Genetic Predictors of Response to Serotonergic and Noradrenergic Antidepressants in Major Depressive Disorder: A Genome-Wide Analysis of Individual-Level Data and a Meta-Analysis
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{"title"=>"Genetic Predictors of Response to Serotonergic and Noradrenergic Antidepressants in Major Depressive Disorder: A Genome-Wide Analysis of Individual-Level Data and a Meta-Analysis", "type"=>"journal", "authors"=>[{"first_name"=>"Katherine E.", "last_name"=>"Tansey", "scopus_author_id"=>"35171626800"}, {"first_name"=>"Michel", "last_name"=>"Guipponi", "scopus_author_id"=>"6603864187"}, {"first_name"=>"Nader", "last_name"=>"Perroud", "scopus_author_id"=>"23470451800"}, {"first_name"=>"Guido", "last_name"=>"Bondolfi", "scopus_author_id"=>"6701771718"}, {"first_name"=>"Enrico", "last_name"=>"Domenici", "scopus_author_id"=>"6603420961"}, {"first_name"=>"David", "last_name"=>"Evans", "scopus_author_id"=>"7601570762"}, {"first_name"=>"Stephanie K.", "last_name"=>"Hall", "scopus_author_id"=>"7402710922"}, {"first_name"=>"Joanna", "last_name"=>"Hauser", "scopus_author_id"=>"7202945962"}, {"first_name"=>"Neven", "last_name"=>"Henigsberg", "scopus_author_id"=>"55933046400"}, {"first_name"=>"Xiaolan", "last_name"=>"Hu", "scopus_author_id"=>"55441093700"}, {"first_name"=>"Borut", "last_name"=>"Jerman", "scopus_author_id"=>"8586872400"}, {"first_name"=>"Wolfgang", "last_name"=>"Maier", "scopus_author_id"=>"36046783700"}, {"first_name"=>"Ole", "last_name"=>"Mors", "scopus_author_id"=>"7004006411"}, {"first_name"=>"Michael", "last_name"=>"O'Donovan", "scopus_author_id"=>"7103147367"}, {"first_name"=>"Tim J.", "last_name"=>"Peters", "scopus_author_id"=>"55675982000"}, {"first_name"=>"Anna", "last_name"=>"Placentino", "scopus_author_id"=>"7801331319"}, {"first_name"=>"Marcella", "last_name"=>"Rietschel", "scopus_author_id"=>"16741658400"}, {"first_name"=>"Daniel", "last_name"=>"Souery", "scopus_author_id"=>"7003619771"}, {"first_name"=>"Katherine J.", "last_name"=>"Aitchison", "scopus_author_id"=>"7003415672"}, {"first_name"=>"Ian", "last_name"=>"Craig", "scopus_author_id"=>"7102548208"}, {"first_name"=>"Anne", "last_name"=>"Farmer", "scopus_author_id"=>"7102158824"}, {"first_name"=>"Jens R.", "last_name"=>"Wendland", "scopus_author_id"=>"12761086700"}, {"first_name"=>"Alain", "last_name"=>"Malafosse", "scopus_author_id"=>"7006578437"}, {"first_name"=>"Peter", "last_name"=>"Holmans", "scopus_author_id"=>"57172024600"}, {"first_name"=>"Glyn", "last_name"=>"Lewis", "scopus_author_id"=>"7402636617"}, {"first_name"=>"Cathryn M.", "last_name"=>"Lewis", "scopus_author_id"=>"7402975073"}, {"first_name"=>"Tine Bryan", "last_name"=>"Stensbøl", "scopus_author_id"=>"6603819475"}, {"first_name"=>"Shitij", "last_name"=>"Kapur", "scopus_author_id"=>"7102708793"}, {"first_name"=>"Peter", "last_name"=>"McGuffin", "scopus_author_id"=>"22954119700"}, {"first_name"=>"Rudolf", "last_name"=>"Uher", "scopus_author_id"=>"6701847176"}], "year"=>2012, "source"=>"PLoS Medicine", "identifiers"=>{"issn"=>"15491277", "scopus"=>"2-s2.0-84868096278", "sgr"=>"84868096278", "pui"=>"365953481", "isbn"=>"10.1371/journal.pmed.1001326", "pmid"=>"23091423", "doi"=>"10.1371/journal.pmed.1001326"}, "id"=>"54727415-2fc2-38d6-abfc-bf9f6c680b0b", "abstract"=>"BACKGROUND: It has been suggested that outcomes of antidepressant treatment for major depressive disorder could be significantly improved if treatment choice is informed by genetic data. This study aims to test the hypothesis that common genetic variants can predict response to antidepressants in a clinically meaningful way.\\n\\nMETHODS AND FINDINGS: The NEWMEDS consortium, an academia-industry partnership, assembled a database of over 2,000 European-ancestry individuals with major depressive disorder, prospectively measured treatment outcomes with serotonin reuptake inhibiting or noradrenaline reuptake inhibiting antidepressants and available genetic samples from five studies (three randomized controlled trials, one part-randomized controlled trial, and one treatment cohort study). After quality control, a dataset of 1,790 individuals with high-quality genome-wide genotyping provided adequate power to test the hypotheses that antidepressant response or a clinically significant differential response to the two classes of antidepressants could be predicted from a single common genetic polymorphism. None of the more than half million genetic markers significantly predicted response to antidepressants overall, serotonin reuptake inhibitors, or noradrenaline reuptake inhibitors, or differential response to the two types of antidepressants (genome-wide significance p<5×10(-8)). No biological pathways were significantly overrepresented in the results. No significant associations (genome-wide significance p<5×10(-8)) were detected in a meta-analysis of NEWMEDS and another large sample (STAR*D), with 2,897 individuals in total. Polygenic scoring found no convergence among multiple associations in NEWMEDS and STAR*D.\\n\\nCONCLUSIONS: No single common genetic variant was associated with antidepressant response at a clinically relevant level in a European-ancestry cohort. Effects specific to particular antidepressant drugs could not be investigated in the current study. Please see later in the article for the Editors' Summary.", "link"=>"http://www.mendeley.com/research/genetic-predictors-response-serotonergic-noradrenergic-antidepressants-major-depressive-disorder-gen", "reader_count"=>77, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Librarian"=>4, "Student > Doctoral Student"=>8, "Researcher"=>11, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>3, "Student > Master"=>13, "Other"=>10, "Student > Bachelor"=>3, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Librarian"=>4, "Student > Doctoral Student"=>8, "Researcher"=>11, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>3, "Student > Master"=>13, "Other"=>10, "Student > Bachelor"=>3, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>9, "Engineering"=>1, "Nursing and Health Professions"=>2, "Biochemistry, Genetics and Molecular Biology"=>1, "Medicine and Dentistry"=>30, "Agricultural and Biological Sciences"=>14, "Neuroscience"=>2, "Physics and Astronomy"=>1, "Psychology"=>12, "Social Sciences"=>3, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>30}, "Neuroscience"=>{"Neuroscience"=>2}, "Social Sciences"=>{"Social Sciences"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>12}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>14}, "Computer Science"=>{"Computer Science"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>9}}, "reader_count_by_country"=>{"Belgium"=>1, "United States"=>3, "China"=>1, "United Kingdom"=>1, "Italy"=>1, "France"=>1, "Germany"=>1}, "group_count"=>6}

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  • {"files"=>["https://ndownloader.figshare.com/files/557349"], "description"=>"<p>The red line indicates the genome-wide significance level (<i>p</i><5.0×10<sup>−8</sup>), and the absence of data points above this line indicates the lack of significant associations in the four analyses: (A) analysis of the whole sample (<i>n</i> = 1,790); (B) analysis of SRI-treated individuals (<i>n</i> = 1,222); (C) analysis of NRI-treated individuals (<i>n</i> = 568); (D) gene-by-drug interaction analysis in the randomly allocated individuals (<i>n</i> = 949).</p>", "links"=>[], "tags"=>["plots", "genome-wide", "pharmacogenetic", "analyses", "chromosome"], "article_id"=>227803, "categories"=>["Mental Health", "Pharmacology"], "users"=>["Katherine E. Tansey", "Michel Guipponi", "Nader Perroud", "Guido Bondolfi", "Enrico Domenici", "David Evans", "Stephanie K. Hall", "Joanna Hauser", "Neven Henigsberg", "Xiaolan Hu", "Borut Jerman", "Wolfgang Maier", "Ole Mors", "Michael O'Donovan", "Tim J. Peters", "Anna Placentino", "Marcella Rietschel", "Daniel Souery", "Katherine J. Aitchison", "Ian Craig", "Anne Farmer", "Jens R. Wendland", "Alain Malafosse", "Peter Holmans", "Glyn Lewis", "Cathryn M. Lewis", "Tine Bryan Stensbøl", "Shitij Kapur", "Peter McGuffin", "Rudolf Uher"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001326.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Manhattan_plots_for_the_genome_wide_pharmacogenetic_analyses_showing_results_by_log10_p_value_and_chromosome_location_/227803", "title"=>"Manhattan plots for the genome-wide pharmacogenetic analyses showing results by −log10 <i>p</i>-value and chromosome location.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-16 02:10:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/557025"], "description"=>"<p>Flow of samples through quality control.</p>", "links"=>[], "tags"=>["samples"], "article_id"=>227478, "categories"=>["Mental Health", "Pharmacology"], "users"=>["Katherine E. Tansey", "Michel Guipponi", "Nader Perroud", "Guido Bondolfi", "Enrico Domenici", "David Evans", "Stephanie K. Hall", "Joanna Hauser", "Neven Henigsberg", "Xiaolan Hu", "Borut Jerman", "Wolfgang Maier", "Ole Mors", "Michael O'Donovan", "Tim J. Peters", "Anna Placentino", "Marcella Rietschel", "Daniel Souery", "Katherine J. Aitchison", "Ian Craig", "Anne Farmer", "Jens R. Wendland", "Alain Malafosse", "Peter Holmans", "Glyn Lewis", "Cathryn M. Lewis", "Tine Bryan Stensbøl", "Shitij Kapur", "Peter McGuffin", "Rudolf Uher"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001326.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Flow_of_samples_through_quality_control_/227478", "title"=>"Flow of samples through quality control.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-16 02:04:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/298024", "https://ndownloader.figshare.com/files/298249"], "description"=>"<div><h3>Background</h3><p>It has been suggested that outcomes of antidepressant treatment for major depressive disorder could be significantly improved if treatment choice is informed by genetic data. This study aims to test the hypothesis that common genetic variants can predict response to antidepressants in a clinically meaningful way.</p> <h3>Methods and Findings</h3><p>The NEWMEDS consortium, an academia–industry partnership, assembled a database of over 2,000 European-ancestry individuals with major depressive disorder, prospectively measured treatment outcomes with serotonin reuptake inhibiting or noradrenaline reuptake inhibiting antidepressants and available genetic samples from five studies (three randomized controlled trials, one part-randomized controlled trial, and one treatment cohort study). After quality control, a dataset of 1,790 individuals with high-quality genome-wide genotyping provided adequate power to test the hypotheses that antidepressant response or a clinically significant differential response to the two classes of antidepressants could be predicted from a single common genetic polymorphism. None of the more than half million genetic markers significantly predicted response to antidepressants overall, serotonin reuptake inhibitors, or noradrenaline reuptake inhibitors, or differential response to the two types of antidepressants (genome-wide significance <em>p</em><5×10<sup>−8</sup>). No biological pathways were significantly overrepresented in the results. No significant associations (genome-wide significance <em>p</em><5×10<sup>−8</sup>) were detected in a meta-analysis of NEWMEDS and another large sample (STAR*D), with 2,897 individuals in total. Polygenic scoring found no convergence among multiple associations in NEWMEDS and STAR*D.</p> <h3>Conclusions</h3><p>No single common genetic variant was associated with antidepressant response at a clinically relevant level in a European-ancestry cohort. Effects specific to particular antidepressant drugs could not be investigated in the current study.</p> <p> <em>Please see later in the article for the Editors' Summary</em></p> </div>", "links"=>[], "tags"=>["predictors", "serotonergic", "noradrenergic", "antidepressants", "depressive", "genome-wide", "individual-level", "meta-analysis"], "article_id"=>118750, "categories"=>["Mental Health", "Pharmacology"], "users"=>["Katherine E. Tansey", "Michel Guipponi", "Nader Perroud", "Guido Bondolfi", "Enrico Domenici", "David Evans", "Stephanie K. Hall", "Joanna Hauser", "Neven Henigsberg", "Xiaolan Hu", "Borut Jerman", "Wolfgang Maier", "Ole Mors", "Michael O'Donovan", "Tim J. Peters", "Anna Placentino", "Marcella Rietschel", "Daniel Souery", "Katherine J. Aitchison", "Ian Craig", "Anne Farmer", "Jens R. Wendland", "Alain Malafosse", "Peter Holmans", "Glyn Lewis", "Cathryn M. Lewis", "Tine Bryan Stensbøl", "Shitij Kapur", "Peter McGuffin", "Rudolf Uher"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001326.s001", "https://dx.doi.org/10.1371/journal.pmed.1001326.s002"], "stats"=>{"downloads"=>31, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Genetic_Predictors_of_Response_to_Serotonergic_and_Noradrenergic_Antidepressants_in_Major_Depressive_Disorder_A_Genome_Wide_Analysis_of_Individual_Level_Data_and_a_Meta_Analysis/118750", "title"=>"Genetic Predictors of Response to Serotonergic and Noradrenergic Antidepressants in Major Depressive Disorder: A Genome-Wide Analysis of Individual-Level Data and a Meta-Analysis", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-10-16 02:25:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/557193"], "description"=>"<p>(A) Analysis of the whole sample (<i>n</i> = 1,790); (B) analysis of SRI-treated individuals (<i>n</i> = 1,222); (C) analysis of NRI-treated individuals (<i>n</i> = 568); (D) gene-by-drug interaction analysis in the randomly allocated individuals (<i>n</i> = 949). The shaded area is the 95% confidence interval of values expected under a uniform distribution.</p>", "links"=>[], "tags"=>["plots", "genome-wide"], "article_id"=>227647, "categories"=>["Mental Health", "Pharmacology"], "users"=>["Katherine E. Tansey", "Michel Guipponi", "Nader Perroud", "Guido Bondolfi", "Enrico Domenici", "David Evans", "Stephanie K. Hall", "Joanna Hauser", "Neven Henigsberg", "Xiaolan Hu", "Borut Jerman", "Wolfgang Maier", "Ole Mors", "Michael O'Donovan", "Tim J. Peters", "Anna Placentino", "Marcella Rietschel", "Daniel Souery", "Katherine J. Aitchison", "Ian Craig", "Anne Farmer", "Jens R. Wendland", "Alain Malafosse", "Peter Holmans", "Glyn Lewis", "Cathryn M. Lewis", "Tine Bryan Stensbøl", "Shitij Kapur", "Peter McGuffin", "Rudolf Uher"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001326.g002", "stats"=>{"downloads"=>4, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Quantile_8211_quantile_plots_for_the_four_genome_wide_analyses_/227647", "title"=>"Quantile–quantile plots for the four genome-wide analyses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-16 02:07:27"}

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