Skip to content
naulon
Get started

No retraction, correction or concern found when last checked on Oct 15, 2026.

Second-generation PLINK: rising to the challenge of larger and richer datasets

Christopher C Chang, Carson C Chow, Laurent CAM Tellier and 3 more

Gigascience · 2015 · CC BY

doi:10.1186/s13742-015-0047-8

Abstract

Background. PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1’s primary data format.

Findings. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $O\left (\sqrt {n}\right)$ \end{document}On-time/constant-space Hardy-Weinberg equilibrium and Fisher’s exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0).

Conclusions. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

Electronic supplementary material. The online version of this article (doi:10.1186/s13742-015-0047-8) contains supplementary material, which is available to authorized users.

Full text

Its licence does not allow us to show the full text. Read it at PubMed Central:

Read at PubMed Central

References 45

  1. 1.

    Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M, Bender D, et al. Plink: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559-75.

    Not in our library10.1086/519795

  2. 2.

    Browning B, Browning S. Improving the accuracy and efficiency of identity by descent detection in population data. Genetics. 2013;194:459-71.

    Not in our library10.1534/genetics.113.150029

  3. 3.
    A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies · 2009
    No notice
  4. 4.

    McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: A mapreduce framework for analyzing next-generation dna sequencing data. Genome Res. 2010;20:1297-303.

    Not in our library10.1101/gr.107524.110

  5. 5.

    Danecek P, Auton A, Abecasis G, Albers C, Banks E, DePristo M, et al. The variant call format and vcftools. Bioinformatics. 2011;27:2156-8.

    Not in our library10.1093/bioinformatics/btr330

  6. 6.

    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, 1000 Genome Project Data Processing Subgroup, et al. The sequence alignment/map format and samtools. Bioinformatics. 2009;25:2078-9.

    Not in our library10.1093/bioinformatics/btp352

  7. 7.

    Yang J, Lee S, Goddard M, Visscher P. Gcta: A tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76-82.

    Not in our library10.1016/j.ajhg.2010.11.011

  8. 8.
    Second-generation PLINK: rising to the challenge of larger and richer datasets · 2015
    No notice
  9. 9.

    Dalke A. Update: Faster Population Counts. http://www.dalkescientific.com/writings/diary/archive/2011/11/02/faster_popcount_update.html.

    Not in our library

  10. 10.

    Lee V, Kim C, Chhugani J, Deisher M, Kim D, Nguyen A, et al. Debunking the 100x gpu vs. cpu myth: an evaluation of throughput computing on cpu and gpu. Proceedings of the 37th Annual International Symposium on Computer Architecture: 19-23 June 2010. 2010.

    Not in our library

  11. 11.

    Haque I, Pande V, Walters W. Anatomy of high-performance 2d similarity calculations. J Chem Inf Model. 2011;51:2345-51.

    Not in our library10.1021/ci200235e

  12. 12.

    Hardy H. Mendelian proportions in a mixed population. Science. 1908;28:49-50.

    Not in our library10.1126/science.28.706.49

  13. 13.

    Wigginton J, Cutler D, Abecasis G. A note on exact tests of hardy-weinberg equilibrium. Am J Hum Genet. 2005;76:887-93.

    Not in our library10.1086/429864

  14. 14.

    Guo S, Thompson E. Performing the exact test of hardy-weinberg proportion for multiple alleles. Biometrics. 1992;48:361-72.

    Not in our library10.2307/2532296

  15. 15.

    Mehta C, Patel N. Algorithm 643: Fexact: a fortran subroutine for fisher’s exact test on unordered r ×c contingency tables. ACM Trans Math Softw. 1986;12:154-61.

    Not in our library10.1145/6497.214326

  16. 16.

    Clarkson D, Fan Y, Joe H. A remark on algorithm 643: Fexact: an algorithm for performing fisher’s exact test in r x c contingency tables. ACM Trans Math Softw. 1993;19:484-8.

    Not in our library10.1145/168173.168412

  17. 17.

    Requena F, Martín Ciudad N. A major improvement to the network algorithm for fisher’s exact test in 2 ×c contingency tables. J Comp Stat & Data Anal. 2006;51:490-8.

    Not in our library10.1016/j.csda.2005.09.004

  18. 18.

    Chang C. Standalone C/C++ Exact Statistical Test Functions. https://github.com/chrchang/stats.

    Not in our library

  19. 19.

    Lydersen S, Fagerland M, Laake P. Recommended tests for association in 2 ×2 tables. Statist Med. 2009;28:1159-75.

    Not in our library10.1002/sim.3531

  20. 20.

    Graffelman J, Moreno V. The mid p-value in exact tests for hardy-weinberg equilibrium. Stat Appl Genet Mol Bio. 2013;12:433-48.

    Not in our library10.1515/sagmb-2012-0039

  21. 21.

    Wall J, Pritchard J. Assessing the performance of the haplotype block model of linkage disequilibrium. Am J Hum Genet. 2003;73:502-15.

    Not in our library10.1086/378099

  22. 22.

    Gabriel S, Schaffner S, Nguyen H, Moore J, Roy J, Blumenstiel B, et al. The structure of haplotype blocks in the human genome. Science. 2002;296:2225-9.

    Not in our library10.1126/science.1069424

  23. 23.

    Barrett J, Fry B, Maller J, Daly M. Haploview: analysis and visualization of ld and haplotype maps. Bioinformatics. 2005;21:263-5.

    Not in our library10.1093/bioinformatics/bth457

  24. 24.

    Hill W. Estimation of linkage disequilibrium in randomly mating populations. Heredity. 1974;33:229-39.

    Not in our library10.1038/hdy.1974.89

  25. 25.
    Cubic exact solutions for the estimation of pairwise haplotype frequencies: implications for linkage disequilibrium analyses and a web tool 'CubeX' · 2007
    No notice
  26. 26.

    Taliun D, Gamper J, Pattaro C. Efficient haplotype block recognition of very long and dense genetic sequences. BMC Bioinformatics. 2014;15:10.

    Not in our library10.1186/1471-2105-15-10

  27. 27.

    Friedman J, Hastie T, Höfling H, Tibshirani R. Pathwise coordinate optimization. Ann Appl Stat. 2007;1:302-32.

    Not in our library10.1214/07-aoas131

  28. 28.

    Vattikuti S, Lee J, Chang C, Hsu S, Chow C. Applying compressed sensing to genome-wide association studies. GigaScience. 2014;3:10.

    Not in our library10.1186/2047-217x-3-10

  29. 29.

    Steiß V, Letschert T, Schäfer H, Pahl R. Permory-mpi: A program for high-speed parallel permutation testing in genome-wide association studies. Bioinformatics. 2012;28:1168-9.

    Not in our library10.1093/bioinformatics/bts086

  30. 30.

    Wan X, Yang C, Yang Q, Xue H, Fan X, Tang N, et al. Boost: A fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am J Hum Genet. 2010;87:325-40.

    Not in our library10.1016/j.ajhg.2010.07.021

  31. 31.

    Ueki M, Cordell H. Improved statistics for genome-wide interaction analysis. PLoS Genet. 2012;8:1002625.

    Not in our library10.1371/journal.pgen.1002625

  32. 32.

    Howey R. CASSI: Genome-Wide Interaction Analysis Software. http://www.staff.ncl.ac.uk/richard.howey/cassi.

    Not in our library

  33. 33.

    GWASSpeedup Problem Statement. http://community.topcoder.com/longcontest/?module=ViewProblemStatement&rd=15637&pm=12525.

    Not in our library

  34. 34.

    Adler M. Pigz: Parallel Gzip. http://zlib.net/pigz/.

    Not in our library

  35. 35.

    Abecasis G, Cardon L, Cookson W. A general test of association for quantitative traits in nuclear families. Am J Hum Genet. 2000;66:279-92.

    Not in our library10.1086/302698

  36. 36.

    Ewens W, Li M, Spielman R. A review of family-based tests for linkage disequilibrium between a quantitative trait and a genetic marker. PLoS Genet. 2008;4:1000180.

    Not in our library10.1371/journal.pgen.1000180

  37. 37.

    Su Z, Marchini J, Donnelly P. Hapgen2: Simulation of multiple disease snps. Bioinformatics. 2011;27:2304-5.

    Not in our library10.1093/bioinformatics/btr341

  38. 38.

    Xu Y, Wu Y, Song C, Zhang H. Simulating realistic genomic data with rare variants. Genet Epidemiol. 2013;37:163-72.

    Not in our library10.1002/gepi.21696

  39. 39.

    The 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56-65.

    Not in our library10.1038/nature11632

  40. 40.

    Defays D. An efficient algorithm for a complete link method. Comput J. 1977;20:364-6.

    Not in our library10.1093/comjnl/20.4.364

All 45 references

Cited by papers we hold 231

and 211 more

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.