Biostatisticians build a better analytic tool

The right tool makes any job easier. That is especially true when the job involves sifting through millions—sometimes billions—of pieces of genetic information. Scientists can use that information to uncover the basis of disease, including the role that inherited genetic variations may play in disease risk, treatment success or the likelihood of treatment-related side effects. Generating data through whole-genome sequencing and other techniques is just the beginning.

St. Jude biostatisticians partnered with colleagues in the U.S. and China to develop such a tool. This new statistical approach can help scientists to learn more from genomic data. The approach is called the set-valued method.

The researchers showed that for certain studies the new method is better than current analytic tools at identifying rare variations associated with secondary traits that may provide clues about disease risk. The tool was designed for case-control studies, an approach to find differences between two groups of people.

“This new method has such profound advantages compared with current approaches that we strongly recommend using it to analyze secondary genetic traits in sequencing studies with a case-control design,” said Guolian Kang, PhD, of the St. Jude Biostatistics department. Kang is pictured here with St. Jude postdoctoral fellow Wenjian Bi, PhD. They are first authors of a report about the novel approach that appeared in the journal Genetics.