Mining gold dust under the genome wide significance level: A two-stage approach to analysis of GWAS
Gang Shi, Eric Boerwinkle, Alanna C. Morrison, Charles C. Gu C., Aravinda Chakravarti, D. C. Rao
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.
https://doi.org/10.1002/gepi.20556
Multi-marker
10.1002/gepi.20556
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Dust Medicine & Life Sciences 100%
Genome-Wide Association Study Medicine & Life Sciences 96%
Gold Medicine & Life Sciences 94%
Atherosclerosis Medicine & Life Sciences 18%
Linkage Disequilibrium Medicine & Life Sciences 13%
Shi, G., Boerwinkle, E., Morrison, A. C., Gu C., C. C., Chakravarti, A., & Rao, D. C. (2011). Mining gold dust under the genome wide significance level: A two-stage approach to analysis of GWAS. Genetic epidemiology, 35(2), 111-118. https://doi.org/10.1002/gepi.20556
Mining gold dust under the genome wide significance level : A two-stage approach to analysis of GWAS. / Shi, Gang; Boerwinkle, Eric; Morrison, Alanna C. et al.
In: Genetic epidemiology, Vol. 35, No. 2, 02.2011, p. 111-118.
Shi, G, Boerwinkle, E, Morrison, AC, Gu C., CC, Chakravarti, A & Rao, DC 2011, 'Mining gold dust under the genome wide significance level: A two-stage approach to analysis of GWAS', Genetic epidemiology, vol. 35, no. 2, pp. 111-118. https://doi.org/10.1002/gepi.20556
Shi G, Boerwinkle E, Morrison AC, Gu C. CC, Chakravarti A, Rao DC. Mining gold dust under the genome wide significance level: A two-stage approach to analysis of GWAS. Genetic epidemiology. 2011 Feb;35(2):111-118. doi: 10.1002/gepi.20556
Shi, Gang ; Boerwinkle, Eric ; Morrison, Alanna C. et al. / Mining gold dust under the genome wide significance level : A two-stage approach to analysis of GWAS. In: Genetic epidemiology. 2011 ; Vol. 35, No. 2. pp. 111-118.
@article{b655232be84343c8bae185251cb18748,
title = "Mining gold dust under the genome wide significance level: A two-stage approach to analysis of GWAS",
abstract = "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.",
keywords = "Association, FDR, LASSO, Multi-marker, Power",
author = "Gang Shi and Eric Boerwinkle and Morrison, {Alanna C.} and {Gu C.}, {Charles C.} and Aravinda Chakravarti and Rao, {D. C.}",
doi = "10.1002/gepi.20556",
T1 - Mining gold dust under the genome wide significance level
T2 - A two-stage approach to analysis of GWAS
AU - Shi, Gang
AU - Boerwinkle, Eric
AU - Morrison, Alanna C.
AU - Gu C., Charles C.
AU - Chakravarti, Aravinda
AU - Rao, D. C.
N2 - 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.
AB - 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.
KW - Association
KW - FDR
KW - LASSO
KW - Multi-marker
KW - Power
U2 - 10.1002/gepi.20556
DO - 10.1002/gepi.20556