mJAM_SuSiE: Run mJAM with SuSiE
In USCbiostats/hJAM: Hierarchical Joint Analysis of Marginal Summary Statistics

mJAM_SuSiE

R Documentation

Run mJAM with SuSiE

Description

fitting mJAM-SuSiE

Usage

mJAM_SuSiE(
  Marg_Result = NULL,
  EAF_Result = NULL,
  N_GWAS,
  X_ref,
  filter_rare = FALSE,
  rare_freq = NULL,
  SuSiE_num_comp = 10,
  SuSiE_coverage = 0.95,
  SuSiE_min_abs_corr = 0.5,
  max_iter = 500,
  estimate_residual_variance = F
)

Arguments

`Marg_Result`	A data frame with marginal summary statistics from all studies. Col1: SNP name; Col2: Effect sizes from study #1; Col3: Std Errors of effect sizes from study #1; ...
`EAF_Result`	A data frame with effect allele frequency (EAF) from all studies. Col1: SNP name; Col2: EAF from study #1; Col3: EAF from study #2; ...
`N_GWAS`	A vector of sample sizes in all original GWAS studies.
`X_ref`	A list of matrices with individual-level SNP dosage data in each study/population.
`filter_rare`	A logical variable indicating whether to filter rare SNPs before the analysis. Default is 'FALSE.' If 'TRUE', then please specify 'rare_freq'.
`rare_freq`	A vector of frequencies between 0 and 0.5 to specify the minor allele frequency cut-off if you want to filter rare SNPs before the analysis. Please also set 'filter_rare' to be TRUE. For example, if there are 3 populations, then rare_freq = c(0.01, 0, 0.01) means SNPs with MAF < 0.01 in pop 1 and MAF < 0.01 in pop 3 will be removed from analysis.
`SuSiE_num_comp`	SuSiE argument. The maximum number of causal SNPs that you want to select. Default is 10.
`SuSiE_coverage`	SuSiE argument. The required coverage of credible sets. Default is 0.95.
`SuSiE_min_abs_corr`	SuSiE argument. Minimum absolute correlation allowed in a credible set.
`max_iter`	SuSiE argument. Maximum iterations to perform.
`estimate_residual_variance`	SuSiE argument. If 'TRUE', then the susie algorithm is updating residual variance estimate during iterations. If 'FALSE', then use the residual variance is a fixed value, which is usually var(Y).