An introduction to the GenomicScores package
30 April 2024
Abstract
GenomicScores provides infrastructure to store and access genomewide position-specific scores within R and Bioconductor.
Package
GenomicScores 2.16.0
1 Getting started
GenomicScores is an R package distributed as part of the Bioconductor project. To install the package, start R and enter:
install.packages("BiocManager")
BiocManager::install("GenomicScores")Once GenomicScores is installed, it can be loaded with the following command.
library(GenomicScores)Often, however, GenomicScores will be automatically loaded when working with an annotation package that uses GenomicScores, such as phastCons100way.UCSC.hg38.
2 Genomewide position-specific scores
Genomewide scores assign each genomic position a numeric value denoting an estimated measure of constraint or impact on variation at that position. They are commonly used to filter single nucleotide variants or assess the degree of constraint or functionality of genomic features. Genomic scores are built on the basis of different sources of information such as sequence homology, functional domains, physical-chemical changes of amino acid residues, etc.
One particular example of genomic scores are phastCons scores. They provide a measure of conservation obtained from genomewide alignments using the program phast (Phylogenetic Analysis with Space/Time models) from Siepel et al. (2005). The GenomicScores package allows one to retrieve these scores through annotation packages (Section 4) or as AnnotationHub resources (Section 5).
Often, genomic scores such as phastCons are used within workflows running on top of R and Bioconductor. The purpose of the GenomicScores package is to enable an easy and interactive access to genomic scores within those workflows.
3 Lossy storage of genomic scores with compressed vectors
Storing and accessing genomic scores within R is challenging when their values cover large regions of the genome, resulting in gigabytes of double-precision numbers. This is the case, for instance, for phastCons (Siepel et al. 2005) or CADD (Kircher et al. 2014).
We address this problem by using lossy compression, also called quantization, coupled with run-length encoding (Rle) vectors. Lossy compression attempts to trade off precision for compression without compromising the scientific integrity of the data (Zender 2016).
Sometimes, measurements and statistical estimates under certain models generate false precision. False precision is essentialy noise that wastes storage space and it is meaningless from the scientific point of view (Zender 2016). In those circumstances, lossy compression not only saves storage space, but also removes false precision.
The use of lossy compression leads to a subset of quantized values much smaller than the original set of genomic scores, resulting in long runs of identical values along the genome. These runs of identical values can be further compressed using the implementation of Rle vectors available in the S4Vectors Bioconductor package.
To enable a seamless access to genomic scores stored with quantized values
in compressed vectors the GenomicScores defines the GScores
class of objects. This class manages the location, loading and dequantization
of genomic scores stored separately on each chromosome, while it also provides
rich metadata on the provenance, citation and licensing of the original data.
4 Availability and retrieval of genomic scores
The access to genomic scores through GScores objects is available either
through annotation packages or as AnnotationHub resources. To
find out what kind of genomic scores are available, through which mechanism,
and in which organism, we may use the function availableGScores().
avgs <- availableGScores()
avgs
Organism Category
AlphaMissense.v2023.hg19 Homo sapiens Pathogenicity
AlphaMissense.v2023.hg38 Homo sapiens Pathogenicity
cadd.v1.3.hg19 <NA> <NA>
cadd.v1.6.hg19 Homo sapiens Pathogenicity
cadd.v1.6.hg38 Homo sapiens Pathogenicity
fitCons.UCSC.hg19 Homo sapiens Constraint
linsight.UCSC.hg19 Homo sapiens Constraint
MafDb.1Kgenomes.phase1.GRCh38 Homo sapiens MAF
MafDb.1Kgenomes.phase1.hs37d5 Homo sapiens MAF
MafDb.1Kgenomes.phase3.GRCh38 Homo sapiens MAF
MafDb.1Kgenomes.phase3.hs37d5 Homo sapiens MAF
MafDb.ExAC.r1.0.GRCh38 Homo sapiens MAF
MafDb.ExAC.r1.0.hs37d5 Homo sapiens MAF
MafDb.ExAC.r1.0.nonTCGA.GRCh38 Homo sapiens MAF
MafDb.ExAC.r1.0.nonTCGA.hs37d5 Homo sapiens MAF
MafDb.gnomAD.r2.1.GRCh38 Homo sapiens MAF
MafDb.gnomAD.r2.1.hs37d5 Homo sapiens MAF
MafDb.gnomADex.r2.1.GRCh38 Homo sapiens MAF
MafDb.gnomADex.r2.1.hs37d5 Homo sapiens MAF
MafDb.TOPMed.freeze5.hg19 Homo sapiens MAF
MafDb.TOPMed.freeze5.hg38 Homo sapiens MAF
MafH5.gnomAD.v3.1.2.GRCh38 Homo sapiens MAF
mcap.v1.0.hg19 Homo sapiens Pathogenicity
phastCons100way.UCSC.hg19 Homo sapiens Conservation
phastCons100way.UCSC.hg38 Homo sapiens Conservation
phastCons27way.UCSC.dm6 Drosophila melanogaster Conservation
phastCons30way.UCSC.hg38 Homo sapiens Conservation
phastCons35way.UCSC.mm39 Mus musculus Conservation
phastCons46wayPlacental.UCSC.hg19 Homo sapiens Conservation
phastCons46wayPrimates.UCSC.hg19 Homo sapiens Conservation
phastCons60way.UCSC.mm10 Mus musculus Conservation
phastCons7way.UCSC.hg38 Homo sapiens Conservation
phyloP100way.UCSC.hg19 Homo sapiens Conservation
phyloP100way.UCSC.hg38 Homo sapiens Conservation
phyloP35way.UCSC.mm39 Mus musculus Conservation
phyloP60way.UCSC.mm10 Mus musculus Conservation
Installed Cached BiocManagerInstall
AlphaMissense.v2023.hg19 FALSE TRUE FALSE
AlphaMissense.v2023.hg38 FALSE TRUE FALSE
cadd.v1.3.hg19 FALSE FALSE FALSE
cadd.v1.6.hg19 FALSE TRUE FALSE
cadd.v1.6.hg38 FALSE TRUE FALSE
fitCons.UCSC.hg19 FALSE FALSE TRUE
linsight.UCSC.hg19 FALSE FALSE FALSE
MafDb.1Kgenomes.phase1.GRCh38 FALSE FALSE TRUE
MafDb.1Kgenomes.phase1.hs37d5 TRUE FALSE TRUE
MafDb.1Kgenomes.phase3.GRCh38 TRUE FALSE TRUE
MafDb.1Kgenomes.phase3.hs37d5 TRUE FALSE TRUE
MafDb.ExAC.r1.0.GRCh38 FALSE FALSE TRUE
MafDb.ExAC.r1.0.hs37d5 TRUE FALSE TRUE
MafDb.ExAC.r1.0.nonTCGA.GRCh38 FALSE FALSE TRUE
MafDb.ExAC.r1.0.nonTCGA.hs37d5 FALSE FALSE TRUE
MafDb.gnomAD.r2.1.GRCh38 FALSE FALSE TRUE
MafDb.gnomAD.r2.1.hs37d5 FALSE FALSE TRUE
MafDb.gnomADex.r2.1.GRCh38 FALSE FALSE TRUE
MafDb.gnomADex.r2.1.hs37d5 TRUE FALSE TRUE
MafDb.TOPMed.freeze5.hg19 FALSE FALSE TRUE
MafDb.TOPMed.freeze5.hg38 FALSE FALSE TRUE
MafH5.gnomAD.v3.1.2.GRCh38 FALSE FALSE TRUE
mcap.v1.0.hg19 FALSE FALSE FALSE
phastCons100way.UCSC.hg19 TRUE FALSE TRUE
phastCons100way.UCSC.hg38 TRUE FALSE TRUE
phastCons27way.UCSC.dm6 FALSE FALSE FALSE
phastCons30way.UCSC.hg38 FALSE FALSE FALSE
phastCons35way.UCSC.mm39 FALSE FALSE FALSE
phastCons46wayPlacental.UCSC.hg19 FALSE FALSE FALSE
phastCons46wayPrimates.UCSC.hg19 FALSE FALSE FALSE
phastCons60way.UCSC.mm10 FALSE FALSE FALSE
phastCons7way.UCSC.hg38 FALSE FALSE TRUE
phyloP100way.UCSC.hg19 FALSE FALSE FALSE
phyloP100way.UCSC.hg38 FALSE FALSE FALSE
phyloP35way.UCSC.mm39 FALSE FALSE FALSE
phyloP60way.UCSC.mm10 FALSE FALSE FALSE
AnnotationHub
AlphaMissense.v2023.hg19 TRUE
AlphaMissense.v2023.hg38 TRUE
cadd.v1.3.hg19 FALSE
cadd.v1.6.hg19 TRUE
cadd.v1.6.hg38 TRUE
fitCons.UCSC.hg19 FALSE
linsight.UCSC.hg19 FALSE
MafDb.1Kgenomes.phase1.GRCh38 FALSE
MafDb.1Kgenomes.phase1.hs37d5 FALSE
MafDb.1Kgenomes.phase3.GRCh38 FALSE
MafDb.1Kgenomes.phase3.hs37d5 FALSE
MafDb.ExAC.r1.0.GRCh38 FALSE
MafDb.ExAC.r1.0.hs37d5 FALSE
MafDb.ExAC.r1.0.nonTCGA.GRCh38 FALSE
MafDb.ExAC.r1.0.nonTCGA.hs37d5 FALSE
MafDb.gnomAD.r2.1.GRCh38 FALSE
MafDb.gnomAD.r2.1.hs37d5 FALSE
MafDb.gnomADex.r2.1.GRCh38 FALSE
MafDb.gnomADex.r2.1.hs37d5 FALSE
MafDb.TOPMed.freeze5.hg19 FALSE
MafDb.TOPMed.freeze5.hg38 FALSE
MafH5.gnomAD.v3.1.2.GRCh38 FALSE
mcap.v1.0.hg19 FALSE
phastCons100way.UCSC.hg19 FALSE
phastCons100way.UCSC.hg38 FALSE
phastCons27way.UCSC.dm6 FALSE
phastCons30way.UCSC.hg38 FALSE
phastCons35way.UCSC.mm39 FALSE
phastCons46wayPlacental.UCSC.hg19 FALSE
phastCons46wayPrimates.UCSC.hg19 FALSE
phastCons60way.UCSC.mm10 FALSE
phastCons7way.UCSC.hg38 FALSE
phyloP100way.UCSC.hg19 FALSE
phyloP100way.UCSC.hg38 FALSE
phyloP35way.UCSC.mm39 FALSE
phyloP60way.UCSC.mm10 FALSEFor example, if we want to use the phastCons conservation scores available through the annotation package phastCons100way.UCSC.hg38, we should first install it (we only need to do this once).
BiocManager::install("phastCons100way.UCSC.hg38")Second, we should load the package, and a GScores object will be created and
named after the package name, during the loading operation. It is often handy
to shorten that name.
library(phastCons100way.UCSC.hg38)
phast <- phastCons100way.UCSC.hg38
class(phast)
[1] "GScores"
attr(,"package")
[1] "GenomicScores"Typing the name of the GScores object shows a summary of its contents and
some of its metadata.
phast
GScores object
# organism: Homo sapiens (UCSC, hg38)
# provider: UCSC
# provider version: 11May2015
# download date: Apr 10, 2018
# loaded sequences: chr5_GL000208v1_random
# number of sites: 2943 millions
# maximum abs. error: 0.05
# use 'citation()' to cite these data in publicationsThe bibliographic reference to cite the genomic score data stored in a GScores
object can be accessed using the citation() method either on the package name
(in case of annotation packages), or on the GScores object.
citation(phast)
Adam Siepel, Gill Berejano, Jakob S. Pedersen, Angie S. Hinrichs,
Minmei Hou, Kate Rosenbloom, Hiram Clawson, John Spieth, LaDeana W.
Hillier, Stephen Richards, George M. Weinstock, Richard K. Wilson,
Richard A. Gibbs, W. James Kent, Webb Miller, David Haussler (2005).
"Evolutionarily conserved elements in vertebrate, insect, worm, and
yeast genomes." _Genome Research_, *15*, 1034-1050.
doi:10.1101/gr.3715005 <https://doi.org/10.1101/gr.3715005>.Other methods tracing provenance and other metadata are provider(),
providerVersion(), organism() and seqlevelsStyle(); please consult
the help page of the GScores class for a comprehensive list of available
methods.
provider(phast)
[1] "UCSC"
providerVersion(phast)
[1] "11May2015"
organism(phast)
[1] "Homo sapiens"
seqlevelsStyle(phast)
[1] "UCSC"To retrieve genomic scores for specific consecutive positions we should use the
method gscores(), as follows.
gscores(phast, GRanges(seqnames="chr22",
IRanges(start=50528591:50528596, width=1)))
GRanges object with 6 ranges and 1 metadata column:
seqnames ranges strand | default
<Rle> <IRanges> <Rle> | <numeric>
[1] chr22 50528591 * | 1.0
[2] chr22 50528592 * | 1.0
[3] chr22 50528593 * | 0.8
[4] chr22 50528594 * | 1.0
[5] chr22 50528595 * | 1.0
[6] chr22 50528596 * | 0.0
-------
seqinfo: 455 sequences (1 circular) from Genome Reference Consortium GRCh38 genomeFor a single position we may use this other GRanges() constructor.
gscores(phast, GRanges("chr22:50528593"))
GRanges object with 1 range and 1 metadata column:
seqnames ranges strand | default
<Rle> <IRanges> <Rle> | <numeric>
[1] chr22 50528593 * | 0.8
-------
seqinfo: 455 sequences (1 circular) from Genome Reference Consortium GRCh38 genomeWe may also retrieve the score values only with the method score().
score(phast, GRanges(seqnames="chr22",
IRanges(start=50528591:50528596, width=1)))
[1] 1.0 1.0 0.8 1.0 1.0 0.0
score(phast, GRanges("chr22:50528593"))
[1] 0.8Let’s illustrate how to retrieve phastCons scores using data from the GWAS
catalog available through the Bioconductor package gwascat. For
the purpose of this vignette, we will filter the GWAS catalog data by (1)
discarding entries with NA values in either chromosome name or position, or
with multiple positions; (2) storing the data into a GRanges object, including
the GWAS catalog columns STRONGEST SNP-RISK ALLELE and MAPPED_TRAIT, and the
reference and alternate alleles, as metadata columns; (4) restricting variants
to those located in chromosomes 20 to 22; and (3) excluding variants with
multinucleotide alleles, or where reference and alternate alleles are identical.
library(BSgenome.Hsapiens.UCSC.hg38)
library(gwascat)
gwc <- get_cached_gwascat()
mask <- !is.na(gwc$CHR_ID) & !is.na(gwc$CHR_POS) &
!is.na(as.integer(gwc$CHR_POS))
gwc <- gwc[mask, ]
grstr <- sprintf("%s:%s-%s", gwc$CHR_ID, gwc$CHR_POS, gwc$CHR_POS)
gwcgr <- GRanges(grstr, RISK_ALLELE=gwc[["STRONGEST SNP-RISK ALLELE"]],
MAPPED_TRAIT=gwc$MAPPED_TRAIT)
seqlevelsStyle(gwcgr) <- "UCSC"
mask <- seqnames(gwcgr) %in% c("chr20", "chr21", "chr22")
gwcgr <- gwcgr[mask]
ref <- as.character(getSeq(Hsapiens, gwcgr))
alt <- gsub("rs[0-9]+-", "", gwcgr$RISK_ALLELE)
mask <- (ref %in% c("A", "C", "G", "T")) & (alt %in% c("A", "C", "G", "T")) &
nchar(alt) == 1 & ref != alt
gwcgr <- gwcgr[mask]
mcols(gwcgr)$REF <- ref[mask]
mcols(gwcgr)$ALT <- alt[mask]gwcgr
GRanges object with 13540 ranges and 4 metadata columns:
seqnames ranges strand | RISK_ALLELE MAPPED_TRAIT
<Rle> <IRanges> <Rle> | <character> <character>
[1] chr20 35321981 * | rs6088792-T body height
[2] chr22 23250864 * | rs5751614-A body height
[3] chr20 6640246 * | rs967417-C body height
[4] chr20 33130847 * | rs6059101-A ulcerative colitis
[5] chr22 38148291 * | rs2284063-G nevus
... ... ... ... . ... ...
[13536] chr20 12979237 * | rs1321940-G sphingomyelin measur..
[13537] chr20 10148004 * | rs2210584-C sphingomyelin measur..
[13538] chr20 11892294 * | rs397865364-C sphingomyelin measur..
[13539] chr20 12823511 * | rs1413019-A sphingomyelin measur..
[13540] chr21 46284982 * | rs9975588-A S100 calcium-binding..
REF ALT
<character> <character>
[1] C T
[2] G A
[3] G C
[4] C A
[5] A G
... ... ...
[13536] A G
[13537] T C
[13538] T C
[13539] C A
[13540] G A
-------
seqinfo: 24 sequences from an unspecified genome; no seqlengthsFinally, let’s obtain the phastCons scores for this GWAS catalog variant set, and examine their summary and cumulative distribution.
pcsco <- score(phast, gwcgr)
summary(pcsco)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0000 0.0000 0.1217 0.0000 1.0000 38
round(cumsum(table(na.omit(pcsco))) / sum(!is.na(pcsco)), digits=2)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0.81 0.85 0.87 0.88 0.88 0.89 0.89 0.90 0.90 0.91 1.00 We can observe that only 10% of the variants in chromosomes 20 to 22 have a conservation phastCons score above 0.5. Let’s examine which traits have more fully conserved variants.
xtab <- table(gwcgr$MAPPED_TRAIT[pcsco == 1])
head(xtab[order(xtab, decreasing=TRUE)])
body height
63
neuroimaging measurement
34
mean corpuscular volume
33
high density lipoprotein cholesterol measurement
30
hemoglobin measurement
26
BMI-adjusted waist-hip ratio
23 5 Genomic scores as AnnotationHub resources
The AnnotationHub (AH), is a Bioconductor web resource that provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard (e.g., UCSC, Ensembl) and distributed sites, can be found. An AH web resource creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access.
We can quickly check for the available AH resources by subsetting as follows
the resources names from the previous table obtained with availableGScores().
rownames(avgs)[avgs$AnnotationHub]
[1] "AlphaMissense.v2023.hg19" "AlphaMissense.v2023.hg38"
[3] "cadd.v1.6.hg19" "cadd.v1.6.hg38" The selected resource can be downloaded with the function getGScores(). After the resource is downloaded the first time, the cached copy will enable a quicker retrieval later. Let’s download other conservation scores, the phyloP scores (Pollard et al. 2010), for human genome version hg38.
phylop <- getGScores("phyloP100way.UCSC.hg38")phylop
GScores object
# organism: Homo sapiens (UCSC, hg38)
# provider: UCSC
# provider version: 11May2015
# download date: May 12, 2017
# loaded sequences: chr20
# maximum abs. error: 0.55
# use 'citation()' to cite these data in publicationsLet’s retrieve the phyloP conservation scores for the previous set of GWAS catalog variants and compare them in Figure @(fig:phastvsphylop).
ppsco <- score(phylop, gwcgr)
plot(pcsco, ppsco, xlab="phastCons", ylab="phyloP",
cex.axis=1.2, cex.lab=1.5, las=1)
Figure 1: Comparison between phastCons and phyloP conservation scores
On the y-axis, phyloP scores as function of phastCons scores on the x-axis, for a set of GWAS catalog variant in the human chromosome 22.
We may observe that the values match in a rather discrete manner due to the
quantization of the scores. In the case of the phastCons annotation package
phastCons100way.UCSC.hg38, the GScore object gives access
in fact to two score populations, the default one in which conservation scores
are rounded to 1 decimal place, and an alternative one, named DP2, in which
they are rounded to 2 decimal places. To figure out what are the available
score populations in a GScores object, we should use the method
populations().
populations(phast)
[1] "default" "DP2" Whenever one of these populations is called default, this is the one used
by default. In other cases we can find out which is the default population as
follows:
defaultPopulation(phast)
[1] "default"To use one of the available score populations we should use the argument pop
in the corresponding method, as follows.
pcsco2 <- score(phast, gwcgr, pop="DP2")
head(pcsco2)
[1] 0.00 0.00 0.00 0.14 0.00 0.00Figure 2 below shows again the comparison of phastCons and phyloP conservation scores, this time at the higher resolution provided by the phastCons scores rounded at two decimal places.
plot(pcsco2, ppsco, xlab="phastCons", ylab="phyloP",
cex.axis=1.2, cex.lab=1.5, las=1)
Figure 2: Comparison between phastCons and phyloP conservation scores at a higher resolution
On the y-axis, phyloP scores as function of phastCons scores on the x-axis, for a set of GWAS catalog variant in the human chromosome 22.
5.1 Building an annotation package from a GScores object
Retrieving genomic scores through AnnotationHub resources requires an internet
connection and we may want to work with such resources offline, for instance in
high-performance computing (HPC) environments. For that purpose, we can create
ourselves an annotation package, such as
phastCons100way.UCSC.hg38,
from a GScores object corresponding to a downloaded AnnotationHub resource.
To do that we use the function makeGScoresPackage() as follows:
makeGScoresPackage(phast, maintainer="Me <me@example.com>",
author="Me", version="1.0.0")Creating package in ./phastCons100way.UCSC.hg38An argument, destDir, which by default points to the current working
directory, can be used to change where in the filesystem the package is created.
Afterwards, we should still build and install the package via, e.g.,
R CMD build and R CMD INSTALL, to be able to use it offline.
6 Retrieval of minor allele frequency data
One particular type of genomic scores that are accessible through
the GScores class is minor allele frequency (MAF) data.
There are currently 15 annotation packages that store MAF values
using the GenomicScores package, named using the
prefix MafDb or MafH5; see Table 1 below.
| Annotation Package | Description |
|---|---|
| MafDb.1Kgenomes.phase1.hs37d5 | MAF data from the 1000 Genomes Project Phase 1 for the human genome version GRCh37. |
| MafDb.1Kgenomes.phase1.GRCh38 | MAF data from the 1000 Genomes Project Phase 1 for the human genome version GRCh38. |
| MafDb.1Kgenomes.phase3.hs37d5 | MAF data from the 1000 Genomes Project Phase 3 for the human genome version GRCh37. |
| MafDb.1Kgenomes.phase3.GRCh38 | MAF data from the 1000 Genomes Project Phase 3 for the human genome version GRCh38. |
| MafDb.ExAC.r1.0.hs37d5 | MAF data from ExAC 60706 exomes for the human genome version GRCh37. |
| MafDb.ExAC.r1.0.GRCh38 | MAF data from ExAC 60706 exomes for the human genome version GRCh38. |
| MafDb.ExAC.r1.0.nonTCGA.hs37d5 | MAF data from ExAC 53105 nonTCGA exomes for the human genome version GRCh37. |
| MafDb.ExAC.r1.0.nonTCGA.GRCh38 | MAF data from ExAC 53105 nonTCGA exomes for the human genome version GRCh38. |
| MafDb.gnomAD.r2.1.hs37d5 | MAF data from gnomAD 15496 genomes for the human genome version GRCh37. |
| MafDb.gnomAD.r2.1.GRCh38 | MAF data from gnomAD 15496 genomes for the human genome version GRCh38. |
| MafDb.gnomADex.r2.1.hs37d5 | MAF data from gnomADex 123136 exomes for the human genome version GRCh37. |
| MafDb.gnomADex.r2.1.GRCh38 | MAF data from gnomADex 123136 exomes for the human genome version GRCh38. |
| MafH5.gnomAD.v4.0.GRCh38 | MAF data from gnomAD 76156 genomes for the human genome version GRCh38. |
| MafDb.TOPMed.freeze5.hg19 | MAF data from NHLBI TOPMed 62784 genomes for the human genome version GRCh37. |
| MafDb.TOPMed.freeze5.hg38 | MAF data from NHLBI TOPMed 62784 genomes for the human genome version GRCh38. |
In this type of package, the scores populations correspond to populations of individuals from which the MAF data were derived, and all MAF data were compressed using a precision of one significant figure for MAF < 0.1 and two significant figures for MAF >= 0.1. Let’s load the MAF package for the release v4.0 of gnomAD (Chen et al. 2024).
library(MafH5.gnomAD.v4.0.GRCh38)
mafh5 <- MafH5.gnomAD.v4.0.GRCh38
mafh5
GScores object
# organism: Homo sapiens (UCSC, hg38)
# provider: BroadInstitute
# provider version: v4.0
# download date: Feb 19, 2024
# default scores population: AF
# number of sites: 639 millions
# maximum abs. error (def. pop.): 0.00251
# use 'citation()' to cite these data in publications
populations(mafh5)
[1] "AF" "AF_allpopmax"Let’s retrieve the gnomAD MAF values for the previous GWAS catalog variant set and examine its distribution, and how many variants occur in less than 1% of all gnomAD populations and what fraction do they represent among the analyzed variants.
mafs <- score(mafh5, gwcgr, pop="AF_allpopmax")
summary(mafs)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.1900 0.3900 0.3248 0.4700 0.5000 414
sum(mafs < 0.01, na.rm=TRUE)
[1] 352
sum(mafs < 0.01, na.rm=TRUE) / sum(!is.na(mafs))
[1] 0.026817Finally, let’s examine which traits have more such rare variants.
xtab <- table(gwcgr$MAPPED_TRAIT[mafs < 0.01])
head(xtab[order(xtab, decreasing=TRUE)])
response to bronchodilator, FEV/FEC ratio
18
mean corpuscular volume
15
platelet component distribution width
13
monocyte count
10
platelet crit
10
forced expiratory volume, response to bronchodilator
8 7 Retrieval of multiple scores per genomic position
Among the score sets available as
AnnotationHub
web resources shown in the previous section, some of them, such as CADD
(Kircher et al. 2014), M-CAP (Jagadeesh et al. 2016) or AlphaMissense (Cheng et al. 2023),
provide multiple scores per genomic position that capture the tolerance to
mutations of single nucleotides. Such type of scores, often used to establish
the potential pathogenicity of variants, are sometimes released under some sort
of license for a non-commercial use. In such cases, the function getGScores()
will ask us interactively to accept the license. We can also set the argument
accept.license=TRUE to accept it non-interactively. We will illustrate such
a case using the AlphaMissense scores (Cheng et al. 2023).
am23 <- getGScores("AlphaMissense.v2023.hg38")
These data is shared under the license CC BY-NC-SA 4.0
(see https://creativecommons.org/licenses/by-nc-sa/4.0),
do you accept it? [y/n]: yLet’s retrieve the AlphaMissense scores for the reference and alternate alleles in our GWAS catalog variant set.
am23
GScores object
# organism: Homo sapiens (UCSC, hg38)
# provider: Google DeepMind
# provider version: v2023
# download date: Oct 10, 2023
# loaded sequences: chr20
# maximum abs. error: 0.005
# license: CC BY-NC-SA 4.0, see https://creativecommons.org/licenses/by-nc-sa/4.0
# use 'citation()' to cite these data in publications
amsco <- score(am23, gwcgr, ref=gwcgr$REF, alt=gwcgr$ALT)
summary(amsco)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.010 0.090 0.150 0.247 0.380 1.000 12609 Using the cutoffs for AlphaMissense scores reported in (Cheng et al. 2023) to classify variants into “likely benign”, “ambiguous” and “likely pathogenic”, and 0.01 and 0.1 as MAF cutoffs, let’s cross-tabulate the proportions of these two factors.
mask <- !is.na(amsco) & !is.na(mafs)
amscofac <- cut(amsco[mask], breaks=c(0, 0.34, 0.56, 1))
amscofac <- relevel(amscofac, ref="(0.56,1]")
maffac <- cut(mafs[mask], breaks=c(0, 0.01, 0.1, 1))
xtab <- table(maffac, amscofac)
t(xtab)
maffac
amscofac (0,0.01] (0.01,0.1] (0.1,1]
(0.56,1] 39 11 7
(0,0.34] 50 141 494
(0.34,0.56] 3 134 39
xtab <- t(xtab / rowSums(xtab))
round(xtab, digits=2)
maffac
amscofac (0,0.01] (0.01,0.1] (0.1,1]
(0.56,1] 0.42 0.04 0.01
(0,0.34] 0.54 0.49 0.91
(0.34,0.56] 0.03 0.47 0.07Figure 3 below displays graphically these proportions in an analogous way to the one shown in Figure 5B from Cheng et al. (2023). While these proportions are quite different to the original figure, due to the much lower number of variants analyzed here, we still can see, like in (Cheng et al. 2023), that the proportion of variants classified as likely pathogenic by AlphaMissense scores is much larger for rare variants with MAF < 0.01 than for common variants with MAF > 0.01.
Figure 3: AlphaMissense predictions
Proportions of three ranges of AlphaMissense pathogenicity scores for three ranges of minor allele frequencies (MAF) derived from gnomAD, on data from the GWAS catalog. This figure is analogous to Figure 5B from Cheng et al. (2023), but with much fewer variants.
8 Summarization of genomic scores
The input genomic ranges to the gscores() method may have widths larger than one
nucleotide. In those cases, and when there is only one score per position, the
gscores() method calculates, by default, the arithmetic mean of the scores across
each range.
gr1 <- GRanges(seqnames="chr22", IRanges(start=50528591:50528596, width=1))
gr1sco <- gscores(phast, gr1)
gr1sco
GRanges object with 6 ranges and 1 metadata column:
seqnames ranges strand | default
<Rle> <IRanges> <Rle> | <numeric>
[1] chr22 50528591 * | 1.0
[2] chr22 50528592 * | 1.0
[3] chr22 50528593 * | 0.8
[4] chr22 50528594 * | 1.0
[5] chr22 50528595 * | 1.0
[6] chr22 50528596 * | 0.0
-------
seqinfo: 455 sequences (1 circular) from Genome Reference Consortium GRCh38 genome
mean(gr1sco$default)
[1] 0.8
gr2 <- GRanges("chr22:50528591-50528596")
gscores(phast, gr2)
GRanges object with 1 range and 1 metadata column:
seqnames ranges strand | default
<Rle> <IRanges> <Rle> | <numeric>
[1] chr22 50528591-50528596 * | 0.8
-------
seqinfo: 455 sequences (1 circular) from Genome Reference Consortium GRCh38 genomeHowever, we may change the way in which scores from multiple-nucleotide ranges are
summarized with the argument summaryFun, as follows.
gscores(phast, gr2, summaryFun=max)
GRanges object with 1 range and 1 metadata column:
seqnames ranges strand | default
<Rle> <IRanges> <Rle> | <numeric>
[1] chr22 50528591-50528596 * | 1
-------
seqinfo: 455 sequences (1 circular) from Genome Reference Consortium GRCh38 genome
gscores(phast, gr2, summaryFun=min)
GRanges object with 1 range and 1 metadata column:
seqnames ranges strand | default
<Rle> <IRanges> <Rle> | <numeric>
[1] chr22 50528591-50528596 * | 0
-------
seqinfo: 455 sequences (1 circular) from Genome Reference Consortium GRCh38 genome
gscores(phast, gr2, summaryFun=median)
GRanges object with 1 range and 1 metadata column:
seqnames ranges strand | default
<Rle> <IRanges> <Rle> | <numeric>
[1] chr22 50528591-50528596 * | 1
-------
seqinfo: 455 sequences (1 circular) from Genome Reference Consortium GRCh38 genome9 Annotating variants with genomic scores
A typical use case of the GenomicScores package is in the context of annotating variants with genomic scores, such as phastCons conservation scores. For this purpose, we load the VariantAnnotaiton and TxDb.Hsapiens.UCSC.hg38.knownGene packages. The former will allow us annotate variants, and the latter contains the gene annotations from UCSC that will be used in this process.
library(VariantAnnotation)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGeneWe annotate the location of previous set of filtered GWAS variants, using the
function locateVariants() from the VariantAnnotation package.
loc <- locateVariants(gwcgr, txdb, AllVariants())
loc[1:3]
GRanges object with 3 ranges and 9 metadata columns:
seqnames ranges strand | LOCATION LOCSTART LOCEND QUERYID
<Rle> <IRanges> <Rle> | <factor> <integer> <integer> <integer>
[1] chr20 35321981 + | intron 25183 25183 1
[2] chr22 23250864 + | intron 76509 76509 2
[3] chr22 23250864 + | intron 70864 70864 2
TXID CDSID GENEID PRECEDEID FOLLOWID
<character> <IntegerList> <character> <CharacterList> <CharacterList>
[1] 210043 7050
[2] 214832 90007
[3] 214833 90007
-------
seqinfo: 24 sequences from an unspecified genome; no seqlengths
table(loc$LOCATION)
spliceSite intron fiveUTR threeUTR coding intergenic promoter
46 58628 440 1500 5407 3683 5975 Now we annotate phastCons conservation scores on the variants and store
those annotations as an additional metadata column of the GRanges object.
For this specific purpose we should use the method score() that returns
the genomic scores as a numeric vector, instead of doing it as a metadata
column in the input ranges object, done by the gscores() function.
loc$PHASTCONS <- score(phast, loc, pop="DP2")
loc[1:3]
GRanges object with 3 ranges and 10 metadata columns:
seqnames ranges strand | LOCATION LOCSTART LOCEND QUERYID
<Rle> <IRanges> <Rle> | <factor> <integer> <integer> <integer>
[1] chr20 35321981 + | intron 25183 25183 1
[2] chr22 23250864 + | intron 76509 76509 2
[3] chr22 23250864 + | intron 70864 70864 2
TXID CDSID GENEID PRECEDEID FOLLOWID
<character> <IntegerList> <character> <CharacterList> <CharacterList>
[1] 210043 7050
[2] 214832 90007
[3] 214833 90007
PHASTCONS
<numeric>
[1] 0
[2] 0
[3] 0
-------
seqinfo: 24 sequences from an unspecified genome; no seqlengthsUsing the following code we can examine the distribution of phastCons conservation scores of variants across the different annotated regions, shown in Figure 4.
x <- split(loc$PHASTCONS, loc$LOCATION)
mask <- elementNROWS(x) > 0
boxplot(x[mask], ylab="phastCons score", las=1, cex.axis=1.2, cex.lab=1.5, col="gray")
points(1:length(x[mask])+0.25, sapply(x[mask], mean, na.rm=TRUE), pch=23, bg="black")
Figure 4: Distribution of phastCons conservation scores in variants across different annotated regions
Diamonds indicate mean values.
Next, we can annotate AlphaMissense and CADD scores as follows. Note that we
use the QUERYID column of the annotations to fetch back reference and
alternative alleles from the original data container.
loc$AM <- score(am23, loc,
ref=gwcgr$REF[loc$QUERYID],
alt=gwcgr$ALT[loc$QUERYID])cadd
GScores object
# organism: Homo sapiens (UCSC, hg38)
# provider: UWashington
# provider version: v1.6
# download date: Oct 11, 2023
# loaded sequences: chr20
# maximum abs. error: 5
# use 'citation()' to cite these data in publications
loc$CADD <- score(cadd, loc, ref=gwcgr$REF[loc$QUERYID], alt=gwcgr$ALT[loc$QUERYID])Using the code below we can produce the plot of Figure 5 comparing AlphaMissense and CADD scores and labeling the location of the variants from which they are derived.
library(RColorBrewer)
par(mar=c(4, 5, 1, 1))
hmcol <- colorRampPalette(brewer.pal(nlevels(loc$LOCATION), "Set1"))(nlevels(loc$LOCATION))
plot(loc$AM, jitter(loc$CADD, factor=2), pch=19,
col=hmcol, xlab="AlphaMissense scores", ylab="CADD scores",
las=1, cex.axis=1.2, cex.lab=1.5, panel.first=grid())
legend("bottomright", levels(loc$LOCATION), pch=19, col=hmcol, inset=0.01)
Figure 5: Comparison of AlphaMissense and CADD scores
Values on the y-axis are jittered to facilitate visualization.
10 Session information
sessionInfo()
R version 4.4.0 beta (2024-04-15 r86425)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: America/New_York
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] RColorBrewer_1.1-3
[2] TxDb.Hsapiens.UCSC.hg38.knownGene_3.18.0
[3] GenomicFeatures_1.56.0
[4] AnnotationDbi_1.66.0
[5] VariantAnnotation_1.50.0
[6] Rsamtools_2.20.0
[7] SummarizedExperiment_1.34.0
[8] Biobase_2.64.0
[9] MatrixGenerics_1.16.0
[10] matrixStats_1.3.0
[11] MafH5.gnomAD.v4.0.GRCh38_3.19.0
[12] gwascat_2.36.0
[13] BSgenome.Hsapiens.UCSC.hg38_1.4.5
[14] BSgenome_1.72.0
[15] rtracklayer_1.64.0
[16] BiocIO_1.14.0
[17] Biostrings_2.72.0
[18] XVector_0.44.0
[19] phastCons100way.UCSC.hg38_3.7.1
[20] GenomicScores_2.16.0
[21] GenomicRanges_1.56.0
[22] GenomeInfoDb_1.40.0
[23] IRanges_2.38.0
[24] S4Vectors_0.42.0
[25] BiocGenerics_0.50.0
[26] knitr_1.46
[27] BiocStyle_2.32.0
loaded via a namespace (and not attached):
[1] tidyselect_1.2.1 dplyr_1.1.4 blob_1.2.4
[4] filelock_1.0.3 bitops_1.0-7 fastmap_1.1.1
[7] RCurl_1.98-1.14 BiocFileCache_2.12.0 GenomicAlignments_1.40.0
[10] XML_3.99-0.16.1 digest_0.6.35 lifecycle_1.0.4
[13] survival_3.6-4 KEGGREST_1.44.0 RSQLite_2.3.6
[16] magrittr_2.0.3 compiler_4.4.0 rlang_1.1.3
[19] sass_0.4.9 tools_4.4.0 utf8_1.2.4
[22] yaml_2.3.8 S4Arrays_1.4.0 bit_4.0.5
[25] curl_5.2.1 DelayedArray_0.30.0 abind_1.4-5
[28] BiocParallel_1.38.0 HDF5Array_1.32.0 withr_3.0.0
[31] purrr_1.0.2 grid_4.4.0 fansi_1.0.6
[34] Rhdf5lib_1.26.0 tinytex_0.50 cli_3.6.2
[37] rmarkdown_2.26 crayon_1.5.2 generics_0.1.3
[40] tzdb_0.4.0 httr_1.4.7 rjson_0.2.21
[43] DBI_1.2.2 cachem_1.0.8 rhdf5_2.48.0
[46] splines_4.4.0 zlibbioc_1.50.0 parallel_4.4.0
[49] BiocManager_1.30.22 restfulr_0.0.15 vctrs_0.6.5
[52] Matrix_1.7-0 jsonlite_1.8.8 bookdown_0.39
[55] hms_1.1.3 bit64_4.0.5 magick_2.8.3
[58] jquerylib_0.1.4 snpStats_1.54.0 glue_1.7.0
[61] codetools_0.2-20 BiocVersion_3.19.1 UCSC.utils_1.0.0
[64] tibble_3.2.1 pillar_1.9.0 rappdirs_0.3.3
[67] htmltools_0.5.8.1 rhdf5filters_1.16.0 GenomeInfoDbData_1.2.12
[70] R6_2.5.1 dbplyr_2.5.0 evaluate_0.23
[73] lattice_0.22-6 highr_0.10 readr_2.1.5
[76] AnnotationHub_3.12.0 png_0.1-8 memoise_2.0.1
[79] bslib_0.7.0 Rcpp_1.0.12 SparseArray_1.4.0
[82] xfun_0.43 pkgconfig_2.0.3 References
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Cheng, Jun, Guido Novati, Joshua Pan, Clare Bycroft, Akvilė Žemgulytė, Taylor Applebaum, Alexander Pritzel, et al. 2023. “Accurate Proteome-Wide Missense Variant Effect Prediction with Alphamissense.” Science, 1284–5.
Jagadeesh, Karthik A, Aaron M Wenger, Mark J Berger, Harendra Guturu, Peter D Stenson, David N Cooper, Jonathan A Bernstein, and Gill Bejerano. 2016. “M-Cap Eliminates a Majority of Variants of Uncertain Significance in Clinical Exomes at High Sensitivity.” Nat. Genet. 48 (12): 1581–6.
Kircher, Martin, Daniela M Witten, Preti Jain, Brian J O’roak, Gregory M Cooper, and Jay Shendure. 2014. “A General Framework for Estimating the Relative Pathogenicity of Human Genetic Variants.” Nat. Genet. 46 (3): 310–15.
Pollard, Katherine S, Melissa J Hubisz, Kate R Rosenbloom, and Adam Siepel. 2010. “Detection of Nonneutral Substitution Rates on Mammalian Phylogenies.” Genome Research 20 (1): 110–21.
Siepel, Adam, Gill Bejerano, Jakob S Pedersen, Angie S Hinrichs, Minmei Hou, Kate Rosenbloom, Hiram Clawson, et al. 2005. “Evolutionarily Conserved Elements in Vertebrate, Insect, Worm, and Yeast Genomes.” Genome Res. 15 (8): 1034–50.
Zender, Charles S. 2016. “Bit Grooming: Statistically Accurate Precision-Preserving Quantization with Compression, Evaluated in the netCDF Operators (Nco, V4. 4.8+).” Geosci. Model Dev. 9 (9): 3199–3211.