{bigsnpr} is an R package for the analysis of massive SNP arrays, primarily designed for human genetics. It enhances the features of package {bigstatsr} for the purpose of analyzing genotype data.
To get you started:
List of functions from bigsnpr and from bigstatsr
Extended documentation with more examples + course recording
Installation
In R, run
# install.packages("remotes")
remotes::install_github("privefl/bigsnpr")or for the CRAN version
install.packages("bigsnpr")Input formats
This 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().
This 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.
This package uses a class called bigSNP for representing SNP data. A bigSNP object is a list with some elements:
-
$genotypes: AFBM.code256. Rows are samples and columns are variants. This stores genotype calls or dosages (rounded to 2 decimal places). -
$fam: Adata.framewith some information on the individuals. -
$map: Adata.framewith some information on the variants.
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.
Package {bigsnpr} also provides functions that directly work on bed files with a few missing values (the bed_*() functions). See paper “Efficient toolkit implementing..”.
Polygenic scores
Polygenic scores are one of the main focus of this package. There are 3 main methods currently available:
Possible upcoming features
Multiple imputation for GWAS (https://doi.org/10.1371/journal.pgen.1006091).
More interactive (visual) QC.
You can request some feature by opening an issue.
Bug report / Support
How to make a great R reproducible example?
Please open an issue if you find a bug.
If you want help using {bigstatsr} (the big_*() functions), please open an issue on {bigstatsr}’s repo, or post on Stack Overflow with the tag bigstatsr.
I will always redirect you to GitHub issues if you email me, so that others can benefit from our discussion.
References
Privé, Florian, et al. “Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr.” Bioinformatics 34.16 (2018): 2781-2787.
Privé, Florian, et al. “Efficient implementation of penalized regression for genetic risk prediction.” Genetics 212.1 (2019): 65-74.
Privé, Florian, et al. “Making the most of Clumping and Thresholding for polygenic scores.” The American Journal of Human Genetics 105.6 (2019): 1213-1221.
Privé, Florian, et al. “Efficient toolkit implementing best practices for principal component analysis of population genetic data.” Bioinformatics 36.16 (2020): 4449-4457.
Privé, Florian, et al. “LDpred2: better, faster, stronger.” Bioinformatics 36.22-23 (2020): 5424-5431.
Privé, Florian. “Optimal linkage disequilibrium splitting.” Bioinformatics 38.1 (2022): 255–256.
Privé, Florian. “Using the UK Biobank as a global reference of worldwide populations: application to measuring ancestry diversity from GWAS summary statistics.” Bioinformatics 38.13 (2022): 3477-3480.
Privé, Florian, et al. “Identifying and correcting for misspecifications in GWAS summary statistics and polygenic scores.” Human Genetics and Genomics Advances 3.4 (2022).
Privé, Florian, et al. Inferring disease architecture and predictive ability with LDpred2-auto. The American Journal of Human Genetics 110.12 (2023): 2042-2055.
