TY - JOUR T1 - An Ancestry Based Approach for Detecting Interactions JF - bioRxiv DO - 10.1101/036640 SP - 036640 AU - Danny S. Park AU - Itamar Eskin AU - Eun Yong Kang AU - Eric R. Gamazon AU - Celeste Eng AU - Christopher R. Gignoux AU - Joshua M. Galanter AU - Esteban Burchard AU - Chun J. Ye AU - Hugues Aschard AU - Eleazar Eskin AU - Eran Halperin AU - Noah Zaitlen Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/05/01/036640.abstract N2 - Background: Epistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes 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 in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals respectively, identifying nine interactions that were significant at p < 5×10−8, we show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low p-values (p < 1.8×10−6).Conclusion: 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. ER -