Mining gold dust under the genome wide significance level: a two-stage approach to analysis of GWAS
Article first published online: 31 DEC 2010
© 2010 Wiley-Liss, Inc.
Volume 35, Issue 2, pages 111–118, February 2011
How to Cite
Shi, G., Boerwinkle, E., Morrison, A. C., Gu, C. C., Chakravarti, A. and Rao, D.C. (2011), Mining gold dust under the genome wide significance level: a two-stage approach to analysis of GWAS. Genet. Epidemiol., 35: 111–118. doi: 10.1002/gepi.20556
- Issue published online: 19 JAN 2011
- Article first published online: 31 DEC 2010
- Manuscript Accepted: 17 NOV 2010
- Manuscript Revised: 27 OCT 2010
- Manuscript Received: 14 JUN 2010
- National Institute of General Medical Sciences. Grant Number: 5R01GM028719
- National Heart, Lung, and Blood Institute. Grant Numbers: 5U01HL054473, 5R01HL086694
We propose a two-stage approach to analyze genome-wide association data in order to identify a set of promising single-nucleotide polymorphisms (SNPs). In stage one, we select a list of top signals from single SNP analyses by controlling false discovery rate. In stage two, we use the least absolute shrinkage and selection operator (LASSO) regression to reduce false positives. The proposed approach was evaluated using simulated quantitative traits based on genome-wide SNP data on 8,861 Caucasian individuals from the Atherosclerosis Risk in Communities (ARIC) Study. Our first stage, targeted at controlling false negatives, yields better power than using Bonferroni-corrected significance level. The LASSO regression reduces the number of significant SNPs in stage two: it reduces false-positive SNPs and it reduces true-positive SNPs also at simulated causal loci due to linkage disequilibrium. Interestingly, the LASSO regression preserves the power from stage one, i.e., the number of causal loci detected from the LASSO regression in stage two is almost the same as in stage one, while reducing false positives further. Real data on systolic blood pressure in the ARIC study was analyzed using our two-stage approach which identified two significant SNPs, one of which was reported to be genome-significant in a meta-analysis containing a much larger sample size. On the other hand, a single SNP association scan did not yield any significant results. Genet. Epidemiol. 32:111-118, 2011.© 2010 Wiley-Liss, Inc.