Original Research ARTICLE
Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma
- 1Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
- 2Department of Pediatric Pneumology and Allergy, KUNO University Children's Hospital Regensburg, Regensburg, Germany
- 3INSERM, Genetic Variation and Human Diseases Unit, U946, Paris, France
- 4Sorbonne Paris Cité, Institut Universitaire d'Hématologie, Université Paris Diderot, Paris, France
- 5Wellcome Trust Centre for Human Genetics, Oxford, UK
- 6Molecular Genetics and Genomics Section, National Heart and Lung Institute, Imperial College London, London, UK
- 7Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
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.
Keywords: asthma, family-wise error rate, false discovery rate, eQTL, genome-wide association study, weighted hypothesis test
Citation: Li L, Kabesch M, Bouzigon E, Demenais F, Farrall M, Moffatt MF, Lin X and Liang L (2013) Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma. Front. Genet. 4:103. doi: 10.3389/fgene.2013.00103
Received: 16 August 2012; Accepted: 21 May 2013;
Published online: 31 May 2013.
Edited by:Barbara E. Stranger, University of Chicago, USA
Reviewed by:Eli Stahl, Mt. Sinai School of Medicine, USA
Matti Pirinen, University of Helsinki, Finland
Copyright © 2013 Li, Kabesch, Bouzigon, Demenais, Farrall, Moffatt, Lin and Liang. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
*Correspondence: Liming Liang, Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Building 2, Room 211A, 655 Huntington Ave., Boston, MA 02115, USA. e-mail: email@example.com
†Present address: Lin Li, Bio Stat Solutions, Inc., Mt Airy, MD, USA.