Investigating splicing variants uncovered by next-generation sequencing the Alzheimer’s disease candidate genes, CLU, PICALM, CR1, ABCA7, BIN1, the MS4A locus, CD2AP, EPHA1 and CD33

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Clement, Naomi ; Braae, Anne ; Turton, James ; Lord, Jenny ; Guetta-Baranes, Tamar ; Medway, Christopher ; Brookes, Keeley ; Barber, Imelda S. ; Patel, Tulsi ; Milla, Lucy ; Azzopardi, Maria ; Lowe, James ; Mann, David ; Pickering-Brown, Stuart ; Kalsheker, Noor ; Passmore, Peter ; Chappell, Sally ; Morgan, Kevin ; Alzheimer’s Research UK (ARUK) Consortium (2016)
  • Publisher: OMICS International

Late onset Alzheimer’s disease (LOAD), the most common cause of late onset dementia, has a strong genetic component. To date, 21 disease-risk loci have been identified through genome wide association studies (GWAS). However, the causative functional variant(s) within these loci are yet to be discovered. This study aimed to identify potential functional splicing mutations in the nine original GWAS-risk genes: CLU, PICALM, CR1, ABCA7, BIN1, the MS4A locus, CD2AP, EPHA1 and CD33. Target enriched next generation sequencing (NGS) was used to resequence the entire genetic region for each of these GWAS-risk loci in 96 LOAD patients and in silico databases were used to annotate the variants for functionality. Predicted splicing variants were further functionally characterised using splicing prediction software and minigene splicing assays. Following in silico annotation, 21 variants were predicted to influence splicing and, upon further annotation, four of these were examined utilising the in vitro minigene assay. Two variants, rs881768 A>G in ABCA7 and a novel variant 11: 60179827 T>G in MS4A6A were shown, in these cell assays, to affect the splicing of these genes. The method employed in the paper successfully identified potential splicing variants in GWAS-risk genes. Further investigation will be needed to understand the full effect of these variants on LOAD risk. However, these results suggest a possible pipeline in order to identify putative functional variants as a result of NGS in disease-associated loci although improvements are needed within the current prediction programme in order to reduce the number of false positives.
  • References (43)
    43 references, page 1 of 5

    1. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, et al. (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet 45:1452-1458.

    2. Schaub MA, Boyle AP, Kundaje A, Batzoglou S, Snyder M (2012) Linking disease associations with regulatory information in the human genome. Genome Res 22: 1748-1759.

    3. Altshuler DM, Gibbs RA, Peltonen L, Dermitzakis E, Schaffner SF, et al. (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467: 52-58.

    4. Bettens K, Brouwers N, Engelborghs S, Lambert JC, Rogaeva E, et al. (2012) Both common variations and rare non-synonymous substitutions and small insertion/deletions in CLU are associated with increased Alzheimer risk. Mol Neurodegener 7: 3.

    5. Lord J, Turton J, Medway C, Shi H, Brown K, et al. (2012) Next generation sequencing of CLU, PICALM and CR1: Pitfalls and potential solutions. Int J Mol Epidemiol Genet 3: 262-275.

    6. Cuyvers E, De Roeck A, Van den Bossche T, Van Cauwenberghe C, Bettens K, et al. (2015) Mutations in ABCA7 in a Belgian cohort of Alzheimer's disease patients: a targeted resequencing study. Lancet Neurol 14: 814-822.

    7. Vardarajan BN, Ghani M, Kahn A, Sheikh S, Sato C, et al. (2015) Rare coding mutations identified by sequencing of Alzheimer disease genome-wide association studies loci. Ann Neurol 78: 487-498.

    8. Belkadi A, Bolze A, Itan Y, Cobat A, Vincent QB, et al. (2015) Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. Proc Natl Acad Sci USA 112: 5473-5478.

    9. Lim KH, Ferraris L, Filloux ME, Raphael BJ, Fairbrother WG (2011) Using positional distribution to identify splicing elements and predict pre-mRNA processing defects in human genes. Proc Natl Acad Sci USA 108: 11093-11098.

    10. Sterne-Weiler T, Howard J, Mort M, Cooper DN, Sanford JR (2011) Loss of exon identity is a common mechanism of human inherited disease. Genome Res 21: 1563-1571.

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