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. Some of these traits are highly correlated with each other, Xing and Kim noted in the PLoS Genetics article, which suggests a common genetic basis. Their technique takes advantage of these tightly correlated traits by analyzing 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," Xing said, "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."

Xing, a member of the Ray and Stephanie Lane Center for Computational Biology at Carnegie Mellon, is working with colleagues at the University of Pittsburgh School of Medicine and Harvard Medical School to use GFlasso to study severe asthma as part of an ongoing study sponsored by the National Institutes of Health.

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