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You can check that gwasrapidd is able to connect to https://www.ebi.ac.uk by making a
connection attempt with the function
is_ebi_reachable():
Returns TRUE if the connection is possible, or
FALSE otherwise. If the connection is not possible, use the
parameter chatty = TRUE to learn at what point the
connection is failing.
The GWAS Catalog is mapped against Ensembl, dbSNP and a specific assembly version of
the human genome. You can get this info with
get_metadata():
The four main retrieval functions get_studies(),
get_associations(), get_variants(), and
get_traits() allow to search by multiple values for the
same search criterion. You only need to pass a vector of queries to each
search criterion parameter. Here are some simple examples.
Get studies by study identifiers (GCST002420 or GCST000392):
get_studies(study_id = c('GCST002420', 'GCST000392'))
#> An object of class "studies"
#> Slot "studies":
#> # A tibble: 2 × 13
#> study_id reported_trait initi…¹ repli…² gxe gxg snp_c…³ quali…⁴ imputed pooled study…⁵ full_…⁶ user_…⁷
#> <chr> <chr> <chr> <chr> <lgl> <lgl> <int> <chr> <lgl> <lgl> <chr> <lgl> <lgl>
#> 1 GCST002420 Binge eating behaviour… 206 Eu… 70 Eur… FALSE FALSE 8466825 <NA> TRUE FALSE <NA> FALSE FALSE
#> 2 GCST000392 Type 1 diabetes 7,514 … 4,267 … FALSE FALSE 841622 <NA> TRUE FALSE <NA> TRUE FALSE
#> # … with abbreviated variable names ¹initial_sample_size, ²replication_sample_size, ³snp_count, ⁴qualifier,
#> # ⁵study_design_comment, ⁶full_pvalue_set, ⁷user_requested
#>
#> Slot "genotyping_techs":
#> # A tibble: 2 × 2
#> study_id genotyping_technology
#> <chr> <chr>
#> 1 GCST002420 Genome-wide genotyping array
#> 2 GCST000392 Genome-wide genotyping array
#>
#> Slot "platforms":
#> # A tibble: 3 × 2
#> study_id manufacturer
#> <chr> <chr>
#> 1 GCST002420 Affymetrix
#> 2 GCST000392 Illumina
#> 3 GCST000392 Affymetrix
#>
#> Slot "ancestries":
#> # A tibble: 4 × 4
#> study_id ancestry_id type number_of_individuals
#> <chr> <int> <chr> <int>
#> 1 GCST002420 1 initial 929
#> 2 GCST002420 2 replication 828
#> 3 GCST000392 1 initial 16559
#> 4 GCST000392 2 replication 13279
#>
#> Slot "ancestral_groups":
#> # A tibble: 4 × 3
#> study_id ancestry_id ancestral_group
#> <chr> <int> <chr>
#> 1 GCST002420 1 European
#> 2 GCST002420 2 European
#> 3 GCST000392 1 European
#> 4 GCST000392 2 European
#>
#> Slot "countries_of_origin":
#> # A tibble: 2 × 5
#> study_id ancestry_id country_name major_area region
#> <chr> <int> <chr> <chr> <chr>
#> 1 GCST002420 1 <NA> <NA> <NA>
#> 2 GCST002420 2 <NA> <NA> <NA>
#>
#> Slot "countries_of_recruitment":
#> # A tibble: 5 × 5
#> study_id ancestry_id country_name major_area region
#> <chr> <int> <chr> <chr> <chr>
#> 1 GCST002420 1 U.S. Northern America <NA>
#> 2 GCST002420 2 U.S. Northern America <NA>
#> 3 GCST000392 1 U.K. Europe Northern Europe
#> 4 GCST000392 2 U.K. Europe Northern Europe
#> 5 GCST000392 2 Denmark Europe Northern Europe
#>
#> Slot "publications":
#> # A tibble: 2 × 7
#> study_id pubmed_id publication_date publication title autho…¹ autho…²
#> <chr> <int> <date> <chr> <chr> <chr> <chr>
#> 1 GCST002420 24882193 2014-04-19 J Affect Disord Bipolar disorder with comorbid binge eating hi… Winham… 0000-0…
#> 2 GCST000392 19430480 2009-05-10 Nat Genet Genome-wide association study and meta-analysi… Barret… <NA>
#> # … with abbreviated variable names ¹author_fullname, ²author_orcidGet associations by variant identifiers (rs3798440 or rs7329174):
get_associations(variant_id = c('rs3798440', 'rs7329174'))
#> An object of class "associations"
#> Slot "associations":
#> # A tibble: 6 × 17
#> associ…¹ pvalue pvalu…² pvalu…³ pvalu…⁴ multi…⁵ snp_i…⁶ snp_t…⁷ stand…⁸ range or_pe…⁹ beta_…˟ beta_…˟ beta_…˟ beta_…˟
#> <chr> <dbl> <chr> <int> <int> <lgl> <lgl> <chr> <dbl> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 24299710 3e-10 <NA> 3 -10 FALSE TRUE novel NA <NA> NA NA <NA> <NA> <NA>
#> 2 16617 1e- 8 <NA> 1 -8 FALSE FALSE novel NA [1.1… 1.26 NA <NA> <NA> <NA>
#> 3 26451 8e- 9 <NA> 8 -9 FALSE FALSE novel NA [1.1… 1.27 NA <NA> <NA> <NA>
#> 4 26394 6e- 6 <NA> 6 -6 FALSE FALSE known NA <NA> 1.45 NA <NA> <NA> <NA>
#> 5 92481688 3e- 6 <NA> 3 -6 FALSE FALSE known NA <NA> NA NA <NA> <NA> <NA>
#> 6 17433639 3e- 6 (Chine… 3 -6 FALSE FALSE known NA <NA> 1.27 NA <NA> <NA> <NA>
#> # … with 2 more variables: last_mapping_date <dttm>, last_update_date <dttm>, and abbreviated variable names
#> # ¹association_id, ²pvalue_description, ³pvalue_mantissa, ⁴pvalue_exponent, ⁵multiple_snp_haplotype,
#> # ⁶snp_interaction, ⁷snp_type, ⁸standard_error, ⁹or_per_copy_number, ˟beta_number, ˟beta_unit, ˟beta_direction,
#> # ˟beta_description
#>
#> Slot "loci":
#> # A tibble: 7 × 4
#> association_id locus_id haplotype_snp_count description
#> <chr> <int> <int> <chr>
#> 1 24299710 1 NA SNP x SNP interaction
#> 2 24299710 2 NA SNP x SNP interaction
#> 3 16617 1 NA Single variant
#> 4 26451 1 NA Single variant
#> 5 26394 1 NA Single variant
#> 6 92481688 1 NA Single variant
#> 7 17433639 1 NA Single variant
#>
#> Slot "risk_alleles":
#> # A tibble: 7 × 7
#> association_id locus_id variant_id risk_allele risk_frequency genome_wide limited_list
#> <chr> <int> <chr> <chr> <dbl> <lgl> <lgl>
#> 1 24299710 1 rs3798440 A NA TRUE FALSE
#> 2 24299710 2 rs9350602 C NA TRUE FALSE
#> 3 16617 1 rs7329174 G NA NA NA
#> 4 26451 1 rs7329174 G NA NA NA
#> 5 26394 1 rs7329174 G NA NA NA
#> 6 92481688 1 rs7329174 G NA FALSE FALSE
#> 7 17433639 1 rs7329174 <NA> 0.211 FALSE FALSE
#>
#> Slot "genes":
#> # A tibble: 10 × 3
#> association_id locus_id gene_name
#> <chr> <int> <chr>
#> 1 24299710 1 MYO6
#> 2 24299710 2 MYO6
#> 3 16617 1 ELF1
#> 4 26451 1 WBP4
#> 5 26451 1 ELF1
#> 6 26451 1 microRNA2276
#> 7 26451 1 SLC25A15
#> 8 26394 1 ELF1
#> 9 92481688 1 <NA>
#> 10 17433639 1 ELF1
#>
#> Slot "ensembl_ids":
#> # A tibble: 10 × 4
#> association_id locus_id gene_name ensembl_id
#> <chr> <int> <chr> <chr>
#> 1 24299710 1 MYO6 ENSG00000196586
#> 2 24299710 2 MYO6 ENSG00000196586
#> 3 16617 1 ELF1 ENSG00000120690
#> 4 26451 1 WBP4 ENSG00000120688
#> 5 26451 1 ELF1 ENSG00000120690
#> 6 26451 1 microRNA2276 <NA>
#> 7 26451 1 SLC25A15 ENSG00000102743
#> 8 26394 1 ELF1 ENSG00000120690
#> 9 92481688 1 <NA> <NA>
#> 10 17433639 1 ELF1 ENSG00000120690
#>
#> Slot "entrez_ids":
#> # A tibble: 10 × 4
#> association_id locus_id gene_name entrez_id
#> <chr> <int> <chr> <chr>
#> 1 24299710 1 MYO6 4646
#> 2 24299710 2 MYO6 4646
#> 3 16617 1 ELF1 1997
#> 4 26451 1 WBP4 11193
#> 5 26451 1 ELF1 1997
#> 6 26451 1 microRNA2276 <NA>
#> 7 26451 1 SLC25A15 10166
#> 8 26394 1 ELF1 1997
#> 9 92481688 1 <NA> <NA>
#> 10 17433639 1 ELF1 1997Get associations by traits (braces or binge eating or gambling):
get_associations(efo_trait = c('braces', 'binge eating', 'gambling'))
#> An object of class "associations"
#> Slot "associations":
#> # A tibble: 14 × 17
#> assoc…¹ pvalue pvalu…² pvalu…³ pvalu…⁴ multi…⁵ snp_i…⁶ snp_t…⁷ stand…⁸ range or_pe…⁹ beta_…˟ beta_…˟ beta_…˟ beta_…˟
#> <chr> <dbl> <chr> <int> <int> <lgl> <lgl> <chr> <dbl> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 15608 4e-7 (brace… 4 -7 FALSE FALSE novel NA <NA> NA NA <NA> <NA> <NA>
#> 2 44592 9e-7 <NA> 9 -7 FALSE FALSE novel NA <NA> 1.99 NA <NA> <NA> <NA>
#> 3 44589 1e-6 <NA> 1 -6 FALSE FALSE novel NA <NA> 1.92 NA <NA> <NA> <NA>
#> 4 44590 4e-6 <NA> 4 -6 FALSE FALSE novel NA <NA> 4.89 NA <NA> <NA> <NA>
#> 5 274608… 1e-6 <NA> 1 -6 FALSE FALSE novel NA <NA> 3.23 NA <NA> <NA> <NA>
#> 6 274608… 1e-7 <NA> 1 -7 FALSE FALSE novel NA <NA> 1.56 NA <NA> <NA> <NA>
#> 7 274608… 7e-7 <NA> 7 -7 FALSE FALSE novel NA <NA> 1.6 NA <NA> <NA> <NA>
#> 8 274608… 3e-8 <NA> 3 -8 FALSE FALSE novel NA <NA> 1.92 NA <NA> <NA> <NA>
#> 9 274608… 1e-6 <NA> 1 -6 FALSE FALSE novel NA <NA> 2.33 NA <NA> <NA> <NA>
#> 10 274608… 1e-8 <NA> 1 -8 FALSE FALSE novel NA <NA> 2.86 NA <NA> <NA> <NA>
#> 11 274608… 3e-7 <NA> 3 -7 FALSE FALSE novel NA <NA> 1.75 NA <NA> <NA> <NA>
#> 12 274608… 3e-7 <NA> 3 -7 FALSE FALSE novel NA <NA> 2.08 NA <NA> <NA> <NA>
#> 13 274608… 1e-6 <NA> 1 -6 FALSE FALSE novel NA <NA> 2 NA <NA> <NA> <NA>
#> 14 274608… 9e-8 <NA> 9 -8 FALSE FALSE novel NA <NA> 3.85 NA <NA> <NA> <NA>
#> # … with 2 more variables: last_mapping_date <dttm>, last_update_date <dttm>, and abbreviated variable names
#> # ¹association_id, ²pvalue_description, ³pvalue_mantissa, ⁴pvalue_exponent, ⁵multiple_snp_haplotype,
#> # ⁶snp_interaction, ⁷snp_type, ⁸standard_error, ⁹or_per_copy_number, ˟beta_number, ˟beta_unit, ˟beta_direction,
#> # ˟beta_description
#>
#> Slot "loci":
#> # A tibble: 14 × 4
#> association_id locus_id haplotype_snp_count description
#> <chr> <int> <int> <chr>
#> 1 15608 1 NA Single variant
#> 2 44592 1 NA Single variant
#> 3 44589 1 NA Single variant
#> 4 44590 1 NA Single variant
#> 5 27460823 1 NA Single variant
#> 6 27460811 1 NA Single variant
#> 7 27460817 1 NA Single variant
#> 8 27460805 1 NA Single variant
#> 9 27460830 1 NA Single variant
#> 10 27460844 1 NA Single variant
#> 11 27460858 1 NA Single variant
#> 12 27460864 1 NA Single variant
#> 13 27460870 1 NA Single variant
#> 14 27460851 1 NA Single variant
#>
#> Slot "risk_alleles":
#> # A tibble: 14 × 7
#> association_id locus_id variant_id risk_allele risk_frequency genome_wide limited_list
#> <chr> <int> <chr> <chr> <dbl> <lgl> <lgl>
#> 1 15608 1 rs1535480 <NA> NA NA NA
#> 2 44592 1 rs6006893 <NA> NA NA NA
#> 3 44589 1 rs10198175 <NA> NA NA NA
#> 4 44590 1 rs13233490 <NA> NA NA NA
#> 5 27460823 1 rs182107583 C 0.04 FALSE FALSE
#> 6 27460811 1 rs7904579 G 0.37 FALSE FALSE
#> 7 27460817 1 rs1950038 T 0.3 FALSE FALSE
#> 8 27460805 1 rs726170 T 0.12 FALSE FALSE
#> 9 27460830 1 rs76087671 T 0.05 FALSE FALSE
#> 10 27460844 1 rs111940429 T 0.04 FALSE FALSE
#> 11 27460858 1 rs7337127 T 0.15 FALSE FALSE
#> 12 27460864 1 rs145763646 A 0.1 FALSE FALSE
#> 13 27460870 1 rs73057489 C 0.07 FALSE FALSE
#> 14 27460851 1 rs17810023 T 0.02 FALSE FALSE
#>
#> Slot "genes":
#> # A tibble: 15 × 3
#> association_id locus_id gene_name
#> <chr> <int> <chr>
#> 1 15608 1 <NA>
#> 2 44592 1 PRR5
#> 3 44589 1 APOB
#> 4 44590 1 PER4
#> 5 27460823 1 LOC101929321
#> 6 27460811 1 CUBN
#> 7 27460817 1 Intergenic
#> 8 27460805 1 PRR5
#> 9 27460805 1 ARHGAP8
#> 10 27460830 1 intergenic
#> 11 27460844 1 AC096669.1
#> 12 27460858 1 intergenic
#> 13 27460864 1 SLC25A26
#> 14 27460870 1 intergenic
#> 15 27460851 1 RP11-250B2.3
#>
#> Slot "ensembl_ids":
#> # A tibble: 15 × 4
#> association_id locus_id gene_name ensembl_id
#> <chr> <int> <chr> <chr>
#> 1 15608 1 <NA> <NA>
#> 2 44592 1 PRR5 ENSG00000186654
#> 3 44589 1 APOB ENSG00000084674
#> 4 44590 1 PER4 <NA>
#> 5 27460823 1 LOC101929321 <NA>
#> 6 27460811 1 CUBN ENSG00000107611
#> 7 27460817 1 Intergenic <NA>
#> 8 27460805 1 PRR5 ENSG00000186654
#> 9 27460805 1 ARHGAP8 ENSG00000241484
#> 10 27460830 1 intergenic <NA>
#> 11 27460844 1 AC096669.1 ENSG00000225588
#> 12 27460858 1 intergenic <NA>
#> 13 27460864 1 SLC25A26 ENSG00000144741
#> 14 27460870 1 intergenic <NA>
#> 15 27460851 1 RP11-250B2.3 <NA>
#>
#> Slot "entrez_ids":
#> # A tibble: 15 × 4
#> association_id locus_id gene_name entrez_id
#> <chr> <int> <chr> <chr>
#> 1 15608 1 <NA> <NA>
#> 2 44592 1 PRR5 55615
#> 3 44589 1 APOB 338
#> 4 44590 1 PER4 <NA>
#> 5 27460823 1 LOC101929321 101929321
#> 6 27460811 1 CUBN 8029
#> 7 27460817 1 Intergenic <NA>
#> 8 27460805 1 PRR5 55615
#> 9 27460805 1 ARHGAP8 23779
#> 10 27460830 1 intergenic <NA>
#> 11 27460844 1 AC096669.1 <NA>
#> 12 27460858 1 intergenic <NA>
#> 13 27460864 1 SLC25A26 115286
#> 14 27460870 1 intergenic <NA>
#> 15 27460851 1 RP11-250B2.3 <NA>Get traits by PubMed identifiers (24882193 or 22780124):
get_traits(pubmed_id = c('24882193', '22780124'))
#> An object of class "traits"
#> Slot "traits":
#> # A tibble: 3 × 3
#> efo_id trait uri
#> <chr> <chr> <chr>
#> 1 EFO_0005924 binge eating http://www.ebi.ac.uk/efo/EFO_0005924
#> 2 EFO_0000289 bipolar disorder http://www.ebi.ac.uk/efo/EFO_0000289
#> 3 EFO_0004699 gambling behaviour http://www.ebi.ac.uk/efo/EFO_0004699The only search parameters that are not vectorised are
user_requested and full_pvalue_set from
get_studies(). These parameters are not vectorised because
they take boolean values (TRUE or FALSE) and
thus only one of the values is sensical to be used as a query at a given
time.
There are two levels of trait description in the GWAS Catalog: (EFO) trait and reported trait.
Studies are assigned one or more terms from the Experimental Factor Ontology (EFO), i.e., an EFO trait, or simply trait, that best represents the phenotype under investigation.
In addition, each study is also assigned a free text reported trait. This is written by the GWAS Catalog curators and reflects the author language, and where necessary, it includes more specific and detailed description of the experimental design, e.g., interaction studies or studies with a background trait.
As an example take the study with accession identifier
GCST000206 by EM Behrens et al. (2008). We can get the EFO
trait with get_traits() and the reported trait with
get_studies():
The (EFO) trait for the Behrens study is
chronic childhood arthritis:
efo_trait <- get_traits(study_id = 'GCST000206')
efo_trait@traits$trait
#> [1] "juvenile idiopathic arthritis"whereas the reported trait is
Arthritis (juvenile idiopathic):
The REST API response for variants contains an element named
genomic contexts. This element is mapped onto the table
genomic_contexts of a variants
S4 object.
Now, there is indeed a server-side bug with the column
chromosome_position of the genomic_contexts
table: the chromosome position returned is that of the variant and not
of the gene (genomic context) as it should be.
The GWAS Catalog team is aware of this bug, and they plan to fix it,
eventually. For the time being, just do not rely on
chromosome_position of the genomic_contexts
table.
For this you may use the function get_variants() with
parameter genomic_range.
For example, to search for variants in chromosome Y in the interval
14692000–14695000, you start by defining a list of 3 elements:
chromosome, start and end that
specify your genomic range:
# 'chromosome' names are case sensitive, and should be uppercase.
# 'start' and 'end' positions should be integer vectors.
my_genomic_range <- list(
chromosome = 'Y',
start = 14692000L,
end = 14695000L)Now you can use my_genomic_range to retrieve the
variants:
To search in multiple regions, construct your genomic range list with those locations just like in the previous example. For example, let’s search now for variants in chromosome X and Y, both in range 13000000–15000000:
my_genomic_range <- list(
chromosome = c('X', 'Y'),
start = c(13000000L, 13000000L),
end = c(15000000L, 15000000L))
chr_XY_variants <- get_variants(genomic_range = my_genomic_range)
chr_XY_variants@variants[c('variant_id',
'chromosome_name',
'chromosome_position')]
#> # A tibble: 24 × 3
#> variant_id chromosome_name chromosome_position
#> <chr> <chr> <int>
#> 1 rs111689944 X 13494655
#> 2 rs7063195 X 13492087
#> 3 rs5978649 X 13596999
#> 4 rs5934106 X 13488259
#> 5 rs749624882 X 14929148
#> 6 rs61273829 X 14150876
#> 7 rs6528024 X 13912803
#> 8 rs66819623 X 13936278
#> 9 rs138331350 X 14841981
#> 10 rs5980075 X 14932409
#> # … with 14 more rowsTo search for variants within a cytogenetic band you can use the
parameter cytogenetic_band of get_variants().
Here is an example, again for chromosome Y, using the cytogenetic band
'Yq11.221' as query:
my_variants <- get_variants(cytogenetic_band = 'Yq11.221')
my_variants@variants[c('variant_id',
'chromosome_name',
'chromosome_position')]
#> # A tibble: 3 × 3
#> variant_id chromosome_name chromosome_position
#> <chr> <chr> <int>
#> 1 rs2032624 Y 12914512
#> 2 rs2115848 Y 14692972
#> 3 rs2032658 Y 13470103How to know what are the cytogenetic bands for querying? We provide a
dataset (dataframe) named cytogenetic_bands that you can
use:
# ?cytogenetic_bands for more details.
cytogenetic_bands
#> # A tibble: 862 × 8
#> cytogenetic_band chromosome start end length assembly stain last_download_date
#> <chr> <chr> <int> <int> <dbl> <chr> <chr> <chr>
#> 1 1p36.33 1 1 2300000 2300000 GRCh38 gneg Thu Jun 27 15:09:22 2019
#> 2 1p36.32 1 2300001 5300000 3000000 GRCh38 gpos25 Thu Jun 27 15:09:22 2019
#> 3 1p36.31 1 5300001 7100000 1800000 GRCh38 gneg Thu Jun 27 15:09:22 2019
#> 4 1p36.23 1 7100001 9100000 2000000 GRCh38 gpos25 Thu Jun 27 15:09:22 2019
#> 5 1p36.22 1 9100001 12500000 3400000 GRCh38 gneg Thu Jun 27 15:09:22 2019
#> 6 1p36.21 1 12500001 15900000 3400000 GRCh38 gpos50 Thu Jun 27 15:09:22 2019
#> 7 1p36.13 1 15900001 20100000 4200000 GRCh38 gneg Thu Jun 27 15:09:22 2019
#> 8 1p36.12 1 20100001 23600000 3500000 GRCh38 gpos25 Thu Jun 27 15:09:22 2019
#> 9 1p36.11 1 23600001 27600000 4000000 GRCh38 gneg Thu Jun 27 15:09:22 2019
#> 10 1p35.3 1 27600001 29900000 2300000 GRCh38 gpos25 Thu Jun 27 15:09:22 2019
#> # … with 852 more rowsLet’s say you want to search for all variants in the shorter arm (p)
of chromosome 21, you can take advantage of the
cytogenetic_bands to get all the corresponding cytogenetic
band names:
# Install package dplyr if you do not have it.
chr21_p_bands <- dplyr::filter(cytogenetic_bands, grepl('^21p', cytogenetic_band)) %>%
dplyr::pull(cytogenetic_band)
chr21_p_bands
#> [1] "21p13" "21p12" "21p11.2" "21p11.1"Now search by cytogenetic_band:
my_variants <- get_variants(cytogenetic_band = chr21_p_bands)
my_variants@variants[c('variant_id',
'chromosome_name',
'chromosome_position',
'chromosome_region')]
#> # A tibble: 4 × 4
#> variant_id chromosome_name chromosome_position chromosome_region
#> <chr> <chr> <int> <chr>
#> 1 rs552964654 21 9053097 21p11.2
#> 2 rs10439884 21 10540506 21p11.2
#> 3 rs75275574 21 9040338 21p11.2
#> 4 rs240444 21 10510446 21p11.2You can get the total length of a chromosome by using the provided
data set: cytogenetic_bands. Here is an example for
chromosome 15:
Currently, there is not an implemented solution in
{gwasrapidd}. For example, if you search for variants by
EFO identifier (efo_id):
my_efo_ids <- c('EFO_0005543', 'EFO_0004762')
my_variants <- get_variants(efo_id = my_efo_ids)
my_variants@variants$variant_id
#> [1] "rs75061358" "rs4252707" "rs72714270" "rs1106639" "rs2736100" "rs648044" "rs78378222" "rs7572263"
#> [9] "rs498872" "rs10852606" "rs4975538" "rs12803321" "rs10131032" "rs6010620" "rs4774756" "rs1938964"
#> [17] "rs10927065" "rs5839764" "rs7107785" "rs634537" "rs72714236" "rs12076373" "rs1275600" "rs11706832"
#> [25] "rs111976262" "rs9841110" "rs80351950" "rs11598018" "rs2297440" "rs77633900" "rs3751667" "rs11599775"
#> [33] "rs10842893" "rs320337" "rs12752552" "rs7125115" "rs688755" "rs2297433" "rs10069690" "rs11979158"
#> [41] "rs2235573" "rs3772190" "rs4977756" "rs55705857" "rs11233250" "rs78355601" "rs7043199" "rs187918360"
#> [49] "rs75455100" "rs10886366" "rs6598475" "rs150821445" "rs6479877" "rs73418461" "rs9787438" "rs6993770"
#> [57] "rs4782371" "rs2375981" "rs9472155" "rs34528081" "rs144160960" "rs12445232" "rs10934631" "rs71779653"
#> [65] "rs117580153" "rs12456390" "rs10822155" "rs7767396" "rs59706856" "rs73872715" "rs181216459" "rs10761731"
#> [73] "rs10738760" "rs186066666" "rs8045833" "rs11392719" "rs180936035" "rs4513773" "rs118086804" "rs114694170"
#> [81] "rs181558074" "rs7763358" "rs6722871" "rs2304058" "rs6921438" "rs61829244" "rs191332118" "rs12214617"
#> [89] "rs1740073" "rs139893147" "rs77961527" "rs7030781" "rs61818787" "rs550057" "rs11639051" "rs10411345"
#> [97] "rs10761750" "rs4714729" "rs4082730" "rs9332599" "rs10761741" "rs12199215" "rs186725382" "rs144820908"
#> [105] "rs10153304" "rs112215592" "rs13206436" "rs2639990" "rs6475938" "rs144085478" "rs34524635" "rs55864163"
#> [113] "rs143479231" "rs34881325"So it is not immediately obvious which variants resulted from the
query 'EFO_0005543' or 'EFO_0004762'.
A possible workaround is to make multiple independent queries and save your results in a list whose names are the respective queries:
# Install purrr first.
# Add names to my_efo_ids
names(my_efo_ids) <- my_efo_ids
my_variants <- purrr::map(my_efo_ids, ~ get_variants(efo_id = .x))Now you can see which variants are associated with each EFO identifier.
For 'EFO_0005543' we got the following variants:
my_variants[['EFO_0005543']]@variants$variant_id
#> [1] "rs75061358" "rs4252707" "rs72714270" "rs1106639" "rs2736100" "rs648044" "rs78378222" "rs7572263"
#> [9] "rs498872" "rs10852606" "rs4975538" "rs12803321" "rs10131032" "rs6010620" "rs4774756" "rs1938964"
#> [17] "rs10927065" "rs5839764" "rs7107785" "rs634537" "rs72714236" "rs12076373" "rs1275600" "rs11706832"
#> [25] "rs111976262" "rs9841110" "rs80351950" "rs11598018" "rs2297440" "rs77633900" "rs3751667" "rs11599775"
#> [33] "rs10842893" "rs320337" "rs12752552" "rs7125115" "rs688755" "rs2297433" "rs10069690" "rs11979158"
#> [41] "rs2235573" "rs3772190" "rs4977756" "rs55705857" "rs11233250"And for 'EFO_0004762':
my_variants[['EFO_0004762']]@variants$variant_id
#> [1] "rs78355601" "rs7043199" "rs187918360" "rs75455100" "rs10886366" "rs6598475" "rs150821445" "rs6479877"
#> [9] "rs73418461" "rs9787438" "rs6993770" "rs4782371" "rs2375981" "rs9472155" "rs34528081" "rs144160960"
#> [17] "rs12445232" "rs10934631" "rs71779653" "rs117580153" "rs12456390" "rs10822155" "rs7767396" "rs59706856"
#> [25] "rs73872715" "rs181216459" "rs10761731" "rs10738760" "rs186066666" "rs8045833" "rs11392719" "rs180936035"
#> [33] "rs4513773" "rs118086804" "rs114694170" "rs181558074" "rs7763358" "rs6722871" "rs2304058" "rs6921438"
#> [41] "rs61829244" "rs191332118" "rs12214617" "rs1740073" "rs139893147" "rs77961527" "rs7030781" "rs61818787"
#> [49] "rs550057" "rs11639051" "rs10411345" "rs10761750" "rs4714729" "rs4082730" "rs9332599" "rs10761741"
#> [57] "rs12199215" "rs186725382" "rs144820908" "rs10153304" "rs112215592" "rs13206436" "rs2639990" "rs6475938"
#> [65] "rs144085478" "rs34524635" "rs55864163" "rs143479231" "rs34881325"The four main retrieval functions get_studies(),
get_associations(), get_variants() and
get_traits() all allow you to search multiple criteria at
once. You can then combine results in an OR or AND fashion using the
parameter set_operation.
Use set_operation = 'union' to combine results in an OR
fashion:
my_variants_OR <- get_variants(
efo_trait = 'triple-negative breast cancer',
gene_name = 'MDM4',
set_operation = 'union')
my_variants_OR@variants[c('variant_id',
'chromosome_name',
'chromosome_position',
'chromosome_region')]
#> # A tibble: 56 × 4
#> variant_id chromosome_name chromosome_position chromosome_region
#> <chr> <chr> <int> <chr>
#> 1 rs3747636 1 204434531 1q32.1
#> 2 rs10793765 1 204580247 1q32.1
#> 3 rs116661163 1 204641544 1q32.1
#> 4 rs12083887 1 118339066 1p12
#> 5 rs12143943 1 204602943 1q32.1
#> 6 rs1008833 1 204457167 1q32.1
#> 7 rs2137255 1 204457245 1q32.1
#> 8 rs2290854 1 204546897 1q32.1
#> 9 rs12036042 1 204500186 1q32.1
#> 10 rs3789045 1 204617684 1q32.1
#> # … with 46 more rowsThe code above retrieves variants whose associated
efo_trait is equal to
'triple-negative breast cancer' or variants that are
associated with gene 'MDM4'.
Alternatively, we may use set_operation = 'intersection'
to combine results in an AND fashion:
my_variants_AND <- get_variants(
efo_trait = 'triple-negative breast cancer',
gene_name = 'MDM4',
set_operation = 'intersection')
my_variants_AND@variants[c('variant_id',
'chromosome_name',
'chromosome_position',
'chromosome_region')]
#> # A tibble: 0 × 4
#> # … with 4 variables: variant_id <chr>, chromosome_name <chr>, chromosome_position <int>, chromosome_region <chr>With set_operation = 'intersection', as in the code
above, we get variants whose associated efo_trait is equal
to 'triple-negative breast cancer' and that are associated
with gene 'MDM4', i.e., only variants meeting both
conditions simultaneously are retrieved.
Please note that almost all search criteria to be used with the retrieval functions are vectorised, meaning that you can use multiple values with the same search criterion. In these cases results are always combined in an OR fashion.
In the following example, we will be using the gene name as the only search criterion. If we pass a vector of gene names then we get all variants that are associated with EITHER (OR) genes.
my_variants <- get_variants(gene_name = c('RNU6-367P', 'ABHD5'))
#> downloading [=======================================================================================] 100% eta: 0s
#> downloading [=======================================================================================] 100% eta: 0s
my_variants@variants[c('variant_id',
'chromosome_name',
'chromosome_position',
'chromosome_region')]
#> # A tibble: 33 × 4
#> variant_id chromosome_name chromosome_position chromosome_region
#> <chr> <chr> <int> <chr>
#> 1 rs142242702 3 44122660 3p21.32
#> 2 rs1554654 3 44002852 3p21.33
#> 3 rs7619427 3 44035549 3p21.33
#> 4 rs11718455 3 44015406 3p21.33
#> 5 rs73076675 3 43924731 3p21.33
#> 6 rs113706999 3 44117664 3p21.32
#> 7 rs6441814 3 44007622 3p21.33
#> 8 rs4017425 3 43987272 3p21.33
#> 9 rs79644353 3 44135707 3p21.32
#> 10 rs7619544 3 43852053 3p21.33
#> # … with 23 more rowsIn this case we retrieved 33 variants. Please note that the
set_operation parameter does not affect this result. The
set_operation only controls the function behaviour when
combining results from different criteria, e.g., when using
efo_trait and gene_name.
To retrieve variants that are concomitantly associated with genes
RNU6-367P and ABHD5, the user needs to place these queries separately
and then intersect them — using the intersect() function,
i.e., combining in an AND fashion. Here we start by retrieving variants
associated with gene RNU6-367P:
my_variants1 <- get_variants(gene_name = 'RNU6-367P')
#> downloading [=======================================================================================] 100% eta: 0s
my_variants1@variants[c('variant_id',
'chromosome_name',
'chromosome_position',
'chromosome_region')]
#> # A tibble: 19 × 4
#> variant_id chromosome_name chromosome_position chromosome_region
#> <chr> <chr> <int> <chr>
#> 1 rs142242702 3 44122660 3p21.32
#> 2 rs1554654 3 44002852 3p21.33
#> 3 rs7619427 3 44035549 3p21.33
#> 4 rs11718455 3 44015406 3p21.33
#> 5 rs73076675 3 43924731 3p21.33
#> 6 rs113706999 3 44117664 3p21.32
#> 7 rs6441814 3 44007622 3p21.33
#> 8 rs4017425 3 43987272 3p21.33
#> 9 rs79644353 3 44135707 3p21.32
#> 10 rs7619544 3 43852053 3p21.33
#> 11 rs58492658 3 44098920 3p21.33
#> 12 rs7635417 3 44032957 3p21.33
#> 13 rs6441813 3 43999831 3p21.33
#> 14 rs189710954 3 43800315 3p21.33
#> 15 rs11130017 3 43932687 3p21.33
#> 16 rs75619216 3 43912969 3p21.33
#> 17 rs1491708 3 43870086 3p21.33
#> 18 rs55807460 3 43903734 3p21.33
#> 19 rs6806257 3 43878471 3p21.33There are 19 variants associated with gene RNU6-367P. Now, for gene ABHD5:
my_variants2 <- get_variants(gene_name = 'ABHD5')
#> downloading [=========================================================>-----------------------------] 67% eta: 4s
#> downloading [=======================================================================================] 100% eta: 0s
my_variants2@variants[c('variant_id',
'chromosome_name',
'chromosome_position',
'chromosome_region')]
#> # A tibble: 23 × 4
#> variant_id chromosome_name chromosome_position chromosome_region
#> <chr> <chr> <int> <chr>
#> 1 rs191867523 3 43774449 3p21.33
#> 2 rs115421670 3 43737217 3p21.33
#> 3 rs6772840 3 43812828 3p21.33
#> 4 rs11720728 3 43832420 3p21.33
#> 5 rs142404191 3 43641812 3p21.33
#> 6 rs192944990 3 43826178 3p21.33
#> 7 rs182923613 3 43791731 3p21.33
#> 8 rs73087085 3 43687796 3p21.33
#> 9 rs75594032 3 43744082 3p21.33
#> 10 rs1468602 3 43701242 3p21.33
#> # … with 13 more rowsThere are 23 variants associated with gene ABHD5. To find those
variants simultaneously associated with both genes, you can intersect
the two variants
objects using gwasrapidd::intersect():
variants_intersect <- gwasrapidd::intersect(my_variants1, my_variants2)
variants_intersect@variants[c('variant_id',
'chromosome_name',
'chromosome_position',
'chromosome_region')]
#> # A tibble: 9 × 4
#> variant_id chromosome_name chromosome_position chromosome_region
#> <chr> <chr> <int> <chr>
#> 1 rs4017425 3 43987272 3p21.33
#> 2 rs7635417 3 44032957 3p21.33
#> 3 rs6441813 3 43999831 3p21.33
#> 4 rs189710954 3 43800315 3p21.33
#> 5 rs11130017 3 43932687 3p21.33
#> 6 rs75619216 3 43912969 3p21.33
#> 7 rs1491708 3 43870086 3p21.33
#> 8 rs55807460 3 43903734 3p21.33
#> 9 rs6806257 3 43878471 3p21.33Apparently only 9 variant(s) are related to both genes RNU6-367P and ABHD5.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.