October 31, 2014

Scrolling Headlines:

Halloween Special Issue -

Thursday, October 30, 2014

UM alumni hopeful for their up-and-coming snowboard company -

Thursday, October 30, 2014

UMass hockey looks to end road trip on a high note with weekend series against Maine -

Thursday, October 30, 2014

#WrongDoor: Why I am not surprised? -

Thursday, October 30, 2014

B-horror films: hits and misses of the nightmare genre -

Thursday, October 30, 2014

Appreciating campus workers -

Thursday, October 30, 2014

UMass hosts Ebola panel to address concerns of the public -

Thursday, October 30, 2014

UMass Democrats hope to get more students connected -

Thursday, October 30, 2014

The broke college student horror comic buyers guide -

Thursday, October 30, 2014

UMass Republican Club: Not just for Republicans -

Thursday, October 30, 2014

Five reasons why Halloween is the best holiday -

Thursday, October 30, 2014

To live and die and live again -

Thursday, October 30, 2014

The anatomy of a horror game -

Thursday, October 30, 2014

Berger has first shot at securing starting role with UMass basketball -

Thursday, October 30, 2014

Robert Johnson’s deal with the devil -

Thursday, October 30, 2014

Humans vs. Zombies: UMass’ most dangerous game -

Thursday, October 30, 2014

Group Halloween costumes inspired by the roles of Hollywood icons -

Thursday, October 30, 2014

A haunting at UMass -

Thursday, October 30, 2014

At the end of your rope? Write about it. -

Thursday, October 30, 2014

‘Gienie’ in a bottle: Pigskin Pick’Em Week nine -

Thursday, October 30, 2014

UMass study reveals genetic links with disease

Chris Roy/Collegian

A new approach to data analysis has led University of Massachusetts biostatisticians to discover new genetic information linked to common diseases such as diabetes and heart disease, according to a UMass press release.

The team of researchers, led by Andrea Foulkes, has applied this new approach to data analysis to pre-existing databases, revealing the genetic information behind that which causes conditions such as high cholesterol and heart diseases, according to the release.

Foulkes directs the Institute for Computational Biology, Biostatistics and Bioinformatics at UMass. Other members of her team include Rongheng Lin, an assistant professor, and Gregory Matthews and Ujjwall Das, who are both postdoctoral researchers. The work done by the team was supported by the National Institutes of Health National Heart, Lung and Blood Institute, the release stated.

“This new approach to data analysis provides opportunities for developing new treatments. It also advances approaches to identifying people at greatest risk for heart disease,” said Foulkes in the release.

The new style of analysis coined “MixMAP,” which was developed by Foulkes and cardiologist Dr. Muredach Reilly at the University of Pennsylvania, stands for “Mixed modeling of Meta-Analysis P-values,” according to the release. Since this method of statistical analysis is based on pre-existing public information, it “represents a low-cost tool” for researchers, according to the release.

“Another important point is that our method is straightforward to use with freely available computer software and can be applied broadly to advance genetic knowledge of many diseases,” Foulkes added in the release.

Foulkes explained that the new method takes the entire human genome into account and can be generalized to figure out many different diseases. Though the other more widely-used methods of gene tracking and analysis look for a “needle in a haystack,” so to speak, as a disease signal, according to the release, Foulkes’ new method makes use of genome knowledge in DNA regions that “contain several genetic signals for disease variation clumped together. … Thus, it is able to detect groups of unusual variants.”

According to the release, Foulkes characterizes the “MixMAP” technique as a discovery method still in need of scientific validation, although it “goes farther than usual by using sophisticated modeling approaches to quantify error.”

“We’ve done better than simply identify the strongest signals, we’ve quantified measures of association to show they are statistically meaningful,” noted Foulkes in the release.

George Felder can be reached at gfelder@student.umass.edu

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