Genetic Analysis of Amyotrophic Lateral Sclerosis Identifies Contributing Pathways and Cell Types
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University of Massachusetts Medical School eScholarship@UMMS Open Access Publications by UMMS Authors 2021-01-15 Genetic analysis of amyotrophic lateral sclerosis identifies contributing pathways and cell types Sara Saez-Atienzar National Institutes of Health Et al. Let us know how access to this document benefits ou.y Follow this and additional works at: https://escholarship.umassmed.edu/oapubs Part of the Genetics and Genomics Commons, Molecular and Cellular Neuroscience Commons, Nervous System Diseases Commons, and the Neurology Commons Repository Citation Saez-Atienzar S, Landers JE. (2021). Genetic analysis of amyotrophic lateral sclerosis identifies contributing pathways and cell types. Open Access Publications by UMMS Authors. https://doi.org/ 10.1126/sciadv.abd9036. Retrieved from https://escholarship.umassmed.edu/oapubs/4570 Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License This material is brought to you by eScholarship@UMMS. It has been accepted for inclusion in Open Access Publications by UMMS Authors by an authorized administrator of eScholarship@UMMS. For more information, please contact [email protected]. SCIENCE ADVANCES | RESEARCH ARTICLE GENETICS Copyright © 2021 The Authors, some rights reserved; Genetic analysis of amyotrophic lateral sclerosis exclusive licensee American Association identifies contributing pathways and cell types for the Advancement Sara Saez-Atienzar1*†, Sara Bandres-Ciga2,3†, Rebekah G. Langston4, Jonggeol J. Kim2, of Science. No claim to 5 6,7,8 original U.S. Government Shing Wan Choi , Regina H. Reynolds , the International ALS Genomics Consortium, Works. Distributed 1,9 1 10 ITALSGEN, Yevgeniya Abramzon , Ramita Dewan , Sarah Ahmed , under a Creative 11 1 7,8 4 2,12 John E. Landers , Ruth Chia , Mina Ryten , Mark R. Cookson , Michael A. Nalls , Commons Attribution Adriano Chiò13,14†, Bryan J. Traynor1,15† NonCommercial License 4.0 (CC BY-NC). Despite the considerable progress in unraveling the genetic causes of amyotrophic lateral sclerosis (ALS), we do not fully understand the molecular mechanisms underlying the disease. We analyzed genome-wide data involv- ing 78,500 individuals using a polygenic risk score approach to identify the biological pathways and cell types involved in ALS. This data-driven approach identified multiple aspects of the biology underlying the disease that resolved into broader themes, namely, neuron projection morphogenesis, membrane trafficking, and signal trans- duction mediated by ribonucleotides. We also found that genomic risk in ALS maps consistently to GABAergic Downloaded from interneurons and oligodendrocytes, as confirmed in human single-nucleus RNA-seq data. Using two-sample Men- delian randomization, we nominated six differentially expressed genes (ATG16L2, ACSL5, MAP1LC3A, MAPKAPK3, PLXNB2, and SCFD1) within the significant pathways as relevant to ALS. We conclude that the disparate genetic etiologies of this fatal neurological disease converge on a smaller number of final common pathways and cell types. http://advances.sciencemag.org/ INTRODUCTION our knowledge of the biological processes involved in ALS is in- Amyotrophic lateral sclerosis [ALS; OMIM (Online Mendelian In- complete, especially for the sporadic form of the disease. heritance in Man) #105400] is a fatal neurological disease characterized To address this gap in our knowledge, we systematically applied by progressive paralysis that leads to death from respiratory failure polygenic risk score analysis to a genomic dataset involving 78,500 typically within 3 to 5 years of symptom onset. Approximately 6000 individuals to distinguish the cellular processes driving ALS. In Americans and 11,000 Europeans die of the condition annually, and essence, our polygenic risk score strategy determines whether a par- the number of ALS cases will increase markedly over the next two ticular pathway participates in the pathogenesis of ALS by compil- decades, mostly due to aging of the global population (1). ing the effect of multiple genetic variants across all of the genes Identifying the genes underlying ALS has provided critical in- involved in that pathway. This approach relies solely on genetic in- sights into the cellular mechanisms leading to neurodegeneration, formation derived from a large cohort and tests all known pathways such as protein homeostasis, cytoskeleton alterations, and RNA in a data-driven manner. Hence, it provides prima facie evidence of on April 7, 2021 metabolism (2). Additional efforts based on reductionist and high- the cellular pathways responsible for the disease. Knowledge of the throughput cell biology experiments have implicated other pathways, cell types involved in a disease process is an essential step to under- such as endoplasmic reticulum (ER) stress (3), nucleocytoplasmic standing a disorder. Recognizing this, we extended our computa- transport (4), and autophagy defects (5). Despite these successes, tional approach to identify the specific cell types that are involved in ALS. To ensure accessibility, we created an online resource so that 1Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National the research community can explore the contribution of the various Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA. 2Molec- pathways and cell types to ALS risk (https://lng-nia.shinyapps.io/ ular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, ALS-Pathways/). National Institutes of Health, Bethesda, MD 20892, USA. 3Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), Granada, Spain. 4Cell Biology and Gene Expression Section, Laboratory of Neurogenetics, National Institute on Aging, Na- tional Institutes of Health, Bethesda, MD 20892, USA. 5Department of Genetics and RESULTS Genomic Sciences, Icahn School of Medicine, Mount Sinai, 1 Gustave L. Levy Pl, Pathway analysis used a three-stage study design New York, NY 10029, USA. 6Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK. 7NIHR Great Overall, we evaluated the involvement of 7296 pathways in the Ormond Street Hospital Biomedical Research Centre, University College London, pathogenesis of ALS using a polygenic risk score approach (see 8 London, UK. Great Ormond Street Institute of Child Health, Genetics and Genomic Fig. 1A for the workflow of our analysis). To ensure the accuracy of Medicine, University College London, London, UK. 9Sobell Department of Motor Neuroscience and Movement Disorders, University College London, Institute of our results, we divided the available ALS genomic data into three Neurology, London, UK. 10Neurodegenerative Diseases Research Unit, Laboratory sections. The first of these independent datasets (hereafter known of Neurogenetics, National Institute of Neurological Disorders and Stroke, National as the reference dataset) was a published genome-wide association Institutes of Health, Bethesda, MD 20892, USA. 11Department of Neurology, Univer- sity of Massachusetts Medical School, Worcester, MA 01605, USA. 12Data Tecnica study (GWAS) involving 12,577 ALS cases and 23,475 controls (6). International, Glen Echo, MD 20812, USA. 13‘Rita Levi Montalcini’ Department of We used the summary statistics from this reference dataset to define Neuroscience, University of Turin, Turin, Italy. 14Azienda Ospedaliero Universitaria 15 the weights of the risk allele so that greater importance was given to Città della Salute e della Scienza, Turin, Italy. Department of Neurology, Johns alleles with higher risk estimates. Hopkins University, Baltimore, MD 21287, USA. *Corresponding author. Email: [email protected] These risk allele weights were then applied to our second dataset †These authors contributed equally to this work. (also known as the training dataset) to generate a polygenic risk Saez-Atienzar et al., Sci. Adv. 2021; 7 : eabd9036 15 January 2021 1 of 13 SCIENCE ADVANCES | RESEARCH ARTICLE Downloaded from http://advances.sciencemag.org/ Fig. 1. Workflow followed in this study. Polygenic risk score analysis was used to identify (A) biological pathways and (B) cell types contributing to the risk of develop- ing ALS. The human frontal cortex single-nucleus dataset was obtained from the North American Brain Expression Consortium (NABEC). The human FTC and hippocam- pus DroNc-seq was obtained from Habib et al. (19). The human Motor Cortex sNUC-seq dataset was obtained from the Allen Cell Types database (20). FTC, prefrontal cortex; M1, primary motor cortex. on April 7, 2021 score estimate for each biological pathway. These training data con- achieved significance in the replication dataset using a raw P value sisted of individual-level genotype and phenotype data from 5605 of <0.05 as the threshold for significance. The FDR-corrected P values ALS cases and 24,110 control subjects that were genotyped in our are also shown in Table 1. laboratory (7). We investigated the pathways defined by the Molec- We applied a similar polygenic risk score approach to determine ular Signatures Database, a compilation of annotated gene sets de- which cell types are associated with the ALS disease process (Fig. 1B). signed for gene set enrichment and pathway analysis. We focused In essence, a cell type associated with a disease will display a pattern our efforts on three collections within the Molecular Signatures Da- whereby more of the polygenic risk score variance is attributable to tabase that