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1. Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population

Author

Chandrashekar, Mayanka, Goethert, Ian, Haque, Md Inzamam Ul, McMahon, Benjamin, Dhaubhadel, Sayera, Knight, Kathryn, Erdos, Joseph, Reagan, Donna, Taylor, Caroline, Kuzmak, Peter, Gaziano, John Michael, McAllister, Eileen, Costa, Lauren, Ho, Yuk-Lam, Cho, Kelly, Tamang, Suzanne, Fodeh-Jarad, Samah, Ovchinnikova, Olga S., Justice, Amy C., Hinkle, Jacob, and Danciu, Ioana

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Objectives: This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. Materials and Methods: We used a DenseNet121 model pretrained MIMIC-CXR dataset for deep learning-based multilabel classification using ground truth labels from radiology reports extracted using the CheXpert and CheXbert Labeler. We compared the performance of the 14 chest X-ray labels on the MIMIC-CXR and Veterans Healthcare Administration chest X-ray dataset (VA-CXR). The VA-CXR dataset comprises over 259k chest X-ray images spanning between the years 2010 and 2022. Results: The validation of ground truth and the assessment of multi-label classification performance across various NLP extraction tools revealed that the VA-CXR dataset exhibited lower disagreement rates than the MIMIC-CXR datasets. Additionally, there were notable differences in AUC scores between models utilizing CheXpert and CheXbert. When evaluating multi-label classification performance across different datasets, minimal domain shift was observed in unseen datasets, except for the label "Enlarged Cardiomediastinum." The study year's subgroup analyses exhibited the most significant variations in multi-label classification model performance. These findings underscore the importance of considering domain shifts in chest X-ray classification tasks, particularly concerning study years. Conclusion: Our study reveals the significant impact of domain shift and demographic factors on chest X-ray classification, emphasizing the need for improved transfer learning and equitable model development. Addressing these challenges is crucial for advancing medical imaging and enhancing patient care.

Published
2024

2. High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning

Author

Dhaubhadel, Sayera, Ganguly, Kumkum, Ribeiro, Ruy M, Cohn, Judith D, Hyman, James M, Hengartner, Nicolas W, Kolade, Beauty, Singley, Anna, Bhattacharya, Tanmoy, Finley, Patrick, Levin, Drew, Thelen, Haedi, Cho, Kelly, Costa, Lauren, Ho, Yuk-Lam, Justice, Amy C, Pestian, John, Santel, Daniel, Zamora-Resendiz, Rafael, Crivelli, Silvia, Tamang, Suzanne, Martins, Susana, Trafton, Jodie, Oslin, David W, Beckham, Jean C, Kimbrel, Nathan A, and McMahon, Benjamin H

Subjects
Clinical and Health Psychology, Health Sciences, Psychology, Behavioral and Social Science, Suicide, Mental Health, Mental health, Good Health and Well Being, Humans, Veterans, Retrospective Studies, Carcinoma, Renal Cell, Cross-Sectional Studies, Prospective Studies, Suicide, Attempted, Kidney Neoplasms, Machine Learning, Million Veteran Program Suicide Exemplar Work Group
Abstract

We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide attempt, and overdose diagnoses with varying choices of study design and prediction methodology. Each model used twenty cross-sectional and 190 longitudinal variables observed in eight time intervals covering 7.5 years prior to the time of prediction. Ensembles of seven base models were created and fine-tuned with ten variables expected to change with study design and outcome definition in order to predict suicide and combined outcome in a prospective cohort. The ensemble models achieved c-statistics of 0.73 on 2-year suicide risk and 0.83 on the combined outcome when predicting on a prospective cohort of [Formula: see text] 4.2 M veterans. The ensembles rely on nonlinear base models trained using a matched retrospective nested case-control (Rcc) study cohort and show good calibration across a diversity of subgroups, including risk strata, age, sex, race, and level of healthcare utilization. In addition, a linear Rcc base model provided a rich set of biological predictors, including indicators of suicide, substance use disorder, mental health diagnoses and treatments, hypoxia and vascular damage, and demographics.

Published
2024

3. Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants.

Author

Wang, Anqi, Shen, Jiayi, Rodriguez, Alex, Saunders, Edward, Chen, Fei, Janivara, Rohini, Darst, Burcu, Sheng, Xin, Xu, Yili, Chou, Alisha, Benlloch, Sara, Dadaev, Tokhir, Brook, Mark, Plym, Anna, Sahimi, Ali, Hoffman, Thomas, Takahashi, Atushi, Matsuda, Koichi, Momozawa, Yukihide, Fujita, Masashi, Laisk, Triin, Figuerêdo, Jéssica, Muir, Kenneth, Ito, Shuji, Liu, Xiaoxi, Uchio, Yuji, Kubo, Michiaki, Kamatani, Yoichiro, Lophatananon, Artitaya, Wan, Peggy, Andrews, Caroline, Lori, Adriana, Choudhury, Parichoy, Schleutker, Johanna, Tammela, Teuvo, Sipeky, Csilla, Auvinen, Anssi, Giles, Graham, Southey, Melissa, MacInnis, Robert, Cybulski, Cezary, Wokolorczyk, Dominika, Lubinski, Jan, Rentsch, Christopher, Cho, Kelly, Mcmahon, Benjamin, Neal, David, Donovan, Jenny, Hamdy, Freddie, Martin, Richard, Nordestgaard, Borge, Nielsen, Sune, Weischer, Maren, Bojesen, Stig, Røder, Andreas, Stroomberg, Hein, Batra, Jyotsna, Chambers, Suzanne, Horvath, Lisa, Clements, Judith, Tilly, Wayne, Risbridger, Gail, Gronberg, Henrik, Aly, Markus, Szulkin, Robert, Eklund, Martin, Nordstrom, Tobias, Pashayan, Nora, Dunning, Alison, Ghoussaini, Maya, Travis, Ruth, Key, Tim, Riboli, Elio, Park, Jong, Sellers, Thomas, Lin, Hui-Yi, Albanes, Demetrius, Weinstein, Stephanie, Cook, Michael, Mucci, Lorelei, Giovannucci, Edward, Lindstrom, Sara, Kraft, Peter, Hunter, David, Penney, Kathryn, Turman, Constance, Tangen, Catherine, Goodman, Phyllis, Thompson, Ian, Hamilton, Robert, Fleshner, Neil, Finelli, Antonio, Parent, Marie-Élise, Stanford, Janet, Ostrander, Elaine, Koutros, Stella, Beane Freeman, Laura, Stampfer, Meir, Wolk, Alicja, and Håkansson, Niclas

Subjects
Humans, Male, Black People, Genetic Predisposition to Disease, Genome-Wide Association Study, Hispanic or Latino, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Risk Factors, White People, Asian People
Abstract

The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.

Published
2023

4. GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.

Author

Docherty, Anna, Mullins, Niamh, Ashley-Koch, Allison, Qin, Xuejun, Coleman, Jonathan, Shabalin, Andrey, Kang, JooEun, Murnyak, Balasz, Wendt, Frank, Adams, Mark, Campos, Adrian, DiBlasi, Emily, Fullerton, Janice, Kranzler, Henry, Bakian, Amanda, Monson, Eric, Rentería, Miguel, Walss-Bass, Consuelo, Andreassen, Ole, Behera, Chittaranjan, Bulik, Cynthia, Edenberg, Howard, Kessler, Ronald, Mann, J, Nurnberger, John, Pistis, Giorgio, Streit, Fabian, Ursano, Robert, Polimanti, Renato, Dennis, Michelle, Garrett, Melanie, Hair, Lauren, Harvey, Philip, Hauser, Elizabeth, Hauser, Michael, Huffman, Jennifer, Jacobson, Daniel, Madduri, Ravi, McMahon, Benjamin, Oslin, David, Trafton, Jodie, Awasthi, Swapnil, Berrettini, Wade, Bohus, Martin, Chang, Xiao, Chen, Hsi-Chung, Chen, Wei, Christensen, Erik, Crow, Scott, Duriez, Philibert, Edwards, Alexis, Fernández-Aranda, Fernando, Galfalvy, Hanga, Gandal, Michael, Gorwood, Philip, Guo, Yiran, Hafferty, Jonathan, Hakonarson, Hakon, Halmi, Katherine, Hishimoto, Akitoyo, Jain, Sonia, Jamain, Stéphane, Jiménez-Murcia, Susana, Johnson, Craig, Kaplan, Allan, Kaye, Walter, Keel, Pamela, Kennedy, James, Kim, Minsoo, Klump, Kelly, Levey, Daniel, Li, Dong, Liao, Shih-Cheng, Lieb, Klaus, Lilenfeld, Lisa, Marshall, Christian, Mitchell, James, Okazaki, Satoshi, Otsuka, Ikuo, Pinto, Dalila, Powers, Abigail, Ramoz, Nicolas, Ripke, Stephan, Roepke, Stefan, Rozanov, Vsevolod, Scherer, Stephen, Schmahl, Christian, Sokolowski, Marcus, Starnawska, Anna, Strober, Michael, Su, Mei-Hsin, Thornton, Laura, Treasure, Janet, Ware, Erin, Watson, Hunna, Witt, Stephanie, Woodside, D, Yilmaz, Zeynep, Zillich, Lea, and Adolfsson, Rolf

Subjects
Biological Markers, Depressive Disorders, Genetics, Schizophrenia Spectrum and Other Psychotic Disorders, Self-Harm, Suicide, Humans, Genome-Wide Association Study, Suicide, Attempted, Depressive Disorder, Major, Risk Factors, Suicidal Ideation, Polymorphism, Single Nucleotide, Genetic Predisposition to Disease, Genetic Loci
Abstract

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

Published
2023

5. Analyses of GWAS signal using GRIN identify additional genes contributing to suicidal behavior

Author

Sullivan, Kyle A., Lane, Matthew, Cashman, Mikaela, Miller, J. Izaak, Pavicic, Mirko, Walker, Angelica M., Cliff, Ashley, Romero, Jonathon, Qin, Xuejun, Mullins, Niamh, Docherty, Anna, Coon, Hilary, Ruderfer, Douglas M., Garvin, Michael R., Pestian, John P., Ashley-Koch, Allison E., Beckham, Jean C., McMahon, Benjamin, Oslin, David W., Kimbrel, Nathan A., Jacobson, Daniel A., and Kainer, David

Published
2024
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8. An examination of the association between altitude and suicide deaths, suicide attempts, and suicidal ideation among veterans at both the patient and geospatial level.

Author

Wang, Xiange, Zamora-Resendiz, Rafael, Shelley, Courtney, Manore, Carrie, Liu, Xinlian, Oslin, David, McMahon, Benjamin, Beckham, Jean, Kimbrel, Nathan, and Crivelli, Silvia

Subjects
Adjusted variables, Altitude, Firearms related suicide, Geospatial, Mental health, Suicide attempts, Suicide ideation, Suicide mortality, Veterans, Altitude, Humans, Male, Risk Factors, Suicidal Ideation, Suicide, Attempted, Veterans
Abstract

Suicide is a major public health problem affecting US Veterans and the US in general. Many variables (e.g., demographic, clinical, biological, geographic) have been associated with risk for suicide and suicidal behavior, including altitude; however, the exact nature of the relationship between altitude and suicide remains unclear in part due to the fact that previous studies have used either geospatial data or individual-level data, but not both. Prior research has also failed to consider the full range of suicidal thoughts and behaviors, ranging from suicidal ideation to suicide deaths. Accordingly, the objective of the present research was to use both geospatial data (county and zip codes) and individual-level data to comprehensively assess the association between altitude and suicide mortality, suicide attempts, and suicidal ideation among US Veterans between 2000 and 2018. Taken together, our results demonstrate that there is a strong correlation between altitude and suicide rates at all the levels investigated and using different statistical analyses and even after controlling for significant covariates such as percent of age >50yr, percent male, percent white, percent non-Hispanic, median household income, and population density. We show that there is a positive correlation between altitude and suicide attempts especially when controlling by the covariates and a weak correlation between altitude and suicide ideation and the combination of suicide, suicide attempts and suicide ideation.

Published
2022

9. Continuous-Time Probabilistic Models for Longitudinal Electronic Health Records

Author

Kaplan, Alan D., Tipnis, Uttara, Beckham, Jean C., Kimbrel, Nathan A., Oslin, David W., and McMahon, Benjamin H.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular sampling of EHR data. We present an unsupervised probabilistic model that captures nonlinear relationships between variables over continuous-time. This method works with arbitrary sampling patterns and captures the joint probability distribution between variable measurements and the time intervals between them. Inference algorithms are derived that can be used to evaluate the likelihood of future using under a trained model. As an example, we consider data from the United States Veterans Health Administration (VHA) in the areas of diabetes and depression. Likelihood ratio maps are produced showing the likelihood of risk for moderate-severe vs minimal depression as measured by the Patient Health Questionnaire-9 (PHQ-9).

Published
2022

12. Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports

Author

Dhaubhadel, Sayera, Mohd-Yusof, Jamaludin, Ganguly, Kumkum, Chennupati, Gopinath, Thulasidasan, Sunil, Hengartner, Nicolas W., Mumphrey, Brent J., Durbin, Eric B., Doherty, Jennifer A., Lemieux, Mireille, Schaefferkoetter, Noah, Tourassi, Georgia, Coyle, Linda, Penberthy, Lynne, McMahon, Benjamin H., and Bhattacharya, Tanmoy

Subjects
Computer Science - Machine Learning
Abstract

Safe deployment of deep learning systems in critical real world applications requires models to make very few mistakes, and only under predictable circumstances. In this work, we address this problem using an abstaining classifier that is tuned to have $>$95% accuracy, and then identify the determinants of abstention using LIME. Essentially, we are training our model to learn the attributes of pathology reports that are likely to lead to incorrect classifications, albeit at the cost of reduced sensitivity. We demonstrate an abstaining classifier in a multitask setting for classifying cancer pathology reports from the NCI SEER cancer registries on six tasks of interest. For these tasks, we reduce the classification error rate by factors of 2--5 by abstaining on 25--45% of the reports. For the specific task of classifying cancer site, we are able to identify metastasis, reports involving lymph nodes, and discussion of multiple cancer sites as responsible for many of the classification mistakes, and observe that the extent and types of mistakes vary systematically with cancer site (e.g., breast, lung, and prostate). When combining across three of the tasks, our model classifies 50% of the reports with an accuracy greater than 95% for three of the six tasks\edit, and greater than 85% for all six tasks on the retained samples. Furthermore, we show that LIME provides a better determinant of classification than measures of word occurrence alone. By combining a deep abstaining classifier with feature identification using LIME, we are able to identify concepts responsible for both correctness and abstention when classifying cancer sites from pathology reports. The improvement of LIME over keyword searches is statistically significant, presumably because words are assessed in context and have been identified as a local determinant of classification.

Published
2020

13. Evidence of Novel Susceptibility Variants for Prostate Cancer and a Multiancestry Polygenic Risk Score Associated with Aggressive Disease in Men of African Ancestry

Author

Chen, Fei, Madduri, Ravi K., Rodriguez, Alex A., Darst, Burcu F., Chou, Alisha, Sheng, Xin, Wang, Anqi, Shen, Jiayi, Saunders, Edward J., Rhie, Suhn K., Bensen, Jeannette T., Ingles, Sue A., Kittles, Rick A., Strom, Sara S., Rybicki, Benjamin A., Nemesure, Barbara, Isaacs, William B., Stanford, Janet L., Zheng, Wei, Sanderson, Maureen, John, Esther M., Park, Jong Y., Xu, Jianfeng, Wang, Ying, Berndt, Sonja I., Huff, Chad D., Yeboah, Edward D., Tettey, Yao, Lachance, Joseph, Tang, Wei, Rentsch, Christopher T., Cho, Kelly, Mcmahon, Benjamin H., Biritwum, Richard B., Adjei, Andrew A., Tay, Evelyn, Truelove, Ann, Niwa, Shelley, Sellers, Thomas A., Yamoah, Kosj, Murphy, Adam B., Crawford, Dana C., Patel, Alpa V., Bush, William S., Aldrich, Melinda C., Cussenot, Olivier, Petrovics, Gyorgy, Cullen, Jennifer, Neslund-Dudas, Christine M., Stern, Mariana C., Kote-Jarai, Zsofia, Govindasami, Koveela, Cook, Michael B., Chokkalingam, Anand P., Hsing, Ann W., Goodman, Phyllis J., Hoffmann, Thomas J., Drake, Bettina F., Hu, Jennifer J., Keaton, Jacob M., Hellwege, Jacklyn N., Clark, Peter E., Jalloh, Mohamed, Gueye, Serigne M., Niang, Lamine, Ogunbiyi, Olufemi, Idowu, Michael O., Popoola, Olufemi, Adebiyi, Akindele O., Aisuodionoe-Shadrach, Oseremen I., Ajibola, Hafees O., Jamda, Mustapha A., Oluwole, Olabode P., Nwegbu, Maxwell, Adusei, Ben, Mante, Sunny, Darkwa-Abrahams, Afua, Mensah, James E., Diop, Halimatou, Van Den Eeden, Stephen K., Blanchet, Pascal, Fowke, Jay H., Casey, Graham, Hennis, Anselm J., Lubwama, Alexander, Thompson, Ian M., Jr., Leach, Robin, Easton, Douglas F., Preuss, Michael H., Loos, Ruth J., Gundell, Susan M., Wan, Peggy, Mohler, James L., Fontham, Elizabeth T., Smith, Gary J., Taylor, Jack A., Srivastava, Shiv, Eeles, Rosaline A., Carpten, John D., Kibel, Adam S., Multigner, Luc, Parent, Marie-Élise, Menegaux, Florence, Cancel-Tassin, Geraldine, Klein, Eric A., Andrews, Caroline, Rebbeck, Timothy R., Brureau, Laurent, Ambs, Stefan, Edwards, Todd L., Watya, Stephen, Chanock, Stephen J., Witte, John S., Blot, William J., Michael Gaziano, J., Justice, Amy C., Conti, David V., and Haiman, Christopher A.

Published
2023
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14. Summary results of the 2014-2015 DARPA Chikungunya challenge

Author

Del Valle, Sara Y, McMahon, Benjamin H, Asher, Jason, Hatchett, Richard, Lega, Joceline C, Brown, Heidi E, Leany, Mark E, Pantazis, Yannis, Roberts, David J, Moore, Sean, Peterson, A Townsend, Escobar, Luis E, Qiao, Huijie, Hengartner, Nicholas W, and Mukundan, Harshini

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Emerging Infectious Diseases, Rare Diseases, Vector-Borne Diseases, Prevention, Biodefense, Infectious Diseases, Vaccine Related, Infection, Good Health and Well Being, Chikungunya Fever, Demography, Dengue, Disease Outbreaks, Forecasting, Humans, Infection Control, Organizational Innovation, Research Design, Security Measures, United States, United States Department of Defense, Zika Virus Infection, Chikungunya, Mechanistic models, Morphological models, Microbiology, Medical Microbiology, Clinical sciences, Medical microbiology, Public health
Abstract

BackgroundEmerging pathogens such as Zika, chikungunya, Ebola, and dengue viruses are serious threats to national and global health security. Accurate forecasts of emerging epidemics and their severity are critical to minimizing subsequent mortality, morbidity, and economic loss. The recent introduction of chikungunya and Zika virus to the Americas underscores the need for better methods for disease surveillance and forecasting.MethodsTo explore the suitability of current approaches to forecasting emerging diseases, the Defense Advanced Research Projects Agency (DARPA) launched the 2014-2015 DARPA Chikungunya Challenge to forecast the number of cases and spread of chikungunya disease in the Americas. Challenge participants (n=38 during final evaluation) provided predictions of chikungunya epidemics across the Americas for a six-month period, from September 1, 2014 to February 16, 2015, to be evaluated by comparison with incidence data reported to the Pan American Health Organization (PAHO). This manuscript presents an overview of the challenge and a summary of the approaches used by the winners.ResultsParticipant submissions were evaluated by a team of non-competing government subject matter experts based on numerical accuracy and methodology. Although this manuscript does not include in-depth analyses of the results, cursory analyses suggest that simpler models appear to outperform more complex approaches that included, for example, demographic information and transportation dynamics, due to the reporting biases, which can be implicitly captured in statistical models. Mosquito-dynamics, population specific information, and dengue-specific information correlated best with prediction accuracy.ConclusionWe conclude that with careful consideration and understanding of the relative advantages and disadvantages of particular methods, implementation of an effective prediction system is feasible. However, there is a need to improve the quality of the data in order to more accurately predict the course of epidemics.

Published
2018

16. A genome-wide association study of suicide attempts in the million veterans program identifies evidence of pan-ancestry and ancestry-specific risk loci

Author

Kimbrel, Nathan A., Ashley-Koch, Allison E., Qin, Xue J., Lindquist, Jennifer H., Garrett, Melanie E., Dennis, Michelle F., Hair, Lauren P., Huffman, Jennifer E., Jacobson, Daniel A., Madduri, Ravi K., Trafton, Jodie A., Coon, Hilary, Docherty, Anna R., Kang, Jooeun, Mullins, Niamh, Ruderfer, Douglas M., Harvey, Philip D., McMahon, Benjamin H., Oslin, David W., Hauser, Elizabeth R., Hauser, Michael A., and Beckham, Jean C.

Published
2022
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18. Genetic variation in CSF2 (5q31.1) is associated with longitudinal susceptibility to pediatric malaria, severe malarial anemia, and all-cause mortality in a high-burden malaria and HIV region of Kenya

Author

Kisia, Lily E., Cheng, Qiuying, Raballah, Evans, Munde, Elly O., McMahon, Benjamin H., Hengartner, Nick W., Ong’echa, John M., Chelimo, Kiprotich, Lambert, Christophe G., Ouma, Collins, Kempaiah, Prakasha, Perkins, Douglas J., Schneider, Kristan A., and Anyona, Samuel B.

Published
2022
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19. Pan‐Cancer Survival Impact of Immune Checkpoint Inhibitors in a National Healthcare System.

Author

Miller, Sean R., Schipper, Matthew, Fritsche, Lars G., Jiang, Ralph, Strohbehn, Garth, Ötleş, Erkin, McMahon, Benjamin H., Crivelli, Silvia, Zamora‐Resendiz, Rafael, Ramnath, Nithya, Yoo, Shinjae, Dai, Xin, Sankar, Kamya, Edwards, Donna M., Allen, Steven G., Green, Michael D., and Bryant, Alex K.

Subjects
SURVIVAL rate, IMMUNE checkpoint inhibitors, OVERALL survival, VETERANS' health, PROGNOSIS
Abstract

Background: The cumulative, health system‐wide survival benefit of immune checkpoint inhibitors (ICIs) is unclear, particularly among real‐world patients with limited life expectancies and among subgroups poorly represented on clinical trials. We sought to determine the health system‐wide survival impact of ICIs. Methods: We identified all patients receiving PD‐1/PD‐L1 or CTLA‐4 inhibitors from 2010 to 2023 in the national Veterans Health Administration (VHA) system (ICI cohort) and all patients who received non‐ICI systemic therapy in the years before ICI approval (historical control). ICI and historical control cohorts were matched on multiple cancer‐related prognostic factors, comorbidities, and demographics. The effect of ICI on overall survival was quantified with Cox regression incorporating matching weights. Cumulative life‐years gained system‐wide were calculated from the difference in adjusted 5‐year restricted mean survival times. Results: There were 27,322 patients in the ICI cohort and 69,801 patients in the historical control cohort. Among ICI patients, the most common cancer types were NSCLC (46%) and melanoma (10%). ICI demonstrated a large OS benefit in most cancer types with heterogeneity across cancer types (NSCLC: adjusted HR [aHR] 0.56, 95% confidence interval [CI] 0.54–0.58, p < 0.001; urothelial: aHR 0.91, 95% CI 0.83–1.01, p = 0.066). The relative benefit of ICI was stable across patient age, comorbidity, and self‐reported race subgroups. Across VHA, 15,859 life‐years gained were attributable to ICI within 5‐years of treatment, with NSCLC contributing the most life‐years gained. Conclusion: We demonstrated substantial increase in survival due to ICIs across a national health system, including in patient subgroups poorly represented on clinical trials. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Transcriptomic and Proteomic Insights into Host Immune Responses in Pediatric Severe Malarial Anemia: Dysregulation in HSP60-70-TLR2/4 Signaling and Altered Glutamine Metabolism.

Author

Onyango, Clinton O., Anyona, Samuel B., Hurwitz, Ivy, Raballah, Evans, Wasena, Sharely A., Osata, Shamim W., Seidenberg, Philip, McMahon, Benjamin H., Lambert, Christophe G., Schneider, Kristan A., Ouma, Collins, Cheng, Qiuying, and Perkins, Douglas J.

Subjects
HEAT shock proteins, GENE expression, GENE regulatory networks, KENYANS, MESSENGER RNA
Abstract

Severe malarial anemia (SMA, Hb < 6.0 g/dL) is a leading cause of childhood morbidity and mortality in holoendemic Plasmodium falciparum transmission zones. This study explored the entire expressed human transcriptome in whole blood from 66 Kenyan children with non-SMA (Hb ≥ 6.0 g/dL, n = 41) and SMA (n = 25), focusing on host immune response networks. RNA-seq analysis revealed 6862 differentially expressed genes, with equally distributed up-and down-regulated genes, indicating a complex host immune response. Deconvolution analyses uncovered leukocytic immune profiles indicative of a diminished antigenic response, reduced immune priming, and polarization toward cellular repair in SMA. Weighted gene co-expression network analysis revealed that immune-regulated processes are central molecular distinctions between non-SMA and SMA. A top dysregulated immune response signaling network in SMA was the HSP60-HSP70-TLR2/4 signaling pathway, indicating altered pathogen recognition, innate immune activation, stress responses, and antigen recognition. Validation with high-throughput gene expression from a separate cohort of Kenyan children (n = 50) with varying severities of malarial anemia (n = 38 non-SMA and n = 12 SMA) confirmed the RNA-seq findings. Proteomic analyses in 35 children with matched transcript and protein abundance (n = 19 non-SMA and n = 16 SMA) confirmed dysregulation in the HSP60-HSP70-TLR2/4 signaling pathway. Additionally, glutamine transporter and glutamine synthetase genes were differentially expressed, indicating altered glutamine metabolism in SMA. This comprehensive analysis underscores complex immune dysregulation and novel pathogenic features in SMA. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Relationship between mutant Cu/Zn superoxide dismutase 1 maturation and inclusion formation in cell models

Author

Ayers, Jacob I, McMahon, Benjamin, Gill, Sabrina, Lelie, Herman L, Fromholt, Susan, Brown, Hilda, Valentine, Joan Selverstone, Whitelegge, Julian P, and Borchelt, David R

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Rare Diseases, Neurodegenerative, ALS, Neurosciences, Brain Disorders, 2.1 Biological and endogenous factors, Aetiology, Animals, CHO Cells, Cricetinae, Cricetulus, HEK293 Cells, Humans, Inclusion Bodies, Mutation, Protein Aggregates, Protein Folding, Superoxide Dismutase-1, aggregation, amyotrophic lateral sclerosis, live imaging, oxidation, superoxide dismutase 1, Neurology & Neurosurgery, Biochemistry and cell biology
Abstract

A common property of Cu/Zn superoxide dismutase 1 (SOD1), harboring mutations associated with amyotrophic lateral sclerosis, is a high propensity to misfold and form abnormal aggregates. The aggregation of mutant SOD1 has been demonstrated in vitro, with purified proteins, in mouse models, in human tissues, and in cultured cell models. In vitro translation studies have determined that SOD1 with amyotrophic lateral sclerosis mutations is slower to mature, and thus perhaps vulnerable to off-pathway folding that could generate aggregates. The aggregation of mutant SOD1 in living cells can be monitored by tagging the protein with fluorescent fluorophores. In this study, we have taken advantage of the Dendra2 fluorophore technology in which excitation can be used to switch the output color from green to red, thereby clearly creating a time stamp that distinguishes pre-existing and newly made proteins. In cells that transiently over-express the Ala 4 to Val variant of SOD1-Dendra2, we observed that newly made mutant SOD1 was rapidly captured by pathologic intracellular inclusions. In cell models of mutant SOD1 aggregation over-expressing untagged A4V-SOD1, we observed that immature forms of the protein, lacking a Cu co-factor and a normal intramolecular disulfide, persist for extended periods. Our findings fit with a model in which immature forms of mutant A4V-SOD1, including newly made protein, are prone to misfolding and aggregation.

Published
2017

24. F81. ELUCIDATING GENETIC AND ENVIRONMENTAL RISK FACTORS FOR ANTIPSYCHOTIC-INDUCED METABOLIC ADVERSE EFFECTS USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN THE MILLION VETERAN PROGRAM

Author

Fanous, Ayman, primary, Bacanu, Silviu-Alin, additional, Mcmahon, Benjamin, additional, Harvey, Philip, additional, Kreyenbuhl, Julie, additional, Marder, Stephen, additional, Agarwal, Khushbu, additional, Abbaszadegan, Hamed, additional, Choudhury, Sutanay, additional, Colby, Ham, additional, Dhaubhadel, Sayera, additional, and Kolade, Beauty, additional

Published
2023
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25. Conformational Dependence of a Protein Kinase Phosphate Transfer Reaction

Author

Henkelman, Graeme, LaBute, Montiago X., Tung, Chang-Shung, Fenimore, P. W., and McMahon, Benjamin H.

Subjects
Physics - Chemical Physics, Physics - Biological Physics
Abstract

Atomic motions and energetics for a phosphate transfer reaction catalyzed by the cAMP-dependent protein kinase (PKA) are calculated by plane-wave density functional theory, starting from structures of proteins crystallized in both the reactant conformation (RC) and the transition-state conformation (TC). In the TC, we calculate that the reactants and products are nearly isoenergetic with a 0.2 eV barrier; while phosphate transfer is unfavorable by over 1.2 eV in the RC, with an even higher barrier. With the protein in the TC, the motions involved in reaction are small, with only P$_\gamma$ and the catalytic proton moving more than 0.5 \AA. Examination of the structures reveals that in the RC the active site cleft is not completely closed and there is insufficient space for the phosphorylated serine residue in the product state. Together, these observations imply that the phosphate transfer reaction occurs rapidly and reversibly in a particular conformation of the protein, and that the reaction can be gated by changes of a few tenths of an \AA in the catalytic site., Comment: revtex4, 7 pages, 4 figures, to be submitted to Science

Published
2004
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27. High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning

Author

Dhaubhadel, Sayera, primary, Ganguly, Kumkum, additional, Ribeiro, Ruy M., additional, Cohn, Judith, additional, Hyman, James M., additional, Hengartner, Nicolas W., additional, Kolade, Beauty, additional, Singley, Anna, additional, Bhattacharya, Tanmoy, additional, Finley, Patrick D., additional, Levin, Drew, additional, Thelen, Haedi, additional, Cho, Kelly, additional, Costa, Lauren, additional, Ho, Yuk-Lam, additional, Justice, Amy C., additional, Pestian, John, additional, Santel, Daniel, additional, Zamora-Resendiz, Rafael, additional, Crivelli, Silvia, additional, Tamang, Suzanne, additional, Martins, Susana, additional, Trafton, Jodie, additional, Oslin, David W., additional, Bekham, Jean C., additional, Kimbrel, Nathan A., additional, Group, Million Vetaran Program Suicide Exemplar Work, additional, and McMahon, Benjamin H., additional

Published
2023
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28. Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts

Author

Pavicic, Mirko, primary, Walker, Angelica M., additional, Sullivan, Kyle A., additional, Lagergren, John, additional, Cliff, Ashley, additional, Romero, Jonathon, additional, Streich, Jared, additional, Garvin, Michael R., additional, Pestian, John, additional, McMahon, Benjamin, additional, Oslin, David W., additional, Beckham, Jean C., additional, Kimbrel, Nathan A., additional, and Jacobson, Daniel A., additional

Published
2023
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30. Cardiovascular Disease Risk Assessment Using Traditional Risk Factors and Polygenic Risk Scores in the Million Veteran Program

Author

Vassy, Jason L., primary, Posner, Daniel C., additional, Ho, Yuk-Lam, additional, Gagnon, David R., additional, Galloway, Ashley, additional, Tanukonda, Vidisha, additional, Houghton, Serena C., additional, Madduri, Ravi K., additional, McMahon, Benjamin H., additional, Tsao, Philip S., additional, Damrauer, Scott M., additional, O’Donnell, Christopher J., additional, Assimes, Themistocles L., additional, Casas, Juan P., additional, Gaziano, J. Michael, additional, Pencina, Michael J., additional, Sun, Yan V., additional, Cho, Kelly, additional, and Wilson, Peter W.F., additional

Published
2023
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32. Deep sequential neural network models improve stratification of suicide attempt risk among US veterans.

Author

Martinez, Carianne, Levin, Drew, Jones, Jessica, Finley, Patrick D, McMahon, Benjamin, Dhaubhadel, Sayera, Cohn, Judith, Program, Million Veteran, Workgroup, MVP Suicide Exemplar, Oslin, David W, Kimbrel, Nathan A, and Beckham, Jean C

Abstract

Objective To apply deep neural networks (DNNs) to longitudinal EHR data in order to predict suicide attempt risk among veterans. Local explainability techniques were used to provide explanations for each prediction with the goal of ultimately improving outreach and intervention efforts. Materials and methods The DNNs fused demographic information with diagnostic, prescription, and procedure codes. Models were trained and tested on EHR data of approximately 500 000 US veterans: all veterans with recorded suicide attempts from April 1, 2005, through January 1, 2016, each paired with 5 veterans of the same age who did not attempt suicide. Shapley Additive Explanation (SHAP) values were calculated to provide explanations of DNN predictions. Results The DNNs outperformed logistic and linear regression models in predicting suicide attempts. After adjusting for the sampling technique, the convolutional neural network (CNN) model achieved a positive predictive value (PPV) of 0.54 for suicide attempts within 12 months by veterans in the top 0.1% risk tier. Explainability methods identified meaningful subgroups of high-risk veterans as well as key determinants of suicide attempt risk at both the group and individual level. Discussion and conclusion The deep learning methods employed in the present study have the potential to significantly enhance existing suicide risk models for veterans. These methods can also provide important clues to explore the relative value of long-term and short-term intervention strategies. Furthermore, the explainability methods utilized here could also be used to communicate to clinicians the key features which increase specific veterans' risk for attempting suicide. [ABSTRACT FROM AUTHOR]

Published
2024
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33. GWAS Meta-Analysis of Suicide Attempt:Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors

Author

Docherty, Anna R., Mullins, Niamh, Ashley-Koch, Allison E., Qin, Xuejun, Coleman, Jonathan R.I., Shabalin, Andrey, Kang, Joo Eun, 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., John Mann, J., 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.M.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., Blake Woodside, D., Yilmaz, Zeynep, Zillich, Lea, Adolfsson, Rolf, Agartz, Ingrid, Alda, Martin, Alfredsson, Lars, Appadurai, Vivek, Artigas, María Soler, Van Der Auwera, Sandra, Helena Azevedo, M., Bass, Nicholas, Bau, Claiton H.D., 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 W.J.H., 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 J.S., Trzaskowski, MacIej, Tsuang, Ming T., Turecki, Gustavo, Vilar-Ribó, Laura, Vincent, John B., Völzke, Henry, Walters, James T.R., 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, Coon, Hilary, Beckham, Jean C., Kimbrel, Nathan A., Ruderfer, Douglas M., Docherty, Anna R., Mullins, Niamh, Ashley-Koch, Allison E., Qin, Xuejun, Coleman, Jonathan R.I., Shabalin, Andrey, Kang, Joo Eun, 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., John Mann, J., 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.M.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., Blake Woodside, D., Yilmaz, Zeynep, Zillich, Lea, Adolfsson, Rolf, Agartz, Ingrid, Alda, Martin, Alfredsson, Lars, Appadurai, Vivek, Artigas, María Soler, Van Der Auwera, Sandra, Helena Azevedo, M., Bass, Nicholas, Bau, Claiton H.D., 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 W.J.H., 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 J.S., Trzaskowski, MacIej, Tsuang, Ming T., Turecki, Gustavo, Vilar-Ribó, Laura, Vincent, John B., Völzke, Henry, Walters, James T.R., 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, Coon, Hilary, Beckham, Jean C., Kimbrel, Nathan A., and Ruderfer, Douglas M.

Abstract

Objective: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide asso-ciation studies (GWASs) recently discovered and cross- validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic co-horts 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 ad-mixtures 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 ge-netic 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 en-richment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major de-pressive 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 pheno-types. These findings provide insight into genetic fa, Objective: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and crossvalidated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS metaanalysis 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 attemp

Published
2023

34. Direct detection of bacteremia by exploiting host-pathogen interactions of lipoteichoic acid and lipopolysaccharide

Author

Kubicek-Sutherland, Jessica Z., Vu, Dung M., Noormohamed, Aneesa, Mendez, Heather M., Stromberg, Loreen R., Pedersen, Christine A., Hengartner, Astrid C., Klosterman, Katja E., Bridgewater, Haley A., Otieno, Vincent, Cheng, Qiuying, Anyona, Samuel B., Ouma, Collins, Raballah, Evans, Perkins, Douglas J., McMahon, Benjamin H., and Mukundan, Harshini

Published
2019
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35. Human NCR3 gene variants rs2736191 and rs11575837 influence susceptibility to the longitudinal development of pediatric severe malarial anemia

Author

Onyango, Clinton O., primary, Cheng, Qiuying, additional, Munde, Elly O., additional, Raballah, Evans, additional, Anyona, Samuel B., additional, McMahon, Benjamin H., additional, Lambert, Christophe G., additional, Onyango, Patrick O., additional, Schneider, Kristan A., additional, Perkins, Douglas J., additional, and Ouma, Collins, additional

Published
2022
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37. MACE prediction using high-dimensional machine learning and mechanistic interpretation: A longitudinal cohort study in US veterans

Author

Dhaubhadel, Sayera, primary, Kolade, Beauty, additional, Ribeiro, Ruy M., additional, Ganguly, Kumkum, additional, Hengartner, Nicolas W., additional, Bhattacharya, Tanmoy, additional, Cohn, Judith D., additional, Agarwal, Khushbu, additional, Cho, Kelly, additional, Costa, Lauren, additional, Ho, Yuk-Lam, additional, Murata, Allison E., additional, Murata, Glen H., additional, Vassy, Jason L., additional, Posner, Daniel C., additional, Gaziano, J. Michael, additional, Sun, Yan V., additional, Wilson, Peter W., additional, Madduri, Ravi, additional, Justice, Amy C., additional, Tsao, Phil, additional, O’Donnell, Christopher J., additional, Damrauer, Scott, additional, and McMahon, Benjamin H., additional

Published
2022
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40. Nonsynonymous amino acid changes in the α-chain of complement component 5 influence longitudinal susceptibility to Plasmodium falciparum infections and severe malarial anemia in kenyan children

Author

Raballah, Evans, primary, Wilding, Kristen, additional, Anyona, Samuel B., additional, Munde, Elly O., additional, Hurwitz, Ivy, additional, Onyango, Clinton O., additional, Ayieko, Cyrus, additional, Lambert, Christophe G., additional, Schneider, Kristan A., additional, Seidenberg, Philip D., additional, Ouma, Collins, additional, McMahon, Benjamin H., additional, Cheng, Qiuying, additional, and Perkins, Douglas J., additional

Published
2022
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41. An examination of the association between altitude and suicide deaths, suicide attempts, and suicidal ideation among veterans at both the patient and geospatial level

Author

Wang, Xiange, primary, Zamora-Resendiz, Rafael, additional, Shelley, Courtney D., additional, Manore, Carrie, additional, Liu, Xinlian, additional, Oslin, David W., additional, McMahon, Benjamin, additional, Beckham, Jean C., additional, Kimbrel, Nathan A., additional, and Crivelli, Silvia, additional

Published
2022
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46. Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Author

Mullins, Niamh, Kang, JooEun, Campos, Adrian I., Coleman, Jonathan R. I., Edwards, Alexis C., Galfalvy, Hanga, Levey, Daniel F., Lori, Adriana, Shabalin, Andrey, Starnawska, Anna, Su, Mei-Hsin, Watson, Hunna J., Adams, Mark, Awasthi, Swapnil, Gandal, Michael, Hafferty, Jonathan D., Hishimoto, Akitoyo, Kim, Minsoo, Okazaki, Satoshi, Otsuka, Ikuo, Ripke, Stephan, Ware, Erin B., Bergen, Andrew W., Berrettini, Wade H., Bohus, Martin, Brandt, Harry, Chang, Xiao, Chen, Wei J., Chen, Hsi-Chung, Crawford, Steven, Crow, Scott, DiBlasi, Emily, Duriez, Philibert, Fernández-Aranda, Fernando, Fichter, Manfred M., Gallinger, Steven, Glatt, Stephen J., Gorwood, Philip, Guo, Yiran, Hakonarson, Hakon, Halmi, Katherine A., Hwu, Hai-Gwo, Jain, Sonia, Jamain, Stéphane, Jiménez-Murcia, Susana, Johnson, Craig, Kaplan, Allan S., Kaye, Walter H., Keel, Pamela K., Kennedy, James L., Klump, Kelly L., Li, Dong, Liao, Shih-Cheng, Lieb, Klaus, Lilenfeld, Lisa, Liu, Chih-Min, Magistretti, Pierre J., Marshall, Christian R., Mitchell, James E., Monson, Eric T., Myers, Richard M., Pinto, Dalila, Powers, Abigail, Ramoz, Nicolas, Roepke, Stefan, Rozanov, Vsevolod, Scherer, Stephen W., Schmahl, Christian, Sokolowski, Marcus, Strober, Michael, Thornton, Laura M., Treasure, Janet, Tsuang, Ming T., Witt, Stephanie H., Woodside, D. Blake, Yilmaz, Zeynep, Zillich, Lea, Adolfsson, Rolf, Agartz, Ingrid, Air, Tracy M., Alda, Martin, Alfredsson, Lars, Andreassen, Ole A., Anjorin, Adebayo, Appadurai, Vivek, Soler Artigas, María, Van der Auwera, Sandra, Azevedo, M. Helena, Bass, Nicholas, Bau, Claiton H. D., Baune, Bernhard T., Bellivier, Frank, Berger, Klaus, Biernacka, Joanna M., Bigdeli, Tim B., Binder, Elisabeth B., Boehnke, Michael, Boks, Marco P., Bosch, Rosa, Braff, David L., Bryant, Richard, Budde, Monika, Byrne, Enda M., Cahn, Wiepke, Casas, Miguel, Castelao, Enrique, Cervilla, Jorge A., Chaumette, Boris, Cichon, Sven, Corvin, Aiden, Craddock, Nicholas, Craig, David, Degenhardt, Franziska, Djurovic, Srdjan, Edenberg, Howard J., Fanous, Ayman H., Foo, Jerome C., Forstner, Andreas J., Frye, Mark, Fullerton, Janice M., Gatt, Justine M., Gejman, Pablo V., Giegling, Ina, Grabe, Hans J., Green, Melissa J., Grevet, Eugenio H., Grigoroiu-Serbanescu, Maria, Gutierrez, Blanca, Guzman-Parra, Jose, Hamilton, Steven P., Hamshere, Marian L., Hartmann, Annette, 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, Konte, Bettina, Krebs, Marie-Odile, Landén, Mikael, Lawrence, Jacob, Leboyer, Marion, Lee, Phil H., Levinson, Douglas F., Liao, Calwing, Lissowska, Jolanta, Lucae, Susanne, Mayoral, Fermin, McElroy, Susan L., McGrath, Patrick, McGuffin, Peter, McQuillin, Andrew, Medland, Sarah E., Mehta, Divya, Melle, Ingrid, Milaneschi, Yuri, Mitchell, Philip B., Molina, Esther, Morken, Gunnar, Mortensen, Preben Bo, Müller-Myhsok, Bertram, Nievergelt, Caroline, Nimgaonkar, Vishwajit, Nöthen, Markus M., O'Donovan, Michael C., Ophoff, Roel A., Owen, Michael J., Pato, Carlos, Pato, Michele T., Penninx, Brenda W. J. H., Pimm, Jonathan, Pistis, Giorgio, 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., Rujescu, Dan, Sánchez-Mora, Cristina, Sanders, Alan R., Schofield, Peter R., Schulze, Thomas G., Scott, Laura J., Serretti, Alessandro, Shi, Jianxin, Shyn, Stanley I., Sirignano, Lea, Sklar, Pamela, Smeland, Olav B., Smoller, Jordan W., Sonuga-Barke, Edmund J. S., Spalletta, Gianfranco, Strauss, John S., Świątkowska, Beata, Trzaskowski, Maciej, Turecki, Gustavo, Vilar-Ribó, Laura, Vincent, John B., Völzke, Henry, Walters, James T. R., Shannon Weickert, Cynthia, Weickert, Thomas W., Weissman, Myrna M., Williams, Leanne M., Wray, Naomi R., Zai, Clement C., Ashley-Koch, Allison E., Beckham, Jean C., Hauser, Elizabeth R., Hauser, Michael A., Kimbrel, Nathan A., Lindquist, Jennifer H., McMahon, Benjamin, Oslin, David W., Qin, Xuejun, Agerbo, Esben, Børglum, Anders D., Breen, Gerome, Erlangsen, Annette, Esko, Tõnu, Gelernter, Joel, Hougaard, David M., Kessler, Ronald C., Kranzler, Henry R., Li, Qingqin S., Martin, Nicholas G., McIntosh, Andrew M., Mors, Ole, Nordentoft, Merete, Olsen, Catherine M., Porteous, David, Ursano, Robert J., Wasserman, Danuta, Werge, Thomas, Whiteman, David C., Bulik, Cynthia M., Coon, Hilary, Demontis, Ditte, Docherty, Anna R., Kuo, Po-Hsiu, Lewis, Cathryn M., Mann, J. John, Rentería, Miguel E., Smith, Daniel J., Stahl, Eli A., Stein, Murray B., Streit, Fabian, Willour, Virginia, Ruderfer, Douglas M., Mullins, Niamh, Kang, JooEun, Campos, Adrian I., Coleman, Jonathan R. I., Edwards, Alexis C., Galfalvy, Hanga, Levey, Daniel F., Lori, Adriana, Shabalin, Andrey, Starnawska, Anna, Su, Mei-Hsin, Watson, Hunna J., Adams, Mark, Awasthi, Swapnil, Gandal, Michael, Hafferty, Jonathan D., Hishimoto, Akitoyo, Kim, Minsoo, Okazaki, Satoshi, Otsuka, Ikuo, Ripke, Stephan, Ware, Erin B., Bergen, Andrew W., Berrettini, Wade H., Bohus, Martin, Brandt, Harry, Chang, Xiao, Chen, Wei J., Chen, Hsi-Chung, Crawford, Steven, Crow, Scott, DiBlasi, Emily, Duriez, Philibert, Fernández-Aranda, Fernando, Fichter, Manfred M., Gallinger, Steven, Glatt, Stephen J., Gorwood, Philip, Guo, Yiran, Hakonarson, Hakon, Halmi, Katherine A., Hwu, Hai-Gwo, Jain, Sonia, Jamain, Stéphane, Jiménez-Murcia, Susana, Johnson, Craig, Kaplan, Allan S., Kaye, Walter H., Keel, Pamela K., Kennedy, James L., Klump, Kelly L., Li, Dong, Liao, Shih-Cheng, Lieb, Klaus, Lilenfeld, Lisa, Liu, Chih-Min, Magistretti, Pierre J., Marshall, Christian R., Mitchell, James E., Monson, Eric T., Myers, Richard M., Pinto, Dalila, Powers, Abigail, Ramoz, Nicolas, Roepke, Stefan, Rozanov, Vsevolod, Scherer, Stephen W., Schmahl, Christian, Sokolowski, Marcus, Strober, Michael, Thornton, Laura M., Treasure, Janet, Tsuang, Ming T., Witt, Stephanie H., Woodside, D. Blake, Yilmaz, Zeynep, Zillich, Lea, Adolfsson, Rolf, Agartz, Ingrid, Air, Tracy M., Alda, Martin, Alfredsson, Lars, Andreassen, Ole A., Anjorin, Adebayo, Appadurai, Vivek, Soler Artigas, María, Van der Auwera, Sandra, Azevedo, M. Helena, Bass, Nicholas, Bau, Claiton H. D., Baune, Bernhard T., Bellivier, Frank, Berger, Klaus, Biernacka, Joanna M., Bigdeli, Tim B., Binder, Elisabeth B., Boehnke, Michael, Boks, Marco P., Bosch, Rosa, Braff, David L., Bryant, Richard, Budde, Monika, Byrne, Enda M., Cahn, Wiepke, Casas, Miguel, Castelao, Enrique, Cervilla, Jorge A., Chaumette, Boris, Cichon, Sven, Corvin, Aiden, Craddock, Nicholas, Craig, David, Degenhardt, Franziska, Djurovic, Srdjan, Edenberg, Howard J., Fanous, Ayman H., Foo, Jerome C., Forstner, Andreas J., Frye, Mark, Fullerton, Janice M., Gatt, Justine M., Gejman, Pablo V., Giegling, Ina, Grabe, Hans J., Green, Melissa J., Grevet, Eugenio H., Grigoroiu-Serbanescu, Maria, Gutierrez, Blanca, Guzman-Parra, Jose, Hamilton, Steven P., Hamshere, Marian L., Hartmann, Annette, 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, Konte, Bettina, Krebs, Marie-Odile, Landén, Mikael, Lawrence, Jacob, Leboyer, Marion, Lee, Phil H., Levinson, Douglas F., Liao, Calwing, Lissowska, Jolanta, Lucae, Susanne, Mayoral, Fermin, McElroy, Susan L., McGrath, Patrick, McGuffin, Peter, McQuillin, Andrew, Medland, Sarah E., Mehta, Divya, Melle, Ingrid, Milaneschi, Yuri, Mitchell, Philip B., Molina, Esther, Morken, Gunnar, Mortensen, Preben Bo, Müller-Myhsok, Bertram, Nievergelt, Caroline, Nimgaonkar, Vishwajit, Nöthen, Markus M., O'Donovan, Michael C., Ophoff, Roel A., Owen, Michael J., Pato, Carlos, Pato, Michele T., Penninx, Brenda W. J. H., Pimm, Jonathan, Pistis, Giorgio, 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., Rujescu, Dan, Sánchez-Mora, Cristina, Sanders, Alan R., Schofield, Peter R., Schulze, Thomas G., Scott, Laura J., Serretti, Alessandro, Shi, Jianxin, Shyn, Stanley I., Sirignano, Lea, Sklar, Pamela, Smeland, Olav B., Smoller, Jordan W., Sonuga-Barke, Edmund J. S., Spalletta, Gianfranco, Strauss, John S., Świątkowska, Beata, Trzaskowski, Maciej, Turecki, Gustavo, Vilar-Ribó, Laura, Vincent, John B., Völzke, Henry, Walters, James T. R., Shannon Weickert, Cynthia, Weickert, Thomas W., Weissman, Myrna M., Williams, Leanne M., Wray, Naomi R., Zai, Clement C., Ashley-Koch, Allison E., Beckham, Jean C., Hauser, Elizabeth R., Hauser, Michael A., Kimbrel, Nathan A., Lindquist, Jennifer H., McMahon, Benjamin, Oslin, David W., Qin, Xuejun, Agerbo, Esben, Børglum, Anders D., Breen, Gerome, Erlangsen, Annette, Esko, Tõnu, Gelernter, Joel, Hougaard, David M., Kessler, Ronald C., Kranzler, Henry R., Li, Qingqin S., Martin, Nicholas G., McIntosh, Andrew M., Mors, Ole, Nordentoft, Merete, Olsen, Catherine M., Porteous, David, Ursano, Robert J., Wasserman, Danuta, Werge, Thomas, Whiteman, David C., Bulik, Cynthia M., Coon, Hilary, Demontis, Ditte, Docherty, Anna R., Kuo, Po-Hsiu, Lewis, Cathryn M., Mann, J. John, Rentería, Miguel E., Smith, Daniel J., Stahl, Eli A., Stein, Murray B., Streit, Fabian, Willour, Virginia, and Ruderfer, Douglas M.

Abstract

BACKGROUND: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. METHODS: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. RESULTS: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. CONCLUSIONS: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.

Published
2022
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47. Genome-wide association study identifies four pan-ancestry loci for suicidal ideation in the Million Veterans Program.

Author

Ashley-Koch, Allison E., Kimbrel, Nathan A., Qin, Xue J., Lindquist, Jennifer H., Garrett, Melanie E., Dennis, Michelle F., Hair, Lauren P., Huffman, Jennifer E., Jacobson, Daniel A., Madduri, Ravi K., Coon, Hilary, Docherty, Anna R., Kang, Jooeun, Mullins, Niamh, Ruderfer, Douglas M., Harvey, Philip D., McMahon, Benjamin H., Oslin, David W., Hauser, Elizabeth R., and Hauser, Michael A.

Subjects
SUICIDAL ideation, GENOME-wide association studies, ATTEMPTED suicide, LOCUS (Genetics), SUICIDAL behavior, DIALECTICAL behavior therapy, VETERANS
Abstract

Suicidal ideation (SI) often precedes and predicts suicide attempt and death, is the most common suicidal phenotype and is over-represented in veterans. The genetic architecture of SI in the absence of suicide attempt (SA) is unknown, yet believed to have distinct and overlapping risk with other suicidal behaviors. We performed the first GWAS of SI without SA in the Million Veteran Program (MVP), identifying 99,814 SI cases from electronic health records without a history of SA or suicide death (SD) and 512,567 controls without SI, SA or SD. GWAS was performed separately in the four largest ancestry groups, controlling for sex, age and genetic substructure. Ancestry-specific results were combined via meta-analysis to identify pan-ancestry loci. Four genome-wide significant (GWS) loci were identified in the pan-ancestry meta-analysis with loci on chromosomes 6 and 9 associated with suicide attempt in an independent sample. Pan-ancestry gene-based analysis identified GWS associations with DRD2, DCC, FBXL19, BCL7C, CTF1, ANNK1, and EXD3. Gene-set analysis implicated synaptic and startle response pathways (q's<0.05). European ancestry (EA) analysis identified GWS loci on chromosomes 6 and 9, as well as GWS gene associations in EXD3, DRD2, and DCC. No other ancestry-specific GWS results were identified, underscoring the need to increase representation of diverse individuals. The genetic correlation of SI and SA within MVP was high (rG = 0.87; p = 1.09e-50), as well as with post-traumatic stress disorder (PTSD; rG = 0.78; p = 1.98e-95) and major depressive disorder (MDD; rG = 0.78; p = 8.33e-83). Conditional analysis on PTSD and MDD attenuated most pan-ancestry and EA GWS signals for SI without SA to nominal significance, with the exception of EXD3 which remained GWS. Our novel findings support a polygenic and complex architecture for SI without SA which is largely shared with SA and overlaps with psychiatric conditions frequently comorbid with suicidal behaviors. Author summary: Suicidal ideation often precedes and predicts suicide attempt and death, is the most common suicidal phenotype and is over-represented in veterans. The underlying genetic risk of suicidal ideation in the absence of suicide attempt is unknown, yet believed to have distinct and overlapping risk with other suicidal behaviors. To find these potential genetic risk variants, we examined blood samples from over 600,000 veterans from Million Veteran Program. Four regions of the genome were significantly associated with risk of suicidal ideation in the absence of suicide attempt across four major ancestral groups (European, African, Hispanic and Asian). Two of those regions on chromosomes 6 and 9, were also significantly associated with suicidal ideation in European ancestry-only veterans, as well as with suicide attempt in an independent sample of mostly civilian individuals. No other genetic regions were identified in other ancestry-only analyses, underscoring the need to increase representation of individuals with diverse ancestry. Suicidal ideation without suicide attempt was genetically correlated with other psychiatric disorders, including suicide attempt, post-traumatic stress disorder and major depressive disorder. Our novel findings suggest that multiple genes contribute to genetic risk for suicidal ideation and that the genetic risk is largely shared with suicide attempt and other psychiatric conditions that frequently co-occur with suicidal ideation. [ABSTRACT FROM AUTHOR]

Published
2023
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49. A Unified Model of Protein Dynamics

Author

Frauenfelder, Hans, Chen, Guo, Berendzen, Joel, Fenimore, Paul W., Jansson, Helén, McMahon, Benjamin H., Stroe, Izabela R., Swenson, Jan, Young, Robert D., and Frauenfelder, Hans

Published
2009
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