Linear mixed model to study multivariate genotype-environment interactions
Project description
Struct-LMM
Structured Linear Mixed Model (StructLMM) is a computationally efficient method to test for and characterize loci that interact with multiple environments [1].
This a standalone module that implements the basic functionalities of StructLMM. However, we recommend using StructLMM via LIMIX2 as this additionally implements:
- Multiple methods for GWAS;
- Methods to characterize GxE at specific variants;
- Command line interface.
Install
From terminal, it can be installed using pip:
pip install struct-lmm
Usage
>>> from numpy import ones, concatenate >>> from numpy.random import RandomState >>> >>> from struct_lmm import StructLMM >>> >>> random = RandomState(1) >>> n = 20 >>> k = 4 >>> y = random.randn(n, 1) >>> E = random.randn(n, k) >>> M = ones((n, 1)) >>> x = 1.0 * (random.rand(n, 1) < 0.2) >>> >>> lmm = StructLMM(y, M, E) >>> lmm.fit(verbose=False) >>> # Association test >>> pv = lmm.score_2dof_assoc(x) >>> print(pv) 0.8470017313426488 >>> # Association test >>> pv, rho = lmm.score_2dof_assoc(x, return_rho=True) >>> print(pv) 0.8470017313426488 >>> print(rho) 0 >>> M = concatenate([M, x], axis=1) >>> lmm = StructLMM(y, M, E) >>> lmm.fit(verbose=False) >>> # Interaction test >>> pv = lmm.score_2dof_inter(x) >>> print(pv) 0.6781100453132024
Problems
If you encounter any problem, please, consider submitting a new issue.
Authors
License
This project is licensed under the MIT License.
[1] Moore, R., Casale, F. P., Bonder, M. J., Horta, D., Franke, L., Barroso, I., & Stegle, O. (2018). A linear mixed-model approach to study multivariate gene–environment interactions (p. 1). Nature Publishing Group.
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