UCL  IRIS
Institutional Research Information Service
UCL Logo
Please report any queries concerning the funding data grouped in the sections named "Externally Awarded" or "Internally Disbursed" (shown on the profile page) to your Research Finance Administrator. Your can find your Research Finance Administrator at https://www.ucl.ac.uk/finance/research/rs-contacts.php by entering your department
Please report any queries concerning the student data shown on the profile page to:

Email: portico-services@ucl.ac.uk

Help Desk: http://www.ucl.ac.uk/ras/portico/helpdesk
Publication Detail
A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Bodea CA, Neale BM, Ripke S, Barclay M, Peyrin-Biroulet L, Chamaillard M, Colombel JF, Cottone M, Croft A, D'Incà R, Halfvarson J, Hanigan K, Henderson P, Hugot JP, Karban A, Kennedy NA, Khan MA, Lémann M, Levine A, Massey D, Milla M, Montgomery GW, Ng SME, Oikonomou I, Peeters H, Proctor DD, Rahier JF, Roberts R, Rutgeerts P, Seibold F, Stronati L, Taylor KM, Törkvist L, Ublick K, Van Limbergen J, Van Gossum A, Vatn MH, Zhang H, Zhang W, Andrews JM, Bampton PA, Florin TH, Gearry R, Krishnaprasad K, Lawrance IC, Mahy G, Radford-Smith G, Roberts RL, Simms LA, Amininijad L, Cleynen I, Dewit O, Franchimont D, Georges M, Laukens D, Theatre E, Vermeire S, Aumais G, Baidoo L, Barrie AM, Beck K, Bernard EJ, Binion DG, Bitton A, Brant SR, Cho JH, Cohen A, Croitoru K, Datta LW, Deslandres C, Duerr RH, Dutridge D, Ferguson J, Fultz J, Goyette P, Greenberg GR, Haritunians T, Jobin G, Katz S, Lahaie RG, McGovern DP, Nelson L, Ng SM, Ning K, Paré P, Regueiro MD, Rioux JD, Ruggiero E, Schumm LP, Schwartz M, Scott R, Sharma Y, Silverberg MS, Spears D
  • Publication date:
    14/04/2016
  • Pagination:
    857, 868
  • Journal:
    American Journal of Human Genetics
  • Volume:
    98
  • Issue:
    5
  • Status:
    Published
  • Print ISSN:
    0002-9297
Abstract
© 2016 The American Society of Human Genetics All rights reserved.One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data.
Publication data is maintained in RPS. Visit https://rps.ucl.ac.uk
 More search options
UCL Researchers
Author
Division of Psychiatry
Author
Clinical and Movement Neurosciences
University College London - Gower Street - London - WC1E 6BT Tel:+44 (0)20 7679 2000

© UCL 1999–2011

Search by