Washington, Apr 2 : Scientists at the Michigan Technological University have successfully identified 11 gene variations linked to type 2 diabetes, with the help of a new mathematical model.
The researchers have developed powerful new tools for winnowing out the genes behind some of humanity's most intractable diseases, including type 2 diabetes.
One of the tools, called Ensemble Learning Approach (ELA), enabled the researchers to isolate 11 variations within genes, called single nucleotide polymorphisms, SNPs or "snips," associated with type 2 diabetes.
Developed by Qiuying Sha, an assistant professor of mathematical sciences, ELA is software that can detect a set of SNPs that jointly have a significant effect on a disease.
"With chronic, complex diseases like Parkinson's, diabetes and ALS Lou Gehrig's disease, multiple genes are involved. You need a powerful test," said Sha.
In case of complex inherited conditions, like type 2 diabetes, it is possible for single genes to precipitate the disease on their own, while other genes cause disease when they act together. Earlier, it was cumbersome to find these gene-gene combinations as the calculations needed to tally suspect genes among the 500,000 or so in the human genome, have been quite difficult.
However, ELA rules out this problem, firstly by reducing the field of potentially dangerous genes, and secondly, by applying statistical methods to find out which SNPs act on their own and which act in combination.
"We thought it was pretty cool," said Sha.
In order to test their model on real data, the researchers examined genes from over 1,000 people in the United Kingdom, half with type 2 diabetes and half without. They singled out 11 SNPs that, singly or in pairs, are linked to the disease with a high degree of probability.
ELA is used to compare the genetic makeup of unrelated individuals to sort out disease-related genes. The researchers 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," said Sha.
But she added: "We don't have the data sets yet to work with. That's the problem with having no medical school."
The researchers have published their findings in the European Journal of Human Genetics.