[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_13":166},"routes/kb",{"_9":10,"_11":12,"_13":14,"_15":16},"owner","coga","slug","coga-kb","session",{"_158":72,"_159":32,"_160":32,"_161":32,"_33":27,"_162":27,"_163":27,"_164":72,"_165":27},"kb",{"_11":12,"_17":10,"_18":19,"_20":21,"_22":23,"_24":25,"_26":27,"_28":27,"_29":30,"_31":32,"_33":27,"_34":27,"_35":27,"_36":27,"_37":38,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":27},"owner_name","description","Collaborative Study on the Genetics of Alcoholism","readme","# About COGA\r\n\r\nSince 1989, researchers from across the country have worked together to identify genes that increase or decrease the risk of alcohol use disorder (AUD), the progression of AUD, and factors related to treatment and remission/recovery. These findings inform prevention and treatment of AUD. \r\n\r\nMore than 725 scientific papers have been written using the COGA data!\r\n\r\nFor more information on COGA, please see here: https://cogastudy.org/","readme_html","

About COGA

Since 1989, researchers from across the country have worked together to identify genes that increase or decrease the risk of alcohol use disorder (AUD), the progression of AUD, and factors related to treatment and remission/recovery. These findings inform prevention and treatment of AUD.

More than 725 scientific papers have been written using the COGA data!

For more information on COGA, please see here: https://cogastudy.org/

\n","system_prompt","==\r\nYou are a research agent that specializes in the genetics of alcohol use disorder (AUD) the underlying molecular mechanisms of AUD. The COGA project (Collaborative Study on the Genetics of Alcoholism) has worked to identify genes that increase or decrease the risk of alcohol use disorder (AUD), the progression of AUD, and factors related to treatment and remission/recovery. These findings inform prevention and treatment of AUD.\r\n\r\nYou have access to a knowledge base (KB) with over 600 AUD papers from the COGA project and the papers that they have cited. Query these papers to find information so you can better answer questions from the user.","is_public",true,"auto_import_cited","created_at","2026-03-13T09:22:37.488478Z","role","","chat_enabled","literature_map","author_map","citation_map","active_jobs",0,"stats",{"_147":148,"_149":150,"_151":152,"_60":153,"_154":155,"_156":157},"highlighted",[52,53,54,55],"zotero_syncs",[],"summary","This knowledge base compiles 644 primary papers that map the genetic and neurobiological architecture of substance‑use disorders (alcohol, nicotine, cannabis, opioid, cocaine) and their comorbid psychiatric traits across 12 research areas. The core themes are large‑scale GWAS and polygenic‑risk‑score (PRS) analyses that identify dozens of shared loci and a common externalizing factor linking alcohol‑use disorder (AUD), other substance‑use phenotypes, PTSD, schizophrenia and related traits. Parallel neurophysiological work—resting‑state EEG, ERP components (P3, theta power), cortical‑thickness and functional‑connectivity measures—serves as endophenotypes that mediate genetic risk and track developmental trajectories in family‑based cohorts (e.g., COGA) and population biobanks. Several groups integrate longitudinal data, trauma exposure, parenting, social support and COVID‑19 stress to show how environmental factors moderate polygenic effects and influence remission, suicide risk, and intergenerational transmission. Methodological advances (gene‑based PRS for admixed samples, PCA to curb collider bias, GAWMerge data integration, multivariate GWAS critiques) broaden ancestry coverage and improve predictive accuracy. Functional validation using iPSC‑derived neurons, CRISPR up‑regulation, and electrophysiology links specific variants (e.g., KCNJ6, OPRM1, ADH1B) to neuronal excitability and behavior. \n\nCollectively, the database can address questions such as: which genetic variants confer cross‑substance liability; how polygenic scores predict onset, severity, and remission across development; what brain‑based biomarkers mediate these effects; and how psychosocial contexts amplify or attenuate genetic risk, especially in diverse populations.","summary_html","

This knowledge base compiles 644 primary papers that map the genetic and neurobiological architecture of substance‑use disorders (alcohol, nicotine, cannabis, opioid, cocaine) and their comorbid psychiatric traits across 12 research areas. The core themes are large‑scale GWAS and polygenic‑risk‑score (PRS) analyses that identify dozens of shared loci and a common externalizing factor linking alcohol‑use disorder (AUD), other substance‑use phenotypes, PTSD, schizophrenia and related traits. Parallel neurophysiological work—resting‑state EEG, ERP components (P3, theta power), cortical‑thickness and functional‑connectivity measures—serves as endophenotypes that mediate genetic risk and track developmental trajectories in family‑based cohorts (e.g., COGA) and population biobanks. Several groups integrate longitudinal data, trauma exposure, parenting, social support and COVID‑19 stress to show how environmental factors moderate polygenic effects and influence remission, suicide risk, and intergenerational transmission. Methodological advances (gene‑based PRS for admixed samples, PCA to curb collider bias, GAWMerge data integration, multivariate GWAS critiques) broaden ancestry coverage and improve predictive accuracy. Functional validation using iPSC‑derived neurons, CRISPR up‑regulation, and electrophysiology links specific variants (e.g., KCNJ6, OPRM1, ADH1B) to neuronal excitability and behavior.

Collectively, the database can address questions such as: which genetic variants confer cross‑substance liability; how polygenic scores predict onset, severity, and remission across development; what brain‑based biomarkers mediate these effects; and how psychosocial contexts amplify or attenuate genetic risk, especially in diverse populations.

\n","summary_generated_at","2026-06-16T05:53:48Z","summary_stale",{"_56":126,"_58":127,"_60":128,"_62":129,"_64":130,"_66":131,"_68":132,"_70":32,"_71":27,"_133":134},{"_56":110,"_58":111,"_60":112,"_62":113,"_64":114,"_66":115,"_68":116,"_70":32,"_71":72},{"_56":94,"_58":95,"_60":96,"_62":63,"_64":65,"_66":97,"_68":98,"_70":32,"_71":72},{"_56":57,"_58":59,"_60":61,"_62":63,"_64":65,"_66":67,"_68":69,"_70":32,"_71":72},"id","79bee4c0-ef78-45cb-b8a5-7cb831f50d08","title","The Collaborative Study on the Genetics of Alcoholism: Overview.","authors",[73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93],"journal","Genes, brain, and behavior","pub_year",2023,"pmid","37736010","doi","10.1111/gbb.12864","url","has_visual_abstract",false,"Agrawal, Arpana","Brislin, Sarah J","Bucholz, Kathleen K","Dick, Danielle","Hart, Ronald P","Johnson, Emma C","Meyers, Jacquelyn","Salvatore, Jessica","Slesinger, Paul","COGA Collaborators","Almasy, Laura","Foroud, Tatiana","Goate, Alison","Hesselbrock, Victor","Kramer, John","Kuperman, Samuel","Merikangas, Alison K","Nurnberger, John I","Tischfield, Jay","Edenberg, Howard J","Porjesz, Bernice","07c8ecd4-e56c-4e3b-b811-2acc26811a5a","The collaborative study on the genetics of alcoholism: Genetics.",[78,99,100,89,90,101,102,103,104,82,105,106,88,84,107,108,93,109,92,73],"37387240","10.1111/gbb.12856","Salvatore, Jessica E","Lai, Dongbing","Tischfield, Jay A","Xuei, Xiaoling","Kamarajan, Chella","Wetherill, Leah","Rice, John P","Kramer, John R","Slesinger, Paul A","Goate, Alison M","Dick, Danielle M","3bb45e32-b880-4dcf-939c-4333595b3a91","Alcohol Use Disorder Polygenic Score Compared With Family History and ADH1B.",[100,117,118,119,120,109,103,121,90,122,123,124,93,125,84],"JAMA network open",2024,"39786404","10.1001/jamanetworkopen.2024.52705","Zhang, Michael","Abreu, Marco","Schwantes-An, Tae-Hwi","Chan, Grace","Kuang, Weipeng","Plawecki, Martin H","Rice, John","Schuckit, Marc","Liu, Yunlong","6dae9ecb-2fac-4c08-8cbb-5eb7168a25e3","Integrated single-cell multiomic profiling of caudate nucleus suggests key mechanisms in alcohol use disorder.",[135,136,137,138,139,100,140,102,141,142,143,108,144,107,77,101,73,145,146,92,125],"Nature communications",2025,"41083468","10.1038/s41467-025-64136-0","visual_abstract_url","/coga/coga-kb/docs/6dae9ecb-2fac-4c08-8cbb-5eb7168a25e3/visual-abstract","Green, Nicholas C","Gao, Hongyu","Chu, Xiaona","Yuan, Qiuyue","McGuire, Patrick","Jiang, Guanglong","Reiter, Jill L","Stevens, Julia","Sutherland, Greg T","Pang, Zhiping P","Wang, Yue","Duren, Zhana","docs_total",67477,"docs_primary",644,"docs_cited",66833,2453,"entities",2347,"claims",5455,"authenticated","username","display_name","system_role","zotero_import","semantic_scholar","must_change_password","paper_chat",{"_158":72,"_159":32,"_160":32,"_161":32,"_33":27,"_162":27,"_163":27,"_164":72,"_165":27}]