Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma

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Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma

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Title: Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma
Author: Li, Lin; Kabesch, Michael; Bouzigon, Emmanuelle; Demenais, Florence; Farrall, Martin; Moffatt, Miriam F.; Lin, Xihong; Liang, Liming

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Citation: Li, Lin, Michael Kabesch, Emmanuelle Bouzigon, Florence Demenais, Martin Farrall, Miriam F. Moffatt, Xihong Lin, and Liming Liang. 2013. “Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma.” Frontiers in Genetics 4 (1): 103. doi:10.3389/fgene.2013.00103. http://dx.doi.org/10.3389/fgene.2013.00103.
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Abstract: Increasing evidence suggests that single nucleotide polymorphisms (SNPs) associated with complex traits are more likely to be expression quantitative trait loci (eQTLs). Incorporating eQTL information hence has potential to increase power of genome-wide association studies (GWAS). In this paper, we propose using eQTL weights as prior information in SNP based association tests to improve test power while maintaining control of the family-wise error rate (FWER) or the false discovery rate (FDR). We apply the proposed methods to the analysis of a GWAS for childhood asthma consisting of 1296 unrelated individuals with German ancestry. The results confirm that eQTLs are enriched for previously reported asthma SNPs. We also find that some SNPs are insignificant using procedures without eQTL weighting, but become significant using eQTL-weighted Bonferroni or Benjamini–Hochberg procedures, while controlling the same FWER or FDR level. Some of these SNPs have been reported by independent studies in recent literature. The results suggest that the eQTL-weighted procedures provide a promising approach for improving power of GWAS. We also report the results of our methods applied to the large-scale European GABRIEL consortium data.
Published Version: doi:10.3389/fgene.2013.00103
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3668139/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11708562
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