bigsnpr

{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.

Quick demo

LIST OF FEATURES

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.

New! Package {bigsnpr} now provides functions that directly work on bed files with a few missing values. See new paper "Efficient toolkit implementing..".

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: A FBM.code256. Rows are samples and columns are SNPs. This stores genotype calls or dosages (rounded to 2 decimal places).
• fam: A data.frame with some information on the SNPs.
• map: A data.frame with some information on the individuals.

New! Package {bigsnpr} now provides functions that directly work on bed files with a few missing values. See new paper "Efficient toolkit implementing..".

Polygenic scores

Polygenic scores are one of the main focus of this package. There are 3 main methods currently available:

• Penalized regressions with individual-level data (see paper and tutorial)

• Clumping and Thresholding (C+T) and Stacked C+T (SCT) with summary statistics and individual level data (see paper and tutorial).

• LDpred2 with summary statistics (see preprint and tutorial)

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

How to make a great R reproducible example?

Please open an issue if you find a bug.

If you want help using {bigstatsr}, 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 (2020).

• Privé, Florian, et al. "LDpred2: better, faster, stronger." BioRxiv (2020).