PRSPGx (version 0.3.0)

PRS_Dis_LDpred2: Construct disease PRS using LDpred2

Description

Using snp_ldpred2_grid function from bigsnpr function

Usage

PRS_Dis_LDpred2(DIS_GWAS, G_reference, pcausal, h2)

Value

A numeric list, the first sublist contains estimated prognostic effect sizes, the second sublist contains estimated predictive effect sizes

Arguments

DIS_GWAS

a numeric matrix containing disease GWAS summary statistics, including SNP ID, position, \(\beta\), SE(\(\beta\)), p-value, N, and MAF

G_reference

a numeric matrix containing the individual-level genotype information from the reference panel (e.g., 1KG)

pcausal

a numeric value indicating the hyper-parameter as the proportion of causal variants

h2

a numeric value indicating the estimated heritability

Author

Song Zhai

Details

PRS-Dis-LDpred2 automatically sets predictive effect sizes equivalent to the prognostic effect sizes; and only need disease GWAS summary statistics and external reference genotype

References

Prive, F., Arbel, J. & Vilhjalmsson, B.J. LDpred2: better, faster, stronger. Bioinformatics 36, 5424-5431 (2020).

Zhai, S., Zhang, H., Mehrotra, D.V. & Shen, J. Paradigm Shift from Disease PRS to PGx PRS for Drug Response Prediction using PRS-PGx Methods (submitted).

Examples

# \donttest{
data(PRSPGx.example); attach(PRSPGx.example)
coef_est <- PRS_Dis_LDpred2(DIS_GWAS, G_reference, pcausal = 0.1, h2 = 0.4)
summary(coef_est$coef.G)
summary(coef_est$coef.TG)
# }