Cumulative Genetic Score and C9orf72 Repeat Status Independently Contribute to Amyotrophic Lateral Sclerosis Risk in 2 Case-Control Studies

Abstract

Background and ObjectivesMost 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.MethodsParticipant 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.ResultsPolygenic 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 (pvalue = 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).DiscussionALS 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.

Metal mixtures associate with higher amyotrophic lateral sclerosis risk and mortality independent of genetic risk and correlate to self-reported exposures: a case-control study

“…Details of this cohort are previously published. [3][4][5][6][7][10][11][12][13][14] Briefly, ALS participants were enrolled at the University of Michigan Pranger ALS Clinic. All ALS patients meeting Gold Coast criteria able to communicate in English were approached for participation.…”

“…In the first batch, 188 samples were genotyped on the OmniExpress array. In a second batch, as previously described, 3 512 samples were genotyped using the Infinium Multi-Ethnic Global-8 array. Lastly, 305 samples were genotyped using the Infinium Global Diversity Array-8 Kit.…”

“…2 In our recent study involving ALS patients in Michigan, we found that polygenic risk contributes to the development of ALS. 3 Additionally, we demonstrated that environmental risk scores (ERS), representing various persistent organic pollutants (POPs) simultaneously, associate with ALS risk and survival. [4][5][6] Thus, both genetic and environmental factors contribute to ALS risk and progression in our Michigan ALS cohort.…”

Improving prediction models of amyotrophic lateral sclerosis (ALS) using polygenic, pre-existing conditions, and survey-based risk scores in the UK Biobank

“…Both PRS were based on weights from an independent ALS GWAS. We benchmarked our findings to a previously published ALS PRS, including 275 SNPs (PRS-275non-C9) [15]. Since this study included the region around C9orf72 to generate the ALS PRSs, but used the same underlying GWAS summary statistics as PRS-275, the poorer performance of the latter may arise because it omitted SNPs from around C9orf72.…”

“…Studies have also identified genetic risk, with over 40 ALS genes, including C9orf72, SOD1, FUS, and TARDBP [9][10][11], as well as environmental risks from exposures (e.g., to metals, pesticides, intense physical activity [5,6,12]). Despite these findings and the resulting advances in ALS correlation and prediction models [13][14][15], a noticeable gap persists in the holistic ALS risk stratification literature due to a lack of approaches incorporating integrated sets of multi-domain risk factors into ALS prediction models [16]. This highlights the need for more comprehensive and integrated approaches to enhance the accuracy and reliability of ALS risk prediction.…”