{"id":16450915,"url":"https://github.com/senresearch/genenetworkapi.jl","last_synced_at":"2026-02-22T20:41:28.159Z","repository":{"id":38215726,"uuid":"73299082","full_name":"senresearch/GeneNetworkAPI.jl","owner":"senresearch","description":"Julia interface for GeneNetwork REST API","archived":false,"fork":false,"pushed_at":"2023-11-07T22:31:09.000Z","size":338,"stargazers_count":7,"open_issues_count":4,"forks_count":3,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-18T19:30:08.630Z","etag":null,"topics":["api","genenetwork","julia"],"latest_commit_sha":null,"homepage":"","language":"Julia","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/senresearch.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-11-09T16:02:06.000Z","updated_at":"2022-06-08T06:57:37.000Z","dependencies_parsed_at":"2022-09-04T14:53:55.847Z","dependency_job_id":"af5f39c1-a88c-4b54-8686-e9105a42d2eb","html_url":"https://github.com/senresearch/GeneNetworkAPI.jl","commit_stats":{"total_commits":77,"total_committers":4,"mean_commits":19.25,"dds":"0.36363636363636365","last_synced_commit":"068f2ad80be5181e254513c59df46d47a6db9f46"},"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/senresearch%2FGeneNetworkAPI.jl","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/senresearch%2FGeneNetworkAPI.jl/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/senresearch%2FGeneNetworkAPI.jl/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/senresearch%2FGeneNetworkAPI.jl/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/senresearch","download_url":"https://codeload.github.com/senresearch/GeneNetworkAPI.jl/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245078067,"owners_count":20557274,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["api","genenetwork","julia"],"created_at":"2024-10-11T10:06:23.921Z","updated_at":"2026-02-22T20:41:28.100Z","avatar_url":"https://github.com/senresearch.png","language":"Julia","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://senresearch.github.io/GeneNetworkAPI.jl/dev)\n[![CI](https://github.com/senresearch/GeneNetworkAPI.jl/actions/workflows/ci.yml/badge.svg)](https://github.com/senresearch/GeneNetworkAPI.jl/actions/workflows/ci.yml)\n\n# GeneNetworkAPI\n\nProvides access to the [GeneNetwork](http://genenetwork.org) database\nand analysis functions using the [GeneNetwork REST\nAPI](https://github.com/genenetwork/gn-docs/blob/master/api/GN2-REST-API.md).\n\nKarl Broman wrote the\n[GNapi](https://github.com/kbroman/GNapi/blob/main/README.md) R\npackage for providing access to GeneNetwork from R.  This package\nfollows the structure and function of that package closely.\n\n## Note on terminology\n\nGeneNetwork collects data on genetically segregating populations\n(called _groups_) in a number of _species_ including humans.  Most of\nthe phenotype data is \"omic\" data which are organized as _datasets_. \n\n## Check connection\n\nTo check if the website is responding properly:\n```\njulia\u003e check_gn()\nGeneNetwork is alive.\n200\n```\n\n## Get species list\n\nWhich species have data on them?\n\n```\njulia\u003e list_species()\n11×4 DataFrame\n Row │ FullName                           Id     Name             TaxonomyId \n     │ String                             Int64  String           Int64      \n─────┼───────────────────────────────────────────────────────────────────────\n   1 │ Mus musculus                           1  mouse                 10090\n   2 │ Rattus norvegicus                      2  rat                   10116\n   3 │ Arabidopsis thaliana                   3  arabidopsis            3702\n   4 │ Homo sapiens                           4  human                  9606\n   5 │ Hordeum vulgare                        5  barley                 4513\n   6 │ Drosophila melanogaster                6  drosophila             7227\n   7 │ Macaca mulatta                         7  macaque monkey         9544\n   8 │ Glycine max                            8  soybean                3847\n   9 │ Solanum lycopersicum                   9  tomato                 4081\n  10 │ Populus trichocarpa                   10  poplar                 3689\n  11 │ Oryzias latipes (Japanese medaka)     11  Oryzias latipes        8090\n```\n\nTo get information on a single species:\n\n```\njulia\u003e list_species(\"rat\")\n1×4 DataFrame\n Row │ FullName           Id     Name    TaxonomyId \n     │ String             Int64  String  Int64      \n─────┼──────────────────────────────────────────────\n   1 │ Rattus norvegicus      2  rat          10116\n```\n\nYou could also subset (safer):\n```\njulia\u003e GeneNetworkAPI.subset(list_species(), :Name =\u003e x-\u003ex.==\"rat\")\n1×4 DataFrame\n Row │ FullName           Id     Name    TaxonomyId \n     │ String             Int64  String  Int64      \n─────┼──────────────────────────────────────────────\n   1 │ Rattus norvegicus      2  rat          10116\n```\n\n## List groups for a species\n\nSince the information is organized by segregating population\n(\"group\"), it is useful to get a list for a particular species you\nmight be interested in.\n\n```\njulia\u003e list_groups(\"rat\")\n7×8 DataFrame\n Row │ DisplayName                        FullName                           GeneticType  Id     MappingMethodId  Name             SpeciesId  public \n     │ String                             String                             String       Int64  String           String           Int64      Int64  \n─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n   1 │ Hybrid Rat Diversity Panel (Incl…  Hybrid Rat Diversity Panel (Incl…  None            10  1                HXBBXH                   2       2\n   2 │ UIOWA SRxSHRSP F2                  UIOWA SRxSHRSP F2                  intercross      24  1                SRxSHRSPF2               2       2\n   3 │ NIH Heterogeneous Stock (RGSMC 2…  NIH Heterogeneous Stock (RGSMC 2…  None            42  1                HSNIH-RGSMC              2       2\n   4 │ NIH Heterogeneous Stock (Palmer)   NIH Heterogeneous Stock (Palmer)   None            55  1                HSNIH-Palmer             2       2\n   5 │ NWU WKYxF344 F2 Behavior           NWU WKYxF344 F2 Behavior           intercross      82  3                NWU_WKYxF344_F2          2       2\n   6 │ HIV-1Tg and Control                HIV-1Tg and Control                None            83  1                HIV-1Tg                  2       2\n   7 │ HRDP-HXB/BXH Brain Proteome        HRDP-HXB/BXH Brain Proteome        None            87  1                HRDP_HXB-BXH-BP          2       2\n```\n\nYou can see the type of population it is.  Note the short name\n(`Name`) as that will be used in queries involving that population\n(group).\n\n\n## Get genotypes for a group\n\nTo get the genotypes of a group:\n\n\n```\njulia\u003e get_geno(\"BXD\") |\u003e (x-\u003efirst(x,10))\n10×240 DataFrame\n Row │ Chr      Locus        cM       Mb       BXD1     BXD2     BXD5     BXD6     BXD8     BXD9     BXD11    BXD12    BXD13    BXD14    BXD15    BXD16    BXD18 ⋯\n     │ String3  String31     Float64  Float64  String1  String1  String1  String1  String1  String1  String1  String1  String1  String1  String1  String1  Strin ⋯\n─────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n   1 │ 1        rs31443144      1.5   3.01027  B        B        D        D        D        B        B        D        B        B        D        D        B     ⋯\n   2 │ 1        rs6269442       1.5   3.4922   B        B        D        D        D        B        B        D        B        B        D        D        B\n   3 │ 1        rs32285189      1.63  3.5112   B        B        D        D        D        B        B        D        B        B        D        D        B\n   4 │ 1        rs258367496     1.63  3.6598   B        B        D        D        D        B        B        D        B        B        D        D        B\n   5 │ 1        rs32430919      1.75  3.77702  B        B        D        D        D        B        B        D        B        B        D        D        B     ⋯\n   6 │ 1        rs36251697      1.88  3.81227  B        B        D        D        D        B        B        D        B        B        D        D        B\n   7 │ 1        rs30658298      2.01  4.43062  B        B        D        D        D        B        B        D        B        B        D        D        B\n   8 │ 1        rs51852623      2.01  4.44674  B        B        D        D        D        B        B        D        B        B        D        D        B\n   9 │ 1        rs31879829      2.14  4.51871  B        B        D        D        D        B        B        D        B        B        D        D        B     ⋯\n  10 │ 1        rs36742481      2.14  4.77632  B        B        D        D        D        B        B        D        B        B        D        D        B\n                                                                                                                                               224 columns omitted\n```\n\nCurrently, we only support the `.geno` format which returns a data\nframe of genotypes with rows as marker and columns as individuals.\n\n## List datasets for a group\n\nTo list the (omic) datasets available for a group, you have to use the\nname as listed in the group list for a species:\n\n```\njulia\u003e list_datasets(\"HSNIH-Palmer\")\n10×11 DataFrame\n Row │ AvgID  CreateTime                     DataScale  FullName                           Id     Long_Abbreviation              ProbeFreezeId  ShortName        ⋯\n     │ Int64  String                         String     String                             Int64  String                         Int64          String           ⋯\n─────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n   1 │    24  Mon, 27 Aug 2018 00:00:00 GMT  log2       HSNIH-Palmer Nucleus Accumbens C…    860  HSNIH-Rat-Acbc-RSeq-Aug18                347  HSNIH-Palmer Nuc ⋯\n   2 │    24  Sun, 26 Aug 2018 00:00:00 GMT  log2       HSNIH-Palmer Infralimbic Cortex …    861  HSNIH-Rat-IL-RSeq-Aug18                  348  HSNIH-Palmer Inf\n   3 │    24  Sat, 25 Aug 2018 00:00:00 GMT  log2       HSNIH-Palmer Lateral Habenula RN…    862  HSNIH-Rat-LHB-RSeq-Aug18                 349  HSNIH-Palmer Lat\n   4 │    24  Fri, 24 Aug 2018 00:00:00 GMT  log2       HSNIH-Palmer Prelimbic Cortex RN…    863  HSNIH-Rat-PL-RSeq-Aug18                  350  HSNIH-Palmer Pre\n   5 │    24  Thu, 23 Aug 2018 00:00:00 GMT  log2       HSNIH-Palmer Orbitofrontal Corte…    864  HSNIH-Rat-VoLo-RSeq-Aug18                351  HSNIH-Palmer Orb ⋯\n   6 │    24  Fri, 14 Sep 2018 00:00:00 GMT  log2       HSNIH-Palmer Nucleus Accumbens C…    868  HSNIH-Rat-Acbc-RSeqlog2-Aug18            347  HSNIH-Palmer Nuc\n   7 │    24  Fri, 14 Sep 2018 00:00:00 GMT  log2       HSNIH-Palmer Infralimbic Cortex …    869  HSNIH-Rat-IL-RSeqlog2-Aug18              348  HSNIH-Palmer Inf\n   8 │    24  Fri, 14 Sep 2018 00:00:00 GMT  log2       HSNIH-Palmer Lateral Habenula RN…    870  HSNIH-Rat-LHB-RSeqlog2-Aug18             349  HSNIH-Palmer Lat\n   9 │    24  Fri, 14 Sep 2018 00:00:00 GMT  log2       HSNIH-Palmer Prelimbic Cortex RN…    871  HSNIH-Rat-PL-RSeqlog2-Aug18              350  HSNIH-Palmer Pre ⋯\n  10 │    24  Fri, 14 Sep 2018 00:00:00 GMT  log2       HSNIH-Palmer Orbitofrontal Corte…    872  HSNIH-Rat-VoLo-RSeqlog2-Aug18            351  HSNIH-Palmer Orb\n                                                                                                                                                 4 columns omitted\n```\n\n## Get sample data for a group\n\nThis gives you a matrix with rows as individuals/samples/strains and\ncolumns as \"clinical\" (non-omic) phenotypes.  The number after the\nunderscore is the phenotype number (to be used later).  Some data may\nbe missing.\n\n```\njulia\u003e get_pheno(\"HSNIH-Palmer\") |\u003e (x-\u003ex[81:100,:]) |\u003e show\n20×509 DataFrame\n Row │ id          HSR_10308  HSR_10309  HSR_10310  HSR_10311  HSR_10312  HSR_10313  HSR_10314  HSR_10315       HSR_10316       HSR_10317      ⋯\n     │ String15    Float64?   Float64?   Float64?   Float64?   Float64?   Float64?   Float64?   Float64?        Float64?        Float64?       ⋯\n─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n   1 │ 000721E489  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing        ⋯\n   2 │ 00072AAC0D  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing\n   3 │ 00072AC972  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing\n   4 │ 00077E61DC  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing\n   5 │ 00077E61EC  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing        ⋯\n   6 │ 00077E61F3       18.0       43.0       25.0       42.0       36.0        8.0       43.0       -0.514286        1.14667         1.125\n   7 │ 00077E61F5  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing\n   8 │ 00077E6204  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing\n   9 │ 00077E6207       22.0       63.0       54.0       77.0       54.0       42.0       77.0        0.914286        1.07959         1.0      ⋯\n  10 │ 00077E6299  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing\n  11 │ 00077E62CD  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing\n  12 │ 00077E62D2       55.0       54.0       31.0       16.0       25.0       18.0       55.0       -2.73333         0.780392        1.22222\n  13 │ 00077E633D       25.0       47.0       58.0       35.0       27.0       35.0       58.0       -0.314286        1.19474         0.925926 ⋯\n  14 │ 00077E634B  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing\n  15 │ 00077E63D9  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing\n  16 │ 00077E641E  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing\n  17 │ 00077E6433      112.0      131.0      117.0       60.0       82.0       70.0      131.0       -3.94286         1.95222         2.54546  ⋯\n  18 │ 00077E64B3  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing\n  19 │ 00077E64BA      135.0      154.0      188.0      267.0       98.0       76.0      267.0       -3.65714         4.19178         4.35484\n  20 │ 00077E64C1  missing    missing    missing    missing    missing    missing    missing    missing         missing         missing\n                                                                                                                             498 columns omitted\n```\n\nTo obtain omics phenotypes, you can utilize the `get_omics()` function, which provides a matrix with individuals/samples/strains as rows and omic phenotypes as columns. This function requires the input of a short abbreviation representing the available (omic) datasets for a particular group. To obtain the short abbreviation, you can refer to the section titled \"List datasets for a group\" and use the `list_dataset()` function.\nFor instance, if you want to acquire the phenotype matrix corresponding to \"HSNIH-Palmer Infralimbic Cortex RNA-Seq (Aug18) rlog,\" you would use its respective short abbreviation.\n```\njulia\u003e get_omics(\"HSNIH-Rat-IL-RSeq-0818\")\n6171×32624 DataFrame\n  Row │ id          ENSRNOG00000000001  ENSRNOG00000000007  ENSRNOG00000000008  ENSRNOG00000000009  ENSRNOG00000000010  ENSRNOG00000000012  ENSRNO ⋯\n      │ String15    Float64?            Float64?            Float64?            Float64?            Float64?            Float64?            Float6 ⋯\n──────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n    1 │ 00071F4FAF             missing             missing             missing             missing             missing             missing         ⋯\n    2 │ 00071F6771             missing             missing             missing             missing             missing             missing        \n    3 │ 00071F768E             missing             missing             missing             missing             missing             missing        \n    4 │ 00071F95F9             missing             missing             missing             missing             missing             missing        \n    5 │ 00071FB160             missing             missing             missing             missing             missing             missing         ⋯\n    6 │ 00071FB747             missing             missing             missing             missing             missing             missing        \n    7 │ 00072069AD             missing             missing             missing             missing             missing             missing\n  ⋮   │     ⋮               ⋮                   ⋮                   ⋮                   ⋮                   ⋮                   ⋮                  ⋱\n 6165 │ 0007929918             missing             missing             missing             missing             missing             missing\n 6166 │ 0007929945             missing             missing             missing             missing             missing             missing         ⋯\n 6167 │ 00077E840E             missing             missing             missing             missing             missing             missing\n 6168 │ 00077E9879             missing             missing             missing             missing             missing             missing\n 6169 │ 00077E9920             missing             missing             missing             missing             missing             missing\n 6170 │ 00077E9D84             missing             missing             missing             missing             missing             missing         ⋯\n 6171 │ 00077E949D             missing             missing             missing             missing             missing             missing\n                                                                                                                 32617 columns and 6157 rows omitted\n```\n\n\nunderscore is the phenotype number (to be used later).  Some data may\nbe missing.\n## Get information about traits\n\nTo get information on a particular (non-omic) trait use the group name\nand the trait number:\n\n```\njulia\u003e info_dataset(\"HSNIH-Palmer\",\"10308\")\n1×4 DataFrame\n Row │ dataset_type  description                        id     name                  \n     │ String        String                             Int64  String                \n─────┼───────────────────────────────────────────────────────────────────────────────\n   1 │ phenotype     Central nervous system, behavior…  10308  reaction_time_pint1_5\n```\n\nTo get information on a dataset (of omic traits) for a group, use:\n\n```\njulia\u003e info_dataset(\"HSNIH-Rat-Acbc-RSeq-Aug18\")\n1×10 DataFrame\n Row │ confidential  data_scale  dataset_type     full_name                          id     name                      public  short_name                         ti ⋯\n     │ Int64         String      String           String                             Int64  String                    Int64   String                             St ⋯\n─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n   1 │            0  log2        mRNA expression  HSNIH-Palmer Nucleus Accumbens C…    860  HSNIH-Rat-Acbc-RSeq-0818       1  HSNIH-Palmer Nucleus Accumbens C…  Nu ⋯\n                                                                                                                                                    2 columns omitted\n```\n\n## Summary information on traits\n\nGet a list of the maximum LRS for each trait and position.\n\n```\njulia\u003e info_pheno(\"HXBBXH\") |\u003e (x-\u003efirst(x,10))\n10×7 DataFrame\n Row │ Additive     Id     LRS       Locus        PhenotypeId  PublicationId  Sequence \n     │ Float64?     Int64  Float64?  String?      Int64        Int64          Int64    \n─────┼─────────────────────────────────────────────────────────────────────────────────\n   1 │  0.0499968   10001  16.2831   rs106114574         1449            319         1\n   2 │ -0.0926364   10002  10.9777   rs63915446          1450            319         1\n   3 │  0.60189     10003  13.6515   rs107486115         1451            319         1\n   4 │ -0.543799    10004   8.43965  D5Rat147            1452            319         1\n   5 │  0.00854221  10005  18.5895   rs106114574         1453            319         1\n   6 │ -0.0142273   10006  11.9965   rs63915446          1454            319         1\n   7 │  0.427167    10007  10.541    rs13452609          1455            319         1\n   8 │ -0.936806    10008  13.2494   rs8143630           1456            319         1\n   9 │ -0.635833    10009   9.97609  rs107549352         1457            319         1\n  10 │ -0.681451    10010   9.59226  D7Mit13             1458            319         1\n```\n\nYou could also specify a group and a trait number or a dataset and a probename.\n\n```\njulia\u003e info_pheno(\"BXD\",\"10001\")\n1×4 DataFrame\n Row │ additive  id     locus       lrs     \n     │ Float64   Int64  String      Float64 \n─────┼──────────────────────────────────────\n   1 │  2.39444      4  rs48756159  13.4975\n```\n\n```\njulia\u003e info_pheno(\"HC_M2_0606_P\",\"1436869_at\")\n1×13 DataFrame\n Row │ additive   alias                              chr     description                id     locus      lrs      mb       mean     name        p_value  se        ⋯\n     │ Float64    String                             String  String                     Int64  String     Float64  Float64  Float64  String      Float64  Nothing   ⋯\n─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n   1 │ -0.214088  HHG1; HLP3; HPE3; SMMCI; Dsh; Hh…  5       sonic hedgehog (hedgehog)  99602  rs8253327  12.7711  28.4572  9.27909  1436869_at    0.306            ⋯\n                                                                                                                                                     1 column omitted\n```\n\n## Analysis commands\n\n\n### GEMMA\n\n```\njulia\u003e run_gemma(\"BXDPublish\",\"10015\",use_loco=true) |\u003e (x-\u003efirst(x,10))\n10×6 DataFrame\n Row │ Mb       additive   chr  lod_score  name         p_value  \n     │ Float64  Float64    Any  Float64    String       Float64  \n─────┼───────────────────────────────────────────────────────────\n   1 │ 3.01027  -0.906398  1     0.448914  rs31443144   0.355702\n   2 │ 3.4922   -0.906398  1     0.448914  rs6269442    0.355702\n   3 │ 3.5112   -0.906398  1     0.448914  rs32285189   0.355702\n   4 │ 3.6598   -0.906398  1     0.448914  rs258367496  0.355702\n   5 │ 3.77702  -0.906398  1     0.448914  rs32430919   0.355702\n   6 │ 3.81227  -0.906398  1     0.448914  rs36251697   0.355702\n   7 │ 4.43062  -0.906398  1     0.448914  rs30658298   0.355702\n   8 │ 4.44674  -0.906398  1     0.448914  rs51852623   0.355702\n   9 │ 4.51871  -0.906398  1     0.448914  rs31879829   0.355702\n  10 │ 4.77632  -0.906398  1     0.448914  rs36742481   0.355702\n```\n\n### R/qtl\n\nThis function performs a one-dimensional genome scan.  The arguments\nare\n\n- db (required) - DB name for trait above (Short_Abbreviation listed\n  when you query for datasets)\n- trait (required) - ID for trait being mapped\n- method - hk (default) | ehk | em | imp | mr | mr-imp |\n  mr-argmax ; Corresponds to the \"method\" option for the R/qtl scanone\n  function.\n- model - normal (default) | binary | 2-part | np ; corresponds\n  to the \"model\" option for the R/qtl scanone function\n- n_perm - number of permutations; 0 by default\n- control_marker - Name of marker to use as control; this relies on\n  the user knowing the name of the marker they want to use as a\n  covariate\n- interval_mapping - Whether to use interval mapping; \"false\" by default\n\n```\njulia\u003e run_rqtl(\"BXDPublish\", \"10015\") |\u003e (x-\u003efirst(x,10))\n10×5 DataFrame\n Row │ Mb       cM       chr  lod_score  name        \n     │ Float64  Float64  Any  Float64    String      \n─────┼───────────────────────────────────────────────\n   1 │ 3.01027  3.01027  1     0.116927  rs31443144\n   2 │ 3.4922   3.4922   1     0.117404  rs6269442\n   3 │ 3.5112   3.5112   1     0.117424  rs32285189\n   4 │ 3.6598   3.6598   1     0.117573  rs258367496\n   5 │ 3.77702  3.77702  1     0.117691  rs32430919\n   6 │ 3.81227  3.81227  1     0.117727  rs36251697\n   7 │ 4.43062  4.43062  1     0.118356  rs30658298\n   8 │ 4.44674  4.44674  1     0.118372  rs51852623\n   9 │ 4.51871  4.51871  1     0.118447  rs31879829\n  10 │ 4.77632  4.77632  1     0.118714  rs36742481\n```\n\n### Correlation\n\nThis function correlates a trait in a dataset against all traits in a\ntarget database.\n\n\n- trait_id (required) - ID for trait used for correlation\n- db (required) - DB name for the trait above (this is the Short_Abbreviation listed when you query for datasets)\n- target_db (required) - Target DB name to be correlated against\n- type - sample (default) | tissue\n- method - pearson (default) | spearman\n- return - Number of results to return (default = 500)\n\n\n```\njulia\u003e run_correlation(\"1427571_at\",\"HC_M2_0606_P\",\"BXDPublish\") |\u003e (x-\u003efirst(x,10))\n10×4 DataFrame\n Row │ #_strains  p_value      sample_r   trait  \n     │ Int64      Float64      Float64    String \n─────┼───────────────────────────────────────────\n   1 │         6  0.00480466   -0.942857  20511\n   2 │         6  0.00480466   -0.942857  20724\n   3 │        12  1.82889e-5   -0.923362  13536\n   4 │         7  0.00680719    0.892857  10157\n   5 │         7  0.00680719   -0.892857  20392\n   6 │         6  0.0188455     0.885714  20479\n   7 │        12  0.000189298  -0.875658  12762\n   8 │        12  0.000245942   0.868653  12760\n   9 │         7  0.0136973    -0.857143  20559\n  10 │        10  0.00222003   -0.842424  10925\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsenresearch%2Fgenenetworkapi.jl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsenresearch%2Fgenenetworkapi.jl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsenresearch%2Fgenenetworkapi.jl/lists"}