Algorithm Can Trace Ancestry from a Single DNA Sample

October 2, 2007

Collaborating scientists from around the world say they have developed a computer algorithm that can trace someone's genetic ancestry based on a single DNA sample from a simple cheek swab. The algorithm works by identifying the critical DNA markers — single nucleotide polymorphisms (SNPs) — that signal genetic similarities among people of the same ancestry.

Requiring no prior knowledge of an individual's ancestry and background, the algorithm has already been used to identify the ancestry of several hundred people, including Native Americans, Europeans, Africans, and individuals from the complex genetic population of Puerto Rico.

"Now that we have found that the program works well, we hope to implement it on a much larger scale, using hundreds of thousands of SNPs and thousands of individuals," said RPI computer scientist Petros Drineas.

Understanding humankind's genetic makeup is critical to unraveling the genetic basis for complex diseases, the team noted. While people share 99% of the genome, it is the 1% difference that can have a major impact on their response to diseases, viruses, medications, and toxins. If researchers can uncover the genetic details that set individuals apart, biomedical studies may lead to customized treatment, Drineas said.

Drineas predicted that the algorithm could also help aid historians and anthropologists in their investigations of the origins of populations and how humans spread across the globe.

Drineas' collaborators included computer scientists, mathematicians, biologists, and biomedical researchers: Peristera Paschou (Democritus University of Thrace); Elad Ziv, Esteban G. Burchard, and Shweta Choudhry (University of California, San Francisco); William Rodriguez-Cintron (University of Puerto Rico School of Medicine); and Michael W. Mahoney (Yahoo! Research).

The findings appear in the paper "PCA-Correlated SNPs for Structure Identification in Worldwide Human Populations," published in PLoS Genetics.

Source: Rensselaer Polytechnic Institute, Sept. 20, 2007.