1. epistasisGA
  2. Commit 273218f18e4ee10e124ae66086ab6f25dffff169
Browse code

Trying GxE fitness score also incorporating parental data.

mnodzenski authored on 19/10/2021 17:13:03
Showing 10 changed files
R/GADGETS.R
History View file @ 273218f
... ... 
@@ -48,7 +48,9 @@
48 48 
 #' @param exposure.risk.levels An integer vector of the hypothesized risk levels corresponding to the elements of \code{case.genetic.data.list}.
49 49 
 #' See argument \code{categorical.exposures.risk.ranks} of \code{preprocess.genetic.data} for more information.
50 50 
 #' @param exposure An integer vector corresponding to environmental exposures of the cases.
51 
-#' @return For each island in the cluster, an rds object containing a list with the following elements will be written to \code{results.dir}:
51 
+#' @return For each island in the cluster, an rds object containing a list with the following elements will be written to \code{results.dir}.
52 
+#' @param 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
53 
+#' if the population is homogenous with no exposure related population structure.
52 54 
 #' \describe{
53 55 
 #'  \item{top.chromosome.results}{A data.table of the final generation chromosomes, their fitness scores, their difference vectors,
54 56 
 #' and the number of risk alleles required for each chromosome SNP for a case or complement to be classified as having the provisional risk set.
... ... 
@@ -87,14 +89,14 @@ GADGETS <- function(cluster.number, results.dir, case.genetic.data, complement.g
87 89 
                     chromosome.size, snp.chisq, weight.lookup, island.cluster.size = 4, n.migrations = 20, n.different.snps.weight = 2,
88 90 
                     n.both.one.weight = 1, migration.interval = 50, gen.same.fitness = 50, max.generations = 500,
89 91 
                     initial.sample.duplicates = FALSE, crossover.prop = 0.8, recessive.ref.prop = 0.75,
90 
-                    recode.test.stat = 1.64, exposure.levels = NULL, exposure.risk.levels = NULL, exposure = NULL) {
92 
+                    recode.test.stat = 1.64, exposure.levels = NULL, exposure.risk.levels = NULL, exposure = NULL, use.parents = FALSE) {
91 93
92 94 
     ### run rcpp version of GADGETS ##
93 95 
     rcpp.res <- run_GADGETS(island.cluster.size, n.migrations, ld.block.vec, n.chromosomes, chromosome.size,
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                             weight.lookup,  snp.chisq, case.genetic.data, complement.genetic.data,
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                             exposure.levels, exposure.risk.levels, exposure, n.different.snps.weight, n.both.one.weight,
96 98 
                             migration.interval, gen.same.fitness, max.generations, initial.sample.duplicates,
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-                            crossover.prop, recessive.ref.prop, recode.test.stat)
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+                            crossover.prop, recessive.ref.prop, recode.test.stat, use.parents)
98 100
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     ### clean up and output results
100 102 
     lapply(seq_along(rcpp.res), function(island.number){
R/RcppExports.R
History View file @ 273218f
... ... 
@@ -113,28 +113,28 @@ chrom_fitness_score <- function(case_genetic_data_in, complement_genetic_data_in
113 113 
     .Call('_epistasisGAGE_chrom_fitness_score', PACKAGE = 'epistasisGAGE', case_genetic_data_in, complement_genetic_data_in, target_snps_in, ld_block_vec, weight_lookup, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test, GxE, both_one_snps_in)
114 114 
 }
115 115
116 
-GxE_fitness_score <- function(case_genetic_data_list, complement_genetic_data_list, target_snps, ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, epi_test = FALSE, check_risk = TRUE) {
117 
-    .Call('_epistasisGAGE_GxE_fitness_score', PACKAGE = 'epistasisGAGE', case_genetic_data_list, complement_genetic_data_list, target_snps, ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test, check_risk)
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+GxE_fitness_score <- function(case_genetic_data_list, complement_genetic_data_list, target_snps, ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, epi_test = FALSE, check_risk = TRUE, use_parents = FALSE) {
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+    .Call('_epistasisGAGE_GxE_fitness_score', PACKAGE = 'epistasisGAGE', case_genetic_data_list, complement_genetic_data_list, target_snps, ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test, check_risk, use_parents)
118 118 
 }
119 119
120 120 
 chrom_fitness_list <- function(case_genetic_data, complement_genetic_data, chromosome_list, ld_block_vec, weight_lookup, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, epi_test = FALSE) {
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     .Call('_epistasisGAGE_chrom_fitness_list', PACKAGE = 'epistasisGAGE', case_genetic_data, complement_genetic_data, chromosome_list, ld_block_vec, weight_lookup, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test)
122 122 
 }
123 123
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-GxE_fitness_list <- function(case_genetic_data_list, complement_genetic_data_list, chromosome_list, ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, epi_test = FALSE, check_risk = TRUE) {
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-    .Call('_epistasisGAGE_GxE_fitness_list', PACKAGE = 'epistasisGAGE', case_genetic_data_list, complement_genetic_data_list, chromosome_list, ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test, check_risk)
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+GxE_fitness_list <- function(case_genetic_data_list, complement_genetic_data_list, chromosome_list, ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, epi_test = FALSE, check_risk = TRUE, use_parents = FALSE) {
125 
+    .Call('_epistasisGAGE_GxE_fitness_list', PACKAGE = 'epistasisGAGE', case_genetic_data_list, complement_genetic_data_list, chromosome_list, ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test, check_risk, use_parents)
126 126 
 }
127 127
128 
-compute_population_fitness <- function(case_genetic_data, complement_genetic_data, ld_block_vec, chromosome_list, weight_lookup, case_genetic_data_list, complement_genetic_data_list, exposure_levels, exposure_risk_levels, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, GxE = FALSE, check_risk = TRUE) {
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-    .Call('_epistasisGAGE_compute_population_fitness', PACKAGE = 'epistasisGAGE', case_genetic_data, complement_genetic_data, ld_block_vec, chromosome_list, weight_lookup, case_genetic_data_list, complement_genetic_data_list, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, GxE, check_risk)
128 
+compute_population_fitness <- function(case_genetic_data, complement_genetic_data, ld_block_vec, chromosome_list, weight_lookup, case_genetic_data_list, complement_genetic_data_list, exposure_levels, exposure_risk_levels, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, GxE = FALSE, check_risk = TRUE, use_parents = FALSE) {
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+    .Call('_epistasisGAGE_compute_population_fitness', PACKAGE = 'epistasisGAGE', case_genetic_data, complement_genetic_data, ld_block_vec, chromosome_list, weight_lookup, case_genetic_data_list, complement_genetic_data_list, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, GxE, check_risk, use_parents)
130 130 
 }
131 131
132 132 
 find_top_chrom <- function(fitness_scores, chromosome_list, chromosome_size) {
133 133 
     .Call('_epistasisGAGE_find_top_chrom', PACKAGE = 'epistasisGAGE', fitness_scores, chromosome_list, chromosome_size)
134 134 
 }
135 135
136 
-initiate_population <- function(n_candidate_snps, case_genetic_data, complement_genetic_data, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, case_genetic_data_list, complement_genetic_data_list, exposure_levels, exposure_risk_levels, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, max_generations = 500L, initial_sample_duplicates = FALSE, GxE = FALSE, check_risk = TRUE) {
137 
-    .Call('_epistasisGAGE_initiate_population', PACKAGE = 'epistasisGAGE', n_candidate_snps, case_genetic_data, complement_genetic_data, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, case_genetic_data_list, complement_genetic_data_list, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, max_generations, initial_sample_duplicates, GxE, check_risk)
136 
+initiate_population <- function(n_candidate_snps, case_genetic_data, complement_genetic_data, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, case_genetic_data_list, complement_genetic_data_list, exposure_levels, exposure_risk_levels, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, max_generations = 500L, initial_sample_duplicates = FALSE, GxE = FALSE, check_risk = TRUE, use_parents = FALSE) {
137 
+    .Call('_epistasisGAGE_initiate_population', PACKAGE = 'epistasisGAGE', n_candidate_snps, case_genetic_data, complement_genetic_data, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, case_genetic_data_list, complement_genetic_data_list, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, max_generations, initial_sample_duplicates, GxE, check_risk, use_parents)
138 138 
 }
139 139
140 140 
 check_convergence <- function(island_cluster_size, island_populations) {
... ... 
@@ -145,8 +145,8 @@ check_max_gens <- function(island_populations, max_generations) {
145 145 
     .Call('_epistasisGAGE_check_max_gens', PACKAGE = 'epistasisGAGE', island_populations, max_generations)
146 146 
 }
147 147
148 
-run_GADGETS <- function(island_cluster_size, n_migrations, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, snp_chisq, case_genetic_data_in, complement_genetic_data_in, exposure_levels_in = NULL, exposure_risk_levels_in = NULL, exposure_in = NULL, n_different_snps_weight = 2L, n_both_one_weight = 1L, migration_interval = 50L, gen_same_fitness = 50L, max_generations = 500L, initial_sample_duplicates = FALSE, crossover_prop = 0.8, recessive_ref_prop = 0.75, recode_test_stat = 1.64) {
149 
-    .Call('_epistasisGAGE_run_GADGETS', PACKAGE = 'epistasisGAGE', island_cluster_size, n_migrations, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, snp_chisq, case_genetic_data_in, complement_genetic_data_in, exposure_levels_in, exposure_risk_levels_in, exposure_in, n_different_snps_weight, n_both_one_weight, migration_interval, gen_same_fitness, max_generations, initial_sample_duplicates, crossover_prop, recessive_ref_prop, recode_test_stat)
148 
+run_GADGETS <- function(island_cluster_size, n_migrations, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, snp_chisq, case_genetic_data_in, complement_genetic_data_in, exposure_levels_in = NULL, exposure_risk_levels_in = NULL, exposure_in = NULL, n_different_snps_weight = 2L, n_both_one_weight = 1L, migration_interval = 50L, gen_same_fitness = 50L, max_generations = 500L, initial_sample_duplicates = FALSE, crossover_prop = 0.8, recessive_ref_prop = 0.75, recode_test_stat = 1.64, use_parents = FALSE) {
149 
+    .Call('_epistasisGAGE_run_GADGETS', PACKAGE = 'epistasisGAGE', island_cluster_size, n_migrations, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, snp_chisq, case_genetic_data_in, complement_genetic_data_in, exposure_levels_in, exposure_risk_levels_in, exposure_in, n_different_snps_weight, n_both_one_weight, migration_interval, gen_same_fitness, max_generations, initial_sample_duplicates, crossover_prop, recessive_ref_prop, recode_test_stat, use_parents)
150 150 
 }
151 151
152 152 
 epistasis_test_permute <- function(case_inf, comp_inf, target_snps_ld_blocks, uni_ld_blocks, n_families, weight_lookup, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64) {
... ... 
@@ -161,11 +161,11 @@ epistasis_test <- function(snp_cols, preprocessed_list, n_permutes = 10000L, n_d
161 161 
     .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)
162 162 
 }
163 163
164 
-GxE_test <- function(snp_cols, 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) {
165 
-    .Call('_epistasisGAGE_GxE_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)
164 
+GxE_test <- function(snp_cols, 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, use_parents = FALSE) {
165 
+    .Call('_epistasisGAGE_GxE_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, use_parents)
166 166 
 }
167 167
168 
-n2log_epistasis_pvals <- function(chromosome_list, in_data_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, GxE = FALSE) {
169 
-    .Call('_epistasisGAGE_n2log_epistasis_pvals', PACKAGE = 'epistasisGAGE', chromosome_list, in_data_list, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, GxE)
168 
+n2log_epistasis_pvals <- function(chromosome_list, in_data_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, GxE = FALSE, use_parents = FALSE) {
169 
+    .Call('_epistasisGAGE_n2log_epistasis_pvals', PACKAGE = 'epistasisGAGE', chromosome_list, in_data_list, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, GxE, use_parents)
170 170 
 }
171 171
R/run.gadgets.R
History View file @ 273218f
... ... 
@@ -50,6 +50,8 @@
50 50 
 #' to determine whether to recode the SNP as recessive. Defaults to 0.75.
51 51 
 #' @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.
52 52 
 #' See the GADGETS paper for specific details.
53 
+#' @param 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
54 
+#' if the population is homogenous with no exposure related population structure.
53 55 
 #' @return For each island, a list of two elements will be written to \code{results.dir}:
54 56 
 #' \describe{
55 57 
 #'  \item{top.chromosome.results}{A data.table of the final generation chromosomes, their fitness scores, their difference vectors,
... ... 
@@ -89,7 +91,7 @@ run.gadgets <- function(data.list, n.chromosomes, chromosome.size, results.dir,
89 91 
     n.chunks = NULL, n.different.snps.weight = 2, n.both.one.weight = 1, weight.function.int = 2,
90 92 
     generations = 500, gen.same.fitness = 50, initial.sample.duplicates = FALSE,
91 93 
     snp.sampling.type = "chisq", crossover.prop = 0.8, n.islands = 1000, island.cluster.size = 4, migration.generations = 50,
92 
-    n.migrations = 20, recessive.ref.prop = 0.75, recode.test.stat = 1.64) {
94 
+    n.migrations = 20, recessive.ref.prop = 0.75, recode.test.stat = 1.64, use.parents = FALSE) {
93 95
94 96 
     ### make sure if island clusters exist, the migration interval is set properly ###
95 97 
     if (island.cluster.size > 1 & migration.generations >= generations & island.cluster.size != 1) {
... ... 
@@ -249,7 +251,7 @@ run.gadgets <- function(data.list, n.chromosomes, chromosome.size, results.dir,
249 251 
         n.both.one.weight = n.both.one.weight, migration.interval = migration.generations, gen.same.fitness = gen.same.fitness,
250 252 
         max.generations = generations, initial.sample.duplicates = initial.sample.duplicates, crossover.prop = crossover.prop,
251 253 
         recessive.ref.prop = recessive.ref.prop, recode.test.stat = recode.test.stat, exposure.levels = data.list$exposure.levels,
252 
-        exposure.risk.levels = data.list$exposure.risk.levels, exposure = data.list$exposure),
254 
+        exposure.risk.levels = data.list$exposure.risk.levels, exposure = data.list$exposure, use.parents = use.parents),
253 255 
         reg = registry)
254 256
255 257 
     # chunk the jobs
man/GADGETS.Rd
History View file @ 273218f
... ... 
@@ -27,7 +27,8 @@ GADGETS(
27 27 
   recode.test.stat = 1.64,
28 28 
   exposure.levels = NULL,
29 29 
   exposure.risk.levels = NULL,
30 
-  exposure = NULL
30 
+  exposure = NULL,
31 
+  use.parents = FALSE
31 32 
 )
32 33 
 }
33 34 
 \arguments{
... ... 
@@ -98,15 +99,18 @@ from \code{exposure}.}
98 99 
 See argument \code{categorical.exposures.risk.ranks} of \code{preprocess.genetic.data} for more information.}
99 100
100 101 
 \item{exposure}{An integer vector corresponding to environmental exposures of the cases.}
101 
-}
102 
-\value{
103 
-For each island in the cluster, an rds object containing a list with the following elements will be written to \code{results.dir}:
102 
+
103 
+\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
104 
+if the population is homogenous with no exposure related population structure.
104 105 
 \describe{
105 106 
  \item{top.chromosome.results}{A data.table of the final generation chromosomes, their fitness scores, their difference vectors,
106 107 
 and the number of risk alleles required for each chromosome SNP for a case or complement to be classified as having the provisional risk set.
107 108 
 See the package vignette for an example and the documentation for \code{chrom.fitness.score} for additional details.}
108 109 
  \item{n.generations}{The total number of generations run.}
110 
+}}
109 111 
 }
112 
+\value{
113 
+For each island in the cluster, an rds object containing a list with the following elements will be written to \code{results.dir}.
110 114 
 }
111 115 
 \description{
112 116 
 This function runs the GADGETS method on a given cluster of islands. It is a wrapper for
man/run.gadgets.Rd
History View file @ 273218f
... ... 
@@ -28,7 +28,8 @@ run.gadgets(
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   migration.generations = 50,
29 29 
   n.migrations = 20,
30 30 
   recessive.ref.prop = 0.75,
31 
-  recode.test.stat = 1.64
31 
+  recode.test.stat = 1.64,
32 
+  use.parents = FALSE
32 33 
 )
33 34 
 }
34 35 
 \arguments{
... ... 
@@ -103,6 +104,9 @@ to determine whether to recode the SNP as recessive. Defaults to 0.75.}
103 104
104 105 
 \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.
105 106 
 See the GADGETS paper for specific details.}
107 
+
108 
+\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
109 
+if the population is homogenous with no exposure related population structure.}
106 110 
 }
107 111 
 \value{
108 112 
 For each island, a list of two elements will be written to \code{results.dir}:
src/RcppExports.cpp
History View file @ 273218f
... ... 
@@ -384,8 +384,8 @@ BEGIN_RCPP
384 384 
 END_RCPP
385 385 
 }
386 386 
 // GxE_fitness_score
387 
-List GxE_fitness_score(ListOf<IntegerMatrix> case_genetic_data_list, ListOf<IntegerMatrix> complement_genetic_data_list, IntegerVector target_snps, IntegerVector ld_block_vec, IntegerVector weight_lookup, IntegerVector exposure_levels, IntegerVector exposure_risk_levels, int n_different_snps_weight, int n_both_one_weight, double recessive_ref_prop, double recode_test_stat, bool epi_test, bool check_risk);
388 
-RcppExport SEXP _epistasisGAGE_GxE_fitness_score(SEXP case_genetic_data_listSEXP, SEXP complement_genetic_data_listSEXP, SEXP target_snpsSEXP, SEXP ld_block_vecSEXP, SEXP weight_lookupSEXP, SEXP exposure_levelsSEXP, SEXP exposure_risk_levelsSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP recessive_ref_propSEXP, SEXP recode_test_statSEXP, SEXP epi_testSEXP, SEXP check_riskSEXP) {
387 
+List GxE_fitness_score(ListOf<IntegerMatrix> case_genetic_data_list, ListOf<IntegerMatrix> complement_genetic_data_list, IntegerVector target_snps, IntegerVector ld_block_vec, IntegerVector weight_lookup, IntegerVector exposure_levels, IntegerVector exposure_risk_levels, int n_different_snps_weight, int n_both_one_weight, double recessive_ref_prop, double recode_test_stat, bool epi_test, bool check_risk, bool use_parents);
388 
+RcppExport SEXP _epistasisGAGE_GxE_fitness_score(SEXP case_genetic_data_listSEXP, SEXP complement_genetic_data_listSEXP, SEXP target_snpsSEXP, SEXP ld_block_vecSEXP, SEXP weight_lookupSEXP, SEXP exposure_levelsSEXP, SEXP exposure_risk_levelsSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP recessive_ref_propSEXP, SEXP recode_test_statSEXP, SEXP epi_testSEXP, SEXP check_riskSEXP, SEXP use_parentsSEXP) {
389 389 
 BEGIN_RCPP
390 390 
     Rcpp::RObject rcpp_result_gen;
391 391 
     Rcpp::RNGScope rcpp_rngScope_gen;
... ... 
@@ -402,7 +402,8 @@ BEGIN_RCPP
402 402 
     Rcpp::traits::input_parameter< double >::type recode_test_stat(recode_test_statSEXP);
403 403 
     Rcpp::traits::input_parameter< bool >::type epi_test(epi_testSEXP);
404 404 
     Rcpp::traits::input_parameter< bool >::type check_risk(check_riskSEXP);
405 
-    rcpp_result_gen = Rcpp::wrap(GxE_fitness_score(case_genetic_data_list, complement_genetic_data_list, target_snps, ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test, check_risk));
405 
+    Rcpp::traits::input_parameter< bool >::type use_parents(use_parentsSEXP);
406 
+    rcpp_result_gen = Rcpp::wrap(GxE_fitness_score(case_genetic_data_list, complement_genetic_data_list, target_snps, ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test, check_risk, use_parents));
406 407 
     return rcpp_result_gen;
407 408 
 END_RCPP
408 409 
 }
... ... 
@@ -427,8 +428,8 @@ BEGIN_RCPP
427 428 
 END_RCPP
428 429 
 }
429 430 
 // GxE_fitness_list
430 
-List GxE_fitness_list(List case_genetic_data_list, List complement_genetic_data_list, List chromosome_list, IntegerVector ld_block_vec, IntegerVector weight_lookup, IntegerVector exposure_levels, IntegerVector exposure_risk_levels, int n_different_snps_weight, int n_both_one_weight, double recessive_ref_prop, double recode_test_stat, bool epi_test, bool check_risk);
431 
-RcppExport SEXP _epistasisGAGE_GxE_fitness_list(SEXP case_genetic_data_listSEXP, SEXP complement_genetic_data_listSEXP, SEXP chromosome_listSEXP, SEXP ld_block_vecSEXP, SEXP weight_lookupSEXP, SEXP exposure_levelsSEXP, SEXP exposure_risk_levelsSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP recessive_ref_propSEXP, SEXP recode_test_statSEXP, SEXP epi_testSEXP, SEXP check_riskSEXP) {
431 
+List GxE_fitness_list(List case_genetic_data_list, List complement_genetic_data_list, List chromosome_list, IntegerVector ld_block_vec, IntegerVector weight_lookup, IntegerVector exposure_levels, IntegerVector exposure_risk_levels, int n_different_snps_weight, int n_both_one_weight, double recessive_ref_prop, double recode_test_stat, bool epi_test, bool check_risk, bool use_parents);
432 
+RcppExport SEXP _epistasisGAGE_GxE_fitness_list(SEXP case_genetic_data_listSEXP, SEXP complement_genetic_data_listSEXP, SEXP chromosome_listSEXP, SEXP ld_block_vecSEXP, SEXP weight_lookupSEXP, SEXP exposure_levelsSEXP, SEXP exposure_risk_levelsSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP recessive_ref_propSEXP, SEXP recode_test_statSEXP, SEXP epi_testSEXP, SEXP check_riskSEXP, SEXP use_parentsSEXP) {
432 433 
 BEGIN_RCPP
433 434 
     Rcpp::RObject rcpp_result_gen;
434 435 
     Rcpp::RNGScope rcpp_rngScope_gen;
... ... 
@@ -445,13 +446,14 @@ BEGIN_RCPP
445 446 
     Rcpp::traits::input_parameter< double >::type recode_test_stat(recode_test_statSEXP);
446 447 
     Rcpp::traits::input_parameter< bool >::type epi_test(epi_testSEXP);
447 448 
     Rcpp::traits::input_parameter< bool >::type check_risk(check_riskSEXP);
448 
-    rcpp_result_gen = Rcpp::wrap(GxE_fitness_list(case_genetic_data_list, complement_genetic_data_list, chromosome_list, ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test, check_risk));
449 
+    Rcpp::traits::input_parameter< bool >::type use_parents(use_parentsSEXP);
450 
+    rcpp_result_gen = Rcpp::wrap(GxE_fitness_list(case_genetic_data_list, complement_genetic_data_list, chromosome_list, ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test, check_risk, use_parents));
449 451 
     return rcpp_result_gen;
450 452 
 END_RCPP
451 453 
 }
452 454 
 // compute_population_fitness
453 
-List compute_population_fitness(IntegerMatrix case_genetic_data, IntegerMatrix complement_genetic_data, IntegerVector ld_block_vec, List chromosome_list, IntegerVector weight_lookup, ListOf<IntegerMatrix> case_genetic_data_list, ListOf<IntegerMatrix> complement_genetic_data_list, IntegerVector exposure_levels, IntegerVector exposure_risk_levels, int n_different_snps_weight, int n_both_one_weight, double recessive_ref_prop, double recode_test_stat, bool GxE, bool check_risk);
454 
-RcppExport SEXP _epistasisGAGE_compute_population_fitness(SEXP case_genetic_dataSEXP, SEXP complement_genetic_dataSEXP, SEXP ld_block_vecSEXP, SEXP chromosome_listSEXP, SEXP weight_lookupSEXP, SEXP case_genetic_data_listSEXP, SEXP complement_genetic_data_listSEXP, SEXP exposure_levelsSEXP, SEXP exposure_risk_levelsSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP recessive_ref_propSEXP, SEXP recode_test_statSEXP, SEXP GxESEXP, SEXP check_riskSEXP) {
455 
+List compute_population_fitness(IntegerMatrix case_genetic_data, IntegerMatrix complement_genetic_data, IntegerVector ld_block_vec, List chromosome_list, IntegerVector weight_lookup, ListOf<IntegerMatrix> case_genetic_data_list, ListOf<IntegerMatrix> complement_genetic_data_list, IntegerVector exposure_levels, IntegerVector exposure_risk_levels, int n_different_snps_weight, int n_both_one_weight, double recessive_ref_prop, double recode_test_stat, bool GxE, bool check_risk, bool use_parents);
456 
+RcppExport SEXP _epistasisGAGE_compute_population_fitness(SEXP case_genetic_dataSEXP, SEXP complement_genetic_dataSEXP, SEXP ld_block_vecSEXP, SEXP chromosome_listSEXP, SEXP weight_lookupSEXP, SEXP case_genetic_data_listSEXP, SEXP complement_genetic_data_listSEXP, SEXP exposure_levelsSEXP, SEXP exposure_risk_levelsSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP recessive_ref_propSEXP, SEXP recode_test_statSEXP, SEXP GxESEXP, SEXP check_riskSEXP, SEXP use_parentsSEXP) {
455 457 
 BEGIN_RCPP
456 458 
     Rcpp::RObject rcpp_result_gen;
457 459 
     Rcpp::RNGScope rcpp_rngScope_gen;
... ... 
@@ -470,7 +472,8 @@ BEGIN_RCPP
470 472 
     Rcpp::traits::input_parameter< double >::type recode_test_stat(recode_test_statSEXP);
471 473 
     Rcpp::traits::input_parameter< bool >::type GxE(GxESEXP);
472 474 
     Rcpp::traits::input_parameter< bool >::type check_risk(check_riskSEXP);
473 
-    rcpp_result_gen = Rcpp::wrap(compute_population_fitness(case_genetic_data, complement_genetic_data, ld_block_vec, chromosome_list, weight_lookup, case_genetic_data_list, complement_genetic_data_list, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, GxE, check_risk));
475 
+    Rcpp::traits::input_parameter< bool >::type use_parents(use_parentsSEXP);
476 
+    rcpp_result_gen = Rcpp::wrap(compute_population_fitness(case_genetic_data, complement_genetic_data, ld_block_vec, chromosome_list, weight_lookup, case_genetic_data_list, complement_genetic_data_list, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, GxE, check_risk, use_parents));
474 477 
     return rcpp_result_gen;
475 478 
 END_RCPP
476 479 
 }
... ... 
@@ -488,8 +491,8 @@ BEGIN_RCPP
488 491 
 END_RCPP
489 492 
 }
490 493 
 // initiate_population
491 
-List initiate_population(int n_candidate_snps, IntegerMatrix case_genetic_data, IntegerMatrix complement_genetic_data, IntegerVector ld_block_vec, int n_chromosomes, int chromosome_size, IntegerVector weight_lookup, ListOf<IntegerMatrix> case_genetic_data_list, ListOf<IntegerMatrix> complement_genetic_data_list, IntegerVector exposure_levels, IntegerVector exposure_risk_levels, int n_different_snps_weight, int n_both_one_weight, double recessive_ref_prop, double recode_test_stat, int max_generations, bool initial_sample_duplicates, bool GxE, bool check_risk);
492 
-RcppExport SEXP _epistasisGAGE_initiate_population(SEXP n_candidate_snpsSEXP, SEXP case_genetic_dataSEXP, SEXP complement_genetic_dataSEXP, SEXP ld_block_vecSEXP, SEXP n_chromosomesSEXP, SEXP chromosome_sizeSEXP, SEXP weight_lookupSEXP, SEXP case_genetic_data_listSEXP, SEXP complement_genetic_data_listSEXP, SEXP exposure_levelsSEXP, SEXP exposure_risk_levelsSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP recessive_ref_propSEXP, SEXP recode_test_statSEXP, SEXP max_generationsSEXP, SEXP initial_sample_duplicatesSEXP, SEXP GxESEXP, SEXP check_riskSEXP) {
494 
+List initiate_population(int n_candidate_snps, IntegerMatrix case_genetic_data, IntegerMatrix complement_genetic_data, IntegerVector ld_block_vec, int n_chromosomes, int chromosome_size, IntegerVector weight_lookup, ListOf<IntegerMatrix> case_genetic_data_list, ListOf<IntegerMatrix> complement_genetic_data_list, IntegerVector exposure_levels, IntegerVector exposure_risk_levels, int n_different_snps_weight, int n_both_one_weight, double recessive_ref_prop, double recode_test_stat, int max_generations, bool initial_sample_duplicates, bool GxE, bool check_risk, bool use_parents);
495 
+RcppExport SEXP _epistasisGAGE_initiate_population(SEXP n_candidate_snpsSEXP, SEXP case_genetic_dataSEXP, SEXP complement_genetic_dataSEXP, SEXP ld_block_vecSEXP, SEXP n_chromosomesSEXP, SEXP chromosome_sizeSEXP, SEXP weight_lookupSEXP, SEXP case_genetic_data_listSEXP, SEXP complement_genetic_data_listSEXP, SEXP exposure_levelsSEXP, SEXP exposure_risk_levelsSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP recessive_ref_propSEXP, SEXP recode_test_statSEXP, SEXP max_generationsSEXP, SEXP initial_sample_duplicatesSEXP, SEXP GxESEXP, SEXP check_riskSEXP, SEXP use_parentsSEXP) {
493 496 
 BEGIN_RCPP
494 497 
     Rcpp::RObject rcpp_result_gen;
495 498 
     Rcpp::RNGScope rcpp_rngScope_gen;
... ... 
@@ -512,7 +515,8 @@ BEGIN_RCPP
512 515 
     Rcpp::traits::input_parameter< bool >::type initial_sample_duplicates(initial_sample_duplicatesSEXP);
513 516 
     Rcpp::traits::input_parameter< bool >::type GxE(GxESEXP);
514 517 
     Rcpp::traits::input_parameter< bool >::type check_risk(check_riskSEXP);
515 
-    rcpp_result_gen = Rcpp::wrap(initiate_population(n_candidate_snps, case_genetic_data, complement_genetic_data, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, case_genetic_data_list, complement_genetic_data_list, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, max_generations, initial_sample_duplicates, GxE, check_risk));
518 
+    Rcpp::traits::input_parameter< bool >::type use_parents(use_parentsSEXP);
519 
+    rcpp_result_gen = Rcpp::wrap(initiate_population(n_candidate_snps, case_genetic_data, complement_genetic_data, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, case_genetic_data_list, complement_genetic_data_list, exposure_levels, exposure_risk_levels, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, max_generations, initial_sample_duplicates, GxE, check_risk, use_parents));
516 520 
     return rcpp_result_gen;
517 521 
 END_RCPP
518 522 
 }
... ... 
@@ -541,8 +545,8 @@ BEGIN_RCPP
541 545 
 END_RCPP
542 546 
 }
543 547 
 // run_GADGETS
544 
-List run_GADGETS(int island_cluster_size, int n_migrations, IntegerVector ld_block_vec, int n_chromosomes, int chromosome_size, IntegerVector weight_lookup, NumericVector snp_chisq, IntegerMatrix case_genetic_data_in, IntegerMatrix complement_genetic_data_in, Nullable<IntegerVector> exposure_levels_in, Nullable<IntegerVector> exposure_risk_levels_in, Nullable<IntegerVector> exposure_in, int n_different_snps_weight, int n_both_one_weight, int migration_interval, int gen_same_fitness, int max_generations, bool initial_sample_duplicates, double crossover_prop, double recessive_ref_prop, double recode_test_stat);
545 
-RcppExport SEXP _epistasisGAGE_run_GADGETS(SEXP island_cluster_sizeSEXP, SEXP n_migrationsSEXP, SEXP ld_block_vecSEXP, SEXP n_chromosomesSEXP, SEXP chromosome_sizeSEXP, SEXP weight_lookupSEXP, SEXP snp_chisqSEXP, SEXP case_genetic_data_inSEXP, SEXP complement_genetic_data_inSEXP, SEXP exposure_levels_inSEXP, SEXP exposure_risk_levels_inSEXP, SEXP exposure_inSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP migration_intervalSEXP, SEXP gen_same_fitnessSEXP, SEXP max_generationsSEXP, SEXP initial_sample_duplicatesSEXP, SEXP crossover_propSEXP, SEXP recessive_ref_propSEXP, SEXP recode_test_statSEXP) {
548 
+List run_GADGETS(int island_cluster_size, int n_migrations, IntegerVector ld_block_vec, int n_chromosomes, int chromosome_size, IntegerVector weight_lookup, NumericVector snp_chisq, IntegerMatrix case_genetic_data_in, IntegerMatrix complement_genetic_data_in, Nullable<IntegerVector> exposure_levels_in, Nullable<IntegerVector> exposure_risk_levels_in, Nullable<IntegerVector> exposure_in, int n_different_snps_weight, int n_both_one_weight, int migration_interval, int gen_same_fitness, int max_generations, bool initial_sample_duplicates, double crossover_prop, double recessive_ref_prop, double recode_test_stat, bool use_parents);
549 
+RcppExport SEXP _epistasisGAGE_run_GADGETS(SEXP island_cluster_sizeSEXP, SEXP n_migrationsSEXP, SEXP ld_block_vecSEXP, SEXP n_chromosomesSEXP, SEXP chromosome_sizeSEXP, SEXP weight_lookupSEXP, SEXP snp_chisqSEXP, SEXP case_genetic_data_inSEXP, SEXP complement_genetic_data_inSEXP, SEXP exposure_levels_inSEXP, SEXP exposure_risk_levels_inSEXP, SEXP exposure_inSEXP, SEXP n_different_snps_weightSEXP, SEXP n_both_one_weightSEXP, SEXP migration_intervalSEXP, SEXP gen_same_fitnessSEXP, SEXP max_generationsSEXP, SEXP initial_sample_duplicatesSEXP, SEXP crossover_propSEXP, SEXP recessive_ref_propSEXP, SEXP recode_test_statSEXP, SEXP use_parentsSEXP) {
546 550 
 BEGIN_RCPP
547 551 
     Rcpp::RObject rcpp_result_gen;
548 552 
     Rcpp::RNGScope rcpp_rngScope_gen;
... ... 
@@ -567,7 +571,8 @@ BEGIN_RCPP
567 571 
     Rcpp::traits::input_parameter< double >::type crossover_prop(crossover_propSEXP);
568 572 
     Rcpp::traits::input_parameter< double >::type recessive_ref_prop(recessive_ref_propSEXP);
569 573 
     Rcpp::traits::input_parameter< double >::type recode_test_stat(recode_test_statSEXP);
570 
-    rcpp_result_gen = Rcpp::wrap(run_GADGETS(island_cluster_size, n_migrations, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, snp_chisq, case_genetic_data_in, complement_genetic_data_in, exposure_levels_in, exposure_risk_levels_in, exposure_in, n_different_snps_weight, n_both_one_weight, migration_interval, gen_same_fitness, max_generations, initial_sample_duplicates, crossover_prop, recessive_ref_prop, recode_test_stat));
574 
+    Rcpp::traits::input_parameter< bool >::type use_parents(use_parentsSEXP);
575 
+    rcpp_result_gen = Rcpp::wrap(run_GADGETS(island_cluster_size, n_migrations, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, snp_chisq, case_genetic_data_in, complement_genetic_data_in, exposure_levels_in, exposure_risk_levels_in, exposure_in, n_different_snps_weight, n_both_one_weight, migration_interval, gen_same_fitness, max_generations, initial_sample_duplicates, crossover_prop, recessive_ref_prop, recode_test_stat, use_parents));
571 576 
     return rcpp_result_gen;
572 577 
 END_RCPP
573 578 
 }
... ... 
@@ -632,8 +637,8 @@ BEGIN_RCPP
632 637 
 END_RCPP
633 638 
 }
634 639 
 // GxE_test
635 
-List GxE_test(IntegerVector snp_cols, 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);
636 
-RcppExport SEXP _epistasisGAGE_GxE_test(SEXP snp_colsSEXP, 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) {
640 
+List GxE_test(IntegerVector snp_cols, 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, bool use_parents);
641 
+RcppExport SEXP _epistasisGAGE_GxE_test(SEXP snp_colsSEXP, 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 use_parentsSEXP) {
637 642 
 BEGIN_RCPP
638 643 
     Rcpp::RObject rcpp_result_gen;
639 644 
     Rcpp::RNGScope rcpp_rngScope_gen;
... ... 
@@ -645,13 +650,14 @@ BEGIN_RCPP
645 650 
     Rcpp::traits::input_parameter< int >::type weight_function_int(weight_function_intSEXP);
646 651 
     Rcpp::traits::input_parameter< double >::type recessive_ref_prop(recessive_ref_propSEXP);
647 652 
     Rcpp::traits::input_parameter< double >::type recode_test_stat(recode_test_statSEXP);
648 
-    rcpp_result_gen = Rcpp::wrap(GxE_test(snp_cols, preprocessed_list, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat));
653 
+    Rcpp::traits::input_parameter< bool >::type use_parents(use_parentsSEXP);
654 
+    rcpp_result_gen = Rcpp::wrap(GxE_test(snp_cols, preprocessed_list, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, use_parents));
649 655 
     return rcpp_result_gen;
650 656 
 END_RCPP
651 657 
 }
652 658 
 // n2log_epistasis_pvals
653 
-NumericVector n2log_epistasis_pvals(ListOf<IntegerVector> chromosome_list, List in_data_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, bool GxE);
654 
-RcppExport SEXP _epistasisGAGE_n2log_epistasis_pvals(SEXP chromosome_listSEXP, SEXP in_data_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 GxESEXP) {
659 
+NumericVector n2log_epistasis_pvals(ListOf<IntegerVector> chromosome_list, List in_data_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, bool GxE, bool use_parents);
660 
+RcppExport SEXP _epistasisGAGE_n2log_epistasis_pvals(SEXP chromosome_listSEXP, SEXP in_data_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 GxESEXP, SEXP use_parentsSEXP) {
655 661 
 BEGIN_RCPP
656 662 
     Rcpp::RObject rcpp_result_gen;
657 663 
     Rcpp::RNGScope rcpp_rngScope_gen;
... ... 
@@ -664,7 +670,8 @@ BEGIN_RCPP
664 670 
     Rcpp::traits::input_parameter< double >::type recessive_ref_prop(recessive_ref_propSEXP);
665 671 
     Rcpp::traits::input_parameter< double >::type recode_test_stat(recode_test_statSEXP);
666 672 
     Rcpp::traits::input_parameter< bool >::type GxE(GxESEXP);
667 
-    rcpp_result_gen = Rcpp::wrap(n2log_epistasis_pvals(chromosome_list, in_data_list, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, GxE));
673 
+    Rcpp::traits::input_parameter< bool >::type use_parents(use_parentsSEXP);
674 
+    rcpp_result_gen = Rcpp::wrap(n2log_epistasis_pvals(chromosome_list, in_data_list, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, GxE, use_parents));
668 675 
     return rcpp_result_gen;
669 676 
 END_RCPP
670 677 
 }
... ... 
@@ -698,20 +705,20 @@ static const R_CallMethodDef CallEntries[] = {
698 705 
     {"_epistasisGAGE_find_high_risk", (DL_FUNC) &_epistasisGAGE_find_high_risk, 9},
699 706 
     {"_epistasisGAGE_compute_dif_vecs", (DL_FUNC) &_epistasisGAGE_compute_dif_vecs, 7},
700 707 
     {"_epistasisGAGE_chrom_fitness_score", (DL_FUNC) &_epistasisGAGE_chrom_fitness_score, 12},
701 
-    {"_epistasisGAGE_GxE_fitness_score", (DL_FUNC) &_epistasisGAGE_GxE_fitness_score, 13},
708 
+    {"_epistasisGAGE_GxE_fitness_score", (DL_FUNC) &_epistasisGAGE_GxE_fitness_score, 14},
702 709 
     {"_epistasisGAGE_chrom_fitness_list", (DL_FUNC) &_epistasisGAGE_chrom_fitness_list, 10},
703 
-    {"_epistasisGAGE_GxE_fitness_list", (DL_FUNC) &_epistasisGAGE_GxE_fitness_list, 13},
704 
-    {"_epistasisGAGE_compute_population_fitness", (DL_FUNC) &_epistasisGAGE_compute_population_fitness, 15},
710 
+    {"_epistasisGAGE_GxE_fitness_list", (DL_FUNC) &_epistasisGAGE_GxE_fitness_list, 14},
711 
+    {"_epistasisGAGE_compute_population_fitness", (DL_FUNC) &_epistasisGAGE_compute_population_fitness, 16},
705 712 
     {"_epistasisGAGE_find_top_chrom", (DL_FUNC) &_epistasisGAGE_find_top_chrom, 3},
706 
-    {"_epistasisGAGE_initiate_population", (DL_FUNC) &_epistasisGAGE_initiate_population, 19},
713 
+    {"_epistasisGAGE_initiate_population", (DL_FUNC) &_epistasisGAGE_initiate_population, 20},
707 714 
     {"_epistasisGAGE_check_convergence", (DL_FUNC) &_epistasisGAGE_check_convergence, 2},
708 715 
     {"_epistasisGAGE_check_max_gens", (DL_FUNC) &_epistasisGAGE_check_max_gens, 2},
709 
-    {"_epistasisGAGE_run_GADGETS", (DL_FUNC) &_epistasisGAGE_run_GADGETS, 21},
716 
+    {"_epistasisGAGE_run_GADGETS", (DL_FUNC) &_epistasisGAGE_run_GADGETS, 22},
710 717 
     {"_epistasisGAGE_epistasis_test_permute", (DL_FUNC) &_epistasisGAGE_epistasis_test_permute, 10},
711 718 
     {"_epistasisGAGE_epistasis_test_null_scores", (DL_FUNC) &_epistasisGAGE_epistasis_test_null_scores, 11},
712 719 
     {"_epistasisGAGE_epistasis_test", (DL_FUNC) &_epistasisGAGE_epistasis_test, 9},
713 
-    {"_epistasisGAGE_GxE_test", (DL_FUNC) &_epistasisGAGE_GxE_test, 8},
714 
-    {"_epistasisGAGE_n2log_epistasis_pvals", (DL_FUNC) &_epistasisGAGE_n2log_epistasis_pvals, 9},
720 
+    {"_epistasisGAGE_GxE_test", (DL_FUNC) &_epistasisGAGE_GxE_test, 9},
721 
+    {"_epistasisGAGE_n2log_epistasis_pvals", (DL_FUNC) &_epistasisGAGE_n2log_epistasis_pvals, 10},
715 722 
     {NULL, NULL, 0}
716 723 
 };
717 724
src/RcppExports.o
History View file @ 273218f
718 725 
Binary files a/src/RcppExports.o and b/src/RcppExports.o differ
src/epistasisGA.cpp
History View file @ 273218f
... ... 
@@ -1043,6 +1043,7 @@ List chrom_fitness_score(IntegerMatrix case_genetic_data_in, IntegerMatrix compl
1043 1043 
                                 Named("sum_dif_vecs") = sum_dif_vecs,
1044 1044 
                                 Named("sigma") = cov_mat,
1045 1045 
                                 Named("w") = 1/invsum_family_weights,
1046 
+                                Named("family_weights") = family_weights,
1046 1047 
                                 Named("risk_set_alleles") = risk_set_alleles,
1047 1048 
                                 Named("n_case_risk_geno") = n_case_high_risk,
1048 1049 
                                 Named("n_comp_risk_geno") = n_comp_high_risk);
... ... 
@@ -1054,6 +1055,7 @@ List chrom_fitness_score(IntegerMatrix case_genetic_data_in, IntegerMatrix compl
1054 1055 
                               Named("sum_dif_vecs") = sum_dif_vecs,
1055 1056 
                               Named("sigma") = cov_mat,
1056 1057 
                               Named("w") = 1/invsum_family_weights,
1058 
+                              Named("family_weights") = family_weights,
1057 1059 
                               Named("risk_set_alleles") = risk_set_alleles,
1058 1060 
                               Named("n_case_risk_geno") = n_case_high_risk,
1059 1061 
                               Named("n_comp_risk_geno") = n_comp_high_risk,
... ... 
@@ -1085,7 +1087,7 @@ List GxE_fitness_score(ListOf<IntegerMatrix> case_genetic_data_list, ListOf<Inte
1085 1087 
                        IntegerVector target_snps, IntegerVector ld_block_vec, IntegerVector weight_lookup,
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                        IntegerVector exposure_levels, IntegerVector exposure_risk_levels, int n_different_snps_weight = 2,
1087 1089 
                        int n_both_one_weight = 1, double recessive_ref_prop = 0.75, double recode_test_stat = 1.64,
1088 
-                       bool epi_test = false, bool check_risk = true){
1090 
+                       bool epi_test = false, bool check_risk = true, bool use_parents = false){
1089 1091
1090 1092 
   // divide the input data based on exposure and get components for fitness score //
1091 1093 
   List score_by_exposure(exposure_levels.length());
... ... 
@@ -1220,8 +1222,10 @@ List GxE_fitness_score(ListOf<IntegerMatrix> case_genetic_data_list, ListOf<Inte
1220 1222
1221 1223 
           // cov mat //
1222 1224 
           double w1 = exp1_list["w"];
1225 
+          arma::vec family_weights1 = exp1_list["family_weights"];
1223 1226 
           arma::mat sigma1 = exp1_list["sigma"];
1224 1227 
           double w2 = exp2_list["w"];
1228 
+          arma::vec family_weights2 = exp2_list["family_weights"];
1225 1229 
           arma::mat sigma2 = exp2_list["sigma"];
1226 1230 
           arma::mat sigma_hat = (1/w1)*sigma1 + (1/w2)*sigma2;
1227 1231 
           //arma::mat sigma_hat = (w1*sigma1 + w2*sigma2)/(w1 + w2);
... ... 
@@ -1233,6 +1237,29 @@ List GxE_fitness_score(ListOf<IntegerMatrix> case_genetic_data_list, ListOf<Inte
1233 1237 
           s = as_scalar(xbar_diff * sigma_hat_inv * xbar_diff.t());
1234 1238 
           s = s / 1000;
1235 1239
1240 
+          // if parents are to be used, compute the t-stat and multiply
1241 
+          if (use_parents){
1242 
+
1243 
+            arma::colvec family_weights_vec = arma::join_cols(family_weights1, family_weights2);
1244 
+            int n_exp1 = family_weights1.size();
1245 
+            int n_exp2 = family_weights2.size();
1246 
+            int n = n_exp1 + n_exp2;
1247 
+            arma::colvec exp1_vals = arma::colvec(n_exp1, fill::zeros);
1248 
+            arma::colvec exp2_vals = arma::colvec(n_exp2, fill::ones);
1249 
+            arma::colvec y = join_cols(exp1_vals, exp2_vals);
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+            arma::colvec intercept = arma::colvec(n, fill::ones);
1251 
+            arma::mat X = arma::join_horiz(intercept, family_weights_vec);
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+            arma::vec coef = solve(X, y, solve_opts::fast);
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+            double beta_hat = arma::as_scalar(coef[1]);
1254 
+            arma::vec resid = y - X*coef;
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+            double sig2 = arma::as_scalar(arma::trans(resid)*resid/(n - 2));
1256 
+            arma::vec se = arma::sqrt(sig2 * arma::diagvec(arma::inv(arma::trans(X)*X)));
1257 
+            double se_hat = arma::as_scalar(se[1]);
1258 
+            double abs_t = std::abs(beta_hat / se_hat);
1259 
+            s = abs_t * s;
1260 
+
1261 
+          }
1262 
+
1236 1263 
           // if the fitness score is zero or undefined (either due to zero variance or mean), reset to small number
1237 1264 
           if ( (s <= 0) | R_isnancpp(s) | !arma::is_finite(s) ){
1238 1265 
             s = pow(10, -10);
... ... 
@@ -1312,7 +1339,7 @@ List GxE_fitness_list(List case_genetic_data_list, List complement_genetic_data_
1312 1339 
                         IntegerVector ld_block_vec, IntegerVector weight_lookup, IntegerVector exposure_levels,
1313 1340 
                         IntegerVector exposure_risk_levels, int n_different_snps_weight = 2, int n_both_one_weight = 1,
1314 1341 
                         double recessive_ref_prop = 0.75, double recode_test_stat = 1.64, bool epi_test = false,
1315 
-                        bool check_risk = true){
1342 
+                        bool check_risk = true, bool use_parents = false){
1316 1343
1317 1344 
   List scores = chromosome_list.length();
1318 1345 
   for (int i = 0; i < chromosome_list.length(); i++){
... ... 
@@ -1320,7 +1347,7 @@ List GxE_fitness_list(List case_genetic_data_list, List complement_genetic_data_
1320 1347 
     IntegerVector target_snps = chromosome_list[i];
1321 1348 
     scores[i] = GxE_fitness_score(case_genetic_data_list, complement_genetic_data_list, target_snps,
1322 1349 
                                   ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels, n_different_snps_weight,
1323 
-                                  n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test, check_risk);
1350 
+                                  n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test, check_risk, use_parents);
1324 1351
1325 1352 
   }
1326 1353 
   return(scores);
... ... 
@@ -1342,7 +1369,7 @@ List compute_population_fitness(IntegerMatrix case_genetic_data, IntegerMatrix c
1342 1369 
                                 IntegerVector exposure_risk_levels,
1343 1370 
                                 int n_different_snps_weight = 2,
1344 1371 
                                 int n_both_one_weight = 1, double recessive_ref_prop = 0.75, double recode_test_stat = 1.64,
1345 
-                                bool GxE = false, bool check_risk = true){
1372 
+                                bool GxE = false, bool check_risk = true, bool use_parents = false){
1346 1373
1347 1374 
   // initiate storage object for fitness scores
1348 1375 
   List chrom_fitness_score_list;
... ... 
@@ -1353,7 +1380,7 @@ List compute_population_fitness(IntegerMatrix case_genetic_data, IntegerMatrix c
1353 1380 
     chrom_fitness_score_list = GxE_fitness_list(case_genetic_data_list, complement_genetic_data_list, chromosome_list,
1354 1381 
                                                 ld_block_vec, weight_lookup, exposure_levels, exposure_risk_levels,
1355 1382 
                                                 n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat,
1356 
-                                                false, check_risk);
1383 
+                                                false, check_risk, use_parents);
1357 1384
1358 1385 
     // for storing generation information
1359 1386
... ... 
@@ -1506,7 +1533,7 @@ List initiate_population(int n_candidate_snps, IntegerMatrix case_genetic_data,
1506 1533 
                          int n_different_snps_weight = 2, int n_both_one_weight = 1,
1507 1534 
                          double recessive_ref_prop = 0.75, double recode_test_stat = 1.64,
1508 1535 
                          int max_generations = 500, bool initial_sample_duplicates = false,
1509 
-                         bool GxE = false, bool check_risk = true){
1536 
+                         bool GxE = false, bool check_risk = true, bool use_parents = false){
1510 1537
1511 1538 
   int n_possible_unique_combn = n_chromosomes * chromosome_size;
1512 1539 
   if ((n_candidate_snps < n_possible_unique_combn) & !initial_sample_duplicates) {
... ... 
@@ -1545,7 +1572,7 @@ List initiate_population(int n_candidate_snps, IntegerMatrix case_genetic_data,
1545 1572 
                                                          weight_lookup, case_genetic_data_list, complement_genetic_data_list,
1546 1573 
                                                          exposure_levels, exposure_risk_levels, n_different_snps_weight,
1547 1574 
                                                          n_both_one_weight, recessive_ref_prop, recode_test_stat, GxE,
1548 
-                                                         check_risk);
1575 
+                                                         check_risk, use_parents);
1549 1576
1550 1577 
   // pick out the top and bottom scores
1551 1578 
   chromosome_list = current_fitness_list["chromosome_list"];
... ... 
@@ -1584,7 +1611,7 @@ List evolve_island(int n_migrations, IntegerMatrix case_genetic_data, IntegerMat
1584 1611 
                    int max_generations = 500,
1585 1612 
                    bool initial_sample_duplicates = false,
1586 1613 
                    double crossover_prop = 0.8, double recessive_ref_prop = 0.75, double recode_test_stat = 1.64,
1587 
-                   bool GxE = false, bool check_risk = true){
1614 
+                   bool GxE = false, bool check_risk = true, bool use_parents = false){
1588 1615
1589 1616 
   // initialize groups of candidate solutions if generation 1
1590 1617 
   int generation = population["generation"];
... ... 
@@ -1831,7 +1858,7 @@ List evolve_island(int n_migrations, IntegerMatrix case_genetic_data, IntegerMat
1831 1858 
                                                       weight_lookup, case_genetic_data_list, complement_genetic_data_list,
1832 1859 
                                                       exposure_levels, exposure_risk_levels, n_different_snps_weight,
1833 1860 
                                                       n_both_one_weight, recessive_ref_prop, recode_test_stat, GxE,
1834 
-                                                      check_risk);
1861 
+                                                      check_risk, use_parents);
1835 1862
1836 1863 
     //7. Increment iterators
1837 1864 
     generation += 1;
... ... 
@@ -2072,7 +2099,7 @@ List run_GADGETS(int island_cluster_size, int n_migrations,
2072 2099 
                  int n_different_snps_weight = 2, int n_both_one_weight = 1, int migration_interval = 50,
2073 2100 
                  int gen_same_fitness = 50, int max_generations = 500,
2074 2101 
                  bool initial_sample_duplicates = false, double crossover_prop = 0.8,
2075 
-                 double recessive_ref_prop = 0.75, double recode_test_stat = 1.64){
2102 
+                 double recessive_ref_prop = 0.75, double recode_test_stat = 1.64, bool use_parents = false){
2076 2103
2077 2104 
   // instantiate input objects
2078 2105 
   IntegerMatrix case_genetic_data;
... ... 
@@ -2129,7 +2156,7 @@ List run_GADGETS(int island_cluster_size, int n_migrations,
2129 2156 
                                                    n_different_snps_weight, n_both_one_weight,
2130 2157 
                                                    recessive_ref_prop, recode_test_stat,
2131 2158 
                                                    max_generations, initial_sample_duplicates, GxE,
2132 
-                                                   check_risk);
2159 
+                                                   check_risk, use_parents);
2133 2160
2134 2161 
     island_populations[i] = evolve_island(n_migrations, case_genetic_data, complement_genetic_data,
2135 2162 
                                           ld_block_vec, n_chromosomes, chromosome_size, weight_lookup,
... ... 
@@ -2138,7 +2165,7 @@ List run_GADGETS(int island_cluster_size, int n_migrations,
2138 2165 
                                           island_population_i, n_different_snps_weight, n_both_one_weight,
2139 2166 
                                           migration_interval, gen_same_fitness, max_generations,
2140 2167 
                                           initial_sample_duplicates, crossover_prop, recessive_ref_prop, recode_test_stat,
2141 
-                                          GxE, check_risk);
2168 
+                                          GxE, check_risk, use_parents);
2142 2169
2143 2170 
   }
2144 2171
... ... 
@@ -2258,7 +2285,7 @@ List run_GADGETS(int island_cluster_size, int n_migrations,
2258 2285 
                                             island_population_i, n_different_snps_weight, n_both_one_weight,
2259 2286 
                                             migration_interval, gen_same_fitness, max_generations,
2260 2287 
                                             initial_sample_duplicates, crossover_prop, recessive_ref_prop, recode_test_stat,
2261 
-                                            GxE, check_risk);
2288 
+                                            GxE, check_risk, use_parents);
2262 2289
2263 2290 
     }
2264 2291
... ... 
@@ -2461,7 +2488,7 @@ List epistasis_test(IntegerVector snp_cols, List preprocessed_list, int n_permut
2461 2488 
 // [[Rcpp::export]]
2462 2489 
 List GxE_test(IntegerVector snp_cols, List preprocessed_list, int n_permutes = 10000,
2463 2490 
               int n_different_snps_weight = 2, int n_both_one_weight = 1, int weight_function_int = 2,
2464 
-              double recessive_ref_prop = 0.75, double recode_test_stat = 1.64){
2491 
+              double recessive_ref_prop = 0.75, double recode_test_stat = 1.64, bool use_parents = false){
2465 2492
2466 2493 
   // pick out target columns in the preprocessed data
2467 2494 
   IntegerVector target_snps = snp_cols;
... ... 
@@ -2518,7 +2545,7 @@ List GxE_test(IntegerVector snp_cols, List preprocessed_list, int n_permutes = 1
2518 2545 
   List obs_fitness_list =  GxE_fitness_score(case_genetic_data_list, complement_genetic_data_list,
2519 2546 
                                              chrom_snps, target_snps_ld_blocks, weight_lookup, exposure_levels,
2520 2547 
                                              exposure_risk_levels, n_different_snps_weight, n_both_one_weight,
2521 
-                                             recessive_ref_prop, recode_test_stat, true, check_risk);
2548 
+                                             recessive_ref_prop, recode_test_stat, true, check_risk, use_parents);
2522 2549
2523 2550 
   double obs_fitness_score = obs_fitness_list["fitness_score"];
2524 2551
... ... 
@@ -2547,7 +2574,7 @@ List GxE_test(IntegerVector snp_cols, List preprocessed_list, int n_permutes = 1
2547 2574 
     List perm_fitness_list =  GxE_fitness_score(case_genetic_data_list_perm, complement_genetic_data_list_perm,
2548 2575 
                                                 chrom_snps, target_snps_ld_blocks, weight_lookup, exposure_levels_perm,
2549 2576 
                                                 exposure_risk_levels_perm, n_different_snps_weight, n_both_one_weight,
2550 
-                                                recessive_ref_prop, recode_test_stat, true, check_risk);
2577 
+                                                recessive_ref_prop, recode_test_stat, true, check_risk, use_parents);
2551 2578 
     double perm_fitness = perm_fitness_list["fitness_score"];
2552 2579 
     perm_fitness_scores[i] = perm_fitness;
2553 2580
... ... 
@@ -2576,7 +2603,8 @@ List GxE_test(IntegerVector snp_cols, List preprocessed_list, int n_permutes = 1
2576 2603 
 // [[Rcpp::export]]
2577 2604 
 NumericVector n2log_epistasis_pvals(ListOf<IntegerVector> chromosome_list, List in_data_list, int n_permutes = 10000,
2578 2605 
                                     int n_different_snps_weight = 2, int n_both_one_weight = 1, int weight_function_int = 2,
2579 
-                                    double recessive_ref_prop = 0.75, double recode_test_stat = 1.64, bool GxE = false){
2606 
+                                    double recessive_ref_prop = 0.75, double recode_test_stat = 1.64, bool GxE = false,
2607 
+                                    bool use_parents = false){
2580 2608
2581 2609 
   NumericVector n2log_epi_pvals(chromosome_list.size());
2582 2610 
   double N = n_permutes + 1;
... ... 
@@ -2588,7 +2616,7 @@ NumericVector n2log_epistasis_pvals(ListOf<IntegerVector> chromosome_list, List
2588 2616 
     if (GxE){
2589 2617
2590 2618 
       perm_res = GxE_test(chromosome, in_data_list, n_permutes, n_different_snps_weight, n_both_one_weight,
2591 
-                          weight_function_int, recessive_ref_prop, recode_test_stat);
2619 
+                          weight_function_int, recessive_ref_prop, recode_test_stat, use_parents);
2592 2620
2593 2621 
     } else {
2594 2622
src/epistasisGA.o
History View file @ 273218f
2595 2623 
Binary files a/src/epistasisGA.o and b/src/epistasisGA.o differ
src/epistasisGAGE.so
History View file @ 273218f
2596 2624 
Binary files a/src/epistasisGAGE.so and b/src/epistasisGAGE.so differ