Dou John, Bakulski Kelly, Guo Kai, Hur Junguk, Zhao Lili, Saez-Atienzar Sara, Stark Ali, Chia Ruth, García-Redondo Alberto, Rojas-Garcia Ricardo, Vázquez Costa Juan Francisco, Fernandez Santiago Ruben, Bandres-Ciga Sara, Gómez-Garre Pilar, Periñán Maria Teresa, Mir Pablo, Pérez-Tur Jordi, Cardona Fernando, Menendez-Gonzalez Manuel, Riancho Javier, Borrego-Hernández Daniel, Galán-Dávila Lucia, Infante Ceberio Jon, Pastor Pau, Paradas Carmen, Dols-Icardo Oriol, Traynor Bryan J, Feldman Eva L, Goutman Stephen A
From the Department of Epidemiology (J.D., K.B.), School of Public Health, Department of Neurology (K.G., E.L.F., S.A.G.), NeuroNetwork for Emerging Therapies (K.G., E.L.F., S.A.G.), University of Michigan, Ann Arbor; Department of Biomedical Sciences (J.H.), University of North Dakota, Grand Forks; Department of Biostatistics (L.Z.), School of Public Health, University of Michigan, Ann Arbor; Neuromuscular Diseases Research Section (S.S.-A., A.S., R.C., B.J.T.), Laboratory of Neurogenetics, National Institute on Aging, NIH, Bethesda, MD; ALS Unit (A.G.-R., D.B.-H.), Instituto de Investigación Sanitaria "i + 12" del Hospital Universitario 12 de Octubre de Madrid, SERMAS, CIBERER (A.G.-R., R.R.-G., J.F.V.C., D.B.-H.), Center for Networked Biomedical Research into Rare Diseases, Madrid; Neuromuscular Disorders Unit (R.R.-G.), Neurology Department and Sant Pau Biomedical Research Institute, Hospital de la Santa Creu I Sant Pau, Universitat Autonoma de Barcelona; Neuromuscular Unit (J.F.V.C.), Hospital Universitario y Politécnico la Fe, IIS La Fe; Department of Medicine (J.F.V.C.), Universitat de València; Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED) (R.F.S., P.G.-G., M.T.P., P.M., J.P.-T., F.C., O.D.-I.), Madrid; Lab of Parkinson's disease and Other Neurodegenerative Movement Disorders (R.F.S.), IDIBAPS-Institut d'Investigacions Biomèdiques, Barcelona; Unitat de Parkinson i Trastorns del Moviment, Servicio de Neurologia (R.F.S.), Hospital Clínic de Barcelona and Institut de Neurociencies de la Universitat de Barcelona (Maria de Maetzu Center), Catalonia, Spain; Center for Alzheimer's and Related Dementias (S.B.-C.), National Institute on Aging, Bethesda, MD; Unidad de Trastornos del Movimiento (P.G.-G., M.T.P., P.M.), Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC; Departamento de Medicina (P.M.), Universidad de Sevilla; Neurology and Molecular Genetics Mixed Investigation Unit (J.P.-T., F.C.), Instituto de Investigación Sanitaria La Fe, Molecular Genetics Unit (J.P.-T., F.C.), Institut de Biomedicina de València-CSIC; Department of Medicine (M.M.-G.), Universidad de Oviedo; Department of Neurology (M.M.-G.), Hospital Universitario Central de Asturias; Instituto de Investigación Sanitaria del Principado de Asturias (M.M.-G.), Oviedo, Spain; Service of Neurology (J.R.), Hospital Sierrallana, IDIVAL University of Cantabria, Torrelavega; Instituto de Investigación Marqués de Valdecilla (J.R., J.I.C.), Santander; Department of Neurology (L.G.-D.), ALS Unit, Hospital Clínico Universitario "San Carlos," Madrid; Unit of Neurodegenerative Diseases (P.P.), Department of Neurology, University Hospital Germans Trias I Pujol; Neurosciences (P.P.), The Germans Trias i Pujol Research Institute (IGTP) Badalona; Department of Neurology (C.P.), Hospital Universitario Virgen del Rocio, Sevilla; and Memory Unit (O.D.-I.), Neurology Department and Sant Pau Biomedical Research Institute, Hospital de la Santa Creu I Sant Pau, Universitat Autonoma de Barcelona, Spain.
Neurol Genet. 2023 May 31;9(4):e200079. doi: 10.1212/NXG.0000000000200079. eCollection 2023 Aug.
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.
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.
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 ( value = 1 × 10). 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).
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.
大多数肌萎缩侧索硬化症（ALS）患者缺乏单基因变异。本研究使用多基因评分评估独立的密歇根州和西班牙复制队列中的ALS累积遗传风险。
对密歇根大学的参与者样本进行基因分型，并检测9号染色体开放阅读框72六核苷酸重复序列。经过基因分型和参与者筛选后，最终队列规模为219例ALS患者和223名健康对照。使用独立的ALS全基因组关联研究（20,806例病例，59,804名对照）生成排除C9区域的多基因评分。调整后的逻辑回归和受试者工作特征曲线分别评估多基因评分与ALS状态之间的关联和分类。进行了人群归因分数和通路分析。使用独立的西班牙研究样本（548例病例，2,756名对照）进行复制。
由275个单核苷酸变异（SNV）构建的多基因评分在密歇根队列中具有最佳模型拟合。ALS多基因评分增加1个标准差与患ALS的几率高出1.28倍（95%可信区间1.04 - 1.57）相关，曲线下面积为0.663，而无ALS多基因评分的模型（P值 = 1×10）。相对于最低的80%，ALS多基因评分最高的第20百分位数的人群归因分数为4.1%的ALS病例。注释到该多基因评分的基因富集了重要的ALS发病机制。使用统一的132个单核苷酸变异多基因评分与西班牙研究进行荟萃分析，得出了类似的逻辑回归结果（优势比：1.13，95%可信区间1.04 - 1.23）。
ALS多基因评分可以解释人群中的累积遗传风险并反映疾病相关通路。如果进一步验证，该多基因评分将为未来的ALS风险模型提供信息。
