Estimation of Significance Thresholds for Genomewide Association Scans

By Frank Dudbridge, Arief Gusnanto. Genetic Epidemiology, 2008

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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.

Subfields: Genetics

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Citations in the corpus

Genetics of bipolar disorder. (2011, background): “To minimize type I error, most researchers have accepted a p value threshold of ~10 8 (Dudbridge and Gusnanto 2008) for samples of European ancestry.”
Basic statistical analysis in genetic case-control studies (2011, positive): “For GWA studies of dense SNPs and resequence data, a standard genome-wide significance threshold of 7.2 × 10 − 8 for the UK Caucasian population has been proposed by Dudbridge and Gusnanto 21 .”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, background): “on a Bonferroni correction on all independent common SNPs after taking account of the LD structure of the genome (Dudbridge and Gusnanto, 2008; Xu et al., 2014).”
Multiple common variants for celiac disease influencing immune gene expression (2010, positive): “); however, in the current, much larger sample set, all known loci meet recently proposed P −8 threshold”
Chapter 11: Genome-Wide Association Studies (2012, neutral): “2e-8 [38].”
A tutorial on conducting genome‐wide association studies: Quality control and statistical analysis (2018, positive): “Various simulations have indicated that the widely used genome ‐ wide significance threshold of 5 × 10 − 8 for studies on European populations adequately controls for the number of independent SNPs in the entire genome, regardless of the actual SNP density of the study (Dudbridge & Gusnanto, 2008).”
Power and Predictive Accuracy of Polygenic Risk Scores (2013, background): “Assuming this is equivalent to about one million independent common SNPs [32], the required sample sizes are shown in Figure 4 and Table 5.”
Meta-Analysis of 28,141 Individuals Identifies Common Variants within Five New Loci That Influence Uric Acid Concentrations (2009, background): “which corresponds to a Bonferroni correction for the estimated one million independent common variant tests in the human genome of European individuals [ 41 ].”