{
  "_id": "6a1edb7eb401979e7340f856",
  "Package": "snplinkage",
  "Title": "Single Nucleotide Polymorphisms Linkage Disequilibrium\nVisualizations",
  "Version": "1.2.0",
  "Authors@R": "c(\nperson(\"Thomas\", \"Charlon\", role = c(\"aut\", \"cre\"), email = \"charlon@protonmail.com\", comment = c(ORCID = \"0000-0001-7497-0470\")),\nperson(\"Karl\", \"Forner\", role = \"aut\"),\nperson(\"Alessandro\", \"Di Cara\", role = \"aut\"),\nperson(\"Jérôme\", \"Wojcik\", role = \"aut\"))",
  "Description": "Linkage disequilibrium visualizations of up to several\nhundreds of single nucleotide polymorphisms (SNPs), annotated\nwith chromosomic positions and gene names. Two types of plots\nare available for small numbers of SNPs (<40) and for large\nnumbers (tested up to 500). Both can be extended by combining\nother ggplots, e.g. association studies results, and functions\nenable to directly visualize the effect of SNP selection\nmethods, as minor allele frequency filtering and TagSNP\nselection, with a second correlation heatmap. The SNPs\ncorrelations are computed on Genotype Data objects from the\n'GWASTools' package using the 'SNPRelate' package, and the\nplots are customizable 'ggplot2' and 'gtable' objects and are\nannotated using the 'biomaRt' package. Usage is detailed in the\nvignette with example data and results from up to 500 SNPs of\n1,200 scans are in Charlon T. (2019)\n<doi:10.13097/archive-ouverte/unige:161795>.",
  "biocViews": "GeneticVariability, MicroArray, SNP",
  "URL": "https://gitlab.com/thomaschln/snplinkage",
  "BugReports": "https://gitlab.com/thomaschln/snplinkage/-/issues",
  "VignetteBuilder": "knitr",
  "License": "GPL-3",
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  "Config/pak/sysreqs": "cmake make libicu-dev libpng-dev libxml2-dev\nlibssl-dev libx11-dev zlib1g-dev",
  "Repository": "https://thomaschln.r-universe.dev",
  "Date/Publication": "2025-04-22 20:28:14 UTC",
  "RemoteUrl": "https://gitlab.com/thomaschln/snplinkage",
  "RemoteRef": "HEAD",
  "RemoteSha": "bd8604c9e266ac0f26feed832accaae047964862",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-20 08:46:14 UTC",
    "User": "root"
  },
  "Author": "Thomas Charlon [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-7497-0470>),\nKarl Forner [aut],\nAlessandro Di Cara [aut],\nJérôme Wojcik [aut]",
  "Maintainer": "Thomas Charlon <charlon@protonmail.com>",
  "MD5sum": "1d444a0a21a909df5d5451a78ea9f2fe",
  "_user": "thomaschln",
  "_type": "src",
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  "_created": "2026-05-20T08:46:14.000Z",
  "_published": "2026-06-02T13:32:46.610Z",
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  "_buildurl": "https://github.com/r-universe/thomaschln/actions/runs/26151473919",
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  "_commit": {
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    "author": "Thomas Charlon <charlon@protonmail.com>",
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    "linkedin": "in/thomas-charlon-meng-phd-aba0a3275",
    "orcid": "0000-0001-7497-0470",
    "description": "Harvard Medical School Researcher\nBiomedical Informatics @hms-dbmi\nCELEHS laboratory @celehs",
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  "_owner": "gitlab-thomaschln",
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  "_updates": [],
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  "_topics": [
    "geneticvariability",
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  "_homeurl": "https://gitlab.com/thomaschln/snplinkage",
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    "gdata_add_gene_annots",
    "gdata_add_gene_annots_aim_example",
    "gdata_add_gene_annots_hladr_example",
    "gdata_scans_annots",
    "gdata_snps_annots",
    "get_biomart_metadb",
    "ggplot_associations",
    "ggplot_ld",
    "ggplot_snp_pos",
    "gtable_ld",
    "gtable_ld_associations",
    "gtable_ld_associations_combine",
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    "gtable_ld_gdata",
    "gtable_ld_grobs",
    "load_gds_as_genotype_data",
    "parallel_apply",
    "print_qc_as_tex_table",
    "save_hgdp_as_gds",
    "select_region_idxs",
    "snprelate_allele_frequencies",
    "snprelate_ld",
    "snprelate_ld_select",
    "snprelate_qc"
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    {
      "page": "chisq_pvalues_gdata",
      "title": "Compute Chi-squared p-values on a Genotype data object",
      "topics": [
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    {
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      "title": "Crohn's disease data",
      "topics": [
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    {
      "page": "diamond_annots",
      "title": "Get diamond ggplot layer.",
      "topics": [
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      "title": "gdata_add_gene_annots_hladr_example",
      "topics": [
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      "page": "get_biomart_metadb",
      "title": "get_biomart_metadb",
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        "get_biomart_metadb"
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    },
    {
      "page": "get_scan_annot.GenotypeData",
      "title": "Get scans annotations (GenotypeData object)",
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    },
    {
      "page": "get_scan_annot.GenotypeDataSubset",
      "title": "Get scans annotations (GenotypeDataSubset object)",
      "topics": [
        "get_scan_annot.GenotypeDataSubset"
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    },
    {
      "page": "get_snp_annot.GenotypeData",
      "title": "Get SNPs annotations (GenotypeData object)",
      "topics": [
        "get_snp_annot.GenotypeData"
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    },
    {
      "page": "get_snp_annot.GenotypeDataSubset",
      "title": "Get SNPs annotations (GenotypeDataSubset object)",
      "topics": [
        "get_snp_annot.GenotypeDataSubset"
      ]
    },
    {
      "page": "ggplot_associations",
      "title": "Ggplot associations",
      "topics": [
        "ggplot_associations"
      ]
    },
    {
      "page": "ggplot_ld",
      "title": "Ggplot linkage disequilibrium",
      "topics": [
        "ggplot_ld"
      ]
    },
    {
      "page": "ggplot_snp_pos",
      "title": "Ggplot SNPs position",
      "topics": [
        "ggplot_snp_pos"
      ]
    },
    {
      "page": "gtable_ld",
      "title": "Gtable of linkage disequilibrium and chromosomic positions",
      "topics": [
        "gtable_ld"
      ]
    },
    {
      "page": "gtable_ld_associations",
      "title": "Gtable of linkage disequilibrium and associations",
      "topics": [
        "gtable_ld_associations"
      ]
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    {
      "page": "gtable_ld_associations_combine",
      "title": "Build gtable by combining ggplots",
      "topics": [
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    },
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