Implementation of a generalized elastic net LMM for (mainly) GWAS analysis. It also implements the exact post selection inference method proposed by Lee et al. to generate accurate confidence interval and p-values adjusted for elastic net.
The implementation is based on Barbara Rakitsch's implementation of LMM-Lasso (https://github.com/BorgwardtLab/LMM-Lasso) , Artem Skolov's implementation of GELnet (https://github.com/cran/gelnet), and selectiveInference by the Selective Inference Team (https://github.com/selective-inference/Python-software/).
For multiprocessing we are using Pathos. (https://github.com/uqfoundation/pathos)
The software is released under the GNU General Public License.
Author:
Benjamin Schubert
Debora Marks and Chris Sander Group
Systems and Cell Biology,
Harvard Medical School,
200 Longwood Avenue, Boston, 02115 MA, USA
References:
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Rakitsch, B., Lippert, C., Stegle, O., & Borgwardt, K. (2012). A Lasso multi-marker mixed model for association mapping with population structure correction. Bioinformatics, 29(2), 206-214.
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Sokolov, A., Carlin, D. E., Paull, E. O., Baertsch, R., & Stuart, J. M. (2016). Pathway-based genomics prediction using generalized elastic net. PLoS Computational Biology, 12(3), e1004790.
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Lee, J. D., Sun, D. L., Sun, Y., & Taylor, J. E. (2016). Exact post-selection inference, with application to the lasso. The Annals of Statistics, 44(3), 907-927. Chicago
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McKerns, M., & Aivazis, M. pathos: a framework for heterogeneous computing, 2010. Chicago