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Linear mixed model to study multivariate genotype-environment interactions

Project description


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.


From terminal, it can be installed using pip:

pip install struct-lmm


>>> 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)
>>> # Association test
>>> pv = lmm.score_2dof_assoc(x)
>>> print(pv)
>>> # Association test
>>> pv, rho = lmm.score_2dof_assoc(x, return_rho=True)
>>> print(pv)
>>> print(rho)
>>> M = concatenate([M, x], axis=1)
>>> lmm = StructLMM(y, M, E)
>>> # Interaction test
>>> pv = lmm.score_2dof_inter(x)
>>> print(pv)


If you encounter any problem, please, consider submitting a new issue.



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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