RapidoPGS (version 2.2.0)

rapidopgs_multi: Compute PGS from GWAS summary statistics using Bayesian sum of single-effect (SuSiE) linear regression using z scores

Description

'rapidopgs_multi computes PGS from a from GWAS summary statistics using Bayesian sum of single-effect (SuSiE) linear regression using z scores

Usage

rapidopgs_multi(
  data,
  trait = c("cc", "quant"),
  reference = NULL,
  LDmatrices = NULL,
  N = NULL,
  ancestry = "EUR",
  pi_i = 1e-04,
  ncores = 1,
  alpha.block = 1e-04,
  alpha.snp = 0.01,
  sd.prior = NULL
)

Value

a data.table containing the sumstats dataset with computed PGS weights.

Arguments

data

a data.table containing GWAS summary statistic dataset with all required information.

trait

a string indicating if trait is a case-control ("cc") or quantitative ("quant").

reference

a string representing the path to the directory containing the reference panel (eg. "../ref-data/").

LDmatrices

a string representing the path to the directory containing the pre-computed LD matrices.

N

a numeric indicating the number of individuals used to generate input GWAS dataset, or a string indicating the column name containing per-SNP sample size. Required for quantitative traits only.

ancestry

a string indicating the ancestral population (DEFAULT: "EUR")

pi_i

a scalar representing the prior probability (DEFAULT: \(1 \times 10^{-4}\)).If you wish SuSiE to estimate this internally, set p=NULL.

ncores

a numeric specifying the number of cores (CPUs) to be used. If using pre-computed LD matrices, one core is enough for best performance.

alpha.block

a numeric threshold for minimum P-value in LD blocks. Blocks with minimum P above alpha.block will be skipped. Default: 1e-4.

alpha.snp

a numeric threshold for P-value pruning within LD block. SNPs with P above alpha.snp will be removed. Default: 0.01.

sd.prior

the prior specifies that BETA at causal SNPs follows a centred normal distribution with standard deviation sd.prior. If NULL (default) it will be automatically estimated (recommended).

Author

Guillermo Reales, Chris Wallace

Details

This function will take a GWAS summary statistic dataset as an input, will assign LD blocks to it, then use user-provided LD matrices or a preset reference panel in Plink format to compute LD matrices for each block. Then SuSiE method will be used to compute posterior probabilities of variants to be causal and generate PGS weights by multiplying those posteriors by effect sizes (\(\beta\)). Unlike rapidopgs_single, this approach will assume one or more causal variants.

The GWAS summary statistics file to compute PGS using our method must contain the following minimum columns, with these exact column names:

CHR

Chromosome

BP

Base position (in GRCh37/hg19).

REF

Reference, or non-effect allele

ALT

Alternative, or effect allele, the one \(\beta\) refers to

BETA

\(\beta\) (or log(OR)), or effect sizes

SE

standard error of \(\beta\)

P

P-value for the association test

In addition, quantitative traits must have the following extra column:

ALT_FREQ

Minor allele frequency.

Also, for quantitative traits, sample size must be supplied, either as a number, or indicating the column name, for per-SNP sample size datasets (see below). Other columns are allowed, and will be ignored.

Reference panel should be divided by chromosome, in Plink format. Both reference panel and summary statistic dataset should be in GRCh37/hg19. For 1000 Genomes panel, you can use create_1000G function to set it up automatically.

If prefer to use LD matrices, you must indicate the path to the directory where they are stored. They must be in RDS format, named LD_chrZ.rds (where Z is the 1-22 chromosome number). If you don't have LD matrices already, we recommend downloading those gently provided by Prive et al., at https://figshare.com/articles/dataset/European_LD_reference/13034123. These matrices were computed using for 1,054,330 HapMap3 variants based on 362,320 European individuals of the UK biobank.

Examples

if (FALSE) {
sumstats <- data.table(
		CHR=c("4","20","14","2","4","6","6","21","13"), 
		BP=c(1479959, 13000913, 29107209, 203573414, 57331393, 11003529, 149256398, 
				25630085, 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))
PGS  <- rapidopgs_multi(sumstats, trait="cc", reference = "ref-data/", ncores=2)
}