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Run GCTA-COJO

Source: R/COJO.R
COJO.Rd

Main function to run either the conditional stepwise procedure (genome-wide) or the conditional analysis (locus-specific) from GCTA-COJO.

Usage

COJO(
  dat,
  locus_dir,
  bfile = file.path(locus_dir, "LD/plink"),
  fullSS_path = NULL,
  conditioned_snps = NULL,
  exclude = NULL,
  prefix = "cojo",
  run_stepwise = TRUE,
  run_conditional = FALSE,
  run_joint = FALSE,
  credset_thresh = 0.95,
  freq_cutoff = 0.1,
  compute_n = "ldsc",
  colmap = echodata::construct_colmap(),
  full_genome = FALSE,
  gcta_path = echoconda::find_executables_remote(tool = "gcta")[[1]],
  verbose = TRUE,
  ...
)

Source

COJO documentation Publication 1 (doi:10.1016/j.ajhg.2010.11.011 ) Publication 2 (doi:10.1038/ng.2213 )

Arguments

dat

Fine-mapping results data.

locus_dir

Locus-specific directory to store results in.

bfile

Input PLINK binary PED files, e.g. test.fam, test.bim and test.bed (see PLINK user manual for details).

fullSS_path

Path to the full summary statistics file (GWAS or QTL) that you want to fine-map. It is usually best to provide the absolute path rather than the relative path.

conditioned_snps

Which SNPs to conditions on when fine-mapping with (e.g. COJO).

exclude

Specify a list of SNPs to be excluded from the analysis.

prefix

Prefix to use for file names.

run_stepwise

--cojo-slct: Perform a stepwise model selection procedure to select independently associated SNPs. Results will be saved in a *.jma file with additional file *.jma.ldr showing the LD correlations between the SNPs.

run_conditional

--cojo-cond: Perform association analysis of the included SNPs conditional on the given list of SNPs. Results will be saved in a *.cma. The conditional SNP effects (i.e. bC) will be labelled as "NA" if the multivariate correlation between the SNP in question and all the covariate SNPs is > 0.9.

run_joint

--cojo-joint: Fit all the included SNPs to estimate their joint effects without model selection. Results will be saved in a *.jma file with additional file *.jma.ldr.

credset_thresh

The minimum mean Posterior Probability (across all fine-mapping methods used) of SNPs to be included in the "mean.CS" column.

freq_cutoff

Minimum variant frequency cutoff.

compute_n

How to compute per-SNP sample size (new column "N").
If the column "N" is already present in dat, this column will be used to extract per-SNP sample sizes and the argument compute_n will be ignored.
If the column "N" is not present in dat, one of the following options can be supplied to compute_n:

0

N will not be computed.

>0

If any number >0 is provided, that value will be set as N for every row. **Note**: Computing N this way is incorrect and should be avoided if at all possible.

"sum"

N will be computed as: cases (N_CAS) + controls (N_CON), so long as both columns are present.

"ldsc"

N will be computed as effective sample size: Neff =(N_CAS+N_CON)*(N_CAS/(N_CAS+N_CON)) / mean((N_CAS/(N_CAS+N_CON))(N_CAS+N_CON)==max(N_CAS+N_CON)).

"giant"

N will be computed as effective sample size: Neff = 2 / (1/N_CAS + 1/N_CON).

"metal"

N will be computed as effective sample size: Neff = 4 / (1/N_CAS + 1/N_CON).

colmap

Column mappings object. Uses construct_colmap by default.

full_genome

Whether to run GCTA-COJO across genome-wide (TRUE), or within a specific locus (default: FALSE)

gcta_path

Path to the GCTA-COJO executable.

verbose

Print messages.

...

Arguments passed on to COJO_args

cojo_file

Input the summary-level statistics from a meta-analysis GWAS (or a single GWAS).

out

Specify output root filename.

cojo_slct

Perform a stepwise model selection procedure to select independently associated SNPs. Results will be saved in a *.jma file with additional file *.jma.ldr showing the LD correlations between the SNPs.

cojo_cond

Perform association analysis of the included SNPs conditional on the given list of SNPs. Results will be saved in a *.cma. The conditional SNP effects (i.e. bC) will be labelled as "NA" if the multivariate correlation between the SNP in question and all the covariate SNPs is > 0.9.

cojo_joint

Fit all the included SNPs to estimate their joint effects without model selection. Results will be saved in a *.jma file with additional file *.jma.ldr showing the LD correlations between the SNPs.

maf

Exclude SNPs with minor allele frequency (MAF) less than a specified value, e.g. 0.01.

max_maf

Include SNPs with MAF less than a specified value, e.g. 0.1.

cojo_top_SNPs

Perform a stepwise model selection procedure to select a fixed number of independently associated SNPs without a p-value threshold. The output format is the same as that from --cojo-slct.

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. Note: it will be extremely time-consuming if you set a very low significance level, e.g. 5e-3.

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.

cojo_collinear

During the model selection procedure, the program will check the collinearity between the SNPs that have already been selected and a SNP to be tested. The testing SNP will not be selected if its multiple regression R2 on the selected SNPs is greater than the cutoff value. By default, the cutoff value is 0.9 if not specified.

diff_freq

To check the difference in allele frequency of each SNP between the GWAS summary datasets and the LD reference sample. SNPs with allele frequency differences greater than the specified threshold value will be excluded from the analysis. The default value is 0.2.

cojo_gc

If this option is specified, p-values will be adjusted by the genomic control method. By default, the genomic inflation factor will be calculated from the summary-level statistics of all the SNPs unless you specify a value, e.g. --cojo-gc 1.05.

Details

Documentation
Columns are SNP, the effect allele, the other allele, frequency of the effect allele, effect size, standard error, p-value and sample size. The headers are not keywords and will be omitted by the program. Important: "A1" needs to be the effect allele with "A2" being the other allele and "freq" should be the frequency of "A1".'
Note: 1) For a case-control study, the effect size should be log(odds ratio) with its corresponding standard error. 2) Please always input the summary statistics of all SNPs even if your analysis only focuses on a subset of SNPs because the program needs the summary data of all SNPs to calculate the phenotypic variance. You can use one of the --extract options (Data management) to limit the COJO analysis in a certain genomic region.

General results columns:

Chr

Chromosome.

SNP

SNP RSID.

bp

Physical position.

refA

Effect allele.

freq

Frequency of the effect allele in the original data.

b

Effect size.

se

Standard error.

p

p-value from the original GWAS or meta-analysis.

n

Estimated effective sample size.

freq_geno

Frequency of the effect allele in the reference sample.

Stepwise analysis results columns:

bJ

Effect size from the joint analysis of all the selected SNPs.

bJ_se

Standard error from the joint analysis of all the selected SNPs.

pJ

p-value from the joint analysis of all the selected SNPs.

LD_r

LD correlation between the SNP i and SNP i + 1 for the SNPs on the list.

LD_r2

LD_r squared.

CS

Whether the SNP is in the Credible Set, defined as any SNP with where pJ<(1-credset_thresh).

Conditional analysis results columns:

bC

Effect size from the conditional analysis.

bC_se

Standard error from the conditional analysis.

pC

p-value from the conditional analysis.

CS

Whether the SNP is in the Credible Set, defined as any SNP with where pC<(1-credset_thresh).

See also

Other COJO: COJO_get_stepwise_results(), COJO_locus_subdir(), COJO_process_results(), COJO_run()

Examples

if (FALSE) { # \dontrun{
vcf <- system.file("extdata", "BST1.1KGphase3.vcf.bgz",
    package = "echodata")
dat <- echodata::BST1
locus_dir <- file.path(tempdir(), echodata::locus_dir)
fullSS_path <- echodata::example_fullSS()
bfile <- echoLD::vcf_to_plink(vcf = vcf)$prefix
cojo_DT <- COJO(dat = dat,
                locus_dir = locus_dir,
                fullSS_path = fullSS_path,
                bfile = bfile)
} # }