Compute Standard deviation prior (SD prior) for quantitative traits using pre-computed heritability.

sd.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)

Arguments

data: a data.table containing the GWAS summary statistic input dataset. Must contain SNPID and SE columns.
h2: a numeric. Heritability estimate or h^2 (See details).
N: a numeric. Sample size of the GWAS input dataset.
pi_i: a numeric. Prior that a given variant is causal. DEFAULT = 1e-4.

Author

Guillermo Reales, Elena Vigorito, Chris Wallace

Examples

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)