Skip to content

Latest commit

 

History

History
301 lines (253 loc) · 17.3 KB

README.md

File metadata and controls

301 lines (253 loc) · 17.3 KB
Raw

Finemaping

5 scripts to perform fine-mapping :

  • run fine-mapping at one specific region finemapping/finemap_region.nf
  • run fine-mapping on full summary statistics finemapping/main.nf
  • Stepwise model selection procedure to select independently associated SNPs with cojo from gcta finemapping/cojo.nf
  • Conditional analysis using gemma finemapping/cond-assoc.nf
  • Conditional analysis using gcta and summary statistics finemapping/cond-gcta.nf

Finemapping a specific region using pipeline: finemapping/finemap_region.nf

The purpose of this pipeline is to perform a initial analysis of finemapping

Our script, finemapping takes as input PLINK files, gwas file

some limits

  • pipeline discarded positions duplicated from genetics file and summary statistics

Running

The pipeline is run: nextflow run finemapping/finemap_region.nf

options

The key options are:

  • finemapping :

    • n_causal_snp : for finemapping causal snp number
    • chro : chromosome to apply chromosome
    • begin_seq : begin sequence to apply sequence
    • end_seq : end sequence to apply sequence
    • prob_cred_set : prob of credible set [default : 0.95], for FINEMAP software
    • used_pval_z : build beta and se using z or pvale [defaul 0:no]

  • threshold_p : treshold to used [default : 5E10-8]

  • genetics data :

    • 2 way are possible, data already in plink format (if possible same data that association has been done) or 1000 genome will be download
    • genetics data user :
      • 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);

  • 1000 genome :

    • ftp_vcf
    • other_cpus_req

  • file_gwas : file contains summary stat of gwas :

    • 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 allele 1 :[default : "ALLELE1" ]
    • head_A2 : column for allele 0 or 2 :[default : "ALLELE0" ]
    • head_freq : freq header [ default : ""],
    • head_chr : freq header [ default : ""],
    • head_bp : freq header [ default : ""],

  • n_pop : number individuals for data set pop [default : 0]

  • gwas_cat : file of gwas catalog for plot

    • headgc_chr : chromosome header of gwas catalog
    • headgc_bp : position head of gwas catalog
    • list_phenogc : from gwas catalog what pheno used (separated by a comma)
    • file_phenogc : from gwas catalog what pheno used inside a file (separated by a comma)

  • cojo parameter for Stepwise model selection procedure to select independently associated SNPs:

    • gcta_mem_req="6GB"
    • 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 ] (need to be implemented)
    • 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_other : other option for slct see manual )(need to be implemented)

  • annotations parameter :

  • paintor_fileannot) : file contains annotation (see paintor manual)

  • binary :

    • finemap_bin : software binary
    • paintor_bin : software binary
    • plink_bin : software binary
    • gcta_bin : software binary

  • mem and cpu :

    • max_plink_cores LD / plink [default 4]
    • plink_mem_req LD / plink [default : "5GB"]
    • gcta_mem_req [default :"6GB"]
    • caviar_mem_req [default :"40GB"]
    • paintor_mem_req [default :"20GB"]
    • fm_mem_req [default : "20G"]
    • plink_mem_req [default :"6GB"]

Installation

need locuszoom, R : (ggplot2), python3, finemap, paintor, gcta, plink, gwama

Example

nextflow run  h3abioneth3agwas/finemapping/finemap_region.nf  --head_pval p_wald --head_bp ps --head_chr chr --head_rs rs --head_beta beta --head_se se --head_A1 allele1 --head_A2 allele0 --list_phenogc "Type 2 diabetes" --input_dir  data/imputed/  --input_pat imput_data --file_gwas data/summarystat/all_pheno.gemma  --output_dir finemapping_pheno1_wind --output finemapping_pheno1 -resume  -profile slurmSingularity --begin_seq 112178657 --end_seq 113178657 --chro 10

finemapping pipeline automated with selection of lead snps using plink : finemapping/main.nf

algorithm :

  • using clump plink to defined independant locus
  • for each snps apply algorithms from pipeline finemapping/finemap_region.nf

options

The key options are:

  • finemapping :
    • n_causal_snp : for finemapping causal snp number
    • prob_cred_set : prob of credible set [default : 0.95], for FINEMAP software
    • used_pval_z : build pvalue using z (need to check) [defaul 0:no]
    • threshold_p : treshold to used [default : 5E10-8]
    • threshold_p2 : Secondary significance threshold for clumped SNPs see clump
    • size_wind_kb : size windows used for clump and around each independant snps
    • clump_r2 : ld used for clump
    • cut_maf minor allele frequencies [ default : 0.01]
  • data :
    • data : optional used individual from data to clean plink file`
    • pheno : phenotype associate to data
    • covariates : covariate must be in data
  • plink :
    • 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);
  • file_gwas : file contains summary stat of gwas :
    • 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 allele 1 :[default : "ALLELE1" ]
    • head_A2 : column for allele 0 or 2 :[default : "ALLELE0" ]
    • head_freq : freq header [ default : TODO],
    • head_chr : freq header [ default : TODO],
    • head_bp : freq header [ default : TODO],
  • n_pop : number individuals for data set pop [default : 0]
  • gwas_cat : file of gwas catalog for plot
    • headgc_chr : chromosome header of gwas catalog
    • headgc_bp : position head of gwas catalog
    • list_phenogc : from gwas catalog what pheno used (separated by a comma)
    • file_phenogc : from gwas catalog what pheno used inside a file (separated by a comma)
  • cojo parameter (Stepwise model selection procedure to select independently associated SNPs.) :
    • gcta_mem_req="6GB"
    • 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_other : other option for slct see manual
  • mem and cpu :
    • max_plink_cores LD / plink [default 4]
    • plink_mem_req LD / plink [default : "5GB"]
    • gcta_mem_req [default :"6GB"]
    • caviar_mem_req [default :"40GB"]
    • paintor_mem_req [default :"20GB"]
    • fm_mem_req [default : "20G"]
    • plink_mem_req [default :"6GB"]
  • annotations parameter :
    • paintor_fileannot : file contains annotation (see paintor manual)

Installation

need locuszoom, R : ggplot2, python3, finemap, paintor, gcta, plink

Output

  • $output/clump/clump_output.clumped result of clump by plink
  • $output/gwascat : file of gwas catalog format
  • $output/data : gene dowmloaded
  • $output/fm/$chr_$bp : each positions where finemapping had been apply :
    • caviarbf :result of caviarbf
    • cojo_gcta : result a stepwise model selection procedure to select independently associated SNPs. showing the LD correlations between the SNPs. using gcta
    • fm_cond : finemap software using option using --cond
    • fm_sss : finemap software using option --sss
    • paintor : paintor output
    • $output/$chr_$bp/Finemapping_.all.out : result merge of all result
      • information relative at position : rsid chromosome position allele1 allele2 rsid chromosome position allele1 allele2
      • result of association : beta, se, p (if used used_pval_z = 1, beta and se had been computed using p-value, otherwise orignel)
      • gcta result, givin your independant : bj bJ_se pJ LD_r logpJ
      • value of your association rsid chromosome position allele1 allele2 maf beta se
      • IsSig : position significant
      • is_cred : is in credible interval set using finemap with --sss
      • cred_$chr_$bp_cond.cred : is in credible of finemap for finemap software with option --cond
      • cred_$chr_$bp_sss.cred : is in credible of finemap for finemap software with option --sss
      • finemap sss result : prob_fm_sss, log10bf_fm_sss, mean_fm_sss, sd_fm_sss,mean_incl_fm_sss, sd_incl_fm_sss

Example

nextflow run  h3abioneth3agwas/finemapping/main.nf --head_pval p_wald --head_bp ps --head_chr chr --head_rs rs --head_beta beta --head_se se --head_A1 allele1 --head_A2 allele0 --list_phenogc "Type 2 diabetes" --input_dir  data/imputed/  --input_pat imput_data --file_gwas data/summarystat/all_pheno.gemma  --output_dir finemapping_pheno1 --output finemapping_pheno1 -resume  -profile slurmSingularity

Conditional & joint (COJO) analysis of GWAS summary statistic

this section describes a pipeline in devlopment, objectives is doing a Stepwise model selection procedure to select independently associated SNPs on full summary statistics see cojo

Installation

need python3, gcta tested for singularity image: yes

Running

The pipeline is run: nextflow run finemapping/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 in file_gwas

  • plink :

  • 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);

  • 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"

Example

nextflow run   h3abionet/h3agwas/finemapping/cojo-assoc.nf --head_pval p_wald --head_bp ps --head_chr chr --head_rs rs --head_beta beta --head_se se --head_A1 allele1 --head_A2 allele0 --input_dir data/imputed/  --input_pat imput_data  --output_dir cojo --data data/pheno/pheno_test.all --pheno pheno_qt1 --file_gwas data/summarystat/all_pheno.gemma  -resume   -profile slurmSingularity

Conditional analysis of GWAS using gemma

cond-assoc.nf

Installation

need python3, gemma, R tested for singularity image: yes

algorithm

  • pipeline used :
    • plink file and phenotype file
    • pos_cond : list position to be conditionned, transform in 0 (homozygote A1), 0.5 (heterozygote) and 1 (homozygote A2) and used as covariable
    • chro_cond : chromosome to be conditionned
    • pos_ref : position de reference where will be extracted positions of interrest
  • pipeline will run :
    • for each pos_cond, run gemma on the region using as covariable pos_cond
    • pos_cond, run gemma on the region using as covariable all pos_cond (call merge)
    • run gemma on the region using just phenotype
  • output :
    • each gemma output
    • plot of LD between ref and cond
    • plot of p-value comparison of initial without conditional and conditional

Example

  • data and command line can be found h3agwas-examples
  • to perform a conditional analysis, using gemma where positions is used as covariable of the phenotype and check pos_ref (--pos_ref) is link or indepependant , argument same than gwas for gemma where you need to add :
    • pipeline will run a raw gwas using phenotype and covariable and after performed gwas using genotypes of each position from pos_cond as covariable
    • chro_cond : chro where pos_cond and pos_ref
    • pos_ref : pos will be tested for independance or not to pos_cond
    • pos_cond : list of position as conditional to verify indpendance with pos_ref, include as covariable
  • pipeline will compute ld between your positions ref and cond and produce figure

Conditional analysis of GWAS using gcta

need python3, gcta, R tested for singularity image: yes

algorithm

pipeline will extract positions pos_cond, pos_ref from summary statistics and genetics data and apply options --cojo-cond of gcta .

main options

  • pipeline used :
  • plink file, summary statistics and optional file for individual to clean
  • pos_cond : list position to be conditionned
  • chro_cond : chromosome to be conditionned
  • pos_ref : 1 position de reference where will be extracted positions of interrest
  • cojo_actual_geno
  • plink :
  • 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 : same option that assoc/main.nf, file is optional, used if need select specific individus for gcta, compute frequencies or N, if mission in file_gwas
  • phenotype
  • 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" ]