Computational biologists at Carnegie Mellon University have developed a new analytical technique. This, they hope, will help detect the genetic causes behind complex disease syndromes (like diabetes, asthma and cancer). Such disease are mostly characterized by multiple clinical and molecular traits.
Instead of going after genetic alterations behind a particular trait one at a time, the scientists used a statistical method that enables them to uncover genome variations underlying an entire regulatory network of genes or traits responsible for complex diseases.
Professor Eric P. Xing, the study leader, said that their graph-guided fused lasso (GFlasso) method showed increased power in detecting gene variants associated with complex symptoms compared with other methods.
In one test, GFlasso successfully detected a gene variant already implicated in severe asthma, and identified two additional variants that had not previously been associated with the condition.
The researchers said that more study of the two variants would be necessary to confirm the association.
"We know that some of the most common and most serious diseases that plague humans are caused not by a single genetic mutation, but by a combination of many genetic and environmental factors. Complicating the situation is that most complex diseases have a large number of clinical traits such as various symptoms, body metrics and family history, and that genome-wide gene expression profiling can identify tens of thousands of molecular traits associated with the disease," said Xing.
Severe asthma, for instance, is characterized by more than 50 clinical traits, some related to environment or activity levels, some to symptoms such as wheeziness and tightness of the chest and others to lung physiology.
The researchers said that some of these traits are highly correlated with each other, which suggests a common genetic basis.
The new technique takes advantage of these tightly correlated traits by analysing them jointly.
This approach also helps detect genetic variations that might otherwise be missed because they have relatively subtle effects on any individual trait, but are important because they contribute to a number of correlated traits.
"This approach will provide a more comprehensive genetic and molecular view of complex diseases, so we can identify the genes that underlie disease processes, understand the role of genes in determining the severity of disease and develop improved methods for diagnosing disease," said Xing,
The study has been published in the online edition of the journal Public Library of Science (PLoS) Genetics.