NeuroX, a fast and efficient genotyping platform for investigation of neurodegenerative diseases

Mike A. Nalls, Jose Bras, Dena G. Hernandez, Margaux F. Keller, Elisa Majounie, Alan E. Renton, Mohamad Saad, Iris Jansen, Rita Guerreiro, Steven Lubbe, Vincent Plagnol, J. Raphael Gibbs, Claudia Schulte, Nathan Pankratz, Margaret Sutherland, Lars Bertram, Christina M. Lill, Anita L. DeStefano, Tatiana Faroud, Nicholas ErikssonJoyce Y. Tung, Connor Edsall, Noah Nichols, Janet Brooks, Sampath Arepalli, Hannah Pliner, Chris Letson, Peter Heutink, Maria Martinez, Thomas Gasser, Bryan J. Traynor, Nick Wood, John Hardy, Andrew B. Singleton

Research output: Contribution to journalArticlepeer-review

76 Scopus citations

Abstract

Our objective was to design a genotyping platform that would allow rapid genetic characterization of samples in the context of genetic mutations and risk factors associated with common neurodegenerative diseases. The platform needed to be relatively affordable, rapid to deploy, and use a common and accessible technology. Central to this project, we wanted to make the content of the platform open to any investigator without restriction. In designing this array we prioritized a number of types of genetic variability for inclusion, such as known risk alleles, disease-causing mutations, putative risk alleles, and other functionally important variants. The array was primarily designed to allow rapid screening of samples for disease-causing mutations and large population studies of risk factors. Notably, an explicit aim was to make this array widely available to facilitate data sharing across and within diseases. The resulting array, NeuroX, is a remarkably cost and time effective solution for high-quality genotyping. NeuroX comprises a backbone of standard Illumina exome content of approximately 240,000 variants, and over 24,000 custom content variants focusing on neurologic diseases. Data are generated at approximately $50-$60 per sample using a 12-sample format chip and regular Infinium infrastructure; thus, genotyping is rapid and accessible to many investigators. Here, we describe the design of NeuroX, discuss the utility of NeuroX in the analyses of rare and common risk variants, and present quality control metrics and a brief primer for the analysis of NeuroX derived data.

Original languageEnglish (US)
Pages (from-to)1605.e7-1605.e12
JournalNeurobiology of Aging
Volume36
Issue number3
DOIs
StatePublished - Mar 1 2015

Bibliographical note

Funding Information:
This work was supported in part by the Intramural Research Program of the National Institute on Aging , National Institutes of Health , Department of Health and Human Services (project numbers Z01-AG000949-02, under human subjects protocol 2003-077), and by the Wellcome Trust and/or MRC Joint Call in Neurodegeneration award (WT089698) to the UK Parkinson's Disease Consortium (UKPDC) whose members are from the UCL/Institute of Neurology, the University of Sheffield, and the MRC Protein Phosphorylation Unit at the University of Dundee. Additional funding information is provided in the consortium information section included as a Supplementary online appendix . Special thanks to Megan L. Grove-Gaona, Jerome Rotter, Eric Boerwinkle, and Christopher O'Donnell on behalf of the CHARGE consortium for the their helpful advice on aspects of this project. The authors thank the NHLBI GO Exome Sequencing Project and its ongoing studies which produced and provided exome variant calls for comparison: the Lung GO Sequencing Project (HL-102923), the WHI Sequencing Project (HL-102924), the Broad GO Sequencing Project (HL-102925), the Seattle GO Sequencing Project (HL-102926), and the Heart GO Sequencing Project (HL-103010). They thank and acknowledge all who made this research possible. This study used the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, MD ( http://biowulf.nih.gov ), and DNA panels, samples, and clinical data from the National Institute of Neurological Disorders and Stroke Human Genetics Resource Center DNA and Cell Line Repository. People who contributed samples are acknowledged in descriptions of every panel on the repository website. They thank the French Parkinson's Disease Genetics Study Group: Y Agid, M Anheim, A-M Bonnet, M Borg, A Brice, E Broussolle, J-C Corvol, P Damier, A Destée, A Dürr, F Durif, S Klebe, E Lohmann, M Martinez, P Pollak, O Rascol, F Tison, C Tranchant, M Vérin, F Viallet, and M Vidailhet. They also thank the members of the French 3C Consortium: A Alpérovitch, C Berr, C Tzourio, and P Amouyel for allowing us to use part of the 3C cohort, and D Zelenika for support in generating the genome-wide molecular data. They thank P Tienari (Molecular Neurology Programme, Biomedicum, University of Helsinki), T Peuralinna (Department of Neurology, Helsinki University Central Hospital), L Myllykangas (Folkhalsan Institute of Genetics and Department of Pathology, University of Helsinki), and R Sulkava (Department of Public Health and General Practice Division of Geriatrics, University of Eastern Finland) for the Finnish controls (Vantaa85+ GWAS data).

Publisher Copyright:
© 2015.

Keywords

  • Genetics
  • Genotyping
  • Imputation
  • Meta-analysis
  • Methods
  • Neurodegeneration
  • Parkinson's

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