gwas.sif container

Description

The gwas.sif container file has multiple tools related to imputation and GWAS analysis, as summarized in the Sofware table below.

Note that some specific tools (e.g. bolt) are added to the path directly from their /tools folder (e.g. /tools/bolt) due to hard-linked dependencies. Either way, all tools can be invoked by their name, as listed above. For example:

>apptainer exec gwas.sif regenie
              |=============================|
              |      REGENIE v2.0.2.gz      |
              |=============================|

Copyright (c) 2020 Joelle Mbatchou and Jonathan Marchini.
Distributed under the MIT License.
Compiled with Boost Iostream library.
Using Intel MKL with Eigen.

ERROR: You must provide an output prefix using '--out'
For more information, use option '--help' or visit the website: https://rgcgithub.github.io/regenie/

Software

List of software included in the container:

OS/tool	version	license
ubuntu	24.04LTS	Creative Commons CC-BY-SA version 3.0 UK licence
bcftools[1]	1.19	MIT/Expat/GPLv3
bedtools[2]	2.31.1	MIT
beagle[3] [4]	22Jul22.46e	GPLv3
bgenix[5]	1.1.7	Boost
bolt[6]	v2.4.1	GPLv3
cat-bgen[7]	same version as bgenix	Boost
duohmm[8]	95bd395	MIT
eagle[9]	v2.4.1	GPLv3
edit-bgen[10]	same version as bgenix	Boost
flashpca_x86-64[11]	2.0	GPLv3
gcta64[12]	1.94.1	GPLv3
gctb[13]	2.04.3	MIT
GWAMA[14]	2.2.2	BSD-3-Clause
HTSlib[15]	1.19.1	MIT/Expat/Modified-BSD
king[16]	2.3.2	permissive
ldak[17]	6	GPLv3
liftOver[18]	latest	permissive
metal[19]	2020-05-05	-
minimac4[20]	v4.1.6	GPLv3
OPERA[21]	v1.18	-
plink[22]	v1.9.0-b.7.11 64-bit (19 Aug 2025)	GPLv3
plink2[23]	v2.0.0-a.6.23LM 64-bit Intel (24 Aug 2025)	GPLv3
plink2_avx2[23]	v2.0.0-a.6.23LM AVX2 Intel (24 Aug 2025)	GPLv3
PRSice_linux[24]	2.3.5	GPLv3
(qctool[25])	2.2.2, revision e5723df2c0c85959	Boost
regenie[26]	v4.1.2	MIT/Boost
samtools[1]	v1.19.2	MIT/ExpatD
shapeit4.2[27]	v4.2.2	MIT
simu_linux[28]	v0.9.4	GPLv3
SMR[29]	v1.3.2	GPLv2
snptest[30]	v2.5.6	permissive
switchError[31]	6e688b1	MIT
vcftools[32]	0.1.17 (git SHA: d511f469e)	GPLv3

[1] (1,2)

Petr Danecek, James K Bonfield, Jennifer Liddle, John Marshall, Valeriu Ohan, Martin O Pollard, Andrew Whitwham, Thomas Keane, Shane A McCarthy, Robert M Davies, Heng Li, Twelve years of SAMtools and BCFtools, GigaScience, Volume 10, Issue 2, February 2021, giab008, https://doi.org/10.1093/gigascience/giab008

[2]

Aaron R. Quinlan, Ira M. Hall, BEDTools: a flexible suite of utilities for comparing genomic features, Bioinformatics, Volume 26, Issue 6, March 2010, Pages 841–842, https://doi.org/10.1093/bioinformatics/btq033

[3]

B L Browning, X Tian, Y Zhou, and S R Browning (2021) Fast two-stage phasing of large-scale sequence data. Am J Hum Genet 108(10):1880-1890. doi:10.1016/j.ajhg.2021.08.005

[4]

B L Browning, Y Zhou, and S R Browning (2018). A one-penny imputed genome from next generation reference panels. Am J Hum Genet 103(3):338-348. doi:10.1016/j.ajhg.2018.07.015

[5]

https://enkre.net/cgi-bin/code/bgen/wiki?name=bgenix

[6]

Loh, P.-R. et al. Efficient Bayesian mixed model analysis increases association power in large cohorts. Nature Genetics 47, 284–290 (2015).

[7]

https://enkre.net/cgi-bin/code/bgen/wiki?name=cat-bgen

[8]

O’Connell J, Gurdasani D, Delaneau O, Pirastu N, Ulivi S, et al. (2014) A General Approach for Haplotype Phasing across the Full Spectrum of Relatedness. PLOS Genetics 10(4): e1004234. https://doi.org/10.1371/journal.pgen.1004234

[9]

Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nature Genetics 48, 1443–1448 (2016)

[10]

https://enkre.net/cgi-bin/code/bgen/wiki?name=edit-bgen

[11]

Gad Abraham, Yixuan Qiu, Michael Inouye, FlashPCA2: principal component analysis of Biobank-scale genotype datasets, Bioinformatics, Volume 33, Issue 17, September 2017, Pages 2776–2778, https://doi.org/10.1093/bioinformatics/btx299

[12]

Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011 Jan 7;88(1):76-82. doi: 10.1016/j.ajhg.2010.11.011. Epub 2010 Dec 17.

[13]

Zeng, J., de Vlaming, R., Wu, Y. et al. Signatures of negative selection in the genetic architecture of human complex traits. Nat Genet 50, 746–753 (2018). https://doi.org/10.1038/s41588-018-0101-4

[14]

Mägi, R., Morris, A.P. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010). https://doi.org/10.1186/1471-2105-11-288

[15]

James K Bonfield, John Marshall, Petr Danecek, Heng Li, Valeriu Ohan, Andrew Whitwham, Thomas Keane, Robert M Davies, HTSlib: C library for reading/writing high-throughput sequencing data, GigaScience, Volume 10, Issue 2, February 2021, giab007, https://doi.org/10.1093/gigascience/giab007

[16]

Ani Manichaikul, Josyf C. Mychaleckyj, Stephen S. Rich, Kathy Daly, Michèle Sale, Wei-Min Chen, Robust relationship inference in genome-wide association studies, Bioinformatics, Volume 26, Issue 22, November 2010, Pages 2867–2873, https://doi.org/10.1093/bioinformatics/btq559

[17]

Speed, D., Cai, N., the UCLEB Consortium. et al. Reevaluation of SNP heritability in complex human traits. Nat Genet 49, 986–992 (2017). https://doi.org/10.1038/ng.3865

[18]

Phuc-Loi Luu, Phuc-Thinh Ong, Thanh-Phuoc Dinh, Susan J Clark, Benchmark study comparing liftover tools for genome conversion of epigenome sequencing data, NAR Genomics and Bioinformatics, Volume 2, Issue 3, September 2020, lqaa054, https://doi.org/10.1093/nargab/lqaa054

[19]

Cristen J. Willer, Yun Li, Gonçalo R. Abecasis, METAL: fast and efficient meta-analysis of genomewide association scans, Bioinformatics, Volume 26, Issue 17, September 2010, Pages 2190–2191, https://doi.org/10.1093/bioinformatics/btq340

[20]

https://genome.sph.umich.edu/wiki/Minimac4

[21]

Wu Y., Qi T., Wray N.R., Visscher P.M., Zeng J. & Yang J. (2023) Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes. Cell Genomics.

[22]

Christopher C Chang, Carson C Chow, Laurent CAM Tellier, Shashaank Vattikuti, Shaun M Purcell, James J Lee, Second-generation PLINK: rising to the challenge of larger and richer datasets, GigaScience, Volume 4, Issue 1, December 2015, s13742–015–0047–8, https://doi.org/10.1186/s13742-015-0047-8

[23] (1,2)

https://www.cog-genomics.org/plink/2.0/

[24]

Shing Wan Choi, Paul F O’Reilly, PRSice-2: Polygenic Risk Score software for biobank-scale data, GigaScience, Volume 8, Issue 7, July 2019, giz082, https://doi.org/10.1093/gigascience/giz082

[25]

https://code.enkre.net/qctool

[26]

Mbatchou, J., Barnard, L., Backman, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat Genet 53, 1097–1103 (2021). https://doi.org/10.1038/s41588-021-00870-7

[27]

Delaneau, O., Zagury, JF., Robinson, M.R. et al. Accurate, scalable and integrative haplotype estimation. Nat Commun 10, 5436 (2019). https://doi.org/10.1038/s41467-019-13225-y

[28]

https://github.com/precimed/simu

[29]

Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, Montgomery GW, Goddard ME, Wray NR, Visscher PM & Yang J (2016) Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nature Genetics, 48:481-487.

[30]

https://www.chg.ox.ac.uk/~gav/snptest/#download

[31]

O’Connell J, Gurdasani D, Delaneau O, Pirastu N, Ulivi S, et al. (2014) A General Approach for Haplotype Phasing across the Full Spectrum of Relatedness. PLOS Genetics 10(4): e1004234. https://doi.org/10.1371/journal.pgen.1004234

[32]

Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R;