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ukb_sim_snakefile
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ukb_sim_snakefile
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wildcard_constraints:
CHR = "|".join(map(str, config["CHR"])),
DATASET = "|".join(map(str, config["DATASET"])),
TRAIT = "|".join(map(str, config["TRAIT"]))
rule all:
input:
expand(config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_mr_mash_rss_effects_{DATASET}.rds",DATASET=config["DATASET"]),
expand(config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_mr_mash_rss_pred_acc_{DATASET}.rds",DATASET=config["DATASET"]),
expand(config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_ldpred2_auto_effects_{DATASET}.rds",DATASET=config["DATASET"]),
expand(config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_ldpred2_auto_pred_acc_{DATASET}.rds",DATASET=config["DATASET"]),
expand(config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_bayesR_effects_{DATASET}.rds",DATASET=config["DATASET"]),
expand(config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_bayesR_pred_acc_{DATASET}.rds",DATASET=config["DATASET"]),
expand(config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_mvbayesC_rest_effects_{DATASET}.rds",DATASET=config["DATASET"]),
expand(config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_mvbayesC_rest_pred_acc_{DATASET}.rds",DATASET=config["DATASET"]),
expand(config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_wmt_sblup_effects_{DATASET}.rds",DATASET=config["DATASET"]),
expand(config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_wmt_sblup_pred_acc_{DATASET}.rds",DATASET=config["DATASET"]),
expand(config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_mtag_ldpred2_auto_effects_{DATASET}.rds",DATASET=config["DATASET"]),
expand(config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_mtag_ldpred2_auto_pred_acc_{DATASET}.rds",DATASET=config["DATASET"]),
expand(config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_mvbayesC_effects_{DATASET}.rds",DATASET=config["DATASET"]),
expand(config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_mvbayesC_pred_acc_{DATASET}.rds",DATASET=config["DATASET"])#,
#expand(config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_bayesC_effects_{DATASET}.rds",DATASET=config["DATASET"]),
#expand(config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_bayesC_pred_acc_{DATASET}.rds",DATASET=config["DATASET"])
rule split_train_test:
input:
config["FAM"]
output:
config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_test_ids_{DATASET}.txt"
params:
script=config["SCRIPT"]+"split_train_test_ukb_list_plink.R",
n=config["N_TRAIN"],
dataset="{DATASET}"
resources: cpus=1, mem_mb=2048, time_min=10
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=1
Rscript {params.script} \
--input {input} \
--n_train {params.n} \
--seed {params.dataset} \
--output {output}
module unload R/4.2.3
"""
#rule compute_genotype_means:
# input:
# geno=config["BED"],
# tids=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_test_ids_{DATASET}.txt"
# output:
# config["DEST"]+"output/misc/"+config["DATA_PREFIX"]+"_chr{CHR}_geno_means_{DATASET}.rds"
# params:
# script=config["SCRIPT"]+"compute_genotype_means_ukb.R",
# dataset="{DATASET}",
# imp_flag=config["IMPUTE_FLAG"],
# tmp=config["TMP"],
# chr="{CHR}"
# resources: cpus=13, mem_mb=100000, time_min=1440
# shell:
# """
# source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
# module load R/4.2.3
# export MKL_NUM_THREADS=1
# Rscript {params.script} \
# --geno {input.geno} \
# --test_ids {input.tids} \
# --output {output} \
# --seed {params.dataset} \
# --chr {params.chr} \
# --impute_missing {params.imp_flag} \
# --temp_dir {params.tmp} \
# --ncores 8
# module unload R/4.2.3
# """
rule simulate_phenotype:
input:
config["BED"]
output:
config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_pheno_{DATASET}.rds"
params:
p_causal=config["P_CAUSAL"],
r=config["R"],
r_causal=config["R_CAUSAL"],
pve=config["PVE"],
B_scale=config["B_SCALE"],
B_cor=config["B_COR"],
V_cor=config["V_COR"],
w=config["W"],
dataset="{DATASET}",
tmp=config["TMP"],
script=config["SCRIPT"]+"sim_pheno_ukb.R"
resources: cpus=16, mem_mb=150000, time_min=1440
shell:
"""
module load gsl/2.7
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=16
Rscript {params.script} \
--geno {input} \
--p_causal {params.p_causal} \
--r {params.r} \
--r_causal "{params.r_causal}" \
--pve "{params.pve}" \
--B_scale "{params.B_scale}" \
--B_cor "{params.B_cor}" \
--V_cor {params.V_cor} \
--w "{params.w}" \
--seed {params.dataset} \
--temp_dir {params.tmp} \
--output {output} \
--ncores 16
module unload R/4.2.3
module unload gsl/2.7
"""
rule compute_summary_stats:
input:
pheno=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_pheno_{DATASET}.rds",
geno=config["BED"],
tids=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_test_ids_{DATASET}.txt"
output:
config["DEST"]+"output/summary_statistics/"+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_{DATASET}.rds"
params:
dataset="{DATASET}",
chr="{CHR}",
stand=config["STANDARDIZE"],
imp_flag=config["IMPUTE_FLAG"],
tmp=config["TMP"],
norm_pheno=config["NORMALIZE_PHENO"],
script=config["SCRIPT"]+"compute_sumstats_ukb.R"
resources: cpus=5, mem_mb=200000, time_min=1440
shell:
"""
module load gsl/2.7
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=1
Rscript {params.script} \
--pheno {input.pheno} \
--geno {input.geno} \
--test_ids {input.tids} \
--output {output} \
--seed {params.dataset} \
--chr {params.chr} \
--standardize {params.stand} \
--impute_missing {params.imp_flag} \
--normalize {params.norm_pheno} \
--temp_dir {params.tmp} \
--ncores 5
module unload R/4.2.3
module unload gsl/2.7
"""
#rule compute_phenotype_means:
# input:
# pheno=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_pheno_{DATASET}.rds",
# tids=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_test_ids_{DATASET}.txt"
# output:
# config["DEST"]+"output/misc/"+config["DATA_PREFIX"]+"_pheno_means_{DATASET}.rds"
# params:
# dataset="{DATASET}",
# script=config["SCRIPT"]+"compute_phenotype_means_ukb.R"
# resources: cpus=1, mem_mb=2000, time_min=30
# shell:
# """
# source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
# module load R/4.2.3
# Rscript {params.script} \
# --pheno {input.pheno} \
# --test_ids {input.tids} \
# --output {output} \
# --seed {params.dataset}
# module unload R/4.2.3
# """
rule compute_phenotype_covariance:
input:
pheno=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_pheno_{DATASET}.rds",
tids=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_test_ids_{DATASET}.txt"
output:
config["DEST"]+"output/misc/"+config["DATA_PREFIX"]+"_phenotypic_cov_{DATASET}.rds"
params:
dataset="{DATASET}",
norm_pheno=config["NORMALIZE_PHENO"],
script=config["SCRIPT"]+"compute_phenotypic_cov_ukb_sim.R"
resources: cpus=8, mem_mb=6000, time_min=120
shell:
"""
module load gsl/2.7
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=8
export OMP_NUM_THREADS=8
Rscript {params.script} \
--pheno {input.pheno} \
--test_ids {input.tids} \
--seed {params.dataset} \
--normalize {params.norm_pheno} \
--output {output}
module unload R/4.2.3
module unload gsl/2.7
"""
def get_sumstats_input_files(wildcards):
DATASET = wildcards.DATASET
return expand(config["DEST"]+"output/summary_statistics/"+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_{DATASET}.rds", DATASET=DATASET, CHR=config["CHR"])
rule compute_residual_covariance_all_chr:
input:
lambda wildcards: get_sumstats_input_files(wildcards)
output:
config["DEST"]+"output/misc/"+config["DATA_PREFIX"]+"_residual_cov_{DATASET}.rds"
params:
prefix="output/summary_statistics/"+config["DATA_PREFIX"]+"_chr",
suffix="_sumstats_{DATASET}.rds",
chr_r=config["CHR_RANGE"],
dataset="{DATASET}",
script=config["SCRIPT"]+"compute_residual_cov_all_chr_ukb.R"
resources: cpus=1, mem_mb=4000, time_min=60
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=1
Rscript {params.script} \
--sumstats_prefix {params.prefix} \
--sumstats_suffix {params.suffix} \
--chr {params.chr_r} \
--output {output} \
--seed {params.dataset}
module unload R/4.2.3
"""
rule compute_data_driven_covariance_all_chr:
input:
sum_stat=lambda wildcards: get_sumstats_input_files(wildcards),
res_cov=config["DEST"]+"output/misc/"+config["DATA_PREFIX"]+"_residual_cov_{DATASET}.rds"
output:
config["DEST"]+"output/misc/"+config["DATA_PREFIX"]+"_"+config["DD_ED"]+"_data_driven_cov_{DATASET}.rds"
params:
prefix="output/summary_statistics/"+config["DATA_PREFIX"]+"_chr",
suffix="_sumstats_{DATASET}.rds",
chr_r=config["CHR_RANGE"],
ed_can_cov=config["DD_ED_CAN_COV"],
str_z_thr=config["DD_STRONG_Z_THR"],
n_pcs=config["DD_N_PCS"],
frs=config["DD_FLASH_REMOVE_SINGLE"],
ed=config["DD_ED"],
ted_zero=config["DD_TED_ZERO_THRESH"],
dataset="{DATASET}",
script=config["SCRIPT"]+"compute_data_driven_cov_all_chr_ukb.R"
resources: cpus=8, mem_mb=7000, time_min=1440
shell:
"""
module load gsl/2.7
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=8
export OMP_NUM_THREADS=8
Rscript {params.script} \
--sumstats_prefix {params.prefix} \
--sumstats_suffix {params.suffix} \
--chr {params.chr_r} \
--residual_cov {input.res_cov} \
--strong_Z_thresh {params.str_z_thr} \
--n_PCs {params.n_pcs} \
--flash_remove_singleton {params.frs} \
--ED_algorithm {params.ed} \
--ted_zero_thresh {params.ted_zero} \
--canonical_cov {params.ed_can_cov} \
--output {output} \
--seed {params.dataset}
module unload R/4.2.3
module unload gsl/2.7
"""
rule fit_ldpred2auto_by_chr_trait:
input:
sumstats=config["DEST"]+"output/summary_statistics/"+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_{DATASET}.rds",
ldmat=config["LDMAT"]+config["LDMAT_PREFIX"]+"{CHR}_LD_sparse.rds"
output:
config["DEST"]+"output/ldpred2_auto_fit/"+config["DATA_PREFIX"]+"_chr{CHR}_ldpred2_auto_fit_trait{TRAIT}_{DATASET}.rds"
params:
n=config["N_TRAIN"],
h2_init=config["H2_INIT"],
burn_in=config["LDPRED_BURN_IN"],
num_iter=config["LDPRED_ITER"],
trait="{TRAIT}",
dataset="{DATASET}",
chr="{CHR}",
tmp=config["TMP"],
script=config["SCRIPT"]+"fit_ldpred2auto_ukb.R"
resources: cpus=4, mem_mb=50000, time_min=720
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=1
Rscript {params.script} \
--sumstats {input.sumstats} \
--LD_matrix {input.ldmat} \
--n {params.n} \
--h2_init {params.h2_init} \
--burn_in {params.burn_in} \
--num_iter {params.num_iter} \
--trait {params.trait} \
--ncores 4 \
--seed {params.dataset} \
--temp_dir {params.tmp} \
--output {output}
module unload R/4.2.3
"""
rule fit_mrmash_rss:
input:
sumstats=config["DEST"]+"output/summary_statistics/"+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_{DATASET}.rds",
ldmat=config["LDMAT"]+config["LDMAT_PREFIX"]+"{CHR}_LD_sparse.rds",
pheno_cov=config["DEST"]+"output/misc/"+config["DATA_PREFIX"]+"_phenotypic_cov_{DATASET}.rds",
res_cov=config["DEST"]+"output/misc/"+config["DATA_PREFIX"]+"_residual_cov_{DATASET}.rds",
dd_cov=config["DEST"]+"output/misc/"+config["DATA_PREFIX"]+"_"+config["DD_ED"]+"_data_driven_cov_{DATASET}.rds" #,
# x_colm=config["DEST"]+"output/misc/"+config["DATA_PREFIX"]+"_chr{CHR}_geno_means_{DATASET}.rds",
# y_colm=config["DEST"]+"output/misc/"+config["DATA_PREFIX"]+"_pheno_means_{DATASET}.rds"
output:
config["DEST"]+"output/mr_mash_rss_fit/"+config["DATA_PREFIX"]+"_chr{CHR}_mr_mash_rss_fit_{DATASET}.rds"
params:
can_cov=config["MRMASH_CAN_COV"],
n=config["N_TRAIN"],
prop_nz=config["MRMASH_PROP_NONZERO"],
stand=config["STANDARDIZE"],
upd_v_met=config["MRMASH_UPDATE_V_METHOD"],
w0_thresh=config["MRMASH_W0_THRESH"],
traits=config["TRAIT_RANGE"],
script=config["SCRIPT"]+"fit_mrmashrss_ukb.R",
chr="{CHR}",
dataset="{DATASET}"
resources: cpus=4, mem_mb=15000, time_min=4320
shell:
"""
module load gsl/2.7
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=1
Rscript {params.script} \
--sumstats {input.sumstats} \
--LD_matrix {input.ldmat} \
--pheno_cov {input.pheno_cov} \
--residual_cov {input.res_cov} \
--data_driven_cov {input.dd_cov} \
--canonical_cov {params.can_cov} \
--n {params.n} \
--prop_nonzero {params.prop_nz} \
--standardize {params.stand} \
--update_V_method {params.upd_v_met} \
--w0_threshold {params.w0_thresh} \
--ncores 4 \
--traits {params.traits} \
--seed {params.dataset} \
--output {output}
module unload R/4.2.3
module unload gsl/2.7
"""
rule fit_ldpred2auto_gwide_by_trait:
input:
sumstats=lambda wildcards: get_sumstats_input_files(wildcards),
ldmat=expand(config["LDMAT"]+config["LDMAT_PREFIX"]+"{CHR}_LD_sparse.rds", CHR=config["CHR"])
output:
config["DEST"]+"output/ldpred2_auto_gwide_fit/"+config["DATA_PREFIX"]+"_chrAll_ldpred2_auto_fit_trait{TRAIT}_{DATASET}.rds"
params:
ss_prefix="output/summary_statistics/"+config["DATA_PREFIX"]+"_chr",
ss_suffix="_sumstats_{DATASET}.rds",
ld_prefix="data/LD_matrices/"+config["LDMAT_PREFIX"],
ld_suffix="_LD.ld.bin",
n=config["N_TRAIN"],
h2_init=config["H2_INIT_GWIDE"],
burn_in=config["LDPRED_BURN_IN"],
num_iter=config["LDPRED_ITER"],
trait="{TRAIT}",
dataset="{DATASET}",
chr_r=config["CHR_RANGE"],
tmp=config["TMP"],
script=config["SCRIPT"]+"fit_ldpred2auto_gwide_ukb.R"
resources: cpus=4, mem_mb=50000, time_min=1440
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=1
Rscript {params.script} \
--sumstats_prefix {params.ss_prefix} \
--sumstats_suffix {params.ss_suffix} \
--LD_matrix_prefix {params.ld_prefix} \
--LD_matrix_suffix {params.ld_suffix} \
--chr {params.chr_r} \
--n {params.n} \
--h2_init {params.h2_init} \
--burn_in {params.burn_in} \
--num_iter {params.num_iter} \
--trait {params.trait} \
--ncores 4 \
--seed {params.dataset} \
--temp_dir {params.tmp} \
--output {output}
module unload R/4.2.3
"""
rule fit_bayesR_by_chr_trait:
input:
sumstats=config["DEST"]+"output/summary_statistics/"+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_{DATASET}.rds",
ldmat=config["LDMAT"]+config["LDMAT_PREFIX"]+"{CHR}_LD_sparse.ld.bin"
output:
config["DEST"]+"output/bayesR_fit/"+config["DATA_PREFIX"]+"_chr{CHR}_bayesR_fit_trait{TRAIT}_{DATASET}.rds"
params:
n=config["N_TRAIN"],
h2_init=config["BAYESR_H2_INIT"],
pi_init=config["BAYESR_PI_INIT"],
nburn=config["BAYESR_NBURN"],
nit=config["BAYESR_NIT"],
nthin=config["BAYESR_NTHIN"],
trait="{TRAIT}",
dataset="{DATASET}",
chr="{CHR}",
script=config["SCRIPT"]+"fit_qgg_sbayes_ukb.R"
resources: cpus=4, mem_mb=7000, time_min=1440
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=4
export OMP_NUM_THREADS=4
Rscript {params.script} \
--sumstats {input.sumstats} \
--LD_matrix {input.ldmat} \
--n {params.n} \
--method bayesR \
--h2 {params.h2_init} \
--pi {params.pi_init} \
--nburn {params.nburn} \
--nit {params.nit} \
--nthin {params.nthin} \
--trait {params.trait} \
--seed {params.dataset} \
--output {output}
module unload R/4.2.3
"""
rule fit_bayesC_by_chr_trait:
input:
sumstats=config["DEST"]+"output/summary_statistics/"+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_{DATASET}.rds",
ldmat=config["LDMAT"]+config["LDMAT_PREFIX"]+"{CHR}_LD_sparse.ld.bin"
output:
config["DEST"]+"output/bayesC_fit/"+config["DATA_PREFIX"]+"_chr{CHR}_bayesC_fit_trait{TRAIT}_{DATASET}.rds"
params:
n=config["N_TRAIN"],
h2_init=config["BAYESC_H2_INIT"],
pi_init=config["BAYESC_PI_INIT"],
nburn=config["BAYESC_NBURN"],
nit=config["BAYESC_NIT"],
nthin=config["BAYESC_NTHIN"],
trait="{TRAIT}",
dataset="{DATASET}",
chr="{CHR}",
script=config["SCRIPT"]+"fit_qgg_sbayes_ukb.R"
resources: cpus=4, mem_mb=7000, time_min=1440
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=4
export OMP_NUM_THREADS=4
Rscript {params.script} \
--sumstats {input.sumstats} \
--LD_matrix {input.ldmat} \
--n {params.n} \
--method bayesC \
--h2 {params.h2_init} \
--pi {params.pi_init} \
--nburn {params.nburn} \
--nit {params.nit} \
--nthin {params.nthin} \
--trait {params.trait} \
--seed {params.dataset} \
--output {output}
module unload R/4.2.3
"""
rule fit_mvbayesC_by_chr:
input:
sumstats=config["DEST"]+"output/summary_statistics/"+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_{DATASET}.rds",
ldmat=config["LDMAT"]+config["LDMAT_PREFIX"]+"{CHR}_LD_sparse.ld.bin"
output:
config["DEST"]+"output/mvbayesC_fit/"+config["DATA_PREFIX"]+"_chr{CHR}_mvbayesC_fit_{DATASET}.rds"
params:
n=config["N_TRAIN"],
h2_init=config["MVBAYESC_H2_INIT"],
pi_init=config["MVBAYESC_PI_INIT"],
nburn=config["MVBAYESC_NBURN"],
nit=config["MVBAYESC_NIT"],
nthin=config["MVBAYESC_NTHIN"],
traits=config["TRAIT_RANGE"],
dataset="{DATASET}",
chr="{CHR}",
script=config["SCRIPT"]+"fit_qgg_smvbayes_ukb.R"
resources: cpus=4, mem_mb=7000, time_min=2880
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=4
export OMP_NUM_THREADS=4
Rscript {params.script} \
--sumstats {input.sumstats} \
--LD_matrix {input.ldmat} \
--n {params.n} \
--method bayesC \
--models NULL \
--h2 {params.h2_init} \
--pi {params.pi_init} \
--nburn {params.nburn} \
--nit {params.nit} \
--nthin {params.nthin} \
--traits {params.traits} \
--seed {params.dataset} \
--output {output}
module unload R/4.2.3
"""
rule fit_mvbayesC_rest_by_chr:
input:
sumstats=config["DEST"]+"output/summary_statistics/"+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_{DATASET}.rds",
ldmat=config["LDMAT"]+config["LDMAT_PREFIX"]+"{CHR}_LD_sparse.ld.bin"
output:
config["DEST"]+"output/mvbayesC_fit/"+config["DATA_PREFIX"]+"_chr{CHR}_mvbayesC_rest_fit_{DATASET}.rds"
params:
n=config["N_TRAIN"],
h2_init=config["MVBAYESCREST_H2_INIT"],
pi_init=config["MVBAYESCREST_PI_INIT"],
nburn=config["MVBAYESCREST_NBURN"],
nit=config["MVBAYESCREST_NIT"],
nthin=config["MVBAYESCREST_NTHIN"],
traits=config["TRAIT_RANGE"],
dataset="{DATASET}",
chr="{CHR}",
script=config["SCRIPT"]+"fit_qgg_smvbayes_ukb.R"
resources: cpus=4, mem_mb=7000, time_min=2880
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=4
export OMP_NUM_THREADS=4
Rscript {params.script} \
--sumstats {input.sumstats} \
--LD_matrix {input.ldmat} \
--n {params.n} \
--method bayesC \
--models restrictive \
--h2 {params.h2_init} \
--pi {params.pi_init} \
--nburn {params.nburn} \
--nit {params.nit} \
--nthin {params.nthin} \
--traits {params.traits} \
--seed {params.dataset} \
--output {output}
module unload R/4.2.3
"""
def get_pred_input_files(wildcards):
DATASET = wildcards.DATASET
return expand(config["DEST"]+"output/ldpred2_auto_fit/"+config["DATA_PREFIX"]+"_chr{CHR}_ldpred2_auto_fit_trait{TRAIT}_{DATASET}.rds", DATASET=DATASET, CHR=config["CHR"],TRAIT=config["TRAIT"])
rule compute_prediction_accuracy_ldpred:
input:
pheno=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_pheno_{DATASET}.rds",
geno=config["BED"],
tids=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_test_ids_{DATASET}.txt",
list=lambda wildcards: get_pred_input_files(wildcards)
output:
eff=config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_ldpred2_auto_effects_{DATASET}.rds",
pred_acc=config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_ldpred2_auto_pred_acc_{DATASET}.rds"
params:
model=config["LDPRED_MODEL"],
model_fit_dir=config["LDPRED_MODEL_FIT_DIR"],
dataset="{DATASET}",
chr_r=config["CHR_RANGE"],
trait_r=config["TRAIT_RANGE"],
imp_flag=config["IMPUTE_FLAG"],
script=config["SCRIPT"]+"compute_prediction_accuracy_ukb.R",
tmp=config["TMP"],
norm_pheno=config["NORMALIZE_PHENO"],
prefix=config["DATA_PREFIX"]
resources: cpus=4, mem_mb=20000, time_min=120
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=4
Rscript {params.script} \
--model {params.model} \
--model_fit_dir {params.model_fit_dir} \
--pheno {input.pheno} \
--geno {input.geno} \
--test_ids {input.tids} \
--data_id {params.dataset} \
--chr {params.chr_r} \
--trait {params.trait_r} \
--impute_missing {params.imp_flag} \
--output_eff {output.eff} \
--output_pred_acc {output.pred_acc} \
--temp_dir {params.tmp} \
--normalize {params.norm_pheno} \
--ncores 4 \
--prefix {params.prefix}
module unload R/4.2.3
"""
def get_mrmashrss_input_files(wildcards):
DATASET = wildcards.DATASET
return expand(config["DEST"]+"output/mr_mash_rss_fit/"+config["DATA_PREFIX"]+"_chr{CHR}_mr_mash_rss_fit_{DATASET}.rds", DATASET=DATASET, CHR=config["CHR"])
rule compute_prediction_accuracy_mrmashrss:
input:
pheno=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_pheno_{DATASET}.rds",
geno=config["BED"],
#pheno_means=config["DEST"]+"output/misc/"+config["DATA_PREFIX"]+"_pheno_means_{DATASET}.rds",
tids=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_test_ids_{DATASET}.txt",
list=lambda wildcards: get_mrmashrss_input_files(wildcards)
output:
eff=config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_mr_mash_rss_effects_{DATASET}.rds",
pred_acc=config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_mr_mash_rss_pred_acc_{DATASET}.rds"
params:
model=config["MRMASH_MODEL"],
model_fit_dir=config["MRMASH_MODEL_FIT_DIR"],
dataset="{DATASET}",
chr_r=config["CHR_RANGE"],
trait_r=config["TRAIT_RANGE"],
imp_flag=config["IMPUTE_FLAG"],
prefix=config["DATA_PREFIX"],
norm_pheno=config["NORMALIZE_PHENO"],
tmp=config["TMP"],
script=config["SCRIPT"]+"compute_prediction_accuracy_ukb.R"
resources: cpus=4, mem_mb=20000, time_min=120
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=4
Rscript {params.script} \
--model {params.model} \
--model_fit_dir {params.model_fit_dir} \
--pheno {input.pheno} \
--geno {input.geno} \
--test_ids {input.tids} \
--data_id {params.dataset} \
--chr {params.chr_r} \
--trait {params.trait_r} \
--impute_missing {params.imp_flag} \
--output_eff {output.eff} \
--output_pred_acc {output.pred_acc} \
--prefix {params.prefix} \
--normalize {params.norm_pheno} \
--temp_dir {params.tmp} \
--ncores 4
module unload R/4.2.3
"""
def get_ldpred_gwide_input_files(wildcards):
DATASET = wildcards.DATASET
return expand(config["DEST"]+"output/ldpred2_auto_gwide_fit/"+config["DATA_PREFIX"]+"_chrAll_ldpred2_auto_fit_trait{TRAIT}_{DATASET}.rds", DATASET=DATASET, TRAIT=config["TRAIT"])
rule compute_prediction_accuracy_ldpred_gwide:
input:
pheno=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_pheno_{DATASET}.rds",
geno=config["BED"],
tids=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_test_ids_{DATASET}.txt",
list=lambda wildcards: get_ldpred_gwide_input_files(wildcards)
output:
eff=config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_ldpred2_auto_gwide_effects_{DATASET}.rds",
pred_acc=config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_ldpred2_auto_gwide_pred_acc_{DATASET}.rds"
params:
model=config["LDPRED_MODEL"],
model_fit_dir=config["LDPRED_MODEL_FIT_GWIDE_DIR"],
dataset="{DATASET}",
chr="0",
trait_r=config["TRAIT_RANGE"],
imp_flag=config["IMPUTE_FLAG"],
script=config["SCRIPT"]+"compute_prediction_accuracy_ukb.R",
tmp=config["TMP"],
norm_pheno=config["NORMALIZE_PHENO"],
prefix=config["DATA_PREFIX"]
resources: cpus=4, mem_mb=20000, time_min=120
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=4
Rscript {params.script} \
--model {params.model} \
--model_fit_dir {params.model_fit_dir} \
--pheno {input.pheno} \
--geno {input.geno} \
--test_ids {input.tids} \
--data_id {params.dataset} \
--chr {params.chr} \
--trait {params.trait_r} \
--impute_missing {params.imp_flag} \
--output_eff {output.eff} \
--output_pred_acc {output.pred_acc} \
--temp_dir {params.tmp} \
--normalize {params.norm_pheno} \
--ncores 4 \
--prefix {params.prefix}
module unload R/4.2.3
"""
def get_bayesR_input_files(wildcards):
DATASET = wildcards.DATASET
return expand(config["DEST"]+"output/bayesR_fit/"+config["DATA_PREFIX"]+"_chr{CHR}_bayesR_fit_trait{TRAIT}_{DATASET}.rds", DATASET=DATASET, CHR=config["CHR"],TRAIT=config["TRAIT"])
rule compute_prediction_accuracy_bayesR:
input:
pheno=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_pheno_{DATASET}.rds",
geno=config["BED"],
tids=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_test_ids_{DATASET}.txt",
list=lambda wildcards: get_bayesR_input_files(wildcards)
output:
eff=config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_bayesR_effects_{DATASET}.rds",
pred_acc=config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_bayesR_pred_acc_{DATASET}.rds"
params:
model=config["BAYESR_MODEL"],
model_fit_dir=config["BAYESR_MODEL_FIT_DIR"],
dataset="{DATASET}",
chr_r=config["CHR_RANGE"],
trait_r=config["TRAIT_RANGE"],
imp_flag=config["IMPUTE_FLAG"],
script=config["SCRIPT"]+"compute_prediction_accuracy_ukb.R",
tmp=config["TMP"],
norm_pheno=config["NORMALIZE_PHENO"],
prefix=config["DATA_PREFIX"]
resources: cpus=4, mem_mb=20000, time_min=120
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=4
Rscript {params.script} \
--model {params.model} \
--model_fit_dir {params.model_fit_dir} \
--pheno {input.pheno} \
--geno {input.geno} \
--test_ids {input.tids} \
--data_id {params.dataset} \
--chr {params.chr_r} \
--trait {params.trait_r} \
--impute_missing {params.imp_flag} \
--output_eff {output.eff} \
--output_pred_acc {output.pred_acc} \
--temp_dir {params.tmp} \
--normalize {params.norm_pheno} \
--ncores 4 \
--prefix {params.prefix}
module unload R/4.2.3
"""
def get_bayesC_input_files(wildcards):
DATASET = wildcards.DATASET
return expand(config["DEST"]+"output/bayesC_fit/"+config["DATA_PREFIX"]+"_chr{CHR}_bayesC_fit_trait{TRAIT}_{DATASET}.rds", DATASET=DATASET, CHR=config["CHR"],TRAIT=config["TRAIT"])
rule compute_prediction_accuracy_bayesC:
input:
pheno=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_pheno_{DATASET}.rds",
geno=config["BED"],
tids=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_test_ids_{DATASET}.txt",
list=lambda wildcards: get_bayesC_input_files(wildcards)
output:
eff=config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_bayesC_effects_{DATASET}.rds",
pred_acc=config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_bayesC_pred_acc_{DATASET}.rds"
params:
model=config["BAYESC_MODEL"],
model_fit_dir=config["BAYESC_MODEL_FIT_DIR"],
dataset="{DATASET}",
chr_r=config["CHR_RANGE"],
trait_r=config["TRAIT_RANGE"],
imp_flag=config["IMPUTE_FLAG"],
script=config["SCRIPT"]+"compute_prediction_accuracy_ukb.R",
tmp=config["TMP"],
norm_pheno=config["NORMALIZE_PHENO"],
prefix=config["DATA_PREFIX"]
resources: cpus=4, mem_mb=20000, time_min=120
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=4
Rscript {params.script} \
--model {params.model} \
--model_fit_dir {params.model_fit_dir} \
--pheno {input.pheno} \
--geno {input.geno} \
--test_ids {input.tids} \
--data_id {params.dataset} \
--chr {params.chr_r} \
--trait {params.trait_r} \
--impute_missing {params.imp_flag} \
--output_eff {output.eff} \
--output_pred_acc {output.pred_acc} \
--temp_dir {params.tmp} \
--normalize {params.norm_pheno} \
--ncores 4 \
--prefix {params.prefix}
module unload R/4.2.3
"""
def get_mvbayesC_input_files(wildcards):
DATASET = wildcards.DATASET
return expand(config["DEST"]+"output/mvbayesC_fit/"+config["DATA_PREFIX"]+"_chr{CHR}_mvbayesC_fit_{DATASET}.rds", DATASET=DATASET, CHR=config["CHR"])
rule compute_prediction_accuracy_mvbayesC:
input:
pheno=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_pheno_{DATASET}.rds",
geno=config["BED"],
tids=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_test_ids_{DATASET}.txt",
list=lambda wildcards: get_mvbayesC_input_files(wildcards)
output:
eff=config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_mvbayesC_effects_{DATASET}.rds",
pred_acc=config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_mvbayesC_pred_acc_{DATASET}.rds"
params:
model=config["MVBAYESC_MODEL"],
model_fit_dir=config["MVBAYESC_MODEL_FIT_DIR"],
dataset="{DATASET}",
chr_r=config["CHR_RANGE"],
trait_r=config["TRAIT_RANGE"],
imp_flag=config["IMPUTE_FLAG"],
script=config["SCRIPT"]+"compute_prediction_accuracy_ukb.R",
tmp=config["TMP"],
norm_pheno=config["NORMALIZE_PHENO"],
prefix=config["DATA_PREFIX"]
resources: cpus=4, mem_mb=20000, time_min=120
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=4
Rscript {params.script} \
--model {params.model} \
--model_fit_dir {params.model_fit_dir} \
--pheno {input.pheno} \
--geno {input.geno} \
--test_ids {input.tids} \
--data_id {params.dataset} \
--chr {params.chr_r} \
--trait {params.trait_r} \
--impute_missing {params.imp_flag} \
--output_eff {output.eff} \
--output_pred_acc {output.pred_acc} \
--temp_dir {params.tmp} \
--normalize {params.norm_pheno} \
--ncores 4 \
--prefix {params.prefix}
module unload R/4.2.3
"""
def get_mvbayesC_rest_input_files(wildcards):
DATASET = wildcards.DATASET
return expand(config["DEST"]+"output/mvbayesC_fit/"+config["DATA_PREFIX"]+"_chr{CHR}_mvbayesC_rest_fit_{DATASET}.rds", DATASET=DATASET, CHR=config["CHR"])
rule compute_prediction_accuracy_mvbayesC_rest:
input:
pheno=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_pheno_{DATASET}.rds",
geno=config["BED"],
tids=config["DEST"]+"data/phenotypes/simulated/"+config["DATA_PREFIX"]+"_test_ids_{DATASET}.txt",
list=lambda wildcards: get_mvbayesC_rest_input_files(wildcards)
output:
eff=config["DEST"]+"output/estimated_effects/"+config["DATA_PREFIX"]+"_mvbayesC_rest_effects_{DATASET}.rds",
pred_acc=config["DEST"]+"output/prediction_accuracy/"+config["DATA_PREFIX"]+"_mvbayesC_rest_pred_acc_{DATASET}.rds"
params:
model=config["MVBAYESCREST_MODEL"],
model_fit_dir=config["MVBAYESCREST_MODEL_FIT_DIR"],
dataset="{DATASET}",
chr_r=config["CHR_RANGE"],
trait_r=config["TRAIT_RANGE"],
imp_flag=config["IMPUTE_FLAG"],
script=config["SCRIPT"]+"compute_prediction_accuracy_ukb.R",
tmp=config["TMP"],
norm_pheno=config["NORMALIZE_PHENO"],
prefix=config["DATA_PREFIX"]
resources: cpus=4, mem_mb=20000, time_min=120
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=4
Rscript {params.script} \
--model {params.model} \
--model_fit_dir {params.model_fit_dir} \
--pheno {input.pheno} \
--geno {input.geno} \
--test_ids {input.tids} \
--data_id {params.dataset} \
--chr {params.chr_r} \
--trait {params.trait_r} \
--impute_missing {params.imp_flag} \
--output_eff {output.eff} \
--output_pred_acc {output.pred_acc} \
--temp_dir {params.tmp} \
--normalize {params.norm_pheno} \
--ncores 4 \
--prefix {params.prefix}
module unload R/4.2.3
"""
rule munge_summary_stats_for_mtag:
input:
sumstats=config["DEST"]+"output/summary_statistics/"+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_{DATASET}.rds",
output:
temp(config["TMP"]+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_for_mtag_trait{TRAIT}_{DATASET}.txt")
params:
n=config["N_TRAIN"],
trait="{TRAIT}",
script=config["SCRIPT"]+"munge_sumstats_for_mtag_ukb.R"
resources: cpus=1, mem_mb=2000, time_min=30
shell:
"""
source /opt/intel/oneapi/mkl/2023.0.0/env/vars.sh intel64
module load R/4.2.3
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=1
Rscript {params.script} \
--sumstats {input.sumstats} \
--n {params.n} \
--trait {params.trait} \
--output {output}
module unload R/4.2.3
"""
# def get_sumstats_for_mtag_input_files(wildcards):
# DATASET = wildcards.DATASET
# CHR = wildcards.CHR
# PATHS = expand(config["TMP"]+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_for_mtag_trait{TRAIT}_{DATASET}.txt", DATASET=DATASET, CHR=CHR, TRAIT=config["TRAIT"])
# return ",".join(PATHS)
# def get_sumstats_mtag_output_files(wildcards):
# DATASET = wildcards.DATASET
# CHR = wildcards.CHR
# PATHS = expand(config["TMP"]+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_mtag_{DATASET}_trait_{TRAIT}.txt", DATASET=DATASET, CHR=CHR, TRAIT=config["TRAIT"])
# return ",".join(PATHS)
rule compute_mtag_summary_stats_by_chr:
input:
#lambda wildcards: get_sumstats_for_mtag_input_files(wildcards)
expand(config["TMP"]+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_for_mtag_trait{TRAIT}_{DATASET}.txt", DATASET="{DATASET}", CHR="{CHR}", TRAIT=config["TRAIT"])
output:
temp(expand(config["TMP"]+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_mtag_{DATASET}_trait_{TRAIT}.txt", DATASET="{DATASET}", CHR="{CHR}", TRAIT=config["TRAIT"]))
params:
ldscores=config["LDSCORES"],
out_prefix=config["TMP"]+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_mtag_{DATASET}",
maf_min=0.0,
n_min=0.0,
input=",".join(expand(config["TMP"]+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_for_mtag_trait{TRAIT}_{DATASET}.txt", DATASET="{DATASET}", CHR="{CHR}", TRAIT=config["TRAIT"]))
resources: cpus=4, mem_mb=10000, time_min=120
shell:
"""
module load python2.7-fm/2.7.18
python2 /data2/morgante_lab/fabiom/software/mtag/mtag.py \
--sumstats {params.input} \
--ld_ref_panel {params.ldscores} \
--out {params.out_prefix} \
--maf_min {params.maf_min} \
--n_min {params.n_min} \
--incld_ambig_snps \
--median_z_cutoff 0.3 \
--force
module unload python2.7-fm/2.7.18
"""
rule postprocess_mtag_summary_stats:
input:
sumstats=config["DEST"]+"output/summary_statistics/"+config["DATA_PREFIX"]+"_chr{CHR}_sumstats_{DATASET}.rds",