Correcting the Coverage of Credible Sets from Bayesian Genetic Fine Mapping
Find approx. Bayes factors (ABFs)
Calculate ABFs from Z scores
Correlation matrix of SNPS
Corrected coverage estimate using Z-scores and MAFs
Corrected coverage estimate using estimated effect sizes and their sta...
Confidence interval for corrected coverage estimate using Z-scores and...
Confidence interval for corrected coverage estimate using estimated ef...
Corrected coverage estimate using Z-scores and MAFs (fixing nvar)
Corrected coverage estimate using estimated effect sizes and their sta...
Corrected coverage estimate of the causal variant in the credible set
Corrected credible set using Z-scores and MAFs
Corrected credible set using estimated effect sizes and their standard...
Credible set of genetic variants
Credible set of variants from matrix of PPs
Obtain credible sets from a matrix of posterior probabilities
Internal function: Simulate nrep ABFs from joint Z-score vector
Simulate posterior probabilities of causality from joint Z-score vecto...
Estimate the true effect at the causal variant using Z-scores and MAFs
Estimate the true effect at the causal variant using estimated effect ...
logsum
logsum rows of a matrix
Pipe operator
Find PPs of SNPs from matrix of Z-scores
Find PPs of SNPs from Z-scores
Proportion of credible sets containing the causal variant
Find PPs for SNPs and null model from P-values and MAFs
Variance of the estimated effect size for case-control data
Simulate marginal Z-scores from joint Z-score vector
Find PPs for SNPs and null model from Z-scores and MAFs
Simulate posterior probabilities of causality from joint Z-score vecto...
Using a computationally efficient method, the package can be used to find the corrected coverage estimate of a credible set of putative causal variants from Bayesian genetic fine-mapping. The package can also be used to obtain a corrected credible set if required; that is, the smallest set of variants required such that the corrected coverage estimate of the resultant credible set is within some user defined accuracy of the desired coverage. Maller et al. (2012) <doi:10.1038/ng.2435>, Wakefield (2009) <doi:10.1002/gepi.20359>, Fortune and Wallace (2018) <doi:10.1093/bioinformatics/bty898>.
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