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Wrapper to a number of SNP web APIs
R Makefile
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README.md

rsnps

Build Status Build status Coverage Status cran version

NOTE

rsnps used to be ropensnp

Install

Install from CRAN

install.packages("rsnps")

Or dev version

install.packages("devtools")
devtools::install_github("ropensci/rsnps")
library("rsnps")

Data sources

This set of functions/package accesses data from:

Quick start

Search for SNPs in Linkage Disequilibrium

Using the Broad Institute data

tmp <- LDSearch("rs420358")
#> Querying SNAP...
#> Querying NCBI for up-to-date SNP annotation information...
#> Done!
head(tmp)
#> $rs420358
#>        Proxy      SNP Distance RSquared DPrime GeneVariant GeneName
#> 4   rs420358 rs420358        0    1.000  1.000  INTERGENIC      N/A
#> 5   rs442418 rs420358      122    1.000  1.000  INTERGENIC      N/A
#> 8   rs718223 rs420358     1168    1.000  1.000  INTERGENIC      N/A
#> 6   rs453604 rs420358     2947    1.000  1.000  INTERGENIC      N/A
#> 3   rs372946 rs420358      -70    0.943  1.000  INTERGENIC      N/A
#> 1 rs10889290 rs420358     3987    0.800  1.000  INTERGENIC      N/A
#> 2 rs10889291 rs420358     4334    0.800  1.000  INTERGENIC      N/A
#> 7  rs4660403 rs420358     7021    0.800  1.000  INTERGENIC      N/A
#>   GeneDescription Major Minor   MAF NObserved Chromosome_NCBI Marker_NCBI
#> 4             N/A     C     A 0.167       120               1    rs420358
#> 5             N/A     C     T 0.167       120               1    rs442418
#> 8             N/A     A     G 0.167       120               1    rs718223
#> 6             N/A     A     G 0.167       120               1    rs453604
#> 3             N/A     G     C 0.175       120               1    rs372946
#> 1             N/A     G     A 0.200       120               1  rs10889290
#> 2             N/A     C     T 0.200       120               1  rs10889291
#> 7             N/A     A     G 0.200       120               1   rs4660403
#>   Class_NCBI Gene_NCBI Alleles_NCBI Major_NCBI Minor_NCBI MAF_NCBI
#> 4        snp      <NA>          G,T          G          T       NA
#> 5        snp      <NA>          A/G          A          G   0.0723
#> 8        snp      <NA>          A/G          A          G   0.0723
#> 6        snp      <NA>          A/G          A          G   0.0727
#> 3        snp      <NA>          C,G          C          G       NA
#> 1        snp      <NA>          A/G          G          A   0.0841
#> 2        snp      <NA>          C/T          C          T   0.0839
#> 7        snp      <NA>          A/G          A          G   0.0827
#>    BP_NCBI
#> 4 40341238
#> 5 40341360
#> 8 40342406
#> 6 40344185
#> 3 40341168
#> 1 40345225
#> 2 40345572
#> 7 40348259

Using NCBI dbSNP data

SNPs <- c("rs332", "rs420358", "rs1837253", "rs1209415715", "rs111068718")
NCBI_snp_query(SNPs)
#>         Query Chromosome      Marker          Class Gene   Alleles Major
#> 1       rs332          7 rs121909001         in-del CFTR     -/TTT  <NA>
#> 2    rs420358          1    rs420358            snp <NA>       G,T     G
#> 3   rs1837253          5   rs1837253            snp <NA>       C/T     C
#> 4 rs111068718       <NA> rs111068718 microsatellite <NA> (GT)21/24  <NA>
#>   Minor    MAF        BP
#> 1  <NA>     NA 117559592
#> 2     T     NA  40341238
#> 3     T 0.3822 111066173
#> 4  <NA>     NA        NA

Using openSNP data

genotypes() function

genotypes('rs9939609', userid='1,6,8', df=TRUE)
#>    snp_name snp_chromosome snp_position         user_name user_id
#> 1 rs9939609             16     53786615  Bastian Greshake       1
#> 2 rs9939609             16     53786615      Nash Parovoz       6
#> 3 rs9939609             16     53786615 Samantha B. Clark       8
#>   genotype_id genotype
#> 1           9       AT
#> 2           5       AT
#> 3           2       TT

phenotypes() function

out <- phenotypes(userid=1)
out$phenotypes$`Hair Type`
#> $phenotype_id
#> [1] 16
#> 
#> $variation
#> [1] "straight"

Meta

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