{"Encoding":"UTF-8","Package":"bigsnpr","Type":"Package","Title":"Analysis of Massive SNP Arrays","Version":"1.6.1","Date":"2021-01-09","Authors@R":"c(\nperson(\"Florian\", \"Priv\",\nemail = \"florian.prive.21@gmail.com\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Michael\", \"Blum\",\nemail = \"michael.blum@univ-grenoble-alpes.fr\",\nrole = \"ths\"),\nperson(\"Hugues\", \"Aschard\",\nemail = \"hugues.aschard@pasteur.fr\",\nrole = \"ths\"))","Description":"Easy-to-use, efficient, flexible and scalable tools\nfor analyzing massive SNP arrays .","License":"GPL-3","LazyData":"TRUE","Language":"en-US","ByteCompile":"TRUE","SystemRequirements":"Package 'bigsnpr' includes a few functions that\nwrap existing software such as 'PLINK'\n. Functions are provided to\ndownload these software. Note that these external software\nmight not work for some operating systems (e.g. 'PLINK' might\nnot work on Solaris).","Depends":"R (>= 3.3), bigstatsr (>= 1.2.2)","Imports":"bigassertr (>= 0.1.3), bigparallelr, bigsparser (>= 0.4.1),\nbigreadr, bigutilsr (>= 0.3), data.table, foreach, ggplot2,\nmagrittr, Matrix, methods, Rcpp, stats, vctrs","LinkingTo":"bigsparser, bigstatsr, Rcpp, RcppArmadillo (>= 0.9.600),\nrmio","Suggests":"bindata, covr, dbplyr (>= 1.4), dplyr, gaston, glue, Hmisc,\npcadapt (>= 4.1), rmutil, RSpectra, RSQLite, R.utils, spelling,\ntestthat, xgboost","RoxygenNote":"7.1.1","URL":"https://privefl.github.io/bigsnpr/","BugReports":"https://github.com/privefl/bigsnpr/issues","NeedsCompilation":"yes","Packaged":"2021-01-11 19:01:10 UTC; au639593","Author":"Florian Priv [aut, cre],\nMichael Blum [ths],\nHugues Aschard [ths]","Maintainer":"Florian Priv ","Repository":"CRAN","Date/Publication":"2021-01-11 22:40:07 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/bigsnpr_1.6.1.tar.gz","jsonAuthors":[{"name":"Florian Priv","email":"florian.prive.21@gmail.com","maintainer":true},{"name":"Michael Blum","email":"michael.blum@univ-grenoble-alpes.fr","maintainer":false},{"name":"Hugues Aschard","email":"hugues.aschard@pasteur.fr","maintainer":false}],"readme":"[![Travis-CI Build Status](https://travis-ci.org/privefl/bigsnpr.svg?branch=master)](https://travis-ci.org/privefl/bigsnpr)\n[![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/privefl/bigsnpr?branch=master&svg=true)](https://ci.appveyor.com/project/privefl/bigsnpr)\n[![Coverage Status](https://img.shields.io/codecov/c/github/privefl/bigsnpr/master.svg)](https://codecov.io/github/privefl/bigsnpr)\n[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/bigsnpr)](https://CRAN.R-project.org/package=bigsnpr)\n[![DOI](https://zenodo.org/badge/doi/10.1093/bioinformatics/bty185.svg)](http://dx.doi.org/10.1093/bioinformatics/bty185)\n \n \n# bigsnpr\n\n{bigsnpr} is an R package for the analysis of massive SNP arrays, primarily designed for human genetics. It enhances the features of [package {bigstatsr}](https://privefl.github.io/bigstatsr/) for the purpose of analyzing genotype data.\n\n[Quick demo](https://privefl.github.io/bigsnpr/articles/demo.html)\n\n[**LIST OF FEATURES**](https://privefl.github.io/bigsnpr/reference/index.html)\n\n**Note that most of the algorithms of this package don't handle missing values.** You can use `snp_fastImpute()` (taking a few hours for a chip of 15K x 300K) and `snp_fastImputeSimple()` (taking a few minutes only) to impute missing values of *genotyped* variants.\n\n**New!** Package {bigsnpr} now provides functions that directly work on bed files with a few missing values. See new paper \"Efficient toolkit implementing..\".\n\n\n## Installation\n\nIn R, run\n\n```r\n# install.packages(\"remotes\")\nremotes::install_github(\"privefl/bigsnpr\")\n```\n\nor for the CRAN version\n\n```r\ninstall.packages(\"bigsnpr\")\n```\n\n\n## Input formats\n\nThis package reads *bed*/*bim*/*fam* files (PLINK preferred format) using functions `snp_readBed()` and `snp_readBed2()`. Before reading into this package's special format, quality control and conversion can be done using PLINK, which can be called directly from R using `snp_plinkQC()` and `snp_plinkKINGQC()`.\n\nThis package can also read **UK Biobank BGEN files** using function `snp_readBGEN()`. This function takes around 40 minutes to read 1M variants for 400K individuals using 15 cores.\n\nThis package uses a class called `bigSNP` for representing SNP data. A `bigSNP` object is a list with some elements:\n\n- `genotypes`: A [`FBM.code256`](https://privefl.github.io/bigstatsr/reference/FBM.code256-class.html). Rows are samples and columns are SNPs. This stores genotype calls or **dosages** (rounded to 2 decimal places).\n- `fam`: A `data.frame` with some information on the SNPs.\n- `map`: A `data.frame` with some information on the individuals.\n\n**New!** Package {bigsnpr} now provides functions that directly work on bed files with a few missing values. See new paper \"Efficient toolkit implementing..\".\n\n\n## Polygenic scores\n\nPolygenic scores are one of the main focus of this package. There are 3 main methods currently available:\n\n- Penalized regressions with individual-level data (see [paper](https://doi.org/10.1534/genetics.119.302019) and [tutorial](https://privefl.github.io/bigstatsr/articles/penalized-regressions.html))\n\n- Clumping and Thresholding (C+T) and Stacked C+T (SCT) with summary statistics and individual level data (see [paper](https://doi.org/10.1016/j.ajhg.2019.11.001) and [tutorial](https://privefl.github.io/bigsnpr/articles/SCT.html)).\n\n- LDpred2 with summary statistics (see [preprint](https://doi.org/10.1101/2020.04.28.066720) and [tutorial](https://privefl.github.io/bigsnpr/articles/LDpred2.html))\n\n\n## Possible upcoming features\n\n- Multiple imputation for GWAS (https://doi.org/10.1371/journal.pgen.1006091).\n\n- More interactive (visual) QC.\n\nYou can request some feature by opening an issue.\n\n\n## Bug report\n\n[How to make a great R reproducible example?](https://stackoverflow.com/q/5963269/6103040)\n\nPlease open an issue if you find a bug.\n\nIf you want help using {bigstatsr}, please open an issue on [{bigstatsr}'s repo](https://github.com/privefl/bigstatsr/issues) or post on Stack Overflow with the tag *bigstatsr*.\n\nI will always redirect you to GitHub issues if you email me, so that others can benefit from our discussion.\n\n\n## References\n\n- Privé, Florian, et al. [\"Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr.\"](https://doi.org/10.1093/bioinformatics/bty185) Bioinformatics 34.16 (2018): 2781-2787.\n\n- Privé, Florian, et al. [\"Efficient implementation of penalized regression for genetic risk prediction.\"](https://doi.org/10.1534/genetics.119.302019) Genetics 212.1 (2019): 65-74.\n\n- Privé, Florian, et al. [\"Making the most of Clumping and Thresholding for polygenic scores.\"](https://doi.org/10.1016/j.ajhg.2019.11.001) The American Journal of Human Genetics 105.6 (2019): 1213-1221.\n\n- Privé, Florian, et al. [\"Efficient toolkit implementing best practices for principal component analysis of population genetic data.\"](https://doi.org/10.1093/bioinformatics/btaa520) Bioinformatics (2020).\n\n- Privé, Florian, et al. [\"LDpred2: better, faster, stronger.\"](https://doi.org/10.1101/2020.04.28.066720) BioRxiv (2020).\n\n
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