Conditional Random Fields for Fast, Large-Scale Genome-Wide Association Studies
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Mendeley | Further Information

{"title"=>"Conditional random fields for fast, Large-Scale Genome-Wide association studies", "type"=>"journal", "authors"=>[{"first_name"=>"Jim C.", "last_name"=>"Huang", "scopus_author_id"=>"56135474300"}, {"first_name"=>"Christopher", "last_name"=>"Meek", "scopus_author_id"=>"36883167000"}, {"first_name"=>"Carl", "last_name"=>"Kadie", "scopus_author_id"=>"55892496800"}, {"first_name"=>"David", "last_name"=>"Heckerman", "scopus_author_id"=>"7004273387"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pui"=>"362118217", "sgr"=>"79960179918", "scopus"=>"2-s2.0-79960179918", "pmid"=>"21765897", "doi"=>"10.1371/journal.pone.0021591"}, "id"=>"24ab6c30-c752-307e-a696-7952f8fad1da", "abstract"=>"Understanding the role of genetic variation in human diseases remains an important problem to be solved in genomics. An important component of such variation consist of variations at single sites in DNA, or single nucleotide polymorphisms (SNPs). Typically, the problem of associating particular SNPs to phenotypes has been confounded by hidden factors such as the presence of population structure, family structure or cryptic relatedness in the sample of individuals being analyzed. Such confounding factors lead to a large number of spurious associations and missed associations. Various statistical methods have been proposed to account for such confounding factors such as linear mixed-effect models (LMMs) or methods that adjust data based on a principal components analysis (PCA), but these methods either suffer from low power or cease to be tractable for larger numbers of individuals in the sample. Here we present a statistical model for conducting genome-wide association studies (GWAS) that accounts for such confounding factors. Our method scales in runtime quadratic in the number of individuals being studied with only a modest loss in statistical power as compared to LMM-based and PCA-based methods when testing on synthetic data that was generated from a generalized LMM. Applying our method to both real and synthetic human genotype/phenotype data, we demonstrate the ability of our model to correct for confounding factors while requiring significantly less runtime relative to LMMs. We have implemented methods for fitting these models, which are available at http://www.microsoft.com/science.", "link"=>"http://www.mendeley.com/research/conditional-random-fields-fast-largescale-genomewide-association-studies-2", "reader_count"=>26, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Researcher"=>10, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>2, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Researcher"=>10, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>2, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Mathematics"=>3, "Agricultural and Biological Sciences"=>13, "Medicine and Dentistry"=>2, "Neuroscience"=>1, "Computer Science"=>6}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>13}, "Computer Science"=>{"Computer Science"=>6}, "Mathematics"=>{"Mathematics"=>3}}, "reader_count_by_country"=>{"France"=>1, "Germany"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/758123"], "description"=>"<p>Runtimes for the CRF and LMM models (in hours) are shown as a function of study size. All experiments were run on a machine with two 3.0 GHz CPUs and 64.0 GB of RAM.</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "computer science", "mathematics"], "article_id"=>428494, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Jim C. Huang", "Christopher Meek", "Carl Kadie", "David Heckerman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021591.g008", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_runtimes_/428494", "title"=>"Comparison of runtimes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 16:52:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/757390"], "description"=>"<p>Nodes correspond to variables in the model and edges correspond to dependencies between variables under the model. Shaded nodes correspond to observed variables under the model. Conditioned on each individual's SNP and covariates, phenotypic labels are modeled using a fully connected undirected graphical model.</p>", "links"=>[], "tags"=>["graphical", "relating"], "article_id"=>427760, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Jim C. Huang", "Christopher Meek", "Carl Kadie", "David Heckerman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021591.g001", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_graphical_model_for_relating_genetic_variation_to_phenotype_/427760", "title"=>"The graphical model for relating genetic variation to phenotype.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 16:48:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/757680"], "description"=>"<p>QQ plots of model negative p-value statistics obtained from our method as a function of expected negative p-values under the null hypothesis for the synthetic GAW14 data with (a) and real data (b). For comparison, negative p-value statistics obtained from a logistic regression that does not account for confounding factors and from Eigenstrat <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021591#pone.0021591-Price1\" target=\"_blank\">[4]</a> are shown for the synthetic data with (c,e) and real data (d,f). Dotted red lines indicate 95% confidence bounds.</p>", "links"=>[], "tags"=>["plots", "gaw14"], "article_id"=>428050, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Jim C. Huang", "Christopher Meek", "Carl Kadie", "David Heckerman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021591.g004", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Quantile_quantile_QQ_plots_for_the_GAW14_data_/428050", "title"=>"Quantile-quantile (QQ) plots for the GAW14 data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 16:50:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/757900"], "description"=>"<p>Negative p-value statistics obtained from our method (a,b), logistic regression that does not account for confounding factors (c,d) and from Eigenstrat <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021591#pone.0021591-Price1\" target=\"_blank\">[4]</a> (e,f) for the WTCCC data. Dotted red lines in the QQ plots (right panel) indicate 95% confidence bounds.</p>", "links"=>[], "tags"=>["quantile-quantile", "plots", "wtccc"], "article_id"=>428275, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Jim C. Huang", "Christopher Meek", "Carl Kadie", "David Heckerman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021591.g006", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Histograms_and_quantile_quantile_QQ_plots_for_the_WTCCC_data_/428275", "title"=>"Histograms and quantile-quantile (QQ) plots for the WTCCC data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 16:51:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/758033"], "description"=>"<p>The plots are shown as receiver operating characteristic (ROC) curves of the true positive rate as a function of the false positive rate for the GAW14 dataset (a,b,c,d) and the GOLDN dataset (e,f,g,h) for various values of the SNP regression weight when using our method (blue), a LMM-based method (red), a PCA-based method (green) and random guessing (black dotted).</p>", "links"=>[], "tags"=>["synthetic"], "article_id"=>428408, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Jim C. Huang", "Christopher Meek", "Carl Kadie", "David Heckerman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021591.g007", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Assessing_statistical_power_on_synthetic_data_/428408", "title"=>"Assessing statistical power on synthetic data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 16:52:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/757586"], "description"=>"<p>a),b) Histograms of p-values obtained from our method for the synthetic (a) and real (b) GOLDN data. For comparison, p-values obtained from a logistic regression that does not account for confounding factors and from Eigenstrat <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021591#pone.0021591-Price1\" target=\"_blank\">[4]</a> are shown for synthetic (c,e) and real (d,f) GOLDN data. Dotted red lines indicate the expected histogram for the uniform distribution under the null hypothesis .</p>", "links"=>[], "tags"=>["histograms", "goldn"], "article_id"=>427961, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Jim C. Huang", "Christopher Meek", "Carl Kadie", "David Heckerman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021591.g003", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_P_value_histograms_for_the_GOLDN_data_/427961", "title"=>"P-value histograms for the GOLDN data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 16:49:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/757461"], "description"=>"<p>a),b) Histograms of p-values obtained from our method for the synthetic (a) and real (b) GAW14 data. For comparison, p-values obtained from a logistic regression that does not account for confounding factors and from Eigenstrat <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021591#pone.0021591-Price1\" target=\"_blank\">[4]</a> are shown for synthetic (c,e) and real (d,f) GAW14 data. Dotted red lines indicate the expected histogram for the uniform distribution under the null hypothesis .</p>", "links"=>[], "tags"=>["histograms", "gaw14"], "article_id"=>427835, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Jim C. Huang", "Christopher Meek", "Carl Kadie", "David Heckerman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021591.g002", "stats"=>{"downloads"=>1, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_P_value_histograms_for_the_GAW14_dataset_/427835", "title"=>"P-value histograms for the GAW14 dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 16:48:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/757786"], "description"=>"<p>QQ plots of model negative p-value statistics obtained from our method as a function of expected negative p-values under the null hypothesis for synthetic GOLDN data with (a) and real data (b). For comparison, negative p-value statistics obtained from a logistic regression that does not account for confounding factors and from Eigenstrat <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021591#pone.0021591-Price1\" target=\"_blank\">[4]</a> are shown for the synthetic data with (c,e) and real data (d,f). Dotted red lines indicate 95% confidence bounds.</p>", "links"=>[], "tags"=>["plots", "goldn"], "article_id"=>428159, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Jim C. Huang", "Christopher Meek", "Carl Kadie", "David Heckerman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0021591.g005", "stats"=>{"downloads"=>4, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Quantile_quantile_QQ_plots_for_the_GOLDN_data_/428159", "title"=>"Quantile-quantile (QQ) plots for the GOLDN data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 16:50:53"}

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Relative Metric

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