R/rapfunc.R
sd.prior.est.Rdsd.prior.est function will take the dataset as an input, a \(h^2\)
value obtained from a public repository such as LDhub,
(http://ldsc.broadinstitute.org/ldhub/), sample size and number of variants,
and will provide a sd.prior estimate that can be used to improve prediction
performance of RapidoPGS functions on quantitative traits.
sd.prior.est(data, h2, N, pi_i = 1e-04)a data.table containing the GWAS summary statistic input dataset. Must contain SNPID and SE columns.
a numeric. Heritability estimate or h^2 (See details).
a numeric. Sample size of the GWAS input dataset.
a numeric. Prior that a given variant is causal. DEFAULT = 1e-4.
sumstats <- data.table(SNPID=c("4:1479959","20:13000913","14:29107209","2:203573414",
"4:57331393","6:11003529","6:149256398","21:25630085","13:79166661"),
REF=c("C","C","C","T","G","C","C","G","T"),
ALT=c("A","T","T","A","A","A","T","A","C"),
ALT_FREQ=c(0.2611,0.4482,0.0321,0.0538,0.574,0.0174,0.0084,0.0304,0.7528),
BETA=c(0.012,0.0079,0.0224,0.0033,0.0153,0.058,0.0742,0.001,-0.0131),
SE=c(0.0099,0.0066,0.0203,0.0171,0.0063,0.0255,0.043,0.0188,0.0074),
P=c(0.2237,0.2316,0.2682,0.8477,0.01473,0.02298,0.08472,0.9573,0.07535))
sd.prior <- sd.prior.est(sumstats, h2 = 0.2456, N = 45658, pi_i=1e-4)