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2. The Human Phenotype Ontology in 2024: phenotypes around the world

Author

Gargano, Michael A, Matentzoglu, Nicolas, Coleman, Ben, Addo-Lartey, Eunice B, Anagnostopoulos, Anna V, Anderton, Joel, Avillach, Paul, Bagley, Anita M, Bakštein, Eduard, Balhoff, James P, Baynam, Gareth, Bello, Susan M, Berk, Michael, Bertram, Holli, Bishop, Somer, Blau, Hannah, Bodenstein, David F, Botas, Pablo, Boztug, Kaan, Čady, Jolana, Callahan, Tiffany J, Cameron, Rhiannon, Carbon, Seth J, Castellanos, Francisco, Caufield, J Harry, Chan, Lauren E, Chute, Christopher G, Cruz-Rojo, Jaime, Dahan-Oliel, Noémi, Davids, Jon R, de Dieuleveult, Maud, de Souza, Vinicius, de Vries, Bert BA, de Vries, Esther, DePaulo, J Raymond, Derfalvi, Beata, Dhombres, Ferdinand, Diaz-Byrd, Claudia, Dingemans, Alexander JM, Donadille, Bruno, Duyzend, Michael, Elfeky, Reem, Essaid, Shahim, Fabrizzi, Carolina, Fico, Giovanna, Firth, Helen V, Freudenberg-Hua, Yun, Fullerton, Janice M, Gabriel, Davera L, Gilmour, Kimberly, Giordano, Jessica, Goes, Fernando S, Moses, Rachel Gore, Green, Ian, Griese, Matthias, Groza, Tudor, Gu, Weihong, Guthrie, Julia, Gyori, Benjamin, Hamosh, Ada, Hanauer, Marc, Hanušová, Kateřina, He, Yongqun, Hegde, Harshad, Helbig, Ingo, Holasová, Kateřina, Hoyt, Charles Tapley, Huang, Shangzhi, Hurwitz, Eric, Jacobsen, Julius OB, Jiang, Xiaofeng, Joseph, Lisa, Keramatian, Kamyar, King, Bryan, Knoflach, Katrin, Koolen, David A, Kraus, Megan L, Kroll, Carlo, Kusters, Maaike, Ladewig, Markus S, Lagorce, David, Lai, Meng-Chuan, Lapunzina, Pablo, Laraway, Bryan, Lewis-Smith, David, Li, Xiarong, Lucano, Caterina, Majd, Marzieh, Marazita, Mary L, Martinez-Glez, Victor, McHenry, Toby H, McInnis, Melvin G, McMurry, Julie A, Mihulová, Michaela, Millett, Caitlin E, Mitchell, Philip B, Moslerová, Veronika, Narutomi, Kenji, Nematollahi, Shahrzad, and Nevado, Julian

Subjects
Biological Sciences, Genetics, Networking and Information Technology R&D (NITRD), Human Genome, Machine Learning and Artificial Intelligence, Good Health and Well Being, Humans, Biological Ontologies, Phenotype, Genomics, Algorithms, Rare Diseases, Environmental Sciences, Information and Computing Sciences, Developmental Biology, Biological sciences, Chemical sciences, Environmental sciences
Abstract

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

Published
2024

3. Focal adhesion is associated with lithium response in bipolar disorder: evidence from a network-based multi-omics analysis

Author

Niemsiri, Vipavee, Rosenthal, Sara Brin, Nievergelt, Caroline M, Maihofer, Adam X, Marchetto, Maria C, Santos, Renata, Shekhtman, Tatyana, Alliey-Rodriguez, Ney, Anand, Amit, Balaraman, Yokesh, Berrettini, Wade H, Bertram, Holli, Burdick, Katherine E, Calabrese, Joseph R, Calkin, Cynthia V, Conroy, Carla, Coryell, William H, DeModena, Anna, Eyler, Lisa T, Feeder, Scott, Fisher, Carrie, Frazier, Nicole, Frye, Mark A, Gao, Keming, Garnham, Julie, Gershon, Elliot S, Goes, Fernando S, Goto, Toyomi, Harrington, Gloria J, Jakobsen, Petter, Kamali, Masoud, Kelly, Marisa, Leckband, Susan G, Lohoff, Falk W, McCarthy, Michael J, McInnis, Melvin G, Craig, David, Millett, Caitlin E, Mondimore, Francis, Morken, Gunnar, Nurnberger, John I, Donovan, Claire O’, Øedegaard, Ketil J, Ryan, Kelly, Schinagle, Martha, Shilling, Paul D, Slaney, Claire, Stapp, Emma K, Stautland, Andrea, Tarwater, Bruce, Zandi, Peter P, Alda, Martin, Fisch, Kathleen M, Gage, Fred H, and Kelsoe, John R

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical and Health Psychology, Clinical Sciences, Psychology, Serious Mental Illness, Genetics, Mental Health, Brain Disorders, Human Genome, Mental health, Bipolar Disorder, Humans, Lithium, Gene Regulatory Networks, Focal Adhesions, Transcriptome, Genome-Wide Association Study, Neurons, Induced Pluripotent Stem Cells, Pharmacogenetics, Antimanic Agents, Male, Female, Lithium Compounds, Gene Expression Profiling, Genomics, Multiomics, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (Phypergeometric = 1.28E-09 and 4.10E-18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD.

Published
2024

7. 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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12. Passive Sensing of Affective and Cognitive Functioning in Mood Disorders by Analyzing Keystroke Kinematics and Speech Dynamics

Author

Hussain, Faraz, Stange, Jonathan P., Langenecker, Scott A., McInnis, Melvin G., Zulueta, John, Piscitello, Andrea, Ross, Mindy K., Demos, Alexander P., Vesel, Claudia, Rashidisabet, Homa, Cao, Bokai, Huang, He, Yu, Philip S., Nelson, Peter, Ajilore, Olusola A., Leow, Alex, Reuter, Martin, Series Editor, Montag, Christian, Series Editor, and Baumeister, Harald, editor

Published
2023
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14. Circadian rhythms in bipolar disorder patient-derived neurons predict lithium response: preliminary studies

Author

Mishra, Himanshu K, Ying, Noelle M, Luis, Angelica, Wei, Heather, Nguyen, Metta, Nakhla, Timothy, Vandenburgh, Sara, Alda, Martin, Berrettini, Wade H, Brennand, Kristen J, Calabrese, Joseph R, Coryell, William H, Frye, Mark A, Gage, Fred H, Gershon, Elliot S, McInnis, Melvin G, Nievergelt, Caroline M, Nurnberger, John I, Shilling, Paul D, Oedegaard, Ketil J, Zandi, Peter P, Kelsoe, John R, Welsh, David K, and McCarthy, Michael J

Subjects
Depression, Sleep Research, Bipolar Disorder, Mental Health, Serious Mental Illness, Neurosciences, Brain Disorders, Mental health, Circadian Rhythm, Humans, Lithium, Lithium Compounds, Neurons, Pharmacogenomics of Bipolar Disorder Study, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Bipolar disorder (BD) is a neuropsychiatric illness defined by recurrent episodes of mania/hypomania, depression and circadian rhythm abnormalities. Lithium is an effective drug for BD, but 30-40% of patients fail to respond adequately to treatment. Previous work has demonstrated that lithium affects the expression of "clock genes" and that lithium responders (Li-R) can be distinguished from non-responders (Li-NR) by differences in circadian rhythms. However, circadian rhythms have not been evaluated in BD patient neurons from Li-R and Li-NR. We used induced pluripotent stem cells (iPSCs) to culture neuronal precursor cells (NPC) and glutamatergic neurons from BD patients characterized for lithium responsiveness and matched controls. We identified strong circadian rhythms in Per2-luc expression in NPCs and neurons from controls and Li-R, but NPC rhythms in Li-R had a shorter circadian period. Li-NR rhythms were low amplitude and profoundly weakened. In NPCs and neurons, expression of PER2 was higher in both BD groups compared to controls. In neurons, PER2 protein levels were higher in BD than controls, especially in Li-NR samples. In single cells, NPC and neuron rhythms in both BD groups were desynchronized compared to controls. Lithium lengthened period in Li-R and control neurons but failed to alter rhythms in Li-NR. In contrast, temperature entrainment increased amplitude across all groups, and partly restored rhythms in Li-NR neurons. We conclude that neuronal circadian rhythm abnormalities are present in BD and most pronounced in Li-NR. Rhythm deficits in BD may be partly reversible through stimulation of entrainment pathways.

Published
2021

16. Contributions of circadian clock genes to cell survival in fibroblast models of lithium-responsive bipolar disorder

Author

Mishra, Himanshu K., Wei, Heather, Rohr, Kayla E., Ko, Insu, Nievergelt, Caroline M., Maihofer, Adam X., Shilling, Paul D., Alda, Martin, Berrettini, Wade H., Brennand, Kristen J., Calabrese, Joseph R., Coryell, William H., Frye, Mark, Gage, Fred, Gershon, Elliot, McInnis, Melvin G., Nurnberger, John, Oedegaard, Ketil J., Zandi, Peter P., Kelsoe, John R., and McCarthy, Michael J.

Published
2023
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18. When to Intervene: Detecting Abnormal Mood using Everyday Smartphone Conversations

Author

Gideon, John, Matton, Katie, Anderau, Steve, McInnis, Melvin G, and Provost, Emily Mower

Subjects
Computer Science - Machine Learning, Computer Science - Human-Computer Interaction, Statistics - Machine Learning
Abstract

Bipolar disorder (BPD) is a chronic mental illness characterized by extreme mood and energy changes from mania to depression. These changes drive behaviors that often lead to devastating personal or social consequences. BPD is managed clinically with regular interactions with care providers, who assess mood, energy levels, and the form and content of speech. Recent work has proposed smartphones for monitoring mood using speech. However, these works do not predict when to intervene. Predicting when to intervene is challenging because there is not a single measure that is relevant for every person: different individuals may have different levels of symptom severity considered typical. Additionally, this typical mood, or baseline, may change over time, making a single symptom threshold insufficient. This work presents an innovative approach that expands clinical mood monitoring to predict when interventions are necessary using an anomaly detection framework, which we call Temporal Normalization. We first validate the model using a dataset annotated for clinical interventions and then incorporate this method in a deep learning framework to predict mood anomalies from natural, unstructured, telephone speech data. The combination of these approaches provides a framework to enable real-world speech-focused mood monitoring., Comment: Submitted to IEEE Transactions on Affective Computing

Published
2019

19. Jointly Aligning and Predicting Continuous Emotion Annotations

Author

Khorram, Soheil, McInnis, Melvin G, and Provost, Emily Mower

Subjects
Computer Science - Machine Learning, Computer Science - Human-Computer Interaction, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning
Abstract

Time-continuous dimensional descriptions of emotions (e.g., arousal, valence) allow researchers to characterize short-time changes and to capture long-term trends in emotion expression. However, continuous emotion labels are generally not synchronized with the input speech signal due to delays caused by reaction-time, which is inherent in human evaluations. To deal with this challenge, we introduce a new convolutional neural network (multi-delay sinc network) that is able to simultaneously align and predict labels in an end-to-end manner. The proposed network is a stack of convolutional layers followed by an aligner network that aligns the speech signal and emotion labels. This network is implemented using a new convolutional layer that we introduce, the delayed sinc layer. It is a time-shifted low-pass (sinc) filter that uses a gradient-based algorithm to learn a single delay. Multiple delayed sinc layers can be used to compensate for a non-stationary delay that is a function of the acoustic space. We test the efficacy of this system on two common emotion datasets, RECOLA and SEWA, and show that this approach obtains state-of-the-art speech-only results by learning time-varying delays while predicting dimensional descriptors of emotions., Comment: IEEE Transactions on Affective Computing

Published
2019

20. Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)

Author

Gideon, John, McInnis, Melvin G, and Provost, Emily Mower

Subjects
Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning
Abstract

Automatic speech emotion recognition provides computers with critical context to enable user understanding. While methods trained and tested within the same dataset have been shown successful, they often fail when applied to unseen datasets. To address this, recent work has focused on adversarial methods to find more generalized representations of emotional speech. However, many of these methods have issues converging, and only involve datasets collected in laboratory conditions. In this paper, we introduce Adversarial Discriminative Domain Generalization (ADDoG), which follows an easier to train "meet in the middle" approach. The model iteratively moves representations learned for each dataset closer to one another, improving cross-dataset generalization. We also introduce Multiclass ADDoG, or MADDoG, which is able to extend the proposed method to more than two datasets, simultaneously. Our results show consistent convergence for the introduced methods, with significantly improved results when not using labels from the target dataset. We also show how, in most cases, ADDoG and MADDoG can be used to improve upon baseline state-of-the-art methods when target dataset labels are added and in-the-wild data are considered. Even though our experiments focus on cross-corpus speech emotion, these methods could be used to remove unwanted factors of variation in other settings.

Published
2019
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21. Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain

Author

Khorram, Soheil, McInnis, Melvin G, and Provost, Emily Mower

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computational Complexity, Statistics - Machine Learning
Abstract

DTW calculates the similarity or alignment between two signals, subject to temporal warping. However, its computational complexity grows exponentially with the number of time-series. Although there have been algorithms developed that are linear in the number of time-series, they are generally quadratic in time-series length. The exception is generalized time warping (GTW), which has linear computational cost. Yet, it can only identify simple time warping functions. There is a need for a new fast, high-quality multisequence alignment algorithm. We introduce trainable time warping (TTW), whose complexity is linear in both the number and the length of time-series. TTW performs alignment in the continuous-time domain using a sinc convolutional kernel and a gradient-based optimization technique. We compare TTW and GTW on 85 UCR datasets in time-series averaging and classification. TTW outperforms GTW on 67.1% of the datasets for the averaging tasks, and 61.2% of the datasets for the classification tasks., Comment: ICASSP 2019

Published
2019

28. Differential contributions of circadian clock genes to cell survival in bipolar disorder patient derived neuronal progenitor cells distinguishes lithium responders and non-responders

Author

Mishra, Himanshu K., primary, Wei, Heather, additional, LeRoux, Melissa, additional, Ko, Insu, additional, Rohr, Kayla E., additional, Nievergelt, Caroline M, additional, Maihofer, Adam X, additional, Shilling, Paul, additional, Alda, Martin, additional, Berrettini, Wade H, additional, Calabrese, Joseph R., additional, Coryell, William H., additional, Frye, Mark, additional, Gershon, Elliot, additional, McInnis, Melvin G., additional, Nurnberger, John, additional, Oedegaard, Ketil J., additional, Zandi, Peter P., additional, Kelsoe, John R., additional, and McCarthy, Michael J., additional

Published
2024
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30. Passive Sensing of Affective and Cognitive Functioning in Mood Disorders by Analyzing Keystroke Kinematics and Speech Dynamics

Author

Hussain, Faraz, primary, Stange, Jonathan P., additional, Langenecker, Scott A., additional, McInnis, Melvin G., additional, Zulueta, John, additional, Piscitello, Andrea, additional, Ross, Mindy K., additional, Demos, Alexander P., additional, Vesel, Claudia, additional, Rashidisabet, Homa, additional, Cao, Bokai, additional, Huang, He, additional, Yu, Philip S., additional, Nelson, Peter, additional, Ajilore, Olusola A., additional, and Leow, Alex, additional

Published
2022
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31. Chronotype and cellular circadian rhythms predict the clinical response to lithium maintenance treatment in patients with bipolar disorder

Author

McCarthy, Michael J, Wei, Heather, Nievergelt, Caroline M, Stautland, Andrea, Maihofer, Adam X, Welsh, David K, Shilling, Paul, Alda, Martin, Alliey-Rodriguez, Ney, Anand, Amit, Andreasson, Ole A, Balaraman, Yokesh, Berrettini, Wade H, Bertram, Holli, Brennand, Kristen J, Calabrese, Joseph R, Calkin, Cynthia V, Claasen, Ana, Conroy, Clara, Coryell, William H, Craig, David W, D’Arcangelo, Nicole, Demodena, Anna, Djurovic, Srdjan, Feeder, Scott, Fisher, Carrie, Frazier, Nicole, Frye, Mark A, Gage, Fred H, Gao, Keming, Garnham, Julie, Gershon, Elliot S, Glazer, Kara, Goes, Fernando, Goto, Toyomi, Harrington, Gloria, Jakobsen, Petter, Kamali, Masoud, Karberg, Elizabeth, Kelly, Marisa, Leckband, Susan G, Lohoff, Falk, McInnis, Melvin G, Mondimore, Francis, Morken, Gunnar, Nurnberger, John I, Obral, Sarah, Oedegaard, Ketil J, Ortiz, Abigail, Ritchey, Megan, Ryan, Kelly, Schinagle, Martha, Schoeyen, Helle, Schwebel, Candice, Shaw, Martha, Shekhtman, Tatyana, Slaney, Claire, Stapp, Emma, Szelinger, Szabolcs, Tarwater, Bruce, Zandi, Peter P, and Kelsoe, John R

Subjects
Bipolar Disorder, Depression, Sleep Research, Serious Mental Illness, Brain Disorders, Clinical Research, Mental Health, Mental health, Adult, Animals, Antimanic Agents, Cells, Cultured, Circadian Rhythm, Fibroblasts, Genotyping Techniques, Humans, Inositol 1, 4, 5-Trisphosphate Receptors, Lithium Compounds, Luminescent Measurements, Mice, NIH 3T3 Cells, Period Circadian Proteins, Polymorphism, Single Nucleotide, Prospective Studies, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Bipolar disorder (BD) is a serious mood disorder associated with circadian rhythm abnormalities. Risk for BD is genetically encoded and overlaps with systems that maintain circadian rhythms. Lithium is an effective mood stabilizer treatment for BD, but only a minority of patients fully respond to monotherapy. Presently, we hypothesized that lithium-responsive BD patients (Li-R) would show characteristic differences in chronotype and cellular circadian rhythms compared to lithium non-responders (Li-NR). Selecting patients from a prospective, multi-center, clinical trial of lithium monotherapy, we examined morning vs. evening preference (chronotype) as a dimension of circadian rhythm function in 193 Li-R and Li-NR BD patients. From a subset of 59 patient donors, we measured circadian rhythms in skin fibroblasts longitudinally over 5 days using a bioluminescent reporter (Per2-luc). We then estimated circadian rhythm parameters (amplitude, period, phase) and the pharmacological effects of lithium on rhythms in cells from Li-R and Li-NR donors. Compared to Li-NRs, Li-Rs showed a difference in chronotype, with higher levels of morningness. Evening chronotype was associated with increased mood symptoms at baseline, including depression, mania, and insomnia. Cells from Li-Rs were more likely to exhibit a short circadian period, a linear relationship between period and phase, and period shortening effects of lithium. Common genetic variation in the IP3 signaling pathway may account for some of the individual differences in the effects of lithium on cellular rhythms. We conclude that circadian rhythms may influence response to lithium in maintenance treatment of BD.

Published
2019

32. Entrainment of Circadian Rhythms to Temperature Reveals Amplitude Deficits in Fibroblasts from Patients with Bipolar Disorder and Possible Links to Calcium Channels

Author

Nudell, Victoria, Wei, Heather, Nievergelt, Caroline, Maihofer, Adam X, Shilling, Paul, Alda, Martin, Berrettini, Wade H, Brennand, Kristen J, Calabrese, Joseph R, Coryell, William H, Covault, Jonathan M, Frye, Mark A, Gage, Fred, Gershon, Elliot, McInnis, Melvin G, Nurnberger, John I, Oedegaard, Ketil J, Shekhtman, Tatyana, Zandi, Peter P, Kelsoe, John R, and McCarthy, Michael J

Subjects
Medical Physiology, Biomedical and Clinical Sciences, Bipolar Disorder, Sleep Research, Brain Disorders, Bipolar disorder, Calcium channel, Circadian rhythm, Gene expression
Abstract

Bipolar disorder (BD) is characterized by recurrent mood episodes, and circadian rhythm disturbances. Past studies have identified calcium channel genes as risk loci for BD. CACNA1C encodes an L-type calcium channel (LTCC) involved in the entrainment of circadian rhythms to light. Another calcium channel, i.e., the ryanodine receptor (RYR), is involved in -circadian phase delays. It is unknown whether variants in CACNA1C or other calcium channels contribute to the circadian phenotype in BD. We hypothesized that, by using temperature cycles, we could model circadian entrainment in fibroblasts from BD patients and controls to interrogate the circadian functions of LTCCs. Using Per2-luc, a bioluminescent reporter, we verified that cells entrain to temperature rhythms in vitro. Under constant temperature conditions, the LTCC antagonist verapamil shortened the circadian period, and the RYR antagonist dantrolene lengthened the period. However, neither drug affected temperature entrainment. Fibroblasts from BD patients and controls also entrained to temperature. In cells from BD patients, the rhythm amplitude was lower under entrained, but not constant, conditions. Temperature entrainment was otherwise similar between BD and control cells. However, the CACNA1C genotype among BD cells predicted the degree to which cells entrained. We conclude that assessment of rhythms under entrained conditions reveals additional rhythm abnormalities in BD that are not observable under constant temperature conditions.

Published
2019

33. 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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36. Human-Induced Pluripotent Stem Cell (iPSC)-Derived GABAergic Neuron Differentiation in Bipolar Disorder.

Author

Schill, Daniel J., Attili, Durga, DeLong, Cynthia J., McInnis, Melvin G., Johnson, Craig N., Murphy, Geoffrey G., and O'Shea, K. Sue

Subjects
INDUCED pluripotent stem cells, GABAERGIC neurons, PLURIPOTENT stem cells, GABA, STEM cells, INTERNEURONS, CHLORIDE channels
Abstract

Bipolar disorder (BP) is a recurring psychiatric condition characterized by alternating episodes of low energy (depressions) followed by manias (high energy). Cortical network activity produced by GABAergic interneurons may be critical in maintaining the balance in excitatory/inhibitory activity in the brain during development. Initially, GABAergic signaling is excitatory; with maturation, these cells undergo a functional switch that converts GABAA channels from depolarizing (excitatory) to hyperpolarizing (inhibitory), which is controlled by the intracellular concentration of two chloride transporters. The earliest, NKCC1, promotes chloride entry into the cell and depolarization, while the second (KCC2) stimulates movement of chloride from the neuron, hyperpolarizing it. Perturbations in the timing or expression of NKCC1/KCC2 may affect essential morphogenetic events including cell proliferation, migration, synaptogenesis and plasticity, and thereby the structure and function of the cortex. We derived induced pluripotent stem cells (iPSC) from BP patients and undiagnosed control (C) individuals, then modified a differentiation protocol to form GABAergic interneurons, harvesting cells at sequential stages of differentiation. qRT-PCR and RNA sequencing indicated that after six weeks of differentiation, controls transiently expressed high levels of NKCC1. Using multi-electrode array (MEA) analysis, we observed that BP neurons exhibit increased firing, network bursting and decreased synchrony compared to C. Understanding GABA signaling in differentiation may identify novel approaches and new targets for treatment of neuropsychiatric disorders such as BP. [ABSTRACT FROM AUTHOR]

Published
2024
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44. The Pharmacogenomics of Bipolar Disorder study (PGBD): identification of genes for lithium response in a prospective sample

Author

Oedegaard, Ketil J, Alda, Martin, Anand, Anit, Andreassen, Ole A, Balaraman, Yokesh, Berrettini, Wade H, Bhattacharjee, Abesh, Brennand, Kristen J, Burdick, Katherine E, Calabrese, Joseph R, Calkin, Cynthia V, Claasen, Ana, Coryell, William H, Craig, David, DeModena, Anna, Frye, Mark, Gage, Fred H, Gao, Keming, Garnham, Julie, Gershon, Elliot, Jakobsen, Petter, Leckband, Susan G, McCarthy, Michael J, McInnis, Melvin G, Maihofer, Adam X, Mertens, Jerome, Morken, Gunnar, Nievergelt, Caroline M, Nurnberger, John, Pham, Son, Schoeyen, Helle, Shekhtman, Tatyana, Shilling, Paul D, Szelinger, Szabolcs, Tarwater, Bruce, Yao, Jun, Zandi, Peter P, and Kelsoe, John R

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Health Sciences, Brain Disorders, Human Genome, Genetics, Depression, Mental Health, Clinical Research, Prevention, Behavioral and Social Science, Detection, screening and diagnosis, Evaluation of treatments and therapeutic interventions, 4.2 Evaluation of markers and technologies, 6.1 Pharmaceuticals, Mental health, Aged, Antidepressive Agents, Bipolar Disorder, Diagnostic and Statistical Manual of Mental Disorders, Female, Follow-Up Studies, Genome-Wide Association Study, Humans, Lithium Compounds, Male, Middle Aged, Pharmacogenetics, Prospective Studies, Retrospective Studies, Secondary Prevention, Valproic Acid, Bipolar disorder, Lithium, Mood stabilizer, GWAS, Prospective trial, Personalized medicine, Precision medicine, Clinical Sciences, Public Health and Health Services, Psychology, Psychiatry, Clinical sciences, Epidemiology, Clinical and health psychology
Abstract

BackgroundBipolar disorder is a serious and common psychiatric disorder characterized by manic and depressive mood switches and a relapsing and remitting course. The cornerstone of clinical management is stabilization and prophylaxis using mood-stabilizing medications to reduce both manic and depressive symptoms. Lithium remains the gold standard of treatment with the strongest data for both efficacy and suicide prevention. However, many patients do not respond to this medication, and clinically there is a great need for tools to aid the clinician in selecting the correct treatment. Large genome wide association studies (GWAS) investigating retrospectively the effect of lithium response are in the pipeline; however, few large prospective studies on genetic predictors to of lithium response have yet been conducted. The purpose of this project is to identify genes that are associated with lithium response in a large prospective cohort of bipolar patients and to better understand the mechanism of action of lithium and the variation in the genome that influences clinical response.Methods/designThis study is an 11-site prospective non-randomized open trial of lithium designed to ascertain a cohort of 700 subjects with bipolar I disorder who experience protocol-defined relapse prevention as a result of treatment with lithium monotherapy. All patients will be diagnosed using the Diagnostic Interview for Genetic Studies (DIGS) and will then enter a 2-year follow-up period on lithium monotherapy if and when they exhibit a score of 1 (normal, not ill), 2 (minimally ill) or 3 (mildly ill) on the Clinical Global Impressions of Severity Scale for Bipolar Disorder (CGI-S-BP Overall Bipolar Illness) for 4 of the 5 preceding weeks. Lithium will be titrated as clinically appropriate, not to exceed serum levels of 1.2 mEq/L. The sample will be evaluated longitudinally using a wide range of clinical scales, cognitive assessments and laboratory tests. On relapse, patients will be discontinued or crossed-over to treatment with valproic acid (VPA) or treatment as usual (TAU). Relapse is defined as a DSM-IV manic, major depressive or mixed episode or if the treating physician decides a change in medication is clinically necessary. The sample will be genotyped for GWAS. The outcome for lithium response will be analyzed as a time to event, where the event is defined as clinical relapse, using a Cox Proportional Hazards model. Positive single nucleotide polymorphisms (SNPs) from past genetic retrospective studies of lithium response, the Consortium on Lithium Genetics (ConLiGen), will be tested in this prospective study sample; a meta-analysis of these samples will then be performed. Finally, neurons will be derived from pluripotent stem cells from lithium responders and non-responders and tested in vivo for response to lithium by gene expression studies. SNPs in genes identified in these cellular studies will also be tested for association to response.DiscussionLithium is an extraordinarily important therapeutic drug in the clinical management of patients suffering from bipolar disorder. However, a significant proportion of patients, 30-40 %, fail to respond, and there is currently no method to identify the good lithium responders before initiation of treatment. Converging evidence suggests that genetic factors play a strong role in the variation of response to lithium, but only a few genes have been tested and the samples have largely been retrospective or quite small. The current study will collect an entirely unique sample of 700 patients with bipolar disorder to be stabilized on lithium monotherapy and followed for up to 2 years. This study will produce useful information to improve the understanding of the mechanism of action of lithium and will add to the development of a method to predict individual response to lithium, thereby accelerating recovery and reducing suffering and cost.Trial registrationClinicalTrials.gov Identifier: NCT01272531 Registered: January 6, 2011.

Published
2016

46. The Genetics of the Mood Disorder Spectrum: Genome-wide Association Analyses of More Than 185,000 Cases and 439,000 Controls

Author

Byrne, Enda M., Forstner, Andreas J., Holmans, Peter A., de Leeuw, Christiaan A., Mattheisen, Manuel, McQuillin, Andrew, Whitehead Pavlides, Jennifer M., Pers, Tune H., Ripke, Stephan, Stahl, Eli A., Steinberg, Stacy, Trubetskoy, Vassily, Trzaskowski, Maciej, Wang, Yunpeng, Abbott, Liam, Abdellaoui, Abdel, Adams, Mark J., Adolfsson, Annelie Nordin, Agerbo, Esben, Akil, Huda, Albani, Diego, Alliey-Rodriguez, Ney, Als, Thomas D., Andlauer, Till F.M., Anjorin, Adebayo, Antilla, Verneri, Van der Auwera, Sandra, Awasthi, Swapnil, Bacanu, Silviu-Alin, Badner, Judith A., Bækvad-Hansen, Marie, Barchas, Jack D., Bass, Nicholas, Bauer, Michael, Beekman, Aartjan T.F., Belliveau, Richard, Bergen, Sarah E., Bigdeli, Tim B., Binder, Elisabeth B., Bøen, Erlend, Boks, Marco, Boocock, James, Budde, Monika, Bunney, William, Burmeister, Margit, Buttenschøn, Henriette N., Bybjerg-Grauholm, Jonas, Byerley, William, Cai, Na, Casas, Miquel, Castelao, Enrique, Cerrato, Felecia, Cervantes, Pablo, Chambert, Kimberly, Charney, Alexander W., Chen, Danfeng, Christensen, Jane Hvarregaard, Churchhouse, Claire, St Clair, David, Clarke, Toni-Kim, Colodro-Conde, Lucía, Coryell, William, Couvy-Duchesne, Baptiste, Craig, David W., Crawford, Gregory E., Cruceanu, Cristiana, Czerski, Piotr M., Dale, Anders M., Davies, Gail, Deary, Ian J., Degenhardt, Franziska, Del-Favero, Jurgen, DePaulo, J Raymond, Derks, Eske M., Direk, Nese, Djurovic, Srdjan, Dobbyn, Amanda L., Dolan, Conor V., Dumont, Ashley, Dunn, Erin C., Eley, Thalia C., Elvsåshagen, Torbjørn, Escott-Price, Valentina, Fan, Chun Chieh, Finucane, Hilary K., Fischer, Sascha B., Flickinger, Matthew, Foo, Jerome C., Foroud, Tatiana M., Forty, Liz, Frank, Josef, Fraser, Christine, Freimer, Nelson B., Frisén, Louise, Gade, Katrin, Gage, Diane, Garnham, Julie, Giambartolomei, Claudia, Goes, Fernando S., Goldstein, Jaqueline, Gordon, Scott D., Gordon-Smith, Katherine, Green, Elaine K., Green, Melissa J., Greenwood, Tiffany A., Grove, Jakob, Guan, Weihua, Hall, Lynsey S., Hamshere, Marian L., Hansen, Christine Søholm, Hansen, Thomas F., Hautzinger, Martin, Heilbronner, Urs, van Hemert, Albert M., Herms, Stefan, Hickie, Ian B., Hipolito, Maria, Hoffmann, Per, Holland, Dominic, Homuth, Georg, Horn, Carsten, Hottenga, Jouke-Jan, Huckins, Laura, Ising, Marcus, Jamain, Stéphane, Jansen, Rick, Johnson, Jessica S., de Jong, Simone, Jorgenson, Eric, Juréus, Anders, Kandaswamy, Radhika, Karlsson, Robert, Kennedy, James L., Hassan Kiadeh, Farnush Farhadi, Kittel-Schneider, Sarah, Knowles, James A., Kogevinas, Manolis, Kohane, Isaac S., Koller, Anna C., Kraft, Julia, Kretzschmar, Warren W., Krogh, Jesper, Kupka, Ralph, Kutalik, Zoltán, Lavebratt, Catharina, Lawrence, Jacob, Lawson, William B., Leber, Markus, Lee, Phil H., Levy, Shawn E., Li, Jun Z., Li, Yihan, Lind, Penelope A., Liu, Chunyu, Olde Loohuis, Loes M., Maaser, Anna, MacIntyre, Donald J., MacKinnon, Dean F., Mahon, Pamela B., Maier, Wolfgang, Maier, Robert M., Marchini, Jonathan, Martinsson, Lina, Mbarek, Hamdi, McCarroll, Steve, McGrath, Patrick, McGuffin, Peter, McInnis, Melvin G., McKay, James D., Medeiros, Helena, Medland, Sarah E., Mehta, Divya, Meng, Fan, Middeldorp, Christel M., Mihailov, Evelin, Milaneschi, Yuri, Milani, Lili, Mirza, Saira Saeed, Mondimore, Francis M., Montgomery, Grant W., Morris, Derek W., Mostafavi, Sara, Mühleisen, Thomas W., Mullins, Niamh, Nauck, Matthias, Ng, Bernard, Nguyen, Hoang, Nievergelt, Caroline M., Nivard, Michel G., Nwulia, Evaristus A., Nyholt, Dale R., O'Donovan, Claire, O'Reilly, Paul F., Ori, Anil P.S., Oruc, Lilijana, Ösby, Urban, Oskarsson, Hogni, Painter, Jodie N., Parra, José Guzman, Pedersen, Carsten Bøcker, Pedersen, Marianne Giørtz, Perry, Amy, Peterson, Roseann E., Pettersson, Erik, Peyrot, Wouter J., Pfennig, Andrea, Pistis, Giorgio, Purcell, Shaun M., Quiroz, Jorge A., Qvist, Per, Regeer, Eline J., Reif, Andreas, Reinbold, Céline S., Rice, John P., Riley, Brien P., Rivas, Fabio, Rivera, Margarita, Roussos, Panos, Ruderfer, Douglas M., Ryu, Euijung, Sánchez-Mora, Cristina, Schatzberg, Alan F., Scheftner, William A., Schoevers, Robert, Schork, Nicholas J., Schulte, Eva C., Shehktman, Tatyana, Shen, Ling, Shi, Jianxin, Shilling, Paul D., Shyn, Stanley I., Sigurdsson, Engilbert, Slaney, Claire, Smeland, Olav B., Smit, Johannes H., Smith, Daniel J., Sobell, Janet L., Spijker, Anne T., Steffens, Michael, Strauss, John S., Streit, Fabian, Strohmaier, Jana, Szelinger, Szabolcs, Tansey, Katherine E., Teismann, Henning, Teumer, Alexander, Thompson, Robert C., Thompson, Wesley, Thomson, Pippa A., Thorgeirsson, Thorgeir E., Traylor, Matthew, Treutlein, Jens, Uitterlinden, André G., Umbricht, Daniel, Vedder, Helmut, Viktorin, Alexander, Visscher, Peter M., Wang, Weiqing, Watson, Stanley J., Webb, Bradley T., Weickert, Cynthia Shannon, Weickert, Thomas W., Weinsheimer, Shantel Marie, Wellmann, Jürgen, Willemsen, Gonneke, Witt, Stephanie H., Wu, Yang, Xi, Hualin S., Xu, Wei, Yang, Jian, Young, Allan H., Zandi, Peter, Zhang, Peng, Zhang, Futao, Zollner, Sebastian, Adolfsson, Rolf, Agartz, Ingrid, Alda, Martin, Arolt, Volker, Backlund, Lena, Baune, Bernhard T., Bellivier, Frank, Berger, Klaus, Berrettini, Wade H., Biernacka, Joanna M., Blackwood, Douglas H.R., Boehnke, Michael, Boomsma, Dorret I., Corvin, Aiden, Craddock, Nicholas, Daly, Mark J., Dannlowski, Udo, Domenici, Enrico, Domschke, Katharina, Esko, Tõnu, Etain, Bruno, Frye, Mark, Fullerton, Janice M., Gershon, Elliot S., de Geus, E.J.C., Gill, Michael, Goes, Fernando, Grabe, Hans J., Grigoroiu-Serbanescu, Maria, Hamilton, Steven P., Hauser, Joanna, Hayward, Caroline, Heath, Andrew C., Hougaard, David M., Hultman, Christina M., Jones, Ian, Jones, Lisa A., Kahn, René S., Kendler, Kenneth S., Kirov, George, Kloiber, Stefan, Landén, Mikael, Leboyer, Marion, Lewis, Glyn, Li, Qingqin S., Lissowska, Jolanta, Lucae, Susanne, Madden, Pamela A.F., Magnusson, Patrik K., Martin, Nicholas G., Mayoral, Fermin, McElroy, Susan L., McIntosh, Andrew M., McMahon, Francis J., Melle, Ingrid, Metspalu, Andres, Mitchell, Philip B., Morken, Gunnar, Mors, Ole, Mortensen, Preben Bo, Müller-Myhsok, Bertram, Myers, Richard M., Neale, Benjamin M., Nimgaonkar, Vishwajit, Nordentoft, Merete, Nöthen, Markus M., O'Donovan, Michael C., Oedegaard, Ketil J., Owen, Michael J., Paciga, Sara A., Pato, Carlos, Pato, Michele T., Pedersen, Nancy L., Penninx, Brenda W.J. H., Perlis, Roy H., Porteous, David J., Posthuma, Danielle, Potash, James B., Preisig, Martin, Ramos-Quiroga, Josep Antoni, Ribasés, Marta, Rietschel, Marcella, Rouleau, Guy A., Schaefer, Catherine, Schalling, Martin, Schofield, Peter R., Schulze, Thomas G., Serretti, Alessandro, Smoller, Jordan W., Stefansson, Hreinn, Stefansson, Kari, Stordal, Eystein, Tiemeier, Henning, Turecki, Gustavo, Uher, Rudolf, Vaaler, Arne E., Vieta, Eduard, Vincent, John B., Völzke, Henry, Weissman, Myrna M., Werge, Thomas, Andreassen, Ole A., Børglum, Anders D., Cichon, Sven, Edenberg, Howard J., Di Florio, Arianna, Kelsoe, John, Levinson, Douglas F., Lewis, Cathryn M., Nurnberger, John I., Ophoff, Roel A., Scott, Laura J., Sklar, Pamela, Sullivan, Patrick F., Wray, Naomi R., Coleman, Jonathan R.I., Gaspar, Héléna A., Bryois, Julien, and Breen, Gerome

Published
2020
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48. The impact of binge eating behavior on lithium- and quetiapine-associated changes in body weight, body mass index, and waist circumference during 6 months of treatment: Findings from the bipolar CHOICE study

Author

Yaramala, Satyanarayana R., McElroy, Susan L., Geske, Jennifer, Winham, Stacey, Gao, Keming, Reilly-Harrington, Noreen A., Ketter, Terence A., Deckersbach, Thilo, Kinrys, Gustavo, Kamali, Masoud, Sylvia, Louisa G., McInnis, Melvin G., Friedman, Edward S., Thase, Machael E., Kocsis, James H., Tohen, Mauricio, Calabrese, Joseph R., Bowden, Charles L., Shelton, Richard C., Nierenberg, Andrew A., and Bobo, William V.

Published
2020
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49. Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder.

Author

Hou, Liping, Bergen, Sarah E, Akula, Nirmala, Song, Jie, Hultman, Christina M, Landén, Mikael, Adli, Mazda, Alda, Martin, Ardau, Raffaella, Arias, Bárbara, Aubry, Jean-Michel, Backlund, Lena, Badner, Judith A, Barrett, Thomas B, Bauer, Michael, Baune, Bernhard T, Bellivier, Frank, Benabarre, Antonio, Bengesser, Susanne, Berrettini, Wade H, Bhattacharjee, Abesh Kumar, Biernacka, Joanna M, Birner, Armin, Bloss, Cinnamon S, Brichant-Petitjean, Clara, Bui, Elise T, Byerley, William, Cervantes, Pablo, Chillotti, Caterina, Cichon, Sven, Colom, Francesc, Coryell, William, Craig, David W, Cruceanu, Cristiana, Czerski, Piotr M, Davis, Tony, Dayer, Alexandre, Degenhardt, Franziska, Del Zompo, Maria, DePaulo, J Raymond, Edenberg, Howard J, Étain, Bruno, Falkai, Peter, Foroud, Tatiana, Forstner, Andreas J, Frisén, Louise, Frye, Mark A, Fullerton, Janice M, Gard, Sébastien, Garnham, Julie S, Gershon, Elliot S, Goes, Fernando S, Greenwood, Tiffany A, Grigoroiu-Serbanescu, Maria, Hauser, Joanna, Heilbronner, Urs, Heilmann-Heimbach, Stefanie, Herms, Stefan, Hipolito, Maria, Hitturlingappa, Shashi, Hoffmann, Per, Hofmann, Andrea, Jamain, Stephane, Jiménez, Esther, Kahn, Jean-Pierre, Kassem, Layla, Kelsoe, John R, Kittel-Schneider, Sarah, Kliwicki, Sebastian, Koller, Daniel L, König, Barbara, Lackner, Nina, Laje, Gonzalo, Lang, Maren, Lavebratt, Catharina, Lawson, William B, Leboyer, Marion, Leckband, Susan G, Liu, Chunyu, Maaser, Anna, Mahon, Pamela B, Maier, Wolfgang, Maj, Mario, Manchia, Mirko, Martinsson, Lina, McCarthy, Michael J, McElroy, Susan L, McInnis, Melvin G, McKinney, Rebecca, Mitchell, Philip B, Mitjans, Marina, Mondimore, Francis M, Monteleone, Palmiero, Mühleisen, Thomas W, Nievergelt, Caroline M, Nöthen, Markus M, Novák, Tomas, Nurnberger, John I, Nwulia, Evaristus A, and Ösby, Urban

Subjects
Genetics, Mental Health, Clinical Research, Human Genome, Bipolar Disorder, Prevention, Brain Disorders, Serious Mental Illness, Mental health, Good Health and Well Being, Chromosomes, Human, X, Female, Genome-Wide Association Study, Humans, Male, Receptor, ErbB-2, Receptor, erbB-2, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behaviour. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of >9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ∼2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the X-chromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, P =  5.87 × 10 -  9; odds ratio (OR) = 1.12) and markers within ERBB2 (rs2517959, P =  4.53 × 10 -  9; OR = 1.13). No significant X-chromosome associations were detected and X-linked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.

Published
2016

50. The Human Phenotype Ontology in 2024: phenotypes around the world

Author

Gargano, Michael A, primary, Matentzoglu, Nicolas, additional, Coleman, Ben, additional, Addo-Lartey, Eunice B, additional, Anagnostopoulos, Anna V, additional, Anderton, Joel, additional, Avillach, Paul, additional, Bagley, Anita M, additional, Bakštein, Eduard, additional, Balhoff, James P, additional, Baynam, Gareth, additional, Bello, Susan M, additional, Berk, Michael, additional, Bertram, Holli, additional, Bishop, Somer, additional, Blau, Hannah, additional, Bodenstein, David F, additional, Botas, Pablo, additional, Boztug, Kaan, additional, Čady, Jolana, additional, Callahan, Tiffany J, additional, Cameron, Rhiannon, additional, Carbon, Seth J, additional, Castellanos, Francisco, additional, Caufield, J Harry, additional, Chan, Lauren E, additional, Chute, Christopher G, additional, Cruz-Rojo, Jaime, additional, Dahan-Oliel, Noémi, additional, Davids, Jon R, additional, de Dieuleveult, Maud, additional, de Souza, Vinicius, additional, de Vries, Bert B A, additional, de Vries, Esther, additional, DePaulo, J Raymond, additional, Derfalvi, Beata, additional, Dhombres, Ferdinand, additional, Diaz-Byrd, Claudia, additional, Dingemans, Alexander J M, additional, Donadille, Bruno, additional, Duyzend, Michael, additional, Elfeky, Reem, additional, Essaid, Shahim, additional, Fabrizzi, Carolina, additional, Fico, Giovanna, additional, Firth, Helen V, additional, Freudenberg-Hua, Yun, additional, Fullerton, Janice M, additional, Gabriel, Davera L, additional, Gilmour, Kimberly, additional, Giordano, Jessica, additional, Goes, Fernando S, additional, Moses, Rachel Gore, additional, Green, Ian, additional, Griese, Matthias, additional, Groza, Tudor, additional, Gu, Weihong, additional, Guthrie, Julia, additional, Gyori, Benjamin, additional, Hamosh, Ada, additional, Hanauer, Marc, additional, Hanušová, Kateřina, additional, He, Yongqun (Oliver), additional, Hegde, Harshad, additional, Helbig, Ingo, additional, Holasová, Kateřina, additional, Hoyt, Charles Tapley, additional, Huang, Shangzhi, additional, Hurwitz, Eric, additional, Jacobsen, Julius O B, additional, Jiang, Xiaofeng, additional, Joseph, Lisa, additional, Keramatian, Kamyar, additional, King, Bryan, additional, Knoflach, Katrin, additional, Koolen, David A, additional, Kraus, Megan L, additional, Kroll, Carlo, additional, Kusters, Maaike, additional, Ladewig, Markus S, additional, Lagorce, David, additional, Lai, Meng-Chuan, additional, Lapunzina, Pablo, additional, Laraway, Bryan, additional, Lewis-Smith, David, additional, Li, Xiarong, additional, Lucano, Caterina, additional, Majd, Marzieh, additional, Marazita, Mary L, additional, Martinez-Glez, Victor, additional, McHenry, Toby H, additional, McInnis, Melvin G, additional, McMurry, Julie A, additional, Mihulová, Michaela, additional, Millett, Caitlin E, additional, Mitchell, Philip B, additional, Moslerová, Veronika, additional, Narutomi, Kenji, additional, Nematollahi, Shahrzad, additional, Nevado, Julian, additional, Nierenberg, Andrew A, additional, Čajbiková, Nikola Novák, additional, Nurnberger, John I, additional, Ogishima, Soichi, additional, Olson, Daniel, additional, Ortiz, Abigail, additional, Pachajoa, Harry, additional, Perez de Nanclares, Guiomar, additional, Peters, Amy, additional, Putman, Tim, additional, Rapp, Christina K, additional, Rath, Ana, additional, Reese, Justin, additional, Rekerle, Lauren, additional, Roberts, Angharad M, additional, Roy, Suzy, additional, Sanders, Stephan J, additional, Schuetz, Catharina, additional, Schulte, Eva C, additional, Schulze, Thomas G, additional, Schwarz, Martin, additional, Scott, Katie, additional, Seelow, Dominik, additional, Seitz, Berthold, additional, Shen, Yiping, additional, Similuk, Morgan N, additional, Simon, Eric S, additional, Singh, Balwinder, additional, Smedley, Damian, additional, Smith, Cynthia L, additional, Smolinsky, Jake T, additional, Sperry, Sarah, additional, Stafford, Elizabeth, additional, Stefancsik, Ray, additional, Steinhaus, Robin, additional, Strawbridge, Rebecca, additional, Sundaramurthi, Jagadish Chandrabose, additional, Talapova, Polina, additional, Tenorio Castano, Jair A, additional, Tesner, Pavel, additional, Thomas, Rhys H, additional, Thurm, Audrey, additional, Turnovec, Marek, additional, van Gijn, Marielle E, additional, Vasilevsky, Nicole A, additional, Vlčková, Markéta, additional, Walden, Anita, additional, Wang, Kai, additional, Wapner, Ron, additional, Ware, James S, additional, Wiafe, Addo A, additional, Wiafe, Samuel A, additional, Wiggins, Lisa D, additional, Williams, Andrew E, additional, Wu, Chen, additional, Wyrwoll, Margot J, additional, Xiong, Hui, additional, Yalin, Nefize, additional, Yamamoto, Yasunori, additional, Yatham, Lakshmi N, additional, Yocum, Anastasia K, additional, Young, Allan H, additional, Yüksel, Zafer, additional, Zandi, Peter P, additional, Zankl, Andreas, additional, Zarante, Ignacio, additional, Zvolský, Miroslav, additional, Toro, Sabrina, additional, Carmody, Leigh C, additional, Harris, Nomi L, additional, Munoz-Torres, Monica C, additional, Danis, Daniel, additional, Mungall, Christopher J, additional, Köhler, Sebastian, additional, Haendel, Melissa A, additional, and Robinson, Peter N, additional

Published
2023
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