Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies - PubMed (original) (raw)
Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies
Hua Zhong et al. Biostatistics. 2008 Oct.
Abstract
Genome-wide association studies (GWAS) provide an important approach to identifying common genetic variants that predispose to human disease. A typical GWAS may genotype hundreds of thousands of single nucleotide polymorphisms (SNPs) located throughout the human genome in a set of cases and controls. Logistic regression is often used to test for association between a SNP genotype and case versus control status, with corresponding odds ratios (ORs) typically reported only for those SNPs meeting selection criteria. However, when these estimates are based on the original data used to detect the variant, the results are affected by a selection bias sometimes referred to the "winner's curse" (Capen and others, 1971). The actual genetic association is typically overestimated. We show that such selection bias may be severe in the sense that the conditional expectation of the standard OR estimator may be quite far away from the underlying parameter. Also standard confidence intervals (CIs) may have far from the desired coverage rate for the selected ORs. We propose and evaluate 3 bias-reduced estimators, and also corresponding weighted estimators that combine corrected and uncorrected estimators, to reduce selection bias. Their corresponding CIs are also proposed. We study the performance of these estimators using simulated data sets and show that they reduce the bias and give CI coverage close to the desired level under various scenarios, even for associations having only small statistical power.
Figures
Fig. 1.
Bias versus β in 1-stage designs. c = _Z_1 − α / 2, σ is set as the mean of in simulation studies, where number of case–control pairs = N. Minor allele frequency used in simulations is 20%; 10 000 studies were simulated for each scenario.
Fig. 2.
Simulation results under various heterozygote OR values. Bias and SE of based on 100 significant associations. N = 2000 case–control pairs in 1-stage design, and n = 1000 case–control pairs at each stage in 2-stage design.
Fig. 3.
Simulation results under various sample size. N is total case–control pairs in 1-stage design and N/2 is case–control pairs at each stage in 2-stage design. Bias and SE of based on 100 significant associations. Heterozygote OR = 1.2.
Similar articles
- Review and further developments in statistical corrections for Winner's Curse in genetic association studies.
Forde A, Hemani G, Ferguson J. Forde A, et al. PLoS Genet. 2023 Sep 18;19(9):e1010546. doi: 10.1371/journal.pgen.1010546. eCollection 2023 Sep. PLoS Genet. 2023. PMID: 37721937 Free PMC article. Review. - Quantifying and correcting for the winner's curse in genetic association studies.
Xiao R, Boehnke M. Xiao R, et al. Genet Epidemiol. 2009 Jul;33(5):453-62. doi: 10.1002/gepi.20398. Genet Epidemiol. 2009. PMID: 19140131 Free PMC article. - A flexible genome-wide bootstrap method that accounts for ranking and threshold-selection bias in GWAS interpretation and replication study design.
Faye LL, Sun L, Dimitromanolakis A, Bull SB. Faye LL, et al. Stat Med. 2011 Jul 10;30(15):1898-912. doi: 10.1002/sim.4228. Epub 2011 May 3. Stat Med. 2011. PMID: 21538984 - Correcting "winner's curse" in odds ratios from genomewide association findings for major complex human diseases.
Zhong H, Prentice RL. Zhong H, et al. Genet Epidemiol. 2010 Jan;34(1):78-91. doi: 10.1002/gepi.20437. Genet Epidemiol. 2010. PMID: 19639606 Free PMC article. - The pursuit of genome-wide association studies: where are we now?
Ku CS, Loy EY, Pawitan Y, Chia KS. Ku CS, et al. J Hum Genet. 2010 Apr;55(4):195-206. doi: 10.1038/jhg.2010.19. Epub 2010 Mar 19. J Hum Genet. 2010. PMID: 20300123 Review.
Cited by
- SumVg: Total Heritability Explained by All Variants in Genome-Wide Association Studies Based on Summary Statistics with Standard Error Estimates.
So HC, Xue X, Ma Z, Sham PC. So HC, et al. Int J Mol Sci. 2024 Jan 22;25(2):1347. doi: 10.3390/ijms25021347. Int J Mol Sci. 2024. PMID: 38279346 Free PMC article. - Genetic architecture and biology of youth-onset type 2 diabetes.
Kwak SH, Srinivasan S, Chen L, Todd J, Mercader JM, Jensen ET, Divers J, Mottl AK, Pihoker C, Gandica RG, Laffel LM, Isganaitis E, Haymond MW, Levitsky LL, Pollin TI, Florez JC, Flannick J; Progress in Diabetes Genetics in Youth (ProDiGY) consortium. Kwak SH, et al. Nat Metab. 2024 Feb;6(2):226-237. doi: 10.1038/s42255-023-00970-0. Epub 2024 Jan 26. Nat Metab. 2024. PMID: 38278947 Free PMC article. - East Asian-specific and cross-ancestry genome-wide meta-analyses provide mechanistic insights into peptic ulcer disease.
He Y, Koido M, Sutoh Y, Shi M, Otsuka-Yamasaki Y, Munter HM; BioBank Japan; Morisaki T, Nagai A, Murakami Y, Tanikawa C, Hachiya T, Matsuda K, Shimizu A, Kamatani Y. He Y, et al. Nat Genet. 2023 Dec;55(12):2129-2138. doi: 10.1038/s41588-023-01569-7. Epub 2023 Nov 30. Nat Genet. 2023. PMID: 38036781 Free PMC article. - Review and further developments in statistical corrections for Winner's Curse in genetic association studies.
Forde A, Hemani G, Ferguson J. Forde A, et al. PLoS Genet. 2023 Sep 18;19(9):e1010546. doi: 10.1371/journal.pgen.1010546. eCollection 2023 Sep. PLoS Genet. 2023. PMID: 37721937 Free PMC article. Review. - Evaluating significance of European-associated index SNPs in the East Asian population for 31 complex phenotypes.
Qiao J, Wu Y, Zhang S, Xu Y, Zhang J, Zeng P, Wang T. Qiao J, et al. BMC Genomics. 2023 Jun 13;24(1):324. doi: 10.1186/s12864-023-09425-y. BMC Genomics. 2023. PMID: 37312035 Free PMC article.
References
- Agresti A. Categorical Data Analysis. New York: Wiley; 1990.
- Armitage P. Tests for linear trends in proportions and frequencies. Biometrics. 1955;11:375–386.
- Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B (Methodological) 1995;57:289–300.
- Capen EC, Clapp RV, Campbell WM. Competitive bidding in high-risk situations. Journal of Petroleum Technology. 1971;23:641–653.
- Cochran WG. Some methods for strengthening the common chi-square tests. Biometrics. 1954;10:417–451.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources