Examining the genetic risk for amyotrophic lateral sclerosis (ALS) may become a lot easier with a user-friendly tool called ALSgeneScanner, which is meant to be used by non-specialists such as health care professionals and patients.
The method is able to analyze DNA sequencing data from patients and distinguish ALS risk gene variants with high accuracy. It reports findings in a simple way and so has the potential to give patients ownership of their DNA data and the ability to understand it, although this should be done with the help of a specialized genetics counselor, researchers say.
The software development process and its performance have been described in the report “ALSgeneScanner: a pipeline for the analysis and interpretation of DNA sequencing data of ALS patients,” and published in the journal Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration.
A large number of genetic variants have been shown to increase the risk for developing ALS or influence the rate of progression. There is convincing evidence linking variations at 25 genes to ALS, and weaker indications for another 120 genes.
“With the increasing availability of next-generation sequencing data, non-specialists, including health care professionals and patients, are obtaining their genomic information without a corresponding ability to analyze and interpret it,” the report stated.
Therefore, postdoctoral researcher Alfredo Iacoangeli and colleagues from King’s College London in the U.K. developed ALSgeneScanner, a user‐friendly tool for the automatic analysis and interpretation of DNA sequencing data for ALS.
The tool is simpler to use and install compared with typical pipelines that require more informatics skills and high-performance computers. It can run on a standard, mid-range computer, perform the same analyses as other widely used tools, and able to complete the analysis in a few hours at most. It can also process two types of next-generation sequencing data: whole genome sequencing and whole exome sequencing.
In total, they identified 149 genomic regions for which some kind of scientific evidence exists (strong for some genes, less convincing for others) supporting a link to ALS development or progression. Based on this knowledge, the tool creates a risk score that ranks genetic variants according to their strength of association with ALS.
The parameters of this score can be adjusted by the user to maximize accuracy or improve precision or sensitivity according to the user’s needs. This means the user can adjust the tool to identify potentially harmful variants, or detect only those that are established as dangerous.
Tests designed to evaluate the method’s performance showed it is able to accurately sort out disease-associated variants from those which are not related to ALS.
At the end of the analysis, ALSgeneScanner produces a report that includes information about the identified variants, risk scores, and graphical utilities which make it easier to interpret the results.
“ALSgeneScanner puts a powerful bioinformatics tool, able to exploit the potentialities of next-generation sequencing data in the hands of patients, ALS researchers, and clinicians,” the team said.
The researchers stress, however, that interpretation of the results should always be done with the help of a specialized genetics counselor.