SAIGE ExWAS is a pipeline for doing whole-exome association study of rare variants and gene burdens with traits using SAIGE software
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Command:
git clone https://github.com/PMBB-Informatics-and-Genomics/geno_pheno_workbench.git -
Navigate to relevant workflow directory run commands (our pipelines assume all of the nextflow files/scripts are in the current working directory)
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Singularity Command:
singularity build saige.sif docker://pennbiobank/saige:latest -
Docker Command:
docker pull pennbiobank/saige:latest -
Command to Pull from Google Container Registry:
docker pull gcr.io/ritchie-aou-psom-9015/saige:latest -
Run Command:
nextflow run workflows/saige_exwas.nf -profile cluster -
Common
nextflow runflags:-
-resumeflag picks up the workflow where it left off, otherwise, the workflow will rerun from the beginning -
-stubperforms a sort of dry run of the whole workflow, checks channels without executing any code -
-profileselects the compute profiles we set up in nextflow.config (see nextflow.config file below) -
-profileselects the compute profiles we set up in nextflow.config (see nextflow.config file below) -
-profile standarduses the docker image to executes the processes -
-profile clusteruses the singularity container and submits processes to a queue- optimal for HPC or LPC computing systems -
-profile all_of_ususes the docker image to execute pipelines on the All of Us Researcher Workbench
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for more information visit the Nextflow documentation
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sex_strat_cohort_list(Type: List)- List of cohorts that are sex stratified
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bin_pheno_list(Type: List)- Binary phenotype list
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quant_pheno_list(Type: List)- Quantitative phenotype list
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id_col(Type: String)- ID column label
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data_csv(Type: File Path)-
A csv table with all of the phenotypes and covariates to be tested
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Corresponding Input File: Phenotypes and Covariates
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table with participants as rows and all needed phenotypes and covariates as columns
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Type: Data Table
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Format: csv
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Input File Header:
PMBB_ID,DATA_FREEZE_AGE,SEX,T2D,AAA,BMI_median,ANCESTRY,Genotype_PC1,Genotype_PC2,Genotype_PC3,Genotype_PC4,Genotype_PC5,Genotype_PC6,Genotype_PC7,Genotype_PC8,Genotype_PC9,Genotype_PC10,Exome_PC1,Exome_PC2,Exome_PC3,Exome_PC4,Exome_PC5,Exome_PC6,Exome_PC7,Exome_PC8,Exome_PC9,Exome_PC10,LDL_median PMBB1000274307312,56.42162902121834,Male,0.0,0.0,36.58,EUR,0.00907865,0.044848,0.0134254,0.0158855,0.00192968,-0.000757675,-0.0255266,-0.0301011,-0.0145376,0.0134223,0.0115325,0.0424594,-0.0141369,-0.0127108,-0.000808328,-0.00954432,-0.0144673,0.017295,-0.0024283,0.0122516,121.5 PMBB1000437739273,60.9719370294319,Female,0.0,0.0,22.285,EUR,0.010133,0.0502986,0.0194905,0.00985774,-0.00588554,0.00254286,-0.000419516,-0.00631418,-0.000249583,-0.000671407,0.0135289,0.050558,-0.0142215,-0.00324765,0.00139103,-0.0103474,0.00415374,-0.00961909,0.00863192,0.00951374,137.0 PMBB1000856639250,78.25872689938399,Female,1.0,NA,34.84,EUR,0.0119364,0.0518561,0.0258603,0.00814667,-0.0128893,-0.00361884,0.0113697,0.0224107,-0.0221819,0.0325879,0.0152482,0.0507727,-0.0277409,-0.00190829,0.00417344,-0.00173211,0.0112445,0.00392322,-0.0107343,-0.00108714,159.0 PMBB1001117453706,44.70088980150582,Female,0.0,0.0,25.6,EUR,0.0104689,0.0506542,0.0215548,0.00545738,-0.0110262,0.0013086,-0.00515618,5.87523e-05,0.0326367,0.0144168,0.012834,0.0491614,-0.0184922,-0.00661846,0.013669,-0.000850077,0.0156829,-0.0160642,0.00361836,0.00197,NA -
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cohort_sets(Type: File Path)-
A binary csv table in which the columns are the cohorts and the rows are the individuals. A 1 means that individual is a member of the column’s cohort, and a 0 means they aren’t.
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Corresponding Input File: Cohort Membership
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0/1 table with cohorts as columns and participants as rows - 1 indicates that that row’s participant is a member of that column’s cohort
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Type: Data Table
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Format: csv
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Input File Header:
PMBB_ID,AFR_M,AFR_F,AFR_ALL,AMR_M,AMR_F,AMR_ALL,EAS_M,EAS_F,EAS_ALL,EUR_M,EUR_F,EUR_ALL,SAS_M,SAS_F,SAS_ALL,ALL_M,ALL_F,ALL_ALL PMBB9640968538122,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1 PMBB4280034922592,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1 PMBB1732740914029,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1 PMBB9470680445956,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1 -
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min_maf(Type: Float)- Minimum minor allele frequency for plink QC
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cont_covars(Type: List)- Continuous covariates list
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sex_strat_cat_covars(Type: List)- Categorical covariates for sex stratified cohorts to ensure model converges
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geno(Type: Float)- Plink parameters for SAIGE Step 1 Input QC which needs a small set of high-quality variants, genotype rate filters out all variants with missing call rates exceeding the provided value
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step1_script(Type: File Path)- Fits the null logistic/linear mixed model using a full or a sparse genetic relationship matrix (GRM). The GRM estimate the genetic relationship between two individuals over a certain number of SNPs
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group_file_prefix(Type: Chr File Prefix)-
Has the variant positions for each gene as well as the variant annotation for each variant in the gene in SAIGE format
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Corresponding Input File: SAIGE Group Annotation Files
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text files formatted like this example from the SAIGE github:
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Type: Data Table
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Format: saige group (txt)
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Input File Header:
ENSG00000000457 var 1_169853716_C_A 1_169853716_C_T 1_169853717_C_CAGTT ENSG00000000457 anno other_missense damaging_missense damaging_missense ENSG00000000460 var 1_169795119_C_T 1_169795121_G_C 1_169795123_C_G ENSG00000000460 anno other_missense other_missense other_missense -
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hwe(Type: Float)- Plink parameters for SAIGE Step 1 Input QC which needs a small set of high-quality variants
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step1_sparse_grm_samples(Type: File Path)-
List of IDs to use in the sparse GRM
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Corresponding Input File: SAIGE Sparse GRM Sample IDs
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(optional) sample IDs for a sparse relatedness matrix
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Type: List File
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Format: txt
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maf(Type: Float)- Plink parameters for SAIGE Step 1 Input QC which needs a small set of high-quality variants
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min_mac(Type: Float)- SAIGE-GENE Step 2 Parameters
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exome_plink_prefix(Type: Plink Fileset Prefix)-
Exome plink input files
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Corresponding Input File: SAIGE Exome Plink Files
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a hard-call plink set of exome data
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Type: Plink Set
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Format: plink binary
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Input File Header:
PMBB-Release-2020-2.0_genetic_exome_GL_norm{.bed,.bim,.fam,.log,.pgen,.psam,.pvar} -
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firth_cutoff(Type: Float)- P-value ()
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use_firth(Type: Bool (R: TRUE or FALSE))- True to use firth logistic regression
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grouptest_annotation(Type: String)- Annotations for variants
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grouptest_maf(Type: String)- MAF cutoffs
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LOCO(Type: Bool (R: TRUE or FALSE))- Usually a GWAS method
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region_col_names(Type: Map (Dictionary))- Default SAIGE Region column names mapped to new ones
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p_cutoff_summarize(Type: Float)- P-Value Threshold for Summarizing Results at the End, arbitrary p-value threshold for creating a table of results combined with low p-values
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gene_location_file(Type: File Path)-
This file is used for getting gene-based coordinates for plotting
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Corresponding Input File: Gene Location File
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CSV file of
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Type: Data Table
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Format: tsv
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Input File Header:
gene_id |chromosome|seq_region_start|seq_region_end|gene_symbol ENSG00000186092|1 |65419 |71585 |OR4F5 ENSG00000284662|1 |685716 |686654 |OR4F16 ENSG00000187634|1 |923923 |944575 |SAMD11 ENSG00000188976|1 |944203 |959309 |NOC2L ENSG00000187961|1 |960584 |965719 |KLHL17 -
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Quantitative Phenotype Violin Plots
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A violin plot for each quantitative phenotype. One file will be generated for each phenotype and all cohorts will be plotted on each.
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Type: Summary Plot
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Format: png
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Parallel By: Phenotype
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Singles QQ Plots
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A QQ Plot of the Null Model vs Log10P results of the analysis for variants. One plot will be created for each unique combination of phenotype, cohort, annotation group (pLof, etc.), and MAF threshold.
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Type: QQ Plot
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Format: png
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Parallel By: Cohort, Phenotype
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Phenotype Summary Table
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A csv file with summary statistics for the phenotypes. Statistics are computed across all cohorts, phenotypes. For binary phenotypes, it also computes: Total Number, number of cases, Number of Controls, and Prevalence, and summary stats.
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Type: Summary Table
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Format: csv
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Output File Header:
COHORT |PHENO |N |Controls|Cases|Prevalence |mean |std |min |25% |50% |75% |max AMR_ALL|AAA |474|464.0 |10.0 |0.02109704641350211| | | | | | | AMR_ALL|T2D |546|425.0 |121.0|0.2216117216117216 | | | | | | | AMR_ALL|BMI_median|531| | | |29.95419020715631|7.10618710646257 |15.17|24.84|28.69|33.435 |64.465 AMR_ALL|LDL_median|310| | | |93.09516129032258|34.95209123947716|9.0 |70.25|90.0 |112.375|291.0 -
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Regions Manhattan Plots
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A dot plot (manhattan plot) of significant gene regions associated with a phenotype. One plot will be created for each unique combination of phenotype, cohort, annotation group (pLof, etc.), and MAF threshold.
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Type: Manhattan Plot
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Format: png
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Parallel By: Cohort, Phenotype, Annot Group, MAF
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Regions QQ Plots
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A QQ Plot of the Null Model vs Log10P results of the analysis for gene regions. One plot will be created for each unique combination of phenotype, cohort, annotation group (pLof, etc.), and MAF threshold.
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Type: QQ Plot
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Format: png
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Parallel By: Cohort, Phenotype, Annot Group, MAF
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Singles Summary Statistics
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A gzipped, unfiltered TSV (tab-separated) file of the results for the variant (singles) analysis. One file will be created for each unique Cohort, Phenotype, and analysis (regular, cauchy, rare, ultra rare) combination.
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Type: Summary Statistics
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Format: tsv.gz
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Parallel By: Cohort, Phenotype
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Output File Header:
phenotype|chromosome|base_pair_location|variant_id |other_allele|effect_allele|effect_allele_count|effect_allele_frequency|missing_rate|beta |standard_error|t_statistic|variance|p_value |p_value_na|is_spa_test|allele_freq_case|allele_freq T2Diab |21 |41801254 |21_41801254_TCTG_T|TCTG |T |277 |0.0046611 |0.0 |-0.099231 |0.167775 |-3.52526 |35.5258 |0.5542179 |0.5542179 |False |0.00426841 |0.00474126 T2Diab |21 |41801360 |21_41801360_C_T |C |T |41 |0.00068991 |0.0 |-0.864441 |0.633121 |-3.98228 |5.38237 |0.08606924|0.08606924|False |0.000297796 |0.000769948 T2Diab |21 |41801603 |21_41801603_C_T |C |T |24 |0.00040385 |0.0 |0.322923 |0.570593 |0.991852 |3.07148 |0.5714322 |0.5714322 |False |0.000496327 |0.000384974 T2Diab |21 |41801645 |21_41801645_G_A |G |A |58 |0.000975971 |0.0 |0.0167811 |0.35132 |0.135962 |8.10206 |0.9619027 |0.9619027 |False |0.00109192 |0.000952304 -
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Singles Manhattan Plots
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A dot plot (manhattan plot) of significant variants associated with a phenotype. One plot will be created for each unique combination of phenotype, cohort, annotation group (pLof, etc.), and MAF threshold.
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Type: Manhattan Plot
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Format: png
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Parallel By: Cohort, Phenotype
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Singles Top Hits Table
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A FILTERED top hits csv summary file of results including cohort, phenotype, gene, group annotation, p-values, and other counts. One single summary file will be aggregated from all the “top hits” in each “Singles (Variant) Summary Statistics” file.
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Type: Summary Table
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Format: csv
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Output File Header:
cohort,phenotype,chromosome,base_pair_location,variant_id,other_allele,effect_allele,effect_allele_count,effect_allele_frequency,missing_rate,beta,standard_error,t_statistic,variance,p_value,p_value_na,is_spa_test,allele_freq_case,allele_freq_ctrl,n_case,n_ctrl,n_case_hom,n_case_het,n_ctrl_hom,n_ctrl_het,n EUR_M,BMI_median,1,1203928,1_1203928_G_A,G,A,13,0.000425393,0.0,6.68476,1.49857,2.97666,0.445291,8.167552e-06,,,,,,,,,,,15280.0 AFR_M,BMI_median,1,6073749,1_6073749_C_T,C,T,14,0.00182292,0.0,8.90012,1.93388,2.37977,0.267386,4.180559e-06,,,,,,,,,,,3840.0 AFR_F,LDL_median,1,11134343,1_11134343_G_C,G,C,13,0.00127226,0.0,36.8257,8.24963,0.541106,0.0146937,8.047218e-06,,,,,,,,,,,5109.0 AFR_ALL,LDL_median,1,11828619,1_11828619_C_T,C,T,12,0.000740466,0.0,45.5826,9.2016,0.538359,0.0118106,7.279194e-07,,,,,,,,,,,8103.0 -
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Regions Summary Statistics
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A gzipped, unfiltered TSV (tab-separated) file of the results for the gene (regions) analysis if run. One file will be created for each unique Cohort, Phenotype, and analysis (regular, cauchy, rare, ultra rare) combination.
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Type: Summary Statistics
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Format: tsv.gz
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Parallel By: Cohort, Phenotype
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Output File Header:
phenotype|gene |annot |max_maf|p_value |p_value_burden |p_value_skat |beta_burden |se_burden |mac |mac_case|mac_control|rare_var_count|ultrarare_var_count T2Diab |ENSG00000141956|pLoF |0.0001 |0.0479451461682565|0.0479451461682565|0.0479451461682565|0.0588652858997042 |0.0297621953331829|12.0 |5.0 |7.0 |0.0 |9.0 T2Diab |ENSG00000141956|pLoF |0.001 |0.0479451461682565|0.0479451461682565|0.0479451461682565|0.0588652858997042 |0.0297621953331829|12.0 |5.0 |7.0 |0.0 |9.0 T2Diab |ENSG00000141956|pLoF |0.01 |0.0479451461682565|0.0479451461682565|0.0479451461682565|0.0588652858997042 |0.0297621953331829|12.0 |5.0 |7.0 |0.0 |9.0 T2Diab |ENSG00000141956|damaging_missense|0.0001 |0.464219450219203 |0.464219450219203 |0.464219450219203 |-0.0110759683619445|0.0151328276810456|52.0 |7.0 |45.0 |0.0 |41.0 -
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Binary Phenotype Bar Plots
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A bar plot for each binary phenotype. One file will be generated for each phenotype and all cohorts will be plotted.
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Type: Summary Plot
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Format: png
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Parallel By: Phenotype
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Regions Top Hits Table
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A FILTERED top hits csv summary file of results including cohort, phenotype, gene, group annotation, p-values, and other counts. One single summary file will be aggregated from all the “top hits” in each “Regions Summary Statistics” file.
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Type: Summary Table
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Format: csv
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Output File Header:
cohort,phenotype,gene,annot,max_maf,p_value,p_value_burden,p_value_skat,beta_burden,se_burden,mac,mac_case,mac_control,rare_var_count,ultrarare_var_count EUR_F,BMI_median,ENSG00000003393,pLoF,0.0001,1.12067607254946e-06,1.12067607254946e-06,1.12067607254946e-06,0.456714431175153,0.0937971711429878,9.0,,,0.0,8.0 EUR_F,BMI_median,ENSG00000003393,pLoF,0.001,1.12067607254946e-06,1.12067607254946e-06,1.12067607254946e-06,0.456714431175153,0.0937971711429878,9.0,,,0.0,8.0 EUR_F,BMI_median,ENSG00000003393,pLoF,0.01,1.12067607254946e-06,1.12067607254946e-06,1.12067607254946e-06,0.456714431175153,0.0937971711429878,9.0,,,0.0,8.0 EUR_M,LDL_median,ENSG00000006530,pLoF,0.01,8.20696106989754e-06,8.20696106989754e-06,8.20696106989754e-06,3.23068908720413,0.724416423633968,3.0,,,0.0,3.0 -
params {
// default assumes use of the docker container
my_python = "/opt/conda/bin/python"
// default paths assume use of the docker container
step1_script = "/usr/local/bin/step1_fitNULLGLMM.R"
step2_script = "/usr/local/bin/step2_SPAtests.R"
// gpu paramater either ON or OFF, need to set config to -c nextflow_gpu.config
GPU = 'OFF'
// Minimum numbers for filtering cohort-phenotype combinations
min_bin_cases = 100
min_quant_n = 1000
// list of cohorts (usually ancestry-stratified and/or sex-stratified)
cohort_list = [
"AMR_ALL", "AMR_F","AMR_M",
"AFR_ALL", "AFR_F", "AFR_M",
"EAS_ALL", "EAS_F", "EAS_M",
"EUR_ALL", "EUR_F", "EUR_M"
]
// subset of cohorts that are female- or male-only which should exclude sex-based covariates
sex_strat_cohort_list = [
"AMR_F", "AMR_M",
"AFR_F", "AFR_M",
"EAS_F", "EAS_M",
"EUR_F", "EUR_M",
]
// list of chromosomes
chromosome_list = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22"]
// binary and quantitative phenotype lists or path to file of newline-separated lists
bin_pheno_list = ["T2D", "AAA"]
quant_pheno_list = ["BMI_median", "LDL_median"]
// categorical and continuous covariates
cat_covars = ["SEX"]
cont_covars = ["DATA_FREEZE_AGE", "Exome_PC1", "Exome_PC2", "Exome_PC3", "Exome_PC4"]
sex_strat_cat_covars = []
sex_strat_cont_covars = cont_covars
// all default paths are for PMBB WES
data_csv = "/project/pmbb_codeworks/datasets/CodeWorks_Test_Data/cleaned_test_pheno_covars.csv"
cohort_sets = "/project/pmbb_codeworks/datasets/PMBB_Extra/Sample_Lists/Exome_sample_table.csv"
// ID column label
id_col = "PMBB_ID"
// Config parameters for using precomputed sparse GRM:
// use_sparse_GRM = true
// step 1 path should be the small subset of markers used to fit the GRM
// step1_plink_prefix = "/project/pmbb_codeworks/datasets/PMBB_Extra/SAIGE_Step0_Exome/input/PMBB_exome_random_autosomal_markers"
// step1_sparse_grm = "/project/pmbb_codeworks/datasets/PMBB_Extra/SAIGE_Step0_Exome/output/PMBB_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseGRM.mtx"
// step1_sparse_grm_samples = "/project/pmbb_codeworks/datasets/PMBB_Extra/SAIGE_Step0_Exome/output/PMBB_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseGRM.mtx.sampleIDs.txt"
// Config parameters for using real-time FULL GRM:
use_sparse_GRM = false
// Genetic Data Inputs:
exome_plink_prefix = "/project/pmbb_codeworks/datasets/PMBB-2.0_exome_GL_norm/plink/PMBB-Release-2020-2.0_genetic_exome_GL_norm"
group_file_prefix = "/project/pmbb_codeworks/datasets/New_VEP_Annotations_2.0/SAIGE_Sets/subset."
// Plink parameters for SAIGE Step 1 Input QC which needs a small set of high-quality variants
// Current defaults are recommended by GBMI analysis plan
maf = 0.01
geno = 0.01
hwe = 1E-6
// SAIGE-GENE Step 2 Parameters
// Current defaults are recommended by BRAVA analysis plan
min_maf = 0
min_mac = 0.5
grouptest_maf = "0.0001,0.001,0.01"
grouptest_annotation = "pLoF,damaging_missense,other_missense,synonymous,pLoF;damaging_missense,pLoF;damaging_missense;other_missense;synonymous"
use_firth = "TRUE"
firth_cutoff = 0.1
LOCO = "FALSE"
// this is for getting gene-based coordinates for plotting
gene_location_file = "/project/pmbb_codeworks/datasets/ENSEMBL/homo_sapiens_111_b38.txt"
// P-Value Threshold for Summarizing Results at the End
p_cutoff_summarize = 0.00001
// Dictionary (Map) with default SAIGE Region column names mapped to new ones
regions_col_names = [
Region: 'gene',
Group: 'annot',
max_MAF: 'max_maf',
Pvalue: 'p_value',
Pvalue_Burden: 'p_value_burden',
BETA_Burden: 'beta_burden',
SE_Burden: 'se_burden',
Pvalue_SKAT: 'p_value_skat',
MAC: 'mac',
MAC_case: 'mac_case',
MAC_control: 'mac_control',
Number_rare: 'rare_var_count',
Number_ultra_rare: 'ultrarare_var_count'
]
// Dictionary (Map) with default SAIGE SingleAssoc column names mapped to new ones
singles_col_names = [
CHR: 'chromosome',
POS: 'base_pair_location',
MarkerID: 'variant_id',
Allele1: 'other_allele',
Allele2: 'effect_allele',
AC_Allele2: 'effect_allele_count',
AF_Allele2: 'effect_allele_frequency',
MissingRate: 'missing_rate',
BETA: 'beta',
SE: 'standard_error',
Tstat: 't_statistic',
var: 'variance',
'p.value': 'p_value',
'p.value.NA': 'p_value_na',
'Is.SPA': 'is_spa_test',
AF_case: 'allele_freq_case',
AF_ctrl: 'allele_freq_ctrl',
N_case: 'n_case',
N_ctrl: 'n_ctrl',
N_case_hom: 'n_case_hom',
N_case_het: 'n_case_het',
N_ctrl_hom: 'n_ctrl_hom',
N_ctrl_het: 'n_ctrl_het',
N: 'n'
]
}
FROM wzhou88/saige:1.3.6
WORKDIR /app
USER root
RUN apt-get update \
&& apt-get install -y --no-install-recommends bash libtiff5-dev libz-dev g++ gcc git wget tar unzip make \
&& rm -rf /var/lib/apt/lists/*
ENV CONDA_DIR /opt/conda
RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
/bin/bash ~/miniconda.sh -b -p /opt/conda
ENV PATH=$CONDA_DIR/bin:$PATH
RUN mkdir plink
RUN wget -P plink https://s3.amazonaws.com/plink2-assets/alpha5/plink2_linux_x86_64_20240526.zip
RUN wget -P plink https://s3.amazonaws.com/plink1-assets/plink_linux_x86_64_20231211.zip
WORKDIR plink
RUN unzip plink2_linux_x86_64_20240526.zip
RUN unzip plink_linux_x86_64_20231211.zip
RUN rm -rf plink2_linux_x86_64_20240526.zip
RUN rm -rf plink_linux_x86_64_20231211.zip
RUN mv plink2 /usr/bin
RUN mv plink /usr/bin
WORKDIR /app
RUN git clone https://github.com/PMBB-Informatics-and-Genomics/NEAT-Plots.git
RUN mv NEAT-Plots/manhattan-plot/ /app/
ARG BIOFILTER_VERSION=2.4.3
RUN wget https://github.com/RitchieLab/biofilter/releases/download/Biofilter-${BIOFILTER_VERSION}/biofilter-${BIOFILTER_VERSION}.tar.gz -O biofilter.tar.gz
RUN mkdir /app/biofilter
RUN tar -zxvf biofilter.tar.gz --strip-components=1 -C /app/biofilter
USER $CONDA_USER
RUN conda install -y -n base -c conda-forge -c bioconda libtiff bgenix dominate wget conda-build scipy pandas seaborn matplotlib numpy apsw sqlite && \
conda clean --all --yes
WORKDIR /app/manhattan-plot/
RUN conda develop .
WORKDIR /app/biofilter/
RUN /opt/conda/bin/python setup.py install
RUN ln -s /opt/conda/lib/libtiff.so.6 /opt/conda/lib/libtiff.so.5
USER root
// includeConfig '${launchDir}/configs/saige_exwas.config'
// includeConfig '${launchDir}/configs/saige_gene_phewas.config'
includeConfig '${launchDir}/configs/saige_variant_phewas.config'
profiles {
non_docker_dev {
process.executor = 'local'
}
standard {
process.executor = 'local'
process.container = 'karlkeat/saige_exwas'
docker.enabled = true
}
cluster {
process.executor = 'lsf'
process.queue = 'epistasis_normal'
executor {
queueSize=500
}
process.memory = '15GB'
process.container = 'saige_family.sif'
singularity.enabled = true
singularity.runOptions = '-B /project/'
}
all_of_us {
process.executor = 'google-lifesciences'
process.memory = '15GB'
process.container = 'gcr.io/ritchie-aou-psom-9015/saige:latest'
google.zone = "us-central1-a"
google.project = 'terra-vpc-sc-bb404549' // change to your project id
google.lifeSciences.debug = true
google.lifeSciences.network = "network"
google.lifeSciences.subnetwork = "subnetwork"
google.lifeSciences.usePrivateAddress = false
google.lifeSciences.serviceAccountEmail = 'pet-2666723902222ba8b8580@terra-vpc-sc-bb404549.iam.gserviceaccount.com' // change to your service email
google.lifeSciences.copyImage = "gcr.io/google.com/cloudsdktool/cloud-sdk:alpine"
google.enableRequesterPaysBuckets = true
workDir='gs://fc-secure-f3e7d01e-18fa-40ba-bb3e-4d7497ba7d5b/work/' // change to your working directory in your workspace bucket
}
}