The Intolerance of Regulatory Sequence to Genetic Variation Predicts Gene Dosage Sensitivity
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{"title"=>"The Intolerance of Regulatory Sequence to Genetic Variation Predicts Gene Dosage Sensitivity", "type"=>"journal", "authors"=>[{"first_name"=>"Slavé", "last_name"=>"Petrovski", "scopus_author_id"=>"26026629300"}, {"first_name"=>"Ayal B.", "last_name"=>"Gussow", "scopus_author_id"=>"53979664700"}, {"first_name"=>"Quanli", "last_name"=>"Wang", "scopus_author_id"=>"55864746200"}, {"first_name"=>"Matt", "last_name"=>"Halvorsen", "scopus_author_id"=>"36683905300"}, {"first_name"=>"Yujun", "last_name"=>"Han", "scopus_author_id"=>"56576713100"}, {"first_name"=>"William H.", "last_name"=>"Weir", "scopus_author_id"=>"56895818500"}, {"first_name"=>"Andrew S.", "last_name"=>"Allen", "scopus_author_id"=>"7402802442"}, {"first_name"=>"David B.", "last_name"=>"Goldstein", "scopus_author_id"=>"7402147672"}], "year"=>2015, "source"=>"PLoS Genetics", "identifiers"=>{"issn"=>"15537404", "scopus"=>"2-s2.0-84943536668", "sgr"=>"84943536668", "pui"=>"606367994", "isbn"=>"10.1371/journal.pgen.1005492", "pmid"=>"26332131", "doi"=>"10.1371/journal.pgen.1005492"}, "id"=>"e29ee02d-b94b-3566-84e8-56d3b8276421", "abstract"=>"Noncoding sequence contains pathogenic mutations. Yet, compared with mutations in protein-coding sequence, pathogenic regulatory mutations are notoriously difficult to recognize. Most fundamentally, we are not yet adept at recognizing the sequence stretches in the human genome that are most important in regulating the expression of genes. For this reason, it is difficult to apply to the regulatory regions the same kinds of analytical paradigms that are being successfully applied to identify mutations among protein-coding regions that influence risk. To determine whether dosage sensitive genes have distinct patterns among their noncoding sequence, we present two primary approaches that focus solely on a gene's proximal noncoding regulatory sequence. The first approach is a regulatory sequence analogue of the recently introduced residual variation intolerance score (RVIS), termed noncoding RVIS, or ncRVIS. The ncRVIS compares observed and predicted levels of standing variation in the regulatory sequence of human genes. The second approach, termed ncGERP, reflects the phylogenetic conservation of a gene's regulatory sequence using GERP++. We assess how well these two approaches correlate with four gene lists that use different ways to identify genes known or likely to cause disease through changes in expression: 1) genes that are known to cause disease through haploinsufficiency, 2) genes curated as dosage sensitive in ClinGen's Genome Dosage Map, 3) genes judged likely to be under purifying selection for mutations that change expression levels because they are statistically depleted of loss-of-function variants in the general population, and 4) genes judged unlikely to cause disease based on the presence of copy number variants in the general population. We find that both noncoding scores are highly predictive of dosage sensitivity using any of these criteria. In a similar way to ncGERP, we assess two ensemble-based predictors of regional noncoding importance, ncCADD and ncGWAVA, and find both scores are significantly predictive of human dosage sensitive genes and appear to carry information beyond conservation, as assessed by ncGERP. These results highlight that the intolerance of noncoding sequence stretches in the human genome can provide a critical complementary tool to other genome annotation approaches to help identify the parts of the human genome increasingly likely to harbor mutations that influence risk of disease.", "link"=>"http://www.mendeley.com/research/intolerance-regulatory-sequence-genetic-variation-predicts-gene-dosage-sensitivity", "reader_count"=>102, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>5, "Student > Doctoral Student"=>8, "Researcher"=>24, "Student > Ph. D. 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Student"=>34, "Student > Postgraduate"=>2, "Student > Master"=>11, "Other"=>5, "Student > Bachelor"=>6, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Biochemistry, Genetics and Molecular Biology"=>21, "Mathematics"=>1, "Medicine and Dentistry"=>13, "Agricultural and Biological Sciences"=>50, "Neuroscience"=>3, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Psychology"=>1, "Chemistry"=>2, "Social Sciences"=>1, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>13}, "Neuroscience"=>{"Neuroscience"=>3}, "Chemistry"=>{"Chemistry"=>2}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>50}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>21}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>5}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Sweden"=>1, "United States"=>3, "Norway"=>1, "United Kingdom"=>2, "South Africa"=>1, "Italy"=>1}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2250460"], "description"=>"<p>To obtain the presented levels of significance in <b>(B)</b>, we used a logistic regression model to regress loss-of-function deficient or control gene status for the combined 2,997 genes on each of the four genic scores.</p>", "links"=>[], "tags"=>["GERP", "variation intolerance score", "copy number variants", "noncoding sequence stretches", "mutation", "genome annotation approaches", "rvis", "Genetic Variation Predicts Gene Dosage Sensitivity Noncoding sequence", "gene", "cause disease", "influence risk", "change expression levels"], "article_id"=>1533488, "categories"=>["Uncategorised"], "users"=>["Slavé Petrovski", "Ayal B. Gussow", "Quanli Wang", "Matt Halvorsen", "Yujun Han", "William H. Weir", "Andrew S. Allen", "David B. Goldstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005492.g003", "stats"=>{"downloads"=>2, "page_views"=>27, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Distribution_of_ncRVIS_scores_for_the_1_235_loss_of_function_deficient_genes_left_compared_to_the_1_762_loss_of_function_control_genes_right_Median_37_95_vs_58_09_Mann_Whitney_U_test_p_6_6x10_34_B_Receiver_operating_characteristic_ROC_curves_measuring_/1533488", "title"=>"(A) Distribution of ncRVIS scores for the 1,235 loss-of-function deficient genes (left) compared to the 1,762 loss-of-function control genes (right). Median 37.95% vs. 58.09%; Mann-Whitney U test, p = 6.6x10<sup>-34</sup>. (B) Receiver operating characteristic (ROC) curves measuring the ability of RVIS, ncRVIS, pcGERP and ncGERP to discriminate between loss-of-function deficient and loss-of-function control genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-02 02:53:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2250461"], "description"=>"<p>At any given point on the X-axis (RVIS-sum percentile) we can determine what percentage of the 82 OMIM haploinsufficiency genes are accounted for.</p>", "links"=>[], "tags"=>["GERP", "variation intolerance score", "copy number variants", "noncoding sequence stretches", "mutation", "genome annotation approaches", "rvis", "Genetic Variation Predicts Gene Dosage Sensitivity Noncoding sequence", "gene", "cause disease", "influence risk", "change expression levels"], "article_id"=>1533489, "categories"=>["Uncategorised"], "users"=>["Slavé Petrovski", "Ayal B. Gussow", "Quanli Wang", "Matt Halvorsen", "Yujun Han", "William H. Weir", "Andrew S. Allen", "David B. Goldstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005492.g004", "stats"=>{"downloads"=>2, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Scatterplot_of_RVIS_sum_RVIS_CHGV_ncRVIS_and_RVIS_diff_RVIS_CHGV_8211_ncRVIS_scores_Each_dot_represents_a_gene_The_grey_dots_represent_the_background_genome_wide_distribution_The_red_dots_highlight_the_82_OMIM_haploinsufficiency_genes_with_reported_cau/1533489", "title"=>"(A) Scatterplot of RVIS-sum (RVIS-CHGV + ncRVIS) and RVIS-diff (RVIS-CHGV–ncRVIS) scores. Each dot represents a gene. The grey dots represent the background genome-wide distribution. The red dots highlight the 82 OMIM haploinsufficiency genes with reported causal de novo mutations. A higher (positive Y-axis value) RVIS-diff score indicates genes where we might have a greater expectation of gene dosage aberrations being important compared with protein structure aberrations. A lower RVIS-sum (X-axis value) highlights genes that are increasingly intolerant in both their noncoding and protein-coding sequence. (B) A cumulative percentage plot for the RVIS-sum percentile accommodating the 82 OMIM halpoinsufficiency genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-02 02:53:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2250462"], "description"=>"<p>These data show that the two form very different genome-wide distributions (medians: ncGERP -0.02 versus pcGERP 2.64). Moreover, pcGERP tends to present with a slightly platykurtic, left-skewed distribution (γ<sub>2</sub> = -0.10, γ<sub>1</sub> = -0.66) compared to ncGERP, which reflects a more leptokurtic, right-skewed distribution (γ<sub>2</sub> = 0.97, γ<sub>1</sub> = 0.96).</p>", "links"=>[], "tags"=>["GERP", "variation intolerance score", "copy number variants", "noncoding sequence stretches", "mutation", "genome annotation approaches", "rvis", "Genetic Variation Predicts Gene Dosage Sensitivity Noncoding sequence", "gene", "cause disease", "influence risk", "change expression levels"], "article_id"=>1533490, "categories"=>["Uncategorised"], "users"=>["Slavé Petrovski", "Ayal B. Gussow", "Quanli Wang", "Matt Halvorsen", "Yujun Han", "William H. Weir", "Andrew S. Allen", "David B. Goldstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005492.g005", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overlaid_histograms_of_ncGERP_blue_and_pcGERP_red_/1533490", "title"=>"Overlaid histograms of ncGERP (blue) and pcGERP (red).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-02 02:53:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2250463"], "description"=>"<p>To enable a matched comparison, the estimates in this table are based on a set of 14,567 CCDS genes with assessable scores across RVIS-CHGV, ncRVIS and ncGERP formulations. Both RVIS-CHGV and ncRVIS are based on the same population of 690 whole-genome sequenced samples from the CHGV.</p><p><sup>a</sup>HI = Haploinsufficiency. To obtain the presented levels of significance, we used a logistic regression model to regress the presence or absence of a gene within the corresponding gene list on each of the genic scores.</p><p>Joint Model: The AUC of a combined logistic regression model that uses all three features. Correlation plots for the pairs of scores are available in <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005492#pgen.1005492.s001\" target=\"_blank\">S1 Fig</a>.</p><p>Comparing protein-coding and noncoding genic intolerance scores.</p>", "links"=>[], "tags"=>["GERP", "variation intolerance score", "copy number variants", "noncoding sequence stretches", "mutation", "genome annotation approaches", "rvis", "Genetic Variation Predicts Gene Dosage Sensitivity Noncoding sequence", "gene", "cause disease", "influence risk", "change expression levels"], "article_id"=>1533491, "categories"=>["Uncategorised"], "users"=>["Slavé Petrovski", "Ayal B. Gussow", "Quanli Wang", "Matt Halvorsen", "Yujun Han", "William H. Weir", "Andrew S. Allen", "David B. Goldstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005492.t001", "stats"=>{"downloads"=>2, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparing_protein_coding_and_noncoding_genic_intolerance_scores_/1533491", "title"=>"Comparing protein-coding and noncoding genic intolerance scores.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-09-02 02:53:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2250464", "https://ndownloader.figshare.com/files/2250465", "https://ndownloader.figshare.com/files/2250466", "https://ndownloader.figshare.com/files/2250467", "https://ndownloader.figshare.com/files/2250468", "https://ndownloader.figshare.com/files/2250469", "https://ndownloader.figshare.com/files/2250470", "https://ndownloader.figshare.com/files/2250471", "https://ndownloader.figshare.com/files/2250472", "https://ndownloader.figshare.com/files/2250473", "https://ndownloader.figshare.com/files/2250474", "https://ndownloader.figshare.com/files/2250475", "https://ndownloader.figshare.com/files/2250476", "https://ndownloader.figshare.com/files/2250477", "https://ndownloader.figshare.com/files/2250478"], "description"=>"<div><p>Noncoding sequence contains pathogenic mutations. Yet, compared with mutations in protein-coding sequence, pathogenic regulatory mutations are notoriously difficult to recognize. Most fundamentally, we are not yet adept at recognizing the sequence stretches in the human genome that are most important in regulating the expression of genes. For this reason, it is difficult to apply to the regulatory regions the same kinds of analytical paradigms that are being successfully applied to identify mutations among protein-coding regions that influence risk. To determine whether dosage sensitive genes have distinct patterns among their noncoding sequence, we present two primary approaches that focus solely on a gene’s proximal noncoding regulatory sequence. The first approach is a regulatory sequence analogue of the recently introduced residual variation intolerance score (RVIS), termed noncoding RVIS, or ncRVIS. The ncRVIS compares observed and predicted levels of standing variation in the regulatory sequence of human genes. The second approach, termed ncGERP, reflects the phylogenetic conservation of a gene’s regulatory sequence using GERP++. We assess how well these two approaches correlate with four gene lists that use different ways to identify genes known or likely to cause disease through changes in expression: 1) genes that are known to cause disease through haploinsufficiency, 2) genes curated as dosage sensitive in ClinGen’s Genome Dosage Map, 3) genes judged likely to be under purifying selection for mutations that change expression levels because they are statistically depleted of loss-of-function variants in the general population, and 4) genes judged unlikely to cause disease based on the presence of copy number variants in the general population. We find that both noncoding scores are highly predictive of dosage sensitivity using any of these criteria. In a similar way to ncGERP, we assess two ensemble-based predictors of regional noncoding importance, ncCADD and ncGWAVA, and find both scores are significantly predictive of human dosage sensitive genes and appear to carry information beyond conservation, as assessed by ncGERP. These results highlight that the intolerance of noncoding sequence stretches in the human genome can provide a critical complementary tool to other genome annotation approaches to help identify the parts of the human genome increasingly likely to harbor mutations that influence risk of disease.</p></div>", "links"=>[], "tags"=>["GERP", "variation intolerance score", "copy number variants", "noncoding sequence stretches", "mutation", "genome annotation approaches", "rvis", "Genetic Variation Predicts Gene Dosage Sensitivity Noncoding sequence", "gene", "cause disease", "influence risk", "change expression levels"], "article_id"=>1533492, "categories"=>["Uncategorised"], "users"=>["Slavé Petrovski", "Ayal B. Gussow", "Quanli Wang", "Matt Halvorsen", "Yujun Han", "William H. Weir", "Andrew S. Allen", "David B. Goldstein"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1005492.s001", "https://dx.doi.org/10.1371/journal.pgen.1005492.s002", "https://dx.doi.org/10.1371/journal.pgen.1005492.s003", "https://dx.doi.org/10.1371/journal.pgen.1005492.s004", "https://dx.doi.org/10.1371/journal.pgen.1005492.s005", "https://dx.doi.org/10.1371/journal.pgen.1005492.s006", "https://dx.doi.org/10.1371/journal.pgen.1005492.s007", "https://dx.doi.org/10.1371/journal.pgen.1005492.s008", "https://dx.doi.org/10.1371/journal.pgen.1005492.s009", "https://dx.doi.org/10.1371/journal.pgen.1005492.s010", "https://dx.doi.org/10.1371/journal.pgen.1005492.s011", "https://dx.doi.org/10.1371/journal.pgen.1005492.s012", "https://dx.doi.org/10.1371/journal.pgen.1005492.s013", "https://dx.doi.org/10.1371/journal.pgen.1005492.s014", "https://dx.doi.org/10.1371/journal.pgen.1005492.s015"], "stats"=>{"downloads"=>16, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Intolerance_of_Regulatory_Sequence_to_Genetic_Variation_Predicts_Gene_Dosage_Sensitivity_/1533492", "title"=>"The Intolerance of Regulatory Sequence to Genetic Variation Predicts Gene Dosage Sensitivity", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-09-02 02:53:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2250458"], "description"=>"<p>Each dot represents the position of a gene in the regression plot and the corresponding regression line is provided. Annotations are made for the 5% extremes: red = 5% most intolerant, blue = 5% most tolerant.</p>", "links"=>[], "tags"=>["GERP", "variation intolerance score", "copy number variants", "noncoding sequence stretches", "mutation", "genome annotation approaches", "rvis", "Genetic Variation Predicts Gene Dosage Sensitivity Noncoding sequence", "gene", "cause disease", "influence risk", "change expression levels"], "article_id"=>1533486, "categories"=>["Uncategorised"], "users"=>["Slavé Petrovski", "Ayal B. Gussow", "Quanli Wang", "Matt Halvorsen", "Yujun Han", "William H. Weir", "Andrew S. Allen", "David B. Goldstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005492.g001", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_regression_plot_that_shows_the_regression_of_noncoding_polymorphisms_Y_on_an_estimate_of_the_noncoding_sequence_mutability_X_S1_Data_/1533486", "title"=>"A regression plot that shows the regression of noncoding polymorphisms (Y) on an estimate of the noncoding sequence mutability (X) (S1 Data).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-02 02:53:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2250459"], "description"=>"<p>Here, for a given score, all assessable genes were used. To obtain the presented levels of significance, we use a logistic regression model to regress the presence or absence of a gene among the ClinGen dosage sensitivity map list on each of the genic scores.</p>", "links"=>[], "tags"=>["GERP", "variation intolerance score", "copy number variants", "noncoding sequence stretches", "mutation", "genome annotation approaches", "rvis", "Genetic Variation Predicts Gene Dosage Sensitivity Noncoding sequence", "gene", "cause disease", "influence risk", "change expression levels"], "article_id"=>1533487, "categories"=>["Uncategorised"], "users"=>["Slavé Petrovski", "Ayal B. Gussow", "Quanli Wang", "Matt Halvorsen", "Yujun Han", "William H. Weir", "Andrew S. Allen", "David B. Goldstein"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005492.g002", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Receiver_operating_characteristic_ROC_curves_to_measure_the_ability_of_RVIS_CHGV_ncRVIS_pcGERP_ncGERP_ncCADD_ncGWAVA_scores_and_two_joint_models_to_discriminate_genes_reported_among_ClinGen_8217_s_dosage_sensitivity_map_from_the_rest_of_the_human_genome_/1533487", "title"=>"Receiver operating characteristic (ROC) curves to measure the ability of RVIS-CHGV, ncRVIS, pcGERP, ncGERP, ncCADD, ncGWAVA scores and two joint models to discriminate genes reported among ClinGen’s dosage sensitivity map from the rest of the human genome.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-02 02:53:58"}

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