{"Package":"popkin","Title":"Estimate Kinship and FST under Arbitrary Population Structure","Version":"1.3.23","Authors@R":"c(\nperson(given = \"Alejandro\",\nfamily = \"Ochoa\",\nrole = c(\"aut\", \"cre\"),\nemail = \"alejandro.ochoa@duke.edu\",\ncomment = c(ORCID = \"0000-0003-4928-3403\")),\nperson(given = \"John D.\",\nfamily = \"Storey\",\nrole = c(\"aut\"),\nemail = \"jstorey@princeton.edu\",\ncomment = c(ORCID = \"0000-0001-5992-402X\"))\n)","Description":"Provides functions to estimate the kinship matrix of individuals from a large set of biallelic SNPs, and extract inbreeding coefficients and the generalized FST (Wright's fixation index). Method described in Ochoa and Storey (2021) .","LazyData":"true","Depends":"R (>= 3.5.0)","Imports":"RColorBrewer, graphics, grDevices, ape","Suggests":"BEDMatrix, testthat, knitr, rmarkdown, bnpsd","License":"GPL-3","Encoding":"UTF-8","RoxygenNote":"7.2.3","VignetteBuilder":"knitr","URL":"https://github.com/StoreyLab/popkin/","BugReports":"https://github.com/StoreyLab/popkin/issues","NeedsCompilation":"no","Packaged":"2023-01-06 23:37:19 UTC; viiia","Author":"Alejandro Ochoa [aut, cre] (),\nJohn D. Storey [aut] ()","Maintainer":"Alejandro Ochoa ","Repository":"CRAN","Date/Publication":"2023-01-07 00:30:02 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/popkin_1.3.23.tar.gz","jsonAuthors":[{"name":"Alejandro Ochoa","email":"alejandro.ochoa@duke.edu","maintainer":true},{"name":"John D. Storey","email":"jstorey@princeton.edu","maintainer":false}],"readme":"# popkin $\"popkin\"$ \n\nThe `popkin` (\"population kinship\") R package estimates the kinship matrix of individuals and FST from their biallelic genotypes.\nOur estimation framework is the first to be practically unbiased under arbitrary population structures.\n\n## Installation\n\nThe stable version of the package is now on CRAN and can be installed using\n```R\ninstall.packages(\"popkin\")\n```\n\nThe current development version can be installed from the GitHub repository using `devtools`:\n```R\ninstall.packages(\"devtools\") # if needed\nlibrary(devtools)\ninstall_github('StoreyLab/popkin', build_vignettes = TRUE)\n```\n\nYou can see the package vignette, which has more detailed documentation, by typing this into your R session:\n```R\nvignette('popkin')\n```\n\n\n## Examples\n\n### Input data\n\nThe examples below assume the following R data variables are present for `n` individuals and `m` loci:\n\n* The `m`-by-`n` genotype matrix `X`, containing only unphased biallelic variants encoded as 0,1,2 counting a given reference allele per locus.\n* The length-`n` vector `subpops` that assigns each individual to a subpopulation.\n\nThe `subpops` vector is not required, but its use is recommended to improve estimation of the baseline kinship value treated as zero.\n\nIf your data is in BED format, `popkin` will process it efficiently using `BEDMatrix`.\nIf `file` is the path to the BED file (excluding .bed extension):\n```R\nlibrary(BEDMatrix)\nX <- BEDMatrix(file) # load genotype matrix object\n```\n\n### `popkin` functions\n\nThis is a quick overview of every `popkin` function, covering estimation and visualization of kinship and FST from a genotype matrix.\n\nFirst estimate the `kinship` matrix from the genotypes `X`.\nAll downstream analysis require `kinship`, none use `X` after this\n```R\nlibrary(popkin)\nkinship <- popkin(X, subpops) # calculate kinship from X and optional subpop labels\n```\n\nPlot the kinship matrix, marking the subpopulations.\nNote `inbr_diag` replaces the diagonal of `kinship` with inbreeding coefficients\n```R\nplot_popkin( inbr_diag(kinship), labs = subpops )\n```\n\nExtract inbreeding coefficients from `kinship`\n```R\ninbreeding <- inbr(kinship)\n```\n\nEstimate FST\n```R\nweights <- weights_subpops(subpops) # weigh individuals so subpopulations are balanced\nFst <- fst(kinship, weights) # use kinship matrix and weights to calculate fst\nFst <- fst(inbreeding, weights) # estimate more directly from inbreeding vector (same result)\n```\n\nEstimate and visualize the pairwise FST matrix\n```R\npairwise_fst <- pwfst(kinship) # estimated matrix\nleg_title <- expression(paste('Pairwise ', F[ST])) # fancy legend label\nplot_popkin(pairwise_fst, labs = subpops, leg_title = leg_title) # NOTE no need for inbr_diag() here!\n```\n\nRescale the kinship matrix using different subpopulations (implicitly changes the most recent common ancestor population used as reference)\n```R\nkinship2 <- rescale_popkin(kinship, subpops2)\n```\n\nEstimate the coancestry matrix from a matrix of allele frequencies `P` (useful when `P` comes from an admixture inference model)\n```R\ncoancestry <- popkin_af( P )\n```\n\nPlease see the `popkin` R vignette for a description of the key parameters and more detailed examples, including complex plots with multiple kinship matrices and multi-level subpopulation labeling.\n\n\n## Citations\n\nAlejandro Ochoa, John D Storey. 2021. \"Estimating FST and kinship for arbitrary population structures.\" PLoS Genet 17(1): e1009241. PubMed ID 33465078. [doi:10.1371/journal.pgen.1009241](https://doi.org/10.1371/journal.pgen.1009241). bioRxiv [doi:10.1101/083923](https://doi.org/10.1101/083923) 2016-10-27.\n\nAlejandro Ochoa, John D Storey. 2019. \"New kinship and FST estimates reveal higher levels of differentiation in the global human population.\" bioRxiv [doi:10.1101/653279](https://doi.org/10.1101/653279).\n\nAlejandro Ochoa, John D Storey. 2016. \"FST And Kinship for Arbitrary Population Structures I: Generalized Definitions.\" bioRxiv [doi:10.1101/083915](https://doi.org/10.1101/083915).\n","jobInfo":{"package":"popkin","version":"1.3.23","parsingStatus":"success","parserVersion":2,"parsedAt":"2023-01-07T02:30:08+0000"}}

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