| Literature DB >> 35045337 |
Sai Zhang1, Johnathan Cooper-Knock2, Annika K Weimer1, Minyi Shi1, Tobias Moll2, Jack N G Marshall2, Calum Harvey2, Helia Ghahremani Nezhad2, John Franklin2, Cleide Dos Santos Souza2, Ke Ning2, Cheng Wang3, Jingjing Li3, Allison A Dilliott4, Sali Farhan4, Eran Elhaik5, Iris Pasniceanu2, Matthew R Livesey2, Chen Eitan6, Eran Hornstein6, Kevin P Kenna7, Jan H Veldink7, Laura Ferraiuolo2, Pamela J Shaw2, Michael P Snyder8.
Abstract
Amyotrophic lateral sclerosis (ALS) is a complex disease that leads to motor neuron death. Despite heritability estimates of 52%, genome-wide association studies (GWASs) have discovered relatively few loci. We developed a machine learning approach called RefMap, which integrates functional genomics with GWAS summary statistics for gene discovery. With transcriptomic and epigenetic profiling of motor neurons derived from induced pluripotent stem cells (iPSCs), RefMap identified 690 ALS-associated genes that represent a 5-fold increase in recovered heritability. Extensive conservation, transcriptome, network, and rare variant analyses demonstrated the functional significance of candidate genes in healthy and diseased motor neurons and brain tissues. Genetic convergence between common and rare variation highlighted KANK1 as a new ALS gene. Reproducing KANK1 patient mutations in human neurons led to neurotoxicity and demonstrated that TDP-43 mislocalization, a hallmark pathology of ALS, is downstream of axonal dysfunction. RefMap can be readily applied to other complex diseases.Entities:
Keywords: ALS; TDP-43 mislocalization; axonal dysfunction; epigenetics; gene discovery; genetics; iPSC; machine learning; motor neurons; multiomics
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Year: 2022 PMID: 35045337 PMCID: PMC9017397 DOI: 10.1016/j.neuron.2021.12.019
Source DB: PubMed Journal: Neuron ISSN: 0896-6273 Impact factor: 18.688