Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies
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{"title"=>"Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies", "type"=>"journal", "authors"=>[{"first_name"=>"Noah", "last_name"=>"Zaitlen", "scopus_author_id"=>"8975849800"}, {"first_name"=>"Sara", "last_name"=>"Lindström", "scopus_author_id"=>"57193814735"}, {"first_name"=>"Bogdan", "last_name"=>"Pasaniuc", "scopus_author_id"=>"16175969900"}, {"first_name"=>"Marilyn", "last_name"=>"Cornelis", "scopus_author_id"=>"6602155166"}, {"first_name"=>"Giulio", "last_name"=>"Genovese", "scopus_author_id"=>"15131527100"}, {"first_name"=>"Samuela", "last_name"=>"Pollack", "scopus_author_id"=>"36632987200"}, {"first_name"=>"Anne", "last_name"=>"Barton", "scopus_author_id"=>"35352277600"}, {"first_name"=>"Heike", "last_name"=>"Bickeböller", "scopus_author_id"=>"6603947964"}, {"first_name"=>"Donald W.", "last_name"=>"Bowden", "scopus_author_id"=>"7101785120"}, {"first_name"=>"Steve", "last_name"=>"Eyre", "scopus_author_id"=>"7004612837"}, {"first_name"=>"Barry I.", "last_name"=>"Freedman", "scopus_author_id"=>"7102825752"}, {"first_name"=>"David J.", "last_name"=>"Friedman", "scopus_author_id"=>"16068504300"}, {"first_name"=>"John K.", "last_name"=>"Field", "scopus_author_id"=>"55813280500"}, {"first_name"=>"Leif", "last_name"=>"Groop", "scopus_author_id"=>"21634758500"}, {"first_name"=>"Aage", "last_name"=>"Haugen", "scopus_author_id"=>"7006262864"}, {"first_name"=>"Joachim", "last_name"=>"Heinrich", "scopus_author_id"=>"7201505289"}, {"first_name"=>"Brian E.", "last_name"=>"Henderson", "scopus_author_id"=>"55659755700"}, {"first_name"=>"Pamela J.", "last_name"=>"Hicks", "scopus_author_id"=>"26030990200"}, {"first_name"=>"Lynne J.", "last_name"=>"Hocking", "scopus_author_id"=>"56543058100"}, {"first_name"=>"Laurence N.", "last_name"=>"Kolonel", "scopus_author_id"=>"7005665016"}, {"first_name"=>"Maria Teresa", "last_name"=>"Landi", "scopus_author_id"=>"7005278728"}, {"first_name"=>"Carl D.", "last_name"=>"Langefeld", "scopus_author_id"=>"7004078855"}, {"first_name"=>"Loic", "last_name"=>"Le Marchand", "scopus_author_id"=>"7006229986"}, {"first_name"=>"Michael", "last_name"=>"Meister", "scopus_author_id"=>"23051382300"}, {"first_name"=>"Ann W.", "last_name"=>"Morgan", "scopus_author_id"=>"7403138399"}, {"first_name"=>"Olaide Y.", "last_name"=>"Raji", "scopus_author_id"=>"35238474200"}, {"first_name"=>"Angela", "last_name"=>"Risch", "scopus_author_id"=>"7003820272"}, {"first_name"=>"Albert", "last_name"=>"Rosenberger", "scopus_author_id"=>"7006701973"}, {"first_name"=>"David", "last_name"=>"Scherf", "scopus_author_id"=>"36661390200"}, {"first_name"=>"Sophia", "last_name"=>"Steer", "scopus_author_id"=>"6603951069"}, {"first_name"=>"Martin", "last_name"=>"Walshaw", "scopus_author_id"=>"7004763680"}, {"first_name"=>"Kevin M.", "last_name"=>"Waters", "scopus_author_id"=>"26428633000"}, {"first_name"=>"Anthony G.", "last_name"=>"Wilson", "scopus_author_id"=>"7404869447"}, {"first_name"=>"Paul", "last_name"=>"Wordsworth", "scopus_author_id"=>"57195375524"}, {"first_name"=>"Shanbeh", "last_name"=>"Zienolddiny", "scopus_author_id"=>"6603464577"}, {"first_name"=>"Eric Tchetgen", "last_name"=>"Tchetgen", "scopus_author_id"=>"32267595100"}, {"first_name"=>"Christopher", "last_name"=>"Haiman", "scopus_author_id"=>"6701722861"}, {"first_name"=>"David J.", "last_name"=>"Hunter", "scopus_author_id"=>"56447806500"}, {"first_name"=>"Robert M.", "last_name"=>"Plenge", "scopus_author_id"=>"6602930396"}, {"first_name"=>"Jane", "last_name"=>"Worthington", "scopus_author_id"=>"26322894900"}, {"first_name"=>"David C.", "last_name"=>"Christiani", "scopus_author_id"=>"35397343500"}, {"first_name"=>"Debra A.", "last_name"=>"Schaumberg", "scopus_author_id"=>"7003730354"}, {"first_name"=>"Daniel I.", "last_name"=>"Chasman", "scopus_author_id"=>"6701612213"}, {"first_name"=>"David", "last_name"=>"Altshuler", "scopus_author_id"=>"7005798316"}, {"first_name"=>"Benjamin", "last_name"=>"Voight", "scopus_author_id"=>"10339684700"}, {"first_name"=>"Peter", "last_name"=>"Kraft", "scopus_author_id"=>"55214760000"}, {"first_name"=>"Nick", "last_name"=>"Patterson", "scopus_author_id"=>"7006515330"}, {"first_name"=>"Alkes L.", "last_name"=>"Price", "scopus_author_id"=>"57199322743"}], "year"=>2012, "source"=>"PLoS Genetics", "identifiers"=>{"scopus"=>"2-s2.0-84870718214", "sgr"=>"84870718214", "issn"=>"15537390", "doi"=>"10.1371/journal.pgen.1003032", "pmid"=>"23144628", "isbn"=>"1553-7390", "pui"=>"366216238"}, "id"=>"89a93913-4e1b-3506-abe2-533f0c4aa4e6", "abstract"=>"Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low-BMI cases are larger than those estimated from high-BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1 x 10(-9)). The improvement varied across diseases with a 16% median increase in chi(2) test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci.", "link"=>"http://www.mendeley.com/research/informed-conditioning-clinical-covariates-increases-power-casecontrol-association-studies", "reader_count"=>92, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>6, "Librarian"=>2, "Researcher"=>28, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>4, "Student > Master"=>10, "Other"=>4, "Student > Bachelor"=>5, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>6, "Librarian"=>2, "Researcher"=>28, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>4, "Student > Master"=>10, "Other"=>4, "Student > Bachelor"=>5, "Professor"=>9}, "reader_count_by_subject_area"=>{"Unspecified"=>8, "Agricultural and Biological Sciences"=>36, "Business, Management and Accounting"=>1, "Chemistry"=>1, "Computer Science"=>4, "Engineering"=>3, "Biochemistry, Genetics and Molecular Biology"=>9, "Mathematics"=>4, "Medicine and Dentistry"=>20, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>1, "Psychology"=>2, "Social Sciences"=>1, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>20}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>2}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>8}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Engineering"=>{"Engineering"=>3}, "Chemistry"=>{"Chemistry"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>36}, "Computer Science"=>{"Computer Science"=>4}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>9}}, "reader_count_by_country"=>{"United States"=>5, "United Kingdom"=>1, "Australia"=>1}, "group_count"=>7}

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  • {"files"=>["https://ndownloader.figshare.com/files/545826"], "description"=>"<p>For each statistic we display average results across 1,000,000 simulations, for various effect sizes <i>γ</i>. All statistics are χ<sup>2</sup>(1 dof). Logistic regression with an interaction term (G+GxE) values been converted from χ<sup>2</sup>(2 dof) to the equivalent χ<sup>2</sup>(1 dof) value. At an effect size of 0 all statistics give the expected value under the null. OR LBMI is the odds ratio computed from cases with BMI = 24. OR HBMI is the odds ratio for cases with BMI = 35.</p>", "links"=>[], "tags"=>["lt", "approaches", "simulated"], "article_id"=>216256, "categories"=>["Immunology", "Mathematics", "Biological Sciences", "Genetics", "Biotechnology"], "users"=>["Noah Zaitlen", "Sara Lindström", "Bogdan Pasaniuc", "Marilyn Cornelis", "Giulio Genovese", "Samuela Pollack", "Anne Barton", "Heike Bickeböller", "Donald W. Bowden", "Steve Eyre", "Barry I. Freedman", "David J. Friedman", "John K. Field", "Leif Groop", "Aage Haugen", "Joachim Heinrich", "Brian E. Henderson", "Pamela J. Hicks", "Lynne J. Hocking", "Laurence N. Kolonel", "Maria Teresa Landi", "Carl D. Langefeld", "Loic Le Marchand", "Michael Meister", "Ann W. Morgan", "Olaide Y. Raji", "Angela Risch", "Albert Rosenberger", "David Scherf", "Sophia Steer", "Martin Walshaw", "Kevin M. Waters", "Anthony G. Wilson", "Paul Wordsworth", "Shanbeh Zienolddiny", "Eric Tchetgen Tchetgen", "Christopher Haiman", "David J. Hunter", "Robert M. Plenge", "Jane Worthington", "David C. Christiani", "Debra A. Schaumberg", "Daniel I. Chasman", "David Altshuler", "Benjamin Voight", "Peter Kraft", "Nick Patterson", "Alkes L. Price"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1003032.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_2_statistics_for_LT_versus_other_approaches_in_simulated_data_/216256", "title"=>"Average χ<sup>2</sup> statistics for LT versus other approaches in simulated data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-08 01:44:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/545732"], "description"=>"<p>LT model is the liability threshold model for each disease with parameters estimated using the LTPub method. For diseases with multiple covariates, models with all covariates and each covariate separately are given. %Variance Explained is the fraction of variance explained on the liability scale in the study data for each of the covariates in each of the diseases when all covariates are used in the model, and is specific to the distribution of covariates in each particular study. BMI30 is a binary variable, which is 1 if an individual's BMI is greater than 30 and 0 otherwise. Type 2 diabetes (T2D), prostate cancer (PC), lung cancer (LC), breast cancer (BC), rheumatoid arthritis (RA), end-stage kidney disease (ESKD), and age-related macular degeneration (AMD).</p>", "links"=>[], "tags"=>["covariates", "sizes"], "article_id"=>216158, "categories"=>["Immunology", "Mathematics", "Biological Sciences", "Genetics", "Biotechnology"], "users"=>["Noah Zaitlen", "Sara Lindström", "Bogdan Pasaniuc", "Marilyn Cornelis", "Giulio Genovese", "Samuela Pollack", "Anne Barton", "Heike Bickeböller", "Donald W. Bowden", "Steve Eyre", "Barry I. Freedman", "David J. Friedman", "John K. Field", "Leif Groop", "Aage Haugen", "Joachim Heinrich", "Brian E. Henderson", "Pamela J. Hicks", "Lynne J. Hocking", "Laurence N. Kolonel", "Maria Teresa Landi", "Carl D. Langefeld", "Loic Le Marchand", "Michael Meister", "Ann W. Morgan", "Olaide Y. Raji", "Angela Risch", "Albert Rosenberger", "David Scherf", "Sophia Steer", "Martin Walshaw", "Kevin M. Waters", "Anthony G. Wilson", "Paul Wordsworth", "Shanbeh Zienolddiny", "Eric Tchetgen Tchetgen", "Christopher Haiman", "David J. Hunter", "Robert M. Plenge", "Jane Worthington", "David C. Christiani", "Debra A. Schaumberg", "Daniel I. Chasman", "David Altshuler", "Benjamin Voight", "Peter Kraft", "Nick Patterson", "Alkes L. Price"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1003032.t003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Inferred_covariates_and_effect_sizes_on_the_liability_scale_/216158", "title"=>"Inferred covariates and effect sizes on the liability scale.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-08 01:42:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/545907"], "description"=>"<p>Posterior mean value of residual quantitative trait <i>ε</i> (adjusted for BMI) as a function of BMI and case-control status. We also list allele frequencies specified in simulated genotype data.</p>", "links"=>[], "tags"=>["simulated", "t2d"], "article_id"=>216340, "categories"=>["Immunology", "Mathematics", "Biological Sciences", "Genetics", "Biotechnology"], "users"=>["Noah Zaitlen", "Sara Lindström", "Bogdan Pasaniuc", "Marilyn Cornelis", "Giulio Genovese", "Samuela Pollack", "Anne Barton", "Heike Bickeböller", "Donald W. Bowden", "Steve Eyre", "Barry I. Freedman", "David J. Friedman", "John K. Field", "Leif Groop", "Aage Haugen", "Joachim Heinrich", "Brian E. Henderson", "Pamela J. Hicks", "Lynne J. Hocking", "Laurence N. Kolonel", "Maria Teresa Landi", "Carl D. Langefeld", "Loic Le Marchand", "Michael Meister", "Ann W. Morgan", "Olaide Y. Raji", "Angela Risch", "Albert Rosenberger", "David Scherf", "Sophia Steer", "Martin Walshaw", "Kevin M. Waters", "Anthony G. Wilson", "Paul Wordsworth", "Shanbeh Zienolddiny", "Eric Tchetgen Tchetgen", "Christopher Haiman", "David J. Hunter", "Robert M. Plenge", "Jane Worthington", "David C. Christiani", "Debra A. Schaumberg", "Daniel I. Chasman", "David Altshuler", "Benjamin Voight", "Peter Kraft", "Nick Patterson", "Alkes L. Price"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1003032.t001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_liability_threshold_model_simulated_T2D_example_/216340", "title"=>"Illustration of liability threshold model: simulated T2D example.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-08 01:45:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/545242"], "description"=>"<p>The posterior mean of <i>ε</i> for low-BMI and high-BMI cases is the expected value of <i>ε</i> given that it exceeds <u>c(t</u>−)+m. High-BMI cases have a lower posterior mean relative to low-BMI cases since they require a smaller contribution from genetics to exceed the threshold in the liability threshold model.</p>", "links"=>[], "tags"=>["simulated", "t2d"], "article_id"=>215665, "categories"=>["Immunology", "Mathematics", "Biological Sciences", "Genetics", "Biotechnology"], "users"=>["Noah Zaitlen", "Sara Lindström", "Bogdan Pasaniuc", "Marilyn Cornelis", "Giulio Genovese", "Samuela Pollack", "Anne Barton", "Heike Bickeböller", "Donald W. Bowden", "Steve Eyre", "Barry I. Freedman", "David J. Friedman", "John K. Field", "Leif Groop", "Aage Haugen", "Joachim Heinrich", "Brian E. Henderson", "Pamela J. Hicks", "Lynne J. Hocking", "Laurence N. Kolonel", "Maria Teresa Landi", "Carl D. Langefeld", "Loic Le Marchand", "Michael Meister", "Ann W. Morgan", "Olaide Y. Raji", "Angela Risch", "Albert Rosenberger", "David Scherf", "Sophia Steer", "Martin Walshaw", "Kevin M. Waters", "Anthony G. Wilson", "Paul Wordsworth", "Shanbeh Zienolddiny", "Eric Tchetgen Tchetgen", "Christopher Haiman", "David J. Hunter", "Robert M. Plenge", "Jane Worthington", "David C. Christiani", "Debra A. Schaumberg", "Daniel I. Chasman", "David Altshuler", "Benjamin Voight", "Peter Kraft", "Nick Patterson", "Alkes L. Price"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1003032.g001", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_liability_threshold_model_simulated_T2D_example_/215665", "title"=>"Illustration of liability threshold model: simulated T2D example.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:34:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/545399"], "description"=>"<p>For each statistic we display power to attain P<5<b>×</b>10<sup>−8</sup> based on 1,000,000 simulations of 3000 cases and 3000 controls, for various effect sizes <i>γ</i>. The increase in power (ratio of y-axis values) for LT versus LogR is 22.8% for <i>γ</i> = 0.1, and 23.0% when computing average power across all values of <i>γ</i>. For γ = 0 the power was 5.0% for all statistics when the P-value threshold is 0.05. G+GxE performs worse due to an extra degree of freedom.</p>", "links"=>[], "tags"=>["calculations", "lt", "approaches", "simulated"], "article_id"=>215823, "categories"=>["Immunology", "Mathematics", "Biological Sciences", "Genetics", "Biotechnology"], "users"=>["Noah Zaitlen", "Sara Lindström", "Bogdan Pasaniuc", "Marilyn Cornelis", "Giulio Genovese", "Samuela Pollack", "Anne Barton", "Heike Bickeböller", "Donald W. Bowden", "Steve Eyre", "Barry I. Freedman", "David J. Friedman", "John K. Field", "Leif Groop", "Aage Haugen", "Joachim Heinrich", "Brian E. Henderson", "Pamela J. Hicks", "Lynne J. Hocking", "Laurence N. Kolonel", "Maria Teresa Landi", "Carl D. Langefeld", "Loic Le Marchand", "Michael Meister", "Ann W. Morgan", "Olaide Y. Raji", "Angela Risch", "Albert Rosenberger", "David Scherf", "Sophia Steer", "Martin Walshaw", "Kevin M. Waters", "Anthony G. Wilson", "Paul Wordsworth", "Shanbeh Zienolddiny", "Eric Tchetgen Tchetgen", "Christopher Haiman", "David J. Hunter", "Robert M. Plenge", "Jane Worthington", "David C. Christiani", "Debra A. Schaumberg", "Daniel I. Chasman", "David Altshuler", "Benjamin Voight", "Peter Kraft", "Nick Patterson", "Alkes L. Price"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1003032.g002", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Power_calculations_for_LogR_G_GxE_and_LT_approaches_in_simulated_data_/215823", "title"=>"Power calculations for LogR, G+GxE, and LT approaches in simulated data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:37:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/292190"], "description"=>"<div><p>Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1<b>×</b>10<sup>−9</sup>). The improvement varied across diseases with a 16% median increase in χ<sup>2</sup> test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci.</p> </div>", "links"=>[], "tags"=>["informed", "conditioning", "covariates", "increases", "case-control", "studies"], "article_id"=>117496, "categories"=>["Immunology", "Mathematics", "Biological Sciences", "Genetics", "Biotechnology"], "users"=>["Noah Zaitlen", "Sara Lindström", "Bogdan Pasaniuc", "Marilyn Cornelis", "Giulio Genovese", "Samuela Pollack", "Anne Barton", "Heike Bickeböller", "Donald W. Bowden", "Steve Eyre", "Barry I. Freedman", "David J. Friedman", "John K. Field", "Leif Groop", "Aage Haugen", "Joachim Heinrich", "Brian E. Henderson", "Pamela J. Hicks", "Lynne J. Hocking", "Laurence N. Kolonel", "Maria Teresa Landi", "Carl D. Langefeld", "Loic Le Marchand", "Michael Meister", "Ann W. Morgan", "Olaide Y. Raji", "Angela Risch", "Albert Rosenberger", "David Scherf", "Sophia Steer", "Martin Walshaw", "Kevin M. Waters", "Anthony G. Wilson", "Paul Wordsworth", "Shanbeh Zienolddiny", "Eric Tchetgen Tchetgen", "Christopher Haiman", "David J. Hunter", "Robert M. Plenge", "Jane Worthington", "David C. Christiani", "Debra A. Schaumberg", "Daniel I. Chasman", "David Altshuler", "Benjamin Voight", "Peter Kraft", "Nick Patterson", "Alkes L. Price"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1003032", "stats"=>{"downloads"=>4, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Informed_Conditioning_on_Clinical_Covariates_Increases_Power_in_Case_Control_Association_Studies__/117496", "title"=>"Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-08 02:04:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/545526"], "description"=>"<p>The sum of each of the test statistics across all of the SNPs in each of the diseases. LTPub vs LogR is the % increase of LTPub compared to LogR. It has a median value of 16%. Type 2 diabetes (T2D), prostate cancer (PC), lung cancer (LC), breast cancer (BC), rheumatoid arthritis (RA), end-stage kidney disease (ESKD), and age-related macular degeneration (AMD).</p>", "links"=>[], "tags"=>["genetics and genomics", "public health and epidemiology", "Computational biology", "epidemiology", "mathematics"], "article_id"=>215958, "categories"=>["Immunology", "Mathematics", "Biological Sciences", "Genetics", "Biotechnology"], "users"=>["Noah Zaitlen", "Sara Lindström", "Bogdan Pasaniuc", "Marilyn Cornelis", "Giulio Genovese", "Samuela Pollack", "Anne Barton", "Heike Bickeböller", "Donald W. Bowden", "Steve Eyre", "Barry I. Freedman", "David J. Friedman", "John K. Field", "Leif Groop", "Aage Haugen", "Joachim Heinrich", "Brian E. Henderson", "Pamela J. Hicks", "Lynne J. Hocking", "Laurence N. Kolonel", "Maria Teresa Landi", "Carl D. Langefeld", "Loic Le Marchand", "Michael Meister", "Ann W. Morgan", "Olaide Y. Raji", "Angela Risch", "Albert Rosenberger", "David Scherf", "Sophia Steer", "Martin Walshaw", "Kevin M. Waters", "Anthony G. Wilson", "Paul Wordsworth", "Shanbeh Zienolddiny", "Eric Tchetgen Tchetgen", "Christopher Haiman", "David J. Hunter", "Robert M. Plenge", "Jane Worthington", "David C. Christiani", "Debra A. Schaumberg", "Daniel I. Chasman", "David Altshuler", "Benjamin Voight", "Peter Kraft", "Nick Patterson", "Alkes L. Price"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1003032.t005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_statistics_across_all_datasets_/215958", "title"=>"Summary statistics across all datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-08 01:39:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/545627"], "description"=>"<p>ORL>ORH is the number of SNPs in which the odds ratio of low risk cases (e.g. low-BMI) is greater than then odds ratio computed from the high risk group (e.g. high-BMI). LTPub>LogR is the number of SNPs in the dataset for which LTPub exceeded the LogR statistic. There are 9 SNPs shared between the two T2D sets. In total there are 157 unique SNPs and 115 unique SNPs with LTPub>LogR. Type 2 diabetes (T2D), prostate cancer (PC), lung cancer (LC), breast cancer (BC), rheumatoid arthritis (RA), end-stage kidney disease (ESKD), and age-related macular degeneration (AMD).</p>", "links"=>[], "tags"=>["genetics and genomics", "public health and epidemiology", "Computational biology", "epidemiology", "mathematics"], "article_id"=>216050, "categories"=>["Immunology", "Mathematics", "Biological Sciences", "Genetics", "Biotechnology"], "users"=>["Noah Zaitlen", "Sara Lindström", "Bogdan Pasaniuc", "Marilyn Cornelis", "Giulio Genovese", "Samuela Pollack", "Anne Barton", "Heike Bickeböller", "Donald W. Bowden", "Steve Eyre", "Barry I. Freedman", "David J. Friedman", "John K. Field", "Leif Groop", "Aage Haugen", "Joachim Heinrich", "Brian E. Henderson", "Pamela J. Hicks", "Lynne J. Hocking", "Laurence N. Kolonel", "Maria Teresa Landi", "Carl D. Langefeld", "Loic Le Marchand", "Michael Meister", "Ann W. Morgan", "Olaide Y. Raji", "Angela Risch", "Albert Rosenberger", "David Scherf", "Sophia Steer", "Martin Walshaw", "Kevin M. Waters", "Anthony G. Wilson", "Paul Wordsworth", "Shanbeh Zienolddiny", "Eric Tchetgen Tchetgen", "Christopher Haiman", "David J. Hunter", "Robert M. Plenge", "Jane Worthington", "David C. Christiani", "Debra A. Schaumberg", "Daniel I. Chasman", "David Altshuler", "Benjamin Voight", "Peter Kraft", "Nick Patterson", "Alkes L. Price"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1003032.t004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_information_for_all_datasets_/216050", "title"=>"Summary information for all datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-08 01:40:50"}

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Relative Metric

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