These were your genes, 20 generations agoMarch 19th, 2008 - 4:16 pm ICT by admin
Washington, March 19 (IANS) Researchers have created a new tool that can decode the biological record or genomic structure of an individual or a population going back 20 generations. The International HapMap Project has made considerable headway in describing common patterns of human genetic variation, yet analysing this data to decode ancestry still remains a formidable task.
Serafim Batzoglou of Stanford University, who led the research team, designed the model that significantly improves scientists’ ability to determine ancestry based upon genomic features.
Though genetic variation data from closely related populations is lacking, Sundquist and co-author Eugene Fratkin overcame this obstacle by constructing simulated population sets to test their model.
“We were then able to conduct tests on these populations and analyse the accuracy of our method as a function of both the population divergence and the number of generations of admixture,” explained Sundquist.
“Our results show that challenges still remain in distinguishing between closely related populations, but that we have vastly improved the state-of-the-art.”
In the second paper, researchers from the International Computer Science Institute and the University of California, Berkeley, have taken another approach to inferring ancestry.
S. Sankararaman and colleagues present a new computational model that improves previous methods for deriving ancestry information from admixed populations, by more accurately modelling linkage disequilibrium and predicting historical recombination events.
The authors utilise their algorithm to tackle problems such as inferring locus-specific ancestry in a population derived from unknown ancestral populations.
Tags: ancestry, co author, computational model, computer science institute, divergence, genes, headway, human genetic variation, international computer science, international hapmap project, linkage disequilibrium, locus, march 19, obstacle, populations, recombination events, stanford university, sundquist, university of california berkeley, variation data