In the past decade, population genetics research has undergone a remarkable paradigm shift as Kimura’s classic “neutral theory of molecular evolution” has been swept aside in light of new research that finds that adaptation is pervasive across animal genomes. The magnitude of this revelation has made clear that population geneticists know very little about the forces in the environment that have driven staggering levels of genomic adaptation.

Quantifying genomic adaptation has, however, proved one of the most difficult questions in population genetics due to the many confounding factors that mimic or mask adaptation. Unfortunately, controls for important confounding factors are still lacking. The recent findings that signals of polygenic adaptation are often confounded by demography are for example very revealing of the task ahead for population geneticists (Berg et al., 2018 bioRxiv). The lab develops new powerful approaches that address the need to quantify adaptation in response to environmental pressures while solving the problem of simultaneously controlling for many confounding factors.

The lab focuses in particular on the study of ancient epidemics through the lens of host genomic adaptation, where signals of adaptation at host genes that interact with specific pathogens help us identify which specific viruses, bacteria or eukaryotic pathogens were responsible for the ancient epidemics that left these adaptive signals. In practice, the projects in the lab involve the use or/and creation of statistical tools to analyze adaptation in whole genomes, intensive population simulations, whole-genome sequencing and the annotation of diverse functional classes of host-pathogen interactions.

We are currently focusing on adaptation in humans and bats through four different projects:

The impact of pathogens on introgression between archaic and modern humans

We use Neanderthal, Denisovan, and other archaic genomic data only known through their introgressed remnants in modern human genomes, to quantify adaptive introgression driven by pathogens. This work expands our previous analysis that showed that viruses, and RNA viruses in particular were major drivers of adaptive introgression between Neanderthals and modern humans (Enard and Petrov, Cell, 2018). Among other questions, we are currently asking if we can rank pathogens in terms of the total amount of adaptive introgression they were responsible for. In other words, of viruses, bacteria or eukaryotic pathogens, which of these drove the most adaptive introgression soon after interbreeding? Does this question have different answers at different times and different parts of the ancient world?

human genomics Enard lab

Dating ancient epidemics

The lab uses both present and ancient modern human genomes to date ancient epidemics driven by specific pathogens in modern human populations in the past 50,000 years. Based on preliminary results, we are currently focusing on ancient lentiviruses, filoviruses and variola virus, the infamous agent of smallpox. This projects relies on the analysis of the behavior of existing statistics to detect selective sweeps over evolutionary time, as well as on the development of powerful new statistics able to detect both recent or old sweeps, complete or incomplete, hard or soft.

Ancient viral epidemics through the lens of host regulatory adaptation

Changes in host factor expression induced by virologists (RNAi, knockout, overexpression, etc.) can be either detrimental or favorable to a specific virus. Host factor expression changes detrimental to a virus in the lab should have also been detrimental in the wild during evolution. The lab studies the coincidence between the thousands of induced host expression changes in the virology literature and the thousands of signals of regulatory adaptation in human genomes.

Ecological determinants of proteome adaptation against viruses in bats: bridging ecology and adaptive molecular evolution

In collaboration with Dr. Hannah Kim Frank from Stanford University, we are in the process of sequencing the whole genomes of ~80 bat species. This large number of bat species, coupled with (i) the thousands of interactions with diverse viruses we previously annotated and (ii) the known ecological parameters of bats (temperature, species richness, etc.) make it possible to conduct correlation analyses to identify which ecological parameters affect the intensity of adaptation against viruses across a representative sample of the bat phylogeny. This project is the first to ever try to connect the present ecology of a group of species with their long term patterns of adaptive molecular evolution.