Speeding disease gene discovery by sequence based candidate prioritization
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Date
14/03/2005Author
Adie, Euan A
Adams, Richard R
Evans, Kathryn L
Porteous, David
Pickard, Ben S
Metadata
Abstract
Background: Regions of interest identified through genetic linkage studies regularly exceed 30
centimorgans in size and can contain hundreds of genes. Traditionally this number is reduced by
matching functional annotation to knowledge of the disease or phenotype in question. However,
here we show that disease genes share patterns of sequence-based features that can provide a good
basis for automatic prioritization of candidates by machine learning.
Results: We examined a variety of sequence-based features and found that for many of them there
are significant differences between the sets of genes known to be involved in human hereditary
disease and those not known to be involved in disease. We have created an automatic classifier
called PROSPECTR based on those features using the alternating decision tree algorithm which
ranks genes in the order of likelihood of involvement in disease. On average, PROSPECTR enriches
lists for disease genes two-fold 77% of the time, five-fold 37% of the time and twenty-fold 11% of
the time.
Conclusion: PROSPECTR is a simple and effective way to identify genes involved in Mendelian and
oligogenic disorders. It performs markedly better than the single existing sequence-based classifier
on novel data. PROSPECTR could save investigators looking at large regions of interest time and
effort by prioritizing