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) Non-linear randomized Haseman-Elston regression for estimation of gene-environment heritability. Bioinformatics (accepted) [bioRxiv]
18 MAy 2020 (v1.0) : First public release
Software registration and license:
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