Genotype-Based Ancestral Background Consistently Predicts Efficacy and Side Effects across Treatments in CATIE and STAR*D
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{"title"=>"Genotype-Based Ancestral Background Consistently Predicts Efficacy and Side Effects across Treatments in CATIE and STAR*D", "type"=>"journal", "authors"=>[{"first_name"=>"Daniel E.", "last_name"=>"Adkins", "scopus_author_id"=>"7005463920"}, {"first_name"=>"Renan P.", "last_name"=>"Souza", "scopus_author_id"=>"13104196000"}, {"first_name"=>"Karolina", "last_name"=>"Åberg", "scopus_author_id"=>"24491833300"}, {"first_name"=>"Shaunna L.", "last_name"=>"Clark", "scopus_author_id"=>"36598249500"}, {"first_name"=>"Joseph L.", "last_name"=>"McClay", "scopus_author_id"=>"8627461600"}, {"first_name"=>"Patrick F.", "last_name"=>"Sullivan", "scopus_author_id"=>"7402033718"}, {"first_name"=>"Edwin J.C.G.", "last_name"=>"van den Oord", "scopus_author_id"=>"7005835163"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84873538780", "sgr"=>"84873538780", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0055239", "pmid"=>"23405125", "isbn"=>"EN :1932-6203", "pui"=>"368304241"}, "id"=>"42331656-3dce-3162-a389-24a0ace89a59", "abstract"=>"Only a subset of patients will typically respond to any given prescribed drug. The time it takes clinicians to declare a treatment ineffective leaves the patient in an impaired state and at unnecessary risk for adverse drug effects. Thus, diagnostic tests robustly predicting the most effective and safe medication for each patient prior to starting pharmacotherapy would have tremendous clinical value. In this article, we evaluated the use of genetic markers to estimate ancestry as a predictive component of such diagnostic tests. We first estimated each patient's unique mosaic of ancestral backgrounds using genome-wide SNP data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) (n = 765) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) (n = 1892). Next, we performed multiple regression analyses to estimate the predictive power of these ancestral dimensions. For 136/89 treatment-outcome combinations tested in CATIE/STAR*D, results indicated 1.67/1.84 times higher median test statistics than expected under the null hypothesis assuming no predictive power (p<0.01, both samples). Thus, ancestry showed robust and pervasive correlations with drug efficacy and side effects in both CATIE and STAR*D. Comparison of the marginal predictive power of MDS ancestral dimensions and self-reported race indicated significant improvements to model fit with the inclusion of MDS dimensions, but mixed evidence for self-reported race. Knowledge of each patient's unique mosaic of ancestral backgrounds provides a potent immediate starting point for developing algorithms identifying the most effective and safe medication for a wide variety of drug-treatment response combinations. As relatively few new psychiatric drugs are currently under development, such personalized medicine offers a promising approach toward optimizing pharmacotherapy for psychiatric conditions.", "link"=>"http://www.mendeley.com/research/genotypebased-ancestral-background-consistently-predicts-efficacy-side-effects-across-treatments-cat", "reader_count"=>30, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>7, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>1, "Other"=>3, "Student > Master"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>7, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>1, "Other"=>3, "Student > Master"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Biochemistry, Genetics and Molecular Biology"=>6, "Nursing and Health Professions"=>1, "Agricultural and Biological Sciences"=>6, "Medicine and Dentistry"=>5, "Neuroscience"=>2, "Business, Management and Accounting"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Psychology"=>3, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Neuroscience"=>{"Neuroscience"=>2}, "Psychology"=>{"Psychology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Computer Science"=>{"Computer Science"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Unspecified"=>{"Unspecified"=>3}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Brazil"=>1, "Denmark"=>1}, "group_count"=>2}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/493701"], "description"=>"<p>PANSS – Positive and Negative Syndrome Scale. QTc - QT interval corrected for heart rate. QIDS – Quick Inventory of Depressive Symptomatology.</p>", "links"=>[], "tags"=>["regression", "coefficients", "treatment-outcome"], "article_id"=>164217, "categories"=>["Biological Sciences", "Mathematics", "Mental Health", "Genetics"], "users"=>["Daniel E. Adkins", "Renan P. Souza", "Karolina Åberg", "Shaunna L. Clark", "Joseph L. McClay", "Patrick F. Sullivan", "Edwin J. C. G. van den Oord"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055239.t002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_the_average_multiple_regression_correlation_coefficients_for_the_treatment_outcome_combinations_/164217", "title"=>"Summary of the average multiple regression correlation coefficients for the treatment-outcome combinations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-06 01:10:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/482796", "https://ndownloader.figshare.com/files/482810"], "description"=>"<div><p>Only a subset of patients will typically respond to any given prescribed drug. The time it takes clinicians to declare a treatment ineffective leaves the patient in an impaired state and at unnecessary risk for adverse drug effects. Thus, diagnostic tests robustly predicting the most effective and safe medication for each patient prior to starting pharmacotherapy would have tremendous clinical value. In this article, we evaluated the use of genetic markers to estimate ancestry as a predictive component of such diagnostic tests. We first estimated each patient’s unique mosaic of ancestral backgrounds using genome-wide SNP data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) (n = 765) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) (n = 1892). Next, we performed multiple regression analyses to estimate the predictive power of these ancestral dimensions. For 136/89 treatment-outcome combinations tested in CATIE/STAR*D, results indicated 1.67/1.84 times higher median test statistics than expected under the null hypothesis assuming no predictive power (<em>p</em><0.01, both samples). Thus, ancestry showed robust and pervasive correlations with drug efficacy and side effects in both CATIE and STAR*D. Comparison of the marginal predictive power of MDS ancestral dimensions and self-reported race indicated significant improvements to model fit with the inclusion of MDS dimensions, but mixed evidence for self-reported race. Knowledge of each patient’s unique mosaic of ancestral backgrounds provides a potent immediate starting point for developing algorithms identifying the most effective and safe medication for a wide variety of drug-treatment response combinations. As relatively few new psychiatric drugs are currently under development, such personalized medicine offers a promising approach toward optimizing pharmacotherapy for psychiatric conditions.</p> </div>", "links"=>[], "tags"=>["genotype-based", "ancestral", "efficacy", "effects", "treatments", "catie"], "article_id"=>155929, "categories"=>["Biological Sciences", "Mathematics", "Mental Health", "Genetics"], "users"=>["Daniel E. Adkins", "Renan P. Souza", "Karolina Åberg", "Shaunna L. Clark", "Joseph L. McClay", "Patrick F. Sullivan", "Edwin J. C. G. van den Oord"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0055239.s001", "https://dx.doi.org/10.1371/journal.pone.0055239.s002"], "stats"=>{"downloads"=>9, "page_views"=>45, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Genotype_Based_Ancestral_Background_Consistently_Predicts_Efficacy_and_Side_Effects_across_Treatments_in_CATIE_and_STAR_D__/155929", "title"=>"Genotype-Based Ancestral Background Consistently Predicts Efficacy and Side Effects across Treatments in CATIE and STAR*D", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-02-06 01:38:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/493659"], "description"=>"<p>PANSS – Positive and Negative Syndrome Scale. QTc - QT interval corrected for heart rate. QIDS – Quick Inventory of Depressive Symptomatology.</p>", "links"=>[], "tags"=>["subjects", "assessed", "observations", "treatment-phenotype", "phenotypes", "tested"], "article_id"=>164170, "categories"=>["Biological Sciences", "Mathematics", "Mental Health", "Genetics"], "users"=>["Daniel E. Adkins", "Renan P. Souza", "Karolina Åberg", "Shaunna L. Clark", "Joseph L. McClay", "Patrick F. Sullivan", "Edwin J. C. G. van den Oord"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055239.t001", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_subjects_assessed_and_mean_number_of_observations_for_each_treatment_phenotype_comparison_We_present_only_a_summary_of_phenotypes_tested_in_this_study_/164170", "title"=>"Number of subjects assessed and mean number of observations for each treatment-phenotype comparison. We present only a summary of phenotypes tested in this study.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-06 01:09:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/493784"], "description"=>"<p>MDS – multidimensional scaling.</p><p>For both samples, in more models than expected by chance the marginal effect of the MDS dimensions significantly improved model fit, over and above self-reported ethnicity. Self-reported ethnicity showed mixed statistical evidence of improving model fit conditional on the MDS dimensions.</p>", "links"=>[], "tags"=>["marginal", "mds", "dimensions", "self-reported"], "article_id"=>164299, "categories"=>["Biological Sciences", "Mathematics", "Mental Health", "Genetics"], "users"=>["Daniel E. Adkins", "Renan P. Souza", "Karolina Åberg", "Shaunna L. Clark", "Joseph L. McClay", "Patrick F. Sullivan", "Edwin J. C. G. van den Oord"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055239.t004", "stats"=>{"downloads"=>4, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_F_tests_of_the_marginal_effects_of_unique_MDS_dimensions_and_self_reported_ethnicity_/164299", "title"=>"F-tests of the marginal effects of unique MDS dimensions and self-reported ethnicity.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-06 01:11:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/493434"], "description"=>"<p>Points represent ordered model fit F-test -log<sub>10</sub>(<i>p-</i>values), each of which quantifies the explanatory power of a model of 5 MDS ancestral dimensions predicting a measure of treatment response. The straight, dark grey lines represent the expected <i>p</i>-value distribution under the null hypothesis of no true associations. Light grey lines represent 95% confidence intervals for rejecting the null hypothesis at each <i>p</i>-value rank. The inflation parameter, lambda, is defined as the ratio of the median observed test statistic to the expected median under the null distribution; thus, lambda quantifies the degree to which the test statistic distribution systematically diverges from the null expectation of no significant effects.</p>", "links"=>[], "tags"=>["plots", "mds", "dimensions"], "article_id"=>163950, "categories"=>["Biological Sciences", "Mathematics", "Mental Health", "Genetics"], "users"=>["Daniel E. Adkins", "Renan P. Souza", "Karolina Åberg", "Shaunna L. Clark", "Joseph L. McClay", "Patrick F. Sullivan", "Edwin J. C. G. van den Oord"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055239.g001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Quantile_quantile_Q_Q_plots_for_the_joint_effects_of_MDS_dimensions_on_various_drug_response_measures_/163950", "title"=>"Quantile-quantile (Q-Q) plots for the joint effects of MDS dimensions on various drug response measures.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-06 01:05:50"}

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Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Molecular biology", "average_usage"=>[272, 466, 589, 702, 806, 903, 995, 1086, 1176, 1258, 1347, 1422, 1493]}, {"subject_area"=>"/Medicine and health sciences/Epidemiology", "average_usage"=>[263, 452, 568, 671, 758, 847, 921, 1023, 1101, 1187, 1264, 1340, 1394]}, {"subject_area"=>"/Medicine and health sciences/Mental health and psychiatry", "average_usage"=>[282, 468, 596, 706, 804, 895, 1000, 1081, 1175, 1272, 1356, 1445, 1531]}, {"subject_area"=>"/Medicine and health sciences/Pharmacology", "average_usage"=>[265, 471, 604, 723, 827, 926, 1022, 1113, 1210, 1290, 1377, 1454, 1532]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[259, 431, 541, 639, 727, 816, 898, 980, 1061, 1136, 1214, 1294, 1356]}]}
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