Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets
Open accessAbstract
Current genome-wide association studies (GWAS) use commercial genotyping microarrays that can assay over a million single nucleotide polymorphisms (SNPs). The number of SNPs is further boosted by advanced statistical genotype-imputation algorithms and large SNP databases for reference human populations. The testing of a huge number of SNPs needs to be taken into account in the interpretation of statistical significance in such genome-wide studies, but this is complicated by the non-independence of SNPs because of linkage disequilibrium (LD). Several previous groups have proposed the use of the effective number of independent markers (M(e)) for the adjustment of multiple testing, but current methods of calculation for M(e) are limited in accuracy or computational speed. Here, we report a more robust and fast method to calculate M(e). Applying this efficient method [implemented in a free software tool named Genetic type 1 error calculator (GEC)], we systematically examined the M(e), and the corresponding p-value thresholds required to control the genome-wide type 1 error rate at 0.05, for 13 Illumina or Affymetrix genotyping arrays, as well as for HapMap Project and 1000 Genomes Project datasets which are widely used in genotype imputation as reference panels. Our results suggested the use of a p-value threshold of ~10(-7) as the criterion for genome-wide significance for early commercial genotyping arrays, but slightly more stringent p-value thresholds ~5 × 10(-8) for current or merged commercial genotyping arrays, ~10(-8) for all common SNPs in the 1000 Genomes Project dataset and ~5 × 10(-8) for the common SNPs only within genes.
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The effective number of independent tests was calculated using the Genetic Type-1 Error Calculator (GEC) to adjust for multiple testing accounting for linkage disequilibrium.
“To appropriately adjust for the multiple testing of many SNPs, accounting for the existing linkage disequilibrium structure, the effective number of independent tests was calculated using the Genetic Type-1 Error Calculator (GEC), with the 1000 Genomes Project VCF as input [21].”
From Neandertal Introgression Sheds Light on Modern Human Endocranial Globularity (2019) · cites this paper as [21]
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- Authors
- Li, M.-X., Yeung, J. M. Y., Cherny, S. S., & Sham, P. C.
- Affiliations
- University of Hong Kong (HK)
- Corresponding author
- Sham, Pak C.
- Venue
- Human Genetics
- Published
- 2011-12-06
- Volume
- 131
- Issue
- 5
- Pages
- 747-756
- ISSN
- 0018-7348 (ISSN-L)
- 0340-6717
- 1432-1203
- PMC
- PMC3325408
- OpenAlex
- W2055850604
- Cited by
- 962
- References
- 32
- Citations by year
- 2026: 69 · 2025: 117 · 2024: 96 · 2023: 107 · 2022: 91 · 2021: 112 · 2020: 107 · 2019: 65 · 2018: 53 · 2017: 43 · 2016: 41 · 2015: 25 · 2014: 23 · 2013: 8 · 2012: 4
- FWCI
- 6.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Human Genetics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion, cc-by-nc-nd)
- Funders
- European Commission (241909)
- University of Hong Kong
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- multiple testing correction, p-value threshold, linkage disequilibrium, HapMap, 1000 Genomes Project, type I error rate, single nucleotide polymorphisms
- MeSH terms
- Algorithms, Genetic Markers, Genotype, Humans, Software, Statistics as Topic, Databases as Topic, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, Genome-Wide Association Study, HapMap Project
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