Title |
Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma
|
---|---|
Published in |
Frontiers in Genetics, January 2013
|
DOI | 10.3389/fgene.2013.00103 |
Pubmed ID | |
Authors |
Lin Li, Michael Kabesch, Emmanuelle Bouzigon, Florence Demenais, Martin Farrall, Miriam F. Moffatt, Xihong Lin, Liming Liang |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 50% |
Switzerland | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 5% |
Germany | 1 | 1% |
Canada | 1 | 1% |
Unknown | 82 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 23 | 26% |
Student > Ph. D. Student | 19 | 22% |
Professor > Associate Professor | 10 | 11% |
Student > Master | 7 | 8% |
Student > Bachelor | 6 | 7% |
Other | 13 | 15% |
Unknown | 10 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 44 | 50% |
Biochemistry, Genetics and Molecular Biology | 10 | 11% |
Computer Science | 6 | 7% |
Medicine and Dentistry | 5 | 6% |
Mathematics | 4 | 5% |
Other | 6 | 7% |
Unknown | 13 | 15% |