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A high-performance implementation of genome-wide association analysis using mixed-models

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CLAK-GWAS is a software for performing Genome-Wide Association Studies
(GWAS). It provides a high-performance implementation of two 
algorithms, CLAK-Chol and CLAK-Eig, for GWAS involving single and 
multiple phenotypes, respectively. 


IMPORTANT NOTICE
-----------

Early 2018, PolyOmica, the main sponsor of the GenABEL project announced that
it would discontinue its support. As PolyOmica was our only sponsor, we 
regretfully have to announce the likely discontinuation of the GenABEL Project 
in its current form, unless a new sponsor steps up or someone takes over 
maintenance and futher development of (some of) the tools. 


0. LICENSE
-----------

OmicABEL is distributed under the GNU GENERAL PUBLIC LICENSE v.3 or later
A copy of the license is available in the file ''COPYING''


1. INSTALLATION
----------------

Please see the doc/INSTALL file. 

Note that if you downloaded one of the binary versions, no 
installation is needed.


2. USING CLAK-GWAS
-------------------

Please see the doc/HOWTO file.


3. KNOWN ISSUES
----------------

3.1  For the sake of ease-of-use, CLAK-GWAS automatically performs
     a number of system checks and tries to maximize performance.
     Should you experience any unexpected behavior, please contact us
     ( fabregat [at] aices [dot] rwth-aachen [dot] de ); we will attend 
     your request as soon as possible.

3.2  Currently, the input data must consist of double precision entries, 
     organized according to the DatABEL (filevector) data format.
     We are working on the support for more datatypes.


4. CONTACT
-----------

For any question, request, suggestion, or comment, please contact us:

Diego Fabregat Traver,  fabregat [at] aices [dot] rwth-aachen [dot] de
Paolo Bientinesi,       pauldj   [at] aices [dot] rwth-aachen [dot] de
Yurii Aulchenko,        yurii [dot] aulchenko [at] gmail [dot] com

You can also seek help in our forum: http://forum.genabel.org/


Happy GWASing!

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A high-performance implementation of genome-wide association analysis using mixed-models

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