A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies

Corneliu A. Bodea, Benjamin M. Neale, Stephan Ripke, Murray Barclay, Laurent Peyrin-Biroulet, Mathias Chamaillard, Jean Frederick Colombel, Mario Cottone, Anthony Croft, Renata D'Incà, Jonas Halfvarson, Katherine Hanigan, Paul Henderson, Jean Pierre Hugot, Amir Karban, Nicholas A. Kennedy, Mohammed Azam Khan, Marc Lémann, Arie Levine, Dunecan MasseyMonica Milla, Grant W. Montgomery, Sok Meng Evelyn Ng, Ioannis Oikonomou, Harald Peeters, Deborah D. Proctor, Jean Francois Rahier, Rebecca Roberts, Paul Rutgeerts, Frank Seibold, Laura Stronati, Kirstin M. Taylor, Leif Törkvist, Kullak Ublick, Johan Van Limbergen, Andre Van Gossum, Morten H. Vatn, Hu Zhang, Wei Zhang, Jane M. Andrews, Peter A. Bampton, Timothy H. Florin, Richard Gearry, Krupa Krishnaprasad, Ian C. Lawrance, Gillian Mahy, Graham Radford-Smith, Rebecca L. Roberts, Lisa A. Simms, Leonard Baidoo, The International IBD Genetics Consortium

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)857-868
Number of pages12
JournalAmerican journal of human genetics
Issue number5
StatePublished - May 5 2016

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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