This vignette shows you how to perform gene-based association tests using GWAS summary statistics in which sets of SNPs are defined by genes.
The snpsettest requires SNP-level p-values to perform gene-based association tests.
library(snpsettest)
# Check an example of GWAS summary file (included in this package)
head(exGWAS, 3)
#> id chr pos A1 A2 pvalue
#> 1 SNP_0 1 50215 G C 0.1969353
#> 2 SNP_2 1 50768 A G 0.6620465
#> 3 SNP_3 1 50833 T G 0.5822596To infer the relationships among SNPs, the snpsettest package requires a reference data set. The GWAS genotype data itself can be used as the reference data (If the GWAS cohort is large, it is impractical to use genotype data of all individuals. It would be sufficient to randomly select 1,000 unrelated individuals for inferring pairwise LD correlations among common SNPs). Otherwise, you could use publicly available data, such as the 1000 Genomes (please see the companion vignette for processing the 1000 Genomes data). This package accepts PLINK 1 binary files (.bed, .bim, .fam) as an input. We can use read_reference_bed to read them into R.
# Path to .bed file
bfile <- system.file("extdata", "example.bed", package = "snpsettest")
# Read a .bed file using bed.matrix-class in gaston package
# Genotypes are retrieved on demand to manage large-scale genotype data
x <- read_reference_bed(bfile, verbose = FALSE)
#> Created a bed.matrix with 300 individuals and 2,942 markers.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 using harmonize_sumstats either 1) by SNP ID or 2) by chromosome code, base-pair position, and allele codes, while taking into account reference allele swap and possible strand flips.
# Harmonize by SNP IDs
hsumstats1 <- harmonize_sumstats(exGWAS, x)
#> -----
#> Checking the reference data for harmonization...
#> Found 0 monomoprhic SNPs in the reference data.
#> Found 0 duplicate SNP IDs in the reference data.
#> Excluded 0 SNPs from the harmonization.
#> -----
#> Checking the GWAS summary statistics...
#> 2,753 variants to be matched.
#> 2,630 variants have been matched.
# Harmonize by genomic position and allele codes
# Reference allele swap will be taken into account (e.g., A/C match C/A)
hsumstats2 <- harmonize_sumstats(exGWAS, x, match_by_id = FALSE)
#> -----
#> Checking the reference data for harmonization...
#> Found 0 monomoprhic SNPs in the reference data.
#> Found 0 duplicate SNPs in the reference data by genomic position and alleles codes.
#> Excluded 0 SNPs from the harmonization.
#> -----
#> Checking the GWAS summary statistics...
#> 2,753 variants to be matched.
#> 2,618 variants have been matched.
# Check matching entries by flipping allele codes (e.g., A/C match T/G)
# Ambiguous SNPs will be excluded from harmonization
hsumstats3 <- harmonize_sumstats(exGWAS, x, match_by_id = FALSE, check_strand_flip = TRUE)
#> -----
#> Checking the reference data for harmonization...
#> Found 0 monomoprhic SNPs in the reference data.
#> Found 0 duplicate SNPs in the reference data by genomic position and alleles codes.
#> Excluded 0 SNPs from the harmonization.
#> -----
#> Checking the GWAS summary statistics...
#> 2,753 variants to be matched.
#> 835 ambiguous SNPs have been removed.
#> 1,795 variants have been matched.To perform gene-based association tests, it is necessary to annotate SNPs onto their neighboring genes. Mapping SNPs to genes (or genomic regions) can be achieved by map_snp_to_genes with gene start/end information.
# Check gene information from the GENCODE project (included in this package)
head(gene.curated.GRCh37, 3)
#> gene.id chr start end strand gene.name gene.type
#> 1 ENSG00000186092.4 1 69091 70008 + OR4F5 protein_coding
#> 2 ENSG00000237683.5 1 134901 139379 - AL627309.1 protein_coding
#> 3 ENSG00000235249.1 1 367640 368634 + OR4F29 protein_coding
# Map SNPs to genes
snp_sets <- map_snp_to_gene(hsumstats1, gene.curated.GRCh37)
str(snp_sets$sets[1:5])
#> List of 5
#> $ ENSG00000186092.4: chr [1:110] "SNP_0" "SNP_2" "SNP_3" "SNP_4" ...
#> $ ENSG00000237683.5: chr [1:109] "SNP_317" "SNP_320" "SNP_321" "SNP_323" ...
#> $ ENSG00000235249.1: chr [1:95] "SNP_1283" "SNP_1285" "SNP_1287" "SNP_1288" ...
#> $ ENSG00000185097.2: chr [1:96] "SNP_2392" "SNP_2396" "SNP_2397" "SNP_2398" ...
#> $ ENSG00000187634.6: chr [1:135] "SNP_3455" "SNP_3456" "SNP_3458" "SNP_3459" ...
# Allows a certain kb window before/after the gene to be included for SNP mapping
snp_sets_50kb <- map_snp_to_gene(
hsumstats1, gene.curated.GRCh37,
extend_start = 50, extend_end = 50 # default is 20kb
)Once we have SNP sets for genes, snpset_test can be used to perform gene-based association tests.
# Perform gene-based association tests for the first 5 genes
res <- snpset_test(hsumstats1, x, snp_sets$sets[1:5])
#> -----
#> 2,630 variants are found in hsumstats1.
#> 5 set-based association tests will be performed.
#> Starting set-based association tests...
#> -----
#> ENSG00000186092.4: nSNP = 110, P = 0.042
#> ENSG00000237683.5: nSNP = 109, P = 0.00936
#> ENSG00000235249.1: nSNP = 95, P = 0.182
#> ENSG00000185097.2: nSNP = 96, P = 0.122
#> ENSG00000187634.6: nSNP = 135, P = 0.0103
# Show output
res
#> set.id tstat pvalue n.snp top.snp.id top.snp.pvalue
#> 1: ENSG00000186092.4 141.7800 0.042027775 110 SNP_78 0.0009143436
#> 2: ENSG00000237683.5 154.2858 0.009362739 109 SNP_363 0.0006419257
#> 3: ENSG00000235249.1 109.0270 0.182400780 95 SNP_1311 0.0047610286
#> 4: ENSG00000185097.2 114.7301 0.122042173 96 SNP_2458 0.0034444534
#> 5: ENSG00000187634.6 185.7576 0.010306441 135 SNP_3601 0.0003350840