Metagenome-wide association studies

A first step in identifying microbiome candidates that influence a clinical parameter, production yields or any parameter of interest, is often to do a microbiome-wide association study.

We have extensive experience in setting up and analysing Microbiome-Wide Association Studies and offer tailored solutions to our clients. The challenge with microbiome-wide association studies is often the complexity and redundancy of the microbiome, as well as the complexity and/or uncertainty of the clinical parameters measured. To address this, we offer a range of systems biological solutions and offer assistance for optimal study design. All our systems biology solutions are tailored specifically to our clients based on the following key principles below.


Reduction of complexity
Most data types including microbiome, metabolomics and often clinical data, can be reduced in complexity with minimal loss of precision. For example, shotgun metagenomics data can be reduced from millions of genes to metagenomic species’ (see article) and gene modules, like plasmids, phages etc. Similarly, metabolomics data can be reduced to metabolite co-abundance modules, such that the complexity of the initial data becomes of a manageable complexity that, in turn, increases statistical power, and often increases conceptualization of the data (i.e. to species, pathways, diseases, etc.).
 

The functional overlapping species concept
– A way to overcome the functional redundancy and complexity of the microbiome
Most microbiomes consist of many microbial species with overlapping functional niches, and across many instances e.g. with individuals, different species may fill a given niche (see figure). Untamed, this property of microbiomes is an obstacle to classical microbiome-wide association studies. However, by grouping the species that have potential to fill equivalent ecological or functional niches, this challenge can be overcome. Furthermore, group association of functional overlapping species is often much more informative than association to individual species, as the shared properties of the species-group may give hints towards underlying mechanisms behind the association.