Gene Hunters

By Victoria Stern Gene hunters The paper: K. Lage et al., “A human phenome-interactome network of protein complexes implicated in genetic disorders,” Nat Biotech, 25: 309–316, 2007. (Cited in 86 papers) The finding: Søren Brunak at the Technical University of Denmark and Kasper Lage, now at the Broad Institute in Boston, developed a computational method to predict which proteins most likely cause a particular disease. By

Victoria Stern
Oct 31, 2009

Gene hunters

The paper:

K. Lage et al., “A human phenome-interactome network of protein complexes implicated in genetic disorders,” Nat Biotech, 25: 309–316, 2007. (Cited in 86 papers)

The finding:

Søren Brunak at the Technical University of Denmark and Kasper Lage, now at the Broad Institute in Boston, developed a computational method to predict which proteins most likely cause a particular disease. By correlating known disease-causing proteins with specific disease phenotypes, the team generated a model that predicts whether similar proteins might also be behind the same diseases. The researchers found new genes linked to ailments such as inflammatory bowel disease and ovarian cancer.

The advantage:

Instead of randomly sequencing or manually searching for genes, this computational method is a more efficient way to look for individual disease-linked genes that were previously unnoticed or might have otherwise been missed, Brunak says.

The relevance:

This study was one of the first to use computational methods to predict a gene–phenotype relationship. “This paper is very interesting and inspiring. It is one of several quantitative ways to infer gene-disease association,” says Guanghui Hu, a computational biologist at GlaxoSmithKline in King of Prussia, Pa, who was not involved in the research. In 2008, Brunak’s group used the same method to identify new genes likely associated with Parkinson’s disease, cardiomyopathies, and muscular dystrophy syndromes (Proc Natl Acad Sci, 105:20870–75, 2008).

The limitations:

The method will likely miss key associations, and return some false positives, Hu notes. “[The study’s] real impact remains to be seen.”

Diseases for which the computational method has helped uncover new genes
Amyotrophic lateral sclerosis (ALS), inflammatory bowel disease, epithelial ovarian cancer, retinitis pigmentosa, Alzheimer disease, type 2 diabetes, coronary heart disease