medRxivGenetic & Genomic MedicinePreprint
Identification of co-segregating RUBCN and KANK1 mutations in a UK amyotrophic lateral sclerosis kindred
Midhat SalmaniD, Intan Barul Akma Bakhtiar, Alex G. Morris, Alexandra Rother, Simon D. ToppiD, Christopher E. ShawiD, Bradley N. SmithiD, Jacqueline de BellerocheiD
Abstract
Bioinformatic analysis of next-generation sequencing data identified novel missense variants in RUBCN (p.Cys517Phe) and KANK1 (p.Pro1332Ala) that co-segregated with amyotrophic lateral sclerosis (ALS) in a United Kingdom kindred and were ultra-rare in European control populations (allele frequency < 0.001%). Likelihood-based segregation analysis demonstrated significant co-segregation with disease, yielding full-likelihood Bayes factor (FLB) scores of 4.6 and 5.5 for RUBCN and KANK1, respectively, exceeding the threshold supporting pathogenicity (FLB [≥] 4.33). Sex-stratified penetrance modelling identified maximal FLB scores of 7.9 for both genes under a model with male and female penetrances of 0.99 and 0.01, respectively. Gene-based burden analysis of deleterious ultra-rare variants in independent ALS case-control cohorts demonstrated exome-wide significant associations with ALS (SKAT-O, p < 1 x 10-9). Functional studies in lymphoblastoid cell lines derived from variant carriers revealed significantly increased apoptosis and dysregulated expression of genes involved in endocytosis, LC3-associated phagocytosis, endoplasmic reticulum stress, and cytoskeletal organisation (p < 0.05). Analysis of spinal cord tissue from ALS cases and controls showed significant upregulation of RUBCN (2.85-fold) and downregulation of KANK1 (0.40-fold) (p < 0.05). Collectively, these findings implicate RUBCN and KANK1 as candidate ALS genes and suggest that disruption of vesicular trafficking, autophagy-related pathways, and cytoskeletal homeostasis may contribute to disease pathogenesis of ALS.
- organism: human
- assay: whole-exome sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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