January 30, 2015

Scrolling Headlines:

UMass athletic director John McCutcheon to take job at UCSB -

Thursday, January 29, 2015

UMass encourages responsible celebrating, modifies guest policy ahead of Super Bowl -

Thursday, January 29, 2015

UMass basketball returns home to Mullins Center with matchup against Dayton -

Thursday, January 29, 2015

Microsoft introduces Windows 10, Codename Spartan and the HoloLens -

Thursday, January 29, 2015

Cheap gas, a speed bump for the planet -

Thursday, January 29, 2015

Friday night a chance at redemption for UMass hockey -

Thursday, January 29, 2015

Beautiful focuses on body image and loving oneself -

Thursday, January 29, 2015

Minutewomen set to redeem themselves against the Bonnies -

Thursday, January 29, 2015

UMass basketball seeks more consistency out of its veterans -

Thursday, January 29, 2015

UMass hockey hopes to ride momentum into Friday’s matchup against Boston University -

Thursday, January 29, 2015

Tips for maintain and transitioning to a healthier lifestyle -

Thursday, January 29, 2015

MASSPIRG urges McDonalds to stop purchasing meat raised with antibiotics -

Wednesday, January 28, 2015

How to avoid, treat and prevent Computer Vision Syndrome as a college student -

Wednesday, January 28, 2015

Obama and Modi strengthen ties between U.S. and India -

Wednesday, January 28, 2015

UMass receives research honor from the Carnegie Foundation -

Wednesday, January 28, 2015

Islamophobia is a form of racism that needs to be stopped -

Wednesday, January 28, 2015

Björk gets personal on breakup album, ‘Vulnicura’ -

Wednesday, January 28, 2015

UMass Dining nominated for Seafood Champion Award -

Wednesday, January 28, 2015

Why UMass basketball isn’t a good brand of basketball -

Wednesday, January 28, 2015

BLOG: Joseph Widmar commits to UMass hockey -

Tuesday, January 27, 2015

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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