With one, they can cast back through generations to pinpoint the genes behind inherited illness. With another, they have isolated 11 variations within genes—called single nucleotide polymorphisms, SNPs or “snips”—associated with type 2 diabetes. “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.
That test is the Ensemble Learning Approach (ELA), software that can detect a set of SNPs that jointly 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 genes among the 500,000 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 applying statistical methods to determine which SNPs act on their own and which act in combination. To test their model on real data, Sha’s team analyzed genes from over 1,000 people in the United Kingdom, 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. ELA is 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.
MEDICA.de; Source: Michigan Technological University