February 26, 2008 - By Amy Adams
Stanford researchers have created the highest resolution map of human genetic diversity to date, providing insight into how groups of people throughout the world are related and adding weight to previous theories that traced human origins to Africa.
The researchers surveyed 650,000 genetic locations in people from 51 populations to derive the map, providing data that will become a valuable tool in the search for disease-related genes. The work was published in the Feb. 22 issue of Science.
The data confirm earlier work that the vast majority of genetic variation occurs within populations rather than between populations, suggesting that, genetically speaking, race is only skin deep. 'Most of the DNA variation we see has nothing to do with what the people who use the term 'race' usually mean,' said Marcus Feldman, PhD, professor of biological sciences.
Feldman, who has spent decades studying human genetic diversity along with co-author Luca Cavalli-Sforza, PhD, professor emeritus of genetics, said the work expands upon many of their earlier ideas. 'This is the definitive study to show variation within populations,' Feldman said.
The data group people according to their similarities at 650,000 DNA locations. At each location, a person has one of the four DNA letters: an A, T, C or G. All those who have a G at a specific location would be grouped together, and so on. Although many genetic researchers over the years have clamored for exactly this kind of extensive sequencing effort, few labs have had the resources to tackle it. The Stanford group was uniquely suited to the project because of the technical capabilities of the Stanford Human Genome Center, which also played a role in sequencing the human genome.
Unlike previous genetic studies, which surveyed fewer DNA locations and could discern only large population groups, this high-resolution map gives a more detailed view of population diversity. One example is in the Middle East, where the data identifies subgroups that had previously been lumped in a single population. Likewise, the people of China fall into northern and southern groups, whose languages are dramatically different.
'This data shows that all humans are related in complex ways,' said co-author Gregory Barsh, MD, PhD, professor of genetics. 'Many of what we'd call populations are really mixtures of people.'
The data also have some new information. A group of people in Siberia share some genetic similarities and, by inference, ancestry with people indigenous to South America. Feldman said this data makes sense based on previous theories about human migration from Siberia across the Bering bridge to the Americas.
In addition to seeing which groups are related, the data give researchers the ability to track human migration. Anthropologists had previously thought humans originated in sub-Saharan Africa and left to colonize the rest of the world in several waves. This theory, known as 'Out of Africa,' was supported by some previous genetic studies but wasn't universally accepted. The new data bolster the Out-of-Africa model, showing that populations have less genetic diversity the farther that population is from Africa. This result is expected if the adventurous people leaving Africa represented only a small portion of the overall diversity of the population. This small group would establish new, less diverse populations elsewhere in the world.
Perhaps even more important than what the researchers found is what others may be able to learn from the data in the future. The researchers made their data publicly available as soon as they completed the analysis. Although that gave competing groups a head start on projects of their own, senior author Richard Myers, PhD, professor of genetics, said it was the right thing to do. The only stipulation was that other groups allow the Stanford team to publish the first paper using the data, following the precedent set by the public effort to sequence the human genome.
'Like the human genome project, this will enable people to learn important new things about human history and disease,' Myers said. Co-author Hua Tang, PhD, assistant professor of genetics, said the work should help propel researchers toward personalized treatments for genetic diseases, such as cancers.
One place where the data will be useful is in the hunt for disease-related genes, said Devin Absher, PhD, senior scientist at the Stanford Human Genome Center, where the experiments were performed and much of the analysis took place. Absher and Jun Li, PhD, senior scientist, are co-first authors of the paper. When looking for disease genes, researchers compare groups of people with and without a given disease and look for genetic differences. Some of those differences may be attributable to the subjects' genetic heritage rather than the disease itself. The data from the Stanford study should help researchers identify which variations are due to genetic heritage, helping them to focus on variations associated with the disease.
The DNA for this work comes from the Human Genome Diversity Panel, a collection initiated by Cavalli-Sforza in a collaboration with the Centre Etude Polymorphism Humain in Paris. Working with bioethicists to ensure samples were collected properly, anthropologists from around the world started sending blood to this collection in the 1990s. The 51 populations used in the Stanford study were chosen mostly on the basis of where anthropologists were able to get samples. Feldman and Cavalli-Sforza hope to one day have access to samples from additional populations, which will add to what's known about the genetic diversity and relatedness of the world's populations.
DNA from the HGDP is freely available. In fact, a paper published in Nature on Feb. 21 from a group led by Noah Rosenberg, PhD, assistant professor of genetics at the University of Michigan and former graduate student of Feldman's, looks at the same samples and reaches many of the same conclusions as the Stanford study.
Additional Stanford researchers who contributed to the study are senior scientist Audrey Southwick, PhD, and graduate students Amanda Casto and Sohini Ramachandran.
The work was funded by grants from the National Institutes of Health.
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