Local Exome Sequences Facilitate Imputation of Less Common Variants and Increase Power of Genome Wide Association Studies
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{"title"=>"Local Exome Sequences Facilitate Imputation of Less Common Variants and Increase Power of Genome Wide Association Studies", "type"=>"journal", "authors"=>[{"first_name"=>"Peter K.", "last_name"=>"Joshi", "scopus_author_id"=>"55771460300"}, {"first_name"=>"James", "last_name"=>"Prendergast", "scopus_author_id"=>"16317569800"}, {"first_name"=>"Ross M.", "last_name"=>"Fraser", "scopus_author_id"=>"14048248200"}, {"first_name"=>"Jennifer E.", "last_name"=>"Huffman", "scopus_author_id"=>"26654219700"}, {"first_name"=>"Veronique", "last_name"=>"Vitart", "scopus_author_id"=>"56920729200"}, {"first_name"=>"Caroline", "last_name"=>"Hayward", "scopus_author_id"=>"35369845200"}, {"first_name"=>"Ruth", "last_name"=>"McQuillan", "scopus_author_id"=>"6701394821"}, {"first_name"=>"Dominik", "last_name"=>"Glodzik", "scopus_author_id"=>"57199418143"}, {"first_name"=>"Ozren", "last_name"=>"Polašek", "scopus_author_id"=>"8232027400"}, {"first_name"=>"Nicholas D.", "last_name"=>"Hastie", "scopus_author_id"=>"7007175006"}, {"first_name"=>"Igor", "last_name"=>"Rudan", "scopus_author_id"=>"7007138278"}, {"first_name"=>"Harry", "last_name"=>"Campbell", "scopus_author_id"=>"7202318417"}, {"first_name"=>"Alan F.", "last_name"=>"Wright", "scopus_author_id"=>"35402433000"}, {"first_name"=>"Chris S.", "last_name"=>"Haley", "scopus_author_id"=>"35477845200"}, {"first_name"=>"James F.", "last_name"=>"Wilson", "scopus_author_id"=>"56920724500"}, {"first_name"=>"Pau", "last_name"=>"Navarro", "scopus_author_id"=>"8724895200"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84880483904", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0068604", "pui"=>"369369128", "sgr"=>"84880483904", "issn"=>"19326203", "pmid"=>"23874685"}, "id"=>"bf79c2b5-a5dc-3ada-a558-ff824f7bb4b8", "abstract"=>"The analysis of less common variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association. We show that addition of population-specific exome sequence data to global reference data allows more accurate imputation, particularly of less common SNPs (minor allele frequency 1-10%) in two very different European populations. The imputation improvement corresponds to an increase in effective sample size of 28-38%, for SNPs with a minor allele frequency in the range 1-3%.", "link"=>"http://www.mendeley.com/research/local-exome-sequences-facilitate-imputation-less-common-variants-increase-power-genome-wide-associat", "reader_count"=>24, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Researcher"=>9, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>5, "Student > Master"=>3, "Other"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Researcher"=>9, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>5, "Student > Master"=>3, "Other"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Mathematics"=>1, "Agricultural and Biological Sciences"=>14, "Medicine and Dentistry"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>14}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1119878"], "description"=>"<p>The genotype data were quality controlled and phased. These data were then used in further downstream analysis.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "genetics", "population genetics", "haplotypes", "Genetics of disease", "Genome-wide association studies", "Trait locus analysis", "Genome sequencing", "Clinical research design", "Statistical methods"], "article_id"=>746220, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Peter K. Joshi", "James Prendergast", "Ross M. Fraser", "Jennifer E. Huffman", "Veronique Vitart", "Caroline Hayward", "Ruth McQuillan", "Dominik Glodzik", "Ozren Polašek", "Nicholas D. Hastie", "Igor Rudan", "Harry Campbell", "Alan F. Wright", "Chris S. Haley", "James F. Wilson", "Pau Navarro"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068604.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Preparation_of_array_data_and_local_reference_panel_for_imputation_/746220", "title"=>"Preparation of array data and local reference panel for imputation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-16 02:22:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/1119892"], "description"=>"<p>MAF bins increase by factors of √10, to create four exponentially increasing bins.</p><p>N SNPs: number of SNPs in MAF bin.</p><p>1kG: 1000 Genomes used as reference panel.</p><p>1kG+LRP: 1000 Genomes plus local reference panel.</p><p>Increase r<sup>2</sup>: Average across all SNPs in MAF bin increase in r<sup>2</sup>.</p><p>Std dev: The standard deviation (across SNPs) of the increase in r<sup>2</sup> at each SNP.</p><p>Inc. Sample: Increase in effective sample size for GWAS.</p><p>The standard errors of mean increases are less than 0.003. All improvements in r<sup>2</sup> are significantly different from zero and significantly different between MAF bands (P<0.001, two-sided t tests).</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "genetics", "population genetics", "haplotypes", "Genetics of disease", "Genome-wide association studies", "Trait locus analysis", "Genome sequencing", "Clinical research design", "Statistical methods", "imputation", "allelic", "dosage", "samples", "averaged", "snps", "allele"], "article_id"=>746229, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Peter K. Joshi", "James Prendergast", "Ross M. Fraser", "Jennifer E. Huffman", "Veronique Vitart", "Caroline Hayward", "Ruth McQuillan", "Dominik Glodzik", "Ozren Polašek", "Nicholas D. Hastie", "Igor Rudan", "Harry Campbell", "Alan F. Wright", "Chris S. Haley", "James F. Wilson", "Pau Navarro"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068604.t001", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_accuracy_of_imputation_r_2_of_allelic_dosage_across_all_samples_for_a_SNP_averaged_across_SNPs_split_by_Minor_Allele_Frequency_MAF_/746229", "title"=>"Mean accuracy of imputation (r<sup>2</sup> of allelic dosage across all samples for a SNP) averaged across SNPs split by Minor Allele Frequency (MAF).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-07-16 02:22:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/1119891"], "description"=>"<p>Plot of mean improvement in imputation accuracy (r<sup>2</sup>) for SNPs with minor allele frequency (MAF) in the range 1–10% in our exome sequence data.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "genetics", "population genetics", "haplotypes", "Genetics of disease", "Genome-wide association studies", "Trait locus analysis", "Genome sequencing", "Clinical research design", "Statistical methods", "imputation", "snps", "allele", "exome"], "article_id"=>746228, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Peter K. Joshi", "James Prendergast", "Ross M. Fraser", "Jennifer E. Huffman", "Veronique Vitart", "Caroline Hayward", "Ruth McQuillan", "Dominik Glodzik", "Ozren Polašek", "Nicholas D. Hastie", "Igor Rudan", "Harry Campbell", "Alan F. Wright", "Chris S. Haley", "James F. Wilson", "Pau Navarro"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068604.g004", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Plot_of_mean_improvement_in_imputation_accuracy_r_2_for_SNPs_with_minor_allele_frequency_MAF_in_the_range_1_8211_10_in_our_exome_sequence_data_/746228", "title"=>"Plot of mean improvement in imputation accuracy (r<sup>2</sup>) for SNPs with minor allele frequency (MAF) in the range 1–10% in our exome sequence data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-16 02:22:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/1119883"], "description"=>"<p>Frequency plot of imputation accuracy (r<sup>2</sup>) using 1000 Genomes data alone against 1000 Genomes plus a local reference panel for SNPs with Minor Allele Frequencies (MAF) of 1–3.2%.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "genetics", "population genetics", "haplotypes", "Genetics of disease", "Genome-wide association studies", "Trait locus analysis", "Genome sequencing", "Clinical research design", "Statistical methods", "imputation", "1000", "genomes", "snps", "allele", "frequencies"], "article_id"=>746225, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Peter K. Joshi", "James Prendergast", "Ross M. Fraser", "Jennifer E. Huffman", "Veronique Vitart", "Caroline Hayward", "Ruth McQuillan", "Dominik Glodzik", "Ozren Polašek", "Nicholas D. Hastie", "Igor Rudan", "Harry Campbell", "Alan F. Wright", "Chris S. Haley", "James F. Wilson", "Pau Navarro"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068604.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Frequency_plot_of_imputation_accuracy_r_2_using_1000_Genomes_data_alone_against_1000_Genomes_plus_a_local_reference_panel_for_SNPs_with_Minor_Allele_Frequencies_MAF_of_1_8211_3_2_/746225", "title"=>"Frequency plot of imputation accuracy (r<sup>2</sup>) using 1000 Genomes data alone against 1000 Genomes plus a local reference panel for SNPs with Minor Allele Frequencies (MAF) of 1–3.2%.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-16 02:22:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/1119880"], "description"=>"<p>We used a drop one-out crossvalidation approach. For the imputation step each subject was removed from the reference panel in turn, and this subject’s exome sequence SNPs were then imputed using either the 1000 Genomes reference panel alone or in conjunction with a second local reference panel. All subjects’ imputed allelic dosages were then compared with the exome sequence genotype data (“gold standard”).</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "genetics", "population genetics", "haplotypes", "Genetics of disease", "Genome-wide association studies", "Trait locus analysis", "Genome sequencing", "Clinical research design", "Statistical methods", "imputation"], "article_id"=>746222, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Peter K. Joshi", "James Prendergast", "Ross M. Fraser", "Jennifer E. Huffman", "Veronique Vitart", "Caroline Hayward", "Ruth McQuillan", "Dominik Glodzik", "Ozren Polašek", "Nicholas D. Hastie", "Igor Rudan", "Harry Campbell", "Alan F. Wright", "Chris S. Haley", "James F. Wilson", "Pau Navarro"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068604.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_the_procedure_to_estimate_imputation_accuracy_/746222", "title"=>"Illustration of the procedure to estimate imputation accuracy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-16 02:22:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/1119893", "https://ndownloader.figshare.com/files/1119895"], "description"=>"<div><p>The analysis of less common variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association. We show that addition of population-specific exome sequence data to global reference data allows more accurate imputation, particularly of less common SNPs (minor allele frequency 1–10%) in two very different European populations. The imputation improvement corresponds to an increase in effective sample size of 28–38%, for SNPs with a minor allele frequency in the range 1–3%.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "genetics", "population genetics", "haplotypes", "Genetics of disease", "Genome-wide association studies", "Trait locus analysis", "Genome sequencing", "Clinical research design", "Statistical methods", "exome", "sequences", "imputation", "variants", "genome"], "article_id"=>746230, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Peter K. Joshi", "James Prendergast", "Ross M. Fraser", "Jennifer E. Huffman", "Veronique Vitart", "Caroline Hayward", "Ruth McQuillan", "Dominik Glodzik", "Ozren Polašek", "Nicholas D. Hastie", "Igor Rudan", "Harry Campbell", "Alan F. Wright", "Chris S. Haley", "James F. Wilson", "Pau Navarro"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0068604.s001", "https://dx.doi.org/10.1371/journal.pone.0068604.s002"], "stats"=>{"downloads"=>6, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Local_Exome_Sequences_Facilitate_Imputation_of_Less_Common_Variants_and_Increase_Power_of_Genome_Wide_Association_Studies_/746230", "title"=>"Local Exome Sequences Facilitate Imputation of Less Common Variants and Increase Power of Genome Wide Association Studies", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-07-16 02:22:48"}

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  • {"unique-ip"=>"6", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"2", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"4", "full-text"=>"1", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"5", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Evolutionary biology", "average_usage"=>[302, 488, 607, 717, 832, 931, 1024, 1117, 1212, 1302, 1389, 1469, 1535]}]}
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