Genetic Scoring System Significantly Enhances Prediction for Risk of Developing ALS
The majority of patients with ALS lack a monogenic mutation, with only 15-20% of patients having a disease that is driven and controlled by a single gene.
Stephen Goutman, MD
A recent published study in Neurology Genetics using a newly created weighed polygenic scoring system showed that patients with amyotrophic lateral sclerosis (ALS) had higher average polygenic scores compared with healthy controls and that an increase in the score was associated with higher risk of ALS.1 These findings suggest that the system, representing important genes and biological functions in the pathophysiology of the disease, has the ability to improve the prediction for an individual’s risk of developing ALS.
Increases in standard deviation on the ALS polygenic score were associated with 1.28 (95% CI, 1.04–1.57) times higher odds of ALS with area under the curve of 0.663 compared with a model without the scoring system (P = 1 × 10−6). Approximately 4.1% of ALS cases represented the highest 20th percentile of ALS polygenic scores, which were relative to the lowest 80th percentile.
“This polygenic scoring system we developed for ALS allows us to better understand the genetic architecture of the disease,” senior author Stephen Goutman, MD, a neurologist and director of the Pranger ALS Clinic at U-M Health, said in a statement.2 “This may help to distinguish which populations have greater odds to develop the disease and inform future prevention studies and interventions.” In the study, investigators explored ALS cumulative genetic risk using polygenic scores in an independent Michigan and Spanish replication cohort.
In the cohort, 219 patients with ALS and 223 healthy controls were included following genotyping and participant filtering. The polygenic scores were generated using an independent ALS genome-wide association study from Michigan featuring 20,806 cases and 59,804 controls as well as 548 cases and 2,756 controls for replication from the Spanish study. The association and classification of the polygenic scores and status of ALS were evaluated using an adjusted logistic regression and receiver operating characteristic curves. Additionally, researchers conducted population attributable fractions and pathway analyses.
In the Michigan cohort, researchers found that polygenic scores constructed from 275 single-nucleotide variations had the best model fit and that genes interpreted to this scoring system were enhanced for critical pathomechanisms in ALS. Notably, in a meta-analysis performed using a harmonized 132 single nucleotide variation polygenic score, findings from the Spanish study yielded similar logistic regression data (OR, 1.13; 95% CI, 1.04–1.23).
“By combining all of the common genetic features previously associated with ALS, we improved ALS case status prediction among study populations in Michigan and in Spain,” coauthor Kelly M. Bakulski, PhD, assistant professor of epidemiology at the U-M School of Public Health, said in a statement.2 “However, there is a lot of room for improvement in ALS prediction so that folks at risk can be identified for prevention and treatment. Future research into additional risk factors, including environmental exposures, will be critical.”
Researchers in the polygenic system study noted that replication of the polygenic scores and larger samples are important for exploring if the findings are generalizable across other ALS populations. "Including samples from a diverse range of populations will improve our ability to predict ALS in a majority of people," coauthor Bryan Traynor, MD, PhD, a neurologist and senior investigator at the National Institute on Aging, part of the National Institutes of Health, said in a statement.2
