magma: Bayesian Multi-marker Analysis of Genomic Annotation...
In psoerensen/qgg: Statistical Tools for Quantitative Genetic Analyses

magma

R Documentation

Bayesian Multi-marker Analysis of Genomic Annotation (Bayesian MAGMA)

Description

This function analyzes feature sets using MAGMA or Bayesian methods for association testing. It supports joint or marginal testing, as well as Bayesian linear regression using different priors ('bayesC', 'bayesR').

Usage

magma(
  stat = NULL,
  sets = NULL,
  method = "magma",
  type = "joint",
  test = "one-sided",
  pi = 0.001,
  nit = 5000,
  nburn = 1000
)

Arguments

`stat`	A numeric vector or matrix of summary statistics, where rows represent features and columns represent phenotypes.
`sets`	A list of feature sets (e.g., genes, SNPs) to be analyzed.
`method`	A string specifying the method to use. Options are '"magma"', '"blr"', '"bayesC"', or '"bayesR"'. Default is '"magma"'.
`type`	A string specifying the type of analysis to perform. Options are '"joint"' (default) or '"marginal"'. Only used with 'method = "magma"'.
`test`	A string specifying the statistical test. Options are '"one-sided"' (default) or '"two-sided"'. Only used with 'method = "magma"'.
`pi`	A numeric value specifying the proportion of non-zero effects. Used for Bayesian methods. Default is '0.001'.
`nit`	An integer specifying the number of iterations for Bayesian methods. Default is '5000'.
`nburn`	An integer specifying the number of burn-in iterations for Bayesian methods. Default is '1000'.

Details

The function uses either the MAGMA approach for set-based testing or Bayesian linear regression to estimate effect sizes and probabilities of association for feature sets. For Bayesian methods, a spike-and-slab prior is applied.

The 'stat' input must have row names corresponding to feature identifiers. The 'sets' input must be a named list, where each element corresponds to a feature set.