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.
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