- subset(exposure_se, SNP %in% dat$SNP)
exposure_se$SNP <- as.character(exposure_se$SNP)
index <- match(exposure_beta$SNP, dat$SNP)
dat <- dat[index, ]
stopifnot(all(dat$SNP == exposure_beta$SNP))
exposure_beta <- as.matrix(exposure_beta[,-1])
exposure_pval <- as.matrix(exposure_pval[,-1])
exposure_se <- as.matrix(exposure_se[,-1])
rownames(exposure_beta) <- dat$SNP
rownames(exposure_pval) <- dat$SNP
rownames(exposure_se) <- dat$SNP
outcome_beta <- dat$beta.outcome
outcome_se <- dat$se.outcome
outcome_pval <- dat$pval.outcome
expname <- subset(exposure_dat, !duplicated(id.exposure), select=c(id.exposure, exposure))
outname <- subset(outcome_dat, !duplicated(id.outcome), select=c(id.outcome, outcome))
return(list(exposure_beta=exposure_beta, exposure_pval=exposure_pval, exposure_se=exposure_se, outcome_beta=outcome_beta, outcome_pval=outcome_pval, outcome_se=outcome_se, expname=expname, outname=outname))
}
#' Perform basic multivariable MR
#'
#' Performs initial multivariable MR analysis from Burgess et al 2015.
#' For each exposure the outcome is residualised for all the other exposures, then unweighted regression is applied.
#'
#' @param mvdat Output from [mv_harmonise_data()].
#' @param intercept Should the intercept by estimated (`TRUE`) or force line through the origin (`FALSE`, default).
#' @param instrument_specific Should the estimate for each exposure be obtained by using all instruments from all exposures (`FALSE`, default) or by using only the instruments specific to each exposure (`TRUE`).
#' @param pval_threshold P-value threshold to include instruments. The default is `5e-8`.
#' @param plots Create plots? The default is `FALSE`.
#'
#' @export
#' @return List of results
mv_residual <- function(mvdat, intercept=FALSE, instrument_specific=FALSE, pval_threshold=5e-8, plots=FALSE)
{
# This is a matrix of
beta.outcome <- mvdat$outcome_beta
beta.exposure <- mvdat$exposure_beta
pval.exposure <- mvdat$exposure_pval
nexp <- ncol(beta.exposure)
effs <- array(1:nexp)
se <- array(1:nexp)
pval <- array(1:nexp)
nsnp <- array(1:nexp)
marginal_outcome <- matrix(0, nrow(beta.exposure), ncol(beta.exposure))
p <- list()
nom <- colnames(beta.exposure)
nom2 <- mvdat$expname$exposure[match(nom, mvdat$expname$id.exposure)]
for (i in 1:nexp) {
# For this exposure, only keep SNPs that meet some p-value threshold
index <- pval.exposure[,i] < pval_threshold
# Get outcome effects adjusted for all effects on all other exposures
if(intercept)
{
if(instrument_specific)
{
marginal_outcome[index,i] <- stats::lm(beta.outcome[index] ~ beta.exposure[index, -c(i), drop=FALSE])$res
mod <- summary(stats::lm(marginal_outcome[index,i] ~ beta.exposure[index, i]))
} else {
marginal_outcome[,i] <- stats::lm(beta.outcome ~ beta.exposure[, -c(i), drop=FALSE])$res
mod <- summary(stats::lm(marginal_outcome[,i] ~ beta.exposure[,i]))
}
} else {
if(instrument_specific)
{
marginal_outcome[index,i] <- stats::lm(beta.outcome[index] ~ 0 + beta.exposure[index, -c(i), drop=FALSE])$res
mod <- summary(stats::lm(marginal_outcome[index,i] ~ 0 + beta.exposure[index, i]))
} else {
marginal_outcome[,i] <- stats::lm(beta.outcome ~ 0 + beta.exposure[, -c(i), drop=FALSE])$res
mod <- summary(stats::lm(marginal_outcome[,i] ~ 0 + beta.exposure[,i]))
}
}
if(sum(index) > (nexp + as.numeric(intercept)))
{
effs[i] <- mod$coef[as.numeric(intercept) + 1, 1]
se[i] <- mod$coef[as.numeric(intercept) + 1, 2]
} else {
effs[i] <- NA
se[i] <- NA
}
pval[i] <- 2 * stats::pnorm(abs(effs[i])/se[i], lower.tail = FALSE)
nsnp[i] <- sum(index)
# Make scatter plot
d <- data.frame(outcome=marginal_outcome[,i], exposure=beta.exposure[,i])
flip <- sign(d$exposure) == -1
d$outcome[flip] <- d$outcome[flip] * -1
d$exposure <- abs(d$exposure)
if(plots)
{
p[[i]] <- ggplot2::ggplot(d[index,], ggplot2::aes(x=exposure, y=outcome)) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept=0, slope=effs[i]) +
# ggplot2::stat_smooth(method="lm") +
ggplot2::labs(x=paste0("SNP effect on ", nom2[i]), y="Marginal SNP effect on outcome")
}
}
result <- data.frame(id.exposure = nom, id.outcome = mvdat$outname$id.outcome, outcome=mvdat$outname$outcome, nsnp = nsnp, b = effs, se = se, pval = pval, stringsAsFactors = FALSE)
result <- merge(mvdat$expname, result)
out <- list(
result=result,
marginal_outcome=marginal_outcome
)
if(plots) out$plots <- p
return(out)
}
#' Perform IVW multivariable MR
#'
#' Performs modified multivariable MR analysis.
#' For each exposure the instruments are selected then all exposures for those SNPs are regressed against the outcome together, weighting for the inverse variance of the outcome.
#'
#' @param mvdat Output from [mv_harmonise_data()].
#' @param intercept Should the intercept by estimated (`TRUE`) or force line through the origin (`FALSE`, default).
#' @param instrument_specific Should the estimate for each exposure be obtained by using all instruments from all exposures (`FALSE`, default) or by using only the instruments specific to each exposure (`TRUE`).
#' @param pval_threshold P-value threshold to include instruments. The default is `5e-8`.
#' @param plots Create plots? The default is `FALSE`.
#'
#' @export
#' @return List of results
mv_multiple <- function(mvdat, intercept=FALSE, instrument_specific=FALSE, pval_threshold=5e-8, plots=FALSE)
{
# This is a matrix of
beta.outcome <- mvdat$outcome_beta
beta.exposure <- mvdat$exposure_beta
pval.exposure <- mvdat$exposure_pval
w <- 1/mvdat$outcome_se^2
nexp <- ncol(beta.exposure)
effs <- array(1:nexp)
se <- array(1:nexp)
pval <- array(1:nexp)
nsnp <- array(1:nexp)
# marginal_outcome <- matrix(0, nrow(beta.exposure), ncol(beta.exposure))
p <- list()
nom <- colnames(beta.exposure)
nom2 <- mvdat$expname$exposure[match(nom, mvdat$expname$id.exposure)]
for (i in 1:nexp)
{
# For this exposure, only keep SNPs that meet some p-value threshold
index <- pval.exposure[,i] < pval_threshold
# # Get outcome effects adjusted for all effects on all other exposures
# marginal_outcome[,i] <- lm(beta.outcome ~ beta.exposure[, -c(i)])$res
# Get the effect of the exposure on the residuals of the outcome
if(!intercept)
{
if(instrument_specific)
{
mod <- summary(stats::lm(beta.outcome[index] ~ 0 + beta.exposure[index, ,drop=FALSE], weights=w[index]))
} else {
mod <- summary(stats::lm(beta.outcome ~ 0 + beta.exposure, weights=w))
}
} else {
if(instrument_specific)
{
mod <- summary(stats::lm(beta.outcome[index] ~ beta.exposure[index, ,drop=FALSE], weights=w[index]))
} else {
mod <- summary(stats::lm(beta.outcome ~ beta.exposure, weights=w))
}
}
if(instrument_specific && sum(index) <= (nexp + as.numeric(intercept)))
{
effs[i] <- NA
se[i] <- NA
} else {
effs[i] <- mod$coef[as.numeric(intercept) + i, 1]
se[i] <- mod$coef[as.numeric(intercept) + i, 2]
}
pval[i] <- 2 * stats::pnorm(abs(effs[i])/se[i], lower.tail = FALSE)
nsnp[i] <- sum(index)
# Make scatter plot
d <- data.frame(outcome=beta.outcome, exposure=beta.exposure[,i])
flip <- sign(d$exposure) == -1
d$outcome[flip] <- d$outcome[flip] * -1
d$exposure <- abs(d$exposure)
if(plots)
{
p[[i]] <- ggplot2::ggplot(d[index,], ggplot2::aes(x=exposure, y=outcome)) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept=0, slope=effs[i]) +
# ggplot2::stat_smooth(method="lm") +
ggplot2::labs(x=paste0("SNP effect on ", nom2[i]), y="Marginal SNP effect on outcome")
}
}
result <- data.frame(id.exposure = nom, id.outcome = mvdat$outname$id.outcome, outcome=mvdat$outname$outcome, nsnp = nsnp, b = effs, se = se, pval = pval, stringsAsFactors = FALSE)
result <- merge(mvdat$expname, result)
out <- list(
result=result
)
if(plots)
out$plots=p
return(out)
}
#' Perform basic multivariable MR
#'
#' Performs initial multivariable MR analysis from Burgess et al 2015.
#' For each exposure the outcome is residualised for all the other exposures, then unweighted regression is applied.
#'
#' @param mvdat Output from [mv_harmonise_data()].
#' @param pval_threshold P-value threshold to include instruments. The default is `5e-8`.
#'
#' @export
#' @return List of results
mv_basic <- function(mvdat, pval_threshold=5e-8)
{
# This is a matrix of
beta.outcome <- mvdat$outcome_beta
beta.exposure <- mvdat$exposure_beta
pval.exposure <- mvdat$exposure_pval
nexp <- ncol(beta.exposure)
effs <- array(1:nexp)
se <- array(1:nexp)
pval <- array(1:nexp)
nsnp <- array(1:nexp)
marginal_outcome <- matrix(0, nrow(beta.exposure), ncol(beta.exposure))
p <- list()
nom <- colnames(beta.exposure)
nom2 <- mvdat$expname$exposure[match(nom, mvdat$expname$id.exposure)]
for (i in 1:nexp) {
# For this exposure, only keep SNPs that meet some p-value threshold
index <- pval.exposure[,i] < pval_threshold
# Get outcome effects adjusted for all effects on all other exposures
marginal_outcome[,i] <- stats::lm(beta.outcome ~ beta.exposure[, -c(i)])$res
# Get the effect of the exposure on the residuals of the outcome
mod <- summary(stats::lm(marginal_outcome[index,i] ~ beta.exposure[index, i]))
effs[i] <- mod$coef[2, 1]
se[i] <- mod$coef[2, 2]
pval[i] <- 2 * stats::pnorm(abs(effs[i])/se[i], lower.tail = FALSE)
nsnp[i] <- sum(index)
# Make scatter plot
d <- data.frame(outcome=marginal_outcome[,i], exposure=beta.exposure[,i])
flip <- sign(d$exposure) == -1
d$outcome[flip] <- d$outcome[flip] * -1
d$exposure <- abs(d$exposure)
p[[i]] <- ggplot2::ggplot(d[index,], ggplot2::aes(x=exposure, y=outcome)) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept=0, slope=effs[i]) +
# ggplot2::stat_smooth(method="lm") +
ggplot2::labs(x=paste0("SNP effect on ", nom2[i]), y="Marginal SNP effect on outcome")
}
result <- data.frame(id.exposure = nom, id.outcome = mvdat$outname$id.outcome, outcome=mvdat$outname$outcome, nsnp = nsnp, b = effs, se = se, pval = pval, stringsAsFactors = FALSE)
result <- merge(mvdat$expname, result)
return(list(result=result, marginal_outcome=marginal_outcome, plots=p))
}
#' Perform IVW multivariable MR
#'
#' Performs modified multivariable MR analysis.
#' For each exposure the instruments are selected then all exposures for those SNPs are regressed against the outcome together, weighting for the inverse variance of the outcome.
#'
#' @param mvdat Output from [mv_harmonise_data()].
#' @param pval_threshold P-value threshold to include instruments. The default is `5e-8`.
#'
#' @export
#' @return List of results
mv_ivw <- function(mvdat, pval_threshold=5e-8)
{
# This is a matrix of
beta.outcome <- mvdat$outcome_beta
beta.exposure <- mvdat$exposure_beta
pval.exposure <- mvdat$exposure_pval
w <- 1/mvdat$outcome_se^2
nexp <- ncol(beta.exposure)
effs <- array(1:nexp)
se <- array(1:nexp)
pval <- array(1:nexp)
nsnp <- array(1:nexp)
# marginal_outcome <- matrix(0, nrow(beta.exposure), ncol(beta.exposure))
p <- list()
nom <- colnames(beta.exposure)
nom2 <- mvdat$expname$exposure[match(nom, mvdat$expname$id.exposure)]
for (i in 1:nexp) {
# For this exposure, only keep SNPs that meet some p-value threshold
index <- pval.exposure[,i] < pval_threshold
# # Get outcome effects adjusted for all effects on all other exposures
# marginal_outcome[,i] <- lm(beta.outcome ~ beta.exposure[, -c(i)])$res
# Get the effect of the exposure on the residuals of the outcome
mod <- summary(stats::lm(beta.outcome[index] ~ 0 + beta.exposure[index, ], weights=w[index]))
effs[i] <- mod$coef[i, 1]
se[i] <- mod$coef[i, 2]
pval[i] <- 2 * stats::pnorm(abs(effs[i])/se[i], lower.tail = FALSE)
nsnp[i] <- sum(index)
# Make scatter plot
d <- data.frame(outcome=beta.outcome, exposure=beta.exposure[,i])
flip <- sign(d$exposure) == -1
d$outcome[flip] <- d$outcome[flip] * -1
d$exposure <- abs(d$exposure)
p[[i]] <- ggplot2::ggplot(d[index,], ggplot2::aes(x=exposure, y=outcome)) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept=0, slope=effs[i]) +
# ggplot2::stat_smooth(method="lm") +
ggplot2::labs(x=paste0("SNP effect on ", nom2[i]), y="Marginal SNP effect on outcome")
}
result <- data.frame(id.exposure = nom, id.outcome = mvdat$outname$id.outcome, outcome=mvdat$outname$outcome, nsnp = nsnp, b = effs, se = se, pval = pval, stringsAsFactors = FALSE)
result <- merge(mvdat$expname, result)
return(list(result=result, plots=p))
}
#' Apply LASSO feature selection to mvdat object
#'
#' @param mvdat Output from [mv_harmonise_data()].
#'
#' @export
#' @return data frame of retained features
mv_lasso_feature_selection <- function(mvdat)
{
message("Performing feature selection")
b <- glmnet::cv.glmnet(x=mvdat$exposure_beta, y=mvdat$outcome_beta, weight=1/mvdat$outcome_se^2, intercept=0)
c <- glmnet::coef.glmnet(b, s = "lambda.min")
i <- !c[,1] == 0
d <- dplyr::tibble(exposure=rownames(c)[i], b=c[i,])
return(d)
}
#' Perform multivariable MR on subset of features
#'
#' The function proceeds as follows:
#' \enumerate{
#' \item Select features (by default this is done using LASSO feature selection).
#' \item Subset the mvdat to only retain relevant features and instruments.
#' \item Perform MVMR on remaining data.
#' }
#' @param mvdat Output from [mv_harmonise_data()].
#' @param features Dataframe of features to retain, must have column with name 'exposure' that has list of exposures to retain from mvdat. The default is `mvdat_lasso_feature_selection(mvdat)`.
#' @param intercept Should the intercept by estimated (`TRUE`) or force line through the origin (`FALSE`, the default).
#' @param instrument_specific Should the estimate for each exposure be obtained by using all instruments from all exposures (`FALSE`, default) or by using only the instruments specific to each exposure (`TRUE`).
#' @param pval_threshold P-value threshold to include instruments. The default is `5e-8`.
#' @param plots Create plots? The default is `FALSE`.
#'
#' @export
#' @return List of results
mv_subset <- function(mvdat, features=mv_lasso_feature_selection(mvdat), intercept=FALSE, instrument_specific=FALSE, pval_threshold=5e-8, plots=FALSE)
{
# Update mvdat object
mvdat$exposure_beta <- mvdat$exposure_beta[, features$exposure, drop=FALSE]
mvdat$exposure_se <- mvdat$exposure_se[, features$exposure, drop=FALSE]
mvdat$exposure_pval <- mvdat$exposure_pval[, features$exposure, drop=FALSE]
# Find relevant instruments
instruments <- apply(mvdat$exposure_pval, 1, function(x) any(x < pval_threshold))
stopifnot(sum(instruments) > nrow(features))
mvdat$exposure_beta <- mvdat$exposure_beta[instruments,,drop=FALSE]
mvdat$exposure_se <- mvdat$exposure_se[instruments,,drop=FALSE]
mvdat$exposure_pval <- mvdat$exposure_pval[instruments,,drop=FALSE]
mvdat$outcome_beta <- mvdat$outcome_beta[instruments]
mvdat$outcome_se <- mvdat$outcome_se[instruments]
mvdat$outcome_pval <- mvdat$outcome_pval[instruments]
mv_multiple(mvdat, intercept=intercept, instrument_specific=instrument_specific, pval_threshold=pval_threshold, plots=plots)
}
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