A Probabilistic Model to Predict Clinical Phenotypic Traits from Genome Sequencing
Publication Date
September 04, 2014
Journal
PLOS Computational Biology
Authors
Yun Ching Chen, Christopher Douville, Cheng Wang, Noushin Niknafs, et al
Volume
10
Issue
9
Pages
e1003825
DOI
https://dx.plos.org/10.1371/journal.pcbi.1003825
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1003825
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25188385
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4154636
Europe PMC
http://europepmc.org/abstract/MED/25188385
Web of Science
000343011700025
Scopus
84907588757
Mendeley
http://www.mendeley.com/research/probabilistic-model-predict-clinical-phenotypic-traits-genome-sequencing-1
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Mendeley | Further Information

{"title"=>"A Probabilistic Model to Predict Clinical Phenotypic Traits from Genome Sequencing", "type"=>"journal", "authors"=>[{"first_name"=>"Yun-Ching", "last_name"=>"Chen"}, {"first_name"=>"Christopher", "last_name"=>"Douville"}, {"first_name"=>"Cheng", "last_name"=>"Wang"}, {"first_name"=>"Noushin", "last_name"=>"Niknafs"}, {"first_name"=>"Grace", "last_name"=>"Yeo"}, {"first_name"=>"Violeta", "last_name"=>"Beleva-Guthrie"}, {"first_name"=>"Hannah", "last_name"=>"Carter"}, {"first_name"=>"Peter D.", "last_name"=>"Stenson"}, {"first_name"=>"David N.", "last_name"=>"Cooper"}, {"first_name"=>"Biao", "last_name"=>"Li"}, {"first_name"=>"Sean", "last_name"=>"Mooney"}, {"first_name"=>"Rachel", "last_name"=>"Karchin"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"1553-7358", "arxiv"=>"arXiv:1401.4290v2", "doi"=>"10.1371/journal.pcbi.1003825", "pmid"=>"25188385", "isbn"=>"10.1371/journal.pcbi.1003825"}, "id"=>"fd9d4d71-d683-399e-84c3-365e08d546bd", "abstract"=>"Genetic screening is becoming possible on an unprecedented scale. However, its utility remains controversial. Although most variant genotypes cannot be easily interpreted, many individuals nevertheless attempt to interpret their genetic information. Initiatives such as the Personal Genome Project (PGP) and Illumina's Understand Your Genome are sequencing thousands of adults, collecting phenotypic information and developing computational pipelines to identify the most important variant genotypes harbored by each individual. These pipelines consider database and allele frequency annotations and bioinformatics classifications. We propose that the next step will be to integrate these different sources of information to estimate the probability that a given individual has specific phenotypes of clinical interest. To this end, we have designed a Bayesian probabilistic model to predict the probability of dichotomous phenotypes. When applied to a cohort from PGP, predictions of Gilbert syndrome, Graves' disease, non-Hodgkin lymphoma, and various blood groups were accurate, as individuals manifesting the phenotype in question exhibited the highest, or among the highest, predicted probabilities. Thirty-eight PGP phenotypes (26%) were predicted with area-under-the-ROC curve (AUC)>0.7, and 23 (15.8%) of these were statistically significant, based on permutation tests. Moreover, in a Critical Assessment of Genome Interpretation (CAGI) blinded prediction experiment, the models were used to match 77 PGP genomes to phenotypic profiles, generating the most accurate prediction of 16 submissions, according to an independent assessor. Although the models are currently insufficiently accurate for diagnostic utility, we expect their performance to improve with growth of publicly available genomics data and model refinement by domain experts.", "link"=>"http://www.mendeley.com/research/probabilistic-model-predict-clinical-phenotypic-traits-genome-sequencing-1", "reader_count"=>50, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>6, "Librarian"=>1, "Researcher"=>17, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>1, "Other"=>1, "Student > Master"=>3, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>6, "Librarian"=>1, "Researcher"=>17, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>1, "Other"=>1, "Student > Master"=>3, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Biochemistry, Genetics and Molecular Biology"=>8, "Mathematics"=>1, "Agricultural and Biological Sciences"=>17, "Medicine and Dentistry"=>10, "Neuroscience"=>1, "Computer Science"=>9}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>10}, "Neuroscience"=>{"Neuroscience"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>17}, "Computer Science"=>{"Computer Science"=>9}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"Korea (South)"=>1, "United States"=>6, "Spain"=>1}, "group_count"=>4}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1663011"], "description"=>"<p>Red nodes in the first layer of the model represent the individual's genotype calls at genomic positions associated with the phenotype of interest. They are sorted into three categories: <i>V<sub>H</sub></i> (HGMD DM variants), <i>V<sub>L</sub></i> (NHGRI GWAS hits), and <i>V<sub>F</sub></i> (<0.01 MAF in any population reported in ESP6500 (<a href=\"http://evs.gs.washington.edu/EVS/\" target=\"_blank\">http://evs.gs.washington.edu/EVS/</a>) or 1000 Genomes <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003825#pcbi.1003825-Genomes1\" target=\"_blank\">[30]</a>), found in genes annotated as associated with the phenotype. Green nodes in the second layer represent genes split into high penetrance G<sub>H</sub> or low penetrance G<sub>L</sub> based on database annotations. Blue nodes in the third layer are Bernoulli random variables, abstractly representing mechanisms that explain the phenotype, sorted into those altered by high penetrance variants S<sub>VH</sub>, low penetrance variants S<sub>VL</sub>, high penetrance genes S<sub>GH</sub>, or low penetrance genes S<sub>GL</sub>. The blue node <i>Y</i> is a Bernoulli random variable representing individual phenotypic status. Directed edges show the dependencies between nodes. A set of model parameters is estimated for each phenotype and each individual.</p>", "links"=>[], "tags"=>["probability", "Variant genotypes", "prediction", "individual", "cagi", "pipeline", "Personal Genome Project", "utility", "allele frequency annotations", "Predict Clinical Phenotypic Traits", "auc", "Genome Sequencing Genetic screening", "phenotypic", "information", "phenotype", "77 PGP genomes", "model"], "article_id"=>1162388, "categories"=>["Uncategorised"], "users"=>["Yun-Ching Chen", "Christopher Douville", "Cheng Wang", "Noushin Niknafs", "Grace Yeo", "Violeta Beleva-Guthrie", "Hannah Carter", "Peter D. Stenson", "David N. Cooper", "Biao Li", "Sean Mooney", "Rachel Karchin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003825.g005", "stats"=>{"downloads"=>0, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Topology_of_the_model_to_predict_phenotype_from_an_individual_s_genome_sequence_/1162388", "title"=>"Topology of the model to predict phenotype from an individual's genome sequence.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-04 04:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1663008"], "description"=>"<p>Each row represents a phenotype predicted with AUC>0.7 by genome sequence (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003825#pcbi-1003825-g002\" target=\"_blank\">Figure 2</a> (b)) and contains five cells. Cells are colored by the area under the ROC curve (AUC) yielded by a model that contains only 1:GWAS hits, 2:Low penetrance genes, 3:High penetrance genes, 4:High penetrance variants. The fifth cell shows AUC of the combination model used to assess results in this work that considers all of 1,2,3, and 4. The combination model generally yields the best performance: however, for most phenotypes, only one or two of 1, 2, 3 or 4 appears to contribute.</p>", "links"=>[], "tags"=>["probability", "Variant genotypes", "prediction", "individual", "cagi", "pipeline", "Personal Genome Project", "utility", "allele frequency annotations", "Predict Clinical Phenotypic Traits", "auc", "Genome Sequencing Genetic screening", "phenotypic", "information", "phenotype", "77 PGP genomes", "model"], "article_id"=>1162385, "categories"=>["Uncategorised"], "users"=>["Yun-Ching Chen", "Christopher Douville", "Cheng Wang", "Noushin Niknafs", "Grace Yeo", "Violeta Beleva-Guthrie", "Hannah Carter", "Peter D. Stenson", "David N. Cooper", "Biao Li", "Sean Mooney", "Rachel Karchin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003825.g003", "stats"=>{"downloads"=>1, "page_views"=>37, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Contribution_of_GWAS_hits_low_penetrance_genes_high_penetrance_genes_and_high_penetrance_variants_to_prediction_results_/1162385", "title"=>"Contribution of GWAS hits, low penetrance genes, high penetrance genes, and high penetrance variants to prediction results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-04 04:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1663007"], "description"=>"<p>Each row represents a phenotype and consists of three cells, representing (a) model predictions based only on phenotype-specific population prevalence (Prevalence Only), (b) model predictions based on genome sequence (with assumption that every phenotype and every individual has the same prevalence), and (c) model predictions that combine genome sequence and phenotype-specific population prevalence. Cells are colored by the area under the ROC curve (AUC) yielded by each model. Contributions vary among phenotypes due to differences in quality of available information with respect to prevalence and database annotations of variant genotypes.</p>", "links"=>[], "tags"=>["probability", "Variant genotypes", "prediction", "individual", "cagi", "pipeline", "Personal Genome Project", "utility", "allele frequency annotations", "Predict Clinical Phenotypic Traits", "auc", "Genome Sequencing Genetic screening", "phenotypic", "information", "phenotype", "77 PGP genomes", "model"], "article_id"=>1162384, "categories"=>["Uncategorised"], "users"=>["Yun-Ching Chen", "Christopher Douville", "Cheng Wang", "Noushin Niknafs", "Grace Yeo", "Violeta Beleva-Guthrie", "Hannah Carter", "Peter D. Stenson", "David N. Cooper", "Biao Li", "Sean Mooney", "Rachel Karchin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003825.g002", "stats"=>{"downloads"=>5, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Contribution_of_population_prevalence_and_genome_sequence_to_prediction_results_in_Fig_1_/1162384", "title"=>"Contribution of population prevalence and genome sequence to prediction results in Fig. 1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-04 04:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1663005"], "description"=>"<p>Each row represents a clinical phenotype and consists of 130 cells, each of which represents a Personal Genome Project (PGP) participant. Cells in each row are ranked by the posterior probability that the participant has the phenotype. Cells are colored by true phenotypic status. Blue cells indicate that a participant has the phenotype, and red cells that a participant does not have the phenotype. If a cell is colored light grey, the true phenotypic status is unknown. If a cell is colored dark grey, the PGP participant is not considered in the evaluation because the phenotype is gender-specific. #PGP = number of participants in each row having the true phenotypic status. AUC = area under the receiver operating characteristic curve, a threshold-free metric of classifier performance. p-value and FDR = statistical significance of the AUC value, based on permutation testing.</p>", "links"=>[], "tags"=>["probability", "Variant genotypes", "prediction", "individual", "cagi", "pipeline", "Personal Genome Project", "utility", "allele frequency annotations", "Predict Clinical Phenotypic Traits", "auc", "Genome Sequencing Genetic screening", "phenotypic", "information", "phenotype", "77 PGP genomes", "model"], "article_id"=>1162382, "categories"=>["Uncategorised"], "users"=>["Yun-Ching Chen", "Christopher Douville", "Cheng Wang", "Noushin Niknafs", "Grace Yeo", "Violeta Beleva-Guthrie", "Hannah Carter", "Peter D. Stenson", "David N. Cooper", "Biao Li", "Sean Mooney", "Rachel Karchin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003825.g001", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_results_of_the_model_on_38_dichotomous_phenotypes_/1162382", "title"=>"Prediction results of the model on 38 dichotomous phenotypes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-04 04:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1663014", "https://ndownloader.figshare.com/files/1663015", "https://ndownloader.figshare.com/files/1663016", "https://ndownloader.figshare.com/files/1663017", "https://ndownloader.figshare.com/files/1663018", "https://ndownloader.figshare.com/files/1663019", "https://ndownloader.figshare.com/files/1663020", "https://ndownloader.figshare.com/files/1663021", "https://ndownloader.figshare.com/files/1663022", "https://ndownloader.figshare.com/files/1663023", "https://ndownloader.figshare.com/files/1663024", "https://ndownloader.figshare.com/files/1663025", "https://ndownloader.figshare.com/files/1663026", "https://ndownloader.figshare.com/files/1663027", "https://ndownloader.figshare.com/files/1663028"], "description"=>"<div><p>Genetic screening is becoming possible on an unprecedented scale. However, its utility remains controversial. Although most variant genotypes cannot be easily interpreted, many individuals nevertheless attempt to interpret their genetic information. Initiatives such as the Personal Genome Project (PGP) and Illumina's Understand Your Genome are sequencing thousands of adults, collecting phenotypic information and developing computational pipelines to identify the most important variant genotypes harbored by each individual. These pipelines consider database and allele frequency annotations and bioinformatics classifications. We propose that the next step will be to integrate these different sources of information to estimate the probability that a given individual has specific phenotypes of clinical interest. To this end, we have designed a Bayesian probabilistic model to predict the probability of dichotomous phenotypes. When applied to a cohort from PGP, predictions of Gilbert syndrome, Graves' disease, non-Hodgkin lymphoma, and various blood groups were accurate, as individuals manifesting the phenotype in question exhibited the highest, or among the highest, predicted probabilities. Thirty-eight PGP phenotypes (26%) were predicted with area-under-the-ROC curve (AUC)>0.7, and 23 (15.8%) of these were statistically significant, based on permutation tests. Moreover, in a Critical Assessment of Genome Interpretation (CAGI) blinded prediction experiment, the models were used to match 77 PGP genomes to phenotypic profiles, generating the most accurate prediction of 16 submissions, according to an independent assessor. Although the models are currently insufficiently accurate for diagnostic utility, we expect their performance to improve with growth of publicly available genomics data and model refinement by domain experts.</p></div>", "links"=>[], "tags"=>["probability", "Variant genotypes", "prediction", "individual", "cagi", "pipeline", "Personal Genome Project", "utility", "allele frequency annotations", "Predict Clinical Phenotypic Traits", "auc", "Genome Sequencing Genetic screening", "phenotypic", "information", "phenotype", "77 PGP genomes", "model"], "article_id"=>1162391, "categories"=>["Uncategorised"], "users"=>["Yun-Ching Chen", "Christopher Douville", "Cheng Wang", "Noushin Niknafs", "Grace Yeo", "Violeta Beleva-Guthrie", "Hannah Carter", "Peter D. Stenson", "David N. Cooper", "Biao Li", "Sean Mooney", "Rachel Karchin"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003825.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s005", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s006", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s007", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s008", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s009", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s010", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s011", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s012", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s013", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s014", "https://dx.doi.org/10.1371/journal.pcbi.1003825.s015"], "stats"=>{"downloads"=>35, "page_views"=>26, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Probabilistic_Model_to_Predict_Clinical_Phenotypic_Traits_from_Genome_Sequencing_/1162391", "title"=>"A Probabilistic Model to Predict Clinical Phenotypic Traits from Genome Sequencing", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-09-04 04:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1663010"], "description"=>"<p>Phenotypes are ordered by total counts of annotated genes and variants that we found. Counts are shown on a log scale for easier visualization. For each phenotype the (log) count of annotated genes is colored red, GWAS hits green, and high penetrance variants blue. Some phenotypes have a very large number of annotations and others have very few. For 42 phenotypes, we did not find any annotated genes or variants.</p>", "links"=>[], "tags"=>["probability", "Variant genotypes", "prediction", "individual", "cagi", "pipeline", "Personal Genome Project", "utility", "allele frequency annotations", "Predict Clinical Phenotypic Traits", "auc", "Genome Sequencing Genetic screening", "phenotypic", "information", "phenotype", "77 PGP genomes", "model"], "article_id"=>1162387, "categories"=>["Uncategorised"], "users"=>["Yun-Ching Chen", "Christopher Douville", "Cheng Wang", "Noushin Niknafs", "Grace Yeo", "Violeta Beleva-Guthrie", "Hannah Carter", "Peter D. Stenson", "David N. Cooper", "Biao Li", "Sean Mooney", "Rachel Karchin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003825.g004", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_annotated_genes_GWAS_hits_and_high_penetrance_variants_for_phenotypes_analyzed_in_this_study_/1162387", "title"=>"Distribution of annotated genes, GWAS hits and high penetrance variants for phenotypes analyzed in this study.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-04 04:48:51"}

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Relative Metric

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