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1,694 results on '"McGrath, Patrick J."'

2. Cover

Author

McGrath, Patrick J.

Published
2019

6. Notes

Author

McGrath, Patrick J.

Published
2019

15. EEG measures of brain arousal in relation to symptom improvement in patients with major depressive disorder: Results from a randomized placebo-controlled clinical trial

Author

Ulke, Christine, Kayser, Jürgen, Tenke, Craig E, Mergl, Roland, Sander, Christian, Panier, Lidia YX, Alvarenga, Jorge E, Fava, Maurizio, McGrath, Patrick J, Deldin, Patricia J, McInnis, Melvin G, Trivedi, Madhukar H, Weissman, Myrna M, Pizzagalli, Diego A, Hegerl, Ulrich, and Bruder, Gerard E

Published
2024
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16. Challenges of Parents with Children with Intellectual and Neurodevelopmental Disorders during COVID-19: Experiences and Their Impact on Mental Health

Author

Kaltenbach, Elisa, Xiong, Ting, Thomson, Donna, and McGrath, Patrick J.

Abstract

Background: Parents of children with intellectual and neurodevelopmental disorders are a highly burdened group that faces additional challenges during the COVID-19 pandemic. Methods: In this study, parents of children with intellectual and neurodevelopmental disorders (N = 600) living in Canada participated in a cross-sectional online survey. Results: More than 96% of the parents reported facing additional challenges due to the pandemic. COVID-19-related stressors correlated with poorer mental and physical health. Parents who reported a higher amount of COVID-19-related stressors also reported more post-traumatic stress disorder (PTSD) symptoms. Discussion: Resuming and implementing new services for children with intellectual and neurodevelopmental disorders and their parents will be key in alleviating the challenges for those parents.

Published
2023
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17. An electroencephalographic signature predicts antidepressant response in major depression

Author

Wu, Wei, Zhang, Yu, Jiang, Jing, Lucas, Molly V, Fonzo, Gregory A, Rolle, Camarin E, Cooper, Crystal, Chin-Fatt, Cherise, Krepel, Noralie, Cornelssen, Carena A, Wright, Rachael, Toll, Russell T, Trivedi, Hersh M, Monuszko, Karen, Caudle, Trevor L, Sarhadi, Kamron, Jha, Manish K, Trombello, Joseph M, Deckersbach, Thilo, Adams, Phil, McGrath, Patrick J, Weissman, Myrna M, Fava, Maurizio, Pizzagalli, Diego A, Arns, Martijn, Trivedi, Madhukar H, and Etkin, Amit

Subjects
Serious Mental Illness, Neurosciences, Mental Health, Major Depressive Disorder, Depression, Clinical Research, Brain Disorders, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Mental health, Good Health and Well Being, Antidepressive Agents, Depressive Disorder, Major, Double-Blind Method, Electroencephalography, Humans, Machine Learning, Membrane Potentials, Models, Neurological, Predictive Value of Tests, Prefrontal Cortex, Reproducibility of Results, Sertraline, Transcranial Magnetic Stimulation, Treatment Outcome
Abstract

Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.

Published
2020

18. Brain regulation of emotional conflict predicts antidepressant treatment response for depression

Author

Fonzo, Gregory A, Etkin, Amit, Zhang, Yu, Wu, Wei, Cooper, Crystal, Chin-Fatt, Cherise, Jha, Manish K, Trombello, Joseph, Deckersbach, Thilo, Adams, Phil, McInnis, Melvin, McGrath, Patrick J, Weissman, Myrna M, Fava, Maurizio, and Trivedi, Madhukar H

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Clinical Trials and Supportive Activities, Neurosciences, Brain Disorders, Clinical Research, Mind and Body, Behavioral and Social Science, Mental Health, Depression, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Mental health, Good Health and Well Being, Antidepressive Agents, Antidepressive Agents, Second-Generation, Brain, Clinical Trials as Topic, Depressive Disorder, Major, Humans, Magnetic Resonance Imaging, Neuroimaging, Treatment Outcome, Biomedical and clinical sciences, Health sciences
Abstract

The efficacy of antidepressant treatment for depression is controversial due to the only modest superiority demonstrated over placebo. However, neurobiological heterogeneity within depression may limit overall antidepressant efficacy. We sought to identify a neurobiological phenotype responsive to antidepressant treatment by testing pretreatment brain activation during response to, and regulation of, emotional conflict as a moderator of the clinical benefit of the antidepressant sertraline versus placebo. Using neuroimaging data from a large randomized controlled trial, we found widespread moderation of clinical benefits by brain activity during regulation of emotional conflict, in which greater downregulation of conflict-responsive regions predicted better sertraline outcomes. Treatment-predictive machine learning using brain metrics outperformed a model trained on clinical and demographic variables. Our findings demonstrate that antidepressant response is predicted by brain activity underlying a key self-regulatory emotional capacity. Leveraging brain-based measures in psychiatry will forge a path toward better treatment personalization, refined mechanistic insights and improved outcomes.

Published
2019

22. Neural substrates of emotional conflict with anxiety in major depressive disorder: Findings from the Establishing Moderators and biosignatures of Antidepressant Response in Clinical Care (EMBARC) randomized controlled trial

Author

Trombello, Joseph M., Cooper, Crystal M., Fatt, Cherise Chin, Grannemann, Bruce D., Carmody, Thomas J., Jha, Manish K., Mayes, Taryn L., Greer, Tracy L., Yezhuvath, Uma, Aslan, Sina, Pizzagalli, Diego A., Weissman, Myrna M., Webb, Christian A., Dillon, Daniel G., McGrath, Patrick J., Fava, Maurizio, Parsey, Ramin V., McInnis, Melvin G., Etkin, Amit, and Trivedi, Madhukar H.

Published
2022
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24. The mental health and traumatic experiences of mothers of children with 22q11DS

Author

Finless, Alexandra, primary, Rideout, Andrea L., additional, Xiong, Ting, additional, Carbyn, Holly, additional, Lingley-Pottie, Patricia, additional, Palmer, Lisa D., additional, Shugar, Andrea, additional, McDonald-McGinn, Donna M., additional, McGrath, Patrick J., additional, Bassett, Anne S., additional, Cytrynbaum, Cheryl, additional, Orr, Matt, additional, Swillen, Ann, additional, and Meier, Sandra, additional

Published
2024
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33. An expert-generated tool for assessing policy capacity

Author

Lawrence, Logan M., Fierlbeck, Katherine, McGrath, Patrick J., and Curran, Janet A.

Subjects
Medical policy, Government
Abstract

'Policy capacity' describes the ability of policy-making systems to do quality work. Better-quality policy work leads to more successful policies. There have been limited attempts to operationalize theoretical understanding of policy capacity, resulting in a lack of shared understanding and assessment metrics. In this Delphi study, health policy experts validated an existing multidimensional conceptual framework for policy capacity. Factors that contribute to policy success were identified and rated to represent aspects of policy capacity, and arranged within the conceptual framework, as were factor indicators. The resulting tool provides expert-generated items for transparently and systematically assessing policy capacity. La << capacite strategique >> definit la capacite des systemes de prise de decisions a faire un travail de qualite. Un meilleur travail de reflexion politique engendre des politiques plus efficaces. Les tentatives d'operationnaliser les connaissances theoriques de la capacite strategique ont ete limitees, ce qui a conduit a un manque de comprehension commune et de mesures devaluation. Dans cette etude Delphi, des specialistes en politique de sante ont valide un cadre conceptuel multidimensionnel preexistant pour la capacite strategique. Les facteurs contribuant a Telaboration d'une politique efficace ont ete identifies et classes afin de representer les divers aspects de la capacite strategique, puis organises au sein du cadre conceptuel, ainsi que les indicateurs de facteurs. L'outil qui en resulte fournit des elements etablis par les experts pour evaluer la capacite strategique de maniere transparente et systematique., Introduction For over two decades, Canadian policy makers have wondered how to better advance the public good in light of the increasing complexity of public problems, technological change, and a [...]

Published
2020

34. Discovery and replication of cerebral blood flow differences in major depressive disorder

Author

Cooper, Crystal M., Chin Fatt, Cherise R., Liu, Peiying, Grannemann, Bruce D., Carmody, Thomas, Almeida, Jorge R. C., Deckersbach, Thilo, Fava, Maurizio, Kurian, Benji T., Malchow, Ashley L., McGrath, Patrick J., McInnis, Melvin, Oquendo, Maria A., Parsey, Ramin V., Bartlett, Elizabeth, Weissman, Myrna, Phillips, Mary L., Lu, Hanzhang, and Trivedi, Madhukar H.

Published
2020
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35. Reward related ventral striatal activity and differential response to sertraline versus placebo in depressed individuals

Author

Greenberg, Tsafrir, Fournier, Jay C., Stiffler, Richelle, Chase, Henry W., Almeida, Jorge R., Aslam, Haris, Deckersbach, Thilo, Cooper, Crystal, Toups, Marisa S., Carmody, Tom, Kurian, Benji, Peltier, Scott, Adams, Phillip, McInnis, Melvin G., Oquendo, Maria A., Fava, Maurizio, Parsey, Ramin, McGrath, Patrick J., Weissman, Myrna, Trivedi, Madhukar, and Phillips, Mary L.

Published
2020
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36. The Structure Of Drinking Motives in First Nations Adolescents in Nova Scotia

Author

Mushquash, Christopher J., Stewart, Sherry H., Comeau, Nancy, and McGrath, Patrick J.

Abstract

Objective: The factor structure of the Drinking Motives Questionnaire-Revised (DMQ-R; Cooper, 1994) was examined in a sample of First Nations (i.e., Mi'kmaq) adolescents. Results: Exploratory principal components analysis indicated a three-factor structure (conformity, coping, and positive reinforcement motives), with the positive reinforcement motives of enhancement and social motives not separating into the expected two distinct factors. Moreover, community informants (e.g., school personnel) anecdotally indicated possible wording problems with some of the social motive items for the cultural group. A qualitative methodology--focus group interviews with Mi'kmaq adolescents--was used to explore potential reasons for these observed differences in the structure of drinking motives from previous findings in the majority culture (i.e., a measurement problem vs. a real difference in the structure of drinking motives in the Mi'kmaq culture). Conclusions: Qualitative findings support the interpretation that a true social motive for alcohol use does not exist in this cultural/age group and that drinking in social contexts for this group seems less motivated by social affiliation than by enhancement motives (e.g., drinking to party). (Contains 3 tables.)

Published
2008

38. Hospital Variation in Management and Outcomes of Acute Respiratory Distress Syndrome Due to COVID-19

Author

Johnson, Shelsey W., Garcia, Michael A., Sisson, Emily K. Q., Sheldrick, Christopher R., Kumar, Vishakha K., Boman, Karen, Bolesta, Scott, Bansal, Vikas, Lal, Amos, Domecq, J. P., Melamed, Roman R., Christie, Amy B., Husain, Abdurrahman, Yus, Santiago, Gajic, Ognjen, Kashyap, Rahul, Walkey, Allan J., Gavidia, Oscar Y., Pachon, Felipe, Sanchez, Yeimy A., Kassas, Mohamed El, Badr, Mohamed, Tawheed, Ahmed, Tawheed, Ahmed, Yahia, Hend, Matsuda, Wataru, Suzuki, Reina, Suzuki, Reina, Sanui, Masamitsu, Horikita, Sho, Itagaki, Yuki, Kodate, Akira, Suzuki, Reina, Kodate, Akira, Takahashi, Yuki, Moriki, Koyo, Sohaib, Muhammad, Syed, Asghar Mashaal, Anosh, Syed, Naqvi, Ali, Zabolotskikh, Borisovich, Zybin, Konstantin Dmitrievich, Sinkov, Sergey Vasilevich, Musaeva, Tatiana Sergeevna, Almazyad, Mohammed A., Alarifi, Mohammed I., Macarambon, Jara M., Bukhari, Ahmad Abdullah, Albahrani, Hussain A., Asfina, Kazi N., Aldossary, Kaltham M., Zdravkovic, Marija, Todorovic, Zoran, Popadic, Viseslav, Klasnja, Slobodan, Stevanovic, Predrag D., Stojakov, Dejan S., Ignjatovic, Duska K., Bojic, Suzana C., Bobos, Marina M., Nenadic, Irina B., Zaric, Milica S., Djuric, Marko D., Djukic, Vladimir R., Yus, Santiago, Martin, Belen C., Sili, Uluhan, Bilgin, Huseyin, Ay, Pinar, Melamed, Roman R., Tierney, David M., Patel, Love A., Raj, Vino S., Dawud, Barite U., Mazumder, Narayana, Sidebottom, Abbey, Guenther, Alena M., Krehbiel, Benjamin D., Schmitz, Nova J., Jepsen, Stacy L., Sipsey, Lynn, Schulte, Anna, Wunderlich, Whitney, Hoyt, Cecely, Dodd, Kenneth W., Goodmanson, Nicholas, Hesse, Kathleen, Bird, Paige, Weinert, Chauncey, Schoenrade, Nathan, Altaher, Abdulrahman, Mayar, Esmael, Aronson, Matthew, Cooper, Tyler, Logan, Monica, Miner, Brianna, Papo, Gisele, Al-Fares, Abdulrahman, Kharbanda, Anmol, Jhajhria, Sunil, Fyffe, Zachary, Christie, Amy B., Ashley, Dennis W., Adiga, Rajani, Newsome, Andrea Sikora, Forehand, Christy C., Bruning, Rebecca, Jones, Timothy W., Sabov, Moldovan, Zaidi, Fatema, Tissavirasingham, Fiona, Malipeddi, Dhatri, Banner-Goodspeed, Valerie M., Bose, Somnath, Kelly, Lauren E., Joseph, Melisa, McGourty, Marie, Capers, Krystal, Hoenig, Benjamin, Karamourtopoulos, Maria C., Law, Anica C., Baedorf Kassis, Elias N., Walkey, Allan J., Waikar, Sushrut S., Garcia, Michael A., Colona, Mia, Kibbelaar, Zoe, Leong, Michael, Wallman, Daniel, Soni, Kanupriya, Maccarone, Jennifer, Gilman, Joshua, Devis, Ycar, Chung, Joseph, Paracha, Munizay, Lumelsky, David N., DiLorenzo, Madeline, Abdurrahman, Najla, Johnson, Shelsey, Zammit, Kimberly, McGrath, Patrick J., Loeffler, William, Chilbert, Maya R., Mesland, Jean-Baptiste, Henin, Pierre, Petre, Hélène, Buelens, Isabelle, Gerard, Anne-Catherine, Ihle, Rayan E., Martin, Shelda A., Davis, Elaine A., Surani, Salim, White, Joshua, Khan, Aftab, Dhahwal, Rahul, Cheruku, Sreekanth, Ahmed, Farzin, Deonarine, Christopher, Jones, Ashley, Shaikh, Mohammad-Ali, Preston, David, Chin, Jeanette, Daugherty, Steven K., Atkinson, Sam, Shrimpton, Kelly, Bartz, Raquel R., Krishnamoorthy, Vijay, Kraft, Bryan, Pulsipher, Aaron, Friedman, Eugene, Mehta, Sachin, Yamane, David P., Benjenk, Ivy, Prasanna, Nivedita, Nault, Katharine, Reilkoff, Ronald A., Heneghan, Julia A., Eichen, Sarah, Goertzen, Lexie, Rajala, Scott, Feussom, Ghislaine, Tang, Ben, Arizona, Mayo Clinic, Cartin-Ceba, Rodrigo, Sen, Ayan, Palacios, Amanda, Mahdi, Giyth M., Kashyap, Rahul, Gajic, Ognjen, Bansal, Vikas, Tekin, Aysun, Lal, Amos, O’Horo, John C., Deo, Neha N., Sharma, Mayank, Qamar, Shahraz, Domecq, Juan Pablo, Singh, Romil, Niven, Alex, Clinic, Mayo, Claire, Eau, La Nou, Abigail T., Bogojevic, Marija, Mullen, Barbara, Sanghavi, Devang, Guru, Pramod, Franco, Pablo Moreno, Gnanapandithan, Karthik, Saunders, Hollie, Fleissner, Zachary, Garcia, Juan, Yu Lee Mateus, Alejandra, Yarrarapu, Siva Naga, Kaur, Nirmaljot, Giri, Abhisekh, Khan, Syed Anjum, Jain, Nitesh Kumar, Koritala, Thoyaja, Venkata, Chakradhar, Engemann, Miriam, Mantese, Annamarie, Tarabichi, Yasir, Perzynski, Adam, Wang, Christine, Kotekal, Dhatri, Aulakh, Bhagat S., Tripathi, Sandeep, Bandy, Jennifer A., Kreps, Lisa M., Bollinger, Dawn R., Bandy, Jennifer A., Stienecker, Roger Scott, Melendez, Andre G., Brunner, Tressa A., Budzon, Sue M., Heffernan, Jessica L., Souder, Janelle M., Miller, Tracy L., Maisonneuve, Andrea G., Vilella, Antonia L., Kutner, Sara B., Clark, Kacie, Moore, Danielle, Anderson, Harry L., III, Rajkumar, Dixy, Abunayla, Ali, Heiter, Jerrilyn, Zaren, Howard A., Smith, Stephanie J., Lewis, Grant C., Seames, Lauren, Farlow, Cheryl, Miller, Judy, Broadstreet, Gloria, Martinez, Anthony, Allison, Micheal, Mittal, Aniket, Ruiz, Rafael, Skaanland, Aleta, Ross, Robert, Gupta, Neha, Jones, Tracy L., Ayers, Shonda C., Harrell, Amy B., Brown, Dr. Brent R., Husain, Abdurrahman, Malhotra, Atul, Zawaydeh, Qais, Markotić, Dragana, Bošnjak, Ivana, Vail, Emily A., Nicholson, Susannah, Jonas, Rachelle B., Dement, AnnaRose E., Tang, William, DeRosa, Mark, Villarreal, Robert E., McGonagill, Patrick W., Galet, Colette, Hubbard, Janice, Wang, David, Allan, Lauren, Badheka, Aditya, Chegondi, Madhuradhar, Alvarez, Roger A., Alarcon-Calderon, Amarilys, Sosa, Marie Anne, Mahabir, Sunita K., Patel, Mausam J., Shah, Faraaz Ali, Chuan, Byron, Rawal, Sagar L., Piracha, Manal, Tonna, Joseph E., Levin, Nicholas M., Suslavich, Kayte, Tsolinas, Rachel, Fica, Zachary T., Skidmore, Chloe R., Stapleton, Renee D., Dixon, Anne E., Johnson, Olivia, Ardren, Sara S., Burns, Stephanie, Raymond, Anna, Gonyaw, Erika, Hodgdon, Kevin, Housenger, Chloe, Lin, Benjamin, McQuesten, Karen, Pecott-Grimm, Heidi, Sweet, Julie, Ventrone, Sebastian, Akhter, Murtaza, Rahman, Rania Abdul, Mulrow, Mary, Khanna, Ashish K., Harris, Lynne, Cusson, Bruce, Fowler, Jacob, Vaneenenaam, David, McKinney, Glen, Udoh, Imoh, Johnson, Kathleen, Pattan, Vishwanath, Papke, Jessica, Jimada, Ismail, Mhid, Nida, and Chakola, Samuel

Published
2022
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39. Cerebral Blood Perfusion Predicts Response to Sertraline versus Placebo for Major Depressive Disorder in the EMBARC Trial

Author

Cooper, Crystal M., Chin Fatt, Cherise R., Jha, Manish, Fonzo, Gregory A., Grannemann, Bruce D., Carmody, Thomas, Ali, Aasia, Aslan, Sina, Almeida, Jorge R.C., Deckersbach, Thilo, Fava, Maurizio, Kurian, Benji T., McGrath, Patrick J., McInnis, Melvin, Parsey, Ramin V., Weissman, Myrna, Phillips, Mary L., Lu, Hanzhang, Etkin, Amit, and Trivedi, Madhukar H.

Published
2019
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40. Anxiety and anhedonia in depression: Associations with neuroticism and cognitive control

Author

Liao, Allen, Walker, Robrina, Carmody, Thomas J., Cooper, Crystal, Shaw, Meredith A., Grannemann, Bruce D., Adams, Phil, Bruder, Gerard E., McInnis, Melvin G., Webb, Christian A., Dillon, Daniel G., Pizzagalli, Diego A., Phillips, Mary L., Kurian, Benji T., Fava, Maurizio, Parsey, Ramin V., McGrath, Patrick J., Weissman, Myrna M., and Trivedi, Madhukar H.

Published
2019
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46. Characterizing anxiety subtypes and the relationship to behavioral phenotyping in major depression: Results from the EMBARC study

Author

Trombello, Joseph M., Pizzagalli, Diego A., Weissman, Myrna M., Grannemann, Bruce D., Cooper, Crystal M., Greer, Tracy L., Malchow, Ashley L., Jha, Manish K., Carmody, Thomas J., Kurian, Benji T., Webb, Christian A., Dillon, Daniel G., McGrath, Patrick J., Bruder, Gerard, Fava, Maurizio, Parsey, Ramin V., McInnis, Melvin G., Adams, Phil, and Trivedi, Madhukar H.

Published
2018
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49. Test-retest reliability of cerebral blood flow in healthy individuals using arterial spin labeling: Findings from the EMBARC study

Author

Almeida, Jorge R.C., Greenberg, Tsafrir, Lu, Hanzhang, Chase, Henry W., Fournier, Jay C., Cooper, Crystal M., Deckersbach, Thilo, Adams, Phil, Carmody, Thomas, Fava, Maurizio, Kurian, Benji, McGrath, Patrick J., McInnis, Melvin G., Oquendo, Maria A., Parsey, Ramin, Weissman, Myrna, Trivedi, Madhukar, and Phillips, Mary L.

Published
2018
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50. A genome-wide association study of a sustained pattern of antidepressant response

Author

Hunter, Aimee M, Leuchter, Andrew F, Power, Robert A, Muthén, Bengt, McGrath, Patrick J, Lewis, Cathryn M, Cook, Ian A, Garriock, Holly A, McGuffin, Peter, Uher, Rudolf, and Hamilton, Steven P

Subjects
Brain Disorders, Clinical Trials and Supportive Activities, Human Genome, Clinical Research, Genetics, Mental Health, Prevention, Depression, Aetiology, 2.1 Biological and endogenous factors, Mental health, Antidepressive Agents, Clinical Trials as Topic, Confidence Intervals, Depressive Disorder, Major, Genome-Wide Association Study, Genotype, Humans, Odds Ratio, Pharmacogenetics, Polymorphism, Single Nucleotide, Antidepressant, STAR*D, GENDEP, Growth mixture modeling, Citalopram, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Genome-wide association studies (GWAS) have failed to replicate common genetic variants associated with antidepressant response, as defined using a single endpoint. Genetic influences may be discernible by examining individual variation between sustained versus unsustained patterns of response, which may distinguish medication effects from non-specific, or placebo responses to active medication. We conducted a GWAS among 1116 subjects with Major Depressive Disorder from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial who were characterized using Growth Mixture Modeling as showing a sustained versus unsustained pattern of clinical response over 12 weeks of treatment with citalopram. Replication analyses examined 585 subjects from the Genome-based Therapeutic Drugs for Depression (GENDEP) trial. The strongest association with sustained as opposed to unsustained response in STAR*D involved a single nucleotide polymorphism (SNP; rs10492002) within the acyl-CoA synthetase short-chain family member 3 gene (ACSS3, p-value=4.5×10(-6), odds ratio=0.61). No SNPs met our threshold for genome-wide significance. SNP data were available in GENDEP for 18 of the top 25 SNPs in STAR*D. The most replicable association was with SNP rs7816924 (p=0.008, OR=1.58); no SNP met the replication p-value threshold of 0.003. Joint analysis of these 18 SNPs resulted in the strongest signal coming from rs7816924 (p=2.11×10(-7)), which resides in chondroitin sulfate N-acetylgalactosaminyltransferase 1 gene (CSGALNACT1). An exploratory genetic pathway analysis revealed evidence for an involvement of the KEGG pathway of long-term potentiation (FDR=.02). Results suggest novel genetic associations to sustained response.

Published
2013

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