GPLEMMA (Gaussian Prior Linear Environment Mixed Model Analysis) non-linear randomized Haseman-Elston regression method for flexible modeling of gene-environment interactions in large datasets such as the UK Biobank.

The method simultaneously estimates a linear combination of environmental variables, called an environmental score (ES), that interacts with genetic markers throughout the genome, and it’s associated heritability. Estimation of the ES provides a readily interpretable way to examine the combined effect of many environmental variables.

This approach is related to the whole-genome regression approach for ES estimation implemented in the LEMMA method.


If you use GPLEMMA in your research, please cite the following publication:

Matthew Kerin and Jonathan Marchini (2020) A non-linear regression method for estimation of gene–environment heritability. Bioinformatics [Journal][bioRxiv]


18 MAy 2020 (v1.0) : First public release

Software registration and license:

The GPLEMMA method is implemented as an option in the LEMMA code, which is freely available under an MIT licence. There is a documentation webpage and source code on Github.


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