{
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  "Package": "SEAGLE",
  "Type": "Package",
  "Title": "Scalable Exact Algorithm for Large-Scale Set-Based\nGene-Environment Interaction Tests",
  "Version": "1.0.1",
  "Authors@R": "c(person(\"Jocelyn\", \"Chi\", email = \"jocetchi@gmail.com\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Ilse\", \"Ipsen\", email = \"ipsen@ncsu.edu\",\nrole = c(\"aut\")),\nperson(\"Jung-Ying\", \"Tzeng\", email = \"jytzeng@ncsu.edu\",\nrole = c(\"aut\"))\n)",
  "Description": "The explosion of biobank data offers immediate\nopportunities for gene-environment (GxE) interaction studies of\ncomplex diseases because of the large sample sizes and rich\ncollection in genetic and non-genetic information. However, the\nextremely large sample size also introduces new computational\nchallenges in GxE assessment, especially for set-based GxE\nvariance component (VC) tests, a widely used strategy to boost\noverall GxE signals and to evaluate the joint GxE effect of\nmultiple variants from a biologically meaningful unit (e.g.,\ngene). We present 'SEAGLE', a Scalable Exact AlGorithm for\nLarge-scale Set-based GxE tests, to permit GxE VC test scalable\nto biobank data. 'SEAGLE' employs modern matrix computations to\nachieve the same “exact” results as the original GxE VC tests,\nand does not impose additional assumptions nor relies on\napproximations. 'SEAGLE' can easily accommodate sample sizes in\nthe order of 10^5, is implementable on standard laptops, and\ndoes not require specialized equipment. The accompanying\nmanuscript for this package can be found at Chi, Ipsen, Hsiao,\nLin, Wang, Lee, Lu, and Tzeng. (2021+) <arXiv:2105.03228>.",
  "URL": "https://github.com/jocelynchi/SEAGLE",
  "License": "GPL-3",
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  "Repository": "https://jocelynchi.r-universe.dev",
  "Date/Publication": "2021-11-05 21:29:54 UTC",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-13 06:16:07 UTC",
    "User": "root"
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  "Author": "Jocelyn Chi [aut, cre],\nIlse Ipsen [aut],\nJung-Ying Tzeng [aut]",
  "Maintainer": "Jocelyn Chi <jocetchi@gmail.com>",
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  "_published": "2026-05-22T10:13:32.493Z",
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      "title": "Synthetic haplotype data generated from COSI software",
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      "page": "applyAt",
      "title": "Function for applying t(A) on the left for REML EM",
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      ]
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      "page": "cosihap",
      "title": "Synthetic haplotype data generated from COSI software",
      "topics": [
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      ]
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      "title": "REML EM Algorithm",
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      ]
    },
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      "title": "Generate synthetic data according to a fixed effects model",
      "topics": [
        "makeSimData"
      ]
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      "title": "Prepare data for input into SEAGLE function",
      "topics": [
        "prep.SEAGLE"
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    },
    {
      "page": "Rinv.AtG",
      "title": "Function for applying R inverse to AtG in REML EM algorithm",
      "topics": [
        "Rinv.AtG"
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    },
    {
      "page": "Rinv.u",
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      "topics": [
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    },
    {
      "page": "SEAGLE",
      "title": "Compute score-like test statistic and p-value for GxE test with SEAGLE algorithm",
      "topics": [
        "SEAGLE"
      ]
    },
    {
      "page": "Vinv",
      "title": "Function for applying V inverse in Algorithm 1",
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      "title": "Example 1: Using SEAGLE with .txt Input Files",
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      "headings": [],
      "created": "2021-05-18 00:34:40",
      "modified": "2021-05-27 17:49:04",
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    },
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      "filename": "example2.html",
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      "headings": [],
      "created": "2021-05-18 00:34:40",
      "modified": "2021-05-27 17:49:04",
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    },
    {
      "source": "example3.Rmd",
      "filename": "example3.html",
      "title": "Example 3: Using SEAGLE with GWAS or Next Generation Sequencing Data",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Preparing GWAS Data with PLINK1.9",
        "Preparing Next Generation Sequencing Data with PLINK1.9",
        "Loading .raw files into R and extracting the genetic marker matrix ${\\bf G}$",
        "Running SEAGLE",
        "Acknowledgments"
      ],
      "created": "2021-05-20 01:42:02",
      "modified": "2021-05-27 17:49:04",
      "commits": 4
    },
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      "filename": "example4.html",
      "title": "Example 4: Using SEAGLE for Chromosome-Wide Gene-Based Analysis",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Writing .raw files for each gene in chromosome 22 with PLINK1.9",
        "Loading .raw files into R and extracting the genetic marker matrix ${\\bf G}$",
        "Acknowledgments"
      ],
      "created": "2021-05-20 01:42:02",
      "modified": "2021-08-23 17:57:40",
      "commits": 8
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