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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:

>singularity 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 20.04 Creative Commons CC-BY-SA version 3.0 UK licence
bcftools1 1.19 MIT/Expat/GPLv3
bedtools2 2.31.1 MIT
beagle3 4 22Jul22.46e GPLv3
bgenix5 1.1.7 Boost
bolt6 v2.4.1 GPLv3
cat-bgen7 same version as bgenix Boost
duohmm8 95bd395 MIT
eagle9 v2.4.1 GPLv3
edit-bgen10 same version as bgenix Boost
flashpca_x86-6411 2.0 GPLv3
gcta6412 1.94.1 GPLv3
gctb13 2.04.3 MIT
GWAMA14 2.2.2 BSD-3-Clause
HTSlib15 1.19.1 MIT/Expat/Modified-BSD
king16 2.3.2 permissive
ldak17 6 GPLv3
liftOver18 latest permissive
metal19 2020-05-05 -
minimac420 v4.1.6 GPLv3
plink21 v1.90b7.2 64-bit (11 Dec 2023) GPLv3
plink222 v2.00a5.10LM 64-bit Intel (5 Jan 2024) GPLv3
plink2_avx222 v2.00a5.10LM AVX2 Intel (5 Jan 2024) GPLv3
PRSice_linux23 2.3.5 GPLv3
qctool24 2.2.2, revision e5723df2c0c85959 Boost
regenie25 v3.6 MIT/Boost
samtools1 v1.19.2 MIT/ExpatD
shapeit4.226 v4.2.2 MIT
shapeit527 phase_rare v5.1.1 MIT
shapeit527 phase_common v5.1.1 MIT
shapeit527 ligate v5.1.1 MIT
shapeit527 switch v5.1.1 MIT
shapeit527 xcftools v5.1.1 MIT
simu_linux28 v0.9.4 GPLv3
snptest29 v2.5.6 permissive
switchError30 6e688b1 MIT
vcftools31 0.1.17 (git SHA: d511f469e) GPLv3

Footnotes

  1. 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

  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. 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 ↩

  22. https://www.cog-genomics.org/plink/2.0/ ↩ ↩2

  23. 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 ↩

  24. https://code.enkre.net/qctool ↩

  25. 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 ↩

  26. 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 ↩

  27. Hofmeister, R.J., Ribeiro, D.M., Rubinacci, S. et al. Accurate rare variant phasing of whole-genome and whole-exome sequencing data in the UK Biobank. Nat Genet 55, 1243–1249 (2023). https://doi.org/10.1038/s41588-023-01415-w ↩ ↩2 ↩3 ↩4 ↩5

  28. https://github.com/precimed/simu ↩

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

  30. 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 ↩

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