Nothing
In rsnps: Get 'SNP' ('Single-Nucleotide' 'Polymorphism') Data on the Web
rsnps tutorial
Install and load library
When available on CRAN
install.packages("rsnps")
Or get from Github
install.packages("devtools")
devtools::install_github("ropensci/rsnps")
library(rsnps)
OpenSNP data
All Genotypes
Get genotype data for all users at a particular SNP from OpenSNP:
x <- allgensnp(snp='rs7412')
head(x)
#> name chromosome position name id genotype_id local_genotype
#> 1 rs7412 19 44908822 R.M. Holston 22 8 CC
#> 2 rs7412 19 44908822 Charles G. Sullivan 5326 3834 CC
#> 3 rs7412 19 44908822 Glenn Allen Nolen 19 7 CC
#> 4 rs7412 19 44908822 Angel Harris 495 223 CC
#> 5 rs7412 19 44908822 Mom to AG 387 173 CC
#> 6 rs7412 19 44908822 kevinmcc 285 118 CC
All Phenotypes
Get all phenotypes, their variations, and how many users have data available for a given phenotype
Get all data
x <- allphenotypes(df = TRUE)
head(x)
#> id characteristic known_variations number_of_users
#> 1 1 Eye color Brown 1665
#> 2 1 Eye color Brown-green 1665
#> 3 1 Eye color Blue-green 1665
#> 4 1 Eye color Blue-grey 1665
#> 5 1 Eye color Green 1665
#> 6 1 Eye color Blue 1665
Output a list, then call the characteristic of interest by 'id' or 'characteristic'
datalist <- allphenotypes()
Get a list of all characteristics you can call
names(datalist)[1:10]
#> [1] "Eye color" "Lactose intolerance" "Handedness"
#> [4] "white skin" "Ability to find a bug in openSNP" "Beard Color"
#> [7] "Hair Color" "Ability to Tan" "Height"
#> [10] "Hair Type"
Get data.frame for ADHD
datalist[["ADHD"]]
#> id characteristic known_variations
#> 1 29 ADHD False
#> 2 29 ADHD True
#> 3 29 ADHD Undiagnosed, but probably true
#> 4 29 ADHD No
#> 5 29 ADHD Yes
#> 6 29 ADHD Not diagnosed
#> 7 29 ADHD Diagnosed as not having but with some signs
#> 8 29 ADHD Mthfr c677t
#> 9 29 ADHD Rs1801260
#> 10 29 ADHD Adult onset
#> 11 29 ADHD Diagnosed as "other hyperkinetic disorder"
#> 12 29 ADHD Blonde, european, green eyes
#> 13 29 ADHD Extreme
#> 14 29 ADHD Diagnosed as hyperactive type, though it is my belief that adhd is simply a normal trait such as eye color.
#> number_of_users
#> 1 325
#> 2 325
#> 3 325
#> 4 325
#> 5 325
#> 6 325
#> 7 325
#> 8 325
#> 9 325
#> 10 325
#> 11 325
#> 12 325
#> 13 325
#> 14 325
Get data.frame for mouth size and SAT Writing
datalist[c("mouth size","SAT Writing")]
#> $`mouth size`
#> id characteristic known_variations number_of_users
#> 1 120 mouth size Medium 202
#> 2 120 mouth size Small 202
#> 3 120 mouth size Large 202
#> 4 120 mouth size Slightly wide mouth 202
#>
#> $`SAT Writing`
#> id characteristic known_variations number_of_users
#> 1 41 SAT Writing 750 110
#> 2 41 SAT Writing Tested before 2005 110
#> 3 41 SAT Writing 800 110
#> 4 41 SAT Writing Country with no sat 110
#> 5 41 SAT Writing N/a 110
#> 6 41 SAT Writing Never & have ba & above 110
#> 7 41 SAT Writing 720 110
#> 8 41 SAT Writing Did well - don't remember score 110
#> 9 41 SAT Writing 511 110
#> 10 41 SAT Writing 760 110
#> 11 41 SAT Writing 780 110
#> 12 41 SAT Writing 700 110
#> 13 41 SAT Writing Not part of sat when i took test in august 1967 at uiuc 110
#> 14 41 SAT Writing Not part of sat in 1961 110
#> 15 41 SAT Writing 620 110
#> 16 41 SAT Writing 560 110
Annotations
Get just the metadata
annotations(snp = 'rs7903146', output = 'metadata')
#> .id V1
#> 1 name rs7903146
#> 2 chromosome 10
#> 3 position 112998590
Just from PLOS journals
annotations(snp = 'rs7903146', output = 'plos')[c(1:2),]
#> author
#> 1 Maggie C. Y. Ng
#> 2 André Gustavo P. Sousa
#> title
#> 1 Meta-Analysis of Genome-Wide Association Studies in African Americans Provides Insights into the Genetic Architecture of Type 2 Diabetes
#> 2 Genetic Variants of Diabetes Risk and Incident Cardiovascular Events in Chronic Coronary Artery Disease
#> publication_date number_of_readers url doi
#> 1 2014-08-07T00:00:00.000Z 11650 https://doi.org/10.1371/journal.pgen.1004517 10.1371/journal.pgen.1004517
#> 2 2011-01-20T00:00:00.000Z 2482 https://doi.org/10.1371/journal.pone.0016341 10.1371/journal.pone.0016341
Just from SNPedia
annotations(snp = 'rs7903146', output = 'snpedia')
#> url summary
#> 1 http://www.snpedia.com/index.php/Rs7903146(C;C) Normal (lower) risk of Type 2 Diabetes and Gestational Diabetes.
#> 2 http://www.snpedia.com/index.php/Rs7903146(C;T) 1.4x increased risk for diabetes (and perhaps colon cancer).
#> 3 http://www.snpedia.com/index.php/Rs7903146(T;T) 2x increased risk for Type-2 diabetes
Get all annotations
annotations(snp = 'rs7903146', output = 'all')[1:5,]
#> .id author
#> 1 mendeley T E Meyer
#> 2 mendeley Camilla Cervin
#> 3 mendeley Nicholette D Palmer
#> 4 mendeley Ashis K Mondal
#> 5 mendeley Julian Munoz
#> title
#> 1 Diabetes genes and prostate cancer in the Atherosclerosis Risk in Communities study
#> 2 Diabetes in Adults , Type 1 Diabetes , and Type 2 Diabetes GENETICS OF LADA
#> 3 Association of TCF7L2 gene polymorphisms with reduced acute insulin response in Hispanic Americans.
#> 4 Genotype and tissue-specific effects on alternative splicing of the transcription factor 7-like 2 gene in humans.
#> 5 Polymorphism in the transcription factor 7-like 2 (TCF7L2) gene is associated with reduced insulin secretion in nondiabetic women.
#> publication_year number_of_readers open_access
#> 1 2010 3 TRUE
#> 2 2008 2 FALSE
#> 3 2008 8 FALSE
#> 4 2010 13 TRUE
#> 5 2006 10 TRUE
#> url
#> 1 http://www.mendeley.com/research/diabetes-genes-prostate-cancer-atherosclerosis-risk-communities-study-4/
#> 2 http://www.mendeley.com/research/diabetes-adults-type-1-diabetes-type-2-diabetes-genetics-lada/
#> 3 http://www.mendeley.com/research/association-tcf7l2-gene-polymorphisms-reduced-acute-insulin-response-hispanic-americans/
#> 4 http://www.mendeley.com/research/genotype-tissuespecific-effects-alternative-splicing-transcription-factor-7like-2-gene-humans/
#> 5 http://www.mendeley.com/research/polymorphism-transcription-factor-7like-2-tcf7l2-gene-associated-reduced-insulin-secretion-nondiabet/
#> doi publication_date summary first_author pubmed_link journal trait pvalue
#> 1 19/2/558 [pii]\\r10.1158/1055-9965.EPI-09-0902 <NA> <NA> <NA> <NA> <NA> <NA> NA
#> 2 10.2337/db07-0299.Leif <NA> <NA> <NA> <NA> <NA> <NA> NA
#> 3 10.1210/jc.2007-1225 <NA> <NA> <NA> <NA> <NA> <NA> NA
#> 4 10.1210/jc.2009-2064 <NA> <NA> <NA> <NA> <NA> <NA> NA
#> 5 10.2337/db06-0574 <NA> <NA> <NA> <NA> <NA> <NA> NA
#> pvalue_description confidence_interval
#> 1 <NA> <NA>
#> 2 <NA> <NA>
#> 3 <NA> <NA>
#> 4 <NA> <NA>
#> 5 <NA> <NA>
Download
Download genotype data for a user from 23andme or other repo. (not evaluated in this example)
data <- users(df=TRUE)
head(data[[1]])
fetch_genotypes(url = data[[1]][1,"genotypes.download_url"], rows=15)
Genotype user data
Genotype data for one or multiple users
genotypes(snp='rs9939609', userid=1)
#> $snp
#> $snp$name
#> [1] "rs9939609"
#>
#> $snp$chromosome
#> [1] "16"
#>
#> $snp$position
#> [1] "53786615"
#>
#>
#> $user
#> $user$name
#> [1] "Bastian Greshake Tzovaras"
#>
#> $user$id
#> [1] 1
#>
#> $user$genotypes
#> $user$genotypes[[1]]
#> $user$genotypes[[1]]$genotype_id
#> [1] 9
#>
#> $user$genotypes[[1]]$local_genotype
#> [1] "AT"
genotypes('rs9939609', userid='1,6,8', df=TRUE)
#> snp_name snp_chromosome snp_position user_name user_id genotype_id genotype
#> 1 rs9939609 16 53786615 Bastian Greshake Tzovaras 1 9 AT
#> 2 rs9939609 16 53786615 Nash Parovoz 6 5 AT
#> 3 rs9939609 16 53786615 Samantha B. Clark 8 2 TT
genotypes('rs9939609', userid='1-2', df=FALSE)
#> [[1]]
#> [[1]]$snp
#> [[1]]$snp$name
#> [1] "rs9939609"
#>
#> [[1]]$snp$chromosome
#> [1] "16"
#>
#> [[1]]$snp$position
#> [1] "53786615"
#>
#>
#> [[1]]$user
#> [[1]]$user$name
#> [1] "Bastian Greshake Tzovaras"
#>
#> [[1]]$user$id
#> [1] 1
#>
#> [[1]]$user$genotypes
#> [[1]]$user$genotypes[[1]]
#> [[1]]$user$genotypes[[1]]$genotype_id
#> [1] 9
#>
#> [[1]]$user$genotypes[[1]]$local_genotype
#> [1] "AT"
#>
#>
#>
#>
#>
#> [[2]]
#> [[2]]$snp
#> [[2]]$snp$name
#> [1] "rs9939609"
#>
#> [[2]]$snp$chromosome
#> [1] "16"
#>
#> [[2]]$snp$position
#> [1] "53786615"
#>
#>
#> [[2]]$user
#> [[2]]$user$name
#> [1] "Senficon"
#>
#> [[2]]$user$id
#> [1] 2
#>
#> [[2]]$user$genotypes
#> list()
Phenotype user data
Get phenotype data for one or multiple users
phenotypes(userid=1)$phenotypes[1:3]
#> $`Caffeine dependence`
#> $`Caffeine dependence`$phenotype_id
#> [1] 538
#>
#> $`Caffeine dependence`$variation
#> [1] "No"
#>
#>
#> $`hair on ear`
#> $`hair on ear`$phenotype_id
#> [1] 254
#>
#> $`hair on ear`$variation
#> [1] "No"
#>
#>
#> $`Third Nipple`
#> $`Third Nipple`$phenotype_id
#> [1] 259
#>
#> $`Third Nipple`$variation
#> [1] "None"
phenotypes(userid='1,6,8', df=TRUE)[[1]][1:10,]
#> phenotype phenotypeID variation
#> 1 Caffeine dependence 538 No
#> 2 hair on ear 254 No
#> 3 Third Nipple 259 None
#> 4 Alcoholism 485 None
#> 5 Alcohol Consumption (per week) 484 0
#> 6 Allergy to artificial grape flavoring 352 No
#> 7 inverted nipples 583 None
#> 8 Do you prefer python, matlab, or R? 585 Python & R
#> 9 Political Compass 276 Economic Left/Right: -8.88 Social Libertarian/Authoritarian: -9.49
#> 10 Sweat eating spicy food 219 Yes
out <- phenotypes(userid='1-8', df=TRUE)
lapply(out, head)
#> $`Bastian Greshake Tzovaras`
#> phenotype phenotypeID variation
#> 1 Caffeine dependence 538 No
#> 2 hair on ear 254 No
#> 3 Third Nipple 259 None
#> 4 Alcoholism 485 None
#> 5 Alcohol Consumption (per week) 484 0
#> 6 Allergy to artificial grape flavoring 352 No
#>
#> $Senficon
#> phenotype phenotypeID variation
#> 1 no data no data no data
#>
#> $`no info on user_3`
#> phenotype phenotypeID variation
#> 1 no data no data no data
#>
#> $`no info on user_4`
#> phenotype phenotypeID variation
#> 1 no data no data no data
#>
#> $`no info on user_5`
#> phenotype phenotypeID variation
#> 1 no data no data no data
#>
#> $`Nash Parovoz`
#> phenotype phenotypeID variation
#> 1 Y-DNA Haplogroup (ISOGG) 150 J-FGC5206
#> 2 The Dress: Perception of colour 338 White and gold
#> 3 Number of wisdom teeth 57 4
#> 4 Ability to find a bug in openSNP 5 extremely high
#> 5 Lactose intolerance 2 lactose-tolerant
#> 6 white skin 4 Caucasian
#>
#> $`no info on user_7`
#> phenotype phenotypeID variation
#> 1 no data no data no data
#>
#> $`Samantha B. Clark`
#> phenotype phenotypeID variation
#> 1 Gambling 539 No
#> 2 Caffeine dependence 538 No
#> 3 Dietary supplements used 534 b12
#> 4 Diet 533 Vegan / plant-based
#> 5 Tooth sensitivity 532 Sweet, cold
#> 6 OCD - Obsessive-Compulsive Disorder 555 No
All known variations
Get all known variations and all users sharing that phenotype for one phenotype(-ID).
phenotypes_byid(phenotypeid=12, return_ = 'desc')
#> $id
#> [1] 12
#>
#> $characteristic
#> [1] "Beard Color"
#>
#> $description
#> [1] "coloration of facial hair"
phenotypes_byid(phenotypeid=12, return_ = 'knownvars')
#> $known_variations
#> $known_variations[[1]]
#> [1] "Red"
#>
#> $known_variations[[2]]
#> [1] "Blonde"
#>
#> $known_variations[[3]]
#> [1] "Red-brown"
#>
#> $known_variations[[4]]
#> [1] "Red-blonde-brown-black(in diferent parts i have different color,for example near the lips blond-red"
#>
#> $known_variations[[5]]
#> [1] "No beard-female"
#>
#> $known_variations[[6]]
#> [1] "Brown-black"
#>
#> $known_variations[[7]]
#> [1] "Blonde-brown"
#>
#> $known_variations[[8]]
#> [1] "Black"
#>
#> $known_variations[[9]]
#> [1] "Dark brown with minor blondish-red"
#>
#> $known_variations[[10]]
#> [1] "Brown-grey"
#>
#> $known_variations[[11]]
#> [1] "Red-blonde-brown-black"
#>
#> $known_variations[[12]]
#> [1] "Blond-brown"
#>
#> $known_variations[[13]]
#> [1] "Brown, some red"
#>
#> $known_variations[[14]]
#> [1] "Brown"
#>
#> $known_variations[[15]]
#> [1] "Brown-gray"
#>
#> $known_variations[[16]]
#> [1] "Never had a beard"
#>
#> $known_variations[[17]]
#> [1] "I'm a woman"
#>
#> $known_variations[[18]]
#> [1] "Black-brown-blonde"
#>
#> $known_variations[[19]]
#> [1] "Was red-brown now mixed with gray,"
#>
#> $known_variations[[20]]
#> [1] "Red-blonde-brown"
#>
#> $known_variations[[21]]
#> [1] "Dark brown w/few blonde & red hairs"
#>
#> $known_variations[[22]]
#> [1] "Dark blonde with red and light blonde on goatee area."
#>
#> $known_variations[[23]]
#> [1] "Black with few red hairs"
#>
#> $known_variations[[24]]
#> [1] "Black, graying"
#>
#> $known_variations[[25]]
#> [1] "Red, moustache still is, beard mostly white"
#>
#> $known_variations[[26]]
#> [1] "Blonde/brown-some black-and red on chin-all starting to gray"
#>
#> $known_variations[[27]]
#> [1] "Dark brown"
#>
#> $known_variations[[28]]
#> [1] "Every possible color, most hair shafts have more than one color at different points along the shaft"
#>
#> $known_variations[[29]]
#> [1] "Black with few white hairs"
#>
#> $known_variations[[30]]
#> [1] "Brown ginger"
#>
#> $known_variations[[31]]
#> [1] "Dark blonde"
#>
#> $known_variations[[32]]
#> [1] "Black - going white due to age"
#>
#> $known_variations[[33]]
#> [1] "N/a"
phenotypes_byid(phenotypeid=12, return_ = 'users')[1:10,]
#> user_id variation
#> 1 22 Red
#> 2 1 Blonde
#> 3 26 red-brown
#> 4 10 Red-Blonde-Brown-Black(in diferent parts i have different color,for example near the lips blond-red
#> 5 14 No beard-female
#> 6 42 Brown-black
#> 7 45 Red-Blonde-Brown-Black(in diferent parts i have different color,for example near the lips blond-red
#> 8 16 blonde-brown
#> 9 8 No beard-female
#> 10 661 Brown-black
NCBI SNP data
dbSNP
Query NCBI's dbSNP for information on a set of SNPs.
An example with four markers, where one has been merged, and one has been withdrawn from NCBI.
snps <- c("rs332", "rs420358", "rs1837253", "rs1209415715", "rs111068718")
(dbsnp_info <- ncbi_snp_query(snps))
#> # A tibble: 4 × 16
#> query chromosome bp class rsid gene alleles ancestral_allele variation_allele seqname hgvs assembly ref_seq minor maf
#> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 rs332 7 1.18e8 del rs12… "CFT… TTT, d… TTT delTTT NC_000… NC_0… GRCh38.… <NA> <NA> NA
#> 2 rs420… 1 4.03e7 snv rs42… "" A,C,G,T A C,G,T NC_000… NC_0… GRCh38.… <NA> <NA> NA
#> 3 rs183… 5 1.11e8 snv rs18… "" T,C T C NC_000… NC_0… GRCh38.… T C 0.726
#> 4 rs120… 9 4.18e7 snv rs12… "" T,A,C T A,C NC_000… NC_0… GRCh38.… <NA> <NA> NA
#> # … with 1 more variable: maf_population <list>
The maf column contains the minor allele frequency from the GnomAD database (if available). All population specific allele frequencies can be accessed through the column maf_population which returns a list.
dbsnp_info$maf_population
#> [[1]]
#> study ref_seq Minor MAF
#> 1 NA NA NA
#>
#> [[2]]
#> study ref_seq Minor MAF
#> 1 ALSPAC A C 0.8227815
#> 2 Estonian A C 0.7895089
#> 3 GENOME_DK A C 0.8750000
#> 4 GoNL A C 0.8266533
#> 5 KOREAN A C 0.9658703
#> 6 NorthernSweden A C 0.8183333
#> 7 Qatari A C 0.8379630
#> 8 SGDP_PRJ A C 0.9175824
#> 9 Siberian A C 0.8333333
#> 10 TOMMO A C 0.9589499
#> 11 TOPMED A C 0.8765689
#> 12 TOPMED A C 0.8767313
#> 13 TWINSUK A C 0.8193096
#> 14 Vietnamese A C 0.9952830
#> 15 dbGaP_PopFreq A C 0.7991653
#> 16 KOREAN A G 0.0000000
#> 17 KOREAN A T 0.0000000
#> 18 dbGaP_PopFreq A T 0.0000000
#>
#> [[3]]
#> study ref_seq Minor MAF
#> 1 1000Genomes T C 0.6178115
#> 2 ALSPAC T C 0.7477945
#> 3 Daghestan T C 0.6856128
#> 4 Estonian T C 0.7037946
#> 5 GENOME_DK T C 0.7250000
#> 6 GnomAD T C 0.7257767
#> 7 GoNL T C 0.7274549
#> 8 HGDP_Stanford T C 0.6602687
#> 9 HapMap T C 0.6054025
#> 10 KOREAN T C 0.3969283
#> 11 Korea1K T C 0.3733624
#> 12 NorthernSweden T C 0.6850000
#> 13 PAGE_STUDY T C 0.6673868
#> 14 PRJEB36033 T C 1.0000000
#> 15 PRJEB37584 T C 0.4141414
#> 16 Qatari T C 0.7824074
#> 17 SGDP_PRJ T C 0.7670940
#> 18 Siberian T C 0.7826087
#> 19 TOMMO T C 0.3405728
#> 20 TOPMED T C 0.7110490
#> 21 TOPMED T C 0.7196758
#> 22 TWINSUK T C 0.7437972
#> 23 Vietnamese T C 0.4074074
#> 24 dbGaP_PopFreq T C 0.7300999
#>
#> [[4]]
#> study ref_seq Minor MAF
#> 1 dbGaP_PopFreq T A 0
Try the rsnps package in your browser
Any scripts or data that you put into this service are public.
rsnps documentation built on Jan. 28, 2022, 5:07 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
rsnps tutorial
Install and load library
When available on CRAN
install.packages("rsnps")
Or get from Github
install.packages("devtools") devtools::install_github("ropensci/rsnps")
library(rsnps)
OpenSNP data
All Genotypes
Get genotype data for all users at a particular SNP from OpenSNP:
x <- allgensnp(snp='rs7412') head(x) #> name chromosome position name id genotype_id local_genotype #> 1 rs7412 19 44908822 R.M. Holston 22 8 CC #> 2 rs7412 19 44908822 Charles G. Sullivan 5326 3834 CC #> 3 rs7412 19 44908822 Glenn Allen Nolen 19 7 CC #> 4 rs7412 19 44908822 Angel Harris 495 223 CC #> 5 rs7412 19 44908822 Mom to AG 387 173 CC #> 6 rs7412 19 44908822 kevinmcc 285 118 CC
All Phenotypes
Get all phenotypes, their variations, and how many users have data available for a given phenotype
Get all data
x <- allphenotypes(df = TRUE) head(x) #> id characteristic known_variations number_of_users #> 1 1 Eye color Brown 1665 #> 2 1 Eye color Brown-green 1665 #> 3 1 Eye color Blue-green 1665 #> 4 1 Eye color Blue-grey 1665 #> 5 1 Eye color Green 1665 #> 6 1 Eye color Blue 1665
Output a list, then call the characteristic of interest by 'id' or 'characteristic'
datalist <- allphenotypes()
Get a list of all characteristics you can call
names(datalist)[1:10] #> [1] "Eye color" "Lactose intolerance" "Handedness" #> [4] "white skin" "Ability to find a bug in openSNP" "Beard Color" #> [7] "Hair Color" "Ability to Tan" "Height" #> [10] "Hair Type"
Get data.frame for ADHD
datalist[["ADHD"]] #> id characteristic known_variations #> 1 29 ADHD False #> 2 29 ADHD True #> 3 29 ADHD Undiagnosed, but probably true #> 4 29 ADHD No #> 5 29 ADHD Yes #> 6 29 ADHD Not diagnosed #> 7 29 ADHD Diagnosed as not having but with some signs #> 8 29 ADHD Mthfr c677t #> 9 29 ADHD Rs1801260 #> 10 29 ADHD Adult onset #> 11 29 ADHD Diagnosed as "other hyperkinetic disorder" #> 12 29 ADHD Blonde, european, green eyes #> 13 29 ADHD Extreme #> 14 29 ADHD Diagnosed as hyperactive type, though it is my belief that adhd is simply a normal trait such as eye color. #> number_of_users #> 1 325 #> 2 325 #> 3 325 #> 4 325 #> 5 325 #> 6 325 #> 7 325 #> 8 325 #> 9 325 #> 10 325 #> 11 325 #> 12 325 #> 13 325 #> 14 325
Get data.frame for mouth size and SAT Writing
datalist[c("mouth size","SAT Writing")] #> $`mouth size` #> id characteristic known_variations number_of_users #> 1 120 mouth size Medium 202 #> 2 120 mouth size Small 202 #> 3 120 mouth size Large 202 #> 4 120 mouth size Slightly wide mouth 202 #> #> $`SAT Writing` #> id characteristic known_variations number_of_users #> 1 41 SAT Writing 750 110 #> 2 41 SAT Writing Tested before 2005 110 #> 3 41 SAT Writing 800 110 #> 4 41 SAT Writing Country with no sat 110 #> 5 41 SAT Writing N/a 110 #> 6 41 SAT Writing Never & have ba & above 110 #> 7 41 SAT Writing 720 110 #> 8 41 SAT Writing Did well - don't remember score 110 #> 9 41 SAT Writing 511 110 #> 10 41 SAT Writing 760 110 #> 11 41 SAT Writing 780 110 #> 12 41 SAT Writing 700 110 #> 13 41 SAT Writing Not part of sat when i took test in august 1967 at uiuc 110 #> 14 41 SAT Writing Not part of sat in 1961 110 #> 15 41 SAT Writing 620 110 #> 16 41 SAT Writing 560 110
Annotations
Get just the metadata
annotations(snp = 'rs7903146', output = 'metadata') #> .id V1 #> 1 name rs7903146 #> 2 chromosome 10 #> 3 position 112998590
Just from PLOS journals
annotations(snp = 'rs7903146', output = 'plos')[c(1:2),] #> author #> 1 Maggie C. Y. Ng #> 2 André Gustavo P. Sousa #> title #> 1 Meta-Analysis of Genome-Wide Association Studies in African Americans Provides Insights into the Genetic Architecture of Type 2 Diabetes #> 2 Genetic Variants of Diabetes Risk and Incident Cardiovascular Events in Chronic Coronary Artery Disease #> publication_date number_of_readers url doi #> 1 2014-08-07T00:00:00.000Z 11650 https://doi.org/10.1371/journal.pgen.1004517 10.1371/journal.pgen.1004517 #> 2 2011-01-20T00:00:00.000Z 2482 https://doi.org/10.1371/journal.pone.0016341 10.1371/journal.pone.0016341
Just from SNPedia
annotations(snp = 'rs7903146', output = 'snpedia') #> url summary #> 1 http://www.snpedia.com/index.php/Rs7903146(C;C) Normal (lower) risk of Type 2 Diabetes and Gestational Diabetes. #> 2 http://www.snpedia.com/index.php/Rs7903146(C;T) 1.4x increased risk for diabetes (and perhaps colon cancer). #> 3 http://www.snpedia.com/index.php/Rs7903146(T;T) 2x increased risk for Type-2 diabetes
Get all annotations
annotations(snp = 'rs7903146', output = 'all')[1:5,] #> .id author #> 1 mendeley T E Meyer #> 2 mendeley Camilla Cervin #> 3 mendeley Nicholette D Palmer #> 4 mendeley Ashis K Mondal #> 5 mendeley Julian Munoz #> title #> 1 Diabetes genes and prostate cancer in the Atherosclerosis Risk in Communities study #> 2 Diabetes in Adults , Type 1 Diabetes , and Type 2 Diabetes GENETICS OF LADA #> 3 Association of TCF7L2 gene polymorphisms with reduced acute insulin response in Hispanic Americans. #> 4 Genotype and tissue-specific effects on alternative splicing of the transcription factor 7-like 2 gene in humans. #> 5 Polymorphism in the transcription factor 7-like 2 (TCF7L2) gene is associated with reduced insulin secretion in nondiabetic women. #> publication_year number_of_readers open_access #> 1 2010 3 TRUE #> 2 2008 2 FALSE #> 3 2008 8 FALSE #> 4 2010 13 TRUE #> 5 2006 10 TRUE #> url #> 1 http://www.mendeley.com/research/diabetes-genes-prostate-cancer-atherosclerosis-risk-communities-study-4/ #> 2 http://www.mendeley.com/research/diabetes-adults-type-1-diabetes-type-2-diabetes-genetics-lada/ #> 3 http://www.mendeley.com/research/association-tcf7l2-gene-polymorphisms-reduced-acute-insulin-response-hispanic-americans/ #> 4 http://www.mendeley.com/research/genotype-tissuespecific-effects-alternative-splicing-transcription-factor-7like-2-gene-humans/ #> 5 http://www.mendeley.com/research/polymorphism-transcription-factor-7like-2-tcf7l2-gene-associated-reduced-insulin-secretion-nondiabet/ #> doi publication_date summary first_author pubmed_link journal trait pvalue #> 1 19/2/558 [pii]\\r10.1158/1055-9965.EPI-09-0902 <NA> <NA> <NA> <NA> <NA> <NA> NA #> 2 10.2337/db07-0299.Leif <NA> <NA> <NA> <NA> <NA> <NA> NA #> 3 10.1210/jc.2007-1225 <NA> <NA> <NA> <NA> <NA> <NA> NA #> 4 10.1210/jc.2009-2064 <NA> <NA> <NA> <NA> <NA> <NA> NA #> 5 10.2337/db06-0574 <NA> <NA> <NA> <NA> <NA> <NA> NA #> pvalue_description confidence_interval #> 1 <NA> <NA> #> 2 <NA> <NA> #> 3 <NA> <NA> #> 4 <NA> <NA> #> 5 <NA> <NA>
Download
Download genotype data for a user from 23andme or other repo. (not evaluated in this example)
data <- users(df=TRUE) head(data[[1]]) fetch_genotypes(url = data[[1]][1,"genotypes.download_url"], rows=15)
Genotype user data
Genotype data for one or multiple users
genotypes(snp='rs9939609', userid=1) #> $snp #> $snp$name #> [1] "rs9939609" #> #> $snp$chromosome #> [1] "16" #> #> $snp$position #> [1] "53786615" #> #> #> $user #> $user$name #> [1] "Bastian Greshake Tzovaras" #> #> $user$id #> [1] 1 #> #> $user$genotypes #> $user$genotypes[[1]] #> $user$genotypes[[1]]$genotype_id #> [1] 9 #> #> $user$genotypes[[1]]$local_genotype #> [1] "AT"
genotypes('rs9939609', userid='1,6,8', df=TRUE) #> snp_name snp_chromosome snp_position user_name user_id genotype_id genotype #> 1 rs9939609 16 53786615 Bastian Greshake Tzovaras 1 9 AT #> 2 rs9939609 16 53786615 Nash Parovoz 6 5 AT #> 3 rs9939609 16 53786615 Samantha B. Clark 8 2 TT
genotypes('rs9939609', userid='1-2', df=FALSE) #> [[1]] #> [[1]]$snp #> [[1]]$snp$name #> [1] "rs9939609" #> #> [[1]]$snp$chromosome #> [1] "16" #> #> [[1]]$snp$position #> [1] "53786615" #> #> #> [[1]]$user #> [[1]]$user$name #> [1] "Bastian Greshake Tzovaras" #> #> [[1]]$user$id #> [1] 1 #> #> [[1]]$user$genotypes #> [[1]]$user$genotypes[[1]] #> [[1]]$user$genotypes[[1]]$genotype_id #> [1] 9 #> #> [[1]]$user$genotypes[[1]]$local_genotype #> [1] "AT" #> #> #> #> #> #> [[2]] #> [[2]]$snp #> [[2]]$snp$name #> [1] "rs9939609" #> #> [[2]]$snp$chromosome #> [1] "16" #> #> [[2]]$snp$position #> [1] "53786615" #> #> #> [[2]]$user #> [[2]]$user$name #> [1] "Senficon" #> #> [[2]]$user$id #> [1] 2 #> #> [[2]]$user$genotypes #> list()
Phenotype user data
Get phenotype data for one or multiple users
phenotypes(userid=1)$phenotypes[1:3] #> $`Caffeine dependence` #> $`Caffeine dependence`$phenotype_id #> [1] 538 #> #> $`Caffeine dependence`$variation #> [1] "No" #> #> #> $`hair on ear` #> $`hair on ear`$phenotype_id #> [1] 254 #> #> $`hair on ear`$variation #> [1] "No" #> #> #> $`Third Nipple` #> $`Third Nipple`$phenotype_id #> [1] 259 #> #> $`Third Nipple`$variation #> [1] "None"
phenotypes(userid='1,6,8', df=TRUE)[[1]][1:10,] #> phenotype phenotypeID variation #> 1 Caffeine dependence 538 No #> 2 hair on ear 254 No #> 3 Third Nipple 259 None #> 4 Alcoholism 485 None #> 5 Alcohol Consumption (per week) 484 0 #> 6 Allergy to artificial grape flavoring 352 No #> 7 inverted nipples 583 None #> 8 Do you prefer python, matlab, or R? 585 Python & R #> 9 Political Compass 276 Economic Left/Right: -8.88 Social Libertarian/Authoritarian: -9.49 #> 10 Sweat eating spicy food 219 Yes
out <- phenotypes(userid='1-8', df=TRUE) lapply(out, head) #> $`Bastian Greshake Tzovaras` #> phenotype phenotypeID variation #> 1 Caffeine dependence 538 No #> 2 hair on ear 254 No #> 3 Third Nipple 259 None #> 4 Alcoholism 485 None #> 5 Alcohol Consumption (per week) 484 0 #> 6 Allergy to artificial grape flavoring 352 No #> #> $Senficon #> phenotype phenotypeID variation #> 1 no data no data no data #> #> $`no info on user_3` #> phenotype phenotypeID variation #> 1 no data no data no data #> #> $`no info on user_4` #> phenotype phenotypeID variation #> 1 no data no data no data #> #> $`no info on user_5` #> phenotype phenotypeID variation #> 1 no data no data no data #> #> $`Nash Parovoz` #> phenotype phenotypeID variation #> 1 Y-DNA Haplogroup (ISOGG) 150 J-FGC5206 #> 2 The Dress: Perception of colour 338 White and gold #> 3 Number of wisdom teeth 57 4 #> 4 Ability to find a bug in openSNP 5 extremely high #> 5 Lactose intolerance 2 lactose-tolerant #> 6 white skin 4 Caucasian #> #> $`no info on user_7` #> phenotype phenotypeID variation #> 1 no data no data no data #> #> $`Samantha B. Clark` #> phenotype phenotypeID variation #> 1 Gambling 539 No #> 2 Caffeine dependence 538 No #> 3 Dietary supplements used 534 b12 #> 4 Diet 533 Vegan / plant-based #> 5 Tooth sensitivity 532 Sweet, cold #> 6 OCD - Obsessive-Compulsive Disorder 555 No
All known variations
Get all known variations and all users sharing that phenotype for one phenotype(-ID).
phenotypes_byid(phenotypeid=12, return_ = 'desc') #> $id #> [1] 12 #> #> $characteristic #> [1] "Beard Color" #> #> $description #> [1] "coloration of facial hair"
phenotypes_byid(phenotypeid=12, return_ = 'knownvars') #> $known_variations #> $known_variations[[1]] #> [1] "Red" #> #> $known_variations[[2]] #> [1] "Blonde" #> #> $known_variations[[3]] #> [1] "Red-brown" #> #> $known_variations[[4]] #> [1] "Red-blonde-brown-black(in diferent parts i have different color,for example near the lips blond-red" #> #> $known_variations[[5]] #> [1] "No beard-female" #> #> $known_variations[[6]] #> [1] "Brown-black" #> #> $known_variations[[7]] #> [1] "Blonde-brown" #> #> $known_variations[[8]] #> [1] "Black" #> #> $known_variations[[9]] #> [1] "Dark brown with minor blondish-red" #> #> $known_variations[[10]] #> [1] "Brown-grey" #> #> $known_variations[[11]] #> [1] "Red-blonde-brown-black" #> #> $known_variations[[12]] #> [1] "Blond-brown" #> #> $known_variations[[13]] #> [1] "Brown, some red" #> #> $known_variations[[14]] #> [1] "Brown" #> #> $known_variations[[15]] #> [1] "Brown-gray" #> #> $known_variations[[16]] #> [1] "Never had a beard" #> #> $known_variations[[17]] #> [1] "I'm a woman" #> #> $known_variations[[18]] #> [1] "Black-brown-blonde" #> #> $known_variations[[19]] #> [1] "Was red-brown now mixed with gray," #> #> $known_variations[[20]] #> [1] "Red-blonde-brown" #> #> $known_variations[[21]] #> [1] "Dark brown w/few blonde & red hairs" #> #> $known_variations[[22]] #> [1] "Dark blonde with red and light blonde on goatee area." #> #> $known_variations[[23]] #> [1] "Black with few red hairs" #> #> $known_variations[[24]] #> [1] "Black, graying" #> #> $known_variations[[25]] #> [1] "Red, moustache still is, beard mostly white" #> #> $known_variations[[26]] #> [1] "Blonde/brown-some black-and red on chin-all starting to gray" #> #> $known_variations[[27]] #> [1] "Dark brown" #> #> $known_variations[[28]] #> [1] "Every possible color, most hair shafts have more than one color at different points along the shaft" #> #> $known_variations[[29]] #> [1] "Black with few white hairs" #> #> $known_variations[[30]] #> [1] "Brown ginger" #> #> $known_variations[[31]] #> [1] "Dark blonde" #> #> $known_variations[[32]] #> [1] "Black - going white due to age" #> #> $known_variations[[33]] #> [1] "N/a"
phenotypes_byid(phenotypeid=12, return_ = 'users')[1:10,] #> user_id variation #> 1 22 Red #> 2 1 Blonde #> 3 26 red-brown #> 4 10 Red-Blonde-Brown-Black(in diferent parts i have different color,for example near the lips blond-red #> 5 14 No beard-female #> 6 42 Brown-black #> 7 45 Red-Blonde-Brown-Black(in diferent parts i have different color,for example near the lips blond-red #> 8 16 blonde-brown #> 9 8 No beard-female #> 10 661 Brown-black
NCBI SNP data
dbSNP
Query NCBI's dbSNP for information on a set of SNPs.
An example with four markers, where one has been merged, and one has been withdrawn from NCBI.
snps <- c("rs332", "rs420358", "rs1837253", "rs1209415715", "rs111068718") (dbsnp_info <- ncbi_snp_query(snps)) #> # A tibble: 4 × 16 #> query chromosome bp class rsid gene alleles ancestral_allele variation_allele seqname hgvs assembly ref_seq minor maf #> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> #> 1 rs332 7 1.18e8 del rs12… "CFT… TTT, d… TTT delTTT NC_000… NC_0… GRCh38.… <NA> <NA> NA #> 2 rs420… 1 4.03e7 snv rs42… "" A,C,G,T A C,G,T NC_000… NC_0… GRCh38.… <NA> <NA> NA #> 3 rs183… 5 1.11e8 snv rs18… "" T,C T C NC_000… NC_0… GRCh38.… T C 0.726 #> 4 rs120… 9 4.18e7 snv rs12… "" T,A,C T A,C NC_000… NC_0… GRCh38.… <NA> <NA> NA #> # … with 1 more variable: maf_population <list>
The maf column contains the minor allele frequency from the GnomAD database (if available). All population specific allele frequencies can be accessed through the column maf_population which returns a list.
dbsnp_info$maf_population #> [[1]] #> study ref_seq Minor MAF #> 1 NA NA NA #> #> [[2]] #> study ref_seq Minor MAF #> 1 ALSPAC A C 0.8227815 #> 2 Estonian A C 0.7895089 #> 3 GENOME_DK A C 0.8750000 #> 4 GoNL A C 0.8266533 #> 5 KOREAN A C 0.9658703 #> 6 NorthernSweden A C 0.8183333 #> 7 Qatari A C 0.8379630 #> 8 SGDP_PRJ A C 0.9175824 #> 9 Siberian A C 0.8333333 #> 10 TOMMO A C 0.9589499 #> 11 TOPMED A C 0.8765689 #> 12 TOPMED A C 0.8767313 #> 13 TWINSUK A C 0.8193096 #> 14 Vietnamese A C 0.9952830 #> 15 dbGaP_PopFreq A C 0.7991653 #> 16 KOREAN A G 0.0000000 #> 17 KOREAN A T 0.0000000 #> 18 dbGaP_PopFreq A T 0.0000000 #> #> [[3]] #> study ref_seq Minor MAF #> 1 1000Genomes T C 0.6178115 #> 2 ALSPAC T C 0.7477945 #> 3 Daghestan T C 0.6856128 #> 4 Estonian T C 0.7037946 #> 5 GENOME_DK T C 0.7250000 #> 6 GnomAD T C 0.7257767 #> 7 GoNL T C 0.7274549 #> 8 HGDP_Stanford T C 0.6602687 #> 9 HapMap T C 0.6054025 #> 10 KOREAN T C 0.3969283 #> 11 Korea1K T C 0.3733624 #> 12 NorthernSweden T C 0.6850000 #> 13 PAGE_STUDY T C 0.6673868 #> 14 PRJEB36033 T C 1.0000000 #> 15 PRJEB37584 T C 0.4141414 #> 16 Qatari T C 0.7824074 #> 17 SGDP_PRJ T C 0.7670940 #> 18 Siberian T C 0.7826087 #> 19 TOMMO T C 0.3405728 #> 20 TOPMED T C 0.7110490 #> 21 TOPMED T C 0.7196758 #> 22 TWINSUK T C 0.7437972 #> 23 Vietnamese T C 0.4074074 #> 24 dbGaP_PopFreq T C 0.7300999 #> #> [[4]] #> study ref_seq Minor MAF #> 1 dbGaP_PopFreq T A 0
Try the rsnps package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.