New algorithm will enhance understanding of relationship between genotype and environmental factors
- Researchers have developed a new method for multi-environment testing of genotype–environment interactions
- Comprehensive analysis of hundreds of environmental factors could enhance understanding of genotype–phenotype relationships
- Previously undiscovered interactions between genomes, environment and phenotype can now be identified
Researchers at EMBL’s European Bioinformatics Institute (EMBL-EBI) and the Wellcome Sanger Institute have developed a new computational method that makes it possible to identify the impact of hundreds of environmental factors on genotype–environment interactions (GxE).
The research article, published in the journal Nature Genetics, produced an algorithm and a bioinformatics method that can be applied to large cohorts of human genome and lifestyle data to identify the impact environmental factors (such as diet, physical activity or living conditions) have on genotype–phenotype relationships.
Applying this method allows scientists to identify areas of the genome that affect human traits in different ways, depending on lifestyle or other environmental factors.
Hundreds of factors
While our genome is unchanged throughout our lifetime, human traits such as height or weight are influenced by lifestyle and environmental factors.
“What we are doing in this study is going beyond the classical genome to phenotype approach by accounting for environmental factors in a comprehensive manner,” explains Oliver Stegle, a group leader at EMBL-EBI. “Our approach allows us to simultaneously use hundreds of environmental factors, measured in human cohorts, to enhance the analysis of genotype–phenotype relationships. Previously such analyses required a narrow hypothesis, choosing a specific environmental factor such as physical activity, and testing for interactions with genetic variables to understand the impact on phenotypes. Now we can analyse everything in one go, meaning we can find and identify interplays between genomes, environment and phenotype in a comprehensive manner.”
By applying the new structured linear mixed model (StructLMM) to body mass index in the UK Biobank(a database holding genome and lifestyle data for 500,000 people), the researchers were able to identify previously known and novel GxE signals that are simultaneously driven by multiple environmental factors.
“Characterising gene–environment interactions using multiple environments is important,” says Paolo Casale, a postdoctoral researcher at Microsoft Research New England and an alumnus of EMBL-EBI. “These analyses can provide a finer characterisation of high-risk groups for certain diseases and help to identify the most relevant environmental factors.”
“This is an exciting new way of thinking about genotype–environment interactions, paving the way to explore the importance of these interactions rather than looking at genotype alone,” adds Rachel Moore, a PhD student at EMBL-EBI and the Wellcome Sanger Institute.
Giving genomes context
Going forward, this method will offer a more comprehensive way of incorporating environment into genetics studies than previously possible. It will also increase the number of discoveries of variants whose function depends on environment or lifestyle.
Inês Barroso, senior group leader at the Wellcome Sanger Institute, adds: “We hope this method stimulates research which incorporates environmental factors, by taking our genome into context and generating understanding of how the function of the genome we are born with is modulated by our life, habits, environment and social interactions.”
This work was supported by the European Molecular Biology Laboratory (EMBL), the European Union’s Horizon 2020 research and innovation programme under grant agreement No 635290 and 664726, and many other funding bodies. Please see the paper for the full list of funders.
This post was originally published on EMBL-EBI’s website.
Stegle, O., et al. (2018). A linear mixed-model approach to study multivariate gene–environment interactions. Nature Genetics, Published online 26 November 2018. DOI:10.1038/s41588-018-0271-0.
European Molecular Biology Laboratory