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A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies
Department of Statistics, Carnegie Mellon University, Pittsburgh PA, USA; Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge MA, USA.
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge MA, USA; Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge MA, USA.
Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge MA, USA; Department of Psychiatry and Psychotherapy, Charite, Campus Mitte, Berlin, Germany.
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2016 (English)In: American Journal of Human Genetics, ISSN 0002-9297, E-ISSN 1537-6605, Vol. 98, no 5, p. 857-868Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
University of Chicago Press, 2016. Vol. 98, no 5, p. 857-868
National Category
Medical and Health Sciences Gastroenterology and Hepatology
Identifiers
URN: urn:nbn:se:oru:diva-66635DOI: 10.1016/j.ajhg.2016.02.025ISI: 000375869300005PubMedID: 27087321Scopus ID: 2-s2.0-84963574963OAI: oai:DiVA.org:oru-66635DiVA, id: diva2:1198718
Funder
NIH (National Institute of Health), MH101244-02
Note

Funding Agency:

National Institute of Mental Health, R37MH057881

Available from: 2018-04-18 Created: 2018-04-18 Last updated: 2018-09-11Bibliographically approved

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Halfvarson, Jonas

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