Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies
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{"title"=>"Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies", "type"=>"journal", "authors"=>[{"first_name"=>"Gleb", "last_name"=>"Kichaev", "scopus_author_id"=>"53984363600"}, {"first_name"=>"Wen Yun", "last_name"=>"Yang", "scopus_author_id"=>"7407758157"}, {"first_name"=>"Sara", "last_name"=>"Lindstrom", "scopus_author_id"=>"57193814735"}, {"first_name"=>"Farhad", "last_name"=>"Hormozdiari", "scopus_author_id"=>"24723879400"}, {"first_name"=>"Eleazar", "last_name"=>"Eskin", "scopus_author_id"=>"7003344359"}, {"first_name"=>"Alkes L.", "last_name"=>"Price", "scopus_author_id"=>"57020794000"}, {"first_name"=>"Peter", "last_name"=>"Kraft", "scopus_author_id"=>"55214760000"}, {"first_name"=>"Bogdan", "last_name"=>"Pasaniuc", "scopus_author_id"=>"16175969900"}], "year"=>2014, "source"=>"PLoS Genetics", "identifiers"=>{"pui"=>"600311040", "pmid"=>"25357204", "doi"=>"10.1371/journal.pgen.1004722", "scopus"=>"2-s2.0-84908324508", "isbn"=>"1553-7404", "issn"=>"15537404", "sgr"=>"84908324508"}, "id"=>"ccf36bfd-8dec-368d-b63d-5d601861686f", "abstract"=>"Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy). Furthermore, we introduce a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data.", "link"=>"http://www.mendeley.com/research/integrating-functional-data-prioritize-causal-variants-statistical-finemapping-studies", "reader_count"=>191, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>11, "Student > Doctoral Student"=>10, "Researcher"=>53, "Student > Ph. D. Student"=>68, "Student > Postgraduate"=>5, "Student > Master"=>11, "Other"=>5, "Student > Bachelor"=>13, "Lecturer"=>2, "Professor"=>10}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>11, "Student > Doctoral Student"=>10, "Researcher"=>53, "Student > Ph. D. Student"=>68, "Student > Postgraduate"=>5, "Student > Master"=>11, "Other"=>5, "Student > Bachelor"=>13, "Lecturer"=>2, "Professor"=>10}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>37, "Mathematics"=>6, "Agricultural and Biological Sciences"=>108, "Medicine and Dentistry"=>14, "Neuroscience"=>2, "Physics and Astronomy"=>1, "Psychology"=>3, "Computer Science"=>12, "Economics, Econometrics and Finance"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>14}, "Neuroscience"=>{"Neuroscience"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>3}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>108}, "Computer Science"=>{"Computer Science"=>12}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>37}, "Mathematics"=>{"Mathematics"=>6}, "Unspecified"=>{"Unspecified"=>5}}, "reader_count_by_country"=>{"Canada"=>1, "Netherlands"=>1, "Belgium"=>1, "United States"=>12, "Denmark"=>1, "United Kingdom"=>2, "Australia"=>1, "Switzerland"=>1, "Spain"=>2}, "group_count"=>11}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1772948"], "description"=>"<p>Using a background and a synthetic functional annotation at a frequency of 1/3 (), we simulated with annotation effect sizes such that in expectation, we attained approximately 100 causal variants while maintaining enrichment at a fixed point. We used the standard simulation parameters, fixing the variance explained by these 100 loci to 0.25 and using genotypes. We discarded simulations where fgwas failed to converge (see <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004722#s4\" target=\"_blank\">Methods</a>). Displayed here are the mean inferred Log2 enrichment estimates ( 1 SD) that were conducted over 500 independent simulations at each enrichment level.</p>", "links"=>[], "tags"=>["snp", "blood lipids traits", "estimate causality probabilities", "risk loci", "Prioritize Causal Variants", "summary association statistics", "1000 Genomes data", "genomic annotation data", "variant", "Integrating Functional Data"], "article_id"=>1223776, "categories"=>["Uncategorised"], "users"=>["Gleb Kichaev", "Wen-Yun Yang", "Sara Lindström", "Farhad Hormozdiari", "Eleazar Eskin", "Alkes L. Price", "Peter Kraft", "Bogdan Pasaniuc"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004722.g003", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accuracy_of_enrichment_estimation_for_a_synthetic_annotation_that_contains_8_fold_depletion_to_8_fold_enrichment_of_causal_variants_across_simulations_of_fine_mapping_data_sets_over_100_loci_/1223776", "title"=>"Accuracy of enrichment estimation for a synthetic annotation that contains 8-fold depletion to 8-fold enrichment of causal variants across simulations of fine-mapping data sets over 100 loci.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-30 04:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772961", "https://ndownloader.figshare.com/files/1772962", "https://ndownloader.figshare.com/files/1772963", "https://ndownloader.figshare.com/files/1772964", "https://ndownloader.figshare.com/files/1772965", "https://ndownloader.figshare.com/files/1772966", "https://ndownloader.figshare.com/files/1772967", "https://ndownloader.figshare.com/files/1772968", "https://ndownloader.figshare.com/files/1772969", "https://ndownloader.figshare.com/files/1772970", "https://ndownloader.figshare.com/files/1772971", "https://ndownloader.figshare.com/files/1772972", "https://ndownloader.figshare.com/files/1772973", "https://ndownloader.figshare.com/files/1772974", "https://ndownloader.figshare.com/files/1772975", "https://ndownloader.figshare.com/files/1772976", "https://ndownloader.figshare.com/files/1772977", "https://ndownloader.figshare.com/files/1772978", "https://ndownloader.figshare.com/files/1772979", "https://ndownloader.figshare.com/files/1772980"], "description"=>"<div><p>Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy). Furthermore, we introduce a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data.</p></div>", "links"=>[], "tags"=>["snp", "blood lipids traits", "estimate causality probabilities", "risk loci", "Prioritize Causal Variants", "summary association statistics", "1000 Genomes data", "genomic annotation data", "variant", "Integrating Functional Data"], "article_id"=>1223789, "categories"=>["Uncategorised"], "users"=>["Gleb Kichaev", "Wen-Yun Yang", "Sara Lindström", "Farhad Hormozdiari", "Eleazar Eskin", "Alkes L. Price", "Peter Kraft", "Bogdan Pasaniuc"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1004722.s001", "https://dx.doi.org/10.1371/journal.pgen.1004722.s002", "https://dx.doi.org/10.1371/journal.pgen.1004722.s003", "https://dx.doi.org/10.1371/journal.pgen.1004722.s004", "https://dx.doi.org/10.1371/journal.pgen.1004722.s005", "https://dx.doi.org/10.1371/journal.pgen.1004722.s006", "https://dx.doi.org/10.1371/journal.pgen.1004722.s007", "https://dx.doi.org/10.1371/journal.pgen.1004722.s008", "https://dx.doi.org/10.1371/journal.pgen.1004722.s009", "https://dx.doi.org/10.1371/journal.pgen.1004722.s010", "https://dx.doi.org/10.1371/journal.pgen.1004722.s011", "https://dx.doi.org/10.1371/journal.pgen.1004722.s012", "https://dx.doi.org/10.1371/journal.pgen.1004722.s013", "https://dx.doi.org/10.1371/journal.pgen.1004722.s014", "https://dx.doi.org/10.1371/journal.pgen.1004722.s015", "https://dx.doi.org/10.1371/journal.pgen.1004722.s016", "https://dx.doi.org/10.1371/journal.pgen.1004722.s017", "https://dx.doi.org/10.1371/journal.pgen.1004722.s018", "https://dx.doi.org/10.1371/journal.pgen.1004722.s019", "https://dx.doi.org/10.1371/journal.pgen.1004722.s020"], "stats"=>{"downloads"=>101, "page_views"=>47, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Integrating_Functional_Data_to_Prioritize_Causal_Variants_in_Statistical_Fine_Mapping_Studies_/1223789", "title"=>"Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-10-30 04:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772957"], "description"=>"<p>SNPs with posterior probability causality for HDL phenotype across the 37 risk loci (Results for TG/TC/LDL in <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004722#pgen.1004722.s015\" target=\"_blank\">Tables S5</a>, <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004722#pgen.1004722.s016\" target=\"_blank\">S6</a>, <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004722#pgen.1004722.s017\" target=\"_blank\">S7</a>).</p><p> denotes a non-synonymous variant.</p><p>HDL SNPs with high confidence for causality.</p>", "links"=>[], "tags"=>["snp", "blood lipids traits", "estimate causality probabilities", "risk loci", "Prioritize Causal Variants", "summary association statistics", "1000 Genomes data", "genomic annotation data", "variant", "Integrating Functional Data"], "article_id"=>1223785, "categories"=>["Uncategorised"], "users"=>["Gleb Kichaev", "Wen-Yun Yang", "Sara Lindström", "Farhad Hormozdiari", "Eleazar Eskin", "Alkes L. Price", "Peter Kraft", "Bogdan Pasaniuc"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004722.t006", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_HDL_SNPs_with_high_confidence_for_causality_/1223785", "title"=>"HDL SNPs with high confidence for causality.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-30 04:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772955"], "description"=>"<p>Average median (minimum) distance in Kb to true causal variant(s) for SNPs in the set of top N SNPs when causal variant is either present or absent from the fine-mapping data set. We simulated one 100 Kb locus with causal status drawn from a uniform prior. We then masked the causal variant(s) to explore how this would effect fine-mapping resolution.</p><p>Fine-mapping resolution when causal variant is missing.</p>", "links"=>[], "tags"=>["snp", "blood lipids traits", "estimate causality probabilities", "risk loci", "Prioritize Causal Variants", "summary association statistics", "1000 Genomes data", "genomic annotation data", "variant", "Integrating Functional Data"], "article_id"=>1223783, "categories"=>["Uncategorised"], "users"=>["Gleb Kichaev", "Wen-Yun Yang", "Sara Lindström", "Farhad Hormozdiari", "Eleazar Eskin", "Alkes L. Price", "Peter Kraft", "Bogdan Pasaniuc"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004722.t004", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fine_mapping_resolution_when_causal_variant_is_missing_/1223783", "title"=>"Fine-mapping resolution when causal variant is missing.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-30 04:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772956"], "description"=>"<p>Displayed are the log2 relative probabilities of SNPs to be causal if they fall within the listed annotation. *Indicates use in final PAINTOR model for the phenotype.</p><p>Top 10 most significant annotations for lipid traits.</p>", "links"=>[], "tags"=>["snp", "blood lipids traits", "estimate causality probabilities", "risk loci", "Prioritize Causal Variants", "summary association statistics", "1000 Genomes data", "genomic annotation data", "variant", "Integrating Functional Data"], "article_id"=>1223784, "categories"=>["Uncategorised"], "users"=>["Gleb Kichaev", "Wen-Yun Yang", "Sara Lindström", "Farhad Hormozdiari", "Eleazar Eskin", "Alkes L. Price", "Peter Kraft", "Bogdan Pasaniuc"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004722.t005", "stats"=>{"downloads"=>1, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Top_10_most_significant_annotations_for_lipid_traits_/1223784", "title"=>"Top 10 most significant annotations for lipid traits.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-30 04:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772954"], "description"=>"<p>To expedite simulations, we used a modified version of the simulation setup. As before, causal SNPs were drawn according to a logistic prior such that in expectation there were a total of 100 causal variants – we did not enrich causal in any annotations. For this experiment, Z-scores were drawn directly from a multivariate normal distribution; this gave virtually identical results to using simulated genotypes derived from HAPGEN (see <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004722#s4\" target=\"_blank\">Methods</a>). We find that PAINTOR increasingly outperforms existing methodologies as the size of the loci become larger.</p><p>Performance of PAINTOR compared to standard methodologies at variable sized loci.</p>", "links"=>[], "tags"=>["snp", "blood lipids traits", "estimate causality probabilities", "risk loci", "Prioritize Causal Variants", "summary association statistics", "1000 Genomes data", "genomic annotation data", "variant", "Integrating Functional Data"], "article_id"=>1223782, "categories"=>["Uncategorised"], "users"=>["Gleb Kichaev", "Wen-Yun Yang", "Sara Lindström", "Farhad Hormozdiari", "Eleazar Eskin", "Alkes L. Price", "Peter Kraft", "Bogdan Pasaniuc"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004722.t003", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_PAINTOR_compared_to_standard_methodologies_at_variable_sized_loci_/1223782", "title"=>"Performance of PAINTOR compared to standard methodologies at variable sized loci.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-30 04:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772951"], "description"=>"<p>Methods were benchmarked using the average number of SNPs per locus selected to find (50%,90%) of all causal variants. We simulated a trait with 100 risk loci explaining fine-mapped through sequencing of N = 10,000 samples and assessed accuracy only at loci that harbor at least one casual variant (64 loci on the average). We explored two methods to prioritizing variants: (1) “Variant Ranking Across All Loci” prioritizes SNPs across all loci while (2) “Variant Ranking Independently at Each Locus”, first prioritizes variants at each risk locus followed by merging across all loci. We note that PAINTOR 1CV and/or no annot corresponds to running PAINTOR assuming a single causal variant and/or not providing access to annotations. PAINTOR True did not empirically estimate enrichment but used the true enrichment values for each functional annotation data.</p><p>Summary of performance for various fine-mapping methods.</p>", "links"=>[], "tags"=>["snp", "blood lipids traits", "estimate causality probabilities", "risk loci", "Prioritize Causal Variants", "summary association statistics", "1000 Genomes data", "genomic annotation data", "variant", "Integrating Functional Data"], "article_id"=>1223779, "categories"=>["Uncategorised"], "users"=>["Gleb Kichaev", "Wen-Yun Yang", "Sara Lindström", "Farhad Hormozdiari", "Eleazar Eskin", "Alkes L. Price", "Peter Kraft", "Bogdan Pasaniuc"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004722.t001", "stats"=>{"downloads"=>2, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_performance_for_various_fine_mapping_methods_/1223779", "title"=>"Summary of performance for various fine-mapping methods.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-30 04:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772950"], "description"=>"<p>We demonstrate how utility curves are optimized by selecting SNPs that achieve a minimum posterior probability threshold at various benefit-to-cost ratios (R). The total number of SNPs selected at the maximum utility for R =  (1.25, 1.5, 2, 5, 10, 20) is (29.8, 39.2, 52.4, 119.1, 221.4, 405.4) which identifies approximately (29.8, 35.6, 43.4, 65.33, 79.9, 91.8) causal variants.</p>", "links"=>[], "tags"=>["snp", "blood lipids traits", "estimate causality probabilities", "risk loci", "Prioritize Causal Variants", "summary association statistics", "1000 Genomes data", "genomic annotation data", "variant", "Integrating Functional Data"], "article_id"=>1223778, "categories"=>["Uncategorised"], "users"=>["Gleb Kichaev", "Wen-Yun Yang", "Sara Lindström", "Farhad Hormozdiari", "Eleazar Eskin", "Alkes L. Price", "Peter Kraft", "Bogdan Pasaniuc"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004722.g004", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Thresholding_on_posterior_probabilities_provides_a_principled_way_to_assess_utility_/1223778", "title"=>"Thresholding on posterior probabilities provides a principled way to assess utility.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-30 04:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772945"], "description"=>"<p>PAINTOR is a statistical model for incorporating functional annotations on top of association statistics to ascribe probabilistic confidence of causality to the SNPs at the loci. Depicted here are two loci with functional annotations from three different cell lines/tissues and three different classes. Causal variants are enriched within the green annotation class while depleted from others. PAINTOR is designed to upweight (with probability mass) SNPs residing in the green annotation while down-weighting SNPs residing in the red annotation.</p>", "links"=>[], "tags"=>["snp", "blood lipids traits", "estimate causality probabilities", "risk loci", "Prioritize Causal Variants", "summary association statistics", "1000 Genomes data", "genomic annotation data", "variant", "Integrating Functional Data"], "article_id"=>1223773, "categories"=>["Uncategorised"], "users"=>["Gleb Kichaev", "Wen-Yun Yang", "Sara Lindström", "Farhad Hormozdiari", "Eleazar Eskin", "Alkes L. Price", "Peter Kraft", "Bogdan Pasaniuc"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004722.g001", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_model_input_/1223773", "title"=>"Illustration of model input.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-30 04:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772959"], "description"=>"<p>List of model parameters for the locus where L is the total number of fine-mapping loci).</p>", "links"=>[], "tags"=>["snp", "blood lipids traits", "estimate causality probabilities", "risk loci", "Prioritize Causal Variants", "summary association statistics", "1000 Genomes data", "genomic annotation data", "variant", "Integrating Functional Data"], "article_id"=>1223787, "categories"=>["Uncategorised"], "users"=>["Gleb Kichaev", "Wen-Yun Yang", "Sara Lindström", "Farhad Hormozdiari", "Eleazar Eskin", "Alkes L. Price", "Peter Kraft", "Bogdan Pasaniuc"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004722.t008", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_List_of_model_parameters_for_the_locus_where_L_is_the_total_number_of_fine_mapping_loci_/1223787", "title"=>"List of model parameters for the locus where L is the total number of fine-mapping loci).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-30 04:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772958"], "description"=>"<p>Shown here are the number of SNPs in the 90% Confidence Set for each of the lipid phenotypes as estimated using PAINTOR. After marginally running PAINTOR on the entire pool of annotations, we selected the top five annotations for each trait and fit full trait-specific models on each of the densely imputed data sets. We compared PAINTOR with or without integration of functional annotation data. The magnitude in the reduction in the size of the confidence set approximately mirrors what we observe in simulations.</p><p>Reduction in the number of SNPs in the 90% Credible Set after incorporating functional annotations.</p>", "links"=>[], "tags"=>["snp", "blood lipids traits", "estimate causality probabilities", "risk loci", "Prioritize Causal Variants", "summary association statistics", "1000 Genomes data", "genomic annotation data", "variant", "Integrating Functional Data"], "article_id"=>1223786, "categories"=>["Uncategorised"], "users"=>["Gleb Kichaev", "Wen-Yun Yang", "Sara Lindström", "Farhad Hormozdiari", "Eleazar Eskin", "Alkes L. Price", "Peter Kraft", "Bogdan Pasaniuc"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004722.t007", "stats"=>{"downloads"=>6, "page_views"=>31, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reduction_in_the_number_of_SNPs_in_the_90_Credible_Set_after_incorporating_functional_annotations_/1223786", "title"=>"Reduction in the number of SNPs in the 90% Credible Set after incorporating functional annotations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-30 04:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772952"], "description"=>"<p>We define an <i>ρ</i>-level confidence set as the number of SNPs we need to select in order to consume an fraction of the total posterior probability mass over all loci. Results in the table correspond to averaging over 500 independent simulations where the average number of true causals SNPs per simulation was 109.2. The size of 90%, 95%, and 99% confidence sets are reduced by 22.8%, 17.5% and 11.1% when incorporating functional annotations as prior probabilities. <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004722#s4\" target=\"_blank\">Methods</a> that assume one causal variant are miss-calibrated due to loci with multiple causals.</p><p>Leveraging functional priors leads to improved fine-mapping resolution.</p>", "links"=>[], "tags"=>["snp", "blood lipids traits", "estimate causality probabilities", "risk loci", "Prioritize Causal Variants", "summary association statistics", "1000 Genomes data", "genomic annotation data", "variant", "Integrating Functional Data"], "article_id"=>1223780, "categories"=>["Uncategorised"], "users"=>["Gleb Kichaev", "Wen-Yun Yang", "Sara Lindström", "Farhad Hormozdiari", "Eleazar Eskin", "Alkes L. Price", "Peter Kraft", "Bogdan Pasaniuc"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004722.t002", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Leveraging_functional_priors_leads_to_improved_fine_mapping_resolution_/1223780", "title"=>"Leveraging functional priors leads to improved fine-mapping resolution.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-30 04:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772947"], "description"=>"<p>We simulated datasets consisting of 10 K genotypes over one hundred 10 KB loci using three synthetic functional annotations randomly dispersed at fixed percentages (2.2%, 2.2%, 30.7%). SNPs falling within these annotations were enriched (9.5, 5.7, 3.65) times more with causal variants relative to unannotated SNPs. We fixed the variance explained by these loci to and repeated the simulation 500 times. The top figure corresponds to the overall performance at causal loci (64 loci) with PAINTOR clearly achieving the greatest overall accuracy. The bottom figures correspond to loci with a single causal variant (an average of 34 per simulation) (left) or multiple causal variants (average of 30 per simulation) (right). At loci where there is one true causal variant, fgwas achieves greater accuracy than PAINTOR due to the fact that fgwas assumes the correct number of causal variants. We note that the version of PAINTOR that assumes a single causal variant yields very similar to fgwas at loci where the truth is of a single causal (both requiring 2.63 SNPs per locus to identify 90% of the causal variants.) However, at loci with multiple causal variants, the power of methods that assume a single causal is greatly deflated leading to PAINTOR's superior overall accuracy.</p>", "links"=>[], "tags"=>["snp", "blood lipids traits", "estimate causality probabilities", "risk loci", "Prioritize Causal Variants", "summary association statistics", "1000 Genomes data", "genomic annotation data", "variant", "Integrating Functional Data"], "article_id"=>1223775, "categories"=>["Uncategorised"], "users"=>["Gleb Kichaev", "Wen-Yun Yang", "Sara Lindström", "Farhad Hormozdiari", "Eleazar Eskin", "Alkes L. Price", "Peter Kraft", "Bogdan Pasaniuc"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004722.g002", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PAINTOR_outperforms_existing_methodologies_for_fine_mapping_/1223775", "title"=>"PAINTOR outperforms existing methodologies for fine-mapping.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-30 04:03:30"}

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