Comparative Population Genetics in the Human Gut Microbiome

Abstract

Genetic variation in the human gut microbiome is responsible for conferring a number of crucial phenotypes like the ability to digest food and metabolize drugs. Yet, our understanding of how this variation arises and is maintained remains relatively poor. Thus, the microbiome remains a largely untapped resource, as the large number of coexisting species in the microbiome presents a unique opportunity to compare and contrast evolutionary processes across species to identify universal trends and deviations. Here we outline features of the human gut microbiome that, while not unique in isolation, as an assemblage make it a system with unparalleled potential for comparative population genomics studies. We consciously take a broad view of comparative population genetics, emphasizing how sampling a large number of species allows researchers to identify universal evolutionary dynamics in addition to new genes, which can then be leveraged to identify exceptional species that deviate from general patterns. To highlight the potential power of comparative population genetics in the microbiome, we reanalyze patterns of purifying selection across ∼40 prevalent species in the human gut microbiome to identify intriguing trends which highlight functional categories in the microbiome that may be under more or less constraint.

Keywords: comparative population genetics; microbial evolution; microbiome; population genetics.

Fig. 1.

(a) The relationship between synonymous divergence on the x axis ($d_S$) and the ratio of nonsynonymous and synonymous divergences ($d_N/d_S)$ on the y axis follows the form predicted by purifying selection across species (Eq. S8 from S1D in Garud et al. 2019). Though by color coding individual species, we see that data points tend to be grouped by species identity, where certain species fall above or below the prediction. (b) By grouping genes by their pathways and generating an appropriate null distribution via permutation, we can identify pathways that, under the assumptions of the model, are under stronger or weaker purifying selection than expected by chance. We can then examine how the mean $d_N/d_S$ ($〈d_N/d_S〉$) of a given pathway relates to its variance (${\sigma }_{d_N/d_S}^2$), where the variance increases slightly faster than the square of $〈d_N/d_S〉$, suggesting that the coefficient of variation is greater than one (inset figure in b). (c) By inverting our permutation scheme, we can identify the set of species that are subject to stronger or weaker purifying selection than expected by chance.