diff --git a/.Rbuildignore b/.Rbuildignore
index 43ff7d8a..f5c6edf5 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -21,3 +21,5 @@ cache$
 ^inst/sandpit$
 ^air\.toml$
 ^vignettes/rf\.rdata$
+^\.positai$
+^\.claude$
diff --git a/.github/workflows/check-full.yaml b/.github/workflows/check-full.yaml
index 4162fc7d..02f7067c 100644
--- a/.github/workflows/check-full.yaml
+++ b/.github/workflows/check-full.yaml
@@ -38,6 +38,7 @@ jobs:
           - {os: ubuntu-latest,   r: '4.3.2'}
           - {os: ubuntu-latest,   r: 'oldrel-3'}
           - {os: ubuntu-latest,   r: 'oldrel-4'}
+          - {os: ubuntu-latest,   r: 'oldrel-5'}  
           - {os: macos-14, r: 'release'}
           # - {os: ubuntu-24.04-arm, r: 'release', rspm: 'no' }
 
diff --git a/.gitignore b/.gitignore
index 2f7eb1ee..2ea57cf4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -26,3 +26,4 @@ vignettes/mr_report.md
 vignettes/figure/*.png
 vignettes/*.html
 rf.rdata
+.positai
diff --git a/DESCRIPTION b/DESCRIPTION
index b0feef5e..81857575 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: TwoSampleMR
 Title: Two Sample MR Functions and Interface to MRC Integrative
     Epidemiology Unit OpenGWAS Database
-Version: 0.7.4
+Version: 0.7.5
 Authors@R: c(
     person("Gibran", "Hemani", , "g.hemani@bristol.ac.uk", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-0920-1055")),
@@ -20,7 +20,7 @@ Authors@R: c(
   )
 Description: A package for performing Mendelian randomization using GWAS
     summary data. It uses the IEU OpenGWAS database
-    <https://gwas.mrcieu.ac.uk/> to automatically obtain data, and a wide
+    <https://opengwas.io> to automatically obtain data, and a wide
     range of methods to run the analysis.
 License: MIT + file LICENSE
 URL: https://github.com/MRCIEU/TwoSampleMR,
@@ -68,6 +68,6 @@ Remotes:
     MRPRESSO=rondolab/MR-PRESSO,
     qingyuanzhao/mr.raps,
     WSpiller/RadialMR
+Config/roxygen2/markdown: TRUE
+Config/roxygen2/version: 8.0.0
 Encoding: UTF-8
-Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.3
diff --git a/NEWS.md b/NEWS.md
index 60a97dde..1105bdb0 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,15 @@
+# TwoSampleMR v0.7.5
+
+(Release date 2026-05-04)
+
+* Amend <https://gwas.mrcieu.ac.uk> URLs to <https://opengwas.io/>
+* Bump version of roxygen2 to 8.0.0 and regenerate documentation
+* Replace `array(1:nexp)` placeholder allocations with `numeric(nexp)`/`integer(nexp)` in multivariable MR functions
+* Replace `1:n` with `seq_len(n)` in loop indices across `singlesnp`, `leaveoneout`, `rucker`, `mr_mode`, and `multivariable_mr` for empty-vector safety
+* Pre-generate jackknife resample indices in `mr_rucker_jackknife_internal` and switch to `lapply`, matching the bootstrap function pattern
+* Vectorise `get_population_allele_frequency` by inlining the quadratic-root logic from `contingency()` and picking the valid root with vectorised constraint checks
+* Skip `mr_wald_ratio` inside `mr()` when more than one SNP is provided and other methods are also requested, suppressing the multi-SNP warning for default method runs while preserving the warning when `mr_wald_ratio` is requested on its own
+
 # TwoSampleMR v0.7.4
 
 (Release date 2026-04-02)
diff --git a/R/add_rsq.r b/R/add_rsq.r
index 8c6c018b..ec333821 100644
--- a/R/add_rsq.r
+++ b/R/add_rsq.r
@@ -347,13 +347,35 @@ allele_frequency <- function(g) {
 get_population_allele_frequency <- function(af, prop, odds_ratio, prevalence) {
   stopifnot(length(af) == length(odds_ratio))
   stopifnot(length(prop) == length(odds_ratio))
-  for (i in seq_along(odds_ratio)) {
-    co <- contingency(af[i], prop[i], odds_ratio[i])
-    af_controls <- co[1, 2] / (co[1, 2] + co[2, 2])
-    af_cases <- co[1, 1] / (co[1, 1] + co[2, 1])
-    af[i] <- af_controls * (1 - prevalence[i]) + af_cases * prevalence[i]
+  eps <- 1e-15
+  a <- odds_ratio - 1
+  b <- (af + prop) * (1 - odds_ratio) - 1
+  c_ <- odds_ratio * af * prop
+
+  z <- numeric(length(odds_ratio))
+  linear <- abs(a) < eps
+  z[linear] <- -c_[linear] / b[linear]
+
+  quad <- !linear
+  if (any(quad)) {
+    d <- pmax(0, b[quad]^2 - 4 * a[quad] * c_[quad])
+    sqrt_d <- sqrt(d)
+    two_a <- 2 * a[quad]
+    z_pos <- (-b[quad] + sqrt_d) / two_a
+    z_neg <- (-b[quad] - sqrt_d) / two_a
+    af_q <- af[quad]
+    prop_q <- prop[quad]
+    tol <- -1e-7
+    valid_pos <- z_pos >= tol &
+      (prop_q - z_pos) >= tol &
+      (af_q - z_pos) >= tol &
+      (1 + z_pos - af_q - prop_q) >= tol
+    z[quad] <- ifelse(valid_pos, z_pos, z_neg)
   }
-  return(af)
+
+  af_controls <- (af - z) / (1 - prop)
+  af_cases <- z / prop
+  return(af_controls * (1 - prevalence) + af_cases * prevalence)
 }
 
 
diff --git a/R/leaveoneout.R b/R/leaveoneout.R
index bd724204..706a048b 100644
--- a/R/leaveoneout.R
+++ b/R/leaveoneout.R
@@ -44,7 +44,7 @@ mr_leaveoneout <- function(dat, parameters = default_parameters(), method = mr_i
       return(d)
     }
     if (nsnp > 2) {
-      l <- lapply(1:nsnp, function(i) {
+      l <- lapply(seq_len(nsnp), function(i) {
         with(
           x,
           method(beta.exposure[-i], beta.outcome[-i], se.exposure[-i], se.outcome[-i], parameters)
diff --git a/R/mr.R b/R/mr.R
index 3f1b88a7..7148d70c 100644
--- a/R/mr.R
+++ b/R/mr.R
@@ -72,7 +72,13 @@ mr <- function(
     } else {
       message("Analysing '", exp_id, "' on '", out_id, "'")
     }
-    res <- lapply(method_list, function(meth) {
+    keep <- if (nrow(x) > 1 && length(method_list) > 1) {
+      method_list != "mr_wald_ratio"
+    } else {
+      rep(TRUE, length(method_list))
+    }
+    methods_to_run <- method_list[keep]
+    res <- lapply(methods_to_run, function(meth) {
       get(meth)(x$beta.exposure, x$beta.outcome, x$se.exposure, x$se.outcome, parameters)
     })
     mr_tab <- data.frame(
@@ -80,7 +86,7 @@ mr <- function(
       id.outcome = out_id,
       outcome = x$outcome[1],
       exposure = x$exposure[1],
-      method = method_names,
+      method = method_names[keep],
       nsnp = vapply(res, function(x) x$nsnp, numeric(1)),
       b = vapply(res, function(x) x$b, numeric(1)),
       se = vapply(res, function(x) x$se, numeric(1)),
diff --git a/R/mr_mode.R b/R/mr_mode.R
index f4de06ab..0247792a 100644
--- a/R/mr_mode.R
+++ b/R/mr_mode.R
@@ -78,7 +78,7 @@ mr_mode <- function(dat, parameters = default_parameters(), mode_method = "all")
     #Set up a matrix to store the results from each bootstrap iteration
     beta.boot <- matrix(nrow = nboot, ncol = length(beta_Mode.in))
 
-    for (i in 1:nboot) {
+    for (i in seq_len(nboot)) {
       BetaIV.boot <- BetaIV.boot_mat[i, ]
       BetaIV.boot_NOME <- BetaIV.boot_NOME_mat[i, ]
 
diff --git a/R/multivariable_mr.R b/R/multivariable_mr.R
index 37cee539..b4e56601 100644
--- a/R/multivariable_mr.R
+++ b/R/multivariable_mr.R
@@ -498,15 +498,15 @@ mv_residual <- function(
   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)
+  effs <- numeric(nexp)
+  se <- numeric(nexp)
+  pval <- numeric(nexp)
+  nsnp <- integer(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 (i in seq_len(nexp)) {
     # For this exposure, only keep SNPs that meet some p-value threshold
     index <- pval.exposure[, i] < pval_threshold
 
@@ -607,15 +607,15 @@ mv_multiple <- function(
   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)
+  effs <- numeric(nexp)
+  se <- numeric(nexp)
+  pval <- numeric(nexp)
+  nsnp <- integer(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 (i in seq_len(nexp)) {
     # For this exposure, only keep SNPs that meet some p-value threshold
     index <- pval.exposure[, i] < pval_threshold
 
@@ -704,15 +704,15 @@ mv_basic <- function(mvdat, pval_threshold = 5e-8) {
   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)
+  effs <- numeric(nexp)
+  se <- numeric(nexp)
+  pval <- numeric(nexp)
+  nsnp <- integer(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 (i in seq_len(nexp)) {
     # For this exposure, only keep SNPs that meet some p-value threshold
     index <- pval.exposure[, i] < pval_threshold
 
@@ -772,15 +772,15 @@ mv_ivw <- function(mvdat, pval_threshold = 5e-8) {
   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)
+  effs <- numeric(nexp)
+  se <- numeric(nexp)
+  pval <- numeric(nexp)
+  nsnp <- integer(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 (i in seq_len(nexp)) {
     # For this exposure, only keep SNPs that meet some p-value threshold
     index <- pval.exposure[, i] < pval_threshold
 
diff --git a/R/rucker.R b/R/rucker.R
index c207abfe..f2e510ed 100644
--- a/R/rucker.R
+++ b/R/rucker.R
@@ -37,7 +37,7 @@ PM <- function(y = y, s = s, Alpha = 0.1) {
   v <- 1 / s^2
   sum.v <- sum(v)
   typS <- sum(v * (k - 1)) / (sum.v^2 - sum(v^2))
-  for (j in 1:L) {
+  for (j in seq_len(L)) {
     tausq <- 0
     Fstat <- 1
     TAUsq <- NULL
@@ -302,7 +302,7 @@ mr_rucker_bootstrap <- function(dat, parameters = default_parameters()) {
 
   dat2 <- dat
   l <- list()
-  for (i in 1:nboot) {
+  for (i in seq_len(nboot)) {
     dat2$beta.exposure <- boot_exp[i, ]
     dat2$beta.outcome <- boot_out[i, ]
     l[[i]] <- mr_rucker(dat2, parameters)
@@ -451,13 +451,15 @@ mr_rucker_jackknife_internal <- function(dat, parameters = default_parameters())
       e_plot = NULL
     ))
   } else {
-    l <- list()
-    for (i in 1:nboot) {
-      # dat2$beta.exposure <- rnorm(nsnp, mean=dat$beta.exposure, sd=dat$se.exposure)
-      # dat2$beta.outcome <- rnorm(nsnp, mean=dat$beta.outcome, sd=dat$se.outcome)
-      dat2 <- dat[sample(seq_len(nrow(dat)), nrow(dat), replace = TRUE), ]
-      l[[i]] <- mr_rucker_internal(dat2, parameters)
-    }
+    n <- nrow(dat)
+    boot_idx <- matrix(
+      sample.int(n, n * nboot, replace = TRUE),
+      nrow = nboot,
+      ncol = n
+    )
+    l <- lapply(seq_len(nboot), function(i) {
+      mr_rucker_internal(dat[boot_idx[i, ], ], parameters)
+    })
 
     modsel <- data.table::rbindlist(
       lapply(l, function(x) x$selected),
@@ -505,7 +507,7 @@ mr_rucker_jackknife_internal <- function(dat, parameters = default_parameters())
 
     p1 <- ggplot2::ggplot(bootstrap, ggplot2::aes_string(x = "Q", y = "Qdash")) +
       ggplot2::geom_point(ggplot2::aes_string(colour = "model")) +
-      ggplot2::geom_point(data = subset(bootstrap, i == "Full")) +
+      ggplot2::geom_point(data = bootstrap[bootstrap$i == "Full", ]) +
       ggplot2::scale_colour_brewer(type = "qual") +
       ggplot2::xlim(0, max(bootstrap$Q, bootstrap$Qdash)) +
       ggplot2::ylim(0, max(bootstrap$Q, bootstrap$Qdash)) +
diff --git a/R/singlesnp.R b/R/singlesnp.R
index 85585d36..23ea9622 100644
--- a/R/singlesnp.R
+++ b/R/singlesnp.R
@@ -59,7 +59,7 @@ mr_singlesnp <- function(
       )
       return(d)
     }
-    l <- lapply(1:nsnp, function(j) {
+    l <- lapply(seq_len(nsnp), function(j) {
       with(
         x,
         get(single_method)(
diff --git a/README.md b/README.md
index c91a7888..02406535 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ badge](https://mrcieu.r-universe.dev/badges/TwoSampleMR)](https://mrcieu.r-unive
 <!-- badges: end -->
 
 A package for performing Mendelian randomization using GWAS summary
-data. It uses the [IEU OpenGWAS database](https://gwas.mrcieu.ac.uk/) to
+data. It uses the [IEU OpenGWAS database](https://opengwas.io) to
 obtain data automatically, and a wide range of methods to run the
 analysis.
 
diff --git a/index.md b/index.md
index 940e9e48..85c4fef8 100644
--- a/index.md
+++ b/index.md
@@ -7,7 +7,7 @@
 [![TwoSampleMR status badge](https://mrcieu.r-universe.dev/badges/TwoSampleMR)](https://mrcieu.r-universe.dev/TwoSampleMR)
 <!-- badges: end -->
 
-A package for performing Mendelian randomization using GWAS summary data. It uses the [IEU OpenGWAS database](https://gwas.mrcieu.ac.uk/) to obtain data automatically, and a wide range of methods to run the analysis.
+A package for performing Mendelian randomization using GWAS summary data. It uses the [IEU OpenGWAS database](https://opengwas.io) to obtain data automatically, and a wide range of methods to run the analysis.
 
 ## January 2020 major update 
 
diff --git a/man/TwoSampleMR-package.Rd b/man/TwoSampleMR-package.Rd
index c1e4647c..9d3a28bc 100644
--- a/man/TwoSampleMR-package.Rd
+++ b/man/TwoSampleMR-package.Rd
@@ -6,7 +6,7 @@
 \alias{TwoSampleMR-package}
 \title{TwoSampleMR: Two Sample MR Functions and Interface to MRC Integrative Epidemiology Unit OpenGWAS Database}
 \description{
-A package for performing Mendelian randomization using GWAS summary data. It uses the IEU OpenGWAS database \url{https://gwas.mrcieu.ac.uk/} to automatically obtain data, and a wide range of methods to run the analysis.
+A package for performing Mendelian randomization using GWAS summary data. It uses the IEU OpenGWAS database \url{https://opengwas.io} to automatically obtain data, and a wide range of methods to run the analysis.
 }
 \seealso{
 Useful links:
@@ -22,6 +22,7 @@ Useful links:
 
 Authors:
 \itemize{
+  \item Gibran Hemani \email{g.hemani@bristol.ac.uk} (\href{https://orcid.org/0000-0003-0920-1055}{ORCID})
   \item Philip Haycock \email{philip.haycock@bristol.ac.uk} (\href{https://orcid.org/0000-0001-5001-3350}{ORCID})
   \item Jie Zheng \email{Jie.Zheng@bristol.ac.uk} (\href{https://orcid.org/0000-0002-6623-6839}{ORCID})
   \item Tom Gaunt \email{Tom.Gaunt@bristol.ac.uk} (\href{https://orcid.org/0000-0003-0924-3247}{ORCID})
diff --git a/man/mr_steiger.Rd b/man/mr_steiger.Rd
index 7024738c..dce0904a 100644
--- a/man/mr_steiger.Rd
+++ b/man/mr_steiger.Rd
@@ -23,7 +23,7 @@ mr_steiger(p_exp, p_out, n_exp, n_out, r_exp, r_out, r_xxo = 1, r_yyo = 1, ...)
 
 \item{r_yyo}{Measurement precision of outcome}
 
-\item{...}{Further arguments to be passed to \code{\link[lattice:cloud]{lattice::wireframe()}}}
+\item{...}{Further arguments to be passed to \code{\link[lattice:wireframe]{lattice::wireframe()}}}
 }
 \value{
 List with the following elements:
diff --git a/man/mr_steiger2.Rd b/man/mr_steiger2.Rd
index 504270c9..8bf4b395 100644
--- a/man/mr_steiger2.Rd
+++ b/man/mr_steiger2.Rd
@@ -19,7 +19,7 @@ mr_steiger2(r_exp, r_out, n_exp, n_out, r_xxo = 1, r_yyo = 1, ...)
 
 \item{r_yyo}{Measurement precision of outcome}
 
-\item{...}{Further arguments to be passed to \code{\link[lattice:cloud]{lattice::wireframe()}}}
+\item{...}{Further arguments to be passed to \code{\link[lattice:wireframe]{lattice::wireframe()}}}
 }
 \value{
 List with the following elements:
diff --git a/man/steiger_sensitivity.Rd b/man/steiger_sensitivity.Rd
index 89b42636..22fd6870 100644
--- a/man/steiger_sensitivity.Rd
+++ b/man/steiger_sensitivity.Rd
@@ -11,7 +11,7 @@ steiger_sensitivity(rgx_o, rgy_o, ...)
 
 \item{rgy_o}{Observed variance of outcome explained by SNPs}
 
-\item{...}{Further arguments to be passed to \code{\link[lattice:cloud]{lattice::wireframe()}}}
+\item{...}{Further arguments to be passed to \code{\link[lattice:wireframe]{lattice::wireframe()}}}
 }
 \value{
 List with the following elements:
diff --git a/vignettes/exposure.Rmd b/vignettes/exposure.Rmd
index 1d59a089..e33af969 100644
--- a/vignettes/exposure.Rmd
+++ b/vignettes/exposure.Rmd
@@ -281,7 +281,7 @@ cg25212131_exp_dat <-
 
 ### IEU OpenGWAS database
 
-The IEU OpenGWAS database contains the entire summary statistics for thousands of GWASs. You can browse them here: https://gwas.mrcieu.ac.uk/
+The IEU OpenGWAS database contains the entire summary statistics for thousands of GWASs. You can browse them here: https://opengwas.io
 
 You can use this database to define the instruments for a particular exposure. You can also use this database to obtain the effects for constructing polygenic risk scores using different p-value thresholds.
 
diff --git a/vignettes/gwas2020.Rmd b/vignettes/gwas2020.Rmd
index f936bed8..0533e0ff 100644
--- a/vignettes/gwas2020.Rmd
+++ b/vignettes/gwas2020.Rmd
@@ -53,7 +53,7 @@ We are using Elasticsearch and Neo4j on an Oracle Cloud Infrastructure to serve
 
 ### Browse available datasets online
 
-We have a new home for the GWAS summary data: <https://gwas.mrcieu.ac.uk/>.
+We have a new home for the GWAS summary data: <https://opengwas.io>.
 
 ### Chromosome and position
 
@@ -89,7 +89,7 @@ It is now possible to perform clumping, or create LD matrices, using your own lo
 
 ### Access the data directly
 
-Previously the data was only accessible through the database. Now the data can be downloaded in "GWAS VCF" format from here https://gwas.mrcieu.ac.uk/. (IEU members can access all the data on RDSF or bluecrystal4 directly). This means that if you want to perform very large or numerous operations, you can do it on HPC or locally in a more performant manner by using the data files directly. Please see the [gwasvcf R package](https://github.com/mrcieu/gwasvcf) on how to work with these data.
+Previously the data was only accessible through the database. Now the data can be downloaded in "GWAS VCF" format from here https://opengwas.io. (IEU members can access all the data on RDSF or bluecrystal4 directly). This means that if you want to perform very large or numerous operations, you can do it on HPC or locally in a more performant manner by using the data files directly. Please see the [gwasvcf R package](https://github.com/mrcieu/gwasvcf) on how to work with these data.
 
 ### Connect the data to different analytical tools
 
@@ -97,7 +97,7 @@ Either the data in the database, or the GWAS VCF files, can be queried and the r
 
 ## Key links
 
-- The IEU OpenGWAS database: https://gwas.mrcieu.ac.uk
+- The IEU OpenGWAS database: https://opengwas.io
 - API to the IEU OpenGWAS database: https://api.opengwas.io/api/
 - ieugwasr package, for R access to the API: https://mrcieu.github.io/ieugwasr/
 - ieugwaspy package, for python access to the API: https://github.com/MRCIEU/ieugwaspy/
diff --git a/vignettes/introduction.Rmd b/vignettes/introduction.Rmd
index de4604d3..d2bf7259 100644
--- a/vignettes/introduction.Rmd
+++ b/vignettes/introduction.Rmd
@@ -82,7 +82,7 @@ install.packages('TwoSampleMR',
 The workflow for performing MR is as follows:
 
 1. Select instruments for the exposure (perform LD clumping if necessary)
-2. Extract the instruments from the [IEU GWAS database](https://gwas.mrcieu.ac.uk/) for the outcomes of interest
+2. Extract the instruments from the [IEU GWAS database](https://opengwas.io) for the outcomes of interest
 3. Harmonise the effect sizes for the instruments on the exposures and the outcomes to be each for the same reference allele
 4. Perform MR analysis, sensitivity analyses, create plots, compile reports
 
diff --git a/vignettes/outcome.Rmd b/vignettes/outcome.Rmd
index 8905d8ad..057f5363 100644
--- a/vignettes/outcome.Rmd
+++ b/vignettes/outcome.Rmd
@@ -49,7 +49,7 @@ Once instruments for the exposure trait have been specified, those variants need
 
 The IEU GWAS database (IGD) contains complete GWAS summary statistics from a large number of studies. You can browse them here:
 
-https://gwas.mrcieu.ac.uk/
+https://opengwas.io
 
 To obtain details about the available GWASs programmatically do the following: