We are analyzing https://link.springer.com/article/10.1007/s00439-011-1118-2.

Title:
Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets | Human Genetics
Description:
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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Keywords {🔍}

snps, association, article, number, effective, pvalue, thresholds, google, scholar, hapmap, project, pubmed, arrays, genomewide, datasets, gwas, method, genotyping, genotype, threshold, data, studies, error, dataset, table, genet, proposed, correlation, genomes, panels, tests, significant, correction, ratio, cas, significance, type, fwer, imputation, markers, multiple, illumina, bonferroni, snp, testing, methods, regions, independent, reference, linkage,

Topics {✒️}

genome-wide linkage studies genome-wide association studies org/web/packages/mvtnorm/index quantitative trait study genome-wide association study genome-wide error rate genome-wide significance levels /csg/abecasis/mach/download/ genome-wide significant association genetic association studies article download pdf family-wise error rate genome-wide significance thresholds genome-wide snp platforms genome-wide significance threshold user-friendly command line gov/downloads/ld_data/latest/ genome-wide studies statistical genotype-imputation algorithms illumina human610-quad beadchip high-throughput genotyping arrays snp-wise error rate genetic association tests genome-wide significance genome association studies pair-wise ld data population-based linkage analyses pair-wise ld coefficients genetic research massive multiple-testing burden multiple-ld-block scenarios genome-wide significant test-wise significance threshold family-wise type family-wise error genomewide association studies give mvn-derived fwers genotype call rate open access privacy choices/manage cookies allelic association test linkage disequilibrium full access single-nucleotide polymorphisms genomewide association scans pair-wise ld real genotype dataset user friendly tool de bakker pi modern human origins

Schema {🗺️}

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         headline:Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets
         description: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.
         datePublished:2011-12-06T00:00:00Z
         dateModified:2011-12-06T00:00:00Z
         pageStart:747
         pageEnd:756
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         keywords:
            Linkage Disequilibrium
            Linkage Disequilibrium Block
            Linkage Disequilibrium Pattern
            Genotyping Array
            Genotype Imputation
            Human Genetics
            Molecular Medicine
            Gene Function
            Metabolic Diseases
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      headline:Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets
      description: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.
      datePublished:2011-12-06T00:00:00Z
      dateModified:2011-12-06T00:00:00Z
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      keywords:
         Linkage Disequilibrium
         Linkage Disequilibrium Block
         Linkage Disequilibrium Pattern
         Genotyping Array
         Genotype Imputation
         Human Genetics
         Molecular Medicine
         Gene Function
         Metabolic Diseases
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         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1007%2Fs00439-011-1118-2/MediaObjects/439_2011_1118_Fig1_HTML.gif
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      name:Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong
      name:Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong
      name:State Key Laboratory for Cognitive and Brain Sciences, The University of Hong Kong, Pokfulam, Hong Kong
      name:Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong
      name:The Centre for Reproduction, Development and Growth, The University of Hong Kong, Pokfulam, Hong Kong
      name:Genome Research Centre, The University of Hong Kong, Pokfulam, Hong Kong
      name:Department of Psychiatry, LKS Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong
      name:State Key Laboratory for Cognitive and Brain Sciences, The University of Hong Kong, Pokfulam, Hong Kong

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