cl_interval: Confidence interval associated with discovery GWAS...

In amandaforde/winnerscurse: Winner's Curse Adjustment Methods for Summary Statistics from Genome-Wide Association Studies

Confidence interval associated with discovery GWAS conditional likelihood methods

Description

cl_interval is a function that allows the user to obtain a confidence interval for the adjusted association estimates of significant SNPs, which have been obtained through the implementation of conditional_likelihood. This function produces one confidence interval for each significant SNP, based on the approach suggested in Ghosh et al. (2008). Note that in order for an appropriate confidence interval to be outputted for each significant SNP, the absolute value of the largest z-statistic in the data set must be less than 150.

Usage

cl_interval(summary_data, alpha = 5e-08, conf_level = 0.95)

Arguments

summary_data	A data frame containing summary statistics from the discovery GWAS. It must have three columns with column names rsid, beta and se, respectively, and columns beta and se must contain numerical values. Each row must correspond to a unique SNP, identified by rsid.
alpha	A numerical value which specifies the desired genome-wide significance threshold. The default is given as 5e-8.
conf_level	A numerical value between 0 and 1 which determines the confidence interval to be computed. The default setting is 0.95 which results in the calculation of a 95% confidence interval for the adjusted association estimate for each SNP.

Value

A data frame which combines the output of conditional_likelihood with two additional columns, namely lower and upper, containing the lower and upper bounds of the required confidence interval for each significant SNP, respectively. However, if no SNPs are detected as significant in the data set, cl_interval returns a warning message: "WARNING: There are no significant SNPs at this threshold."

References

Ghosh, A., Zou, F., & Wright, F. A. (2008). Estimating odds ratios in genome scans: an approximate conditional likelihood approach. American journal of human genetics, 82(5), 1064-1074. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ajhg.2008.03.002")}

See Also

conditional_likelihood for details on operation of conditional likelihood methods with summary statistics from discovery GWAS.

https://amandaforde.github.io/winnerscurse/articles/standard_errors_confidence_intervals.html for illustration of the use of cl_interval with a toy data set and further information regarding the manner in which the confidence interval is computed.

amandaforde/winnerscurse documentation built on March 9, 2024, 6:09 p.m.