config_default.txt

# !! Do not edit !!
# Copy this file("config_default.txt") to "config.txt" and make your modifications there

# reference file, such as ref9545380_1kgPhase3eur_LDr2p1.mat, which contains variables
# LDmat (binary sparse LD matrix), chrnumvec, posvec, mafvec, is_ambiguous, is_intergenic.
# Feel free to specify full path to the file.
reffile=ref9545380_1kgPhase3eur_LDr2p1.mat

# Set path to the folder with traits files (PGC_SCZ_2014.mat, etc)
# It is OK to keep 'traitfolder' empty. In this case 'traitfiles' must contain full path to each file.
# Otherwise 'traitfiles' must give a file name within 'traitfolder', including .mat extension.
traitfolder=.

# Set your GWAS file here for trait1 and other traits to condition on
traitfile1=CTG_COG_2018.mat
traitname1=COG
traitfiles={'SSGAC_EDU_2016.mat'}
traitnames={'EDU'}

# Output directory
outputdir=results

# stattype can be either conjfdr (conjunctive fdr) or condfdr (conditional fdr).
# recommended values of fdrthresh is 0.05 for conjfdr, and 0.01 for condfdr.
stattype=conjfdr
fdrthresh=0.01

# Random pruning options (on/off, number of iterations)
randprune=true
randprune_n=20

# Exclusion regions in format [CHR BP_from BP_to],
# where CHR is chromosome and BP_from and BP_to are genomic coordinates (hg19 build).
# To specify multiple regions use ;
# Example for MHC and chr8 inversion: [6 25119106 33854733; 8 7200000 12500000]
exclude_chr_pos=[6 25119106 33854733]

# Customizations for manhattan plot
manh_fontsize_genenames=12
manh_yspace=0.75
manh_ymargin=0.25
manh_colorlist=[1 0 0; 1 0.5 0 ; 0 0.75 0.75; 0 0.5 0; 0.75 0 0.75; 0 0 1; 0 1 0; 0 1 1]

# An optional file containing additional reference information (e.g. the 9545380.ref file)
# 'SNP' column - for SNP rs# or other marker names
# 'A1' and 'A2' columns - alleles information
refinfo=

# ========= Other miscellaneous options (expert usage only) =========

# Force random prune indices (pruneidx) to be re-generated for each run.
# Setting this to false will preserve pruneidx across re-runs (unless you restart matlab or clear workspace).
# Be careful with setting this to false.
# Remember to clear matlab workspace whenever you change ldmatfile or randprune_n.
reset_pruneidx=true

# randprune_repeats allows to choose from three resampling options ('default', 'maxout', 'none')
randprune_repeats=default

# Threshold on Fisher's combined statistics (flp)
# SNPs with flp below this thresh will not make it into the resulting loci table.
# Set this parameter to 1 results to disable filtering on flp.
pthresh=1

# Genomic correction options
# perform_gc turns genomic correction on or off
# use_standard_gc=true uses median genomic correction (standard from the literature),
# while use_standard_gc=false imply our in-house developed genomic correction procedure (typically more conservative).
# randprune_gc=true imply that lambdaGC factor will be calculated after random pruning.
perform_gc=true
use_standard_gc=false
randprune_gc=true

# By default the exclusion list (exclude_chr_pos) is applied only when cond/conj FDR model is fit, but not during discovery.
# Setting exclude_from_discovery=true will apply exclusion list also to the discovery.
# This flag has not effect on 'exclude_ambiguous_snps' option.
exclude_from_discovery=false

# Minor allele frequency threshold to exclude rare SNPs.
# Note that if some SNPs have undefined (unknown) MAF they will be excluded too.
# The likely reason for a SNP to have an undefined MAF is because it was not found in 1kG genotypes.
# For such SNPs we also don't know their LD structure,
# and hence can't perform random pruning on them.
# You may set mafthresh=nan to keep such SNPs in the analysis,
# but this is not recommended.
mafthresh = 0.005

# A flag indicating whether to exclude ambiguous SNPs (A/T and C/G) from the analysis.
# When set to 'true' ambigous SNPs will be excluded both from fit and from discovery,
# regardless of the 'exclude_from_discovery' setting.
exclude_ambiguous_snps = true

# A flag indicating whether to show plots on screen.
# This applies to manhattan plot, QQ plots, Fold enrichment plots, and FDR lookup heatmap.
onscreen = true

# pleioFDR analysis does not require effect dirrection, and is based on p-values.
# For historical reason the pleioFDR pipeline require also zscore as input, and report files with z-score, to allow user to asses effect direction.
# However, if effect direction info is not available for your GWAS summary statistics, then you may want to overwrite the above behavior.
# Setting `dummy_zscore = true` will force pleioFDR code to calcualte z-score from p-value (forcing them to be positive).
dummy_zscore = false

# A flag indicating whether to exit matlab upon completion of pleioFDR analysis.
exit_matlab_upon_completion = false