gwascat: structuring and querying the NHGRI GWAS catalog
November 08, 2022
Contents
1 Introduction
NHGRI maintains and routinely updates a database of selected genome-wide association studies. This document describes R/Bioconductor facilities for working with contents of this database.
1.1 Installation
The package can be installed using Bioconductor’s package, with the sequence1.2 Attachment and access to documentation
Once the package has been installed, use to obtain interactive access to all the facilities. After executing this command, use to obtain an overview. The current version of this vignette can always be accessed at www.bioconductor.org, or by suitably navigating the web pages generated with .
1.3 Using tidy methods – added August 2022
gwcat = get_cached_gwascat()
library(dplyr)
gwcat |> arrange(DATE)## # A tibble: 402,121 × 38
## `DATE ADDED TO CATAL…` PUBMEDID `FIRST AUTHOR` DATE JOURNAL LINK STUDY
## <date> <dbl> <chr> <date> <chr> <chr> <chr>
## 1 2008-06-16 15761122 Klein RJ 2005-03-10 Science www.… Comp…
## 2 2008-06-16 16252231 Maraganore DM 2005-09-09 Am J H… www.… High…
## 3 2008-06-16 16648850 Arking DE 2006-04-30 Nat Ge… www.… A co…
## 4 2008-06-16 17052657 Fung HC 2006-09-28 Lancet… www.… Geno…
## 5 2008-06-16 17052657 Fung HC 2006-09-28 Lancet… www.… Geno…
## 6 2008-06-16 17052657 Fung HC 2006-09-28 Lancet… www.… Geno…
## 7 2008-06-16 17053108 Dewan A 2006-10-19 Science www.… HTRA…
## 8 2008-06-16 17068223 Duerr RH 2006-10-26 Science www.… A ge…
## 9 2008-06-16 17158188 Bierut LJ 2006-12-07 Hum Mo… www.… Nove…
## 10 2008-06-16 17158188 Bierut LJ 2006-12-07 Hum Mo… www.… Nove…
## # … with 402,111 more rows, and 31 more variables: `DISEASE/TRAIT` <chr>,
## # `INITIAL SAMPLE SIZE` <chr>, `REPLICATION SAMPLE SIZE` <chr>, REGION <chr>,
## # CHR_ID <chr>, CHR_POS <chr>, `REPORTED GENE(S)` <chr>, MAPPED_GENE <chr>,
## # UPSTREAM_GENE_ID <chr>, DOWNSTREAM_GENE_ID <chr>, SNP_GENE_IDS <chr>,
## # UPSTREAM_GENE_DISTANCE <dbl>, DOWNSTREAM_GENE_DISTANCE <dbl>,
## # `STRONGEST SNP-RISK ALLELE` <chr>, SNPS <chr>, MERGED <dbl>,
## # SNP_ID_CURRENT <dbl>, CONTEXT <chr>, INTERGENIC <dbl>, …We can produce a GRanges in two forms. By default
we get an mcols that has a small set of columns. Note
that records that lack a CHR_POS value are omitted.
Records that have complicated CHR_POS values, including
semicolons or ” x ” notation are kept, but only the first
position is retained. The CHR_ID field may have
complicated character values, these are not normalized,
and are simply used as seqnames “as is”.
gg = gwcat |> as_GRanges()## dropping 18239 records that have NA for CHR_POS## 261 records have semicolon in CHR_POS; splitting and using first entry.## 251 records have ' x ' in CHR_POS indicating multiple SNP effects, using first.gg## GRanges object with 383882 ranges and 4 metadata columns:
## seqnames ranges strand | PUBMEDID DATE
## <Rle> <IRanges> <Rle> | <numeric> <Date>
## [1] 1 161509955 * | 19915573 2009-11-15
## [2] 6 31143579 * | 19915573 2009-11-15
## [3] 13 26957130 * | 19915573 2009-11-15
## [4] 9 5213687 * | 19915573 2009-11-15
## [5] 6 32465390 * | 19915573 2009-11-15
## ... ... ... ... . ... ...
## [383878] 10 68192278 * | 34077760 2021-06-01
## [383879] 13 110066208 * | 34077760 2021-06-01
## [383880] 21 28133942 * | 34077760 2021-06-01
## [383881] 1 91730044 * | 34077760 2021-06-01
## [383882] 11 58646437 * | 34077760 2021-06-01
## DISEASE/TRAIT SNPS
## <character> <character>
## [1] Ulcerative colitis rs1801274
## [2] Ulcerative colitis rs9263739
## [3] Ulcerative colitis rs17085007
## [4] Ulcerative colitis rs10975003
## [5] Ulcerative colitis rs2395185
## ... ... ...
## [383878] Vertical cup-disc ra.. rs10998007
## [383879] Vertical cup-disc ra.. rs12875868
## [383880] Vertical cup-disc ra.. rs6516818
## [383881] Vertical cup-disc ra.. rs10783002
## [383882] Vertical cup-disc ra.. rs1938598
## -------
## seqinfo: 209 sequences from GRCh38 genome; no seqlengthsWe can set the seqinfo as follows, retaining only records that employ standard chromosomes.
library(GenomeInfoDb)
data(si.hs.38)
gg = keepStandardChromosomes(gg, pruning="coarse")
seqlevels(gg) = seqlevels(si.hs.38)
seqinfo(gg) = si.hs.38
gg## GRanges object with 383370 ranges and 4 metadata columns:
## seqnames ranges strand | PUBMEDID DATE
## <Rle> <IRanges> <Rle> | <numeric> <Date>
## [1] 1 161509955 * | 19915573 2009-11-15
## [2] 6 31143579 * | 19915573 2009-11-15
## [3] 13 26957130 * | 19915573 2009-11-15
## [4] 9 5213687 * | 19915573 2009-11-15
## [5] 6 32465390 * | 19915573 2009-11-15
## ... ... ... ... . ... ...
## [383366] 10 68192278 * | 34077760 2021-06-01
## [383367] 13 110066208 * | 34077760 2021-06-01
## [383368] 21 28133942 * | 34077760 2021-06-01
## [383369] 1 91730044 * | 34077760 2021-06-01
## [383370] 11 58646437 * | 34077760 2021-06-01
## DISEASE/TRAIT SNPS
## <character> <character>
## [1] Ulcerative colitis rs1801274
## [2] Ulcerative colitis rs9263739
## [3] Ulcerative colitis rs17085007
## [4] Ulcerative colitis rs10975003
## [5] Ulcerative colitis rs2395185
## ... ... ...
## [383366] Vertical cup-disc ra.. rs10998007
## [383367] Vertical cup-disc ra.. rs12875868
## [383368] Vertical cup-disc ra.. rs6516818
## [383369] Vertical cup-disc ra.. rs10783002
## [383370] Vertical cup-disc ra.. rs1938598
## -------
## seqinfo: 24 sequences from GRCh38 genome1.4 Getting a recent version of the GWAS catalog
We use BiocFileCache to manage downloaded TSV from EBI’s download site. The file is provided without compression, so prepare for 200+MB download if you are not working from a cache. There is no etag set, so you have to check for updates on your own.
args(get_cached_gwascat)## function (url = "http://www.ebi.ac.uk/gwas/api/search/downloads/alternative",
## cache = BiocFileCache::BiocFileCache(), refresh = FALSE,
## ...)
## NULLThis is converted to a manageable extension of GRanges using
process_gwas_dataframe.
args(process_gwas_dataframe)## function (df)
## NULL2 Illustrations: computing
Available functions are:
library(gwascat)## gwascat loaded. Use makeCurrentGwascat() to extract current image.## from EBI. The data folder of this package has some legacy extracts.objects("package:gwascat")## [1] "as_GRanges" "bindcadd_snv" "getRsids"
## [4] "getTraits" "get_cached_gwascat" "gwascat_from_AHub"
## [7] "gwcat_snapshot" "gwcex2gviz" "ldtagr"
## [10] "locs4trait" "makeCurrentGwascat" "obo2graphNEL"
## [13] "process_gwas_dataframe" "riskyAlleleCount" "subsetByChromosome"
## [16] "subsetByTraits" "topTraits" "traitsManh"An extended GRanges instance with a sample of 50000 SNP-disease associations reported
on 30 April 2020 is
obtained as follows, with addresses based on the GRCh38 genome build.
We use gwtrunc to refer to it in the sequel.
data(ebicat_2020_04_30)
gwtrunc = ebicat_2020_04_30To determine the most frequently occurring traits in this sample:
topTraits(gwtrunc)##
## Blood protein levels
## 1941
## Heel bone mineral density
## 1309
## Body mass index
## 1283
## Height
## 1238
## Metabolite levels
## 691
## Systolic blood pressure
## 654
## Schizophrenia
## 643
## Educational attainment (years of education)
## 642
## Post bronchodilator FEV1/FVC ratio
## 479
## Type 2 diabetes
## 466For a given trait, obtain a GRanges with all recorded associations; here only three associations are shown:
subsetByTraits(gwtrunc, tr="LDL cholesterol")[1:3]## gwasloc instance with 3 records and 38 attributes per record.
## Extracted: 2020-04-30 23:24:51
## metadata()$badpos includes records for which no unique locus was given.
## Genome: GRCh38
## Excerpt:
## GRanges object with 3 ranges and 3 metadata columns:
## seqnames ranges strand | DISEASE/TRAIT SNPS P-VALUE
## <Rle> <IRanges> <Rle> | <character> <character> <numeric>
## [1] 7 21567734 * | LDL cholesterol rs12670798 6e-09
## [2] 5 75355259 * | LDL cholesterol rs3846662 2e-11
## [3] 2 43837951 * | LDL cholesterol rs6756629 3e-10
## -------
## seqinfo: 24 sequences from GRCh38 genome3 Some visualizations
3.1 Basic Manhattan plot
A basic Manhattan plot is easily constructed with the ggbio package facilities. Here we confine attention to chromosomes 4:6. First, we create a version of the catalog with \(-log_{10} p\) truncated at a maximum value of 25.
requireNamespace("S4Vectors")
mcols = S4Vectors::mcols
mlpv = mcols(gwtrunc)$PVALUE_MLOG
mlpv = ifelse(mlpv > 25, 25, mlpv)
S4Vectors::mcols(gwtrunc)$PVALUE_MLOG = mlpv
library(GenomeInfoDb)
# seqlevelsStyle(gwtrunc) = "UCSC" # no more!
seqlevels(gwtrunc) = paste0("chr", seqlevels(gwtrunc))
gwlit = gwtrunc[ which(as.character(seqnames(gwtrunc)) %in% c("chr4", "chr5", "chr6")) ]
library(ggbio)
mlpv = mcols(gwlit)$PVALUE_MLOG
mlpv = ifelse(mlpv > 25, 25, mlpv)
S4Vectors::mcols(gwlit)$PVALUE_MLOG = mlpvautoplot(gwlit, geom="point", aes(y=PVALUE_MLOG), xlab="chr4-chr6")
3.2 Annotated Manhattan plot
A simple call permits visualization of GWAS results for a small number of traits. Note the defaults in this call.
traitsManh(gwtrunc)
% %
3.3 Integrative view of potential genetic determinants
The following chunk uses GFF3 data on eQTL and related phenomena distributed at the GBrowse instance at eqtl.uchicago.edu. A request for all information at 43-45 Mb was made on 2 June 2012, yielding the GFF3 referenced below. Of interest are locations and scores of genetic associations with DNaseI hypersensitivity (scores identifying dsQTL, see Degner et al 2012).
gffpath = system.file("gff3/chr17_43000000_45000000.gff3", package="gwascat")
library(rtracklayer)
c17tg = import(gffpath)We make a Gviz DataTrack of the dsQTL scores.
c17td = c17tg[ which(S4Vectors::mcols(c17tg)$type == "Degner_dsQTL") ]
library(Gviz)## Loading required package: griddsqs = DataTrack( c17td, chrom="chr17", genome="hg19", data="score",
name="dsQTL")We start the construction of the graph here.
g2 = GRanges(seqnames="chr17", IRanges(start=4.3e7, width=2e6))
basic = gwcex2gviz(basegr = gwtrunc, contextGR=g2, plot.it=FALSE) ## ## 'select()' returned 1:1 mapping between keys and columnsWe also collect locations of eQTL in the Stranger 2007 multipopulation eQTL study.
c17ts = c17tg[ which(S4Vectors::mcols(c17tg)$type == "Stranger_eqtl") ]
eqloc = AnnotationTrack(c17ts, chrom="chr17", genome="hg19", name="Str eQTL")
displayPars(eqloc)$col = "black"
displayPars(dsqs)$col = "red"
integ = list(basic[[1]], eqloc, dsqs, basic[[2]], basic[[3]])Now use Gviz.
plotTracks(integ)
4 SNP sets and trait sets
4.1 SNPs by name
We can regard the content of a SNP chip as a set of SNP, referenced by name. The pd.genomewidesnp.6 package describes the Affymetrix SNP 6.0 chip. We can determine which traits are associated with loci interrogated by the chip as follows. We work with a subset of the 1 million loci for illustration.
The data frame has information on 10000 probes, acquired through the following code (not executed here to reduce dependence on the pd.genomewidesnp.6 package, which is very large.
library(pd.genomewidesnp.6)
con = pd.genomewidesnp.6@getdb()
locon6 = dbGetQuery(con,
"select dbsnp_rs_id, chrom, physical_pos from featureSet limit 10000")Instead use the serialized information:
data(locon6)
rson6 = as.character(locon6[[1]])
rson6[1:5]## [1] "rs2887286" "rs1496555" "rs41477744" "rs3890745" "rs10492936"We subset the GWAS ranges structure with rsids that are common to both the chip and the GWAS catalog. We then tabulate the diseases associated with the common loci.
intr = gwtrunc[ intersect(getRsids(gwtrunc), rson6) ]
sort(table(getTraits(intr)), decreasing=TRUE)[1:10]##
## Adolescent idiopathic scoliosis Height
## 4 4
## Metabolite levels Blood protein levels
## 4 3
## Body mass index Asthma
## 3 2
## Diastolic blood pressure Post bronchodilator FEV1/FVC ratio
## 2 2
## Type 2 diabetes Age-related diseases and mortality
## 2 14.2 Traits by genomic location
We will assemble genomic coordinates for SNP on the Affymetrix 6.0 chip and show the effects of identifying the trait-associated loci with regions of width 1000bp instead of 1bp.
The following code retrieves coordinates for SNP interrogated on 10000 probes (to save time) on the 6.0 chip, in a GRanges instance that was lifted over to GRCh38. The data statement is preceded by legacy code that produced an instance with hg19 coordinates.
#gr6.0 = GRanges(seqnames=ifelse(is.na(locon6$chrom),0,locon6$chrom),
# IRanges(ifelse(is.na(locon6$phys),1,locon6$phys), width=1))
#S4Vectors::mcols(gr6.0)$rsid = as.character(locon6$dbsnp_rs_id)
#seqlevels(gr6.0) = paste("chr", seqlevels(gr6.0), sep="")
data(gr6.0_hg38)Here we compute overlaps with both the raw disease-associated locus addresses, and with the locus address \(\pm\) 500bp.
ag = function(x) as(x, "GRanges")
ovraw = suppressWarnings(subsetByOverlaps(ag(gwtrunc), gr6.0_hg38))
length(ovraw)## [1] 125ovaug = suppressWarnings(subsetByOverlaps(ag(gwtrunc+500), gr6.0_hg38))
length(ovaug)## [1] 248To acquire the subset of the catalog to which 6.0 probes are within 500bp, use:
rawrs = mcols(ovraw)$SNPS
augrs = mcols(ovaug)$SNPS
gwtrunc[augrs]## gwasloc instance with 248 records and 38 attributes per record.
## Extracted: 2020-04-30 23:24:51
## metadata()$badpos includes records for which no unique locus was given.
## Genome: GRCh38
## Excerpt:
## GRanges object with 5 ranges and 3 metadata columns:
## seqnames ranges strand | DISEASE/TRAIT SNPS P-VALUE
## <Rle> <IRanges> <Rle> | <character> <character> <numeric>
## [1] chr11 78380104 * | Alzheimer's disease .. rs2373115 1e-10
## [2] chr11 45851540 * | Fasting blood glucose rs11605924 1e-14
## [3] chr1 101475100 * | Bipolar disorder rs1419103 6e-06
## [4] chr11 43064544 * | Cognitive performance rs10501293 5e-06
## [5] chr1 2622185 * | Rheumatoid arthritis rs3890745 1e-07
## -------
## seqinfo: 24 sequences from GRCh38 genomeRelaxing the intersection criterion in this limited case leads to a larger set of traits.
nn = setdiff( getTraits(gwtrunc[augrs]), getTraits(gwtrunc[rawrs]) )
length(nn)## [1] 65head(nn)## [1] "Fasting blood glucose" "Bipolar disorder"
## [3] "Obesity" "Osteonecrosis of the jaw"
## [5] "Optic nerve measurement (disc area)" "Venous thromboembolism"tail(nn)## [1] "Medication use (diuretics)"
## [2] "Pre-treatment viral load in HIV-1 infection"
## [3] "Medication use (agents acting on the renin-angiotensin system)"
## [4] "Macular thickness"
## [5] "Systolic blood pressure (alcohol consumption interaction)"
## [6] "Skin pigmentation"5 Counting alleles associated with traits
We can use to count risky alleles enumerated in the GWAS catalog. This particular function assumes that we have genotyped at the catalogued loci. Below we will discuss how to impute from non-catalogued loci to those enumerated in the catalog.
data(gg17N) # translated from GGdata chr 17 calls using ABmat2nuc
gg17N[1:5,1:5]## rs6565733 rs1106175 rs17054921 rs8064924 rs8070440
## NA06985 "G/G" "A/G" "C/C" "G/G" "G/G"
## NA06991 "G/G" "A/A" "C/C" "G/G" "G/G"
## NA06993 "G/G" "A/A" "C/C" "G/G" "G/G"
## NA06994 "A/G" "A/G" "C/C" "A/G" "G/G"
## NA07000 "G/G" "A/A" "C/C" "G/G" "G/G"This function can use genotype information in the A/B format, assuming that B denotes the alphabetically later nucleotide. Because we have direct nucleotide coding in our matrix, we set the parameter to false in this call.
h17 = riskyAlleleCount(gg17N, matIsAB=FALSE, chr="ch17",
gwwl = gwtrunc)
h17[1:5,1:5]## rs2034088 rs741677 rs9907102 rs8066620 rs12603526
## NA06985 0 0 0 2 0
## NA06991 1 1 0 2 0
## NA06993 1 2 0 2 0
## NA06994 2 2 0 2 0
## NA07000 1 2 0 1 0table(as.numeric(h17))##
## 0 1 2
## 34530 14477 10483It is of interest to bind the counts back to the catalog data.
gwr = gwtrunc
gwr = gwr[colnames(h17),]
S4Vectors::mcols(gwr) = cbind(mcols(gwr), DataFrame(t(h17)))
sn = rownames(h17)
gwr[,c("DISEASE/TRAIT", sn[1:4])]## gwasloc instance with 661 records and 5 attributes per record.
## Extracted: 2020-04-30 23:24:51
## metadata()$badpos includes records for which no unique locus was given.
## Genome: GRCh38
## Excerpt:
## GRanges object with 5 ranges and 5 metadata columns:
## seqnames ranges strand | DISEASE/TRAIT NA06985 NA06991
## <Rle> <IRanges> <Rle> | <character> <integer> <integer>
## [1] chr17 519811 * | Hip circumference ad.. 0 1
## [2] chr17 560603 * | Waist circumference .. 0 1
## [3] chr17 561592 * | Metabolite levels 0 0
## [4] chr17 835613 * | Heel bone mineral de.. 2 2
## [5] chr17 897353 * | Colorectal cancer 0 0
## NA06993 NA06994
## <integer> <integer>
## [1] 1 2
## [2] 2 2
## [3] 0 0
## [4] 2 2
## [5] 0 0
## -------
## seqinfo: 24 sequences from GRCh38 genomeNow by programming on the metadata columns, we can identify individuals with particular risk profiles.
6 Formal management of trait vocabularies
6.1 Diseases: Disease Ontology
The Disease Ontology project Osborne et al. (2009) formalizes a vocabulary for human diseases. Bioconductor’s DO.db package is a curated representation.
library(DO.db)## Loading required package: AnnotationDbi## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.##
## Attaching package: 'AnnotationDbi'## The following object is masked from 'package:dplyr':
##
## selectDO()## Quality control information for DO:
##
##
## This package has the following mappings:
##
## DOANCESTOR has 6569 mapped keys (of 6570 keys)
## DOCHILDREN has 1811 mapped keys (of 6570 keys)
## DOOBSOLETE has 2374 mapped keys (of 2374 keys)
## DOOFFSPRING has 1811 mapped keys (of 6570 keys)
## DOPARENTS has 6569 mapped keys (of 6570 keys)
## DOTERM has 6570 mapped keys (of 6570 keys)
##
##
## Additional Information about this package:
##
## DB schema: DO_DB
## DB schema version: 1.0All tokens of the ontology are acquired via:
alltob = unlist(mget(mappedkeys(DOTERM), DOTERM))
allt = sapply(alltob, Term)
allt[1:5]## DOID:0001816 DOID:0002116
## "angiosarcoma" "pterygium"
## DOID:0014667 DOID:0050004
## "disease of metabolism" "seminal vesicle acute gonorrhea"
## DOID:0050012
## "chikungunya"Direct mapping from disease trait tokens in the catalog to this vocabulary succeeds for a modest proportion of records.
cattra = mcols(gwtrunc)$`DISEASE/TRAIT`
mat = match(tolower(cattra), tolower(allt))
catDO = names(allt)[mat]
na.omit(catDO)[1:50]## [1] "DOID:8778" "DOID:8778" "DOID:8778" "DOID:8778" "DOID:8778"
## [6] "DOID:8778" "DOID:8778" "DOID:8778" "DOID:8778" "DOID:8778"
## [11] "DOID:8778" "DOID:8778" "DOID:8778" "DOID:8577" "DOID:8577"
## [16] "DOID:8577" "DOID:10652" "DOID:8778" "DOID:8778" "DOID:8778"
## [21] "DOID:8778" "DOID:8778" "DOID:9256" "DOID:9256" "DOID:9256"
## [26] "DOID:8893" "DOID:5419" "DOID:1909" "DOID:1324" "DOID:1324"
## [31] "DOID:8577" "DOID:8577" "DOID:10652" "DOID:3312" "DOID:3312"
## [36] "DOID:3312" "DOID:3312" "DOID:1324" "DOID:1324" "DOID:1324"
## [41] "DOID:7148" "DOID:1040" "DOID:3910" "DOID:8778" "DOID:8778"
## [46] "DOID:8577" "DOID:8577" "DOID:8577" "DOID:8577" "DOID:0050425"mean(is.na(catDO))## [1] 0.90354Approximate matching of unmatched tokens can proceed by various routes. Some traits are not diseases, and will not be mappable using Disease Ontology. However, consider
unique(cattra[is.na(catDO)])[1:20]## [1] "Alcohol consumption (transferrin glycosylation)"
## [2] "Sudden cardiac arrest"
## [3] "Orofacial clefts (maternal alcohol consumption interaction)"
## [4] "Height"
## [5] "Pulmonary function"
## [6] "Glioma"
## [7] "Bone mineral density (hip)"
## [8] "Bone mineral density (spine)"
## [9] "Idiopathic dilated cardiomyopathy"
## [10] "Osteoporosis-related phenotypes"
## [11] "Cutaneous nevi"
## [12] "Primary biliary cholangitis"
## [13] "Cardiac hypertrophy"
## [14] "Adiposity"
## [15] "Uric acid levels"
## [16] "Type 1 diabetes"
## [17] "Alzheimer's disease (late onset)"
## [18] "Fasting blood insulin"
## [19] "Fasting blood glucose"
## [20] "Homeostasis model assessment of beta-cell function"nomatch = cattra[is.na(catDO)]
unique(nomatch)[1:5]## [1] "Alcohol consumption (transferrin glycosylation)"
## [2] "Sudden cardiac arrest"
## [3] "Orofacial clefts (maternal alcohol consumption interaction)"
## [4] "Height"
## [5] "Pulmonary function"Manual searching shows that a number of these have very close matches.
6.2 Other phenotypic traits: Human Phenotype Ontology
Bioconductor does not possess an annotation package for phenotype ontology, but the standardized OBO format can be parsed and modeled into a graph.
hpobo = gzfile(dir(system.file("obo", package="gwascat"), pattern="hpo", full=TRUE))
HPOgraph = obo2graphNEL(hpobo)
close(hpobo)The phenotypic terms are obtained via:
requireNamespace("graph")
hpoterms = unlist(graph::nodeData(HPOgraph, graph::nodes(HPOgraph), "name"))
hpoterms[1:10]## HP:0000001
## "All"
## HP:0000002
## "Abnormality of body height"
## HP:0000003
## "Multicystic kidney dysplasia"
## HP:0000004
## "Onset and clinical course"
## HP:0000005
## "Mode of inheritance"
## HP:0000006
## "Autosomal dominant inheritance"
## HP:0000007
## "Autosomal recessive inheritance"
## HP:0000008
## "Abnormality of female internal genitalia"
## HP:0000009
## "Functional abnormality of the bladder"
## HP:0000010
## "Recurrent urinary tract infections"Exact hits to unmatched GWAS catalog traits exist:
intersect(tolower(nomatch), tolower(hpoterms))## [1] "glioma" "stroke"
## [3] "autism" "glioblastoma"
## [5] "knee osteoarthritis" "coronary artery calcification"
## [7] "hearing impairment" "nephropathy"
## [9] "hypertriglyceridemia" "insomnia"
## [11] "depression" "eczema"
## [13] "nasal polyps" "eating disorders"
## [15] "ischemic stroke" "iga nephropathy"
## [17] "hematuria" "neurofibrillary tangles"
## [19] "hashimoto thyroiditis" "selective iga deficiency"
## [21] "headache" "thrombosis"
## [23] "dysphagia" "excessive daytime sleepiness"
## [25] "benign prostatic hyperplasia" "bicuspid aortic valve"
## [27] "orthostatic hypotension" "freckling"
## [29] "impulsivity"More work on formalization of trait terms is underway.
%## {Curation of approximate matches}
% NB the graph stuff can be used for other OBO without .db packages.. %The package includes an OBO-formatted %image of the ontology and a model for it as a instance %as defined in the Bioconductor package .
%The graph model is constructed as follows. %<<dogrmod} %doobo = dir(system.file(“obo”, package=“gwascat”), full=TRUE, patt=“HumanDO”) %DOgraph = obo2graphNEL(doobo) %DOgraph %@ % %Nodes are the formal disease term identifiers; node data provides additional %metadata. %<<lkno} %nodeData(DOgraph)[1:5] %@
7 CADD scores
Kircher et al. (2014) define combined annotation-dependent depletion scores measuring variant pathogenicity in an integrative way. Small requests to bind scores for SNV to GRanges can be resolved through HTTP; large requests can be carried out on a local tabix-indexed selection from their archive.
g3 = as(gwtrunc, "GRanges")
bg3 = bindcadd_snv( g3[which(seqnames(g3)=="chr3")][1:20] )
inds = ncol(mcols(bg3))
bg3[, (inds-3):inds]This requires cooperation of network interface and server, so we don’t evaluate in vignette build but on 1 Apr 2014 the response was:
GRanges with 20 ranges and 4 metadata columns:
seqnames ranges strand | Ref Alt
<Rle> <IRanges> <Rle> | <character> <character>
[1] 3 [109789570, 109789570] * | A G
[2] 3 [ 25922285, 25922285] * | G A
[3] 3 [109529550, 109529550] * | T C
[4] 3 [175055759, 175055759] * | T G
[5] 3 [191912870, 191912870] * | C T
... ... ... ... ... ... ...
[16] 3 [187716886, 187716886] * | A G
[17] 3 [160820524, 160820524] * | G C
[18] 3 [169518455, 169518455] * | T C
[19] 3 [179172979, 179172979] * | G T
[20] 3 [171785168, 171785168] * | G C
CScore PHRED
<numeric> <numeric>
[1] -0.182763 3.110
[2] -0.289708 2.616
[3] 0.225373 5.216
[4] -0.205689 3.003
[5] -0.172189 3.161
... ... ...
[16] -0.019710 3.913
[17] -0.375183 2.235
[18] -0.695270 0.987
[19] -0.441673 1.949
[20] 0.231972 5.252
---
seqlengths:
1 2 3 4 ... 21 22 X
249250621 243199373 198022430 191154276 ... 48129895 51304566 1552705608 Appendix: Adequacy of location annotation
A basic question concerning the use of archived SNP identifiers is durability of the association between asserted location and SNP identifier. The function uses a current Bioconductor SNPlocs package to check this.
For example, to verify that locations asserted on chromosome 20 agree between the Bioconductor dbSNP image and the gwas catalog,
if ("SNPlocs.Hsapiens.dbSNP144.GRCh37" %in% installed.packages()[,1]) {
library(SNPlocs.Hsapiens.dbSNP144.GRCh37)
chklocs("20", gwtrunc)
}This is not a fast procedure.
9 Acknowledgment
The development of this software was supported in part by Robert Gentleman and the Computational Biology Group of Genentech, Inc.