Washington, Mar 25 : With the help of a new mathematical model scientists at Michigan Technological University have successfully identified 11 gene variations associated with type 2 diabetes.
They have developed new mathematical tools for isolating genes responsible for the most intractable diseases, including type 2 diabetes.
With the help of these tools, scientists have successfully isolated 1 gene variations called single nucleotide polymorphisms, SNPs or 'snips' associated with type 2 diabetes.
The new model called Ensemble Learning Approach (ELA), is a software that can detect a set of SNPs having a significant effect on a disease.
"With chronic, complex diseases like Parkinson's, diabetes and ALS [Lou Gehrig's disease], multiple genes are involved," said Qiuying Sha, an assistant professor of mathematical sciences.
"You need a powerful test," Sha added.
With complex inherited conditions, including type 2 diabetes, there are some genes that may precipitate the disease individually while some work jointly. Identifying the suspected genes was tough as the calculations needed to match up were also difficult.
ELA drastically narrows the field of potentially dangerous genes, and applies the statistical methods to determine which SNPs act on their own and which act in combination.
With the help of the model, the team led by Sha examined the genes of over 1,000 British people, half with type 2 diabetes and half without.
They identified 11 SNPs that, singly or in pairs, are linked to the disease with a high degree of probability.
Scientists have also developed another tool through which they can cast back through generations to identify the genes behind inherited illness of the intractable diseases.
This model 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.