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{"title"=>"Assessment of Genotype Imputation Performance Using 1000 Genomes in African American Studies", "type"=>"journal", "authors"=>[{"first_name"=>"Dana B.", "last_name"=>"Hancock", "scopus_author_id"=>"14828970000"}, {"first_name"=>"Joshua L.", "last_name"=>"Levy", "scopus_author_id"=>"55317413400"}, {"first_name"=>"Nathan C.", "last_name"=>"Gaddis", "scopus_author_id"=>"6603464337"}, {"first_name"=>"Laura J.", "last_name"=>"Bierut", "scopus_author_id"=>"6701374894"}, {"first_name"=>"Nancy L.", "last_name"=>"Saccone", "scopus_author_id"=>"6603709921"}, {"first_name"=>"Grier P.", "last_name"=>"Page", "scopus_author_id"=>"7102496507"}, {"first_name"=>"Eric O.", "last_name"=>"Johnson", "scopus_author_id"=>"7404681430"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84870616969", "sgr"=>"84870616969", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pmid"=>"23226329", "doi"=>"10.1371/journal.pone.0050610", "pui"=>"366207311"}, "id"=>"339ccccc-ce89-3d76-8d70-6265a8fbc9bb", "abstract"=>"Genotype imputation, used in genome-wide association studies to expand coverage of single nucleotide polymorphisms (SNPs), has performed poorly in African Americans compared to less admixed populations. Overall, imputation has typically relied on HapMap reference haplotype panels from Africans (YRI), European Americans (CEU), and Asians (CHB/JPT). The 1000 Genomes project offers a wider range of reference populations, such as African Americans (ASW), but their imputation performance has had limited evaluation. Using 595 African Americans genotyped on Illumina's HumanHap550v3 BeadChip, we compared imputation results from four software programs (IMPUTE2, BEAGLE, MaCH, and MaCH-Admix) and three reference panels consisting of different combinations of 1000 Genomes populations (February 2012 release): (1) 3 specifically selected populations (YRI, CEU, and ASW); (2) 8 populations of diverse African (AFR) or European (AFR) descent; and (3) all 14 available populations (ALL). Based on chromosome 22, we calculated three performance metrics: (1) concordance (percentage of masked genotyped SNPs with imputed and true genotype agreement); (2) imputation quality score (IQS; concordance adjusted for chance agreement, which is particularly informative for low minor allele frequency [MAF] SNPs); and (3) average r2hat (estimated correlation between the imputed and true genotypes, for all imputed SNPs). Across the reference panels, IMPUTE2 and MaCH had the highest concordance (91%-93%), but IMPUTE2 had the highest IQS (81%-83%) and average r2hat (0.68 using YRI+ASW+CEU, 0.62 using AFR+EUR, and 0.55 using ALL). Imputation quality for most programs was reduced by the addition of more distantly related reference populations, due entirely to the introduction of low frequency SNPs (MAF≤2%) that are monomorphic in the more closely related panels. While imputation was optimized by using IMPUTE2 with reference to the ALL panel (average r2hat = 0.86 for SNPs with MAF>2%), use of the ALL panel for African American studies requires careful interpretation of the population specificity and imputation quality of low frequency SNPs.", "link"=>"http://www.mendeley.com/research/assessment-genotype-imputation-performance-using-1000-genomes-african-american-studies", "reader_count"=>62, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Researcher"=>21, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>1, "Student > Master"=>8, "Other"=>4, "Student > Bachelor"=>4, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Researcher"=>21, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>1, "Student > Master"=>8, "Other"=>4, "Student > Bachelor"=>4, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Biochemistry, Genetics and Molecular Biology"=>6, "Mathematics"=>3, "Medicine and Dentistry"=>10, "Agricultural and Biological Sciences"=>36, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Psychology"=>1, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>10}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>36}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>3}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"United States"=>5, "Finland"=>1, "Denmark"=>1, "France"=>2}, "group_count"=>2}

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• {"id"=>"275655960423432192", "text"=>"This is how you impute genotypes in African-Americans (summary: use IMPUTE2 with the latest 1000 Genomes panel) - http://t.co/P4Wim31R", "created_at"=>"2012-12-03T17:41:17Z", "user"=>"bryan_howie", "user_name"=>"Bryan Howie", "user_profile_image"=>"http://a0.twimg.com/profile_images/2209520166/shades_normal.jpg"}
• {"id"=>"275675302674432001", "text"=>"Thx 4 tip @bryan_howie We should assess trying on Puerto Ricans w 3-way admixture http://t.co/SZbDVGBo", "created_at"=>"2012-12-03T18:58:08Z", "user"=>"larry_parnell", "user_name"=>"Larry Parnell", "user_profile_image"=>"http://a0.twimg.com/profile_images/1554548447/LP_030_2bw_normal.jpg"}
• {"id"=>"278424398489337856", "text"=>"Assessment of Genotype Imputation Performance Using 1000 Genomes as reference = #CaveatEmptor @ http://t.co/bKIsHrNt", "created_at"=>"2012-12-11T09:02:04Z", "user"=>"SAGRudd", "user_name"=>"Stephen Rudd", "user_profile_image"=>"http://a0.twimg.com/profile_images/1387284629/n1350228140_30403185_7501029_normal.jpg"}
• {"id"=>"278427418212061184", "text"=>"RT @SAGRudd: Assessment of Genotype Imputation Performance Using 1000 Genomes as reference = #CaveatEmptor @ http://t.co/bKIsHrNt", "created_at"=>"2012-12-11T09:14:04Z", "user"=>"PhilippBayer", "user_name"=>"Philipp Bayer", "user_profile_image"=>"http://a0.twimg.com/profile_images/2013711027/IMG_1088_normal.png"}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/532770"], "description"=>"<p>r2hat values were averaged across all imputed SNPs on chromosome 22. The number of subjects corresponding to each reference panel is shown in parentheses.</p>", "links"=>[], "tags"=>["r2hat", "resulting", "imputation", "programs", "1000", "genomes"], "article_id"=>203253, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Dana B. Hancock", "Joshua L. Levy", "Nathan C. Gaddis", "Laura J. Bierut", "Nancy L. Saccone", "Grier P. Page", "Eric O. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050610.g003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_r2hat_values_resulting_from_four_different_imputation_programs_and_three_different_1000_Genomes_February_2012_reference_panels_/203253", "title"=>"Average r2hat values resulting from four different imputation programs and three different 1000 Genomes (February 2012) reference panels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-30 00:54:13"}
• {"files"=>["https://ndownloader.figshare.com/files/532848"], "description"=>"<p>Imputation was conducted for all SNPs available on the YRI+CEU+ASW (N = 234, in red), AFR+EUR (N = 625, in green), or the ALL (N = 1,092, in blue) reference panel from 1000 Genomes. Imputed polymorphic SNPs were divided into MAF intervals of 1%, and their average r2hat values were calculated within each interval.</p>", "links"=>[], "tags"=>["imputation", "allele"], "article_id"=>203343, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Dana B. Hancock", "Joshua L. Levy", "Nathan C. Gaddis", "Laura J. Bierut", "Nancy L. Saccone", "Grier P. Page", "Eric O. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050610.g004", "stats"=>{"downloads"=>3, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_r2hat_based_on_imputation_using_IMPUTE2_across_the_minor_allele_frequency_MAF_spectrum_/203343", "title"=>"Average r2hat, based on imputation using IMPUTE2, across the minor allele frequency (MAF) spectrum.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-30 00:55:43"}
• {"files"=>["https://ndownloader.figshare.com/files/286140", "https://ndownloader.figshare.com/files/286204", "https://ndownloader.figshare.com/files/286272", "https://ndownloader.figshare.com/files/286321", "https://ndownloader.figshare.com/files/286365", "https://ndownloader.figshare.com/files/286417", "https://ndownloader.figshare.com/files/286476", "https://ndownloader.figshare.com/files/286530", "https://ndownloader.figshare.com/files/286598", "https://ndownloader.figshare.com/files/286660", "https://ndownloader.figshare.com/files/286723"], "description"=>"<div><p>Genotype imputation, used in genome-wide association studies to expand coverage of single nucleotide polymorphisms (SNPs), has performed poorly in African Americans compared to less admixed populations. Overall, imputation has typically relied on HapMap reference haplotype panels from Africans (YRI), European Americans (CEU), and Asians (CHB/JPT). The 1000 Genomes project offers a wider range of reference populations, such as African Americans (ASW), but their imputation performance has had limited evaluation. Using 595 African Americans genotyped on Illumina’s HumanHap550v3 BeadChip, we compared imputation results from four software programs (IMPUTE2, BEAGLE, MaCH, and MaCH-Admix) and three reference panels consisting of different combinations of 1000 Genomes populations (February 2012 release): (1) 3 specifically selected populations (YRI, CEU, and ASW); (2) 8 populations of diverse African (AFR) or European (AFR) descent; and (3) all 14 available populations (ALL). Based on chromosome 22, we calculated three performance metrics: (1) concordance (percentage of masked genotyped SNPs with imputed and true genotype agreement); (2) imputation quality score (IQS; concordance adjusted for chance agreement, which is particularly informative for low minor allele frequency [MAF] SNPs); and (3) average r2hat (estimated correlation between the imputed and true genotypes, for all imputed SNPs). Across the reference panels, IMPUTE2 and MaCH had the highest concordance (91%–93%), but IMPUTE2 had the highest IQS (81%–83%) and average r2hat (0.68 using YRI+ASW+CEU, 0.62 using AFR+EUR, and 0.55 using ALL). Imputation quality for most programs was reduced by the addition of more distantly related reference populations, due entirely to the introduction of low frequency SNPs (MAF≤2%) that are monomorphic in the more closely related panels. While imputation was optimized by using IMPUTE2 with reference to the ALL panel (average r2hat = 0.86 for SNPs with MAF>2%), use of the ALL panel for African American studies requires careful interpretation of the population specificity and imputation quality of low frequency SNPs.</p> </div>", "links"=>[], "tags"=>["genotype", "imputation", "1000", "genomes", "african", "american", "studies"], "article_id"=>116403, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Dana B. Hancock", "Joshua L. Levy", "Nathan C. Gaddis", "Laura J. Bierut", "Nancy L. Saccone", "Grier P. Page", "Eric O. Johnson"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0050610.s001", "https://dx.doi.org/10.1371/journal.pone.0050610.s002", "https://dx.doi.org/10.1371/journal.pone.0050610.s003", "https://dx.doi.org/10.1371/journal.pone.0050610.s004", "https://dx.doi.org/10.1371/journal.pone.0050610.s005", "https://dx.doi.org/10.1371/journal.pone.0050610.s006", "https://dx.doi.org/10.1371/journal.pone.0050610.s007", "https://dx.doi.org/10.1371/journal.pone.0050610.s008", "https://dx.doi.org/10.1371/journal.pone.0050610.s009", "https://dx.doi.org/10.1371/journal.pone.0050610.s010", "https://dx.doi.org/10.1371/journal.pone.0050610.s011"], "stats"=>{"downloads"=>23, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Assessment_of_Genotype_Imputation_Performance_Using_1000_Genomes_in_African_American_Studies__/116403", "title"=>"Assessment of Genotype Imputation Performance Using 1000 Genomes in African American Studies", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-11-30 01:46:43"}
• {"files"=>["https://ndownloader.figshare.com/files/532683"], "description"=>"<p>IQS results were based on masking 2% of the genotyped SNPs and adjusting the concordance rate chance agreement between imputed and true genotypes. The number of subjects corresponding to each reference panel is shown in parentheses.</p>", "links"=>[], "tags"=>["resulting", "imputation", "programs", "1000", "genomes"], "article_id"=>203175, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Dana B. Hancock", "Joshua L. Levy", "Nathan C. Gaddis", "Laura J. Bierut", "Nancy L. Saccone", "Grier P. Page", "Eric O. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050610.g002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Imputation_quality_score_IQS_resulting_from_four_different_imputation_programs_and_three_different_1000_Genomes_February_2012_reference_panels_/203175", "title"=>"Imputation quality score (IQS) resulting from four different imputation programs and three different 1000 Genomes (February 2012) reference panels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-30 00:52:55"}
• {"files"=>["https://ndownloader.figshare.com/files/532606"], "description"=>"<p>Concordance rates were based on masking 2% of the genotyped SNPs on chromosome 22 and comparing imputed and true genotypes. The number of subjects corresponding to each reference panel is shown in parentheses.</p>", "links"=>[], "tags"=>["resulting", "imputation", "programs", "1000", "genomes", "2012"], "article_id"=>203087, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Dana B. Hancock", "Joshua L. Levy", "Nathan C. Gaddis", "Laura J. Bierut", "Nancy L. Saccone", "Grier P. Page", "Eric O. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050610.g001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Concordance_resulting_from_four_different_imputation_programs_and_three_different_1000_Genomes_February_2012_release_reference_panels_/203087", "title"=>"Concordance resulting from four different imputation programs and three different 1000 Genomes (February 2012 release) reference panels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-30 00:51:27"}

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Relative Metric

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F1000Prime