mv_iwas_summ: MV-IWAS using Summary Statistics
In kathalexknuts/MVIWAS: Runs MV-IWAS and MV-IWAS-Egger

Description Usage Arguments Examples

View source: R/mv_iwas_summ.R

This function produces causal estimates for phenotype-disease associations using MV-IWAS and MV-IWAS-Egger using GWAS summary statistics for a disease and exposure phenotypes (Knutson, Deng, and Pan, 2020)

mv_iwas_summ(
  betaZY,
  se_betaZY,
  betaZX,
  corr_mat,
  n,
  trait_type,
  n_case = NULL,
  n_control = NULL
)

`betaZY`	A one column matrix with estimated SNP effects on the disease from GWAS summary data. There should be a separate row for each SNP. The order of SNPs in rows should match across betaZY, se_betaZY, betaZX, se_betaZX, and corr_mat.
`se_betaZY`	A one column matrix with estimated SE of SNP effects on the disease from GWAS summary data. There should be a separate row for each SNP. The order of SNPs in rows should match across betaZY, se_betaZY, betaZX, se_betaZX, and corr_mat.
`betaZX`	A matrix with estimated SNP effects on each exposure phenotype of interest, taken from GWAS summary data. There should be a separate column for each exposure phenotype of interest and a separate row for each SNP. The order of SNPs in rows should match across betaZY, se_betaZY, betaZX, se_betaZX, and corr_mat. For SNPs which are not to be included in Stage 1 for a given exposure phenotype, set the corresponding SNP row to 0 for that phenotype's column. Do not use NA. If the number of columns is equal to 1, this test will be equivalent to the so-called UV-IWAS test.
`corr_mat`	The covariance or correlation matrix of SNPs, estimated from a reference panel of similar ancestry to the GWAS summary data. The order of SNPs in the columns and rows should match each other and the row orders of betaZY, se_betaZY, betaZX, se_betaZX.
`n`	The sample size used to estimate the disease GWAS summary data
`trait_type`	Either "Continuous" or "Binary", for continous or binary disease traits
`n_case`	The number of cases used in the disease GWAS sample. Required for trait_type = "Binary", default for Continuous trait is NULL
`n_control`	The number of controls used in the disease GWAS sample. Required for trait_type = "Binary", default for Continuous trait is NULL
`se_betaZX`	A matrix with estimated SE of SNP effects on each exposure phenotype of interest, taken from GWAS summary data. There should be a separate column for each exposure phenotype of interest and a separate row for each SNP. The order of SNPs in rows should match across betaZY, se_betaZY, betaZX, se_betaZX, and corr_mat. For SNPs which are not to be included in Stage 1 for a given exposure phenotype, set the corresponding SNP row to 0 for that phenotype's column. Do not use NA.