Shuanglin Zhang Profile

Dr. Shuanglin Zhang

Shuanglin Zhang

PhD, Statistics

Expertise

Statistics

Contact

906-487-2146
shuzhang@mtu.edu

A team of Michigan Tech mathematicians led by Professor Shuanglin Zhang, who was recently awarded the Richard and Elizabeth Henes Professorship in Mathematical Sciences, has developed powerful new tools for winnowing out the genes linked to some of humanity's most intractable diseases.

With one, they can cast back through generations to pinpoint the genes behind inherited illness. With another, they have isolated eleven genes associated with type-2 diabetes. The team spokesperson is Qiuying Sha, Zhang's wife and an assistant professor of mathematical sciences. Zhang has contracted another genetically driven condition: amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig's disease, which has made speaking difficult. Ironically, his work may one day pave the way to a cure. "With chronic, complex diseases like Parkinson's, diabetes, and ALS, multiple genes are involved," said Sha. "It is critical to develop statistical methods that can account for gene-gene interactions and can analyze these genes jointly."

This team has developed the Ensemble Learning Approach (ELA), software used to detect a set of genes that together have a significant effect on a disease.

With complex inherited conditions, including type-2 diabetes, single genes may precipitate the disease on their own, while other genes cause disease when they act together.

In the past, finding these gene-gene combinations has been especially unwieldy because the calculations needed to match up suspect markers among the five-hundred thousand or so in the human genome have been virtually impossible. ELA sidesteps this problem, first by drastically narrowing the field of potentially dangerous genes, and second, by determining which genetic variants act on their own and which act in combination. "We thought it will be a powerful tool to help finding disease-related genes for complex diseases," Sha said.

Their work has been published in Genetic Epidemiology and is available online at http://www3.interscience.wiley.com/cgi-bin/abstract/117890704/ABSTRACT.

ELA is also used to compare the genetic makeup of unrelated individuals to sort out disease-related genes. The team has also developed another approach, which uses a two-stage association test that incorporates founders' phenotypes, called TTFP, that can examine the genomes of family members going back generations.

"In the past, researchers have dealt with the nuclear family, parents, and children, but this could go back to grandparents, great-grandparents . . . as far back as you want." The team has published their findings in the European Journal of Human Genetics. An abstract is available at www.nature.com/ejhg/journal/v15/n11/abs/5201902a.html. Other members of Michigan Tech's statistical genetics group are postdoctoral scientists Zhaogong Zhang and Tao Feng.

Now that they've developed the software, the analysis is relatively simple, says Sha. But getting the genetic data to work on is not. "We don't have the data sets yet to work with," she says, clearly frustrated.

Those who do have data sets, however, can use the team's software to help find the cause—and hopefully, the cures—for a multitude of illnesses. Maybe even Lou Gehrig's disease.

ELA is available in Windows and Linux versions at www.math.mtu.edu/~shuzhang/software.html, and TTFP is available by request to Sha, qsha@mtu.edu.