PRS_Dis_LDpred2: Construct disease PRS using LDpred2

In PRSPGx: Construct PGx PRS

Construct disease PRS using LDpred2

Description

Using snp_ldpred2_grid function from bigsnpr function

Usage

PRS_Dis_LDpred2(DIS_GWAS, G_reference, pcausal, h2)

Arguments

DIS_GWAS	a numeric matrix containing disease GWAS summary statistics, including SNP ID, position, β, SE(β), 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

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

Value

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

Author(s)

Song Zhai

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

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)

PRSPGx documentation built on July 20, 2022, 5:07 p.m.