PRS_PGx_Bayes {PRSPGx}

Construct PGx PRS using Bayesian regression

Description

Flexibly shrink prognostic and predictive effect sizes simutaneously with glocal-local shrinkage parameters

Usage

PRS_PGx_Bayes(
  PGx_GWAS,
  G_reference,
  n.itr = 1000,
  n.burnin = 500,
  n.gap = 10,
  paras,
  standardize = TRUE
)

Arguments

PGx_GWAS

a numeric list containing PGx GWAS summary statistics (with SNP ID, position, \beta, \alpha, 2-df p-value, MAF and N), SD(Y), and mean(T)

G_reference

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

n.itr

a numeric value indicating the total number of MCMC iteration

n.burnin

a numeric value indicating the number of burn in

n.gap

a numeric value indicating the MCMC gap

paras

a numeric vector containg hyper-parameters (v, \phi)

standardize

a logical flag indicating should phenotype and genotype be standardized

Details

PRS-PGx-Bayes only needs PGx 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

Ge, T., Chen, CY., Ni, Y. et al. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).

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)
paras = c(3, 5)
coef_est <- PRS_PGx_Bayes(PGx_GWAS, G_reference, paras = paras, n.itr = 10, n.burnin = 5, n.gap = 1)
summary(coef_est$coef.G)
summary(coef_est$coef.TG)