Minor allele data from grch38 giving NAs from getBM ?
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Abiologist • 0
@2b534cfa
Last seen 4 weeks ago
Poland

Does anyone know whether there is something wrong with the coding below to extract minor allele data from grch38 ? The data is apparently available on Ensembl (e.g. https://www.ensembl.org/Homo_sapiens/Variation/Exploredb=core;r=10:120308754-120309754;v=rs10788066;vdb=variation;vf=654619804) but the data extraction method doesn't appear to extract minor allele data if I use host = "https://www.ensembl.org/" - it gives NAs for minor allele and minor allele frequency - is there something else I should do ? Or is there a better method ? Or is the minor allele data from grch37 actually up-to-date ? I give several examples - the first gives edge effects (which I presume is not so important) with only two SNPs analyzed. The second gives the required output (but from grch37). The third gives all NAs for minor allele data from grch38 - I presume the formulation of snp.db3 is correct although I'm waiting for some feedback ! Alternatively perhaps this has not been implemented for grch38 ? (this appears to be of considerable importance ???) I include three other parameters ("chrom_start", "chrom_strand", "associated_gene") which are giving correct results for nt.biomart2 and nt.biomart3 (but edge effect for "associated gene" with nt.biomart1).

nt.biomart1 - edge effects:

refsnp_id            minor_allele     minor_allele_freq                chrom_start chrom_strand.   associated_gene

1 rs10788066 TRUE 0.465056 122068766 1 NA

2 rs765840164 NA NA 21830103 1 NA

nt.biomart2 - from grch37 (only first 10 rows are shown here):

refsnp_id       minor_allele minor_allele_freq       chrom_start           chrom_strand          associated_gene

1 rs1064213 A 0.379393 198950240 1

2 rs1064213 A 0.379393 198950240 1 PLCL1

3 rs1106090 G 0.472045 58068741 1 EIF2S2P7,VRK2

4 rs1106090 G 0.472045 58068741 1

5 rs12089815 G 0.380391 91189933 1

6 rs12089815 G 0.380391 91189933 1 BARHL2

7 rs12140153 T 0.026558 62579891 1

8 rs12140153 T 0.026558 62579891 1 <NA>

9 rs12140153 T 0.026558 62579891 1 PATJ

10 rs12140153 T 0.026558 62579891 1 INADL

nt.biomart3 - from grch38 (only first 10 rows shown here):

refsnp_id       minor_allele minor_allele_freq         chrom_start         chrom_strand          associated_gene

1 rs1064213 NA NA 198085516 1

2 rs1064213 NA NA 198085516 1 PLCL1

3 rs1106090 NA NA 57841606 1 EIF2S2P7,VRK2

4 rs1106090 NA NA 57841606 1

5 rs12089815 NA NA 90724376 1

6 rs12089815 NA NA 90724376 1 BARHL2

7 rs12140153 NA NA 62114219 1 PATJ

8 rs12140153 NA NA 62114219 1

9 rs12140153 NA NA 62114219 1 INADL

10 rs12140153 NA NA 62114219 1 <NA>

library("biomaRt")
## nt.biomart1 - edge effects:
snp.db1 <- useMart(host = "https://grch37.ensembl.org", biomart = "ENSEMBL_MART_SNP", dataset = "hsapiens_snp")
nt.biomart1 <- getBM(attributes = c("refsnp_id", "minor_allele", "minor_allele_freq", "chrom_start", "chrom_strand", "associated_gene"), filters = c("snp_filter"), values = c("rs10788066", "rs765840164"), mart = snp.db1, uniqueRows = TRUE)
nt.biomart1
## nt.biomart2 - from grch37:
nt.biomart2 <- getBM(attributes = c("refsnp_id", "minor_allele", "minor_allele_freq", "chrom_start", "chrom_strand", "associated_gene"), filters = c("snp_filter"), values = c("rs3762444", "rs284262", "rs655598", "rs12089815", "rs12140153", "rs788163", "rs1064213", "rs1106090", "rs7557796", "rs16825008"), mart = snp.db1, uniqueRows = TRUE)
nt.biomart2
## nt.biomart3 - from grch38:
snp.db3 <- useMart(host = "https://www.ensembl.org/", biomart = "ENSEMBL_MART_SNP", dataset = "hsapiens_snp")
nt.biomart3 <- getBM(attributes = c("refsnp_id", "minor_allele", "minor_allele_freq", "chrom_start", "chrom_strand", "associated_gene"), filters = c("snp_filter"), values = c("rs3762444", "rs284262", "rs655598", "rs12089815", "rs12140153", "rs788163", "rs1064213", "rs1106090", "rs7557796", "rs16825008"), mart = snp.db3, uniqueRows = TRUE)
nt.biomart3


sessionInfo( )
R version 4.3.3 (2024-02-29)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.4

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Berlin
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] biomaRt_2.58.2

loaded via a namespace (and not attached):
 [1] rappdirs_0.3.3          utf8_1.2.4              generics_0.1.3         
 [4] bitops_1.0-7            xml2_1.3.6              RSQLite_2.3.6          
 [7] stringi_1.8.4           hms_1.1.3               digest_0.6.35          
[10] magrittr_2.0.3          fastmap_1.2.0           blob_1.2.4             
[13] progress_1.2.3          AnnotationDbi_1.64.1    GenomeInfoDb_1.38.8    
[16] DBI_1.2.2               httr_1.4.7              purrr_1.0.2            
[19] fansi_1.0.6             XML_3.99-0.16.1         Biostrings_2.70.3      
[22] cli_3.6.2               rlang_1.1.3             crayon_1.5.2           
[25] dbplyr_2.5.0            XVector_0.42.0          Biobase_2.62.0         
[28] bit64_4.0.5             withr_3.0.0             cachem_1.0.8           
[31] tools_4.3.3             memoise_2.0.1           dplyr_1.1.4            
[34] GenomeInfoDbData_1.2.11 filelock_1.0.3          BiocGenerics_0.48.1    
[37] curl_5.2.1              vctrs_0.6.5             R6_2.5.1               
[40] png_0.1-8               stats4_4.3.3            lifecycle_1.0.4        
[43] BiocFileCache_2.10.2    zlibbioc_1.48.2         KEGGREST_1.42.0        
[46] stringr_1.5.1           S4Vectors_0.40.2        IRanges_2.36.0         
[49] bit_4.0.5               pkgconfig_2.0.3         pillar_1.9.0           
[52] glue_1.7.0              tibble_3.2.1            tidyselect_1.2.1       
[55] compiler_4.3.3          prettyunits_1.2.0       RCurl_1.98-1.14
BioMartGOGeneSets allele_freq biomaRt getBM • 90 views
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