snpsettest: A Set-Based Association Test using GWAS Summary Statistics

Description

The goal of 'snpsettest' is to provide simple tools that perform set-based association tests (e.g., gene-based association tests) using GWAS (genome-wide association study) summary statistics. A set-based association test in this package is based on the statistical model described in VEGAS (versatile gene-based association study), which combines the effects of a set of SNPs accounting for linkage disequilibrium between markers. This package uses a different approach from the original VEGAS implementation to compute set-level p values more efficiently, as described in https://github.com/HimesGroup/snpsettest/wiki/Statistical-test-in-snpsettest.

Author(s)

Maintainer: Jaehyun Joo jaehyunjoo@outlook.com

Authors:

• Blanca Himes bhimes@pennmedicine.upenn.edu

See Also

Useful links:

• https://github.com/HimesGroup/snpsettest

• Report bugs at https://github.com/HimesGroup/snpsettest/issues

An example file of GWAS summary statistics

Description

An example file of GWAS summary statistics

Usage

exGWAS

Format

Data frame with columns

id

SNP ID.

chr

chromosome.

pos

base-pair position.

A1, A2

allele codes.

pvalue

p value.

Examples

head(exGWAS)

Human gene information from the GENCODE GRCh37 version

Description

Human gene information was extracted from the GENCODE release 19. This data only contains 'KNOWN' status genes with the following gene biotypes: protein-coding, Immunoglobulin (Ig) variable chain and T-cell receptor (TcR) genes.

Usage

gene.curated.GRCh37

Format

Data frame with columns

gene.id

SNP ID.

chr

chromosome.

start

genomic start location (1-based).

end

genomic end location.

strand

genomic strand.

gene.name

gene symbols mapped to the GENCODE genes.

gene.type

gene biotypes in the GENCODE genes.

Source

https://www.gencodegenes.org/human/release_19.html

Examples

head(gene.curated.GRCh37)

Human gene information from the GENCODE GRCh38 version

Description

Human gene information was extracted from the GENCODE release 37. This data only contains genes with the following gene biotypes: protein-coding, Immunoglobulin (Ig) variable chain and T-cell receptor (TcR) genes.

Usage

gene.curated.GRCh38

Format

Data frame with columns

gene.id

SNP ID.

chr

chromosome.

start

genomic start location (1-based).

end

genomic end location.

strand

genomic strand.

gene.name

gene symbols mapped to the GENCODE genes.

gene.type

gene biotypes in the GENCODE genes.

Source

https://www.gencodegenes.org/human/release_37.html

Examples

head(gene.curated.GRCh38)

Harmonizing GWAS summary to reference data

Description

Finds an intersection of variants between GWAS summary and reference data.

Usage

harmonize_sumstats(
  sumstats,
  x,
  match_by_id = TRUE,
  check_strand_flip = FALSE,
  return_indice = FALSE
)

Arguments

Details

Pre-processing of GWAS summary data is required because the sets of variants available in a particular GWAS might be poorly matched to the variants in reference data. SNP matching can be performed either 1) by SNP ID or 2) by chromosome code, base-pair position, and allele codes, while taking into account possible strand flips and reference allele swap. For matched entries, the SNP IDs in GWAS summary data are replaced with the ones in the reference data.

Value

A data frame with columns: "id", "chr", "pos", "A1", "A2" and "pvalue". If return_indice = TRUE, the data frame includes additional columns key_, swapped_, and flipped_. key_ is "chr_pos_A1_A2" in sumstat (the original input before harmonization). swapped_ contains a logical vector indicating reference allele swap. flipped_ contains a logical vector indicating strand flip.

Examples

## GWAS summary statistics
head(exGWAS)

## Load reference genotype data
bfile <- system.file("extdata", "example.bed", package = "snpsettest")
x <- read_reference_bed(path = bfile)

## Harmonize by SNP IDs
hsumstats1 <- harmonize_sumstats(exGWAS, x)

## Harmonize by genomic position and allele codes
## Reference allele swap will be taken into account
hsumstats2 <- harmonize_sumstats(exGWAS, x, match_by_id = FALSE)

## Check matching entries by flipping allele codes
## Ambiguous SNPs will be excluded from harmonization
hsumstats3 <- harmonize_sumstats(exGWAS, x, match_by_id = FALSE,
                                 check_strand_flip = TRUE)

Map SNPs to genes

Description

Annotate SNPs onto their neighboring genes (or arbitrary genomic regions) to perform set-based association tests.

Usage

map_snp_to_gene(
  info_snp,
  info_gene,
  extend_start = 20L,
  extend_end = 20L,
  only_sets = FALSE
)

Arguments

Value

A nested list containing following components:

• sets: a named list where each index represents a separate set of SNPs

• map: a data frame containing SNP mapping information

Examples

## GWAS summary statistics
head(exGWAS)

## Gene information data
head(gene.curated.GRCh37)

## Map SNPs to genes
snp_sets <- map_snp_to_gene(exGWAS, gene.curated.GRCh37)

## Better to use harmonized GWAS data for gene mapping
bfile <- system.file("extdata", "example.bed", package = "snpsettest")
x <- read_reference_bed(path = bfile)
hsumstats <- harmonize_sumstats(exGWAS, x)
snp_sets <- map_snp_to_gene(hsumstats, gene.curated.GRCh37)

Read a PLINK bed file for reference data

Description

Create a bed.matrix object from a .bed file. The function expects .fam and .bim files under the same directory. See gaston::read.bed.matrix for more details.

Usage

read_reference_bed(path, ...)

Arguments

Value

A gaston::bed.matrix object with a Z-standardized genotype matrix

Examples

## Get a path to the example .bed file
bfile <- system.file("extdata", "example.bed", package = "snpsettest")

## Read a .bed file
x <- read_reference_bed(path = bfile)

Set-based association tests

Description

Perform set-based association tests between multiple sets of SNPs and a phenotype using GWAS summary statistics. If the function encounters missing genotypes in the reference data, they will be imputed with genotype means.

Usage

snpset_test(hsumstats, x, snp_sets, method = c("saddle", "davies"))

Arguments

Value

A data.table with columns: "set.id", "pvalue", "n.snp", "top.snp.id" and "top.snp.pvalue"

• set.id = a name of SNP set

• tstat = a test statistic

• pvalue = a set-level p value

• n.snp = the number of SNPs used in a test

• top.snp.id = SNP ID with the smallest p-value within a set of SNPs

• top.snp.pvalue = The smallest p-value within a set of SNPs

References

Kuonen, D. Saddlepoint Approximations for Distributions of Quadratic Forms in Normal Variables. Biometrika 86, 929–935 (1999).

Davies, R. B. Algorithm AS 155: The Distribution of a Linear Combination of Chi-Square Random Variables. Journal of the Royal Statistical Society. Series C (Applied Statistics) 29, 323–333 (1980).

Examples

## GWAS summary statistics
head(exGWAS)

## Load reference genotype data
bfile <- system.file("extdata", "example.bed", package = "snpsettest")
x <- read_reference_bed(path = bfile)

## GWAS harmonization with reference data
hsumstats <- harmonize_sumstats(exGWAS, x)

## Perform a set-based test with an arbitrary SNP set
snpset_test(hsumstats, x, list(test = c("SNP_880", "SNP_1533", "SNP_4189")))

## Gene information data
head(gene.curated.GRCh37)

## Map SNPs to genes
snp_sets <- map_snp_to_gene(hsumstats, gene.curated.GRCh37)

## Perform gene-based association tests
out <- snpset_test(hsumstats, x, snp_sets$sets)