Cumulative genetic score and C9orf72 repeat status independently contribute to amyotrophic lateral sclerosis risk in 2 case-control studies

Abstract

Background and Objectives Most patients with amyotrophic lateral sclerosis (ALS) lack a monogenic mutation. This study evaluates ALS cumulative genetic risk in an independent Michigan and Spanish replication cohort using polygenic scores.

Methods Participant samples from University of Michigan were genotyped and assayed for the chromosome 9 open reading frame 72 hexanucleotide expansion. Final cohort size was 219 ALS and 223 healthy controls after genotyping and participant filtering. Polygenic scores excluding the C9 region were generated using an independent ALS genome-wide association study (20,806 cases, 59,804 controls). Adjusted logistic regression and receiver operating characteristic curves evaluated the association and classification between polygenic scores and ALS status, respectively. Population attributable fractions and pathway analyses were conducted. An independent Spanish study sample (548 cases, 2,756 controls) was used for replication.

Results Polygenic scores constructed from 275 single-nucleotide variation (SNV) had the best model fit in the Michigan cohort. An SD increase in ALS polygenic score associated with 1.28 (95% CI 1.04-1.57) times higher odds of ALS with area under the curve of 0.663 vs a model without the ALS polygenic score (p value = 1 × 10-6). The population attributable fraction of the highest 20th percentile of ALS polygenic scores, relative to the lowest 80th percentile, was 4.1% of ALS cases. Genes annotated to this polygenic score enriched for important ALS pathomechanisms. Meta-analysis with the Spanish study, using a harmonized 132 single nucleotide variation polygenic score, yielded similar logistic regression findings (odds ratio: 1.13, 95% CI 1.04-1.23).

Discussion ALS polygenic scores can account for cumulative genetic risk in populations and reflect disease-relevant pathways. If further validated, this polygenic score will inform future ALS risk models