Journey Through the Genetic Evolution of Stone Age Europe

Ancient DNA analysis, the study of DNA extracted from ancient archeological specimens, is carving a new understanding of human history. Written in nucleotide bases are complex stories of how ancient groups socialized, insights into their agricultural practices and even clues on which diseases they were exposed to.

Over recent years, ancient DNA studies have contributed to a growing picture of how our ancestors lived during the European Stone Age. These insights are possible thanks to the development of sophisticated next-generation sequencing (NGS) technologies, capable of analyzing small and heavily degraded DNA fragments.

Before agriculture spread into the region during the Mesolithic period, approximately 8500 years ago, hunter-gatherer populations inhabited Europe, divided broadly into two groups: Western Hunter-Gatherers (WHG) and East European Hunter-Gatherers (EHG). It’s likely that intermingling occured between these two core groups, but our understanding of the dynamics and timelines relating to genetic blending during this period remains limited. 

An international team of scientists – led by researchers at Uppsala University – analyzed genome data from 56 specimens dating back to the Mesolithic, Neolithic and Eneolithic periods across Central and Eastern Europe. The analyses, published earlier this month in Communications Biology, further enhance our understanding of population dynamics during the Stone Age. While some groups clearly intermingled, others appear to have been isolated from one another, most likely due to their geographical location.

Dr. Tiina Mattila, a population geneticist at Uppsala University and the study’s lead author, sat down with Technology Networks to discuss the genetic evolution of Stone Age Europe.

Molly Campbell (MC): What did we already know about the different groups occupying Stone Age Europe prior to this study?

Tiina Mattila (TM): Prior to this study, it was known that the spread of farming in Europe was strongly associated with the migration of people from Anatolia. This genetic cluster has been labeled as Anatolian Neolithic (AN). The migrating farmers were genetically distinguishable from the hunter-gatherers living in Europe before this migratory wave during the Mesolithic.

The major Mesolithic European genetic hunter-gatherer lineages were labeled as WHG and EHG. The former was prevalent in Central and Western Europe, while the latter occupied regions further to the east. In Central, Western, and Southern Europe, the AN group quickly became dominant when farming spread. However, in some regions, the hunter-gatherer lineages remained dominant. The Mesolithic populations from the Baltic region, Scandinavia and Eastern Europe were a mixture of the WHG and EHG lineages, meaning that they had ancestors from both of these groups.

MC: Your study shows that the intermingling of hunter-gatherers in Eurasia’s genetic lines was strongly linked to geography – can you explain how, and what this means for our understanding of their history?

TM: In one part of our study, we extended the investigation on the previously defined Mesolithic WHG–EHG admixture population. We generated new data from Mesolithic individuals from Central and Eastern Europe and combined this with previously published Mesolithic individuals from nearby regions.

We next measured a topology-aware geographic distance from the region occupied by the WHG lineage and found a linear decrease in the admixture proportion deriving from WHG lineage when moving away from the WHG region. This means these populations were separated but after some time met again, had offspring and merged together.

MC: You discovered that common graves during the European Stone Age were not always indicative of family relations. Can you discuss the data behind this conclusion and what it tells us about social systems during this period?

TM: The genome of an individual is a mixture of their parent's genomes: half of the genome derives from the mother’s and half from the father's genome. For this reason, we also share, on average, half of our genome with our siblings and decreasing levels with other relatives, creating a hierarchical relatedness network.

When we compare the genetic variation between the genomes of, for example, parent–offspring pairs, they are expected to be more similar to each other than two non-related individuals drawn from a population. We used the ancient DNA data from multiple individuals to search for close relatives in our dataset. In some cases, the organization of the burials suggested kinship. For example, in one grave from a farming context, an adult female and a child were buried close to each other indicating some type of bond between the individuals. However, we show that, genetically, they were not closely related (at least they were not first- or second-degree relatives), suggesting a potential social rather than genetic bond between individuals. Complex social systems are unique characteristics of humans and knowledge of universal patterns of human populations helps us to understand the evolution of these human-specific properties.

MC: What challenges did you encounter during this research project, and are there any limitations you wish to highlight to our readers?

TM: Sampling in ancient DNA research is extremely tedious and time-consuming, due to the fact that samples are buried underground, there is a shortage of suitable material, and a high proportion of environmental DNA contaminating the sample.

When an organism dies, the biological material degrades through time. Its preservation strongly depends on the environmental conditions. Hence, sometimes (and actually quite often when sampling thousands of years old material), the sample does not contain enough DNA from the individual of interest. This obviously sets some frame for the sample size in an individual study, and the older periods are covered by fewer samples. Luckily, however, the genome of one individual does not only represent the individual itself, but also its ancestors. Hence, a single individual can tell us a lot about the broad history of the population to which the person belonged. This was, for example, the main purpose of our study. However, our sampling design, covering thousands of years in a relatively large geographic area was not optimal for studying intra-population social structure. A wider sampling of single, well-defined sites is more powerful in such investigations.

MC: What are your next steps in this research space?

TM: In the future, methodological developments and growth of the comparative datasets will allow a deeper understanding of the process of admixture and factors affecting inter-population structure. For example, we found variable degrees of mixing between groups. However, we can only guess what caused these differences in the mixing patterns. If we get more information on the early steps of admixture, it may be possible to discuss the causes of the observed patterns. In addition to optimized sampling, this will require collaboration across large research groups in future projects.

Dr. Tiina Mattila was speaking to Molly Campbell, Senior Science Writer at Technology Networks.