Construction and estimation of mtmlSEM models :)
There are a plethora of methods for genome-wide association studies. However, only a few of them may be classified as multi-trait and multi-locus, i.e. consider the influence of multiple genetic variants to several correlated phenotypes. We propose a multi-trait multi-locus model which employs structural equation modeling (SEM) to describe complex associations between SNPs and traits - multi-trait multi-locus SEM (mtmlSEM). The structure of our model makes it possible to discriminate pleiotropic and single-trait SNPs of direct and indirect effect. We also propose an automatic procedure to construct the model using factor analysis and the maximum likelihood method. For estimating a large number of parameters in the model, we performed Bayesian inference and implemented Gibbs sampling. An important feature of the model is that it correctly copes with non-normally distributed variables, such as some traits and variants.
To construct the model and add SNPs then, use the following scripts:
pipeline_spart.py and pipeline_add_snps.py.
To optimise parameters and get predictions, follow the next notebooks:
mcmc_estimate.ipynb and mcmc_predict.ipynb
A.A.Igolkina et al., Multi-trait multi-locus SEM model discriminates SNPs of different effects
Anna Igolkina e-mail.
Georgy Meshcheryakov
Scripts licensed under the MIT license.