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An Ancestry Based Approach for Detecting Interactions

Danny S. Park, Itamar Eskin, Eun Yong Kang, Eric R. Gamazon, Celeste Eng, Christopher R. Gignoux, Joshua M. Galanter, Esteban Burchard, Chun J. Ye, Hugues Aschard, Eleazar Eskin, Eran Halperin, Noah Zaitlen
doi: https://doi.org/10.1101/036640
Danny S. Park
1Department of Bioengineering and Therapeutic Sciences. University of California San Francisco. San Francisco, CA.
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  • For correspondence: danny.park@ucsf.edu noah.zaitlen@ucsf.edu
Itamar Eskin
2The Blavatnik School of Computer Science. Tel-Aviv University. Tel Aviv, Israel.
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Eun Yong Kang
3Department of Computer Science. University of California Los Angeles. Los Angeles, CA.
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Eric R. Gamazon
4Division of Genetic Medicine, Department of Medicine. Vanderbilt University. Nashville, TN.
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Celeste Eng
5Department of Medicine. University of California San Francisco. San Francisco, CA.
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Christopher R. Gignoux
1Department of Bioengineering and Therapeutic Sciences. University of California San Francisco. San Francisco, CA.
6Department of Genetics. Stanford University. Palo Alto, CA.
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Joshua M. Galanter
5Department of Medicine. University of California San Francisco. San Francisco, CA.
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Esteban Burchard
1Department of Bioengineering and Therapeutic Sciences. University of California San Francisco. San Francisco, CA.
5Department of Medicine. University of California San Francisco. San Francisco, CA.
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Chun J. Ye
7Institute of Human Genetics. University of California San Francisco. San Francisco, CA.
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Hugues Aschard
8Department of Epidemiology. Harvard School of Public Health. Boston, MA.
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Eleazar Eskin
3Department of Computer Science. University of California Los Angeles. Los Angeles, CA.
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Eran Halperin
2The Blavatnik School of Computer Science. Tel-Aviv University. Tel Aviv, Israel.
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Noah Zaitlen
1Department of Bioengineering and Therapeutic Sciences. University of California San Francisco. San Francisco, CA.
5Department of Medicine. University of California San Francisco. San Francisco, CA.
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  • For correspondence: danny.park@ucsf.edu noah.zaitlen@ucsf.edu
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I. Abstract

Background: Gene-gene and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human associations studies remains challenging for myriad reasons. In the case of gene-gene interactions, the large number of potential interacting pairs presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies.

Results: In this work, we develop a new statistical approach to address these issues that leverages genetic ancestry [Θ] in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, identifying nine interactions that were significant at a threshold of p < 5 × 10−8. We replicate two of these interactions and show that a third has previously been identified in a genetic interaction screen for rheumatoid arthritis.

Conclusion: We show that genetic ancestry can be a useful proxy for unknown and unmeasured environmental exposures with which it is correlated

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Posted January 13, 2016.
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An Ancestry Based Approach for Detecting Interactions
Danny S. Park, Itamar Eskin, Eun Yong Kang, Eric R. Gamazon, Celeste Eng, Christopher R. Gignoux, Joshua M. Galanter, Esteban Burchard, Chun J. Ye, Hugues Aschard, Eleazar Eskin, Eran Halperin, Noah Zaitlen
bioRxiv 036640; doi: https://doi.org/10.1101/036640
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An Ancestry Based Approach for Detecting Interactions
Danny S. Park, Itamar Eskin, Eun Yong Kang, Eric R. Gamazon, Celeste Eng, Christopher R. Gignoux, Joshua M. Galanter, Esteban Burchard, Chun J. Ye, Hugues Aschard, Eleazar Eskin, Eran Halperin, Noah Zaitlen
bioRxiv 036640; doi: https://doi.org/10.1101/036640

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