Uricchio lab @Tufts

The big picture

Many species worldwide are experiencing rapid, human-induced changes in their environment that threaten their persistence, but we still struggle to predict which species will adapt to these changing circumstances, and how adaptation may affect species composition in nature. We develop evolutionary models and statistical inference techniques to detect selection signals in genomic and phenotypic data. Long-term goals of our work are to use these models to predict when species maintain sufficient genetic variation to adapt to rapid environmental change, and to understand how evolutionary processes affect our ability to detect the genetic variation underlying heritable traits.


When does adaptation occur in response to environmental change?


Evolutionary biologists have catalogued many recent adaptation events across species. But most of inferences of adaptation proceed backwards in time -- we use a combination of genomic, phenptypic and ecological data to understand how evolution may have favored particular genetic variants in particular environments. But how can we translate these methods to understand when adaptation is likely to occur? Is adaptation always inherently unpredictable, or are there certain traits and environmental changes that result in predictable outcomes?

We are developing new methods to infer adaptation rate and strength from genomic data along with my colleague David Enard . Most classic methods to infer adaptation rate and strength have assumed that beneficial mutations provide large fitness advantages, but we are increasingly aware that adaptation can sometimes proceed through polygenic adaptation and/or selection on standing variation. The extent to which these processes occur and contribute to species persistence is an open question. Our work argues that weakly beneficial alleles are likely to be a major contributor to the adaptation process. If we are able to learn more about the specific traits and genetic architectures that are likely be evolvable through polygenic adaptation or selection on standing variation, we may be able to improve our ability to predict when species will (or will not) adapt to their changing environment. This work relies heavily on our ability to perform computationally efficient simulations of linked selection and complex demography.


Predicting the outcome of competition in plant communities

Exotic species can dramatically affect species composition by directly out-competing native species, but in some cases native and introduced species may coexist stably. We studied competition between native and exotic species in California grasslands. Our analysis suggests that invaders dominate over California native species, but that the likelihood of persistence of natives depends integrally on the order of arrival of their exotic competitors. Our results provide a contrast to many earlier studies that focused on undisturbed communities and found stabilizing niche differences between species, perhaps due to the long co-evolutionary history of naturally co-occurring species. Our ongoing work seeks to incorporate genomic data into ecological models to predict when species composition is likely to depend on rapid evolutionary processes.



How does natural selection affect genetically inherited traits?

Many traits have a genetic component. In humans, examples include eye color, height, and body mass index, and also many common diseases, such as asthma and cardiovascular disease. Evolutionarily, this poses a question: if some diseases have a genetic component, how can they be common? Put another way, why hasn't natural selection constrained disease-causing genetic variation to be very rare, thereby making diseases very rare? The short answer is: although we have many promising leads, we're still working on figuring it out. Some hypotheses include:

  • Natural selection did not act on disease variation for common human diseases in ancestral human populations.
  • Disease genotypes are detrimental in the context of the disease, but advantageous for another reason (e.g., a disease variant increased the rate of surviving childhood in ancestral human populations, but shortens the expected lifespan in adulthood).
  • Disease variation is under very strong negative selection and most disease variation is very rare, but there are so many sites in the 3 billion base pairs of the human genome that confer disease risk that disease is still common (i.e., there are many different ways to get same the same disease with different rare genetic variants).
  • Heritable human diseases are not as heritable as we think they are (i.e., genetics plays a smaller role than environment).

To date, it's been very difficult to tease apart these possible explanations and make sense of how selection does (or does not) act on common, heritable human diseases. Our work has mostly focused on the first three possibilities. We use population genetic models of natural selection and complex phenotypes to model recent (~100,000 yrs) human evolutionary history, and to infer how natural selection shapes genetic variation. During my PhD, we showed that state-of-the-art techniques for detecting risk loci in the genome are not as effective as one would hope when selection acts directly on trait-related variation. We also showed that selection on RNA expression is likely to dramatically increase the relative importance of rare variation in driving gene expression, suggesting that evolutionary constraint has controlled the rate of evolution of genetic elements underlying gene expression.