Browsing by Author "Binder, Elisabeth B"

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    ItemOpen Access
    GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.
    (The American journal of psychiatry, 2023-10) Docherty, Anna R; Mullins, Niamh; Ashley-Koch, Allison E; Qin, Xuejun; Coleman, Jonathan RI; Shabalin, Andrey; Kang, JooEun; Murnyak, Balasz; Wendt, Frank; Adams, Mark; Campos, Adrian I; DiBlasi, Emily; Fullerton, Janice M; Kranzler, Henry R; Bakian, Amanda V; Monson, Eric T; Rentería, Miguel E; Walss-Bass, Consuelo; Andreassen, Ole A; Behera, Chittaranjan; Bulik, Cynthia M; Edenberg, Howard J; Kessler, Ronald C; Mann, J John; Nurnberger, John I; Pistis, Giorgio; Streit, Fabian; Ursano, Robert J; Polimanti, Renato; Dennis, Michelle; Garrett, Melanie; Hair, Lauren; Harvey, Philip; Hauser, Elizabeth R; Hauser, Michael A; Huffman, Jennifer; Jacobson, Daniel; Madduri, Ravi; McMahon, Benjamin; Oslin, David W; Trafton, Jodie; Awasthi, Swapnil; Berrettini, Wade H; Bohus, Martin; Chang, Xiao; Chen, Hsi-Chung; Chen, Wei J; Christensen, Erik D; Crow, Scott; Duriez, Philibert; Edwards, Alexis C; Fernández-Aranda, Fernando; Galfalvy, Hanga; Gandal, Michael; Gorwood, Philip; Guo, Yiran; Hafferty, Jonathan D; Hakonarson, Hakon; Halmi, Katherine A; Hishimoto, Akitoyo; Jain, Sonia; Jamain, Stéphane; Jiménez-Murcia, Susana; Johnson, Craig; Kaplan, Allan S; Kaye, Walter H; Keel, Pamela K; Kennedy, James L; Kim, Minsoo; Klump, Kelly L; Levey, Daniel F; Li, Dong; Liao, Shih-Cheng; Lieb, Klaus; Lilenfeld, Lisa; Marshall, Christian R; Mitchell, James E; Okazaki, Satoshi; Otsuka, Ikuo; Pinto, Dalila; Powers, Abigail; Ramoz, Nicolas; Ripke, Stephan; Roepke, Stefan; Rozanov, Vsevolod; Scherer, Stephen W; Schmahl, Christian; Sokolowski, Marcus; Starnawska, Anna; Strober, Michael; Su, Mei-Hsin; Thornton, Laura M; Treasure, Janet; Ware, Erin B; Watson, Hunna J; Witt, Stephanie H; Woodside, D Blake; Yilmaz, Zeynep; Zillich, Lea; Adolfsson, Rolf; Agartz, Ingrid; Alda, Martin; Alfredsson, Lars; Appadurai, Vivek; Artigas, María Soler; Van der Auwera, Sandra; Azevedo, M Helena; Bass, Nicholas; Bau, Claiton HD; Baune, Bernhard T; Bellivier, Frank; Berger, Klaus; Biernacka, Joanna M; Bigdeli, Tim B; Binder, Elisabeth B; Boehnke, Michael; Boks, Marco P; Braff, David L; Bryant, Richard; Budde, Monika; Byrne, Enda M; Cahn, Wiepke; Castelao, Enrique; Cervilla, Jorge A; Chaumette, Boris; Corvin, Aiden; Craddock, Nicholas; Djurovic, Srdjan; Foo, Jerome C; Forstner, Andreas J; Frye, Mark; Gatt, Justine M; Giegling, Ina; Grabe, Hans J; Green, Melissa J; Grevet, Eugenio H; Grigoroiu-Serbanescu, Maria; Gutierrez, Blanca; Guzman-Parra, Jose; Hamshere, Marian L; Hartmann, Annette M; Hauser, Joanna; Heilmann-Heimbach, Stefanie; Hoffmann, Per; Ising, Marcus; Jones, Ian; Jones, Lisa A; Jonsson, Lina; Kahn, René S; Kelsoe, John R; Kendler, Kenneth S; Kloiber, Stefan; Koenen, Karestan C; Kogevinas, Manolis; Krebs, Marie-Odile; Landén, Mikael; Leboyer, Marion; Lee, Phil H; Levinson, Douglas F; Liao, Calwing; Lissowska, Jolanta; Mayoral, Fermin; McElroy, Susan L; McGrath, Patrick; McGuffin, Peter; McQuillin, Andrew; Mehta, Divya; Melle, Ingrid; Mitchell, Philip B; Molina, Esther; Morken, Gunnar; Nievergelt, Caroline; Nöthen, Markus M; O'Donovan, Michael C; Ophoff, Roel A; Owen, Michael J; Pato, Carlos; Pato, Michele T; Penninx, Brenda WJH; Potash, James B; Power, Robert A; Preisig, Martin; Quested, Digby; Ramos-Quiroga, Josep Antoni; Reif, Andreas; Ribasés, Marta; Richarte, Vanesa; Rietschel, Marcella; Rivera, Margarita; Roberts, Andrea; Roberts, Gloria; Rouleau, Guy A; Rovaris, Diego L; Sanders, Alan R; Schofield, Peter R; Schulze, Thomas G; Scott, Laura J; Serretti, Alessandro; Shi, Jianxin; Sirignano, Lea; Sklar, Pamela; Smeland, Olav B; Smoller, Jordan W; Sonuga-Barke, Edmund JS; Trzaskowski, Maciej; Tsuang, Ming T; Turecki, Gustavo; Vilar-Ribó, Laura; Vincent, John B; Völzke, Henry; Walters, James TR; Weickert, Cynthia Shannon; Weickert, Thomas W; Weissman, Myrna M; Williams, Leanne M; Wray, Naomi R; Zai, Clement C; Agerbo, Esben; Børglum, Anders D; Breen, Gerome; Demontis, Ditte; Erlangsen, Annette; Gelernter, Joel; Glatt, Stephen J; Hougaard, David M; Hwu, Hai-Gwo; Kuo, Po-Hsiu; Lewis, Cathryn M; Li, Qingqin S; Liu, Chih-Min; Martin, Nicholas G; McIntosh, Andrew M; Medland, Sarah E; Mors, Ole; Nordentoft, Merete; Olsen, Catherine M; Porteous, David; Smith, Daniel J; Stahl, Eli A; Stein, Murray B; Wasserman, Danuta; Werge, Thomas; Whiteman, David C; Willour, Virginia; VA Million Veteran Program (MVP); MVP Suicide Exemplar Workgroup; Suicide Working Group of the Psychiatric Genomics Consortium; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium; Bipolar Disorder Working Group of the Psychiatric Genomics Consortium; Schizophrenia Working Group of the Psychiatric Genomics Consortium; Eating Disorder Working Group of the Psychiatric Genomics Consortium; German Borderline Genomics Consortium; Coon, Hilary; Beckham, Jean C; Kimbrel, Nathan A; Ruderfer, Douglas M

    Objective

    Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures.

    Methods

    This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses.

    Results

    Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors.

    Conclusions

    This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.
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    ItemOpen Access
    Pharmacogenomics-Driven Prediction of Antidepressant Treatment Outcomes: A Machine-Learning Approach With Multi-trial Replication.
    (Clinical pharmacology and therapeutics, 2019-10) Athreya, Arjun P; Neavin, Drew; Carrillo-Roa, Tania; Skime, Michelle; Biernacka, Joanna; Frye, Mark A; Rush, A John; Wang, Liewei; Binder, Elisabeth B; Iyer, Ravishankar K; Weinshilboum, Richard M; Bobo, William V
    We set out to determine whether machine learning-based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN-AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine-learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN-AMPS patients, with comparable prediction accuracies > 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.
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    ItemOpen Access
    Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings.
    (Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2021-06) Athreya, Arjun P; Brückl, Tanja; Binder, Elisabeth B; John Rush, A; Biernacka, Joanna; Frye, Mark A; Neavin, Drew; Skime, Michelle; Monrad, Ditlev; Iyer, Ravishankar K; Mayes, Taryn; Trivedi, Madhukar; Carter, Rickey E; Wang, Liewei; Weinshilboum, Richard M; Croarkin, Paul E; Bobo, William V
    Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians' ability to accurately predict a specific patient's eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming.