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Center for High Performance Computing

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In addition to deploying and operating high-performance computational resources and providing advanced user support and training, CHPC serves as an expert team to broadly support the increasingly diverse research computing and data needs on campus. These needs include support for big data, big data movement, data analytics, security, virtual machines, Windows science application servers, protected environments for data mining and analysis of protected health information, advanced networking, and more.

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Announcing the Retirements of Anita M. Orendt and Julia Harrison
Julia Harrison
Julia Harrison

After nearly four decades of dedicated service at the University of Utah, Julia Harrison retired as the Operations Director of the Center for High Performance Computing.

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Anita M. Orendt
Anita M. Orendt

Anita M. Orendt is a dedicated educator and researcher with a rich background in physical chemistry. Anita has made significant contributions to the academic community at the University of Utah.

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Upcoming Events:

CHPC PE DOWNTIME: Partial Protected Environment Downtime  -- Oct 24-25, 2023

Posted October 18th, 2023


CHPC INFORMATION: MATLAB and Ansys updates

Posted September 22, 2023


CHPC SECURITY REMINDER

Posted September 8th, 2023

CHPC is reaching out to remind our users of their responsibility to understand what the software being used is doing, especially software that you download, install, or compile yourself. Read More...

News History...

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The Deep History of Human Populations

By Alan R. Rogers

Departments of Anthropology and Biology, University of Utah

Our lab has developed a new statistical method, called “Legofit,” which uses genetic data to estimate the history of population size, subdivision, and gene flow. Our recent publications have used it to study human evolution over the past 2 million years. Legofit studies the frequencies of “nucleotide site patterns,” which are illustrated in the figure. The solid black lines and arrows represent a network of populations. The dashed and colored lines show one of many possible gene genealogies that might occur at different nucleotide sites within the genome. Upper-case letters refer to populations. X represents an African population (the Yorubans), Y a European population, A Altai Neanderthals, and D Denisovans. S is an unsampled “superarchaic” population that is distantly related to other humans. Lowercase letters at the bottom of the figure label nucleotide site patterns. A nucleotide site exhibits pattern xya if random nucleotides sampled from X, Y, and A carry the derived allele, but those sampled from other populations are ancestral. Site pattern probabilities can be calculated from models of population history, and their frequencies can be estimated from data. Legofit estimates parameters by fitting models to these relative frequencies. 

Nucleotide site patterns contain only a portion of the information available in genome sequence data. This portion, however, is of particular relevance to the study of deep population history. Site pattern frequencies are unaffected by recent population history because they ignore the within population component of variation. This reduces the number of parameters we must estimate and allows us to focus on the distant past. The archaeology of the early middle Pleistocene provided an additional clue. At this time, the “neandersovan” ancestors of Neanderthals and Denisovans separated from the ancestors of modern humans. Modern humans seem to have evolved in Africa, so it seemed plausible that neandersovans separated from an African population and emigrated to Eurasia. Had they done so, they would have encountered the previous “superarchaic” inhabitants of Eurasia, who had been there since about 1.85 million years ago. This suggested a fourth episode of admixture, labeled δ in the figure, from superarchaics into neandersovans.

For more information, see our Spring 2020 newsletter here.



 

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