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Estimation of significance thresholds for genomewide association scans
Frank Dudbridge, Arief Gusnanto
Genetic Epidemiology · 2008
Abstract
The question of what significance threshold is appropriate for genomewide association studies is somewhat unresolved. Previous theoretical suggestions have yet to be validated in practice, whereas permutation testing does not resolve a discrepancy between the genomewide multiplicity of the experiment and the subset of markers actually tested. We used genotypes from the Wellcome Trust Case‐Control Consortium to estimate a genomewide significance threshold for the UK Caucasian population. We subsampled the genotypes at increasing densities, using permutation to estimate the nominal P ‐value for 5% family‐wise error. By extrapolating to infinite density, we estimated the genomewide significance threshold to be about 7.2 × 10 −8 . To reduce the computation time, we considered Patterson's eigenvalue estimator of the effective number of tests, but found it to be an order of magnitude too low for multiplicity correction. However, by fitting a Beta distribution to the minimum P ‐value from permutation replicates, we showed that the effective number is a useful heuristic and suggest that its estimation in this context is an open problem. We conclude that permutation is still needed to obtain genomewide significance thresholds, but with subsampling, extrapolation and estimation of an effective number of tests, the threshold can be standardized for all studies of the same population. Genet. Epidemiol . 2008. © 2008 Wiley‐Liss, Inc.
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References 24
- 1.
Barrett JC, Cardon LR. Evaluating coverage of genome-wide association studies. Nat Genet. 2006;38:659-662.
Not in our library10.1038/ng1801
- 2.
Benjamini Y, Hochberg Y. Controlling the false discovery rate—a practical and powerful approach to multiple testing. J R Stat Soc B. 1995;57:289-300.
Not in our library
- 3.
Churchill GA, Doerge RW. Empirical threshold values for quantitative trait mapping. Genetics. 1994;138:963-971.
Not in our library10.1093/genetics/138.3.963
- 4.
Clayton DG. P-values, false discovery rates, and Bayes factors: how should we assess the “significance” of genetic associations? Ann Hum Genet. 2003;67:630.
Not in our library
- 5.
Cohen JE, Gürtler RE. Modeling household transmission of American trypanosomiasis. Science. 2001;293:694-698.
Not in our library10.1126/science.1060638
- 6.
Dudbridge F, Koeleman BP. Rank truncated product of P-values, with application to genomewide association scans. Genet Epidemiol. 2003;25:360-366.
Not in our library10.1002/gepi.10264
- 7.
Dudbridge F, Koeleman BP. Efficient computation of significance levels for multiple associations in large studies of correlated data, including genomewide association studies. Am J Hum Genet. 2004;75:424-435.
Not in our library10.1086/423738
- 8.
Efron B, Tibshirani R. Empirical Bayes methods and false discovery rates for microarrays. Genet Epidemiol. 2002;23:70-86.
Not in our library10.1002/gepi.1124
- 9.
Hochberg Y, Tamhane AC. Multiple Comparison Procedures. 1987.
Not in our library
- 10.
A haplotype map of the human genome. Nature. 2005;437:1299-1320.
Not in our library10.1038/nature04226
- 11.
- 12.
Lander ES, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet. 1995;11:241-247.
Not in our library10.1038/ng1195-241
- 13.
Manly KF, Nettleton D, Hwang JT. Genomics, prior probability, and statistical tests of multiple hypotheses. Genome Res. 2004;14:997-1001.
Not in our library10.1101/gr.2156804
- 14.
Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007;39:906-913.
Not in our library10.1038/ng2088
- 15.
Morton NE. Sequential tests for the detection of linkage. Am J Hum Genet. 1955;7:277-318.
Not in our library
- 16.
- 17.
Pe'er I, de Bakker PI, Maller J, Yelensky R, Altshuler D, Daly MJ. Evaluating and improving power in whole-genome association studies using fixed marker sets. Nat Genet. 2006;38:663-667.
Not in our library10.1038/ng1816
- 18.
Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996;273:1516-1517.
Not in our library10.1126/science.273.5281.1516
- 19.
Šidák Z. Rectangular confidence regions for the means of multivariate normal distributions. J Am Stat Assoc. 1967;78:626-633.
Not in our library
- 20.
Thomas DC, Clayton DG. Betting odds and genetic associations. J Nat Cancer Inst. 2004;96:421-423.
Not in our library10.1093/jnci/djh094
- 21.
Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Nat Cancer Inst. 2004;96:434-442.
Not in our library10.1093/jnci/djh075
- 22.
Wakefield J. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am J Hum Genet. 2007;81:208-227.
Not in our library10.1086/519024
- 23.
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447:661-678.
Not in our library10.1038/nature05911
- 24.
Westfall PH, Young SS. Resampling-Based Multiple Testing: Examples and Methods for P-value Adjustment. 1993.
Not in our library
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