The demographic consequences of the Neolithic transition

Upper panel shows four people with spears hunting a mastodon, while lower panel shows four thatched dwellings with a few dozen people harvesting grain and constructing a settlement

The widespread adoption of farming coincided with some of the most dramatic demographic shifts – including major population growth, decline, mixture, and movement – in human history. These past events have left distinctive signatures in modern genomes, which represent a trove of information about our past.

In a recent paper, I analyzed population-level genetic data to investigate the history of the Chabu, a population of traditional hunter-gatherers living in the Southwest Ethiopian highlands. I found that the Chabu population has declined significantly over the last few millenia, mirroring trends observed in other Eastern African hunter-gatherers. However, I also observed that this trend was not universal among the descendants of ancient hunter-gatherers of this region. See our paper, my twitter thread, and this commentary for more.

My current research aims to integrate archeological and genetic data to better understand the transition to farming throughout the world. We still don’t know what the primary motivators were for humans to give up hunting and gathering and adopt farming. Furthermore, the main driving factors – which could have included population pressure, climate, and human innovation – may not have been the same across different regions of the world. By building inferential frameworks that integrate evidence from both archeology and genetics, I am seeking a better understanding of the tempo and mode of this major cultural transition across the globe.

Integrating genetics and epigenetics to understand human diversity

An illustration of a segment of a DNA double helix with a central CpG site that is methylated on both strands

Understanding how particular traits arise from the combination of complex factors, both genetic and environmental, is a major goal of genomics research. Epigenetic mechanisms integrate the effects of genetics, lifestyle, age, and stress to influence gene expression and, potentially, phenotypic expression.

As part of my PhD dissertation, I studied how DNA methylation patterns change with age, and how these patterns differ across different human populations and across tissue types. Since DNA methylation is known to be strongly influenced by heritable factors (i.e. genotype), I also became interested in exploring how common genetic variants might influence epigenetic aging patterns.

My postdoctoral work extended this line of inquiry to gene expression, which, like DNA methylation, has a strong heritable component. I built genotype-based models for gene expression and helped develop a novel fine-mapping approach that improves performance by leveraging information from across diverse genetic ancestries. We found that by using inferred genome-wide gene expression from genetically diverse cohorts, we could significantly improve our ability to pinpoint the genes that causally impact traits.

The genetics of host and pathogen evolution

Red blood cells infected with malaria-causing parasites

For most of human history, pathogens have been the primary drivers of mortality and are thought to have driven some of the strongest selection that human populations experienced. Genomic data can provide insights into the dynamics of these evolutionary arms races.

My current research aims to understand patterns of genetic diversity in Plasmodium vivax, a malaria-causing pathogen. I am developing integrated epidemiological and genomic models that are designed to leverage increasing quantities of P. vivax genomic data to gain insights into this pathogens’ origins, spread, and evolution.

Over the last few years, we have seen a devastating pandemic sicken and kill millions of people worldwide. Research has shown that genetic factors contribute to an individual’s risk of severe COVID-19 outcomes. This recent paper leverages genotype-based models of gene expression that I built to dissect the genetic etiology of hospitalization following SARS-Cov-2 infection, identifying putative causal genes in the immune pathway.