Zhang's team used a new statistical method to analyze the genetic codes of 547 individuals, 276 with sporadic ALS and 271 without. Their method, a two-locus interaction analysis approach, allows the researchers to identify multiple genes associated with a complex illness.

The data set they analyzed was provided by National Institute of Neurological Disorders and Stroke (NINDS) Human Genetics Resource Center at the Coriell Institute (ccrriell/ninds), a publicly funded "bank" or repository for human cells, DNA samples, clinical data, and other information that aims to accelerate research on the genetics of nervous system disorders.

"Ideally, we should confirm our results in a second data set, but we don't have one available," Sha says.

ALS is not the first condition they have tackled. Using data sets provided by University of Cambridge, Zhang, Sha and their colleagues have also identified 11 genes linked to type 2 diabetes, which has reached epidemic proportions in the U.S.

The team hopes to apply their methods to other medical conditions, but has been hampered by the lack of genetic information: most data sets are not freely available to researchers. Zhang found out about the ALS data sets serendipitously, while searching the ALS Association website for information on his condition.

"Unfortunately, we don't have access to more data sets," said Sha. "If we did, we could analyze even more diseases."

Source: Michigan Technological University