Credits
PLINK 1.9 is developed, tested, and documented primarily by Christopher Chang at GRAIL, Inc., Carson Chow and Shashaank Vattikuti at the NIH-NIDDK's Laboratory of Biological Modeling, Laurent Tellier at the BGI Cognitive Genomics Lab, and James Lee at the University of Minnesota, with additional funding from the Purcell Lab at Brigham & Women's Hospital.
- All previous versions of PLINK are the work of Shaun Purcell at Brigham & Women's Hospital and Harvard University. Since our update started as an independent project, its level of compatibility with PLINK 1.07 would have been all but impossible to achieve if PLINK was not a free and open source program.
- GCTA is the work of
Jian Yang et al. at the University of Queensland. Their release of the
GCTA 1.2 source code under GPLv3 terms is also greatly appreciated by us.
- Thanks to Stephen Hsu at the BGI-CGL for motivating the initial weighted distance calculation.
- Thanks to Sanja Franić at VU University Amsterdam for early testing.
- Thanks to Mike Keehan for additional testing and a bugfix.
- Thanks to Masahiro Kanai for improving the robustness of the VCF parser, fixing some other plink_data.c bugs, and adding some filtering flags.
- The SSE2 population count algorithm used in many of PLINK 1.9's inner loops is based on work and discussion by Andrew Dalke, Robert Harley, Cédric Lauradoux, Terje Mathisen, and Kim Walisch.
- The Hardy-Weinberg equilibrium and Fisher exact tests are based on an algorithm developed by
Jan Wigginton and Gonçalo Abecasis at the University of
Michigan Center for Statistical Genetics.
- The Hardy-Weinberg equilibrium test 'midp' option was added due to work by Jan Graffelman and Victor Moreno.
- The parallel gzip implementation was developed by Mark Adler at the Caltech/NASA Jet Propulsion Laboratory.
- The BGZF library was developed by Bob Handsaker, Petr Danecek, Heng Li, and John Marshall.
- PLINK 1.9's permutation procedures extend work by Brian Browning (PRESTO) and Roman Pahl (PERMORY).
- PLINK 1.9's fast epistasis test implements methods developed by Xiang Wan et al. in BOOST and Masao Ueki, Heather Cordell, and Richard Howey in CASSI.
- The logistic regression algorithm is based on the winning submission of
Pascal Pons in the GWAS Speedup crowdsourcing contest run in April 2013
by Babbage Analytics & Innovation and TopCoder, who have donated the results to
be used in PLINK 2. The contest was designed by Po-Ru Loh; subsequent
analysis and code preparation were performed by Andrew Hill, Ragu
Bharadwaj, and Scott Jelinsky. A manuscript is in preparation by
these authors and Iain Kilty, Kevin Boudreau, Karim Lakhani and Eva Guinan.
- Thanks to David Fischer for GitHub hygiene improvements.
- Thanks to numerous PLINK 1.9 alpha testers for bug reports and helpful suggestions.
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