Package: simulateGP 0.1.3

Gibran Hemani

simulateGP: Functions for Simulating Genotype-Phenotype Relationships

Functions for simulating SNP effects on phenotypes. Functions for simulating disease risk on probability and liability scales. Simulations to quickly obtain estimates of effect sizes for simulated genotype-phenotypes.

Authors:Gibran Hemani [aut, cre], John Ferguson [aut], Rita Rasteiro [aut]

simulateGP_0.1.3.tar.gz
simulateGP_0.1.3.zip(r-4.7-any)simulateGP_0.1.3.zip(r-4.6-any)simulateGP_0.1.3.zip(r-4.5-any)
simulateGP_0.1.3.tgz(r-4.6-any)simulateGP_0.1.3.tgz(r-4.5-any)
simulateGP_0.1.3.tar.gz(r-4.7-any)simulateGP_0.1.3.tar.gz(r-4.6-any)
simulateGP_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
simulateGP/json (API)

# Install 'simulateGP' in R:

install.packages('simulateGP', repos = c('https://mrcieu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/explodecomputer/simulategp/issues

Pkgdown/docs site:https://explodecomputer.github.io

Datasets:

ldetect - Data frame of independent LD regions

On CRAN:

genetics simulations statistics

5.41 score 9 stars 238 scripts 47 exports 88 dependencies

Last updated from:dd16b78fcb (on refs/pull/6/head). Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	160
source / vignettes	OK	298
linux-release	OK	184
macos-release	OK	130
macos-oldrel	OK	109
windows-devel	OK	81
windows-release	OK	83
windows-oldrel	OK	96
wasm-release	OK	155

Exports:%>%add_u allele_frequency arbitrary_map ascertain_samples choose_effects contingency correlated_binomial create_system estimate_system_effects expected_mse expected_se expected_ssx fast_assoc generate_gwas_params generate_gwas_ss generate_gwas_ss_1 generate_ldobj get_effs get_ld get_population_allele_frequency get_regions_from_ldobjdir gwas gx_to_gp hap_freqs init_parameters lor_to_rsq make_geno make_mvdat make_phen merge_exp_out range01 read_ldobjdir recode_dat recode_dat_intercept recode_dat_simple risk_cross_plot risk_simulation sample_beta sample_system_effects simulate_geno simulate_haplotypes simulate_population summary_set test_ldobj test_system y_to_binary

Dependencies:askpass base64enc bslib cachem cli codetools cowplot cpp11 crosstalk curl data.table digest dplyr evaluate farver fastmap fontawesome foreach fs generics genetics.binaRies ggplot2 glmnet glue GPArotation gridExtra gtable gwasglue2 highr htmltools htmlwidgets httr ieugwasr isoband iterators jquerylib jsonlite knitr labeling later lattice lazyeval lifecycle magrittr MASS Matrix memoise mime mnormt MRMix MRPRESSO nlme openssl otel pbapply pillar pkgconfig plotly promises psych purrr R6 RadialMR rappdirs RColorBrewer Rcpp RcppEigen rlang rmarkdown S7 sass scales shape stringi stringr survival sys tibble tidyr tidyselect tinytex TwoSampleMR utf8 vctrs viridisLite withr xfun yaml

Data generating process for genetic effects on complex traits with interactions

Genetic effects | Model 1 - simple linear relationship between x and y with large numbers of instruments of small effect | Model 2 - Heterogeneous treatment effect of x on y based on covariate | Model 3 - The interacting variable from (2) is proxied by a large number of proxies | Model 4 - Heterogeneous treatment effect at the instrument level

Rendered frommr_dgp.rmdusingknitr::rmarkdown

Last update: 2026-09-30
Started: 2020-04-07

Simulating GWAS summary data

Genetic architecture | Linkage disequilibrium | Sample overlap | Synthesising sample overlap, LD and architecture

Rendered fromsimulate_gwas.Rmdusingknitr::rmarkdown

Last update: 2024-04-02
Started: 2021-03-06

Comparing SuSIE results with summary level vs individual level simulations

Compare summary level simulations against individual level simulations | Compare two different populations

Rendered fromsusie_check.Rmdusingknitr::rmarkdown

Last update: 2021-06-28
Started: 2021-04-06

Background notes: Simulating GWAS summary data

Expected standard error | Sampling the effect estimate | Sampling a set of effect estimates | Sensitivitiy to HWE

Rendered fromgwas_summary_data.Rmdusingknitr::rmarkdown

Last update: 2021-04-06
Started: 2020-03-03

Simulating GWAS summary data with LD

Rendered fromgwas_summary_data_ld.Rmdusingknitr::rmarkdown

Last update: 2021-04-06
Started: 2020-09-10

Generating LD matrices

Obtain independent LD regions | Generate LD matrix objects | Try it out for generating summary data

Rendered fromgenerate_ldobj.Rmdusingknitr::rmarkdown

Last update: 2021-03-07
Started: 2021-03-06

Sample overlap in GWAS summary datasets

Rendered fromsample_overlap.Rmdusingknitr::rmarkdown

Last update: 2021-03-06
Started: 2020-10-11

Simple two-sample Mendelian randomisation examples

Generating summary data from individual level data | More complex example | Generating summary data directly | No sample overlap case | Sample overlap case

Rendered fromtwosamplemr.Rmdusingknitr::rmarkdown

Last update: 2021-03-06
Started: 2021-03-04

Simulating summary data for weak instruments

Weak instrument bias | Winner's curse schematic

Rendered fromweak_instruments.Rmdusingknitr::rmarkdown

Last update: 2021-03-05
Started: 2020-03-12

Directly simulating summary datasets with sample overlap

Rendered fromsample_overlap_theory.Rmdusingknitr::rmarkdown

Last update: 2021-03-05
Started: 2021-03-05

Simple Mendelian randomisation examples

Rendered fromsimplemr.Rmdusingknitr::rmarkdown

Last update: 2021-03-05
Started: 2021-03-04

LD matrices

Rendered fromld_matrices.Rmdusingknitr::rmarkdown

Last update: 2021-03-04
Started: 2021-03-04

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
simulateGP: Functions for Simulating Genotype-Phenotype Relationships	simulateGP-package simulateGP simulategp
Add confounder variables and their instruments	add_u
Estimate allele frequency from SNP	allele_frequency
Create an arbitrary map	arbitrary_map
Ascertain some proportion of cases and controls from binary phenotype	ascertain_samples
Get vector of effects that explain some amount of variance	choose_effects
Obtain 2x2 contingency table from marginal parameters and odds ratio	contingency
Simulate two correlated binomial variables	correlated_binomial
Wrapper for simulation pipeline	create_system
Estimate the effects of all SNPs on all phenotypes	estimate_system_effects
Calculate expected MSE	expected_mse
Expected se given beta, af, n and vy	expected_se
Calculate expected SSX	expected_ssx
Get summary statistics in simple linear regression	fast_assoc
Generate SNP effects given MAF, h2 and selection	generate_gwas_params
Modify SNP effects to account for LD	generate_gwas_ss
Create a GWAS summary dataset	generate_gwas_ss_1
Generate LD matrix objects from reference panel	generate_ldobj
Get effs for two traits and make dat format	get_effs
Get LD matrix for a specified region from bfile reference panel	get_ld
Estimate the allele frequency in population from case/control summary data	get_population_allele_frequency
Determine regions from LD file	get_regions_from_ldobjdir
Perform association of many SNPs against phenotype	gwas
Translate risk from liability to probability scale	gx_to_gp
Estimate haplotype frequencies for two loci	hap_freqs
Choose initial parameters for direct effects on X and Y	init_parameters
Data frame of independent LD regions	ldetect
Estimate proportion of variance of liability explained by SNP in general population	lor_to_rsq
Create genotype matrix	make_geno
Take several exposures and one outcome and make the data required for multivariable MR	make_mvdat
Simulate variable based on the influences of other variables	make_phen
Organise outputs from 'gwas' into harmonised dat format	merge_exp_out
Scale variable to have range between 0 and 1	range01
Read in LD objects into list	read_ldobjdir
Recode data to make every effect on x positive	recode_dat
Intercept recoding to have every effect on x positive	recode_dat_intercept
Simple recoding to have every effect on x positive	recode_dat_simple
Plot liability vs probability disease risk	risk_cross_plot
Make simulation to compare disease and liability scales	risk_simulation
Sample beta values given standard errors	sample_beta
Sample the actual effects based on initial parameters	sample_system_effects
Simulate genotypes from haplotypes	simulate_geno
Simulate haplotypes of two loci	simulate_haplotypes
Simulate individual level data from initial parameters	simulate_population
General funcs	stuff
Wrapper for generating a summary set	summary_set
Create test LD object	test_ldobj
Apply MR tests to system	test_system
Convert continuous trait to binary	y_to_binary