Family tree geography goes genetic

The team created a genetic algorithm, cleverly called the Geographical Population Structure (GPS) algorithm, using accurate genetic and geographical information provided anonymously by volunteer participants of the Genographic Project. The algorithm can accurately predict 83% of a living individual’s country of origin, and when tested on over 200 Sardinians, GPS was able to place a quarter of them in their villages and most of the rest within 50 kilometres of their home villages.

Potential use as a genealogy tool

Genetic information can already be used to identify the population of origin of other organisms, and in humans, it has been used as evidence of historical migrations, including the out-of-Africa event. However, this is the first time a database and complex algorithm has been developed to successfully extrapolate a modern individual’s geographical origin. The method has potential uses as a genealogy tool, for example, it could help adoptees trace their origins. It may also have applications in forensic and health research, for example, assigning ethnic and geographic classifications to DNA evidence.

It also raises the possibility of precise genetic ancestry testing as more biogeographical datasets are added to the database. For example, while the dataset is reasonably well filled for Europeans, there is little data for Māori or Polynesian people at present.

In their published paper, the authors stress that the tool only works well where appropriate samples are available in the reference population dataset. They also emphasise that GPS may not necessarily yield the most recent region of residency for “populations that experienced recent admixture or have recently migrated and maintained a certain degree of endogamy, but rather a historical residency. Therefore, the region of origin can be intuitively interpreted as where the last major admixture took place at the population level since the establishment of the reference populations in their provided geographical location.”

New thinking about factors that shape human population structure

The authors went on to say, “We envision that, with time, biogeographical applications will become enhanced for more worldwide communities due to the addition of populations to the reference panel. Therefore, our results should be considered a lower bound to the full potential of GPS for biogeography. We hope that our study will promote new thinking about how population size, genetic diversity and environment have shaped human population structure.”