@@ -13,13 +13,7 @@

 #' @param weight.function.int An integer used to assign family weights. Specifically, we use \code{weight.function.int} in a function that takes the weighted sum

 #' of the number of different SNPs and SNPs both equal to one as an argument, denoted as x, and

 #' returns a family weight equal to \code{weight.function.int}^x. Defaults to 2.

-#' @param recessive.ref.prop The proportion to which the observed proportion of informative cases with the provisional risk genotype(s) will be compared

-#' to determine whether to recode the SNP as recessive. Defaults to 0.75.

-#' @param recode.test.stat For a given SNP, the minimum test statistic required to recode and recompute the fitness score using recessive coding. Defaults to 1.64.

-#' See the GADGETS paper for specific details.

-#' @param use.parents A integer indicating whether family level informativeness should be used alongside transmissions in computing GxE fitness scores. Defaults to 1,

-#' indicating family level informativeness will be used. Specify 0 to only use transmission data.

-#' @return A list of thee elements:

+#' @return A list of three elements:

 #' \describe{

 #'  \item{pval}{The p-value of the test.}

 #'  \item{obs.fitness.score}{The fitness score from the observed data}

@@ -31,39 +25,39 @@

 #' data(dad.gxe)

 #' data(mom.gxe)

 #' data(exposure)

+#' exposure <- matrix(exposure - 1, ncol = 1, drop = FALSE)

 #' data(snp.annotations)

 #' pp.list <- preprocess.genetic.data(case.gxe, father.genetic.data = dad.gxe,

 #'                                mother.genetic.data = mom.gxe,

 #'                                ld.block.vec = rep(25, 4),

-#'                                categorical.exposures = exposure)

+#'                                continuous.exposures = exposure)

+#'  run.gadgets(pp.list, n.chromosomes = 5, chromosome.size = 3,

+#'              results.dir = "tmp_gxe", cluster.type = "interactive",

+#'              registryargs = list(file.dir = "tmp_reg_gxe", seed = 1300),

+#'              n.islands = 8, island.cluster.size = 4,

+#'              n.migrations = 1)

 #'

-#' run.gadgets(pp.list, n.chromosomes = 5, chromosome.size = 3,

-#'        results.dir = "tmp_gxe", cluster.type = "interactive",

-#'        registryargs = list(file.dir = "tmp_reg_gxe", seed = 1300),

-#'        n.islands = 8, island.cluster.size = 4,

-#'        n.migrations = 1)

 #'

 #' combined.res <- combine.islands('tmp_gxe', snp.annotations, pp.list, 1)

-#' null.info <- readRDS("tmp_gxe/null.mean.cov.info.rds")

+#' null.info <- readRDS("tmp_gxe/null.mean.sd.info.rds")

 #' null.mean <- null.info[["null.mean"]]

-#' null.cov <- null.info[["null.cov"]]

+#' null.sd <- null.info[["null.sd"]]

 #'

 #' top.snps <- as.vector(t(combined.res[1, 1:3]))

 #' set.seed(10)

-#' GxE.test.res <- GxE.test(top.snps, pp.list, null.mean, null.cov)

+#' GxE.test.res <- GxE.test(top.snps, pp.list, null.mean, null.sd)

 #'

 #' unlink('tmp_gxe', recursive = TRUE)

 #' unlink('tmp_reg_gxe', recursive = TRUE)

 #'

 #' @export

-GxE.test <- function(snp.cols, preprocessed.list, null.mean.vec, null.se.vec,

+GxE.test <- function(snp.cols, preprocessed.list, null.mean.vec, null.sd.vec,

                      n.permutes = 10000, n.different.snps.weight = 2, n.both.one.weight = 1,

                      weight.function.int = 2) {

     # run the test via cpp

-    GxE_test(snp.cols, preprocessed.list, null.mean.vec, null.se.vec, n.permutes,

-             n.different.snps.weight, n.both.one.weight, weight.function.int,

-             recessive.ref.prop, recode.test.stat, use.parents)

+    GxE_test(snp.cols, preprocessed.list, null.mean.vec, null.sd.vec, n.permutes,

+             n.different.snps.weight, n.both.one.weight, weight.function.int)

 }

@@ -181,11 +181,11 @@ epistasis_test <- function(snp_cols, preprocessed_list, n_permutes = 10000L, n_d

     .Call('_epistasisGAGE_epistasis_test', PACKAGE = 'epistasisGAGE', snp_cols, preprocessed_list, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, warn)

 }

-GxE_test <- function(snp_cols, preprocessed_list, null_mean_vec, null_se_vec, n_permutes = 10000L, n_different_snps_weight = 2L, n_both_one_weight = 1L, weight_function_int = 2L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, use_parents = 1L) {

-    .Call('_epistasisGAGE_GxE_test', PACKAGE = 'epistasisGAGE', snp_cols, preprocessed_list, null_mean_vec, null_se_vec, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, use_parents)

+GxE_test <- function(target_snps, preprocessed_list, null_mean_vec, null_se_vec, n_permutes = 10000L, n_different_snps_weight = 2L, n_both_one_weight = 1L, weight_function_int = 2L) {

+    .Call('_epistasisGAGE_GxE_test', PACKAGE = 'epistasisGAGE', target_snps, preprocessed_list, null_mean_vec, null_se_vec, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int)

 }

-n2log_epistasis_pvals <- function(chromosome_list, in_data_list, null_mean_vec, null_se_vec, n_permutes = 10000L, n_different_snps_weight = 2L, n_both_one_weight = 1L, weight_function_int = 2L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, GxE = FALSE, use_parents = 1L) {

-    .Call('_epistasisGAGE_n2log_epistasis_pvals', PACKAGE = 'epistasisGAGE', chromosome_list, in_data_list, null_mean_vec, null_se_vec, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, GxE, use_parents)

+n2log_epistasis_pvals <- function(chromosome_list, preprocessed_list, n_permutes = 10000L, n_different_snps_weight = 2L, n_both_one_weight = 1L, weight_function_int = 2L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, null_mean_vec_ = NULL, null_se_vec_ = NULL) {

+    .Call('_epistasisGAGE_n2log_epistasis_pvals', PACKAGE = 'epistasisGAGE', chromosome_list, preprocessed_list, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, null_mean_vec_, null_se_vec_)

 }

@@ -9,7 +9,7 @@

 #' @param preprocessed.list The list output by \code{preprocess.genetic.data} run on the observed data.

 #' @param null.mean.vec A vector of null means used for comparison in the Mahalanobis distance based version of the

 #' GxE fitness score. Does not need to be specified otherwise, and can be left at its default.

-#' @param null.se.vec A vector or estimated null standard errors for the components of the

+#' @param null.sd.vec A vector or estimated null standard deviations for the components of the

 #' GxE fitness score. Does not need to be specified otherwise, and can be left at its default.

 #' @param score.type A character string specifying the method for aggregating SNP-pair scores across chromosome sizes. Options are

 #' 'max', 'sum', or 'logsum', defaulting to "logsum". For a given SNP-pair, it's graphical score will be the \code{score.type} of all

@@ -93,7 +93,7 @@

 compute.graphical.scores <- function(results.list, preprocessed.list, score.type = "logsum", pval.thresh = 0.05,

                                      n.permutes = 10000, n.different.snps.weight = 2, n.both.one.weight = 1,

                                      weight.function.int = 2, recessive.ref.prop = 0.75, recode.test.stat = 1.64,

-                                     bp.param = bpparam(), null.mean.vec = NULL, null.se.vec = NULL) {

+                                     bp.param = bpparam(), null.mean.vec = NULL, null.sd.vec = NULL) {

     if (pval.thresh > 0.6){

@@ -102,9 +102,9 @@ compute.graphical.scores <- function(results.list, preprocessed.list, score.type

     }

     #need to specify both or neither of null mean and sd vec

-    if (is.null(null.mean.vec) & !is.null(null.se.vec) | !is.null(null.mean.vec) & is.null(null.se.vec)){

+    if (is.null(null.mean.vec) & !is.null(null.sd.vec) | !is.null(null.mean.vec) & is.null(null.sd.vec)){

-        stop("null.mean.vec and null.se.vec must both be NULL or both not NULL")

+        stop("null.mean.vec and null.sd.vec must both be NULL or both not NULL")

     }

@@ -123,14 +123,14 @@ compute.graphical.scores <- function(results.list, preprocessed.list, score.type

     ## compute graphical scores based on epistasis test

     GxE <- !is.null(preprocessed.list$exposure)

     n2log.epi.pvals <- bplapply(chrom.list, function(chrom.size.list, preprocessed.list, null.mean.vec,

-                                                     null.se.vec, n.permutes, n.different.snps.weight, n.both.one.weight,

+                                                     null.sd.vec, n.permutes, n.different.snps.weight, n.both.one.weight,

                                                      weight.function.int, recessive.ref.prop, recode.test.stat){

         n2log_epistasis_pvals(chrom.size.list, preprocessed.list, n.permutes,

                               n.different.snps.weight, n.both.one.weight, weight.function.int,

-                              recessive.ref.prop, recode.test.stat, null.mean.vec, null.se.vec)

+                              recessive.ref.prop, recode.test.stat, null.mean.vec, null.sd.vec)

-    }, preprocessed.list = preprocessed.list, null.mean.vec = null.mean.vec, null.se.vec = null.se.vec,

+    }, preprocessed.list = preprocessed.list, null.mean.vec = null.mean.vec, null.sd.vec = null.sd.vec,

     n.permutes = n.permutes, n.different.snps.weight = n.different.snps.weight, n.both.one.weight = n.both.one.weight,

     weight.function.int = weight.function.int, recessive.ref.prop = recessive.ref.prop,

     recode.test.stat = recode.test.stat, BPPARAM = bp.param)

@@ -58,10 +58,10 @@

 #' GxE fitness score. This does not need to be specified unless an analyst wants to replicate the results of a previous GADGETS

 #' GxE run, or if some of the islands of a run failed to complete, and the analyst forgot to set the seed prior to running this command.

 #' In that case, to use the same null mean vector in computing the fitness score, the analyst can find the

-#' previously used null mean vector in the file "null.mean.se.info.rds" stored in the \code{results.dir} directory.

-#' @param null.se.vec A vector of estimated null standard errors for the three components of the

+#' previously used null mean vector in the file "null.mean.sd.info.rds" stored in the \code{results.dir} directory.

+#' @param null.sd.vec A vector of estimated null standard errors for the three components of the

 #' GxE fitness score. See argument \code{null.mean.vec} for reasons this argument might be specified. For a given run, the

-#' previously used vector can also be found in the file "null.mean.se.info.rds" stored in the \code{results.dir} directory.

+#' previously used vector can also be found in the file "null.mean.sd.info.rds" stored in the \code{results.dir} directory.

 #' @return For each island, a list of two elements will be written to \code{results.dir}:

 #' \describe{

 #'  \item{top.chromosome.results}{A data.table of the final generation chromosomes, their fitness scores, their difference vectors,

@@ -101,7 +101,7 @@ run.gadgets <- function(data.list, n.chromosomes, chromosome.size, results.dir,

     generations = 500, gen.same.fitness = 50, initial.sample.duplicates = FALSE,

     snp.sampling.type = "chisq", crossover.prop = 0.8, n.islands = 1000, island.cluster.size = 4, migration.generations = 50,

     n.migrations = 20, recessive.ref.prop = 0.75, recode.test.stat = 1.64, n.random.chroms = 10000, null.mean.vec = NULL,

-    null.se.vec = NULL) {

+    null.sd.vec = NULL) {

     ### make sure if island clusters exist, the migration interval is set properly ###

     if (island.cluster.size > 1 & migration.generations >= generations & island.cluster.size != 1) {

@@ -242,12 +242,9 @@ run.gadgets <- function(data.list, n.chromosomes, chromosome.size, results.dir,

     }

-    ### if running GxE, compute the elements required for mahalanobis distance fitness score ###

-    #exposure <- data.list$exposure

-    # null.mean <- rep(0, 3)

-    # null.se <- rep(1, 3)

+    ### if running GxE, compute the elements required for standardizing elements of the fitness score ###

     null.mean <- rep(0, 2)

-    null.se <- rep(1, 2)

+    null.sd <- rep(1, 2)

     #if (!is.null(exposure)){

         #storage.mode(exposure) <- "integer"

@@ -255,10 +252,10 @@ run.gadgets <- function(data.list, n.chromosomes, chromosome.size, results.dir,

         if (use.parents == 1){

-            if (is.null(null.mean.vec) & is.null(null.se.vec)){

+            if (is.null(null.mean.vec) & is.null(null.sd.vec)){

                 # make sure we're not accidentally redoing this

-                out.file.name <- file.path(results.dir, "null.mean.se.info.rds")

+                out.file.name <- file.path(results.dir, "null.mean.sd.info.rds")

                 # # split genetic data by exposure status

                 # case.genetic.data <- data.frame(data.list$case.genetic.data)

@@ -292,7 +289,7 @@ run.gadgets <- function(data.list, n.chromosomes, chromosome.size, results.dir,

                                                          rep(0, 2), rep(1, 2), n.different.snps.weight,

                                                          n.both.one.weight)

                 null.mean <- colMeans(null.vec.mat)

-                null.se <- sqrt(diag(cov(null.vec.mat)))

+                null.sd <- sqrt(diag(cov(null.vec.mat)))

                 #save these if needed later

                 if (file.exists(out.file.name)){

@@ -317,10 +314,10 @@ run.gadgets <- function(data.list, n.chromosomes, chromosome.size, results.dir,

                 }

-            } else if (!is.null(null.mean.vec) & !is.null(null.se.vec)){

+            } else if (!is.null(null.mean.vec) & !is.null(null.sd.vec)){

                 # make sure we're not accidentally redoing this

-                out.file.name <- file.path(results.dir, "null.mean.se.info.rds")

+                out.file.name <- file.path(results.dir, "null.mean.sd.info.rds")

                 if (file.exists(out.file.name)){

                     prev.res <- readRDS(out.file.name)

@@ -329,18 +326,18 @@ run.gadgets <- function(data.list, n.chromosomes, chromosome.size, results.dir,

                     if (any(null.mean != prev.mean) | any(prev.se != null.se)){

-                        stop(paste("null mean and/or se vector  do not match the previous values in",

+                        stop(paste("null mean and/or sd vector  do not match the previous values in",

                                    out.file.name))

                     }

                     null.mean <- null.mean.vec

-                    null.se <- null.se.vec

+                    null.sd <- null.sd.vec

                 }

             } else {

-                stop("null.mean.vec and null.se.vec must both be null or both not be null")

+                stop("null.mean.vec and null.sd.vec must both be null or both not be null")

             }

@@ -404,21 +401,12 @@ run.gadgets <- function(data.list, n.chromosomes, chromosome.size, results.dir,

     }

     # write jobs to registry

-    # ids <- batchMap(GADGETS, cluster.number = cluster.ids, more.args = list(results.dir = results.dir, n.migrations = n.migrations,

-    #     case.genetic.data = data.list$case.genetic.data, complement.genetic.data = data.list$complement.genetic.data,

-    #     ld.block.vec = data.list$ld.block.vec, n.chromosomes = n.chromosomes, chromosome.size = chromosome.size, snp.chisq = snp.chisq,

-    #     weight.lookup = weight.lookup, null.mean.vec = null.mean, null.se.vec = null.se, island.cluster.size = island.cluster.size,

-    #     n.different.snps.weight = n.different.snps.weight, n.both.one.weight = n.both.one.weight, migration.interval = migration.generations,

-    #     gen.same.fitness = gen.same.fitness, max.generations = generations, initial.sample.duplicates = initial.sample.duplicates, crossover.prop = crossover.prop,

-    #     recessive.ref.prop = recessive.ref.prop, recode.test.stat = recode.test.stat, exposure.levels = data.list$exposure.levels,

-    #     exposure = exposure, use.parents = use.parents),

-    #     reg = registry)

     ids <- batchMap(GADGETS, cluster.number = cluster.ids,

                     more.args = list(results.dir = results.dir, n.migrations = n.migrations,

                                     case.genetic.data = case.genetic.data, complement.genetic.data = complement.genetic.data, case.genetic.data.n = case.genetic.data.n,

                                     complement.genetic.data.n = complement.genetic.data.n, exposure.mat = exposure.mat, weight.lookup.n = weight.lookup.n,

                                     ld.block.vec = data.list$ld.block.vec, n.chromosomes = n.chromosomes, chromosome.size = chromosome.size, snp.chisq = snp.chisq,

-                                    weight.lookup = weight.lookup, null.mean.vec = null.mean, null.se.vec = null.se, island.cluster.size = island.cluster.size,

+                                    weight.lookup = weight.lookup, null.mean.vec = null.mean, null.se.vec = null.sd, island.cluster.size = island.cluster.size,

                                     n.different.snps.weight = n.different.snps.weight, n.both.one.weight = n.both.one.weight, migration.interval = migration.generations,

                                     gen.same.fitness = gen.same.fitness, max.generations = generations, initial.sample.duplicates = initial.sample.duplicates,

                                     crossover.prop = crossover.prop, recessive.ref.prop = recessive.ref.prop, recode.test.stat = recode.test.stat,

@@ -8,14 +8,11 @@ GxE.test(

   snp.cols,

   preprocessed.list,

   null.mean.vec,

-  null.se.vec,

+  null.sd.vec,

   n.permutes = 10000,

   n.different.snps.weight = 2,

   n.both.one.weight = 1,

-  weight.function.int = 2,

-  recessive.ref.prop = 0.75,

-  recode.test.stat = 1.64,

-  use.parents = 1

+  weight.function.int = 2

 )

 }

 \arguments{

@@ -34,18 +31,9 @@ is multiplied in computing the family weights. Defaults to 1.}

 \item{weight.function.int}{An integer used to assign family weights. Specifically, we use \code{weight.function.int} in a function that takes the weighted sum

 of the number of different SNPs and SNPs both equal to one as an argument, denoted as x, and

 returns a family weight equal to \code{weight.function.int}^x. Defaults to 2.}

-

-\item{recessive.ref.prop}{The proportion to which the observed proportion of informative cases with the provisional risk genotype(s) will be compared

-to determine whether to recode the SNP as recessive. Defaults to 0.75.}

-

-\item{recode.test.stat}{For a given SNP, the minimum test statistic required to recode and recompute the fitness score using recessive coding. Defaults to 1.64.

-See the GADGETS paper for specific details.}

-

-\item{use.parents}{A integer indicating whether family level informativeness should be used alongside transmissions in computing GxE fitness scores. Defaults to 1,

-indicating family level informativeness will be used. Specify 0 to only use transmission data.}

 }

 \value{

-A list of thee elements:

+A list of three elements:

 \describe{

  \item{pval}{The p-value of the test.}

  \item{obs.fitness.score}{The fitness score from the observed data}

@@ -62,26 +50,27 @@ data(case.gxe)

 data(dad.gxe)

 data(mom.gxe)

 data(exposure)

+exposure <- matrix(exposure - 1, ncol = 1, drop = FALSE)

 data(snp.annotations)

 pp.list <- preprocess.genetic.data(case.gxe, father.genetic.data = dad.gxe,

                                mother.genetic.data = mom.gxe,

                                ld.block.vec = rep(25, 4),

-                               categorical.exposures = exposure)

+                               continuous.exposures = exposure)

+ run.gadgets(pp.list, n.chromosomes = 5, chromosome.size = 3,

+             results.dir = "tmp_gxe", cluster.type = "interactive",

+             registryargs = list(file.dir = "tmp_reg_gxe", seed = 1300),

+             n.islands = 8, island.cluster.size = 4,

+             n.migrations = 1)

-run.gadgets(pp.list, n.chromosomes = 5, chromosome.size = 3,

-       results.dir = "tmp_gxe", cluster.type = "interactive",

-       registryargs = list(file.dir = "tmp_reg_gxe", seed = 1300),

-       n.islands = 8, island.cluster.size = 4,

-       n.migrations = 1)

 combined.res <- combine.islands('tmp_gxe', snp.annotations, pp.list, 1)

-null.info <- readRDS("tmp_gxe/null.mean.cov.info.rds")

+null.info <- readRDS("tmp_gxe/null.mean.sd.info.rds")

 null.mean <- null.info[["null.mean"]]

-null.cov <- null.info[["null.cov"]]

+null.sd <- null.info[["null.sd"]]

 top.snps <- as.vector(t(combined.res[1, 1:3]))

 set.seed(10)

-GxE.test.res <- GxE.test(top.snps, pp.list, null.mean, null.cov)

+GxE.test.res <- GxE.test(top.snps, pp.list, null.mean, null.sd)

 unlink('tmp_gxe', recursive = TRUE)

 unlink('tmp_reg_gxe', recursive = TRUE)

@@ -7,8 +7,6 @@

 compute.graphical.scores(

   results.list,

   preprocessed.list,

-  null.mean.vec = rep(0, 3),

-  null.se.vec = rep(1, 3),

   score.type = "logsum",

   pval.thresh = 0.05,

   n.permutes = 10000,

@@ -17,7 +15,9 @@ compute.graphical.scores(

   weight.function.int = 2,

   recessive.ref.prop = 0.75,

   recode.test.stat = 1.64,

-  bp.param = bpparam()

+  bp.param = bpparam(),

+  null.mean.vec = NULL,

+  null.sd.vec = NULL

 )

 }

 \arguments{

@@ -28,12 +28,6 @@ otherwise an error will be returned.}

 \item{preprocessed.list}{The list output by \code{preprocess.genetic.data} run on the observed data.}

-\item{null.mean.vec}{A vector of null means used for comparison in the Mahalanobis distance based version of the

-GxE fitness score. Does not need to be specified otherwise, and can be left at its default.}

-

-\item{null.se.vec}{A vector or estimated null standard errors for the components of the

-GxE fitness score. Does not need to be specified otherwise, and can be left at its default.}

-

 \item{score.type}{A character string specifying the method for aggregating SNP-pair scores across chromosome sizes. Options are

 'max', 'sum', or 'logsum', defaulting to "logsum". For a given SNP-pair, it's graphical score will be the \code{score.type} of all

 graphical scores of chromosomes containing that pair across chromosome sizes. The choice of 'logsum' rather than 'sum'

@@ -64,6 +58,12 @@ to determine whether to recode the SNP as recessive. Defaults to 0.75.}

 See the GADGETS paper for specific details.}

 \item{bp.param}{The BPPARAM argument to be passed to bplapply. See \code{BiocParallel::bplapply} for more details.}

+

+\item{null.mean.vec}{A vector of null means used for comparison in the Mahalanobis distance based version of the

+GxE fitness score. Does not need to be specified otherwise, and can be left at its default.}

+

+\item{null.sd.vec}{A vector or estimated null standard deviations for the components of the

+GxE fitness score. Does not need to be specified otherwise, and can be left at its default.}

 }

 \value{

 A list of two elements:

@@ -31,7 +31,7 @@ run.gadgets(

   recode.test.stat = 1.64,

   n.random.chroms = 10000,

   null.mean.vec = NULL,

-  null.se.vec = NULL

+  null.sd.vec = NULL

 )

 }

 \arguments{

@@ -114,11 +114,11 @@ compute the GxE fitness score.}

 GxE fitness score. This does not need to be specified unless an analyst wants to replicate the results of a previous GADGETS

 GxE run, or if some of the islands of a run failed to complete, and the analyst forgot to set the seed prior to running this command.

 In that case, to use the same null mean vector in computing the fitness score, the analyst can find the

-previously used null mean vector in the file "null.mean.se.info.rds" stored in the \code{results.dir} directory.}

+previously used null mean vector in the file "null.mean.sd.info.rds" stored in the \code{results.dir} directory.}

-\item{null.se.vec}{A vector of estimated null standard errors for the three components of the

+\item{null.sd.vec}{A vector of estimated null standard errors for the three components of the

 GxE fitness score. See argument \code{null.mean.vec} for reasons this argument might be specified. For a given run, the

-previously used vector can also be found in the file "null.mean.se.info.rds" stored in the \code{results.dir} directory.}

+previously used vector can also be found in the file "null.mean.sd.info.rds" stored in the \code{results.dir} directory.}

 \item{use.parents}{A logical indicating whether parent data should be used in computing the fitness score. Defaults to FALSE. This should only be set to true

 if the population is homogenous with no exposure related population structure.}

@@ -770,12 +770,12 @@ BEGIN_RCPP

 END_RCPP

 }

 // GxE_test

-List GxE_test(IntegerVector snp_cols, List preprocessed_list, arma::vec null_mean_vec, arma::vec null_se_vec, int n_permutes, int n_different_snps_weight, int n_both_one_weight, int weight_function_int, double recessive_ref_prop, double recode_test_stat, int use_parents);

-RcppExport SEXP _epistasisGAGE_GxE_test(SEXP snp_colsSEXP, SEXP preprocessed_listSEXP, SEXP null_mean_vecSEXP, SEXP null_se_vecSEXP, SEXP n_permutesSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP weight_function_intSEXP, SEXP recessive_ref_propSEXP, SEXP recode_test_statSEXP, SEXP use_parentsSEXP) {

+List GxE_test(arma::uvec target_snps, List preprocessed_list, arma::vec null_mean_vec, arma::vec null_se_vec, int n_permutes, int n_different_snps_weight, int n_both_one_weight, int weight_function_int);

+RcppExport SEXP _epistasisGAGE_GxE_test(SEXP target_snpsSEXP, SEXP preprocessed_listSEXP, SEXP null_mean_vecSEXP, SEXP null_se_vecSEXP, SEXP n_permutesSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP weight_function_intSEXP) {

 BEGIN_RCPP

     Rcpp::RObject rcpp_result_gen;

     Rcpp::RNGScope rcpp_rngScope_gen;

-    Rcpp::traits::input_parameter< IntegerVector >::type snp_cols(snp_colsSEXP);

+    Rcpp::traits::input_parameter< arma::uvec >::type target_snps(target_snpsSEXP);

     Rcpp::traits::input_parameter< List >::type preprocessed_list(preprocessed_listSEXP);

     Rcpp::traits::input_parameter< arma::vec >::type null_mean_vec(null_mean_vecSEXP);

     Rcpp::traits::input_parameter< arma::vec >::type null_se_vec(null_se_vecSEXP);

@@ -783,32 +783,27 @@ BEGIN_RCPP

     Rcpp::traits::input_parameter< int >::type n_different_snps_weight(n_different_snps_weightSEXP);

     Rcpp::traits::input_parameter< int >::type n_both_one_weight(n_both_one_weightSEXP);

     Rcpp::traits::input_parameter< int >::type weight_function_int(weight_function_intSEXP);

-    Rcpp::traits::input_parameter< double >::type recessive_ref_prop(recessive_ref_propSEXP);

-    Rcpp::traits::input_parameter< double >::type recode_test_stat(recode_test_statSEXP);

-    Rcpp::traits::input_parameter< int >::type use_parents(use_parentsSEXP);

-    rcpp_result_gen = Rcpp::wrap(GxE_test(snp_cols, preprocessed_list, null_mean_vec, null_se_vec, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, use_parents));

+    rcpp_result_gen = Rcpp::wrap(GxE_test(target_snps, preprocessed_list, null_mean_vec, null_se_vec, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int));

     return rcpp_result_gen;

 END_RCPP

 }

 // n2log_epistasis_pvals

-NumericVector n2log_epistasis_pvals(ListOf<IntegerVector> chromosome_list, List in_data_list, arma::vec null_mean_vec, arma::vec null_se_vec, int n_permutes, int n_different_snps_weight, int n_both_one_weight, int weight_function_int, double recessive_ref_prop, double recode_test_stat, bool GxE, int use_parents);

-RcppExport SEXP _epistasisGAGE_n2log_epistasis_pvals(SEXP chromosome_listSEXP, SEXP in_data_listSEXP, SEXP null_mean_vecSEXP, SEXP null_se_vecSEXP, SEXP n_permutesSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP weight_function_intSEXP, SEXP recessive_ref_propSEXP, SEXP recode_test_statSEXP, SEXP GxESEXP, SEXP use_parentsSEXP) {

+NumericVector n2log_epistasis_pvals(ListOf<IntegerVector> chromosome_list, List preprocessed_list, int n_permutes, int n_different_snps_weight, int n_both_one_weight, int weight_function_int, double recessive_ref_prop, double recode_test_stat, Nullable<NumericVector> null_mean_vec_, Nullable<NumericVector> null_se_vec_);

+RcppExport SEXP _epistasisGAGE_n2log_epistasis_pvals(SEXP chromosome_listSEXP, SEXP preprocessed_listSEXP, SEXP n_permutesSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP weight_function_intSEXP, SEXP recessive_ref_propSEXP, SEXP recode_test_statSEXP, SEXP null_mean_vec_SEXP, SEXP null_se_vec_SEXP) {

 BEGIN_RCPP

     Rcpp::RObject rcpp_result_gen;

     Rcpp::RNGScope rcpp_rngScope_gen;

     Rcpp::traits::input_parameter< ListOf<IntegerVector> >::type chromosome_list(chromosome_listSEXP);

-    Rcpp::traits::input_parameter< List >::type in_data_list(in_data_listSEXP);

-    Rcpp::traits::input_parameter< arma::vec >::type null_mean_vec(null_mean_vecSEXP);

-    Rcpp::traits::input_parameter< arma::vec >::type null_se_vec(null_se_vecSEXP);

+    Rcpp::traits::input_parameter< List >::type preprocessed_list(preprocessed_listSEXP);

     Rcpp::traits::input_parameter< int >::type n_permutes(n_permutesSEXP);

     Rcpp::traits::input_parameter< int >::type n_different_snps_weight(n_different_snps_weightSEXP);

     Rcpp::traits::input_parameter< int >::type n_both_one_weight(n_both_one_weightSEXP);

     Rcpp::traits::input_parameter< int >::type weight_function_int(weight_function_intSEXP);

     Rcpp::traits::input_parameter< double >::type recessive_ref_prop(recessive_ref_propSEXP);

     Rcpp::traits::input_parameter< double >::type recode_test_stat(recode_test_statSEXP);

-    Rcpp::traits::input_parameter< bool >::type GxE(GxESEXP);

-    Rcpp::traits::input_parameter< int >::type use_parents(use_parentsSEXP);

-    rcpp_result_gen = Rcpp::wrap(n2log_epistasis_pvals(chromosome_list, in_data_list, null_mean_vec, null_se_vec, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, GxE, use_parents));

+    Rcpp::traits::input_parameter< Nullable<NumericVector> >::type null_mean_vec_(null_mean_vec_SEXP);

+    Rcpp::traits::input_parameter< Nullable<NumericVector> >::type null_se_vec_(null_se_vec_SEXP);

+    rcpp_result_gen = Rcpp::wrap(n2log_epistasis_pvals(chromosome_list, preprocessed_list, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, null_mean_vec_, null_se_vec_));

     return rcpp_result_gen;

 END_RCPP

 }

@@ -859,8 +854,8 @@ static const R_CallMethodDef CallEntries[] = {

     {"_epistasisGAGE_epistasis_test_permute", (DL_FUNC) &_epistasisGAGE_epistasis_test_permute, 10},

     {"_epistasisGAGE_epistasis_test_null_scores", (DL_FUNC) &_epistasisGAGE_epistasis_test_null_scores, 11},

     {"_epistasisGAGE_epistasis_test", (DL_FUNC) &_epistasisGAGE_epistasis_test, 9},

-    {"_epistasisGAGE_GxE_test", (DL_FUNC) &_epistasisGAGE_GxE_test, 11},

-    {"_epistasisGAGE_n2log_epistasis_pvals", (DL_FUNC) &_epistasisGAGE_n2log_epistasis_pvals, 12},

+    {"_epistasisGAGE_GxE_test", (DL_FUNC) &_epistasisGAGE_GxE_test, 8},

+    {"_epistasisGAGE_n2log_epistasis_pvals", (DL_FUNC) &_epistasisGAGE_n2log_epistasis_pvals, 10},

     {NULL, NULL, 0}

 };

Binary files a/src/RcppExports.o and b/src/RcppExports.o differ

Binary files a/src/epistasisGA.o and b/src/epistasisGA.o differ

Binary files a/src/epistasisGAGE.so and b/src/epistasisGAGE.so differ