COJO: Run _GCTA-COJO_

In RajLabMSSM/echofinemap: echoverse module: Fine-mapping functions

Run GCTA-COJO

Description

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,
  ...
)

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).

Source

COJO documentation Publication 1 Publication 2

See Also

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

Examples

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)

RajLabMSSM/echofinemap documentation built on Jan. 3, 2023, 1:42 a.m.