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An ancestry-based approach for detecting interactions.

Authors
  • Park, Danny S1
  • Eskin, Itamar2
  • Kang, Eun Yong3
  • Gamazon, Eric R4, 5
  • Eng, Celeste6
  • Gignoux, Christopher R1, 7
  • Galanter, Joshua M6
  • Burchard, Esteban1, 6
  • Ye, Chun J8
  • Aschard, Hugues9
  • Eskin, Eleazar3
  • Halperin, Eran2
  • Zaitlen, Noah1, 6
  • 1 Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.
  • 2 The Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv, Israel. , (Israel)
  • 3 Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
  • 4 Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA.
  • 5 Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. , (Netherlands)
  • 6 Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
  • 7 Department of Genetics, Stanford University, Palo Alto, CA, USA.
  • 8 Institute of Human Genetics, University of California San Francisco, San Francisco, CA, USA.
  • 9 Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
Type
Published Article
Journal
Genetic Epidemiology
Publisher
Wiley (John Wiley & Sons)
Publication Date
Nov 08, 2017
Identifiers
DOI: 10.1002/gepi.22087
PMID: 29114909
Source
Medline
Keywords
License
Unknown

Abstract

We show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.

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