Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis

Sai Zhang, Johnathan Cooper-Knock, Annika K Weimer, Minyi Shi, Tobias Moll, Jack N G Marshall, Calum Harvey, Helia Ghahremani Nezhad, John Franklin, Cleide Dos Santos Souza, Ke Ning, Cheng Wang, Jingjing Li, Allison A Dilliott, Sali Farhan, Eran Elhaik, Iris Pasniceanu, Matthew R Livesey, Chen Eitan, Eran HornsteinKevin P Kenna, Jan H Veldink, Laura Ferraiuolo, Pamela J Shaw, Michael P Snyder*,

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)992-1008.e11
Issue number6
Publication statusPublished - 16 Mar 2022


  • ALS
  • axonal dysfunction
  • epigenetics
  • gene discovery
  • genetics
  • iPSC
  • machine learning
  • motor neurons
  • multiomics
  • TDP-43 mislocalization


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