Journal of Alzheimers Disease & Parkinsonism
Open Access

Research Article

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

¹Human Genetics Group, School of Life Sciences, Queens Medical Centre, University of Nottingham, Nottingham, UK

²Neuropathology, School of Medicine, Queens Medical Centre, University of Nottingham, Nottingham, UK

³Clinical Neuroscience Research Group, Greater Manchester Neurosciences Centre, University of Manchester, Salford, UK

⁴Centre for Public Health, School of Medicine, Dentistry, and Biomedical Sciences, Queen’s University Belfast, Belfast, Northern Ireland, UK

⁵†The Alzheimer’s Research UK (ARUK) Consortium comprises Peter Passmore, David Craig, Janet Johnston, Bernadette McGuinness, Stephen Todd, Queen’s University Belfast, UK; Reinhard Heun, Royal Derby Hospital, UK; Heike Kölsch, University of Bonn, Germany; Patrick G. Kehoe, University of Bristol, UK; Emma R.L.C. Vardy, University of Salford, UK; Nigel M. Hooper, Stuart Pickering-Brown, University of Manchester, UK; Julie Snowden, Anna Richardson, Matt Jones, David Neary, Jenny Harris, Salford Royal NHS Foundation Trust, UK & University of Manchester, UK; Keeley Brookes, Christopher Medway, James Lowe, Kevin Morgan, University of Nottingham, UK; A. David Smith, Gordon Wilcock, Donald Warden, University of Oxford (OPTIMA), UK; Clive Holmes, University of Southampton, UK

⁶#Equal contribution

*Corresponding Author:

Kevin Morgan
Human Genetics, School of Life Sciences, A Floor
West Block, Room 1306, Queens Medical Centre
University of Nottingham, Nottingham, NG7 2UH
United Kingdom
Tel: +44 115 8230724
E-mail: Kevin.Morgan@nottingham.ac.uk

Received date: October 03, 2016; Accepted date: October 22, 2016; Published date: October 29, 2016

Citation: Clement N, Braae A, Turton J, Lord J, Guetta-Baranes T, et al. (2016) 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. J Alzheimers Dis Parkinsonism 6:276. doi:10.4172/2161-0460.1000276

Copyright: © 2016 Clement N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

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.

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