Search

Your search keyword '"Ashley, Euan A."' showing total 1,913 results
Start Over Author "Ashley, Euan A."
1,913 results on '"Ashley, Euan A."'

1. Towards Democratization of Subspeciality Medical Expertise

Author

O'Sullivan, Jack W., Palepu, Anil, Saab, Khaled, Weng, Wei-Hung, Cheng, Yong, Chu, Emily, Desai, Yaanik, Elezaby, Aly, Kim, Daniel Seung, Lan, Roy, Tang, Wilson, Tapaskar, Natalie, Parikh, Victoria, Jain, Sneha S., Kulkarni, Kavita, Mansfield, Philip, Webster, Dale, Gottweis, Juraj, Barral, Joelle, Schaekermann, Mike, Tanno, Ryutaro, Mahdavi, S. Sara, Natarajan, Vivek, Karthikesalingam, Alan, Ashley, Euan, and Tu, Tao

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence
Abstract

The scarcity of subspecialist medical expertise, particularly in rare, complex and life-threatening diseases, poses a significant challenge for healthcare delivery. This issue is particularly acute in cardiology where timely, accurate management determines outcomes. We explored the potential of AMIE (Articulate Medical Intelligence Explorer), a large language model (LLM)-based experimental AI system optimized for diagnostic dialogue, to potentially augment and support clinical decision-making in this challenging context. We curated a real-world dataset of 204 complex cases from a subspecialist cardiology practice, including results for electrocardiograms, echocardiograms, cardiac MRI, genetic tests, and cardiopulmonary stress tests. We developed a ten-domain evaluation rubric used by subspecialists to evaluate the quality of diagnosis and clinical management plans produced by general cardiologists or AMIE, the latter enhanced with web-search and self-critique capabilities. AMIE was rated superior to general cardiologists for 5 of the 10 domains (with preference ranging from 9% to 20%), and equivalent for the rest. Access to AMIE's response improved cardiologists' overall response quality in 63.7% of cases while lowering quality in just 3.4%. Cardiologists' responses with access to AMIE were superior to cardiologist responses without access to AMIE for all 10 domains. Qualitative examinations suggest AMIE and general cardiologist could complement each other, with AMIE thorough and sensitive, while general cardiologist concise and specific. Overall, our results suggest that specialized medical LLMs have the potential to augment general cardiologists' capabilities by bridging gaps in subspecialty expertise, though further research and validation are essential for wide clinical utility.

Published
2024

2. Physiological Adaptations to Progressive Endurance Exercise Training in Adult and Aged Rats: Insights from the Molecular Transducers of Physical Activity Consortium (MoTrPAC)

Author

Schenk, Simon, Sagendorf, Tyler J, Many, Gina M, Lira, Ana K, de Sousa, Luis GO, Bae, Dam, Cicha, Michael, Kramer, Kyle S, Muehlbauer, Michael, Hevener, Andrea L, Rector, R Scott, Thyfault, John P, Williams, John P, Goodyear, Laurie J, Esser, Karyn A, Newgard, Christopher B, Bodine, Sue C, Adkins, Joshua N, Albertson, Brent G, Amar, David, Amper, Mary Anne S, Ashley, Euan, Bamman, Marcas M, Barnes, Jerry, Bergman, Bryan C, Bessesen, Daniel H, Buford, Thomas W, Burant, Charles F, Cutter, Gary R, De Sousa, Luis Gustavo Oliveria, Fernández, Facundo M, Gaul, David A, Ge, Yongchao, Goodpaster, Bret H, Guevara, Kristy, Hirshman, Michael F, Huffman, Kim M, Jackson, Bailey E, Jankowski, Catherine M, Jimenez-Morales, David, Kohrt, Wendy M, Kraus, William E, Lessard, Sarah J, Lester, Bridget, Lindholm, Malene E, Many, Gina, Marjanovic, Nada, Marshall, Andrea G, Melanson, Edward L, Miller, Michael E, Moreau, Kerrie L, Nair, Venugopalan D, Ortlund, Eric A, Qian, Wei-Jun, Rasmussen, Blake B, Richards, Collyn Z-T, Rushing, Scott, Sanford, James A, Schauer, Irene E, Schwartz, Robert S, Sealfon, Stuart C, Seenarine, Nitish, Sparks, Lauren M, Stowe, Cynthia L, Talton, Jennifer W, Teng, Christopher, Tesfa, Nathan D, Thalacker-Mercer, Anna, Trappe, Scott, Trappe, Todd A, Vasoya, Mital, Wheeler, Matthew T, Walkup, Michael P, Yan, Zhen, and Zhen, Jimmy

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Physical Activity, Cardiovascular, Prevention, Behavioral and Social Science, Animals, Male, Rats, Inbred F344, Female, Physical Conditioning, Animal, Adaptation, Physiological, Rats, Aging, Physical Endurance, Muscle, Skeletal, Endurance Training, training, treadmill, maximal oxygen uptake, body composition, citrate synthase, skeletal muscle, biorepository, aging, MoTrPAC Study Group, Medical physiology
Abstract

While regular physical activity is a cornerstone of health, wellness, and vitality, the impact of endurance exercise training on molecular signaling within and across tissues remains to be delineated. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to characterize molecular networks underlying the adaptive response to exercise. Here, we describe the endurance exercise training studies undertaken by the Preclinical Animal Sites Studies component of MoTrPAC, in which we sought to develop and implement a standardized endurance exercise protocol in a large cohort of rats. To this end, Adult (6-mo) and Aged (18-mo) female (n = 151) and male (n = 143) Fischer 344 rats were subjected to progressive treadmill training (5 d/wk, ∼70%-75% VO2max) for 1, 2, 4, or 8 wk; sedentary rats were studied as the control group. A total of 18 solid tissues, as well as blood, plasma, and feces, were collected to establish a publicly accessible biorepository and for extensive omics-based analyses by MoTrPAC. Treadmill training was highly effective, with robust improvements in skeletal muscle citrate synthase activity in as little as 1-2 wk and improvements in maximum run speed and maximal oxygen uptake by 4-8 wk. For body mass and composition, notable age- and sex-dependent responses were observed. This work in mature, treadmill-trained rats represents the most comprehensive and publicly accessible tissue biorepository, to date, and provides an unprecedented resource for studying temporal-, sex-, and age-specific responses to endurance exercise training in a preclinical rat model.

Published
2024

4. Temporal dynamics of the multi-omic response to endurance exercise training

Author

Bae, Dam, Dasari, Surendra, Dennis, Courtney, Evans, Charles R, Gaul, David A, Ilkayeva, Olga, Ivanova, Anna A, Kachman, Maureen T, Keshishian, Hasmik, Lanza, Ian R, Lira, Ana C, Muehlbauer, Michael J, Nair, Venugopalan D, Piehowski, Paul D, Rooney, Jessica L, Smith, Kevin S, Stowe, Cynthia L, Zhao, Bingqing, Clark, Natalie M, Jimenez-Morales, David, Lindholm, Malene E, Many, Gina M, Sanford, James A, Smith, Gregory R, Vetr, Nikolai G, Zhang, Tiantian, Almagro Armenteros, Jose J, Avila-Pacheco, Julian, Bararpour, Nasim, Ge, Yongchao, Hou, Zhenxin, Marwaha, Shruti, Presby, David M, Natarajan Raja, Archana, Savage, Evan M, Steep, Alec, Sun, Yifei, Wu, Si, Zhen, Jimmy, Bodine, Sue C, Esser, Karyn A, Goodyear, Laurie J, Schenk, Simon, Montgomery, Stephen B, Fernández, Facundo M, Sealfon, Stuart C, Snyder, Michael P, Adkins, Joshua N, Ashley, Euan, Burant, Charles F, Carr, Steven A, Clish, Clary B, Cutter, Gary, Gerszten, Robert E, Kraus, William E, Li, Jun Z, Miller, Michael E, Nair, K Sreekumaran, Newgard, Christopher, Ortlund, Eric A, Qian, Wei-Jun, Tracy, Russell, Walsh, Martin J, Wheeler, Matthew T, Dalton, Karen P, Hastie, Trevor, Hershman, Steven G, Samdarshi, Mihir, Teng, Christopher, Tibshirani, Rob, Cornell, Elaine, Gagne, Nicole, May, Sandy, Bouverat, Brian, Leeuwenburgh, Christiaan, Lu, Ching-ju, Pahor, Marco, Hsu, Fang-Chi, Rushing, Scott, Walkup, Michael P, Nicklas, Barbara, Rejeski, W Jack, Williams, John P, Xia, Ashley, Albertson, Brent G, Barton, Elisabeth R, Booth, Frank W, Caputo, Tiziana, Cicha, Michael, De Sousa, Luis Gustavo Oliveira, Farrar, Roger, Hevener, Andrea L, Hirshman, Michael F, Jackson, Bailey E, Ke, Benjamin G, Kramer, Kyle S, Lessard, Sarah J, Makarewicz, Nathan S, Marshall, Andrea G, and Nigro, Pasquale

Subjects
Health Sciences, Sports Science and Exercise, Prevention, Human Genome, Cardiovascular, Behavioral and Social Science, Genetics, Physical Activity, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Generic health relevance, Inflammatory and immune system, Good Health and Well Being, Animals, Female, Humans, Male, Rats, Acetylation, Blood, Cardiovascular Diseases, Databases, Factual, Endurance Training, Epigenome, Inflammatory Bowel Diseases, Internet, Lipidomics, Metabolome, Mitochondria, Multiomics, Non-alcoholic Fatty Liver Disease, Organ Specificity, Phosphorylation, Physical Conditioning, Animal, Physical Endurance, Proteome, Proteomics, Time Factors, Transcriptome, Ubiquitination, Wounds and Injuries, MoTrPAC Study Group, Lead Analysts, MoTrPAC Study Group, General Science & Technology
Abstract

Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood1-3. Here, the Molecular Transducers of Physical Activity Consortium4 profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and female Rattus norvegicus over eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository ( https://motrpac-data.org/ ).

Published
2024

5. Regional variation in cardiovascular genes enables a tractable genome editing strategy

Author

Krysov, Vikki, Wilson, Rachel, Ten, Nicholas, Youlton, Nathan, De Jong, Hannah, Sutton, Shirley, Huang, Yong, Reuter, Choloe, Grove, Megan, Wheeler, Matthew, Ashley, Euan, and Parikh, Victoria

Abstract

Recent rise in the number and therapeutic potential of genome engineering technologies has generated excitement for their application in cardiovascular therapeutics. One significant barrier to their implementation is costly and time-consuming reagent development for novel variants. We have previously shown that disease-associated variants cluster in functional protein domains where variants are found in the general population at lower frequency.1,2 These findings may provide an opportunity to pre-emptively target multiple pathogenic variant clusters (“pathogenic hotspots”) that address a majority of pathogenic and likely pathogenic (P/LP) variants for a given disease with a small number of pre-designed reagents. Prime editing, a search-and-replace editing technology, can overwrite genomic sequences with a reverse transcriptase guided by a Cas9 nickase and prime editing guide (peg)RNA.3 We hypothesized that most cardiovascular disease-relevant genes in ClinVar would display regional variant clustering, and that multiple variants within a regional hotspot could be targeted with a limited number of prime editing reagents.

Published
2024

6. A Generalizable Deep Learning System for Cardiac MRI

Author

Shad, Rohan, Zakka, Cyril, Kaur, Dhamanpreet, Fong, Robyn, Filice, Ross Warren, Mongan, John, Kalianos, Kimberly, Khandwala, Nishith, Eng, David, Leipzig, Matthew, Witschey, Walter, de Feria, Alejandro, Ferrari, Victor, Ashley, Euan, Acker, Michael A., Langlotz, Curtis, and Hiesinger, William

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, I.2.10
Abstract

Cardiac MRI allows for a comprehensive assessment of myocardial structure, function, and tissue characteristics. Here we describe a foundational vision system for cardiac MRI, capable of representing the breadth of human cardiovascular disease and health. Our deep learning model is trained via self-supervised contrastive learning, by which visual concepts in cine-sequence cardiac MRI scans are learned from the raw text of the accompanying radiology reports. We train and evaluate our model on data from four large academic clinical institutions in the United States. We additionally showcase the performance of our models on the UK BioBank, and two additional publicly available external datasets. We explore emergent zero-shot capabilities of our system, and demonstrate remarkable performance across a range of tasks; including the problem of left ventricular ejection fraction regression, and the diagnosis of 35 different conditions such as cardiac amyloidosis and hypertrophic cardiomyopathy. We show that our deep learning system is capable of not only understanding the staggering complexity of human cardiovascular disease, but can be directed towards clinical problems of interest yielding impressive, clinical grade diagnostic accuracy with a fraction of the training data typically required for such tasks., Comment: 21 page main manuscript, 4 figures. Supplementary Appendix and code will be made available on publication

Published
2023

7. Learning epistatic polygenic phenotypes with Boolean interactions.

Author

Arnaout, Rima, Brown, Ben, Priest, James, Yu, Bin, Behr, Merle, Kumbier, Karl, Cordova-Palomera, Aldo, Aguirre, Matthew, Ronen, Omer, Ye, Chengzhong, Ashley, Euan, and Butte, Atul

Subjects
Humans, Epistasis, Genetic, Genome-Wide Association Study, Phenotype, Multifactorial Inheritance, Logistic Models, Polymorphism, Single Nucleotide
Abstract

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

Published
2024

8. A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease.

Author

Hughes, J, Tooley, James, Torres Soto, Jessica, Ostropolets, Anna, Poterucha, Tim, Christensen, Matthew, Yuan, Neal, Ehlert, Ben, Kaur, Dhamanpreet, Kang, Guson, Rogers, Albert, Narayan, Sanjiv, Elias, Pierre, Ouyang, David, Ashley, Euan, Zou, James, and Perez, Marco

Abstract

The electrocardiogram (ECG) is the most frequently performed cardiovascular diagnostic test, but it is unclear how much information resting ECGs contain about long term cardiovascular risk. Here we report that a deep convolutional neural network can accurately predict the long-term risk of cardiovascular mortality and disease based on a resting ECG alone. Using a large dataset of resting 12-lead ECGs collected at Stanford University Medical Center, we developed SEER, the Stanford Estimator of Electrocardiogram Risk. SEER predicts 5-year cardiovascular mortality with an area under the receiver operator characteristic curve (AUC) of 0.83 in a held-out test set at Stanford, and with AUCs of 0.78 and 0.83 respectively when independently evaluated at Cedars-Sinai Medical Center and Columbia University Irving Medical Center. SEER predicts 5-year atherosclerotic disease (ASCVD) with an AUC of 0.67, similar to the Pooled Cohort Equations for ASCVD Risk, while being only modestly correlated. When used in conjunction with the Pooled Cohort Equations, SEER accurately reclassified 16% of patients from low to moderate risk, uncovering a group with an actual average 9.9% 10-year ASCVD risk who would not have otherwise been indicated for statin therapy. SEER can also predict several other cardiovascular conditions such as heart failure and atrial fibrillation. Using only lead I of the ECG it predicts 5-year cardiovascular mortality with an AUC of 0.80. SEER, used alongside the Pooled Cohort Equations and other risk tools, can substantially improve cardiovascular risk stratification and aid in medical decision making.

Published
2023

12. Almanac: Retrieval-Augmented Language Models for Clinical Medicine

Author

Zakka, Cyril, Chaurasia, Akash, Shad, Rohan, Dalal, Alex R., Kim, Jennifer L., Moor, Michael, Alexander, Kevin, Ashley, Euan, Boyd, Jack, Boyd, Kathleen, Hirsch, Karen, Langlotz, Curt, Nelson, Joanna, and Hiesinger, William

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Large-language models have recently demonstrated impressive zero-shot capabilities in a variety of natural language tasks such as summarization, dialogue generation, and question-answering. Despite many promising applications in clinical medicine, adoption of these models in real-world settings has been largely limited by their tendency to generate incorrect and sometimes even toxic statements. In this study, we develop Almanac, a large language model framework augmented with retrieval capabilities for medical guideline and treatment recommendations. Performance on a novel dataset of clinical scenarios (n = 130) evaluated by a panel of 5 board-certified and resident physicians demonstrates significant increases in factuality (mean of 18% at p-value < 0.05) across all specialties, with improvements in completeness and safety. Our results demonstrate the potential for large language models to be effective tools in the clinical decision-making process, while also emphasizing the importance of careful testing and deployment to mitigate their shortcomings.

Published
2023

13. Genetic architecture of cardiac dynamic flow volumes

Author

Gomes, Bruna, Singh, Aditya, O’Sullivan, Jack W., Schnurr, Theresia M., Goddard, Pagé C., Loong, Shaun, Amar, David, Hughes, J. Weston, Kostur, Mykhailo, Haddad, Francois, Salerno, Michael, Foo, Roger, Montgomery, Stephen B., Parikh, Victoria N., Meder, Benjamin, and Ashley, Euan A.

Published
2024
Full Text
View/download PDF

14. Long-Term Outcomes After Septal Reduction Therapies in Obstructive Hypertrophic Cardiomyopathy: Insights From the SHARE Registry

Author

Maurizi, Niccolò, Antiochos, Panagiotis, Owens, Anjali, Lakdwala, Neal, Saberi, Sara, Russell, Mark W., Fumagalli, Carlo, Skalidis, Ioannis, Lin, Kimberly Y., Nathan, Ashwin S., De Feria Alsina, Alejandro, Reza, Nosheen, Stendahl, John C., Abrams, Dominic, Semsarian, Christopher, Clagget, Brian, Lampert, Rachel, Wheeler, Matthew, Parikh, Victoria N., Ashley, Euan, Michels, Michelle, Rossano, Joseph, Ryan, Thomas D., Ingles, Jodie, Ware, James, Ho, Carolyn Y., Helms, Adam S., Day, Sharlene M., and Olivotto, Iacopo

Published
2024
Full Text
View/download PDF

15. Re-envisioning community genetics: community empowerment in preventive genomics

Author

Wand, Hannah, Martschenko, Daphne O, Smitherman, Annamaria, Michelson, Sheryl, Pun, Ting, Witte, John S, Scott, Stuart A, Cho, Mildred K, and Ashley, Euan A

Subjects
Biological Sciences, Genetics, Clinical Research, Health Services, Prevention, Generic health relevance, Good Health and Well Being, Preventive Genomics Program Co-Design Working Group, Community engagement, Implementation science, Polygenic scores, Precision public health, Program development
Abstract

As genomic technologies rapidly develop, polygenic scores (PGS) are entering into a growing conversation on how to improve precision in public health and prevent chronic disease. While the integration of PGS into public health and clinical services raises potential benefits, it also introduces potential harms. In particular, there is a high level of uncertainty about how to incorporate PGS into clinical settings in a manner that is equitable, just, and aligned with the long-term goals of many healthcare systems to support person-centered and value-based care. This paper argues that any conversation about whether and how to design and implement PGS clinical services requires dynamic engagement with local communities, patients, and families. These parties often face the consequences, both positive and negative, of such uncertainties and should therefore drive clinical translation. As a collaborative effort between hospital stakeholders, community partners, and researchers, this paper describes a community-empowered co-design process for addressing uncertainty and making programmatic decisions about the implementation of PGS into clinical services. We provide a framework for others interested in designing clinical programs that are responsive to, and inclusive and respectful of, local communities.

Published
2023

16. Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department

Author

Ram-Mohan, Nikhil, Rogers, Angela J, Blish, Catherine A, Nadeau, Kari C, Zudock, Elizabeth J, Kim, David, Quinn, James V, Sun, Lixian, Liesenfeld, Oliver, Group, The Stanford COVID-19 Biobank Study, Yang, Samuel, follows:, The additional author members of the Stanford COVID-19 Biobank Study Group are as, Hashemi, Marjan M, Tjandra, Kristel C, Newberry, Jennifer A, Blomkalns, Andra L, O’Hara, Ruth, Ashley, Euan, Mann, Rosen, Visweswaran, Anita, Ranganath, Thanmayi, Roque, Jonasel, Manohar, Monali, Din, Hena Naz, Kumar, Komal, Jee, Kathryn, Noon, Brigit, Anderson, Jill, Fay, Bethany, Schreiber, Donald, Zhao, Nancy, Vergara, Rosemary, McKechnie, Julia, Wilk, Aaron, de la Parte, Lauren, Dantzler, Kathleen Whittle, Ty, Maureen, Kathale, Nimish, Rustagi, Arjun, Martinez-Colon, Giovanny, Ivison, Geoff, Pi, Ruoxi, Lee, Maddie, Brewer, Rachel, Hollis, Taylor, Baird, Andrea, Ugur, Michele, Bogusch, Drina, Nahass, Georgie, Haider, Kazim, Tran, Kim Quyen Thi, Simpson, Laura, Tal, Michal, Chang, Iris, Do, Evan, Fernandes, Andrea, Lee, Allie, Ahuja, Neera, Snow, Theo, and Krempski, James

Subjects
Microbiology, Biological Sciences, Lung, Emerging Infectious Diseases, Vaccine Related, Infectious Diseases, Clinical Research, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, 4.1 Discovery and preclinical testing of markers and technologies, Infection, Good Health and Well Being, Humans, Female, Middle Aged, Male, COVID-19, SARS-CoV-2, Coinfection, RNA, Messenger, Bacteria, Bacterial Infections, Stanford COVID-19 Biobank Study Group, bacterial superinfection, coinfection, diagnosis, emergency department, host response classifier, mortality prediction, prognosis
Abstract

Clinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial coinfection, and determining illness severity since current practices require separate workflows. Here, we explore the accuracy of the IMX-BVN-3/IMX-SEV-3 29 mRNA host response classifiers in simultaneously detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and bacterial coinfections and predicting clinical severity of COVID-19. A total of 161 patients with PCR-confirmed COVID-19 (52.2% female; median age, 50.0 years; 51% hospitalized; 5.6% deaths) were enrolled at the Stanford Hospital ED. RNA was extracted (2.5 mL whole blood in PAXgene blood RNA), and 29 host mRNAs in response to the infection were quantified using Nanostring nCounter. The IMX-BVN-3 classifier identified SARS-CoV-2 infection in 151 patients with a sensitivity of 93.8%. Six of 10 patients undetected by the classifier had positive COVID tests more than 9 days prior to enrollment, and the remaining patients oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial coinfection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e., Clostridioides difficile colitis (n = 1), urinary tract infection (n = 1), and clinically diagnosed bacterial infections (n = 3), for a specificity of 99.4%. Two of 101 (2.8%) patients in the IMX-SEV-3 "Low" severity classification and 7/60 (11.7%) in the "Moderate" severity classification died within 30 days of enrollment. IMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19 and bacterial coinfections and predicted patients' risk of death. A point-of-care version of these classifiers, under development, could improve ED patient management, including more accurate treatment decisions and optimized resource utilization. IMPORTANCE We assay the utility of the single-test IMX-BVN-3/IMX-SEV-3 classifiers that require just 2.5 mL of patient blood in concurrently detecting viral and bacterial infections as well as predicting the severity and 30-day outcome from the infection. A point-of-care device, in development, will circumvent the need for blood culturing and drastically reduce the time needed to detect an infection. This will negate the need for empirical use of broad-spectrum antibiotics and allow for antibiotic use stewardship. Additionally, accurate classification of the severity of infection and the prediction of 30-day severe outcomes will allow for appropriate allocation of hospital resources.

Published
2022

17. Endocannabinoid dysfunction in neurological disease: neuro-ocular DAGLA-related syndrome (NODRS)

Author

Bainbridge, Matthew N, Mazumder, Aloran, Ogasawara, Daisuke, Jamra, Rami Abou, Bernard, Geneviève, Bertini, Enrico, Burglen, Lydie, Cope, Heidi, Crawford, Ali, Derksen, Alexa, Dure, Leon, Gantz, Emily, Koch-Hogrebe, Margarete, Hurst, Anna CE, Mahida, Sonal, Marshall, Paige, Micalizzi, Alessia, Novelli, Antonio, Peng, Hongfan, Medicine, Rady Children's Institute for Genomic, Rodriguez, Diana, Robbins, Shira L, Rutledge, S Lane, Scalise, Roberta, Schließke, Sophia, Shashi, Vandana, Srivastava, Siddharth, Thiffault, Isabella, Topol, Sarah, Acosta, Maria T, Adam, Margaret, Adams, David R, Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A, Azamian, Mahshid S, Bacino, Carlos A, Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H, Behrens, Edward, Bejerano, Gill, Bennet, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A, Berry, Gerard T, Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C, Brokamp, Elly, Brown, Gabrielle, Burke, Elizabeth A, Burrage, Lindsay C, Butte, Manish J, Byers, Peter, Byrd, William E, Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Chang, Ta Chen Peter, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Clark, Gary D, Coakley, Terra R, Cobban, Laurel A, Cogan, Joy D, Coggins, Matthew, Cole, F Sessions, Colley, Heather A, Cooper, Cynthia M, Craigen, William J, Crouse, Andrew B, Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G, Deardorff, Matthew, Dell’Angelica, Esteban C, Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L, Douine, Emilie D, Duncan, Laura, and Earl, Dawn

Subjects
Biotechnology, Brain Disorders, Genetics, Neurosciences, Clinical Research, Rare Diseases, Eye Disease and Disorders of Vision, Pediatric, Aetiology, 2.1 Biological and endogenous factors, Neurological, Humans, Child, Endocannabinoids, Phenotype, Nervous System Diseases, Heterozygote, Syndrome, Mutant Proteins, endocannabinoid, episodic ataxia, paroxysmal tonic upgaze, nystagmus, developmental delay, Rady Children's Institute for Genomic Medicine, Undiagnosed Disease Network, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

The endocannabinoid system is a highly conserved and ubiquitous signalling pathway with broad-ranging effects. Despite critical pathway functions, gene variants have not previously been conclusively linked to human disease. We identified nine children from eight families with heterozygous, de novo truncating variants in the last exon of DAGLA with a neuro-ocular phenotype characterized by developmental delay, ataxia and complex oculomotor abnormality. All children displayed paroxysms of nystagmus or eye deviation accompanied by compensatory head posture and worsened incoordination most frequently after waking. RNA sequencing showed clear expression of the truncated transcript and no differences were found between mutant and wild-type DAGLA activity. Immunofluorescence staining of patient-derived fibroblasts and HEK cells expressing the mutant protein showed distinct perinuclear aggregation not detected in control samples. This report establishes truncating variants in the last DAGLA exon as the cause of a unique paediatric syndrome. Because enzymatic activity was preserved, the observed mislocalization of the truncated protein may account for the observed phenotype. Potential mechanisms include DAGLA haploinsufficiency at the plasma membrane or dominant negative effect. To our knowledge, this is the first report directly linking an endocannabinoid system component with human genetic disease and sets the stage for potential future therapeutic avenues.

Published
2022

18. Cohort Expansion and Genotype-Phenotype Analysis of RAB11A-Associated Neurodevelopmental Disorder

Author

Acosta, Maria T., Adam, Margaret, Adams, David R., Alvarez, Raquel L., Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A., Bacino, Carlos A., Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bellen, Hugo J., Bennett, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C., Brokamp, Elly, Brown, Gabrielle, Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Peter Chang, Ta Chen, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Cole, F. Sessions, Colley, Heather A., Cooper, Cynthia M., Cope, Heidi, Corona, Rosario, Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D'Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G., Dell'Angelica, Esteban C., Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L., Douine, Emilie D., Duncan, Laura, Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Falk, Marni, Fieg, Elizabeth L., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Gahl, William A., Glass, Ian, Gochuico, Bernadette, Goddard, Page C., Godfrey, Rena A., Golden-Grant, Katie, Grajewski, Alana, Hadley, Don, Hahn, Sihoun, Halley, Meghan C., Hamid, Rizwan, Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Hutchison, Sarah, Introne, Wendy, Isasi, Rosario, Izumi, Kosuke, Jamal, Fariha, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Jean-Marie, Orpa, Jobanputra, Vaidehi, Karaviti, Lefkothea, Kennedy, Jennifer, Ketkar, Shamika, Kiley, Dana, Kilich, Gonench, Kobren, Shilpa N., Kohane, Isaac S., Kohler, Jennefer N., Korrick, Susan, Kozuira, Mary, Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Lalani, Seema R., Lam, Byron, Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., LeBlanc, Kimberly, Lee, Brendan H., Levitt, Roy, Lewis, Richard A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Maghiro, AudreyStephannie, Mahoney, Rachel, Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Maravilla, Kenneth, Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McConkie-Rosell, Allyn, McCray, Alexa T., McGee, Elisabeth, Mefford, Heather, Merritt, J. Lawrence, Might, Matthew, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo, Mulvihill, John, Nakano-Okuno, Mariko, Nelson, Stanley F., Newman, John H., Nicholas, Sarah K., Nickerson, Deborah, Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Pallais, J. Carl, Palmer, Christina G.S., Papp, Jeanette C., Parker, Neil H., Phillips, John A., III, Posey, Jennifer E., Potocki, Lorraine, Pusey Swerdzewski, Barbara N., Quinlan, Aaron, Rao, Deepak A., Raper, Anna, Raskind, Wendy, Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenfeld, Jill A., Rosenwasser, Natalie, Rossignol, Francis, Ruzhnikov, Maura, Sacco, Ralph, Sampson, Jacinda B., Saporta, Mario, Schaechter, Judy, Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Scott, C. Ron, Shashi, Vandana, Shin, Jimann, Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solem, Emily, Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stoler, Joan M., Sullivan, Kathleen, Sullivan, Jennifer A., Sun, Angela, Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tan, Queenie K.-G., Tan, Amelia L.M., Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Ungar, Rachel A., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Walker, Melissa, Wallace, Stephanie, Walley, Nicole M., Wambach, Jennifer, Wan, Jijun, Wang, Lee-kai, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Hubshman, Monika Weisz, Wener, Mark, Wenger, Tara, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Worley, Kim, Xiao, Changrui, Yamamoto, Shinya, Yang, John, Zhang, Zhe, Zuchner, Stephan, Borroto, Maria Carla, Patel, Heena, Srivastava, Siddharth, Swanson, Lindsay C., Keren, Boris, Whalen, Sandra, Mignot, Cyril, Wang, Xiaodong, Chen, Qian, McLean, Scott, Littlejohn, Rebecca O., Emrick, Lisa, Attali, Ruben, Lesca, Gaetan, Acquaviva-Bourdain, Cecile, Sarret, Catherine, Seaver, Laurie H., Platzer, Konrad, Bartolomaeus, Tobias, Wünsch, Cornelia, Fischer, Susann, Rodriguez Barreto, Ana Maria, Granadillo, Jorge L., Schreiner, Elisabeth, Brunet, Theresa, Schatz, Ulrich A., Thiffault, Isabelle, Mullegama, Sureni V., Michaud, Jacques L., Hamdan, Fadi F., Rossignol, Elsa, and Campeau, Philippe M.

Published
2024
Full Text
View/download PDF

19. Loss-of-function in RBBP5 results in a syndromic neurodevelopmental disorder associated with microcephaly

Author

Acosta, Maria T., Adams, David R., Raquel, Alvarez, L., Alvey, Justin, Allworth, Aimee, Andrews, Ashley, Ashley, Euan A., Bacino, Carlos A., Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bellen, Hugo J., Bennett, Jimmy, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Briere, Lauren C., Brown, Gabrielle, Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Carvhalo Neto, George D., Cassini, Thomas, Peter Chang, Ta Chen, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Chinn, Ivan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Cole, F. Sessions, Colley, Heather A., Cope, Heidi, Corona, Rosario, Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G., Dell'Angelica, Esteban C., Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L., Douine, Emilie D., Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Falk, Marni, Fieg, Elizabeth L., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Gahl, William A., Glass, Ian, Gochuico, Bernadette, Goddard, Page C., Godfrey, Rena A., Grajewski, Alana, Hadley, Don, Halley, Meghan C., Hamid, Rizwan, Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Hutchison, Sarah, Introne, Wendy, Isasi, Rosario, Izumi, Kosuke, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Jean-Marie, Orpa, Jobanputra, Vaidehi, Kaitryn, Emerald, Ketkar, Shamika, Kiley, Dana, Kilich, Gonench, Kobren, Shilpa N., Kohane, Isaac S., Kohler, Jennefer N., Korrick, Susan, Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Lalani, Seema R., Lam, Byron, Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., LeBlanc, Kimberly, Lee, Brendan H., Levitt, Roy, Lewis, Richard A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Maghiro, Audrey Stephannie, Mahoney, Rachel, Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McConkie-Rosell, Allyn, McCray, Alexa T., McGee, Elisabeth, Might, Matthew, Miller, Danny, Mirzaa, Ghayda, Moore, Ryan M., Morava, Eva, Moretti, Paolo, Mulvihill, John J., Nakano-Okuno, Mariko, Nelson, Stanley F., Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Pallais, J. Carl, Palmer, Christina G.S., Papp, Jeanette C., Parker, Neil H., Phillips, John A., III, Posey, Jennifer E., Potocki, Lorraine, Pusey Swerdzewski, Barbara N., Quinlan, Aaron, Rao, Deepak A., Raper, Anna, Raskind, Wendy, Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenfeld, Jill A., Rosenthal, Elizabeth, Rossignol, Francis, Ruzhnikov, Maura, Sacco, Ralph, Sampson, Jacinda B., Saporta, Mario, Schaechter, Judy, Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Seto, Elaine, Shashi, Vandana, Shelkowitz, Emily, Sheppeard, Sam, Shin, Jimann, Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stergachis, Andrew, Stoler, Joan M., Sullivan, Kathleen, Sullivan, Jennifer A., Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tan, Queenie K.-G., Tan, Amelia L.M., Tarakad, Arjun, Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Ungar, Rachel A., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Walker, Melissa, Walley, Nicole M., Wambach, Jennifer, Wan, Jijun, Wang, Lee-kai, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Hubshman, Monika Weisz, Wener, Mark, Wenger, Tara, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Worley, Kim, Xiao, Changrui, Yamamoto, Shinya, Yang, John, Zhang, Zhe, Zuchner, Stephan, Huang, Yue, Jay, Kristy L., Yen-Wen Huang, Alden, Jangam, Sharayu V., Chorin, Odelia, Rothschild, Annick, Barel, Ortal, Mariani, Milena, Iascone, Maria, Xue, Han, Huang, Jing, Mignot, Cyril, Keren, Boris, Saillour, Virginie, Mah-Som, Annelise Y., Sacharow, Stephanie, Rajabi, Farrah, Costin, Carrie, Kanca, Oguz, and Martinez-Agosto, Julian A.

Published
2024
Full Text
View/download PDF

20. The Undiagnosed Diseases Network: Characteristics of solvable applicants and diagnostic suggestions for nonaccepted ones

Author

Acosta, Maria T., Adams, David R., Afzali, Ben, Al-Beshri, Ali, Allworth, Aimee, Alvarez, Raquel L., Alvey, Justin, Andrews, Ashley, Ashley, Euan A., Bacino, Carlos A., Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bellen, Hugo J., Bennett, Jimmy, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Borja, Nicholas, Botto, Lorenzo, Briere, Lauren C., Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Callaway, Kaitlin, Carey, John, Carvalho, George, Cassini, Thomas, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Chinn, Ivan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Cole, F. Sessions, Corner, Brian, Corona, Rosario I., Craigen, William J., Crouse, Andrew B., Cuddapah, Vishnu, Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Delgado, Margaret, Dell’Angelica, Esteban C., Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Douglas, Jessica, Douine, Emilie D., Earl, Dawn, Emrick, Lisa T., Eng, Christine M., Ezell, Kimberly, Fieg, Elizabeth L., Fisher, Paul G., Fogel, Brent L., Fu, Jiayu, Gahl, William A., Ganetzky, Rebecca, Glanton, Emily, Glass, Ian, Goddard, Page C., Gonzalez, Joanna M., Gropman, Andrea, Halley, Meghan C., Hamid, Rizwan, Hanchard, Neal, Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Huang, Yan, Hurst, Anna, Introne, Wendy, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Marie, Orpa Jean, Jobanputra, Vaidehi, Kaitryn, Emerald, Kanca, Oguz, Karasozen, Yigit, Ketkar, Shamika, Kiley, Dana, Kilich, Gonench, Kobren, Shilpa N., Kohane, Isaac S., Kohler, Jennefer N., Korf, Bruce, Korrick, Susan, Krakow, Deborah, Kravets, Elijah, Lalani, Seema R., Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., Latchman, Kumarie, LeBlanc, Kimberly, Lee, Brendan H., Lewis, Richard A., Liu, Pengfei, Longo, Nicola, Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Maghiro, Audrey Stephannie, Mahoney, Rachel, Malicdan, May Christine V., Mao, Rong, Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McConkie-Rosell, Allyn, McCray, Alexa T., Might, Matthew, Mikati, Mohamad, Miller, Danny, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo, Morimoto, Marie, Mulvihill, John J., Nakano-Okuno, Mariko, Nelson, Stanley F., Neumann, Serena, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Pallais, J. Carl, Parker, Neil H., Peart, LéShon, Petcharet, Leoyklang, Phillips, John A., III, Posey, Jennifer E., Potocki, Lorraine, Pusey Swerdzewski, Barbara N., Quinlan, Aaron, Rajagopalan, Ramakrishnan, Rao, Deepak A., Raper, Anna, Raskind, Wendy, Rebelo, Adriana, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rodriguez, Martin, Rosenfeld, Jill A., Rosenthal, Elizabeth, Rossignol, Francis, Ruzhnikov, Maura, Sabaii, Marla, Sampson, Jacinda B., Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Seto, Elaine, Shashi, Vandana, Shelkowitz, Emily, Sheppeard, Sam, Shin, Jimann, Silverman, Edwin K., Sisco, Kathy, Skelton, Tammi, Skraban, Cara, Smith, Carson A., Smith, Kevin S., Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stergachis, Andrew, Stoler, Joan M., Sullivan, Kathleen, Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tan, Queenie K.-G., Tan, Amelia L.M., Tarakad, Arjun, Taylor, Herman, Tekin, Mustafa, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Ungar, Rachel A., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Walker, Melissa, Walley, Nicole M., Wambach, Jennifer, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Hubshman, Monika Weisz, Wener, Mark, Wenger, Tara, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Wood, Heidi, Worley, Kim, Yamamoto, Shinya, Zhang, Zhe, Zuchner, Stephan, Findley, Laura, Ni, Weihong, Sinsheimer, Janet S., Cole, F. Session, Esteves, Cecilia, Newman, John H., and Mokry, Jill R.

Published
2024
Full Text
View/download PDF

21. Considerations for reporting variants in novel candidate genes identified during clinical genomic testing

Author

Abouhala, Siwaar, Albert, Jessica, Almalvez, Miguel, Alvarez, Raquel, Amin, Mutaz, Anderson, Peter, Aradhya, Swaroop, Ashley, Euan, Assimes, Themistocles, Auriga, Light, Austin-Tse, Christina, Bamshad, Mike, Barseghyan, Hayk, Baxter, Samantha, Behera, Sairam, Beheshti, Shaghayegh, Bejerano, Gill, Berger, Seth, Bernstein, Jon, Best, Sabrina, Blankenmeister, Benjamin, Blue, Elizabeth, Boerwinkle, Eric, Bonkowski, Emily, Bonner, Devon, Boone, Philip, Bornhorst, Miriam, Brand, Harrison, Buckingham, Kati, Calame, Daniel, Carter, Jennefer, Casadei, Silvia, Chadwick, Lisa, Chavez, Clarisa, Chen, Ziwei, Chinn, Ivan, Chong, Jessica, Coban-Akdemir, Zeynep, Cohen, Andrea J., Conner, Sarah, Conomos, Matthew, Coveler, Karen, Cui, Ya Allen, Currin, Sara, Daber, Robert, Dardas, Zain, Davis, Colleen, Dawood, Moez, de Dios, Ivan, de Esch, Celine, Delaney, Meghan, Delot, Emmanuele, DiTroia, Stephanie, Doddapaneni, Harsha, Du, Haowei, Duan, Ruizhi, Dugan-Perez, Shannon, Duong, Nhat, Duyzend, Michael, Eichler, Evan, Emami, Sara, Fraser, Jamie, Fusaro, Vincent, Galey, Miranda, Ganesh, Vijay, Garcia, Brandon, Garimella, Kiran, Gibbs, Richard, Gifford, Casey, Ginsburg, Amy, Goddard, Page, Gogarten, Stephanie, Gogate, Nikhita, Gordon, William, Gorzynski, John E., Greenleaf, William, Grochowski, Christopher, Groopman, Emily, Sousa, Rodrigo Guarischi, Gudmundsson, Sanna, Gulati, Ashima, Hall, Stacey, Harvey, William, Hawley, Megan, Heavner, Ben, Horike-Pyne, Martha, Hu, Jianhong, Huang, Yongqing, Hwang, James, Jarvik, Gail, Jensen, Tanner, Jhangiani, Shalini, Jimenez-Morales, David, Jin, Christopher, Saad, Ahmed K., Kahn-Kirby, Amanda, Kain, Jessica, Kaur, Parneet, Keehan, Laura, Knoblach, Susan, Ko, Arthur, Kundaje, Anshul, Kundu, Soumya, Lancaster, Samuel M., Larsson, Katie, Lee, Arthur, Lemire, Gabrielle, Lewis, Richard, Li, Wei, Li, Yidan, Liu, Pengfei, LoTempio, Jonathan, Lupski, James (Jim), Ma, Jialan, MacArthur, Daniel, Mahmoud, Medhat, Malani, Nirav, Mangilog, Brian, Marafi, Dana, Marmolejos, Sofia, Marten, Daniel, Martinez, Eva, Marvin, Colby, Marwaha, Shruti, Mastrorosa, Francesco Kumara, Matalon, Dena, May, Susanne, McGee, Sean, Meador, Lauren, Mefford, Heather, Mendez, Hector Rodrigo, Miller, Alexander, Miller, Danny E., Mitani, Tadahiro, Montgomery, Stephen, Moyses, Mariana, Munderloh, Chloe, Muzny, Donna, Nelson, Sarah, Nguyen, Thuy-mi P., Nguyen, Jonathan, Nussbaum, Robert, Nykamp, Keith, O'Callaghan, William, O'Heir, Emily, O'Leary, Melanie, Olsen, Jeren, Osei-Owusu, Ikeoluwa, O'Donnell-Luria, Anne, Padhi, Evin, Pais, Lynn, Pan, Miao, Panchal, Piyush, Patterson, Karynne, Payne, Sheryl, Pehlivan, Davut, Petrowski, Paul, Pham, Alicia, Pitsava, Georgia, Podesta, Astaria`Sara, Ponce, Sarah, Porter, Elizabeth, Posey, Jennifer, Prosser, Jaime, Quertermous, Thomas, Rai, Archana, Ramani, Arun, Rehm, Heidi, Reuter, Chloe, Reuter, Jason, Richardson, Matthew, Rivera-Munoz, Andres, Rubio, Oriane, Sabo, Aniko, Salani, Monica, Samocha, Kaitlin, Sanchis-Juan, Alba, Savage, Sarah, Scott, Evette, Scott, Stuart, Sedlazeck, Fritz, Shah, Gulalai, Shojaie, Ali, Singh, Mugdha, Smith, Kevin, Smith, Josh, Snow, Hana, Snyder, Michael, Socarras, Kayla, Starita, Lea, Stark, Brigitte, Stenton, Sarah, Stergachis, Andrew, Stilp, Adrienne, Sutton, V. Reid, Tai, Jui-Cheng, Talkowski, Michael (Mike), Tise, Christina, Tong, Catherine (Cat), Tsao, Philip, Ungar, Rachel, VanNoy, Grace, Vilain, Eric, Voutos, Isabella, Walker, Kim, Wei, Chia-Lin, Weisburd, Ben, Weiss, Jeff, Wellington, Chris, Weng, Ziming, Westheimer, Emily, Wheeler, Marsha, Wheeler, Matthew, Wiel, Laurens, Wilson, Michael, Wojcik, Monica, Wong, Quenna, Xiao, Changrui, Yadav, Rachita, Yi, Qian, Yuan, Bo, Zhao, Jianhua, Zhen, Jimmy, Zhou, Harry, Chong, Jessica X., Berger, Seth I., Smith, Erica, Calame, Daniel G., Hawley, Megan H., Rivera-Munoz, E. Andres, Bamshad, Michael J., and Rehm, Heidi L.

Published
2024
Full Text
View/download PDF

22. Dominant missense variants in SREBF2 are associated with complex dermatological, neurological, and skeletal abnormalities

Author

Bacino, Carlos A., Balasubramanyam, Ashok, Burrage, Lindsay C., Chao, Hsiao-Tuan, Chinn, Ivan, Clark, Gary D., Craigen, William J., Dai, Hongzheng, Emrick, Lisa T., Ketkar, Shamika, Lalani, Seema R., Lee, Brendan H., Lewis, Richard A., Marom, Ronit, Orengo, James P., Posey, Jennifer E., Potocki, Lorraine, Rosenfeld, Jill A., Seto, Elaine, Scott, Daryl A., Tarakad, Arjun, Tran, Alyssa A., Vogel, Tiphanie P., Hubshman, Monika Weisz, Worley, Kim, Bellen, Hugo J., Wangler, Michael F., Yamamoto, Shinya, Kanca, Oguz, Eng, Christine M., Liu, Pengfei, Ward, Patricia A., Behrens, Edward, Falk, Marni, Hassey, Kelly, Izumi, Kosuke, Kilich, Gonench, Sullivan, Kathleen, Vanderver, Adeline, Zhang, Zhe, Raper, Anna, Jobanputra, Vaidehi, Mikati, Mohamad, McConkie-Rosell, Allyn, Schoch, Kelly, Shashi, Vandana, Spillmann, Rebecca C., Tan, Queenie K.-G., Walley, Nicole M., Beggs, Alan H., Berry, Gerard T., Briere, Lauren C., Cobban, Laurel A., Coggins, Matthew, Fieg, Elizabeth L., High, Frances, Holm, Ingrid A., Korrick, Susan, Loscalzo, Joseph, Maas, Richard L., MacRae, Calum A., Pallais, J. Carl, Rao, Deepak A., Rodan, Lance H., Silverman, Edwin K., Stoler, Joan M., Sweetser, David A., Walker, Melissa, Douglas, Jessica, Glanton, Emily, Kobren, Shilpa N., Kohane, Isaac S., LeBlanc, Kimberly, Maghiro, Audrey Stephannie C., Mahoney, Rachel, McCray, Alexa T., Tan, Amelia L.M., Dasari, Surendra, Lanpher, Brendan C., Lanza, Ian R., Morava, Eva, Oglesbee, Devin, Bademci, Guney, Barbouth, Deborah, Bivona, Stephanie, Borja, Nicholas, Gonzalez, Joanna M., Latchman, Kumarie, Peart, LéShon, Rebelo, Adriana, Smith, Carson A., Tekin, Mustafa, Thorson, Willa, Zuchner, Stephan, Taylor, Herman, Colley, Heather A., Dayal, Jyoti G., Doss, Argenia L., Eckstein, David J., Hutchison, Sarah, Krasnewich, Donna M., Mamounas, Laura A., Manolio, Teri A., Urv, Tiina K., Acosta, Maria T., D'Souza, Precilla, Gropman, Andrea, Macnamara, Ellen F., Maduro, Valerie V., Mulvihill, John J., Novacic, Donna, Pusey Swerdzewski, Barbara N., Toro, Camilo, Wahl, Colleen E., Adams, David R., Afzali, Ben, Burke, Elizabeth A., Davis, Joie, Delgado, Margaret, Fu, Jiayu, Gahl, William A., Hanchard, Neil, Huang, Yan, Introne, Wendy, Jean-Marie, Orpa, Malicdan, May Christine V., Morimoto, Marie, Petcharet, Leoyklang, Rossignol, Francis, Sabaii, Marla, Solomon, Ben, Tifft, Cynthia J., Wolfe, Lynne A., Wood, Heidi, Allworth, Aimee, Bamshad, Michael, Beck, Anita, Bennett, Jimmy, Blue, Elizabeth, Byers, Peter, Chanprasert, Sirisak, Cunningham, Michael, Dipple, Katrina, Doherty, Daniel, Earl, Dawn, Glass, Ian, Hing, Anne, Hisama, Fuki M., Horike-Pyne, Martha, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Kaitryn, Emerald, Lam, Christina, Miller, Danny, Mirzaa, Ghayda, Raskind, Wendy, Rosenthal, Elizabeth, Shelkowitz, Emily, Sheppeard, Sam, Stergachis, Andrew, Sybert, Virginia, Wener, Mark, Wenger, Tara, Alvarez, Raquel L., Bejerano, Gill, Bernstein, Jonathan A., Bonner, Devon, Coakley, Terra R., Fisher, Paul G., Goddard, Page C., Halley, Meghan C., Hom, Jason, Kohler, Jennefer N., Kravets, Elijah, Martin, Beth A., Marwaha, Shruti, Reuter, Chloe M., Ruzhnikov, Maura, Sampson, Jacinda B., Smith, Kevin S., Sutton, Shirley, Tabor, Holly K., Ungar, Rachel A., Wheeler, Matthew T., Ashley, Euan A., Byrd, William E., Crouse, Andrew B., Might, Matthew, Nakano-Okuno, Mariko, Whitlock, Jordan, Butte, Manish J., Corona, Rosario, Dell'Angelica, Esteban C., Dorrani, Naghmeh, Douine, Emilie D., Fogel, Brent L., Huang, Alden, Krakow, Deborah, Loo, Sandra K., Martin, Martin G., Martínez-Agosto, Julian A., McGee, Elisabeth, Nelson, Stanley F., Nieves-Rodriguez, Shirley, Papp, Jeanette C., Parker, Neil H., Renteria, Genecee, Sinsheimer, Janet S., Wan, Jijun, Alvey, Justin, Andrews, Ashley, Bale, Jim, Bohnsack, John, Botto, Lorenzo, Carey, John, Longo, Nicola, Moretti, Paolo, Pace, Laura, Quinlan, Aaron, Velinder, Matt, Viskochil, Dave, Marth, Gabor, Bayrak-Toydemir, Pinar, Mao, Rong, Westerfield, Monte, Bican, Anna, Cassini, Thomas, Corner, Brian, Hamid, Rizwan, Neumann, Serena, Phillips, John A., III, Rives, Lynette, Robertson, Amy K., Ezell, Kimberly, Cogan, Joy D., Hayes, Nichole, Kiley, Dana, Sisco, Kathy, Wambach, Jennifer, Wegner, Daniel, Baldridge, Dustin, Cole, F. Sessions, Pak, Stephen, Schedl, Timothy, Shin, Jimann, Solnica-Krezel, Lilianna, Moulton, Matthew J., Atala, Kristhen, Zheng, Yiming, Dutta, Debdeep, Grange, Dorothy K., Lin, Wen-Wen, Wegner, Daniel J., Wambach, Jennifer A., Duker, Angela L., Bober, Michael B., Kratz, Lisa, Wise, Carol A., Oxendine, Ila, Khanshour, Anas, and Rios, Jonathan

Published
2024
Full Text
View/download PDF

23. Loss of function of FAM177A1, a Golgi complex localized protein, causes a novel neurodevelopmental disorder

Author

Acosta, Maria T., Adam, Margaret, Adams, David R., Alvarez, Raquel L., Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A., Bacino, Carlos A., Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bellen, Hugo J., Bennett, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C., Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Chang, Ta Chen Peter, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Chinn, Ivan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Sessions Cole, F., Colley, Heather A., Cope, Heidi, Corner, Brian, Corona, Rosario, Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G., Dell’Angelica, Esteban C., Dickson, Patricia, Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L., Douine, Emilie D., Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Ezell, Kimberly, Falk, Marni, Fieg, Elizabeth L., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Gahl, William A., Glass, Ian, Gochuico, Bernadette, Goddard, Page C., Godfrey, Rena A., Golden-Grant, Katie, Grajewski, Alana, Hadley, Don, Hahn, Sihoun, Halley, Meghan C., Hamid, Rizwan, Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Hutchison, Sarah, Introne, Wendy, Isasi, Rosario, Izumi, Kosuke, Jamal, Fariha, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Jean-Marie, Orpa, Jobanputra, Vaidehi, Karaviti, Lefkothea, Ketkar, Shamika, Kiley, Dana, Kilich, Gonench, Kobren, Shilpa N., Kohane, Isaac S., Kohler, Jennefer N., Korrick, Susan, Kozuira, Mary, Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Lalani, Seema R., Lam, Byron, Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., LeBlanc, Kimberly, Lee, Brendan H., Levitt, Roy, Lewis, Richard A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Maghiro, AudreyStephannie, Mahoney, Rachel, Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Maravilla, Kenneth, Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McConkie-Rosell, Allyn, McCray, Alexa T., McGee, Elisabeth, Mefford, Heather, Lawrence Merritt, J., Might, Matthew, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo, Mulvihill, John, Nakano-Okuno, Mariko, Nelson, Stanley F., Neumann, Serena, Newman, John H., Nicholas, Sarah K., Nickerson, Deborah, Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Carl Pallais, J., Palmer, Christina G.S., Papp, Jeanette C., Parker, Neil H., Phillips, John A., III, Posey, Jennifer E., Potocki, Lorraine, Pusey Swerdzewski, Barbara N., Quinlan, Aaron, Rao, Deepak A., Raper, Anna, Raskind, Wendy, Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenfeld, Jill A., Rosenwasser, Natalie, Rossignol, Francis, Ruzhnikov, Maura, Sacco, Ralph, Sampson, Jacinda B., Saporta, Mario, Schaechter, Judy, Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Ron Scott, C., Seto, Elaine, Shashi, Vandana, Shin, Jimann, Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stoler, Joan M., Sullivan, Kathleen, Sullivan, Jennifer A., Sun, Angela, Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tan, Queenie K.-G., Tan, Amelia L.M., Tarakad, Arjun, Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Ungar, Rachel A., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Walker, Melissa, Wallace, Stephanie, Walley, Nicole M., Wambach, Jennifer, Wan, Jijun, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Hubshman, Monika Weisz, Wener, Mark, Wenger, Tara, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Worley, Kim, Xiao, Changrui, Yamamoto, Shinya, Yang, John, Zhang, Zhe, Zuchner, Stephan, Legro, Nicole R., Bowman, Angela, Ugur, Berrak, Jackstadt, Madelyn M., Shriver, Leah P., Patti, Gary J., Zhang, Bo, Feng, Wenjia, McAdow, Anthony R., Goddard, Pagé, Jensen, Tanner, Fresard, Laure, Alvarez, Raquel, McCormack, Colleen, Holt, James M., Worthey, Elizabeth A., Montgomery, Stephen B., Postlethwait, John, De Camilli, Pietro, and Solnica-Krezel, Lila

Published
2024
Full Text
View/download PDF

24. Rare variant associations with plasma protein levels in the UK Biobank

Author

Dhindsa, Ryan S., Burren, Oliver S., Sun, Benjamin B., Prins, Bram P., Matelska, Dorota, Wheeler, Eleanor, Mitchell, Jonathan, Oerton, Erin, Hristova, Ventzislava A., Smith, Katherine R., Carss, Keren, Wasilewski, Sebastian, Harper, Andrew R., Paul, Dirk S., Fabre, Margarete A., Runz, Heiko, Viollet, Coralie, Challis, Benjamin, Platt, Adam, Vitsios, Dimitrios, Ashley, Euan A., Whelan, Christopher D., Pangalos, Menelas N., Wang, Quanli, and Petrovski, Slavé

Published
2023
Full Text
View/download PDF

25. Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy

Author

Parikh, Victoria N, Ioannidis, Alexander G, Jimenez-Morales, David, Gorzynski, John E, De Jong, Hannah N, Liu, Xiran, Roque, Jonasel, Cepeda-Espinoza, Victoria P, Osoegawa, Kazutoyo, Hughes, Chris, Sutton, Shirley C, Youlton, Nathan, Joshi, Ruchi, Amar, David, Tanigawa, Yosuke, Russo, Douglas, Wong, Justin, Lauzon, Jessie T, Edelson, Jacob, Mas Montserrat, Daniel, Kwon, Yongchan, Rubinacci, Simone, Delaneau, Olivier, Cappello, Lorenzo, Kim, Jaehee, Shoura, Massa J, Raja, Archana N, Watson, Nathaniel, Hammond, Nathan, Spiteri, Elizabeth, Mallempati, Kalyan C, Montero-Martín, Gonzalo, Christle, Jeffrey, Kim, Jennifer, Kirillova, Anna, Seo, Kinya, Huang, Yong, Zhao, Chunli, Moreno-Grau, Sonia, Hershman, Steven G, Dalton, Karen P, Zhen, Jimmy, Kamm, Jack, Bhatt, Karan D, Isakova, Alina, Morri, Maurizio, Ranganath, Thanmayi, Blish, Catherine A, Rogers, Angela J, Nadeau, Kari, Yang, Samuel, Blomkalns, Andra, O’Hara, Ruth, Neff, Norma F, DeBoever, Christopher, Szalma, Sándor, Wheeler, Matthew T, Gates, Christian M, Farh, Kyle, Schroth, Gary P, Febbo, Phil, deSouza, Francis, Cornejo, Omar E, Fernandez-Vina, Marcelo, Kistler, Amy, Palacios, Julia A, Pinsky, Benjamin A, Bustamante, Carlos D, Rivas, Manuel A, and Ashley, Euan A

Subjects
Biological Sciences, Genetics, Pneumonia & Influenza, Lung, Prevention, Human Genome, Clinical Research, Infectious Diseases, Infection, Good Health and Well Being, COVID-19, Genome, Viral, Genome-Wide Association Study, Humans, Pandemics, SARS-CoV-2
Abstract

The SARS-CoV-2 pandemic has differentially impacted populations across race and ethnicity. A multi-omic approach represents a powerful tool to examine risk across multi-ancestry genomes. We leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from nasopharyngeal swabs of 1049 individuals (736 SARS-CoV-2 positive and 313 SARS-CoV-2 negative) and integrate them with digital phenotypes from electronic health records from a diverse catchment area in Northern California. Genome-wide association disaggregated by admixture mapping reveals novel COVID-19-severity-associated regions containing previously reported markers of neurologic, pulmonary and viral disease susceptibility. Phylodynamic tracking of consensus viral genomes reveals no association with disease severity or inferred ancestry. Summary data from multiomic investigation reveals metagenomic and HLA associations with severe COVID-19. The wealth of data available from residual nasopharyngeal swabs in combination with clinical data abstracted automatically at scale highlights a powerful strategy for pandemic tracking, and reveals distinct epidemiologic, genetic, and biological associations for those at the highest risk.

Published
2022

26. A novel DPH5-related diphthamide-deficiency syndrome causing embryonic lethality or profound neurodevelopmental disorder

Author

Shankar, Suma P, Grimsrud, Kristin, Lanoue, Louise, Egense, Alena, Willis, Brandon, Hörberg, Johanna, AlAbdi, Mayer, Klaus, Ütkür, Koray, Monaghan, Kristin G, Krier, Joel, Stoler, Joan, Alnemer, Maha, Shankar, Prabhu R, Schaffrath, Raffael, Alkuraya, Fowzan S, Brinkmann, Ulrich, Eriksson, Leif A, Lloyd, Kent, Rauen, Katherine A, Network, Undiagnosed Diseases, Acosta, Maria T, Adam, Margaret, Adams, David R, Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A, Azamian, Mahshid S, Bacino, Carlos A, Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H, Behrens, Edward, Bejerano, Gill, Bennet, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A, Berry, Gerard T, Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C, Brokamp, Elly, Brown, Gabrielle, Burke, Elizabeth A, Burrage, Lindsay C, Butte, Manish J, Byers, Peter, Byrd, William E, Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Chang, Ta Chen Peter, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Clark, Gary D, Coakley, Terra R, Cobban, Laurel A, Cogan, Joy D, Coggins, Matthew, Cole, F Sessions, Colley, Heather A, Cooper, Cynthia M, Cope, Heidi, Craigen, William J, Crouse, Andrew B, Cunningham, Michael, D'Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G, Deardorff, Matthew, Dell'Angelica, Esteban C, Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L, Douine, Emilie D, Duncan, Laura, Earl, Dawn, Eckstein, David J, Emrick, Lisa T, Eng, Christine M, Esteves, Cecilia, Falk, Marni, Fernandez, Liliana, Fieg, Elizabeth L, and Fisher, Paul G

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Pediatric, Human Genome, 2.1 Biological and endogenous factors, Adenosine Diphosphate, Animals, Histidine, Humans, Methyltransferases, Mice, Mice, Inbred C57BL, Neurodevelopmental Disorders, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Syndrome, Nonverbal neurodevelopment delays, Novel gene discovery, Precision animal modeling, Precision genomics, Translational genetics, Undiagnosed Diseases Network, Clinical Sciences, Genetics & Heredity
Abstract

PurposeDiphthamide is a post-translationally modified histidine essential for messenger RNA translation and ribosomal protein synthesis. We present evidence for DPH5 as a novel cause of embryonic lethality and profound neurodevelopmental delays (NDDs).MethodsMolecular testing was performed using exome or genome sequencing. A targeted Dph5 knockin mouse (C57BL/6Ncrl-Dph5em1Mbp/Mmucd) was created for a DPH5 p.His260Arg homozygous variant identified in 1 family. Adenosine diphosphate-ribosylation assays in DPH5-knockout human and yeast cells and in silico modeling were performed for the identified DPH5 potential pathogenic variants.ResultsDPH5 variants p.His260Arg (homozygous), p.Asn110Ser and p.Arg207Ter (heterozygous), and p.Asn174LysfsTer10 (homozygous) were identified in 3 unrelated families with distinct overlapping craniofacial features, profound NDDs, multisystem abnormalities, and miscarriages. Dph5 p.His260Arg homozygous knockin was embryonically lethal with only 1 subviable mouse exhibiting impaired growth, craniofacial dysmorphology, and multisystem dysfunction recapitulating the human phenotype. Adenosine diphosphate-ribosylation assays showed absent to decreased function in DPH5-knockout human and yeast cells. In silico modeling of the variants showed altered DPH5 structure and disruption of its interaction with eEF2.ConclusionWe provide strong clinical, biochemical, and functional evidence for DPH5 as a novel cause of embryonic lethality or profound NDDs with multisystem involvement and expand diphthamide-deficiency syndromes and ribosomopathies.

Published
2022

27. Molecular adaptations in response to exercise training are associated with tissue-specific transcriptomic and epigenomic signatures

Author

Adkins, Joshua N., Almagro Armenteros, Jose Juan, Ashley, Euan, Bae, Dam, Bamman, Marcas, Bararpour, Nasim, Bennett, Alexis, Bergman, Bryan C., Bessesen, Daniel H., Bodine, Sue C., Buford, Thomas W., Chambers, Toby L., Chavez, Clarisa, Cortes, Tiffany, Cutter, Gary, Espinoza, Sara, Fernández, Facundo M., Gagne, Nicole, Gaul, David A., Goodpaster, Bret H., Hung, Chia-Jui, Jackson, Bailey E., Jankowski, Catherine M., Jin, Christopher A., Kohrt, Wendy M., Kramer, Kyle S., Lester, Bridget, Lira, Ana K., May, Sandy, Marjanovic, Nada, Melanson, Edward L., Moore, Samuel G., Moreau, Kerrie L., Musi, Nicolas, Nachum, Daniel, Nudelman, German, Pahor, Marco, Patel, Darpan, Piehowski, Paul D., Qian, Wei-Jun, Rasmussen, Blake B., Rirak, Stas, Rooney, Jessica L., Sanford, James A., Savage, Evan M., Schauer, Irene E., Serra, Monica, Stowe, Cynthia L., Thalacker-Mercer, Anna, Trappe, Todd A., Trappe, Scott, Vetr, Nikolai G., Volpi, Elena, Wheeler, Matthew T., Wu, Si, Zebarjadi, Navid, Zhen, Jimmy, Nair, Venugopalan D., Pincas, Hanna, Smith, Gregory R., Zaslavsky, Elena, Ge, Yongchao, Amper, Mary Anne S., Vasoya, Mital, Chikina, Maria, Sun, Yifei, Raja, Archana Natarajan, Mao, Weiguang, Gay, Nicole R., Esser, Karyn A., Smith, Kevin S., Zhao, Bingqing, Wiel, Laurens, Singh, Aditya, Lindholm, Malene E., Amar, David, Montgomery, Stephen, Snyder, Michael P., Walsh, Martin J., and Sealfon, Stuart C.

Published
2024
Full Text
View/download PDF

28. The mitochondrial multi-omic response to exercise training across rat tissues

Author

Adkins, Joshua N., Armenteros, Jose Juan Almagro, Amper, Mary Anne S., Bae, Dam, Bamman, Marcas, Bararpour, Nasim, Barnes, Jerry, Bergman, Bryan C., Bessesen, Daniel H., Broskey, Nicholas T., Buford, Thomas W., Carr, Steven, Chambers, Toby L., Chavez, Clarisa, Chiu, Roxanne, Clark, Natalie, Cutter, Gary, Evans, Charles R., Franczak, Edziu, Gagne, Nicole, Ge, Yongchao, Hennig, Krista M., Houmard, Joseph A., Huffman, Kim M., Hung, Chia-Jui, Hutchinson-Bunch, Chelsea, Ilkayeva, Olga, Jackson, Bailey E., Jankowski, Catherine M., Jin, Christopher A., Johannsen, Neil M., Katz, Daniel H., Keshishian, Hasmik, Kohrt, Wendy M., Kramer, Kyle S., Kraus, William E., Lester, Bridget, Li, Jun Z., Lira, Ana K., Lowe, Adam, Mani, D.R., Many, Gina M., May, Sandy, Melanson, Edward L., Moore, Samuel G., Moreau, Kerrie L., Musi, Nicolas, Nachun, Daniel, Nair, Venugopalan D., Newgard, Christopher, Nudelman, German, Piehowski, Paul D., Pincas, Hanna, Qian, Wei-Jun, Rankinen, Tuomo, Rasmussen, Blake B., Ravussin, Eric, Rooney, Jessica L., Rushing, Scott, Samdarshi, Mihir, Sanford, James A., Schauer, Irene E., Sealfon, Stuart C., Smith, Kevin S., Smith, Gregory R., Snyder, Michael, Stowe, Cynthia L., Talton, Jennifer W., Teng, Christopher, Thalacker-Mercer, Anna, Tracy, Russell, Trappe, Scott, Trappe, Todd A., Vasoya, Mital, Vetr, Nikolai G., Volpi, Elena, Walkup, Michael P., Wiel, Laurens, Wu, Si, Yan, Zhen, Yu, Jiye, Zaslavsky, Elena, Zebarjadi, Navid, Zhen, Jimmy, Amar, David, Gay, Nicole R., Jimenez-Morales, David, Jean Beltran, Pierre M., Ramaker, Megan E., Raja, Archana Natarajan, Zhao, Bingqing, Sun, Yifei, Marwaha, Shruti, Gaul, David A., Hershman, Steven G., Ferrasse, Alexis, Xia, Ashley, Lanza, Ian, Fernández, Facundo M., Montgomery, Stephen B., Hevener, Andrea L., Ashley, Euan A., Walsh, Martin J., Sparks, Lauren M., Burant, Charles F., Rector, R. Scott, Thyfault, John, Wheeler, Matthew T., Goodpaster, Bret H., Coen, Paul M., Schenk, Simon, Bodine, Sue C., and Lindholm, Malene E.

Published
2024
Full Text
View/download PDF

29. Exome and genome sequencing in a heterogeneous population of patients with rare disease: Identifying predictors of a diagnosis

Author

Acosta, Maria T., Adams, David R., Alvarez, Raquel L., Alvey, Justin, Allworth, Aimee, Andrews, Ashley, Ashley, Euan A., Bacino, Carlos A., Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bellen, Hugo J., Bennett, Jimmy, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Briere, Lauren C., Brown, Gabrielle, Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Peter Chang, Ta Chen, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Chinn, Ivan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Cole, F. Sessions, Colley, Heather A., Cope, Heidi, Corona, Rosario, Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G., Dell'Angelica, Esteban C., Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L., Douine, Emilie D., Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Falk, Marni, Fieg, Elizabeth L., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Gahl, William A., Glass, Ian, Gochuico, Bernadette, Goddard, Page C., Godfrey, Rena A., Grajewski, Alana, Hadley, Don, Halley, Meghan C., Hamid, Rizwan, Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Hutchison, Sarah, Introne, Wendy, Isasi, Rosario, Izumi, Kosuke, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Jean-Marie, Orpa, Jobanputra, Vaidehi, Kaitryn, Emerald, Ketkar, Shamika, Kiley, Dana, Kilich, Gonench, Kobren, Shilpa N., Kohane, Isaac S., Kohler, Jennefer N., Korrick, Susan, Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Lalani, Seema R., Lam, Byron, Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., LeBlanc, Kimberly, Lee, Brendan H., Levitt, Roy, Lewis, Richard A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Maghiro, AudreyStephannie, Mahoney, Rachel, Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McConkie-Rosell, Allyn, McCray, Alexa T., McGee, Elisabeth, Might, Matthew, Miller, Danny, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo, Mulvihill, John J., Nakano-Okuno, Mariko, Nelson, Stanley F., Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Pallais, J. Carl, Palmer, Christina G.S., Papp, Jeanette C., Parker, Neil H., Phillips, John A., III, Posey, Jennifer E., Potocki, Lorraine, Pusey Swerdzewski, Barbara N., Quinlan, Aaron, Rao, Deepak A., Raper, Anna, Raskind, Wendy, Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenfeld, Jill A., Rosenthal, Elizabeth, Rossignol, Francis, Ruzhnikov, Maura, Sacco, Ralph, Sampson, Jacinda B., Saporta, Mario, Schaechter, Judy, Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Seto, Elaine, Shashi, Vandana, Shelkowitz, Emily, Sheppeard, Sam, Shin, Jimann, Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stergachis, Andrew, Stoler, Joan M., Sullivan, Kathleen, Sullivan, Jennifer A., Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tan, Queenie K.-G., Tan, Amelia L.M., Tarakad, Arjun, Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Ungar, Rachel A., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Walker, Melissa, Walley, Nicole M., Wambach, Jennifer, Wan, Jijun, Wang, Lee-kai, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Hubshman, Monika Weisz, Wener, Mark, Wenger, Tara, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Worley, Kim, Xiao, Changrui, Yamamoto, Shinya, Yang, John, Zhang, Zhe, Zuchner, Stephan, Pucel, Jenna, Reuter, Chloe, and Gochyyev, Perman

Published
2024
Full Text
View/download PDF

30. Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices

Author

Gupta, Varsha, Kariotis, Sokratis, Rajab, Mohammed D., Errington, Niamh, Alhathli, Elham, Jammeh, Emmanuel, Brook, Martin, Meardon, Naomi, Collini, Paul, Cole, Joby, Wild, Jim M., Hershman, Steven, Javed, Ali, Thompson, A. A. Roger, de Silva, Thushan, Ashley, Euan A., Wang, Dennis, and Lawrie, Allan

Published
2023
Full Text
View/download PDF

31. Author Correction: COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning

Author

Salek, Mahyar, Li, Nianzhen, Chou, Hou-Pu, Saini, Kiran, Jovic, Andreja, Jacobs, Kevin B., Johnson, Chassidy, Lu, Vivian, Lee, Esther J., Chang, Christina, Nguyen, Phuc, Mei, Jeanette, Pant, Krishna P., Wong-Thai, Amy Y., Smith, Quillan F., Huang, Stephanie, Chow, Ryan, Cruz, Janifer, Walker, Jeff, Chan, Bryan, Musci, Thomas J., Ashley, Euan A., and Masaeli, Maddison (Mahdokht)

Published
2023
Full Text
View/download PDF

32. COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning

Author

Salek, Mahyar, Li, Nianzhen, Chou, Hou-Pu, Saini, Kiran, Jovic, Andreja, Jacobs, Kevin B., Johnson, Chassidy, Lu, Vivian, Lee, Esther J., Chang, Christina, Nguyen, Phuc, Mei, Jeanette, Pant, Krishna P., Wong-Thai, Amy Y., Smith, Quillan F., Huang, Stephanie, Chow, Ryan, Cruz, Janifer, Walker, Jeff, Chan, Bryan, Musci, Thomas J., Ashley, Euan A., and Masaeli, Maddison (Mahdokht)

Published
2023
Full Text
View/download PDF

33. High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning

Author

Duffy, Grant, Cheng, Paul P, Yuan, Neal, He, Bryan, Kwan, Alan C., Shun-Shin, Matthew J., Alexander, Kevin M., Ebinger, Joseph, Lungren, Matthew P., Rader, Florian, Liang, David H., Schnittger, Ingela, Ashley, Euan A., Zou, James Y., Patel, Jignesh, Witteles, Ronald, Cheng, Susan, and Ouyang, David

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

Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrophic cardiomyopathy, and cardiac amyloidosis. Early detection and characterization of LVH can significantly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating etiologies of LVH. To overcome this challenge, we present EchoNet-LVH - a deep learning workflow that automatically quantifies ventricular hypertrophy with precision equal to human experts and predicts etiology of LVH. Trained on 28,201 echocardiogram videos, our model accurately measures intraventricular wall thickness (mean absolute error [MAE] 1.4mm, 95% CI 1.2-1.5mm), left ventricular diameter (MAE 2.4mm, 95% CI 2.2-2.6mm), and posterior wall thickness (MAE 1.2mm, 95% CI 1.1-1.3mm) and classifies cardiac amyloidosis (area under the curve of 0.83) and hypertrophic cardiomyopathy (AUC 0.98) from other etiologies of LVH. In external datasets from independent domestic and international healthcare systems, EchoNet-LVH accurately quantified ventricular parameters (R2 of 0.96 and 0.90 respectively) and detected cardiac amyloidosis (AUC 0.79) and hypertrophic cardiomyopathy (AUC 0.89) on the domestic external validation site. Leveraging measurements across multiple heart beats, our model can more accurately identify subtle changes in LV geometry and its causal etiologies. Compared to human experts, EchoNet-LVH is fully automated, allowing for reproducible, precise measurements, and lays the foundation for precision diagnosis of cardiac hypertrophy. As a resource to promote further innovation, we also make publicly available a large dataset of 23,212 annotated echocardiogram videos.

Published
2021
Full Text
View/download PDF

37. De novo variants in DENND5B cause a neurodevelopmental disorder

Author

Acosta, Maria T., Adams, David R., Alvarez, Raquel L., Alvey, Justin, Allworth, Aimee, Andrews, Ashley, Ashley, Euan A., Afzali, Ben, Bacino, Carlos A., Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bellen, Hugo J., Bennett, Jimmy, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Briere, Lauren C., Brown, Gabrielle, Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Chang, Ta Chen Peter, Chanprasert, Sirisak, Chao, HsiaoTuan, Chinn, Ivan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Cole, F. Sessions, Colley, Heather A., Cope, Heidi, Corona, Rosario, Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G., Delgado, Margaret, Dell'Angelica, Esteban C., Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L., Douine, Emilie D., Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Falk, Marni, Fieg, Elizabeth L., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Fu, Jiayu, Gahl, William A., Glass, Ian, Goddard, Page C., Godfrey, Rena A., Grajewski, Alana, Gropman, Andrea, Halley, Meghan C., Hamid, Rizwan, Hanchard, Neal, Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Huang, Yan, Hutchison, Sarah, Introne, Wendy, Isasi, Rosario, Izumi, Kosuke, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Jean-Marie, Orpa, Jobanputra, Vaidehi, Kaitryn, Emerald, Ketkar, Shamika, Kiley, Dana, Kilich, Gonench, Kobren, Shilpa N., Kohane, Isaac S., Kohler, Jennefer N., Korrick, Susan, Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Lalani, Seema R., Lam, Byron, Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., LeBlanc, Kimberly, Lee, Brendan H., Levitt, Roy, Lewis, Richard A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Maghiro, AudreyStephannie, Mahoney, Rachel, Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McConkie-Rosell, Allyn, McCray, Alexa T., McGee, Elisabeth, Might, Matthew, Miller, Danny, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo, Morimoto, Marie, Mulvihill, John J., Nakano-Okuno, Mariko, Nelson, Stanley F., Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Pallais, J. Carl, Papp, Jeanette C., Parker, Neil H., Petcharet, Leoyklang, Phillips, John A., III, Posey, Jennifer E., Potocki, Lorraine, Swerdzewski, Barbara N. Pusey, Quinlan, Aaron, Rao, Deepak A., Raper, Anna, Raskind, Wendy, Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenfeld, Jill A., Rosenthal, Elizabeth, Rossignol, Francis, Ruzhnikov, Maura, Sabaii, Marla, Sacco, Ralph, Sampson, Jacinda B., Saporta, Mario, Schaechter, Judy, Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Seto, Elaine, Sharma, Prashant, Shashi, Vandana, Shelkowitz, Emily, Sheppeard, Sam, Shin, Jimann, Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stergachis, Andrew, Stoler, Joan M., Sullivan, Kathleen, Sullivan, Jennifer A., Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tan, Queenie K.-G., Tan, Amelia L.M., Tarakad, Arjun, Taylor, Herman, Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Ungar, Rachel A., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Walker, Melissa, Walley, Nicole M., Wambach, Jennifer, Wan, Jijun, Wang, Lee-kai, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Weisz Hubshman, Monika, Wener, Mark, Wenger, Tara, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Worley, Kim, Yamamoto, Shinya, Zhang, Zhe, Zuchner, Stephan, Scala, Marcello, Tomati, Valeria, Ferla, Matteo, Lena, Mariateresa, Cohen, Julie S., Fatemi, Ali, Brokamp, Elly, Koziura, Mary E., Nicouleau, Michael, Rio, Marlene, Siquier, Karine, Boddaert, Nathalie, Musante, Ilaria, Tamburro, Serena, Baldassari, Simona, Iacomino, Michele, Scudieri, Paolo, Bellus, Gary, Reed, Sara, Al Saif, Hind, Russo, Rossana Sanchez, Walsh, Matthew B., Cantagrel, Vincent, Crunk, Amy, Gustincich, Stefano, Ruggiero, Sarah M., Fitzgerald, Mark P., Helbig, Ingo, Striano, Pasquale, Severino, Mariasavina, Salpietro, Vincenzo, Pedemonte, Nicoletta, and Zara, Federico

Published
2024
Full Text
View/download PDF

38. Clinical application of a scale to assess genomic healthcare empowerment (GEmS): Process and illustrative case examples

Author

McConkie‐Rosell, Allyn, Schoch, Kelly, Sullivan, Jennifer, Spillmann, Rebecca C, Cope, Heidi, Tan, Queenie K‐G, Palmer, Christina GS, Hooper, Stephen R, Shashi, Vandana, Acosta, Maria T, Adam, Margaret, Adams, David R, Agrawal, Pankaj B, Alejandro, Mercedes E, Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A, Azamian, Mahshid S, Bacino, Carlos A, Bademci, Guney, Baker, Eva, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak‐Toydemir, Pinar, Beck, Anita, Beggs, Alan H, Behrens, Edward, Bejerano, Gill, Bennet, Jimmy, Berg‐Rood, Beverly, Bernstein, Jonathan A, Berry, Gerard T, Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonnenmann, Carsten, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C, Brokamp, Elly, Brown, Gabrielle, Burke, Elizabeth A, Burrage, Lindsay C, Butte, Manish J, Byers, Peter, Byrd, William E, Carey, John, Carrasquillo, Olveen, Chang, Ta Chen Peter, Chanprasert, Sirisak, Chao, Hsiao‐Tuan, Clark, Gary D, Coakley, Terra R, Cobban, Laurel A, Cogan, Joy D, Coggins, Matthew, Sessions Cole, F, Colley, Heather A, Cooper, Cynthia M, Craigen, William J, Crouse, Andrew B, Cunningham, Michael, D'Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davids, Mariska, Dayal, Jyoti G, Deardorff, Matthew, Dell'Angelica, Esteban C, Dhar, Shweta U, Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Douine, Emilie D, Draper, David D, Duncan, Laura, Earl, Dawn, Eckstein, David J, Emrick, Lisa T, Eng, Christine M, Esteves, Cecilia, Estwick, Tyra, Falk, Marni, Fernandez, Liliana, Ferreira, Carlos, Fieg, Elizabeth L, Findley, Laurie C, Fisher, Paul G, Fogel, Brent L, Forghani, Irman, Fresard, Laure, Gahl, William A, Glass, Ian, and Godfrey, Rena A

Subjects
Pediatric, Biotechnology, Human Genome, Clinical Research, Genetics, Good Health and Well Being, Child, Delivery of Health Care, Family, Genomics, Humans, Parents, Exome Sequencing, exome and genomic sequencing, undiagnosed disorders, healthcare empowerment, genetic counseling, parental perspectives, rare disorders, Undiagnosed Disease Network, Clinical Sciences, Genetics & Heredity
Abstract

The Genome Empowerment Scale (GEmS), developed as a research tool, assesses perspectives of parents of children with undiagnosed disorders about to undergo exome or genome sequencing related to the process of empowerment. We defined genomic healthcare empowerment as follows: perceived ability to understand and seek new information related to the genomic sequencing, manage emotions related to the diagnostic process and outcomes, and utilize genomic sequencing information to the betterment of the individual/child and family. The GEmS consists of four scales, two are primarily emotion-focused (Meaning of a Diagnosis, and Emotional Management of the Process) and two are action-oriented (Seeking Information and Support, and Implications and Planning). The purpose of this research was to provide a strategy for interpreting results from the GEmS and present illustrative cases. These illustrations should serve to facilitate use of the GEmS in the clinical and research arena, particularly with respect to guiding genetic counseling processes for parents of children with undiagnosed conditions.

Published
2022

39. Disruption of protein quality control of the human ether-à-go-go related gene K+ channel results in profound long QT syndrome

Author

Ledford, Hannah A, Ren, Lu, Thai, Phung N, Park, Seojin, Timofeyev, Valeriy, Sirish, Padmini, Xu, Wilson, Emigh, Aiyana M, Priest, James R, Perez, Marco V, Ashley, Euan A, Yarov-Yarovoy, Vladimir, Yamoah, Ebenezer N, Zhang, Xiao-Dong, and Chiamvimonvat, Nipavan

Subjects
Medical Physiology, Biomedical and Clinical Sciences, Stem Cell Research - Embryonic - Human, Stem Cell Research - Induced Pluripotent Stem Cell, Pediatric, Stem Cell Research - Induced Pluripotent Stem Cell - Human, Stem Cell Research, Cardiovascular, Genetics, Heart Disease, Congenital Heart Disease, Rare Diseases, 2.1 Biological and endogenous factors, Aetiology, ERG1 Potassium Channel, HEK293 Cells, Humans, Long QT Syndrome, Myocytes, Cardiac, Patch-Clamp Techniques, Ubiquitin-Protein Ligases, Cardiac ion channels, E3 ubiquitin ligase, Endoplasmic reticulum-associated degradation, Human ether a-go-go related gene (hERG)-encoded potassium channels, Human induced pluripotent stem cells, Human induced pluripotent stem cell-derived cardiomyocytes, Long QT syndrome, Protein quality control, RING finger protein 207, Human ether a-go-go related gene (hERG)–encoded potassium channels, Biomedical Engineering, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundLong QT syndrome (LQTS) is a hereditary disease that predisposes patients to life-threatening cardiac arrhythmias and sudden cardiac death. Our previous study of the human ether-à-go-go related gene (hERG)-encoded K+ channel (Kv11.1) supports an association between hERG and RING finger protein 207 (RNF207) variants in aggravating the onset and severity of LQTS, specifically T613M hERG (hERGT613M) and RNF207 frameshift (RNF207G603fs) mutations. However, the underlying mechanistic underpinning remains unknown.ObjectiveThe purpose of the present study was to test the role of RNF207 in the function of hERG-encoded K+ channel subunits.MethodsWhole-cell patch-clamp experiments were performed in human embryonic kidney (HEK 293) cells and human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) together with immunofluorescent confocal and high resolution microscopy, auto-ubiquitinylation assays, and co-immunoprecipitation experiments to test the functional interactions between hERG and RNF207.ResultsHere, we demonstrated that RNF207 serves as an E3 ubiquitin ligase and targets misfolded hERGT613M proteins for degradation. RNF207G603fs exhibits decreased activity and hinders the normal degradation pathway; this increases the levels of hERGT613M subunits and their dominant-negative effect on the wild-type subunits, ultimately resulting in decreased current density. Similar findings are shown for hERGA614V, a known dominant-negative mutant subunit. Finally, the presence of RNF207G603fs with hERGT613M results in significantly prolonged action potential durations and reduced hERG current in human-induced pluripotent stem cell-derived cardiomyocytes.ConclusionOur study establishes RNF207 as an interacting protein serving as a ubiquitin ligase for hERG-encoded K+ channel subunits. Normal function of RNF207 is critical for the quality control of hERG subunits and consequently cardiac repolarization. Moreover, our study provides evidence for protein quality control as a new paradigm in life-threatening cardiac arrhythmias in patients with LQTS.

Published
2022

40. SARS-CoV-2 RNAemia predicts clinical deterioration and extrapulmonary complications from COVID-19

Author

Ram-Mohan, Nikhil, Kim, David, Zudock, Elizabeth J, Hashemi, Marjan M, Tjandra, Kristel C, Rogers, Angela J, Blish, Catherine A, Nadeau, Kari C, Newberry, Jennifer A, Quinn, James V, O’Hara, Ruth, Ashley, Euan, Nguyen, Hien, Jiang, Lingxia, Hung, Paul, Blomkalns, Andra L, Yang, Samuel, Mann, Rosen, Visweswaran, Anita, Ranganath, Thanmayi, Roque, Jonasel, Manohar, Monali, Din, Hena Naz, Kumar, Komal, Jee, Kathryn, Noon, Brigit, Anderson, Jill, Fay, Bethany, Schreiber, Donald, Zhao, Nancy, Vergara, Rosemary, McKechnie, Julia, Wilk, Aaron, de la Parte, Lauren, Dantzler, Kathleen Whittle, Ty, Maureen, Kathale, Nimish, Rustagi, Arjun, Martinez-Colon, Giovanny, Ivison, Geoff, Pi, Ruoxi, Lee, Maddie, Brewer, Rachel, Hollis, Taylor, Baird, Andrea, Ugur, Michele, Bogusch, Drina, Nahass, George R, Haider, Kazim, Tran, Kim Quyen Thi, Simpson, Laura, Tal, Michal, Chang, Iris, Do, Evan, Fernandes, Andrea, Lee, Alexandra S, Ahuja, Neera, Snow, Theo, and Krempski, James

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Lung, Pneumonia, Infectious Diseases, Emerging Infectious Diseases, Clinical Research, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Good Health and Well Being, Stanford COVID-19 Biobank Study Group, RNAemia, SARS-CoV-2, digital PCR, extrapulmonary complications, severity prediction, Biological Sciences, Medical and Health Sciences, Microbiology, Clinical sciences
Abstract

BackgroundThe determinants of coronavirus disease 2019 (COVID-19) disease severity and extrapulmonary complications (EPCs) are poorly understood. We characterized relationships between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNAemia and disease severity, clinical deterioration, and specific EPCs.MethodsWe used quantitative and digital polymerase chain reaction (qPCR and dPCR) to quantify SARS-CoV-2 RNA from plasma in 191 patients presenting to the emergency department with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterized the role of RNAemia in predicting clinical severity and EPCs using elastic net regression.ResultsOf SARS-CoV-2-positive patients, 23.0% (44 of 191) had viral RNA detected in plasma by dPCR, compared with 1.4% (2 of 147) by qPCR. Most patients with serial measurements had undetectable RNAemia within 10 days of symptom onset, reached maximum clinical severity within 16 days, and symptom resolution within 33 days. Initially RNAemic patients were more likely to manifest severe disease (odds ratio, 6.72 [95% confidence interval, 2.45-19.79]), worsening of disease severity (2.43 [1.07-5.38]), and EPCs (2.81 [1.26-6.36]). RNA loads were correlated with maximum severity (r = 0.47 [95% confidence interval, .20-.67]).ConclusionsdPCR is more sensitive than qPCR for the detection of SARS-CoV-2 RNAemia, which is a robust predictor of eventual COVID-19 severity and oxygen requirements, as well as EPCs. Because many COVID-19 therapies are initiated on the basis of oxygen requirements, RNAemia on presentation might serve to direct early initiation of appropriate therapies for the patients most likely to deteriorate.

Published
2022

41. Medical Imaging and Machine Learning

Author

Shad, Rohan, Cunningham, John P., Ashley, Euan A., Langlotz, Curtis P., and Hiesinger, William

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

Advances in computing power, deep learning architectures, and expert labelled datasets have spurred the development of medical imaging artificial intelligence systems that rival clinical experts in a variety of scenarios. The National Institutes of Health in 2018 identified key focus areas for the future of artificial intelligence in medical imaging, creating a foundational roadmap for research in image acquisition, algorithms, data standardization, and translatable clinical decision support systems. Among the key issues raised in the report: data availability, need for novel computing architectures and explainable AI algorithms, are still relevant despite the tremendous progress made over the past few years alone. Furthermore, translational goals of data sharing, validation of performance for regulatory approval, generalizability and mitigation of unintended bias must be accounted for early in the development process. In this perspective paper we explore challenges unique to high dimensional clinical imaging data, in addition to highlighting some of the technical and ethical considerations in developing high-dimensional, multi-modality, machine learning systems for clinical decision support., Comment: 9 pages, 4 figures

Published
2021
Full Text
View/download PDF

42. Deep learning evaluation of biomarkers from echocardiogram videos

Author

Hughes, J Weston, Yuan, Neal, He, Bryan, Ouyang, Jiahong, Ebinger, Joseph, Botting, Patrick, Lee, Jasper, Theurer, John, Tooley, James E, Nieman, Koen, Lungren, Matthew P, Liang, David H, Schnittger, Ingela, Chen, Jonathan H, Ashley, Euan A, Cheng, Susan, Ouyang, David, and Zou, James Y

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Hematology, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Good Health and Well Being, Algorithms, Biomarkers, Deep Learning, Echocardiography, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, ROC Curve, Software, Artificial intelligence, Deep learning, Public Health and Health Services, Clinical sciences, Epidemiology
Abstract

BackgroundLaboratory testing is routinely used to assay blood biomarkers to provide information on physiologic state beyond what clinicians can evaluate from interpreting medical imaging. We hypothesized that deep learning interpretation of echocardiogram videos can provide additional value in understanding disease states and can evaluate common biomarkers results.MethodsWe developed EchoNet-Labs, a video-based deep learning algorithm to detect evidence of anemia, elevated B-type natriuretic peptide (BNP), troponin I, and blood urea nitrogen (BUN), as well as values of ten additional lab tests directly from echocardiograms. We included patients (n = 39,460) aged 18 years or older with one or more apical-4-chamber echocardiogram videos (n = 70,066) from Stanford Healthcare for training and internal testing of EchoNet-Lab's performance in estimating the most proximal biomarker result. Without fine-tuning, the performance of EchoNet-Labs was further evaluated on an additional external test dataset (n = 1,301) from Cedars-Sinai Medical Center. We calculated the area under the curve (AUC) of the receiver operating characteristic curve for the internal and external test datasets.FindingsOn the held-out test set of Stanford patients not previously seen during model training, EchoNet-Labs achieved an AUC of 0.80 (0.79-0.81) in detecting anemia (low hemoglobin), 0.86 (0.85-0.88) in detecting elevated BNP, 0.75 (0.73-0.78) in detecting elevated troponin I, and 0.74 (0.72-0.76) in detecting elevated BUN. On the external test dataset from Cedars-Sinai, EchoNet-Labs achieved an AUC of 0.80 (0.77-0.82) in detecting anemia, of 0.82 (0.79-0.84) in detecting elevated BNP, of 0.75 (0.72-0.78) in detecting elevated troponin I, and of 0.69 (0.66-0.71) in detecting elevated BUN. We further demonstrate the utility of the model in detecting abnormalities in 10 additional lab tests. We investigate the features necessary for EchoNet-Labs to make successful detection and identify potential mechanisms for each biomarker using well-known and novel explainability techniques.InterpretationThese results show that deep learning applied to diagnostic imaging can provide additional clinical value and identify phenotypic information beyond current imaging interpretation methods.FundingJ.W.H. and B.H. are supported by the NSF Graduate Research Fellowship. D.O. is supported by NIH K99 HL157421-01. J.Y.Z. is supported by NSF CAREER 1942926, NIH R21 MD012867-01, NIH P30AG059307 and by a Chan-Zuckerberg Biohub Fellowship.

Published
2021

43. Biallelic CRELD1 variants cause a multisystem syndrome, including neurodevelopmental phenotypes, cardiac dysrhythmias, and frequent infections

Author

Borras, Silvia, Clark, Caroline, Dean, John, Miedzybrodzka, Zosia, Ross, Alison, Tennant, Stephen, Dabir, Tabib, Donnelly, Deirdre, Humphreys, Mervyn, Magee, Alex, McConnell, Vivienne, McKee, Shane, McNerlan, Susan, Morrison, Patrick J., Rea, Gillian, Stewart, Fiona, Cole, Trevor, Cooper, Nicola, Cooper-Charles, Lisa, Cox, Helen, Islam, Lily, Jarvis, Joanna, Keelagher, Rebecca, Lim, Derek, McMullan, Dominic, Morton, Jenny, Naik, Swati, O’Driscoll, Mary, Ong, Kai-Ren, Osio, Deborah, Ragge, Nicola, Turton, Sarah, Vogt, Julie, Williams, Denise, Bodek, Simon, Donaldson, Alan, Hills, Alison, Low, Karen, Newbury-Ecob, Ruth, Norman, Andrew M., Roberts, Eileen, Scurr, Ingrid, Smithson, Sarah, Tooley, Madeleine, Abbs, Steve, Armstrong, Ruth, Dunn, Carolyn, Holden, Simon, Park, Soo-Mi, Paterson, Joan, Raymond, Lucy, Reid, Evan, Sandford, Richard, Simonic, Ingrid, Tischkowitz, Marc, Woods, Geoff, Bradley, Lisa, Comerford, Joanne, Green, Andrew, Lynch, Sally, McQuaid, Shirley, Mullaney, Brendan, Berg, Jonathan, Goudie, David, Mavrak, Eleni, McLean, Joanne, McWilliam, Catherine, Reavey, Eleanor, Azam, Tara, Cleary, Elaine, Jackson, Andrew, Lam, Wayne, Lampe, Anne, Moore, David, Porteous, Mary, Baple, Emma, Baptista, Júlia, Brewer, Carole, Castle, Bruce, Kivuva, Emma, Owens, Martina, Rankin, Julia, Shaw-Smith, Charles, Turner, Claire, Turnpenny, Peter, Tysoe, Carolyn, Bradley, Therese, Davidson, Rosemarie, Gardiner, Carol, Joss, Shelagh, Kinning, Esther, Longman, Cheryl, McGowan, Ruth, Murday, Victoria, Pilz, Daniela, Tobias, Edward, Whiteford, Margo, Williams, Nicola, Barnicoat, Angela, Clement, Emma, Faravelli, Francesca, Hurst, Jane, Jenkins, Lucy, Jones, Wendy, Ajith Kumar, V.K., Lees, Melissa, Loughlin, Sam, Male, Alison, Morrogh, Deborah, Rosser, Elisabeth, Scott, Richard, Wilson, Louise, Beleza, Ana, Deshpande, Charu, Flinter, Frances, Holder, Muriel, Irving, Melita, Izatt, Louise, Josifova, Dragana, Mohammed, Shehla, Molenda, Aneta, Robert, Leema, Roworth, Wendy, Ruddy, Deborah, Ryten, Mina, Yau, Shu, Bennett, Christopher, Blyth, Moira, Campbell, Jennifer, Coates, Andrea, Dobbie, Angus, Hewitt, Sarah, Hobson, Emma, Jackson, Eilidh, Jewell, Rosalyn, Kraus, Alison, Prescott, Katrina, Sheridan, Eamonn, Thomson, Jenny, Bradshaw, Kirsty, Dixit, Abhijit, Eason, Jacqueline, Haines, Rebecca, Harrison, Rachel, Mutch, Stacey, Sarkar, Ajoy, Searle, Claire, Shannon, Nora, Sharif, Abid, Suri, Mohnish, Vasudevan, Pradeep, Canham, Natalie, Ellis, Ian, Greenhalgh, Lynn, Howard, Emma, Stinton, Victoria, Swale, Andrew, Weber, Astrid, Banka, Siddharth, Breen, Catherine, Briggs, Tracy, Burkitt-Wright, Emma, Chandler, Kate, Clayton-Smith, Jill, Donnai, Dian, Douzgou, Sofia, Gaunt, Lorraine, Jones, Elizabeth, Kerr, Bronwyn, Langley, Claire, Metcalfe, Kay, Smith, Audrey, Wright, Ronnie, Bourn, David, Burn, John, Fisher, Richard, Hellens, Steve, Henderson, Alex, Montgomery, Tara, Splitt, Miranda, Straub, Volker, Wright, Michael, Zwolinski, Simon, Allen, Zoe, Bernhard, Birgitta, Brady, Angela, Brooks, Claire, Busby, Louise, Clowes, Virginia, Ghali, Neeti, Holder, Susan, Ibitoye, Rita, Wakeling, Emma, Blair, Edward, Carmichael, Jenny, Cilliers, Deirdre, Clasper, Susan, Gibbons, Richard, Kini, Usha, Lester, Tracy, Nemeth, Andrea, Poulton, Joanna, Price, Sue, Shears, Debbie, Stewart, Helen, Wilkie, Andrew, Albaba, Shadi, Baker, Duncan, Balasubramanian, Meena, Johnson, Diana, Parker, Michael, Quarrell, Oliver, Stewart, Alison, Willoughby, Josh, Crosby, Charlene, Elmslie, Frances, Homfray, Tessa, Jin, Huilin, Lahiri, Nayana, Mansour, Sahar, Marks, Karen, McEntagart, Meriel, Saggar, Anand, Tatton-Brown, Kate, Butler, Rachel, Clarke, Angus, Corrin, Sian, Fry, Andrew, Kamath, Arveen, McCann, Emma, Mugalaasi, Hood, Pottinger, Caroline, Procter, Annie, Sampson, Julian, Sansbury, Francis, Varghese, Vinod, Baralle, Diana, Callaway, Alison, Cassidy, Emma J., Daniels, Stacey, Douglas, Andrew, Foulds, Nicola, Hunt, David, Kharbanda, Mira, Lachlan, Katherine, Mercer, Catherine, Side, Lucy, Temple, I. Karen, Wellesley, Diana, Ambrose, J.C., Arumugam, P., Baple, E.L., Bleda, M., Boardman-Pretty, F., Boissiere, J.M., Boustred, C.R., Caulfield, M.J., Chan, G.C., Craig, C.E.H., Daugherty, L.C., de Burca, A., Devereau, A., Elgar, G., Foulger, R.E., Fowler, T., FurióTarí, P., Hackett, J.M., Halai, D., Hamblin, A., Henderson, S., Holman, J.E., Hubbard, T.J.P., Ibáñez, K., Jackson, R., Jones, L.J., Kasperaviciute, D., Kayikci, M., Lahnstein, L., Lawson, K., Leigh, S.E.A., Leong, I.U.S., Lopez, F.J., MaleadyCrowe, F., Mason, J., McDonagh, E.M., Moutsianas, L., Mueller, M., Murugaesu, N., Need, A.C., Odhams, C.A., Patch, C., Perez-Gil, D., Polychronopoulos, D., Pullinger, J., Rahim, T., Rendon, A., Riesgo-Ferreiro, P., Rogers, T., Ryten, M., Savage, K., Sawant, K., Scott, R.H., Siddiq, A., Sieghart, A., Smedley, D., Smith, K.R., Sosinsky, A., Spooner, W., Stevens, H.E., Stuckey, A., Sultana, R., Thomas, E.R.A., Thompson, S.R., Tucci, A., Walsh, E., Watters, S.A., Welland, M.J., Williams, E., Witkowska, K., Acosta, Maria T., Adam, Margaret, Adams, David R., Agrawal, Pankaj B., Alejandro, Mercedes E., Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A., Azamian, Mahshid S., Bacino, Carlos A., Bademci, Guney, Baker, Eva, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bennet, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonnenmann, Carsten, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C., Brokamp, Elly, Brown, Gabrielle, Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Peter Chang, Ta Chen, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Cole, F. Sessions, Colley, Heather A., Cooper, Cynthia M., Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D'Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davids, Mariska, Dayal, Jyoti G., Deardorff, Matthew, Dell'Angelica, Esteban C., Dhar, Shweta U., Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Douine, Emilie D., Draper, David D., Duncan, Laura, Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Esteves, Cecilia, Estwick, Tyra, Falk, Marni, Fernandez, Liliana, Ferreira, Carlos, Fieg, Elizabeth L., Findley, Laurie C., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Fresard, Laure, Gahl, William A., Glass, Ian, Godfrey, Rena A., Golden-Grant, Katie, Goldman, Alica M., Goldstein, David B., Grajewski, Alana, Groden, Catherine A., Gropman, Andrea L., Gutierrez, Irma, Hahn, Sihoun, Hamid, Rizwan, Hanchard, Neil A., Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Huang, Yong, Isasi, Rosario, Jamal, Fariha, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Johnston, Jean M., Karaviti, Lefkothea, Kelley, Emily G., Kennedy, Jennifer, Kiley, Dana, Kohane, Isaac S., Kohler, Jennefer N., Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Korrick, Susan, Koziura, Mary, Krier, Joel B., Lalani, Seema R., Lam, Byron, Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., Lau, C. Christopher, LeBlanc, Kimberly, Lee, Brendan H., Lee, Hane, Levitt, Roy, Lewis, Richard A., Lincoln, Sharyn A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Majcherska, Marta M., Mak, Bryan, Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Maravilla, Kenneth, Markello, Thomas C., Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McCormack, Colleen E., McCray, Alexa T., McGee, Elisabeth, Mefford, Heather, Merritt, J. Lawrence, Might, Matthew, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo M., Morimoto, Marie, Mulvihill, John J., Murdock, David R., Nakano-Okuno, Mariko, Nath, Avi, Nelson, Stan F., Newman, John H., Nicholas, Sarah K., Nickerson, Deborah, Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Pallais, J. Carl, Papp, Jeanette C., Parker, Neil H., Phillips, John A., Posey, Jennifer E., Potocki, Lorraine, Pusey, Barbara N., Quinlan, Aaron, Raskind, Wendy, Raja, Archana N., Rao, Deepak A., Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenfeld, Jill A., Rosenwasser, Natalie, Ruzhnikov, Maura, Sacco, Ralph, Sampson, Jacinda B., Samson, Susan L., Saporta, Mario, Scott, C. Ron, Schaechter, Judy, Schedl, Timothy, Scott, Daryl A., Sharma, Prashant, Shin, Jimann, Signer, Rebecca, Sillari, Catherine H., Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solem, Emily, Solnica-Krezel, Lilianna, Stoler, Joan M., Stong, Nicholas, Sullivan, Jennifer A., Sun, Angela, Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tamburro, Cecelia P., Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Tucker, Brianna M., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Wallace, Stephanie, Walley, Nicole M., Walsh, Chris A., Walker, Melissa, Wambach, Jennifer, Wan, Jijun, Wang, Lee-Kai, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Wener, Mark, Wenger, Tara, Perry, Katherine Wesseling, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Woods, Jeremy D., Yamamoto, Shinya, Yang, John, Yu, Guoyun, Zastrow, Diane B., Zhao, Chunli, Zuchner, Stephan, Jeffries, Lauren, Mis, Emily K., McWalter, Kirsty, Donkervoort, Sandra, Brodsky, Nina N., Carpier, Jean-Marie, Ji, Weizhen, Ionita, Cristian, Roy, Bhaskar, Morrow, Jon S., Darbinyan, Armine, Iyer, Krishna, Aul, Ritu B., Chao, Katherine R., Cobbold, Laura, Cohen, Stacey, Custodio, Helena M., Drummond-Borg, Margaret, Finanger, Erika, Hainline, Bryan E., Helbig, Ingo, Hewson, Stacy, Hu, Ying, Jackson, Adam, Konstantino, Monica, Leach, Meganne E., McCormick, David, Nelson, Stanley, Nguyen, Joanne, Nugent, Kimberly, Ortega, Lucy, Goodkin, Howard P., Roeder, Elizabeth, Roy, Sani, Sapp, Katie, Saade, Dimah, Sisodiya, Sanjay M., Stals, Karen, Towner, Shelley, Wilson, William, Khokha, Mustafa K., Bönnemann, Carsten G., Lucas, Carrie L., and Lakhani, Saquib A.

Published
2024
Full Text
View/download PDF

44. A syndromic neurodevelopmental disorder caused by rare variants in PPFIA3

Author

Acosta, Maria T., Adam, Margaret, Adams, David R., Alvarez, Raquel L., Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A., Bacino, Carlos A., Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bellen, Hugo J., Bennett, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C., Brown, Gabrielle, Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Chang, Ta Chen Peter, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Chinn, Ivan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Cole, F. Sessions, Colley, Heather A., Cope, Heidi, Corona, Rosario, Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G., Dell'Angelica, Esteban C., Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L., Douine, Emilie D., Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Falk, Marni, Fieg, Elizabeth L., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Gahl, William A., Glass, Ian, Gochuico, Bernadette, Goddard, Page C., Godfrey, Rena A., Golden-Grant, Katie, Grajewski, Alana, Hadley, Don, Hahn, Sihoun, Halley, Meghan C., Hamid, Rizwan, Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Hutchison, Sarah, Introne, Wendy, Isasi, Rosario, Izumi, Kosuke, Jamal, Fariha, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Jean-Marie, Orpa, Jobanputra, Vaidehi, Karaviti, Lefkothea, Ketkar, Shamika, Kiley, Dana, Kilich, Gonench, Kobren, Shilpa N., Kohane, Isaac S., Kohler, Jennefer N., Korrick, Susan, Kozuira, Mary, Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Lalani, Seema R., Lam, Byron, Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., LeBlanc, Kimberly, Lee, Brendan H., Levitt, Roy, Lewis, Richard A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Maghiro, Audrey Stephannie, Mahoney, Rachel, Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Maravilla, Kenneth, Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McConkie-Rosell, Allyn, McCray, Alexa T., McGee, Elisabeth, Mefford, Heather, Merritt, J. Lawrence, Might, Matthew, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo, Mulvihill, John, Nakano-Okuno, Mariko, Nelson, Stanley F., Newman, John H., Nicholas, Sarah K., Nickerson, Deborah, Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Pallais, J. Carl, Palmer, Christina G.S., Papp, Jeanette C., Parker, Neil H., Phillips III, John A., Posey, Jennifer E., Potocki, Lorraine, Pusey Swerdzewski, Barbara N., Quinlan, Aaron, Rao, Deepak A., Raper, Anna, Raskind, Wendy, Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenfeld, Jill A., Rosenwasser, Natalie, Rossignol, Francis, Ruzhnikov, Maura, Sacco, Ralph, Sampson, Jacinda B., Saporta, Mario, Schaechter, Judy, Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Scott, C. Ron, Seto, Elaine, Shashi, Vandana, Shin, Jimann, Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stoler, Joan M., Sullivan, Kathleen, Sullivan, Jennifer A., Sun, Angela, Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tan, Queenie K.-G., Tan, Amelia L.M., Tarakad, Arjun, Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Ungar, Rachel A., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Walker, Melissa, Wallace, Stephanie, Walley, Nicole M., Wambach, Jennifer, Wan, Jijun, Wang, Lee-kai, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Weisz Hubshman, Monika, Wener, Mark, Wenger, Tara, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Worley, Kim, Xiao, Changrui, Yamamoto, Shinya, Yang, John, Zhang, Zhe, Zuchner, Stephan, Paul, Maimuna S., Michener, Sydney L., Pan, Hongling, Chan, Hiuling, Pfliger, Jessica M., Lerma, Vanesa C., Tran, Alyssa, Longley, Megan A., Weisz-Hubshman, Monika, Bekheirnia, Mir Reza, Bekheirnia, Nasim, Massingham, Lauren, Zech, Michael, Wagner, Matias, Engels, Hartmut, Cremer, Kirsten, Mangold, Elisabeth, Peters, Sophia, Trautmann, Jessica, Perne, Claudia, Mester, Jessica L., Guillen Sacoto, Maria J., Person, Richard, McDonnell, Pamela P., Cohen, Stacey R., Lusk, Laina, Cohen, Ana S.A., Le Pichon, Jean-Baptiste, Pastinen, Tomi, Zhou, Dihong, Engleman, Kendra, Racine, Caroline, Faivre, Laurence, Moutton, Sébastien, Denommé-Pichon, Anne-Sophie, Koh, Hyun Yong, Poduri, Annapurna, Bolton, Jeffrey, Knopp, Cordula, Julia Suh, Dong Sun, Maier, Andrea, Toosi, Mehran Beiraghi, Karimiani, Ehsan Ghayoor, Maroofian, Reza, Schaefer, Gerald Bradley, Ramakumaran, Vijayalakshmi, Vasudevan, Pradeep, Banos-Pinero, Benito, Pagnamenta, Alistair T., Prasad, Chitra, Osmond, Matthew, Schuhmann, Sarah, Vasileiou, Georgia, Russ-Hall, Sophie, Scheffer, Ingrid E., and Carvill, Gemma L.

Published
2024
Full Text
View/download PDF

46. DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices

Author

Soto, Jessica Torres and Ashley, Euan

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements like step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist where noise remains an unsolved problem. Here, we develop a multi-task deep learning method to assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation (AF). We train our algorithm on over one million simulated unlabeled physiological signals and fine-tune on a curated dataset of over 500K labeled signals from over 100 individuals from 3 different wearable devices. We demonstrate that in comparison with a traditional random forest-based approach (precision:0.24, recall:0.58, f1:0.34, auPRC:0.44) and a single task CNN (precision:0.59, recall:0.69, f1:0.64, auPRC:0.68) our architecture using unsupervised transfer learning through convolutional denoising autoencoders dramatically improves the performance of AF detection in participants at rest (pr:0.94, rc:0.98, f1:0.96, auPRC:0.96). In addition, we validate algorithm performance on a prospectively derived replication cohort of ambulatory subjects using data derived from an independently engineered device. We show that two-stage training can help address the unbalanced data problem common to biomedical applications where large well-annotated datasets are scarce. In conclusion, though a combination of simulation and transfer learning and we develop and apply a multitask architecture to the problem of AF detection from wearable wrist sensors demonstrating high levels of accuracy and a solution for the vexing challenge of mechanical noise.

Published
2020

47. The landscape of reported VUS in multi-gene panel and genomic testing: Time for a change

Author

Marshall, Christian, Meng, Linyan, Jobanputra, Vaidehi, Taft, Ryan, Ashley, Euan, Nakouzi, Ghunwa, Shen, Wei, Kingsmore, Stephen, Rehm, Heidi, Rehm, Heidi L., Alaimo, Joseph T., Aradhya, Swaroop, Bayrak-Toydemir, Pinar, Best, Hunter, Brandon, Rhonda, Buchan, Jillian G., Chao, Elizabeth C., Chen, Elaine, Clifford, Jacob, Cohen, Ana S.A., Conlin, Laura K., Das, Soma, Davis, Kyle W., del Gaudio, Daniela, Del Viso, Florencia, DiVincenzo, Christina, Eisenberg, Marcia, Guidugli, Lucia, Hammer, Monia B., Harrison, Steven M., Hatchell, Kathryn E., Dyer, Lindsay Havens, Hoang, Lily U., Holt, James M., Karbassi, Izabela D., Kearney, Hutton M., Kelly, Melissa A., Kelly, Jacob M., Kluge, Michelle L., Komala, Timothy, Kruszka, Paul, Lau, Lynette, Lebo, Matthew S., Marshall, Christian R., McKnight, Dianalee, McWalter, Kirsty, Meng, Yan, Nagan, Narasimhan, Neckelmann, Christian S., Neerman, Nir, Niu, Zhiyv, Paolillo, Vitoria K., Paolucci, Sarah A., Perry, Denise, Pesaran, Tina, Radtke, Kelly, Rasmussen, Kristen J., Retterer, Kyle, Saunders, Carol J., Spiteri, Elizabeth, Stanley, Christine, Szuto, Anna, Taft, Ryan J., Thiffault, Isabelle, Thomas, Brittany C., Thomas-Wilson, Amanda, Thorpe, Erin, Tidwell, Timothy J., Towne, Meghan C., and Zouk, Hana

Published
2023
Full Text
View/download PDF

50. Genomics Research with Undiagnosed Children: Ethical Challenges at the Boundaries of Research and Clinical Care

Author

Acosta, Maria T., Adam, Margaret, Adams, David R., Alvarez, Raquel L., Alvey, Justin, Amendola, Laura, Andrews, Ashley, Bacino, Carlos A., Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bellen, Hugo J., Bennett, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C., Brown, Gabrielle, Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Peter Chang, Ta Chen, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Chinn, Ivan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Sessions Cole, F., Colley, Heather A., Cope, Heidi, Corona, Rosario, Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D'Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G., Dell'Angelica, Esteban C., Dickson, Patricia, Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L., Douine, Emilie D., Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Falk, Marni, Fieg, Elizabeth L., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Gahl, William A., Glass, Ian, Gochuico, Bernadette, Goddard, Page C., Godfrey, Rena A., Golden-Grant, Katie, Grajewski, Alana, Hadley, Don, Hahn, Sihoun, Hamid, Rizwan, Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Hutchison, Sarah, Introne, Wendy, Isasi, Rosario, Izumi, Kosuke, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Jean-Marie, Orpa, Jobanputra, Vaidehi, Ketkar, Shamika, Kiley, Dana, Kilich, Gonench, Kobren, Shilpa N., Kohane, Isaac S., Kohler, Jennefer N., Korrick, Susan, Kozuira, Mary, Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Lalani, Seema R., Lam, Byron, Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., LeBlanc, Kimberly, Lee, Brendan H., Levitt, Roy, Lewis, Richard A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Mahoney, Rachel, Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Maravilla, Kenneth, Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McConkie-Rosell, Allyn, McCray, Alexa T., McGee, Elisabeth, Mefford, Heather, Merritt, J. Lawrence, Might, Matthew, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo, Mulvihill, John J., Nakano-Okuno, Mariko, Nelson, Stanley F., Newman, John H., Nickerson, Deborah, Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Carl Pallais, J., Palmer, Christina G.S., Papp, Jeanette C., Parker, Neil H., Phillips, John A., III, Posey, Jennifer E., Potocki, Lorraine, Pusey Swerdzewski, Barbara N., Quinlan, Aaron, Rao, Deepak A., Raper, Anna, Raskind, Wendy, Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenfeld, Jill A., Rosenwasser, Natalie, Rossignol, Francis, Ruzhnikov, Maura, Sacco, Ralph, Sampson, Jacinda B., Saporta, Mario, Schaechter, Judy, Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Scott, C. Ron, Seto, Elaine, Shashi, Vandana, Shin, Jimann, Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stoler, Joan M., Sullivan, Kathleen, Sullivan, Jennifer A., Sun, Angela, Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tan, Queenie K.-G., Tan, Amelia L.M., Tarakad, Arjun, Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Ungar, Rachel A., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Walker, Melissa, Wallace, Stephanie, Walley, Nicole M., Wambach, Jennifer, Wan, Jijun, Wang, Lee-kai, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Hubshman, Monika Weisz, Wener, Mark, Wenger, Tara, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Worley, Kim, Xiao, Changrui, Yamamoto, Shinya, Yang, John, Zhang, Zhe, Zuchner, Stephan, Halley, Meghan C., Young, Jennifer L., Tang, Charis, Mintz, Kevin T., Lucas-Griffin, Sawyer, Maghiro, AudreyStephannie, Ashley, Euan A., and Tabor, Holly K.

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results