Ever looked at a world map and wondered where your ancestors are from? Well, it may be possible to find out just by sampling your genome, thanks to a new genetic method developed by researchers in the US and Israel that can pinpoint an individual’s geographic origin.
The team, from the University of California – Los Angeles (UCLA) Henry Samueli School of Engineering and Applied Science, UCLA’s Department of Ecology and Evolutionary Biology, and Tel Aviv University, write about their work in a paper published online in Nature Genetics on 20 May.
The researchers hope their method, which they call “spatial ancestry analysis” or SPA, will increase understanding of genetic diversity among populations, which in turn helps us better understand human disease and evolution.
Research areas that may benefit from the new method include finding links between genetic variants and disease and locating parts of genomes that have been subject to positive selection.
SPA is a software tool for analyzing spatial structure in genetic data. It models genotypes in two- and three-dimensional space.
With SPA researchers can model the spatial distributon of each genetic variant. And in this study, the team showed that particular frequency patterns of spatial distribution of gene variants are tied to particular geographic locations.
For genetic variants the team used SNPs (“snips”, short for single-nucleotide polymorphisms) from various parts of the genome, including “the well-characterized LCT region, as well as at loci including FOXP2, OCA2 and LRP1B”.
An SNP is a DNA sequence variation where there is a single nucleotide (A, T, C or G) difference in the “spelling” of the sequence.
Co-author Eleazar Eskin is an associate professor of computer science at UCLA Engineering. He told the press:
“If we know from where each individual in our study originated, what we observe is that some variation is more common in one part of the world and less common in another part of the world.”
Eskin and colleagues developed the SPA software as a “simple probabilistic model” that plots the frequency with which a genetic variant changes as a function of the geographic location of the individual. In other words, the gene frequency is a function of the x and y coordinates of an individual on a map.
They fed genetic variation and geographic location data from a European and a worldwide collection of individuals into the model.
From this they found they were able to place individuals on a world map on the basis of their genetic information alone, thus showing you can look at frequency of genetic variation as being tied to a particular location in the world.
Eskin said this gives us a different way of thinking about populations. Instead of ancestry having discrete properties, you see it as a “continuum”:
“If you think about a person’s ancestry, it is no longer about being from a specific population – but instead, each person’s ancestry is defined by the location they’re from”, he explained.
First author Wen-Yun Yang, a UCLA computer science graduate student, explained how the model can then be useful:
“If the location of an individual is unknown, our model can actually infer geographic origins for each individual using only their genetic data with surprising accuracy.”
Co-author John Novembre, an assistant professor in UCLA’s department of ecology and evolution, said:
“The model makes it possible to infer the geographic ancestry of an individual’s parents, even if those parents differ in ancestry. Existing approaches falter when it comes to this task.”
The researchers also propose that the model provides a method for finding genes that are undergoing selection in humans.
The National Science Foundation and the National Institutes of Health provided financial support for the study.
Written by Catharine Paddock PhD