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build genotype using 1000 Genotype pipeline and gcta: utils/build_example_data/main.nf

Running

The pipeline is run: nextflow run utils/build_example_data/main.nf

The key options are:

  • output_dir : output direction

  • output : [default "out"]

  • pos_allgeno : file contains chromosome and position that will be extracted from vcf file, each lines is chromosome and position [need, no default]

  • list_chro : list chro extracted fromm 1000 genome [1-22,X : from 1 to 22 and X chromosome]

  • ftp1000genome : download 1000 genome for build dataset no : 0 yes :1 [default :1]

  • list_vcf : file contains list of vcf if ftp1000genome is no,

  • information relative of gwas catalog to extract:

  • gwas_cat : file of gwas catalog, for the moment just uscs format is allowed (not inplemented)

  • gwas_cat_ftp : file to download gwas catalog

  • list_pheno : list pheno extracted from gwas catalog, split by one comma each pheno (default : "Type 2 diabetes")

  • extraction and simulation :

  • simu_hsq : variance explained of disease by genetics [default 0.3]

  • simu_k : Specify the disease prevalence for binary phenotype [default 0.1]

  • simu_rep : repetition number using same snps and heriability effect [default 10]

  • used_effect : two behavious for effect, used gwas catalog effect (option 1) or simulated value using a normal law (option 0) [default : 1]

  • clump position of gwas catalog :

  • clump_p1 [default :0.0001]

  • clump_p2 [default :0.01]

  • clump_r2 [ default : 0.50]

  • clump_kb :[ default : 250 ]

  • other :

  • plk_cpus : cpus number used for plink and gcta[default 10]

  • gcta_bin : binary for gcta [default gcta64]

Example

created input file

awk '{print $1"\t"$4}' data/array_plk/array.bim  > utils/list_posarray

command line

nextflow run ~/Travail/git/h3agwas//utils/build_example_data/main.nf -profile slurmSingularity   --pos_allgeno utils/list_posarray -resume --nb_snp 3 --output_dir simul_gcta_main

Installation

need tabix, TODO tested for singularity image: yes

Simulation using gcta and plink file : utils/build_example_data/simul-assoc_gcta.nf

Pipeline used same argument than previous pipeline without download 1000 genomes

Example

nextflow run ~/Travail/git/h3agwas/utils/build_example_data/simul-assoc_gcta.nf -profile slurmSingularity  --input_dir data/imputed/  --input_pat  imput_data --output_dir simul_gcta

Simulation using phenosim and plink file : utils/build_example_data/simul-assoc_phenosim.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

Option

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

Example :

nextflow run ~/Travail/git/h3agwas/utils/build_example_data/simul-assoc_phenosim.nf -profile slurmSingularity  --ph_normalise 0 --input_dir data/imputed/ --input_pat  imput_data --gemma 1