# Population reference dataset GDS files

Package: RAIDS
Authors: Pascal Belleau [cre, aut] (ORCID: https://orcid.org/0000-0002-0802-1071), Astrid Deschênes [aut] (ORCID: https://orcid.org/0000-0001-7846-6749), David A. Tuveson [aut] (ORCID: https://orcid.org/0000-0002-8017-2712), Alexander Krasnitz [aut]
Version: 1.9.0
Compiled date: 2026-02-08
License: Apache License (>= 2)

This vignette explains, in further details, the format of the population reference files that are required to run the ancestry inference tool.

Two different files are generated from a reference dataset:

- The Population Reference GDS File
- The Population Reference SNV Annotation GDS file

# Population Reference GDS File

The Population Reference GDS file should contain the genome-wide SNV information related to the population data set with known genetic ancestry. This reference data set will be used to generate the simulated samples. It is also used to generate the PCA on which the samples of interest are going to be projected.

The Population Reference GDS file is a GDS object of class SNPGDSFileClass from SNPRelate package (Zheng et al. 2012).

Beware that related profiles should be flagged in the Population Reference GDS file files.

    #############################################################################
    ## Load required packages
    #############################################################################
    library(RAIDS)
    library(SNPRelate)

    pathRef <- system.file("extdata/", package="RAIDS")

    fileReferenceGDS <- file.path(pathRef, "PopulationReferenceDemo.gds")

    gdsRef <- snpgdsOpen(fileReferenceGDS)

    ## Show the file format
    print(gdsRef)
    ## File: /tmp/RtmpahXo1A/Rinst1eab54082d81/RAIDS/extdata/PopulationReferenceDemo.gds (3.2K)
    ## +    [  ]
    ## |--+ sample.id   { Str8 10, 80B }
    ## |--+ sample.annot   [ data.frame ] *
    ## |  |--+ sex   { Str8 10, 20B }
    ## |  |--+ pop.group   { Str8 10, 40B }
    ## |  |--+ superPop   { Str8 10, 40B }
    ## |  \--+ batch   { Float64 10, 80B }
    ## |--+ snp.id   { Str8 7, 21B }
    ## |--+ snp.chromosome   { UInt16 7, 14B }
    ## |--+ snp.position   { Int32 7, 28B }
    ## |--+ snp.allele   { Str8 7, 28B }
    ## |--+ snp.AF   { PackedReal24 7, 21B }
    ## |--+ snp.EAS_AF   { PackedReal24 7, 21B }
    ## |--+ snp.EUR_AF   { PackedReal24 7, 21B }
    ## |--+ snp.AFR_AF   { PackedReal24 7, 21B }
    ## |--+ snp.AMR_AF   { PackedReal24 7, 21B }
    ## |--+ snp.SAS_AF   { PackedReal24 7, 21B }
    ## |--+ genotype   { Bit2 7x10, 18B }
    ## \--+ sample.ref   { Bit1 10, 2B }

    closefn.gds(gdsRef)

This output lists all variables stored in the Population Reference GDS file. At the first level, it stores variables sample.id, snp.id, etc. The additional information displayed in the braces indicate the data type, size, compressed or not with compression ratio.

The mandatory fields are:

- sample.id: a character string (saved in Str8 format) used as unique identifier for each sample
- sample.annot: a data.frame where each row correspond to a sample and containing those columns:
  - sex: a character string (saved in Str8 format) used as identifier of the sex of the sample
  - pop.Group: a character string (saved in Str8 format) representing the sub-population ancestry of the sample (ex:GBR, etc)
  - superPop: a character string (saved in Str8 format) representing the super-population ancestry of the sample (ex:EUR, AFR, EAS, SAS, AMR)
  - batch: an integer (saved in Float64 format) representing the batch of provenance of the sample
- snp.id: a a character string (saved in Str8 format) used as unique identifier for each SNV
- snp.chromosome: an integer or character (saved in UInt16 format) mapping for each chromosome. Integer: numeric values 1-26, mapped in order from 1-22, 23=X, 24=XY (the pseudoautosomal region), 25=Y, 26=M (the mitochondrial probes), and 0 for probes with unknown positions; it does not allow NA. Character: “X”, “XY”, “Y” and “M” can be used here, and a blank string indicating unknown position
- snp.position: an integer (saved in Int32 format) representing the base position of each SNV on the chromosome, and 0 for unknown position; it does not allow NA.
- snp.allele: a character string (saved as Str8 format) representing the reference allele and alternative allele for each of the SNVs present in the snp.id field
- snp.AF: a numeric value between 0 and 1 (saved as PackedReal24 format) representing the allelic frequency of the alternative allele in the general population for each of the SNVs present in the snp.id field
- snp.EAS_AF: a numeric value between 0 and 1 (saved as PackedReal24 format) representing the allelic frequency of the alternative allele in the East Asian population for each of the SNVs present in the snp.id field
- snp.EUR_AF: a numeric value between 0 and 1 (saved as PackedReal24 format) representing the allelic frequency of the alternative allele in the European population for each of the SNVs present in the snp.id field
- snp.AFR_AF: a numeric value between 0 and 1 (saved as PackedReal24 format) representing the allelic frequency of the alternative allele in the African population for each of the SNVs present in the snp.id field
- snp.AMR_AF: a numeric value between 0 and 1 (saved as PackedReal24 format) representing the allelic frequency of the alternative allele in the American population for each of the SNVs present in the snp.id field
- snp.SAS_AF: a numeric value between 0 and 1 (saved as PackedReal24 format) representing the allelic frequency of the alternative allele in the South Asian population for each of the SNVs present in the snp.id field
- genotype: a SNV genotypic matrix of integer values (saved in Bit2 format) (i.e., the number of A alleles) with SNVs as rows and samples as columns (number of SNVs × number of Samples)
- sample.ref: an integer (saved in Bit1 format) indicating if the sample is retained to be used as reference (=1) or removed (=0) as related samples have to be discarded

This following example shows how to create a Population GDS Reference file. This example is for demonstration purpose only and use hard coded values. A working Population GDS Reference file would have to contain multiple samples from each continental population and would also have to contain the SNVs from the entire genome.

To generate a real Population GDS Reference file, the pipeline to process the information would depend of the selected source. If the source files are in VCF format, you can use Bioconductor VariationAnnotation package to extract the genotypic information (beware it may use a lot of memory). Often, you will need to parse metadata files to get information such as the sex and population of the profiles. In addition, the Bioconductor GENESIS package can be used to compute kinship coefficients to identify the unrelated profiles.

    #############################################################################
    ## Load required packages
    #############################################################################
    library(RAIDS)
    library(SNPRelate)
    library(gdsfmt)

    ## Create a temporary GDS Reference file in the temporary directory
    fileNewReferenceGDS <- file.path(tempdir(), "reference_DEMO.gds")

    gdsRefNew <- createfn.gds(fileNewReferenceGDS)

    ## The entry 'sample.id' contain the unique identifiers of 10 samples
    ## that constitute the reference dataset
    sample.id <- c("HG00243", "HG00150", "HG00149", "HG00246", "HG00138",
                        "HG01334", "HG00268", "HG00275", "HG00290", "HG00364")
    add.gdsn(node=gdsRefNew, name="sample.id", val=sample.id,
                storage="string", check=TRUE)

    ## A data frame containing the information about the 10 samples
    ## (in the same order than in the 'sample.id') is created and added to
    ## the 'sample.annot' entry
    ## The data frame must contain those columns:
    ##     'sex': '1'=male, '2'=female
    ##     'pop.group': acronym for the population (ex: GBR, CDX, MSL, ASW, etc..)
    ##     'superPop': acronym for the super-population (ex: AFR, EUR, etc...)
    ##     'batch': number identifying the batch of provenance
    sampleInformation <- data.frame(sex=c("1", "2", "1", "1", "1",
            "1", "2", "2", "1", "2"), pop.group=c(rep("GBR", 6), rep("FIN", 4)),
            superPop=c(rep("EUR", 10)), batch=rep(0, 10), stringsAsFactors=FALSE)
    add.gdsn(node=gdsRefNew, name="sample.annot", val=sampleInformation,
                check=TRUE)

    ## The identifier of each SNV is added in the 'snp.id' entry
    snvID <- c("s29603", "s29605", "s29633", "s29634", "s29635", "s29637",
                "s29638", "s29663", "s29664", "s29666", "s29667", "s29686",
                "s29687", "s29711", "s29741", "s29742", "s29746", "s29750",
                "s29751", "s29753")
    add.gdsn(node=gdsRefNew, name="snp.id", val=snvID, check=TRUE)

    ## The chromosome of each SNV is added to the 'snp.chromosome' entry
    ## The order of the SNVs is the same than in the 'snp.id' entry
    snvChrom <- c(rep(1, 20))
    add.gdsn(node=gdsRefNew, name="snp.chromosome", val=snvChrom, storage="uint16",
                check=TRUE)

    ## The position on the chromosome of each SNV is added to
    ## the 'snp.position' entry
    ## The order of the SNVs is the same than in the 'snp.id' entry
    snvPos <- c(3467333, 3467428, 3469375, 3469387, 3469502, 3469527,
                        3469737, 3471497, 3471565, 3471618)
    add.gdsn(node=gdsRefNew, name="snp.position", val=snvPos, storage="int32",
                check=TRUE)

    ## The allele information of each SNV is added to the 'snp.allele' entry
    ## The order of the SNVs is the same than in the 'snp.allele' entry
    snvAllele <- c("A/G", "C/G", "C/T", "C/T", "T/G", "C/T",
                        "G/A", "A/G", "G/A", "G/A")
    add.gdsn(node=gdsRefNew, name="snp.allele", val=snvAllele, storage="string",
                check=TRUE)

    ## The allele frequency in the general population (between 0 and 1) of each
    ## SNV is added to the 'snp.AF' entry
    ## The order of the SNVs is the same than in the 'snp.id' entry
    snvAF <- c(0.86, 0.01, 0.00, 0.00, 0.01, 0.00, 0.00, 0.00, 0.00, 0.01)
    add.gdsn(node=gdsRefNew, name="snp.AF", val=snvAF, storage="packedreal24",
                check=TRUE)

    ## The allele frequency in the East Asian population (between 0 and 1) of each
    ## SNV is added to the 'snp.EAS_AF' entry
    ## The order of the SNVs is the same than in the 'snp.id' entry
    snvAF <- c(0.80, 0.00, 0.00, 0.01, 0.00, 0.00, 0.01, 0.00, 0.02, 0.00)
    add.gdsn(node=gdsRefNew, name="snp.EAS_AF", val=snvAF, storage="packedreal24",
                check=TRUE)

    ## The allele frequency in the European population (between 0 and 1) of each
    ## SNV is added to the 'snp.EUR_AF' entry
    ## The order of the SNVs is the same than in the 'snp.id' entry
    snvAF <- c(0.91, 0.00, 0.01, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.03)
    add.gdsn(node=gdsRefNew, name="snp.EUR_AF", val=snvAF, storage="packedreal24",
                check=TRUE)

    ## The allele frequency in the African population (between 0 and 1) of each
    ## SNV is added to the 'snp.AFR_AF' entry
    ## The order of the SNVs is the same than in the 'snp.id' entry
    snvAF <- c(0.85, 0.04, 0.00, 0.00, 0.00, 0.01, 0.00, 0.00, 0.00, 0.00)
    add.gdsn(node=gdsRefNew, name="snp.AFR_AF", val=snvAF, storage="packedreal24",
                check=TRUE)

    ## The allele frequency in the American population (between 0 and 1) of each
    ## SNV is added to the 'snp.AMR_AF' entry
    ## The order of the SNVs is the same than in the 'snp.id' entry
    snvAF <- c(0.83, 0.01, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02)
    add.gdsn(node=gdsRefNew, name="snp.AMR_AF", val=snvAF, storage="packedreal24",
                check=TRUE)

    ## The allele frequency in the South Asian population (between 0 and 1) of each
    ## SNV is added to the 'snp.SAS_AF' entry
    ## The order of the SNVs is the same than in the 'snp.id' entry
    snvAF <- c(0.89, 0.00, 0.00, 0.00, 0.05, 0.00, 0.00, 0.01, 0.00, 0.00)
    add.gdsn(node=gdsRefNew, name="snp.SAS_AF", val=snvAF, storage="packedreal24",
                check=TRUE)

    ## The genotype of each SNV for each sample is added to the 'genotype' entry
    ## The genotype correspond to the number of A alleles
    ## The rows represent the SNVs is the same order than in 'snp.id' entry
    ## The columns represent the samples is the same order than in 'sample.id' entry
    genotypeInfo <- matrix(data=c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
                            0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
                            0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                            0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                            0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                            0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                            0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                            0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                            0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                            0, 0, 0, 0, 0, 0, 0, 0, 0, 0), nrow=10, byrow=TRUE)
    add.gdsn(node=gdsRefNew, name="genotype", val=genotypeInfo,
                storage="bit2", check=TRUE)

    ## The entry 'sample.ref' is filled with 1 indicating that all 10
    ## samples are retained to be used as reference
    ## The order of the samples is the same than in the 'sample.id' entry
    add.gdsn(node=gdsRefNew, name="sample.ref", val=rep(1L, 10),
                storage="bit1", check=TRUE)

    ## Show the file format
    print(gdsRefNew)
    ## File: /tmp/RtmpEAgRrM/reference_DEMO.gds (1.6K)
    ## +    [  ]
    ## |--+ sample.id   { Str8 10, 80B }
    ## |--+ sample.annot   [ data.frame ] *
    ## |  |--+ sex   { Str8 10, 20B }
    ## |  |--+ pop.group   { Str8 10, 40B }
    ## |  |--+ superPop   { Str8 10, 40B }
    ## |  \--+ batch   { Float64 10, 80B }
    ## |--+ snp.id   { Str8 20, 140B }
    ## |--+ snp.chromosome   { UInt16 20, 40B }
    ## |--+ snp.position   { Int32 10, 40B }
    ## |--+ snp.allele   { Str8 10, 40B }
    ## |--+ snp.AF   { PackedReal24 10, 30B }
    ## |--+ snp.EAS_AF   { PackedReal24 10, 30B }
    ## |--+ snp.EUR_AF   { PackedReal24 10, 30B }
    ## |--+ snp.AFR_AF   { PackedReal24 10, 30B }
    ## |--+ snp.AMR_AF   { PackedReal24 10, 30B }
    ## |--+ snp.SAS_AF   { PackedReal24 10, 30B }
    ## |--+ genotype   { Bit2 10x10, 25B }
    ## \--+ sample.ref   { Bit1 10, 2B }

    closefn.gds(gdsRefNew)

    unlink(fileNewReferenceGDS, force=TRUE)

# Population Reference Annotation GDS file

The Population Reference Annotation GDS file contains phase information and block group information for all the SNVs present in Population Reference GDS file.
If the source files are in VCF format, you can use Bioconductor VariationAnnotation package to extract the phase information (beware it may use a lot of memory). A block can be a linkage disequelibrium block relative to a population or a gene. A bioconductor package like GENESIS can be used to get the block information.

    #############################################################################
    ## Load required packages
    #############################################################################
    library(RAIDS)
    library(SNPRelate)

    pathReference <- system.file("extdata/tests", package="RAIDS")

    fileReferenceAnnotGDS <- file.path(pathReference, "ex1_good_small_1KG.gds")

    gdsRefAnnot <- openfn.gds(fileReferenceAnnotGDS)

    ## Show the file format
    print(gdsRefAnnot)
    ## File: /tmp/RtmpahXo1A/Rinst1eab54082d81/RAIDS/extdata/tests/ex1_good_small_1KG.gds (830.0K)
    ## +    [  ] *
    ## |--+ sample.id   { Str8 156, 1.2K }
    ## |--+ sample.annot   [ data.frame ] *
    ## |  |--+ sex   { Str8 156, 312B }
    ## |  |--+ pop.group   { Str8 156, 624B }
    ## |  |--+ superPop   { Str8 156, 624B }
    ## |  \--+ batch   { Float64 156, 1.2K }
    ## |--+ snp.id   { Str8 11000, 103.5K }
    ## |--+ snp.chromosome   { UInt16 11000, 21.5K }
    ## |--+ snp.position   { Int32 11000, 43.0K }
    ## |--+ snp.allele   { Str8 11000, 43.0K }
    ## |--+ snp.AF   { PackedReal24 11000, 32.2K }
    ## |--+ snp.EAS_AF   { PackedReal24 11000, 32.2K }
    ## |--+ snp.EUR_AF   { PackedReal24 11000, 32.2K }
    ## |--+ snp.AFR_AF   { PackedReal24 11000, 32.2K }
    ## |--+ snp.AMR_AF   { PackedReal24 11000, 32.2K }
    ## |--+ snp.SAS_AF   { PackedReal24 11000, 32.2K }
    ## |--+ genotype   { Bit2 11000x156, 418.9K }
    ## \--+ sample.ref   { Bit1 156, 20B }

    closefn.gds(gdsRefAnnot)

This output lists all variables stored in the Population Reference Annotation GDS file. At the first level, it stores variables phase, block.annot, etc. The additional information displayed in the braces indicate the data type, size, compressed or not + compression ratio.

The mandatory fields are:

- phase: a integer (saved in Bit2 format) representing the phase of the SNVs in the Population Annotation GDS file; 0 means the first allele is a reference; 1 means the first allele is the alternative and 3 means unknown. The first allele combine with the genotype of the variant determine the phase for a biallelic variant. The SNVs (rows) and samples (columns) in phase are in the same order as in the Population Annotation GDS file.
- block.annot: a data.frame containing those columns:
  - block.id: a character string (saved in Str8 format) representing an identifier of block group. A block can be linkage disequilibrium block relative to a population or a gene.
  - block.desc: a character string (saved in Str8 format) describing the block group.
- bloc: a matrix of integer values (saved in Int32 format) where each row representing a SNV in the Population Annotation GDS file in the same order. The columns are the block groups described in block.annot. Each integer in the matrix representing a specific block.

This following example shows how to create a Population Reference Annotation GDS file. This example is for demonstration purpose only. A working Population Reference Annotation GDS file would have to contain multiple samples from each continental population and would also have to contain the SNVs from the entire genome.

    #############################################################################
    ## Load required packages
    #############################################################################
    library(RAIDS)
    library(gdsfmt)

    ## Create a temporary GDS Reference file in the temporary directory
    fileNewReferenceAnnotGDS <-
            file.path(tempdir(), "reference_SNV_Annotation_DEMO.gds")

    gdsRefAnnotNew <- createfn.gds(fileNewReferenceAnnotGDS)

    ## The entry 'phase' contain the phase of the SNVs in the
    ## Population Annotation GDS file
    ## 0 means the first allele is a reference; 1 means the first allele is
    ## the alternative and 3 means unknown
    ## The SNVs (rows) and samples (columns) in phase are in the same order as
    ## in the Population Annotation GDS file.
    phase <- matrix(data=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                            1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
                            0, 0, 0, 1, 1, 0, 0, 0, 1, 1,
                            0, 0, 0, 1, 1, 0, 0, 0, 0, 1,
                            1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
                            0, 1, 0, 1, 1, 0, 1, 1, 1, 1,
                            0, 1, 0, 1, 1, 0, 1, 1, 1, 1,
                            0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
                            0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                            0, 1, 0, 1, 1, 0, 1, 1, 1, 1), ncol=10, byrow=TRUE)
    add.gdsn(node=gdsRefAnnotNew, name="phase", val=phase, storage="bit2",
                    check=TRUE)

    ## The entry 'blockAnnot' contain the information for each group of blocks
    ## that are present in the 'block' entry.
    blockAnnot <- data.frame(block.id=c("EAS.0.05.500k", "EUR.0.05.500k",
                        "AFR.0.05.500k", "AMR.0.05.500k", "SAS.0.05.500k"),
                    block.desc=c(
                        "EAS populationblock base on SNP 0.05 and windows 500k",
                        "EUR populationblock base on SNP 0.05 and windows 500k",
                        "AFR populationblock base on SNP 0.05 and windows 500k",
                        "AMR populationblock base on SNP 0.05 and windows 500k",
                        "SAS populationblock base on SNP 0.05 and windows 500k"),
                    stringsAsFactors=FALSE)
    add.gdsn(node=gdsRefAnnotNew, name="block.annot", val=blockAnnot, check=TRUE)

    ## The entry 'block' contain the block information for the SNVs in the
    ## Population Annotation GDS file.
    ## The SNVs (rows) are in the same order as in
    ## the Population Annotation GDS file.
    ## The block groups (columns) are in the same order as in
    ## the 'block.annot' entry.
    block <- matrix(data=c(-1, -1, -1, -1, -1,
                            -2, -2,  1, -2, -2,
                            -2,  1,  1,  1, -2,
                            -2,  1,  1,  1, -2,
                            -2, -3, -2, -3, -2,
                             1,  2,  2,  2,  1,
                             1,  2,  2,  2,  1,
                            -3, -4, -3, -4, -3,
                             2, -4,  3, -4, -3,
                             2, -4,  3, -4, -3), ncol=5, byrow=TRUE)
    add.gdsn(node=gdsRefAnnotNew, name="block", val=block, storage="int32",
                check=TRUE)

    ## Show the file format
    print(gdsRefAnnotNew)
    ## File: /tmp/RtmpEAgRrM/reference_SNV_Annotation_DEMO.gds (427B)
    ## +    [  ]
    ## |--+ phase   { Bit2 10x10, 25B }
    ## |--+ block.annot   [ data.frame ] *
    ## |  |--+ block.id   { Str8 5, 70B }
    ## |  \--+ block.desc   { Str8 5, 270B }
    ## \--+ block   { Int32 10x5, 200B }

    closefn.gds(gdsRefAnnotNew)

    unlink(fileNewReferenceAnnotGDS, force=TRUE)

# Pre-processed files, from 1000 Genomes in hg38, are available

Pre-processed files used in the RAIDS associated publication, are available at this address:

https://labshare.cshl.edu/shares/krasnitzlab/aicsPaper

Beware that some of those files are voluminous.

# Session info

Here is the output of `sessionInfo()` on the system on which this document was compiled:

    ## R version 4.5.2 (2025-10-31)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## time zone: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] RAIDS_1.9.0          Rsamtools_2.27.0     Biostrings_2.79.4
    ##  [4] XVector_0.51.0       GenomicRanges_1.63.1 IRanges_2.45.0
    ##  [7] S4Vectors_0.49.0     Seqinfo_1.1.0        BiocGenerics_0.57.0
    ## [10] generics_0.1.4       dplyr_1.2.0          GENESIS_2.41.0
    ## [13] SNPRelate_1.45.0     gdsfmt_1.47.0        knitr_1.51
    ## [16] BiocStyle_2.39.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] RColorBrewer_1.1-3          sys_3.4.3
    ##   [3] jsonlite_2.0.0              shape_1.4.6.1
    ##   [5] magrittr_2.0.4              jomo_2.7-6
    ##   [7] GenomicFeatures_1.63.1      farver_2.1.2
    ##   [9] logistf_1.26.1              nloptr_2.2.1
    ##  [11] rmarkdown_2.30              BiocIO_1.21.0
    ##  [13] vctrs_0.7.1                 memoise_2.0.1
    ##  [15] minqa_1.2.8                 RCurl_1.98-1.17
    ##  [17] quantsmooth_1.77.0          htmltools_0.5.9
    ##  [19] S4Arrays_1.11.1             curl_7.0.0
    ##  [21] broom_1.0.12                pROC_1.19.0.1
    ##  [23] SparseArray_1.11.10         mitml_0.4-5
    ##  [25] sass_0.4.10                 bslib_0.10.0
    ##  [27] sandwich_3.1-1              zoo_1.8-15
    ##  [29] cachem_1.1.0                buildtools_1.0.0
    ##  [31] GenomicAlignments_1.47.0    lifecycle_1.0.5
    ##  [33] iterators_1.0.14            pkgconfig_2.0.3
    ##  [35] Matrix_1.7-4                R6_2.6.1
    ##  [37] fastmap_1.2.0               rbibutils_2.4.1
    ##  [39] MatrixGenerics_1.23.0       digest_0.6.39
    ##  [41] GWASTools_1.57.0            AnnotationDbi_1.73.0
    ##  [43] RSQLite_2.4.6               httr_1.4.7
    ##  [45] abind_1.4-8                 mgcv_1.9-4
    ##  [47] compiler_4.5.2              bit64_4.6.0-1
    ##  [49] S7_0.2.1                    backports_1.5.0
    ##  [51] BiocParallel_1.45.0         DBI_1.2.3
    ##  [53] pan_1.9                     MASS_7.3-65
    ##  [55] quantreg_6.1                DelayedArray_0.37.0
    ##  [57] rjson_0.2.23                DNAcopy_1.85.0
    ##  [59] tools_4.5.2                 lmtest_0.9-40
    ##  [61] nnet_7.3-20                 glue_1.8.0
    ##  [63] restfulr_0.0.16             nlme_3.1-168
    ##  [65] grid_4.5.2                  operator.tools_1.6.3.1
    ##  [67] gtable_0.3.6                BSgenome_1.79.1
    ##  [69] formula.tools_1.7.1         class_7.3-23
    ##  [71] tidyr_1.3.2                 ensembldb_2.35.0
    ##  [73] data.table_1.18.2.1         GWASExactHW_1.2
    ##  [75] stringr_1.6.0               foreach_1.5.2
    ##  [77] pillar_1.11.1               splines_4.5.2
    ##  [79] lattice_0.22-7              survival_3.8-6
    ##  [81] rtracklayer_1.71.3          bit_4.6.0
    ##  [83] SparseM_1.84-2              tidyselect_1.2.1
    ##  [85] SeqVarTools_1.49.0          maketools_1.3.2
    ##  [87] reformulas_0.4.4            ProtGenerics_1.43.0
    ##  [89] SummarizedExperiment_1.41.1 RhpcBLASctl_0.23-42
    ##  [91] xfun_0.56                   Biobase_2.71.0
    ##  [93] matrixStats_1.5.0           stringi_1.8.7
    ##  [95] UCSC.utils_1.7.1            lazyeval_0.2.2
    ##  [97] yaml_2.3.12                 boot_1.3-32
    ##  [99] evaluate_1.0.5              codetools_0.2-20
    ## [101] cigarillo_1.1.0             tibble_3.3.1
    ## [103] BiocManager_1.30.27         cli_3.6.5
    ## [105] rpart_4.1.24                Rdpack_2.6.5
    ## [107] jquerylib_0.1.4             Rcpp_1.1.1
    ## [109] GenomeInfoDb_1.47.2         png_0.1-8
    ## [111] XML_3.99-0.20               parallel_4.5.2
    ## [113] MatrixModels_0.5-4          ggplot2_4.0.2
    ## [115] blob_1.3.0                  AnnotationFilter_1.35.0
    ## [117] bitops_1.0-9                lme4_1.1-38
    ## [119] glmnet_4.1-10               SeqArray_1.51.1
    ## [121] VariantAnnotation_1.57.1    scales_1.4.0
    ## [123] purrr_1.2.1                 crayon_1.5.3
    ## [125] rlang_1.1.7                 KEGGREST_1.51.1
    ## [127] mice_3.19.0

# References

Zheng, Xiuwen, David Levine, Jess Shen, Stephanie M. Gogarten, Cathy Laurie, and Bruce S. Weir. 2012. “A high-performance computing toolset for relatedness and principal component analysis of SNP data.” Bioinformatics 28 (24): 3326–28. https://doi.org/10.1093/bioinformatics/bts606.