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New statistical method links multiple genes to common complex diseases

Lance Hahn analyzed 10 functional polymorphisms that alter the levels of the suspect estrogen metabolites. When they looked at the polymorphisms individually, he and his colleagues found no indication of increased cancer risk. It was only when they looked at different combinations that they found that women with four specific polymorphisms were significantly more likely to develop breast cancer than those with only three, two or one of these gene variants.

They found this complex association by applying their technique to the genetic information that Parl had compiled on 200 women with breast cancer and an age-matched group of 200 female patients without the illness. First, they constructed a series of tables that compare pairs of polymorphisms. Because a person can inherit a given polymorphism from her father, mother or both parents, each table has nine cells. In each cell, they calculate the number of subjects with breast cancer and the number of control subjects without the illness who have the indicated pair of polymorphisms. If the number with cancer is higher, then the cell is listed as high risk; if the number without cancer is higher, then it is considered low risk. If none of the subjects have a particular combination of the two polymorphisms, the cell is labeled as empty.

The researchers constructed these tables for all possible pairs of polymorphisms. Then they constructed similar tables for all combinations of three, four, five all the way up to nine polymorphisms. By examining the pattern of high and low risks in these tables, the geneticists determined that only one combinationfour polymorphisms in three genesincreased an individuals cancer risk. Moores approach isnt limited to analyzing interactions among three or four genes. Using machine learning algorithms and a new multi-processor supercomputer on campus, he estimates that they will be able to search for similar interactions among as many as 20 genes selected from a list of thousands
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Contact: David F. Salisbury
david.salisbury@vanderbilt.edu
615-343-6803
Vanderbilt University
27-Jun-2001


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