PRS_Dis_CT {PRSPGx}

Construct disease PRS unadjusted or using clumping and thresholding

Description

Shrink prognostic effect sizes by p-value cutoff (PRS-Dis-CT turns out to be PRS-Dis-Unadj when setting p-value cutoff = 1)

Usage

PRS_Dis_CT(
  DIS_GWAS,
  G_reference,
  pcutoff = 1e-05,
  clumping = TRUE,
  p1 = 1e-04,
  d1 = 250000,
  r1 = 0.8
)

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)

pcutoff

a numeric value indicating the p-value cutoff

clumping

a logical flag indicating should clumping be performed

p1

a numeric value indicating p-value threshold to decide flag SNPs in clumping

d1

a numeric value indicating window size in clumping

r1

a numeric value indicating correlation in clumping

Details

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

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

Euesden, J., Lewis, C.M. & O'Reilly, P.F. PRSice: Polygenic Risk Score software. Bioinformatics 564, 1466-1468 (2015).

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_CT(DIS_GWAS, G_reference, pcutoff = 0.01, clumping = TRUE)
summary(coef_est$coef.G)
summary(coef_est$coef.TG)