A team of researchers from Washington University and the Israeli Institute of Technology (Technion) in Haifa, Israel, has developed a technique to detect the ancestry of disease genes in hybrid, or mixed, human populations.
The technique, called expected mutual information (EMI), determines how a set of DNA markers is likely to show the ancestral origin of locations on each chromosome. The team constructed an algorithm for the technique that selects panels of DNA markers in order to render the best picture of the ancestral origin of disease genes. They then tested the algorithm to show that it is more powerful and accurate than standard algorithms that are in use.
The result is easier identification of inherited genes that cause diseases in people of mixed races, which researchers call “population admixture.” Nephrologists, for instance, have noted that African-Americans are far more likely than Americans of European descent to die rapidly of end-stage, progressive renal failure due to kidney disease. Many African-Americans, though, have genes that originated in Europe due to ethnic mixing.
The technique helps researchers isolate the genetic causes of disease by detecting from which continent the recurrent disease genes originated.
A current research goal is to treat or even prevent kidney disease with gene or drug therapies.
“This technique will allow researchers to analyze which regions of the genome are associated with end-stage, progressive renal failure,” said Alan R. Templeton, Ph.D., the Charles Rebstock Professor of Biology in Arts & Sciences and associate professor of biomedical engineering.
“Once the regions are identified, then you look at the individual genes and ask: Are there genetic factors involved with this, and, if so, what are the candidates?” Templeton said.
It’s a good bet, Templeton said, that the disease genes are highly likely to have emerged from Africa, as African-Americans have shown the tendency to die more quickly of the disease.
The technique and algorithm apply beyond this particular disease, Templeton added.
“We can look at many different hybrid human populations with this algorithm and use it on a diversity of diseases,” he said.
“Our novel approach extends previous methods by incorporating knowledge on population admixture, drawing a more precise picture of the mosaic of ancestries along an individual’s genome,” said Sivan Bercovici, Templeton’s colleague at Technion and primary author of a research paper about this study published in the current issue of Genome Research.
The researchers analyzed DNA from 575 cases of African-Americans with end-stage progressive renal failure and compared it to controls who did not have the disease. They came up with a panel of approximately 2,000 genetic markers. Enough, Templeton said, “to cover the whole genome.”
To tease out the origins of disease-causing genes, researchers use a technique called mapping by admixture linkage disequilibrium (MALD), a powerful approach to identify regions of the genome that have genes associated with disease.
MALD takes advantage of differences in disease prevalence between populations to look for variation patterns that are over-represented in groups with high susceptibility to a certain disorder.
Both EMI and the algorithm make MALD more accurate and efficient.