assoc

Association pipeline: assoc/main.nf

This workflow has been extensively expanded by Jean-Tristan Brandenburg

An association study is a complex analysis and each analysis has to consider

• the disease/phenotype being studied and its mode of inheritance
• population structure
• other covariates

For this reason it is difficult to build a high quality, generic pipeline to do an association study.

The purpose of this pipeline is to perform a very superficial initial analysis that can be used as one piece of information to guide a rigorous analysis. Of course, we would encourage users to build their own Nextflow script for their rigorous analysis, perhaps using our script as a start.

Our script, assoc takes as input PLINK files that have been through quality control and

• does a principal component analysis on the data, and produces pictures from that;
• performs a simple association test giving odds ratio and raw and adjusted p values

Running

The pipeline is run: nextflow run assoc

The key options are:

• input_dir, output_dir: where input and output goes to and comes from;
• input_pat: the base of set of PLINK bed,bim and fam files (this should only match one);
• data: a tab-separated file that contains phenotype data for your particpants. The row is a header line, with one participant per line after that. The first two columns should FID IID followed by any phenotype values you want to use for testing or for covariates. It can contain other data too -- as long as the ones that you need are in this file.
• pheno: a comma-separated list of phenotypes that you want to test. Each phenotype is tested separately. If you wish to do a transform to the phenottype, you can suffix the phenotype with /function where function is a numpy function. For example you might say --pheno bmi/np.log which will apply the log function to the phenotype. Any numpy function can be used, typical uses being np.log and np.sqrt. We plan to support user provision of a user-given function.
• covariates: a comma-separated list of phenotypes that you want to use
• exclude_snps option to exclude some snps active just for boltlmm (see --exclude in boltlmm manual) : SNP ID must be first column (default none)
• print_pca : by default pipeline compute and print pca (print_pca=1), if you want avoid this step (print_pca = 0)
• file_rs_buildrelat : file with rs list (one by lines) to build genetics models (relatdness), for gemma -snps for boltlmm --modelSnps
• genetic_map_file : genetic map used in boltlmm

By default a chi2 test for association is done. But you can do multiple different tests in one run by settintg the appropriate parameter to 1. Note at least one must be set to 1

• assoc : should a chi2 test be used (0 or 1)
• fisher: Fisher exact test (default 0)
• linear: should linear regreession be used? (default 0)
• logistic: should linear regression be used? (default 0)
• gemma: should gemma be used? (default 0)
  • see manual
  • gemma_num_cores: if gemma is used set this up to 8
  • gemma_mem_req: For 10k samples, 2 million SNPs, we needed 4GB of RAM (default : 6GB)
  • gemma_mat_rel : file contains in gemma format matrix relatdness used by gemma (format 1, see manual), matrix must be in same order than fam file. Avoid computation of relatdness by pipeline.
  • gemma_multi : option run gemma by chromosome separately (is not a loco). to increase time computation and used more cpus, 0 no 1 yes [default : 0 (No)]
• boltlmm: should boltlmm be used?
  • see manual
  • if SNPs is higher than 950,000, 950,000 SNPs are chosen randomly to build the model (see --modelSnps option in bolt)
  • covariates_type / bolt_covariates_type : for bolt need to define if covariate is binary (0) or continue (1), a comma-separated list as same order than covariates
  • bolt_ld_score_file : A table of reference LD scores for boltlmm is needed to calibrate the BOLT-LMM statistic (option in boltlmm --LDscoresFile),to choice a specific column in Ld file you can use bolt_ld_scores_col option (by default : LDSCORE) if option is not provided --LDscoresUseChip used.
  • bolt_use_missing_cov : option to "missing indicator method", by default no activate (0), to activate (1) (--covarUseMissingIndic option in boltlmm), which adds indicator variables demarcating missing status as additional covariates.
  • bolt_num_cores if bolt is used set this up to 8
  • bolt_mem_req memory required for boltlmm, (default : 6GB)
  • impute2 data in bolt :
    • bolt_impute2filelist : list of impute2 files, each line contains : chronumber file, file must be in full pattern *bolt_impute2fidiid : list of individual in same order than bolt_impute2filelist
• fastlmm: should fastlmm be used?
  • see manual
  • fastlmm_num_cores: if fastmll is used set this up to 8
  • fastlmm_mem_req: memory required for fasttlmm (default : 15GB)
  • fastlmm_multi : memory used by fastmll is very big and increase with snp number, option run fastlmm by chromosome, with relatedness matrix computed before with gemma (-gk 1)
  • fastlmmc_bin : should change binary for fastlmmc (default fastlmmc) and then for all the tests except gemma, boltlmm and fastlmm, do you want to adjust for multiple testing
• plink gwas option :
  • adjust: do we want to do explicit testing for Bonferroni correction et al that PLINK odes
  • mperm: do you want to test doing permutation testing. If so, how many tests? By default this is 1000.
• fastGWA : do an analyse with fastGWA (GCTA), specific option fastGWA manual
• fastgwa (if 1 perfom fastGWA, otherwise no [default 0])
• to build grm :
• fastgwa_type : [default : --fastGWA-mlm-exact]
• fastgwa_memory : memory for fastgwa and grm [default: 10G]
• fastgwa_cpus : cpus for fastgw and grm [default: 5]
• covariates_type : similar to bolt_covariates_type, give for each covariable type of covariable qualitatif (0) or quantitatif (1), must be len of covariates, if nothing, consider all covariable just as quantitatif covariable [default ""]
• grm : * gcta_grmfile : index of file with grm with gcta_grmfile.grm.id and gcta_grmfile.grm.sp, if extension will not here, grm will build see below * grm_nbpart : nb part to build grm [default : 100] * gcta64_bin : binary for gcta64 [default : gcta64] * grm_cutoff : cutoff value for grm matrix (using option --make-bK-sparse) [default : 100]

with pipeline, do a GxE interaction with Gemma and Plink, arguments :

• gxe : environmental variables to do gxe analysis with pheno, must be coded in 1 and 2 for plink
• gemma_gxe : GxE interation with gemma [default : 0], see covariates to add covariates in gemma models
• plink_gxe : GxE interation with plink (see option -gxe, in plink manual) [default : 0], no covariate could be provided.

For example

nextflow run assoc/assoc.nf --input_pat raw-GWA-data --chi2 1 --logistic 1 --adjust 1

analyses the files raw-GWA-data bed, bim, fam files and performs a chi2 and logistic regression test, and also does multiple testing correction.

Other flags are:

• thin. You can set this to a floating point number in the range (0, 1] and then the PLINK data files are thinned leaving only that proportion of the SNPs. This allows pipeline to be tested with a small proportion of the data This is probably only needed for debugging purposes and usually this should not be be set.
• chrom. Only do testing on this chromosome.

Post-Analysis script

##1. Computed a p.value par permutation with gemma

##2. Conditional & joint (COJO) analysis of GWAS summary statistic

this section describes a pipeline in devloment, objectives is doing a conditional and joint association using GWAS summary data and gcta see cojo

Installation

need python3, gcta tested for singularity image: no

Running

The pipeline is run: nextflow run assoc/annot-assoc.nf

The key options are:

• work_dir : the directory in which you will run the workflow. This will typically be the h3agwas directory which you cloned;
• output_dir : output directory
• output : output pattern
• ̀ data: same option that _assoc/main.nf_, file is optional, used if need select specific individus for gcta, compute frequencies or N, if mission infile_gwas`
• input_pat: the base of set of PLINK bed,bim and fam files (this should only match one);
• pheno : optional, header in data, if present select individuals with no missiong individual to keep individuals for computed frequencie or gcta
• cut_maf minor allele frequencies [ default : 0.0001]
• ̀file_gwas : file contains gwas result, if N or frequencies is not available, it is computed with plink file and data file, to change format header must be defined :
  • ̀ head_pval` : pvalue header [ default : "P_BOLT_LMM" ]
  • head_freq : freq header [ default : None], if not present computed with plink, (and data/pheno if present)
  • head_n : N (individuals number) [ default : None ], if not present computed with plink (and data/pheno if present)
  • head_rs : rs header column [default : "SNP"]
  • head_beta : beta header colum [default : "BETA"]
  • head_se : column for standard error of beta "SE"
  • head_A1 : column for A0 :[default : "ALLELE0" ]
  • head_A2 : column for A1 :[default : "ALLELE1" ]

Cojo parameter :

• cojo_wind : Specify a distance d (in Kb unit). It is assumed that SNPs more than d Kb away from each other are in complete linkage equilibrium. The default value is 10000 Kb (i.e. 10 Mb) if not specified. [ default : 10000 ]
• cojo_actual_geno : If the individual-level genotype data of the discovery set are available (e.g. a single-cohort GWAS), you can use the discovery set as the reference sample. option to avoid due to a various bug [default 0]
• cojo_slct : Perform a stepwise model selection procedure to select independently associated SNPs? 1 : yes 0 : no [default 1]
  • cojo_p : Threshold p-value to declare a genome-wide significant hit. The default value is 5e-8 if not specified. This option is only valid in conjunction with the option cojo_slct.
  • cojo_slct_other : other option for slct see manual
• cojo_top_snps_chro : Perform a stepwise model selection procedure to select a fixed number of independently associated SNPs by chromosome without a p-value threshold. [integer between 0 and n, to define top snp number. default : 0].
• gcta_mem_req="6GB"

##3. Annotation of position

This section describes a pipeline in devlopment, objectives is annotation of rs using annotation, locuszoom, and phenotype in function of genotype

Installation

need locuszoom, R : (ggplot2), python3

4. Simulation pipeline: assoc/simul-assoc.nf

This section describes a pipeline in devlopment, purpose of this pipeline is to estimate false positive and false negative with simulated phenotype, Our script, assoc/simul-assoc.nf takes as input PLINK files that have been through quality control and

• Simulate quantitative phenotypes with phenosim based on genetics data
• perform a GWAS on phenotype simulated using gemma, boltlmm.
• Perform summary statistics.

Installation

a version of phenosim adapted is already in nextflow binary, write in python2. plink, gemma and bolt must be installed

Running

The pipeline is run: nextflow run assoc/simul-assoc.nf

The key options are:

• work_dir : the directory in which you will run the workflow. This will typically be the h3agwas directory which you cloned;
• input, output and script directories: the default is that these are subdirectories of the work_dir and there'll seldom be reason to change these;
• input_pat : this typically will be the base name of the PLINK files you want to process (i.e., do not include the file suffix). But you could be put any Unix-style glob here. The workflow will match files in the relevant input_dir directory;
• num_cores : cores number used
• ph_mem_req : memory request for phenosim
• Simulation option :
  • phs_nb_sim : simulation number (default : 5)
  • phs_quant_trait : quantitative trait simulation : 1, qualitative not develop yet (default : 1, -q option in phenosim)
  • Quantitative trait option :
    • ph_nb_qtl : number of simulated QTN (default: 2, option -n in phenosim)
    • ph_list_qtl : proportion of variance explained by each QTNs, separate the values by commas (default : 0.05 -q in phenosim)
    • ph_maf_r : MAF range for causal markers (upper and lower bound, separated by a comma, no space) (default: 0.05,1.0, -maf_r in phenosim)
    • option to do a linear transformation of phenotype with co factor of external data and normatisation:
      • ph_normalise : perform a normalisation (1) or not 0 (Default)
      • each phenotype i be normalise using newpheno = norm(pheno)+var0ia+var1ib+ ... + intercept
      • ph_cov_norm : contains coefficients for relation separed by a comma (ex "sex=0.2,age=-0.1)
      • data : contains cofactor data for each individuals used to normalise with
      • ph_cov_range : normalisation range for initial phenotype
      • ph_intercept : intercept
• Association option :
  • boltlmm : 1 perform boltlmm (default 0), see boltlmm option in assoc/main.nf
  • gemma : 1 perform gemma (default 0) see gemma option in assoc/main.nf
  • covariates : covariates to include in model (if ph_normalise is 1)
• Statistics option :
  • ph_alpha_lim : list of alpha used to computed significance (separated by comma)
  • ph_windows_size : windows size around position used to simulate phenotype to define if was detected, in bp ex 1000bp in CM ex 0.1CM

output

different output is provided :

• simul folder : contains position used to defined phenotype
• in boltlmm/gemma folder, res_boltlmm/gemma.stat contains summary stat for each alpha:
  • we defined windows true as the windows around snp used to build phenotype (size is defined previously)
  • nsig_simall_alpha : number significant snp in all windows true
  • nsig_sim_alpha : number windows true where at least one snps is significant
  • nsig_simaround_alpha : number significant windows true where one snp is significant and has been excluded snps used to build pheno
  • nsig_nosim_alpha : snp significant snp not in windows true
  • nsnp : snp number total in dataset
  • nsnpsima : snp number used to build phenotype (see ph_nb_qtl)
• in boltlmm/gemma/simul/ : contains p.value compute for each simulation

###Note

• for phenotype simulation all missing values is discarded and replaced by more frequent allele
• phenosim use a lot of memory and time, subsample of snp/samples improve times / memory used

5. Estimation of heritabilies

This section describes a pipeline in devlopment, objectives is estimated heritabilities with various way, we developped : ldlc, grmel of bolt and greml of gcta, gemma two distincs approaches should be considered :

• based on relatdness matrix and phenotype as gcta, bolt, gemma
• based on gwas result as implemented in ldlc and gemma

Installation

need python3, gcta, ldlc, bolt and gemma

Running

The pipeline is run: nextflow run assoc/esth2-assoc.nf

The key options are:

• work_dir : the directory in which you will run the workflow. This will typically be the h3agwas directory which you cloned;
• output_dir : output directory
• output_pat : output pattern
• ̀ data` : same option that assoc/plink-assoc.nf, file is optional, used for gemma, bolt and gcta
  • pheno :phenotypes used in data to computed in gemma, bolt
• file_gwas : one ore more one file gwas, used for ldsc and gemma, to defined column of files :
  • ̀ head_pval` : pvalue header [ default : "P_BOLT_LMM" ]
  • head_n : N (individuals number) [ default : None ], if not present computed with plink (and data/pheno if present)
  • head_rs : rs header column [default : "SNP"]
  • head_beta : beta header colum [default : "BETA"]
  • head_se : column for standard error of beta "SE"
  • head_A1 : column for A0 :[default : "ALLELE0" ]
  • head_A2 : column for A0 :[default : "ALLELE2" ]
  • head_freq : freq header [ default : A1Freq],
  • head_n: N header, used just for ldsc, if not present, Nind must be initialize.
  • Nind : if head_n not initialise, must be initialise, individuals number for each gwas file, separate by comma
• ldsc_h2 : need a estimation of h2 by ldc : 1 [default : 0]:
  • LDSC computes heritabilies between gwas value using LD information.
  • ldsc_bin : binary for ldsc
  • dir_ref_ld_chr : folder containing ld information, 1 file by begin by chromosome num without chr, see : --ref-ld-chr in ldsc manual
  • ̀ldsc_mem_req
  • ldsc_h2_multi : computing genetic correlation. between different gwas result
  • munge_sumstats_bin : binary for munge
  • ldsc_h2opt : other option for ldsc
  • output :
• gemma_h2 : estimation using gemma,
  • gemma_bin : gemma binary [ default "gemma"]
  • gemma_h2 : do you want a estimation with gemma of heritabilies with relatdness matrix [default : 0] :
  • gemma_h2_pval : do you want a estimation of heritabilities with gemma using p-value [ default : 0]
  • need file of pvalue see file_gwas
  • Z obtained with beta/se
  • gemma_h2_typeest do you wang a (1) or reml (2)[default : "1"]
  • gemma_mat_rel for gemma_h2, have you a pre estimated relatdness otherwise it wil be computed with plink file [default : none]
  • gemma_num_cores : Core number [ default : 6]
  • gemma_mem_req : memory for gemma [ default : "10GB" ]
  • gemma_relopt = 1
  • file_rs_buildrelat : list of rs used to build relatdness [default : None ]
• bolt_h2 : estimation using bolt
  • see manual
  • file_rs_buildrelat : list of rs used to build relatdness [default : None ]
    • if SNPs is higher than 950,000, 950,000 SNPs are chosen randomly to build the model (see --modelSnps option in bolt)
  • bolt_covariates_type : for bolt need to define if covariate is binary (0) or continue (1), a comma-separated list as same order than covariates
  • bolt_ld_score_file : A table of reference LD scores for boltlmm is needed to calibrate the BOLT-LMM statistic (option in boltlmm --LDscoresFile),to choice a specific column in Ld file you can use bolt_ld_scores_col option (by default : LDSCORE) if option is not provided --LDscoresUseChip used.
  • bolt_num_cores if bolt is used set this up to 8
  • bolt_mem_req memory required for boltlmm, (default : 6GB)
  • bolt_h2_multi : to have multi variance between traits [ default : 0]
• gcta :
  • general option :
  • output_dir : plink directory
  • output_pat : basename plink
  • data : data phenotypes
  • pheno : pheno to estimate h2
  • covariates : covariate to estimate heritabilities
  • gcta_mem_req : [default 20GB]
  • gcta_bin : binary for gcta
  • gcta_h2_ldscore [default 200kb]
  • gcta_h2_multi : computed co heritability between phenotype
  • gcta_h2_imp : 0 : version for low density of snps as dna chip
  • gcta_h2_imp : 1 version for high density of snps as data imputed, described here with au
  • gcta_h2_grmfile : extension of grm output for 3 files gcta_h2_grmfile.grm.bin, gcta_h2_grmfile.grm.id gcta_h2_grmfile.grm.N.bin otbained with command line : gcta64 --bfile plk --make-grm --out outhead, if gcta_h2_grmfile is empty default, pipeline will generate files and output will be in gctagrm folder, if not present will be generated :
  • for imputed data, we used algorithm of here and build :
  • grm_cutoff : grm-cutoff in gcta (alias of --rel-cutoff) If used in conjunction with a later calculation (see the order of operations page for details), --rel-cutoff excludes one member of each pair of samples with observed genomic relatedness greater than the given cutoff value (default 0.025) from the analysis. Alternatively, you can invoke this on its own to write a pruned list of sample IDs to plink.rel.id. [default : 0.025] * Step 1: segment based LD score * Step 2 : stratify the SNPs by LD scores of individual SNPs in R * Step 3: making GRMs using SNPs stratified into different groups * steps was very long you can provide gcta_h2_grmfile option to skip with already snps stratified * gcta_mem_reqmgrm : option memory for the step
  • gcta_h2_mgrmfile : if file not provide pipeline will do step described before[default None]
  • gcta_mem_reqmgrm : [default 40GB]
  • params.gcta_reml_alg : see reml-alg : Specify the algorithm to run REML iterations, 0 for average information (AI), 1 for Fisher-scoring and 2 for EM. The default option is 0, i.e. AI-REML, if this option is not specified. [oa]

5. MetaAnalysis pipeline : assoc/meta-assoc.nf

This section describes a pipeline in devlopment, purpose of this pipeline is to do a meta analysis with a various format files.Our script, meta-assoc.nf takes as input various GWAS results files and rsid to do a metanalysis with METAL, GWAMA and Metasoft

Installation

need python3, METAL, GWAMA, MR-MEGA and MetaSoft

Running

The pipeline is run: nextflow run meta-assoc.nf

The key options are:

• work_dir : the directory in which you will run the workflow. This will typically be the h3agwas directory which you cloned;
• input, output and script directories: the default is that these are subdirectories of the work_dir and there'll seldom be reason to change these;
• output_dir = "all"
• meta analysis option :
  • metal : 1 perform metal (default 0)
  • gwama : 1 perform gwama (default 0)
  • metasoft : 1 perform metasoft(default 0)
    • metasoft_pvalue_table : for metasoft need files : HanEskinPvalueTable.txt
  • mrmega : 1 perform MR-MEGA (default 0)
• file_config
  • describe all informations for each gwas result used for meta analysis
  • file is comma separated (csv), each line is to describe one file
  • header of config file is : rsID,Chro,Pos,A1,A2,Beta,Se,Pval,N,freqA1,direction,Imputed,Sep,File,IsRefFile
    • rsID : column name for rsID in gwas file
    • Chro : column name for Chro in gwas file
    • Pos : column name for Pos in gwas file
    • A1 : column name for reference allele in gwas file
    • A2 : column name for alternative allele in gwas file
    • Beta : column name for B values in gwas file
    • Se : column name for sterr values in gwas file
    • N : column name for size in gwas file
    • freqA1 : column name for freqA1 or maf in gwas file
    • direction : column name of strand for association -/+ in gwas file
    • Imputed : column name of imputed or not for position in gwas file
    • Sep : what separator is in gwas file :
      • you could use characters as ; . : but to avoid some trouble you can use :
        • COM : for comma
        • TAB : for tabulation
        • WHI : for white space
    • File : gwas file with full path
    • IsRefFile : you need to define a reference file to define what rs should be considered in other files
    • if one of the column is missing in your GWAS file, replace by NA
• optional option :
  • binaries :
    • metal_bin : binarie for metal (default : metal )
    • gwama_bin : binarie for gwam ( default : GWAMA_ )
    • metasoft_bin : binarie for java of metasoft ( default Metasoft.jar)
    • mrmega_bin : binarie for java of metasoft ( default Metasoft.jar)
  • options softwares :
    • ma_metasoft_opt : append other option in metasoft command line(default : null)
    • ma_genomic_cont : use a genomic_control use in METAL and GWAMA(default, 0)
    • ma_inv_var_weigth: do a invert variance weight usefull for metal (default, 0)
    • ma_random_effect : do mixed model (default 1)
    • ma_mrmega_pc : how many pcs used for mrmega (default : 4)
    • ma_mrmega_opt : append other option in MR-MEGA command line (default : null)

specificity

MR-MEGA

MR-MEGA need chromosomes, positions and N (sample number) for each position, so in pipeline referent file (in file_config, 1 in IsRefFile) must be have chromosome and poosition

6. Mtag analysis

reference

TODO

parameters

TODO

• file_gwas : one ore more one file gwas of differents phenotype
  • ̀ head_pval` : pvalue header [ default : "P_BOLT_LMM" ]
  • head_n : N (individuals number) [ default : None ], if not present computed with plink (and data/pheno if present)
  • head_rs : rs header column [default : "SNP"]
  • head_beta : beta header colum [default : "BETA"]
  • head_se : column for standard error of beta "SE"
  • head_A1 : column for A0 :[default : "ALLELE0" ]
  • head_A2 : column for A0 :[default : "ALLELE2" ]
  • head_freq : freq header [ default : A1Freq],
  • head_n: N header, used just for ldsc, if not present, Nind must be initialize.
• if n not initialise :
  • used plink file to computed each position with n :
    • input_pat : input pattern of plink file
    • input_dir : input dir of plink file
  • list_n : need to be implemented