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{"title"=>"A new statistic to evaluate imputation reliability", "type"=>"journal", "authors"=>[{"first_name"=>"Peng", "last_name"=>"Lin", "scopus_author_id"=>"36662364100"}, {"first_name"=>"Sarah M.", "last_name"=>"Hartz", "scopus_author_id"=>"35329642500"}, {"first_name"=>"Zhehao", "last_name"=>"Zhang", "scopus_author_id"=>"37002313100"}, {"first_name"=>"Scott F.", "last_name"=>"Saccone", "scopus_author_id"=>"7003791233"}, {"first_name"=>"Jia", "last_name"=>"Wang", "scopus_author_id"=>"57200019152"}, {"first_name"=>"Jay A.", "last_name"=>"Tischfield", "scopus_author_id"=>"7004449404"}, {"first_name"=>"Howard J.", "last_name"=>"Edenberg", "scopus_author_id"=>"7006853081"}, {"first_name"=>"John R.", "last_name"=>"Kramer", "scopus_author_id"=>"55729093800"}, {"first_name"=>"Alison M.", "last_name"=>"Goate", "scopus_author_id"=>"7006308333"}, {"first_name"=>"Laura J.", "last_name"=>"Bierut", "scopus_author_id"=>"6701374894"}, {"first_name"=>"John P.", "last_name"=>"Rice", "scopus_author_id"=>"35355165300"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "pui"=>"368277848", "sgr"=>"78149417737", "doi"=>"10.1371/journal.pone.0009697", "scopus"=>"2-s2.0-78149417737", "pmid"=>"20300623"}, "id"=>"4bbbe774-7bd6-39ed-bb8f-9919352d2428", "abstract"=>"As the amount of data from genome wide association studies grows dramatically, many interesting scientific questions require imputation to combine or expand datasets. However, there are two situations for which imputation has been problematic: (1) polymorphisms with low minor allele frequency (MAF), and (2) datasets where subjects are genotyped on different platforms. Traditional measures of imputation cannot effectively address these problems.", "link"=>"http://www.mendeley.com/research/new-statistic-evaluate-imputation-reliability", "reader_count"=>83, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>5, "Researcher"=>25, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>5, "Student > Bachelor"=>5, "Professor"=>6}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>5, "Researcher"=>25, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>5, "Student > Bachelor"=>5, "Professor"=>6}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Unspecified"=>5, "Biochemistry, Genetics and Molecular Biology"=>7, "Mathematics"=>5, "Agricultural and Biological Sciences"=>44, "Medicine and Dentistry"=>9, "Neuroscience"=>1, "Chemical Engineering"=>1, "Psychology"=>2, "Computer Science"=>5, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>9}, "Neuroscience"=>{"Neuroscience"=>1}, "Psychology"=>{"Psychology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>44}, "Computer Science"=>{"Computer Science"=>5}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>7}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>5}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "reader_count_by_country"=>{"New Zealand"=>2, "Colombia"=>1, "Belgium"=>1, "United States"=>4, "Ireland"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>5}

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• http://doi.org/10.1002/gepi.21690
• http://doi.org/10.1371/journal.pone.0063526
• http://doi.org/10.1101/gr.236315.118
• http://doi.org/10.1038/mp.2011.142
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• http://doi.org/10.1186/1471-2164-15-81
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• http://doi.org/10.1186/s12863-015-0253-5

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/858544"], "description"=>"<p>IQS adjusts for chance agreement. As the minor allele frequency approaches 0, the difference between IQS and imputation accuracy increases. The standard deviation is shown for every other point.</p>", "links"=>[], "tags"=>["means", "iqs", "imputation", "allele"], "article_id"=>528990, "categories"=>["Genetics"], "users"=>["Peng Lin", "Sarah M. Hartz", "Zhehao Zhang", "Scott F. Saccone", "Jia Wang", "Jay A. Tischfield", "Howard J. Edenberg", "John R. Kramer", "Alison M.Goate", "Laura J. Bierut", "John P. Rice"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009697.g001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_means_of_IQS_and_imputation_accuracy_within_each_minor_allele_frequency_interval_/528990", "title"=>"The means of IQS and imputation accuracy within each minor allele frequency interval.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-15 02:29:50"}
• {"files"=>["https://ndownloader.figshare.com/files/858665"], "description"=>"<p>Samples were divided randomly into cases and controls. (A) All Illumina 1 M SNPs are directly genotyped indicating there is no population stratification or other non-random factors in cases and controls. (B) Cases were genotyped on the Illumina 550 K array and the remaining Illumina 1 M SNPs were imputed. (C) An IQS filter (IQS>0.9) was applied, retaining 92% of the SNPs. (D) An imputation accuracy filter (>0.99) was applied, retaining 91% of the SNPs.</p>", "links"=>[], "tags"=>["q-q", "plots", "randomly", "dividing", "cases"], "article_id"=>529114, "categories"=>["Genetics"], "users"=>["Peng Lin", "Sarah M. Hartz", "Zhehao Zhang", "Scott F. Saccone", "Jia Wang", "Jay A. Tischfield", "Howard J. Edenberg", "John R. Kramer", "Alison M.Goate", "Laura J. Bierut", "John P. Rice"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009697.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Q_Q_plots_based_on_randomly_dividing_data_into_cases_and_controls_/529114", "title"=>"The Q-Q plots based on randomly dividing data into cases and controls.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-15 02:31:54"}
• {"files"=>["https://ndownloader.figshare.com/files/858760"], "description"=>"<p>European Americans (A) and African Americans(B) datasets were split in half and Illumina 550 K SNPs were imputed to Illumina 1 M SNPs. IQS score for the two halves of the data were plotted against each other. SNPs with minor allele frequency less than 0.01 were excluded to avoid zero in the denominator.</p>", "links"=>[], "tags"=>["robustness", "iqs"], "article_id"=>529210, "categories"=>["Genetics"], "users"=>["Peng Lin", "Sarah M. Hartz", "Zhehao Zhang", "Scott F. Saccone", "Jia Wang", "Jay A. Tischfield", "Howard J. Edenberg", "John R. Kramer", "Alison M.Goate", "Laura J. Bierut", "John P. Rice"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009697.g003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_of_the_robustness_of_IQS_score_/529210", "title"=>"Evaluation of the robustness of IQS score.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-15 02:33:30"}
• {"files"=>["https://ndownloader.figshare.com/files/426754", "https://ndownloader.figshare.com/files/426805"], "description"=>"<div><h3>Background</h3><p>As the amount of data from genome wide association studies grows dramatically, many interesting scientific questions require imputation to combine or expand datasets. However, there are two situations for which imputation has been problematic: (1) polymorphisms with low minor allele frequency (MAF), and (2) datasets where subjects are genotyped on different platforms. Traditional measures of imputation cannot effectively address these problems.</p><h3>Methodology/Principal Findings</h3><p>We introduce a new statistic, the imputation quality score (IQS). In order to differentiate between well-imputed and poorly-imputed single nucleotide polymorphisms (SNPs), IQS adjusts the concordance between imputed and genotyped SNPs for chance. We first evaluated IQS in relation to minor allele frequency. Using a sample of subjects genotyped on the Illumina 1 M array, we extracted those SNPs that were also on the Illumina 550 K array and imputed them to the full set of the 1 M SNPs. As expected, the average IQS value drops dramatically with a decrease in minor allele frequency, indicating that IQS appropriately adjusts for minor allele frequency. We then evaluated whether IQS can filter poorly-imputed SNPs in situations where cases and controls are genotyped on different platforms. Randomly dividing the data into “cases” and “controls”, we extracted the Illumina 550 K SNPs from the cases and imputed the remaining Illumina 1 M SNPs. The initial Q-Q plot for the test of association between cases and controls was grossly distorted (λ = 1.15) and had 4016 false positives, reflecting imputation error. After filtering out SNPs with IQS<0.9, the Q-Q plot was acceptable and there were no longer false positives. We then evaluated the robustness of IQS computed independently on the two halves of the data. In both European Americans and African Americans the correlation was >0.99 demonstrating that a database of IQS values from common imputations could be used as an effective filter to combine data genotyped on different platforms.</p><h3>Conclusions/Significance</h3><p>IQS effectively differentiates well-imputed and poorly-imputed SNPs. It is particularly useful for SNPs with low minor allele frequency and when datasets are genotyped on different platforms.</p></div>", "links"=>[], "tags"=>["statistic", "imputation"], "article_id"=>144232, "categories"=>["Genetics"], "users"=>["Peng Lin", "Sarah M. Hartz", "Zhehao Zhang", "Scott F. Saccone", "Jia Wang", "Jay A. Tischfield", "Howard J. Edenberg", "John R. Kramer", "Alison M.Goate", "Laura J. Bierut", "John P. Rice"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0009697.s001", "https://dx.doi.org/10.1371/journal.pone.0009697.s002"], "stats"=>{"downloads"=>7, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_New_Statistic_to_Evaluate_Imputation_Reliability/144232", "title"=>"A New Statistic to Evaluate Imputation Reliability", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-03-15 01:10:32"}
• {"files"=>["https://ndownloader.figshare.com/files/858998"], "description"=>"<p>The sample is based on 2,597 European Americans that were randomized to cases and controls. Cases used genotypes from the Illumina 550 K platform and were imputed to the 1 M platform and controls were genotyped on the 1 M platform. Genome-wide significance is set as p<5×10⁻⁸. There were 792,563 SNPs available. False positives refer to the absolute number of SNPs that reached genome-wide significance despite the filter. The retained percentage is the proportion of SNPs that passed the filter.</p>", "links"=>[], "tags"=>["empirical", "evaluations", "imputation", "iqs", "european"], "article_id"=>529446, "categories"=>["Genetics"], "users"=>["Peng Lin", "Sarah M. Hartz", "Zhehao Zhang", "Scott F. Saccone", "Jia Wang", "Jay A. Tischfield", "Howard J. Edenberg", "John R. Kramer", "Alison M.Goate", "Laura J. Bierut", "John P. Rice"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009697.t003", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_empirical_evaluations_of_imputation_quality_to_IQS_in_European_Americans_/529446", "title"=>"Comparison of empirical evaluations of imputation quality to IQS in European Americans.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-15 02:37:26"}
• {"files"=>["https://ndownloader.figshare.com/files/858899"], "description"=>"<p>The set of hard-to-impute SNPs compiled from one dataset can be used to filter the imputed data in another dataset. (A) Cases were European Americans genotyped on the Illumina 550 K array and the remaining Illumina 1 M SNPs were imputed. Controls were European Americans genotyped on the Illumina 1 M array. The QQ plot was shown for the 790,965 available SNPs. (B) An IQS filter (IQS>0.9) was applied, retaining 92% of the SNPs. IQS was calculated from an independent dataset. (C) A similar QQ plot for African Americans. Cases were genotyped on the Illumina 550 K array and the remaining Illumina 1 M SNPs were imputed. Controls were genotyped on the Illumina 1 M array. The QQ plot was shown for the 836,993 available SNPs. (D) An IQS filter (IQS>0.9) was applied, retaining 78% of the SNPs. IQS was calculated from an independent dataset.</p>", "links"=>[], "tags"=>["iqs", "poorly-imputed"], "article_id"=>529347, "categories"=>["Genetics"], "users"=>["Peng Lin", "Sarah M. Hartz", "Zhehao Zhang", "Scott F. Saccone", "Jia Wang", "Jay A. Tischfield", "Howard J. Edenberg", "John R. Kramer", "Alison M.Goate", "Laura J. Bierut", "John P. Rice"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009697.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_database_of_IQS_can_be_used_to_filter_poorly_imputed_SNPs_/529347", "title"=>"A database of IQS can be used to filter poorly-imputed SNPs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-15 02:35:47"}
• {"files"=>["https://ndownloader.figshare.com/files/859080"], "description"=>"<p>IQS adjusts for minor allele frequency by comparing observed frequencies to expected frequencies.</p>", "links"=>[], "tags"=>["classification", "genotypes", "computation"], "article_id"=>529526, "categories"=>["Genetics"], "users"=>["Peng Lin", "Sarah M. Hartz", "Zhehao Zhang", "Scott F. Saccone", "Jia Wang", "Jay A. Tischfield", "Howard J. Edenberg", "John R. Kramer", "Alison M.Goate", "Laura J. Bierut", "John P. Rice"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009697.t001", "stats"=>{"downloads"=>6, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Marginal_cross_classification_of_the_genotypes_used_for_the_computation_of_IQS_/529526", "title"=>"Marginal cross classification of the genotypes used for the computation of IQS.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-15 02:38:46"}
• {"files"=>["https://ndownloader.figshare.com/files/859030"], "description"=>"<p>Summary of evaluation measures for European American and African American samples.</p>", "links"=>[], "tags"=>["measures", "european", "american", "african"], "article_id"=>529478, "categories"=>["Genetics"], "users"=>["Peng Lin", "Sarah M. Hartz", "Zhehao Zhang", "Scott F. Saccone", "Jia Wang", "Jay A. Tischfield", "Howard J. Edenberg", "John R. Kramer", "Alison M.Goate", "Laura J. Bierut", "John P. Rice"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009697.t002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_evaluation_measures_for_European_American_and_African_American_samples_/529478", "title"=>"Summary of evaluation measures for European American and African American samples.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-15 02:37:58"}

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Relative Metric

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F1000Prime