A new statistical software may be a valuable tool in identifying genetic variations that lead to chronic diseases like diabetes. Mathematicians at Michigan Technological University have used this software to identify 11 genetic variations that might lead to type 2 diabetes.
The procedure that the software employs, known as the Ensemble Learning Approach (ELA), identifies single nucleotide polymorphisms (SNPs), which are the genetic variations under consideration, and makes sets of them which appear to relate to a specific disease. The 11 genetic variations found by the Michigan mathematicians that relate to type 2 diabetes are 11 sets of SNPs that appear to help lead to the disease.
The human genome is an extremely complex network of nearly 500 thousand genes, which in the past has been impractical to sort through: “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 genes among the 500,000 or so in the human genome have been virtually impossible.”
The ELA procedure allows this daunting task to be accomplished “first by drastically narrowing the field of potentially dangerous genes, and second, by applying statistical methods to determine which SNPs act on their own and which act in combination.”
By performing the ELA procedure on 1,000 individuals, half suffering from type 2 diabetes, mathematicians “identified 11 SNPs that, singly or in pairs, are linked to the disease with a high degree of probability.”
This is a new procedure, and is highly reliant on the availability of genetic data, which at present is not in high abundance. Further decoding of the human genome, along with improvements to ELA that will come with further testing, could make this a valuable tool in the future for identifying the genetic origins of diseases.
Source: Defeat Diabetes Foundation: Goodrich, Marcia. MichiganTech news release. April 2008.