When available on CRAN
install.packages("rsnps")
Or get from Github
install.packages("devtools")
devtools::install_github("ropensci/rsnps")
library(rsnps)
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
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
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 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 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$genotypes
#> list()
Get phenotype data for one or multiple users
phenotypes(userid=1)$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
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"
#>
#> $known_variations[[33]]
#> [1] "N/a"
phenotypes_byid(phenotypeid=12, return_ = 'users')[1:10,]
#> user_id variation
#> 1 22 Red
#> 2 1 Blonde
#> 3 26 red-brown
#> 4 10 Red-Blonde-Brown-Black(in diferent parts i have different color,for example near the lips blond-red
#> 5 14 No beard-female
#> 6 42 Brown-black
#> 7 45 Red-Blonde-Brown-Black(in diferent parts i have different color,for example near the lips blond-red
#> 8 16 blonde-brown
#> 9 8 No beard-female
#> 10 661 Brown-black
Query NCBI's dbSNP for information on a set of SNPs.
An example with four markers, where one has been merged, and one has been withdrawn from NCBI.
snps <- c("rs332", "rs420358", "rs1837253", "rs1209415715", "rs111068718")
(dbsnp_info <- ncbi_snp_query(snps))
#> # A tibble: 4 × 16
#> query chromosome bp class rsid gene alleles ancestral_allele variation_allele seqname hgvs assembly ref_seq minor maf
#> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 rs332 7 1.18e8 del rs12… "CFT… TTT, d… TTT delTTT NC_000… NC_0… GRCh38.… <NA> <NA> NA
#> 2 rs420… 1 4.03e7 snv rs42… "" A,C,G,T A C,G,T NC_000… NC_0… GRCh38.… <NA> <NA> NA
#> 3 rs183… 5 1.11e8 snv rs18… "" T,C T C NC_000… NC_0… GRCh38.… T C 0.726
#> 4 rs120… 9 4.18e7 snv rs12… "" T,A,C T A,C NC_000… NC_0… GRCh38.… <NA> <NA> NA
#> # … with 1 more variable: maf_population <list>
The maf column contains the minor allele frequency from the GnomAD database (if available). All population specific allele frequencies can be accessed through the column maf_population which returns a list.
dbsnp_info$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