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Bayesian finemapping analysis

Source: R/claudia.R
finemap.abf.Rd

Bayesian finemapping analysis

Usage

finemap.abf(dataset, p1 = 1e-04, prior_weights = NULL)

Arguments

dataset

a list with specifically named elements defining the dataset to be analysed. See check_dataset for details.

p1

prior probability a SNP is associated with the trait 1, default 1e-4

prior_weights

Non-negative weights for the prior probability a SNP is causal

Value

a data.frame:

  • an annotated version of the input data containing log Approximate Bayes Factors and intermediate calculations, and the posterior probability of the SNP being causal

Details

This function calculates posterior probabilities of different causal variant for a single trait.

If regression coefficients and variances are available, it calculates Bayes factors for association at each SNP. If only p values are available, it uses an approximation that depends on the SNP's MAF and ignores any uncertainty in imputation. Regression coefficients should be used if available.

Author

Chris Wallace