Title:	A Fast and Light Package to Compute Polygenic Risk Scores
Version:	2.3.1
Description:	Quickly computes polygenic scores from GWAS summary statistics of either case-control or quantitative traits without parameter tuning. Reales,G., Vigorito, E., Kelemen,M., Wallace,C. (2021) <doi:10.1101/2020.07.24.220392> "RápidoPGS: A rapid polygenic score calculator for summary GWAS data without a test dataset".
License:	GPL-3
Depends:	R (≥ 4.3), data.table, RCurl, curl, magrittr
Imports:	dplyr (≥ 1.1.3), GenomicRanges (≥ 1.52.0), IRanges (≥ 2.34.1), bigsnpr (≥ 1.12.2), coloc (≥ 5.2.3), bigreadr (≥ 0.2.5)
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Suggests:	knitr, rmarkdown
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-02-19 11:42:09 UTC; gr440
Author:	Guillermo Reales [aut, cre], Chris Wallace [aut], Olly Burren [ctb]
Maintainer:	Guillermo Reales <gr440@cam.ac.uk>
Repository:	CRAN
Date/Publication:	2025-02-19 12:10:02 UTC

LD block architecture for European populations (hg19).

Description

A GRanges object containing the LD block for European ancestry, in hg19 build. This dataset was obtained from doi:10.1093/bioinformatics/btv546, in bed format, then converted to GRanges. See manuscript for more details.

Usage

EUR_ld.blocks19

Format

A GRanges object containing 1703 ranges

seqnames

chromosome

ranges

start and stop positions for the block

strand

genomic strand, irrelevant here

Source

https://bitbucket.org/nygcresearch/ldetect-data/src

LD block architecture for European populations (hg38).

Description

A GRanges object containing the LD block for European ancestry, in hg38 build. This dataset was obtained from doi:10.1101/2022.03.04.483057, in bed format, then transformed to GRanges. See manuscript for more details.

Usage

EUR_ld.blocks38

Format

A GRanges object containing 1361 ranges

seqnames

chromosome

ranges

start and stop positions for the block

strand

genomic strand, irrelevant here

Source

https://github.com/jmacdon/LDblocks_GRCh38/tree/master/data

Download 1000 Genomes Phase III panel

Description

create_1000G downloads and gets 1000 Genomes Phase III panel (hg19) in PLINK format, and apply quality control for being used to compute PGS using rapidopgs_multi. Given the size of the files, running this function can take long, depending on broadband speed and server status. We also recommend to ensure that there is at least 60GB free space available in disk.

Usage

create_1000G(
  directory = "ref-data",
  remove.related = TRUE,
  qc.maf = 0.01,
  qc.hwe = 1e-10,
  qc.geno = 0,
  autosomes.only = TRUE
)

Arguments

Value

bed, fam and bim files for each chromosome in the chosen directory.

Author(s)

Guillermo Reales

Examples

## Not run: 
create_1000G()

## End(Not run)

Finding a file in an FTP directory This is an internal function to help gwascat.download find the right file

Description

Finding a file in an FTP directory This is an internal function to help gwascat.download find the right file

Usage

find_file_in_ftp(ftp_address, acc, hm)

Arguments

Value

a data.table containing the dataset.

Author(s)

Guillermo Reales

Retrieve GWAS summary datasets from GWAS catalog 'gwascat.download takes a PMID from the user and downloads the associated summary statistics datasets published in GWAS catalog

Description

This function, takes PUBMED ids as an input, searches at the GWAS catalog for harmonised datasets associated to that, interactively asking the user to choose if there are more than one, and fetches the dataset.

Usage

gwascat.download(ID, harmonised = TRUE)

Arguments

Details

If multiple files are available for the same study, R will prompt an interactive dialogue to select a specific file, by number.

Value

a character vector containing the url(s) to the dataset(s).

Author(s)

Guillermo Reales

Helper function to sum logs without loss of precision

Description

Sums logs without loss of precision This function is verbatim of its namesake in cupcake package (github.com/ollyburren/cupcake/)

Usage

logsum(x)

Arguments

Value

a scalar

Author(s)

Chris Wallace

Subset of Michailidou BRCA GWAS sumstat dataset.

Description

A data.table containing a subset of doi:10.1038/nature24284 breast cancer summary statistic dataset, in hg19 build. This dataset is freely available in GWAS catalog (see link below). We used "chromosome", "base_pair_location columns", removed unnecessary and all-missing columns, and took a random sample of 100,000 SNPs without replacement.

Usage

michailidou19

Format

A data.table object containing 100,000 SNPs

SNPID, CHR, BP, REF, ALT, ALT_FREQ, BETA, SE, P

SNPID

rsids, or SNP ids

CHR

chromosome

BP

base position, in hg38

REF

reference, or non-effect allele

ALT

alternative, or effect allele

ALT_FREQ

effect allele frequency

BETA

beta, log(OR), or effect size

SE

standard error of beta

P

p-value

Source

http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004988/harmonised/29059683-GCST004988-EFO_0000305-build37.f.tsv.gz

Subset of Michailidou BRCA GWAS sumstat dataset.

Description

A data.table containing a subset of doi:10.1038/nature24284 breast cancer summary statistic dataset, in hg38 build. This dataset is freely available in GWAS catalog (see link below). We removed unnecessary and all-missing columns, and rows with missing data at hm_beta and hm_effect_allele_frequency, and took a random sample of 100,000 SNPs without replacement.

Usage

michailidou38

Format

A data.table object containing 100,000 SNPs

hm_rsid

rsids, or SNP ids

hm_chrom

chromosome

hm_pos

base position, in hg38

hm_other_allele

reference, or non-effect allele

hm_effect_allele

alternative, or effect allele

hm_beta

beta, log(OR), or effect size

hm_effect_allele_frequency

effect allele frequency

standard_error

standard error of beta

p_value

p-value

Source

http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004988/harmonised/29059683-GCST004988-EFO_0000305.h.tsv.gz

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,
  reference = NULL,
  LDmatrices = NULL,
  N = NULL,
  build = c("hg19", "hg38"),
  trait = c("cc", "quant"),
  ncores = 1,
  alpha.block = 1e-04,
  alpha.snp = 0.01,
  sd.prior = NULL,
  ancestry = "EUR",
  LDblocks = NULL
)

Arguments

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.

Value

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

Author(s)

Guillermo Reales, Chris Wallace

Examples

## Not run: 
ss <- 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"), 
		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(ss, reference = "ref-data/", N = 20000, build = "hg19", trait="cc", ncores=5)

## End(Not run)

Compute PGS from GWAS summary statistics using posteriors from Wakefield's approximate Bayes Factors

Description

'rapidopgs_single computes PGS from a from GWAS summary statistics using posteriors from Wakefield's approximate Bayes Factors

Usage

rapidopgs_single(
  data,
  N = NULL,
  trait = c("cc", "quant"),
  build = "hg19",
  pi_i = 1e-04,
  sd.prior = if (trait == "quant") {
     0.15
 } else {
     0.2
 },
  filt_threshold = NULL,
  recalc = TRUE,
  reference = NULL
)

Arguments

Details

This function will take a GWAS summary statistic dataset as an input, will assign align it to a reference panel file (if provided), then it will assign SNPs to LD blocks and compute Wakefield's ppi by LD block, then will use it to generate PGS weights by multiplying those posteriors by effect sizes (\beta). Optionally, it will filter SNPs by a custom filter on ppi and then recalculate weights, to improve accuracy.

Alternatively, if filt_threshold is larger than one, RapidoPGS will select the top filt_threshold SNPs by absolute weights (note, not ppi but weights).

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 or GRCh38/hg38). If using hg38, use build = "hg38" in parameters

REF

Reference, or non-effect allele

ALT

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

ALT_FREQ

Minor/ALT allele frequency in the tested population, or in a close population from a reference panel. Required for Quantitative traits only

BETA

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

SE

standard error of \beta

If a reference is provided, it should have 5 columns: CHR, BP, SNPID, REF, and ALT. Also, it should be in the same build as the summary statistics. In both files, column order does not matter.

Value

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

Author(s)

Guillermo Reales, Chris Wallace

Examples

sumstats <- data.table(SNPID=c("rs139096444","rs3843766","rs61977545", "rs544733737",
		"rs2177641", "rs183491817", "rs72995775","rs78598863", "rs1411315"), 
		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"), 
		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))

PGS  <- rapidopgs_single(sumstats,  trait = "cc")

Compute Standard deviation prior (SD prior) for quantitative traits using pre-computed heritability.

Description

sd.prior.est function will take the dataset as an input, a h^2 value obtained from a public repository such as LDhub, (http://ldsc.broadinstitute.org/ldhub/), sample size and number of variants, and will provide a sd.prior estimate that can be used to improve prediction performance of RapidoPGS functions on quantitative traits.

Usage

sd.prior.est(data, h2, N, pi_i = 1e-04)

Arguments

Author(s)

Guillermo Reales, Elena Vigorito, Chris Wallace

Examples

sumstats <- data.table(SNPID=c("4:1479959","20:13000913","14:29107209","2:203573414",
"4:57331393","6:11003529","6:149256398","21:25630085","13:79166661"), 
		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)) 
sd.prior <- sd.prior.est(sumstats, h2 = 0.2456, N = 45658, pi_i=1e-4)

Estimate trait variance, internal function

Description

Estimate trait standard deviation given vectors of variance of coefficients, MAF and sample size

Usage

sdY.est(vbeta, maf, n)

Arguments

Details

Estimate is based on var(beta-hat) = var(Y) / (n * var(X)) var(X) = 2* maf*(1-maf) so we can estimate var(Y) by regressing n*var(X) against 1/var(beta) This function is verbatim from its namesake in coloc package (github.com/chr1swallace/coloc/), by Chris Wallace

Value

estimated standard deviation of Y

Author(s)

Chris Wallace

compute posterior probabilities using Wakefield's approximate Bayes Factors wakefield_pp computes posterior probabilities for a given SNP to be causal for a given SNP under the assumption of a single causal variant.

Description

This function was adapted from its namesake in cupcake package (github.com/ollyburren/cupcake/) to no longer require allele frequencies.

Usage

wakefield_pp(beta, se, pi_i = 1e-04, sd.prior = 0.2)

Arguments

Value

a vector of posterior probabilities.

Author(s)

Olly Burren, Chris Wallace, Guillermo Reales

Compute posterior probabilities using Wakefield's approximate Bayes Factors for quantitative traits

Description

wakefield_pp_quant computes posterior probabilities for a given SNP to be causal for a given SNP under the assumption of a single causal variant.

Usage

wakefield_pp_quant(beta, se, sdY, sd.prior = 0.15, pi_i = 1e-04)

Arguments

Details

This function was adapted from wakefield_pp in cupcake package (github.com/ollyburren/cupcake/)

Value

a vector of posterior probabilities.

Author(s)

Guillermo Reales, Chris Wallace