Rapid and Accurate Multiple Testing Correction and Power Estimation for Millions of Correlated Markers
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{"title"=>"Rapid and accurate multiple testing correction and power estimation for millions of correlated markers", "type"=>"journal", "authors"=>[{"first_name"=>"Buhm", "last_name"=>"Han", "scopus_author_id"=>"14034165200"}, {"first_name"=>"Hyun Min", "last_name"=>"Kang", "scopus_author_id"=>"9739748700"}, {"first_name"=>"Eleazar", "last_name"=>"Eskin", "scopus_author_id"=>"7003344359"}], "year"=>2009, "source"=>"PLoS Genetics", "identifiers"=>{"isbn"=>"1553-7404 (Electronic)\\r1553-7390 (Linking)", "scopus"=>"2-s2.0-66349103652", "pmid"=>"19381255", "issn"=>"15537390", "pui"=>"354669617", "doi"=>"10.1371/journal.pgen.1000456", "sgr"=>"66349103652"}, "id"=>"e74b0749-4dd4-35f3-9b0e-e7d907052288", "abstract"=>"With the development of high-throughput sequencing and genotyping technologies, the number of markers collected in genetic association studies is growing rapidly, increasing the importance of methods for correcting for multiple hypothesis testing. The permutation test is widely considered the gold standard for accurate multiple testing correction, but it is often computationally impractical for these large datasets. Recently, several studies proposed efficient alternative approaches to the permutation test based on the multivariate normal distribution (MVN). However, they cannot accurately correct for multiple testing in genome-wide association studies for two reasons. First, these methods require partitioning of the genome into many disjoint blocks and ignore all correlations between markers from different blocks. Second, the true null distribution of the test statistic often fails to follow the asymptotic distribution at the tails of the distribution. We propose an accurate and efficient method for multiple testing correction in genome-wide association studies--SLIDE. Our method accounts for all correlation within a sliding window and corrects for the departure of the true null distribution of the statistic from the asymptotic distribution. In simulations using the Wellcome Trust Case Control Consortium data, the error rate of SLIDE's corrected p-values is more than 20 times smaller than the error rate of the previous MVN-based methods' corrected p-values, while SLIDE is orders of magnitude faster than the permutation test and other competing methods. We also extend the MVN framework to the problem of estimating the statistical power of an association study with correlated markers and propose an efficient and accurate power estimation method SLIP. SLIP and SLIDE are available at http://slide.cs.ucla.edu.", "link"=>"http://www.mendeley.com/research/rapid-accurate-multiple-testing-correction-power-estimation-millions-correlated-markers", "reader_count"=>151, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>20, "Student > Doctoral Student"=>2, "Researcher"=>52, "Student > Ph. D. Student"=>47, "Student > Postgraduate"=>5, "Other"=>3, "Student > Master"=>5, "Student > Bachelor"=>4, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>10}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>20, "Student > Doctoral Student"=>2, "Researcher"=>52, "Student > Ph. D. Student"=>47, "Student > Postgraduate"=>5, "Other"=>3, "Student > Master"=>5, "Student > Bachelor"=>4, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>10}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>4, "Mathematics"=>17, "Medicine and Dentistry"=>19, "Agricultural and Biological Sciences"=>93, "Chemistry"=>1, "Psychology"=>1, "Computer Science"=>13, "Immunology and Microbiology"=>1, "Decision Sciences"=>1, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>19}, "Chemistry"=>{"Chemistry"=>1}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Psychology"=>{"Psychology"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>93}, "Computer Science"=>{"Computer Science"=>13}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Mathematics"=>{"Mathematics"=>17}}, "reader_count_by_country"=>{"Austria"=>1, "Sweden"=>1, "Belgium"=>1, "United States"=>11, "Philippines"=>1, "Finland"=>1, "United Kingdom"=>5, "Italy"=>1, "France"=>1, "Switzerland"=>1, "Germany"=>3, "India"=>1}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/900743"], "description"=>"*<p>Inaccurate (average error is not within 1%).</p>", "links"=>[], "tags"=>["estimating", "genome-wide", "10"], "article_id"=>571201, "categories"=>["Genetics", "Mathematics", "Cancer", "Biological Sciences"], "users"=>["Buhm Han", "Hyun Min Kang", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000456.t002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Running_time_for_estimating_genome_wide_power_with_10_K_samplings_/571201", "title"=>"Running time for estimating genome-wide power with 10 K samplings.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-17 00:20:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/900623"], "description"=>"<p>Using the WTCCC T2D case/control chromosome 22 data, we plot the ratios between the corrected p-value and the permutation p-value for varying window sizes for SLIDE. We use the pointwise p-value corresponding to the permutation p-value .05. The window size zero denotes the Bonferroni correction. The dashed lines denote the interval where an accurate methods' estimate will be found more than 95% of the time.</p>", "links"=>[], "tags"=>["Computational biology", "computer science/applications", "genetics and genomics/bioinformatics", "genetics and genomics/complex traits", "genetics and genomics/population genetics", "mathematics/statistics"], "article_id"=>571079, "categories"=>["Genetics", "Mathematics", "Cancer", "Biological Sciences"], "users"=>["Buhm Han", "Hyun Min Kang", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000456.g008", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_window_size_on_SLIDE_s_performance_/571079", "title"=>"Effect of window size on SLIDE's performance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-17 00:17:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/900203"], "description"=>"<p>The area outside the rectangle is the critical region. (A) Under the null hypothesis, the MVN is centered at zero. The outside-rectangle probability is the corrected p-value (or the significance level). (B) Under the alternative hypothesis, the MVN is shifted by the non-centrality parameter. The outside-rectangle probability is power.</p>", "links"=>[], "tags"=>["bivariate", "mvn"], "article_id"=>570664, "categories"=>["Genetics", "Mathematics", "Cancer", "Biological Sciences"], "users"=>["Buhm Han", "Hyun Min Kang", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000456.g002", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_density_function_of_a_bivariate_MVN_at_two_markers_/570664", "title"=>"Probability density function of a bivariate MVN at two markers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-17 00:11:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/900556"], "description"=>"<p>A dataset of 2,000 cases and 2,000 controls is generated from the HapMap CEU data. Using each method, we estimate the per-marker threshold corresponding to a significance level of .05. The effective number of test is simply .05 divided by the per-marker threshold. The dashed lines denote the interval where an accurate methods' estimate will be found more than 95% of the time.</p>", "links"=>[], "tags"=>["hapmap", "snps", "simulated"], "article_id"=>571015, "categories"=>["Genetics", "Mathematics", "Cancer", "Biological Sciences"], "users"=>["Buhm Han", "Hyun Min Kang", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000456.g007", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effective_number_of_tests_of_the_2_7_million_HapMap_SNPs_for_a_simulated_dataset_/571015", "title"=>"Effective number of tests of the 2.7 million HapMap SNPs for a simulated dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-17 00:16:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/900377"], "description"=>"<p>The probability density function of the asymptotic bivariate MVN is depicted as a grid. The probability mass function of the true distribution is depicted as a histogram. (A) The asymptotic distribution often shows a discrepancy from the true distribution. (The discrepancy is exaggerated in this figure.) (B) After scaling down the asymptotic distribution, the discrepancy is removed.</p>", "links"=>[], "tags"=>["scaling"], "article_id"=>570834, "categories"=>["Genetics", "Mathematics", "Cancer", "Biological Sciences"], "users"=>["Buhm Han", "Hyun Min Kang", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000456.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_SLIDE_s_scaling_procedure_/570834", "title"=>"SLIDE's scaling procedure.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-17 00:13:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/900687"], "description"=>"<p>We use the HapMap CEU reference data. We assume a multiplicative disease model with relative risk 1.2, disease prevalence .01, and a uniform distribution of causal SNPs over common SNPs (MAF≥.05). We use the significance threshold of .05.</p>", "links"=>[], "tags"=>["affymetrix", "500"], "article_id"=>571150, "categories"=>["Genetics", "Mathematics", "Cancer", "Biological Sciences"], "users"=>["Buhm Han", "Hyun Min Kang", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000456.g009", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genome_wide_power_of_the_Affymetrix_500_k_chip_estimated_by_different_methods_/571150", "title"=>"Genome-wide power of the Affymetrix 500 k chip estimated by different methods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-17 00:19:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/900497"], "description"=>"<p>We use the WTCCC T2D case/control chromosome 22 data. The vertical axis is the average error in corrected p-values relative to the Bonferroni correction. The horizontal axis is the approximated time for correcting 10 genome-wide p-values for 500 K SNPs assuming 100 K permutations.</p>", "links"=>[], "tags"=>["compared"], "article_id"=>570960, "categories"=>["Genetics", "Mathematics", "Cancer", "Biological Sciences"], "users"=>["Buhm Han", "Hyun Min Kang", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000456.g006", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_SLIDE_s_accuracy_and_efficiency_compared_to_other_methods_/570960", "title"=>"SLIDE's accuracy and efficiency compared to other methods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-17 00:16:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/900270"], "description"=>"<p>Given a threshold , the asymptotic p-value is . The true p-value is obtained by listing all possible contingency tables. The number of individuals (N) denotes the number of haplotypes, half control and half case.</p>", "links"=>[], "tags"=>["asymptotic", "p-value", "snp"], "article_id"=>570736, "categories"=>["Genetics", "Mathematics", "Cancer", "Biological Sciences"], "users"=>["Buhm Han", "Hyun Min Kang", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000456.g003", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Discrepancy_between_asymptotic_p_value_and_true_p_value_in_a_single_SNP_experiment_/570736", "title"=>"Discrepancy between asymptotic p-value and true p-value in a single SNP experiment.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-17 00:12:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/900439"], "description"=>"<p>We use the WTCCC T2D case/control chromosome 22 data. Approximated time is for correcting 10 p-values with respect to 500 K SNPs assuming 100 K permutations. The dashed lines denote the interval where an accurate methods' estimate will be found more than 95% of the time.</p>", "links"=>[], "tags"=>["corrected", "p-values", "permutation", "p-value"], "article_id"=>570898, "categories"=>["Genetics", "Mathematics", "Cancer", "Biological Sciences"], "users"=>["Buhm Han", "Hyun Min Kang", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000456.g005", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ratios_between_corrected_p_values_and_permutation_p_values_for_ten_different_p_value_thresholds_/570898", "title"=>"Ratios between corrected p-values and permutation p-values for ten different p-value thresholds.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-17 00:14:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/900149"], "description"=>"<p>(A) Correlations between 10 markers are depicted. (B) Correlations taken into account by a block-wise strategy with a block size of 5. The ignored correlations are shown as black. (C) Correlations taken into account by a sliding-window approach with a window size of 5. The ignored correlations are shown as black.</p>", "links"=>[], "tags"=>["sliding-window"], "article_id"=>570613, "categories"=>["Genetics", "Mathematics", "Cancer", "Biological Sciences"], "users"=>["Buhm Han", "Hyun Min Kang", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000456.g001", "stats"=>{"downloads"=>2, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Block_wise_strategy_and_sliding_window_approach_/570613", "title"=>"Block-wise strategy and sliding-window approach.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-17 00:10:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/445828", "https://ndownloader.figshare.com/files/445835", "https://ndownloader.figshare.com/files/445838", "https://ndownloader.figshare.com/files/445847"], "description"=>"<div><p>With the development of high-throughput sequencing and genotyping technologies, the number of markers collected in genetic association studies is growing rapidly, increasing the importance of methods for correcting for multiple hypothesis testing. The permutation test is widely considered the gold standard for accurate multiple testing correction, but it is often computationally impractical for these large datasets. Recently, several studies proposed efficient alternative approaches to the permutation test based on the multivariate normal distribution (MVN). However, they cannot accurately correct for multiple testing in genome-wide association studies for two reasons. First, these methods require partitioning of the genome into many disjoint blocks and ignore all correlations between markers from different blocks. Second, the true null distribution of the test statistic often fails to follow the asymptotic distribution at the tails of the distribution. We propose an accurate and efficient method for multiple testing correction in genome-wide association studies—SLIDE. Our method accounts for all correlation within a sliding window and corrects for the departure of the true null distribution of the statistic from the asymptotic distribution. In simulations using the Wellcome Trust Case Control Consortium data, the error rate of SLIDE's corrected <em>p</em>-values is more than 20 times smaller than the error rate of the previous MVN-based methods' corrected <em>p</em>-values, while SLIDE is orders of magnitude faster than the permutation test and other competing methods. We also extend the MVN framework to the problem of estimating the statistical power of an association study with correlated markers and propose an efficient and accurate power estimation method SLIP. SLIP and SLIDE are available at <a href=\"http://slide.cs.ucla.edu\">http://slide.cs.ucla.edu</a>.</p></div>", "links"=>[], "tags"=>["correction", "estimation", "millions", "correlated", "markers"], "article_id"=>147866, "categories"=>["Genetics", "Mathematics", "Cancer", "Biological Sciences"], "users"=>["Buhm Han", "Hyun Min Kang", "Eleazar Eskin"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1000456.s001", "https://dx.doi.org/10.1371/journal.pgen.1000456.s002", "https://dx.doi.org/10.1371/journal.pgen.1000456.s003", "https://dx.doi.org/10.1371/journal.pgen.1000456.s004"], "stats"=>{"downloads"=>44, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Rapid_and_Accurate_Multiple_Testing_Correction_and_Power_Estimation_for_Millions_of_Correlated_Markers/147866", "title"=>"Rapid and Accurate Multiple Testing Correction and Power Estimation for Millions of Correlated Markers", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-04-17 02:11:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/900771"], "description"=>"*<p>Often anti-conservative.</p><p>All values are extrapolated from the chromosome 22 results.</p>", "links"=>[], "tags"=>["correcting", "genome-wide", "p-values", "500", "snps"], "article_id"=>571230, "categories"=>["Genetics", "Mathematics", "Cancer", "Biological Sciences"], "users"=>["Buhm Han", "Hyun Min Kang", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000456.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Running_time_for_correcting_genome_wide_p_values_in_a_study_with_500_K_SNPs_over_5_000_individuals_/571230", "title"=>"Running time for correcting genome-wide p-values in a study with 500 K SNPs over 5,000 individuals.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-17 00:20:30"}

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