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149 results on '"Haendel, M."'

2. Cultural evolution and environmental change in Central Europe between 40 and 15 ka

Author

Maier, A., Stojakowits, P., Mayr, C., Pfeifer, S., Preusser, F., Zolitschka, B., Anghelinu, M., Bobak, D., Duprat-Oualid, F., Einwögerer, T., Hambach, U., Händel, M., Kaminská, L., Kämpf, L., Łanczont, M., Lehmkuhl, F., Ludwig, P., Magyari, E., Mroczek, P., Nemergut, A., Nerudová, Z., Niţă, L., Polanská, M., Połtowicz-Bobak, M., Rius, D., Römer, W., Simon, U., Škrdla, P., Újvári, G., and Veres, D.

Published
2021
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3. Tools for exploring mouse models of human disease

Author

Haendel, M, Papatheodorou, I, Oellrich, A, Mungall, CJ, Washington, N, Lewis, SE, Robinson, PN, and Smedley, D

Subjects
Networking and Information Technology R&D, Biotechnology, Genetics, 5.1 Pharmaceuticals, 2.1 Biological and endogenous factors, Neurology & Neurosurgery, Neurosciences, Pharmacology and Pharmaceutical Sciences
Abstract

Despite significant computational challenges, a number of tools have been developed recently to leverage the mouse to model human disease. Here we review these tools and show how they can be applied in the identification of candidate genes and therapeutic targets as well as mouse models for mechanistic studies and drug validation.

Published
2016

7. BRCA Challenge: BRCA Exchange as a global resource for variants in BRCA1 and BRCA2

Author

Cline, M.S., Liao, R.G., Parsons, M.T., Paten, B., Alquaddoomi, F., Antoniou, A., Baxter, S., Brody, L., Cook-Deegan, R., Coffin, A., Couch, F.J., Craft, B., Currie, R., Dlott, C.C., Dolman, L., Dunnen, J.T. den, Dyke, S.O.M., Domchek, S.M., Easton, D., Fischmann, Z., Foulkes, W.D., Garber, J., Goldgar, D., Goldman, M.J., Goodhand, P., Harrison, S., Haussler, D., Kato, K., Knoppers, B., Markello, C., Nussbaum, R., Offit, K., Plon, S.E., Rashbass, J., Rehm, H.L., Robson, M., Rubinstein, W.S., Stoppa-Lyonnet, D., Tavtigian, S., Thorogood, A., Zhang, C., Zimmermann, M., Burn, J., Chanock, S., Ratsch, G., Spurdle, A.B., Andreoletti, G., Baker, D., Brenner, S., Brush, M., Caputo, S., Castera, L., Cunningham, F., Hoya, M. de la, Diekhans, M., Dolinsky, J., Dwight, S., Eccles, D., Feng, B., Fiume, M., Flicek, P., Gaudet, P., Garcia, E.G., Haendel, M., Haeussler, M., Hahnen, E., Houdayer, C., Hunt, S., James, P., Lebo, M., Lee, J., Lerner-Ellis, J., Lin, M., Lincoln, S., Malheiro, A., Mesenkamp, A., Monteiro, A., Natzijl-Visser, E., Ngeow, J., North, K., Parkinson, H., Paschall, J., Patrinos, G., Phimister, B., Radice, P., Rainville, I., Rasmussen, M., Riley, G., Rouleau, E., Schmutzler, R., Shefchek, K., Sofia, H., Southey, M., Stuart, J., Thomas, J., Toland, A., Truty, R., Turn-Bull, C., Vaur, D., Vreeswijk, M.P.G., Walker, L., Walsh, M., Wappenschmidt, B., Weitzel, J., Wright, M., Zalunin, V., Zaranek, A., Zerbino, D., Zhou, A., Zhou, J., Zook, J., BRCA Challenge Authors, Eng, Charis, Liao, Rachel G [0000-0002-7830-1976], Parsons, Michael T [0000-0003-3242-8477], Alquaddoomi, Faisal [0000-0003-4297-8747], Baxter, Samantha [0000-0003-4616-9234], Coffin, Amy [0000-0003-2723-8222], Currie, Robert [0000-0003-1828-1827], Dlott, Chloe C [0000-0002-7268-7230], Dolman, Lena [0000-0002-3938-588X], Fischmann, Zachary [0000-0002-7687-0972], Foulkes, William D [0000-0001-7427-4651], Goldman, Mary J [0000-0002-9808-6388], Goodhand, Peter [0000-0002-2624-2820], Harrison, Steven [0000-0002-9614-9111], Haussler, David [0000-0003-1533-4575], Markello, Charles [0000-0002-3653-7155], Plon, Sharon E [0000-0002-9626-0936], Rehm, Heidi L [0000-0002-6025-0015], Rubinstein, Wendy S [0000-0002-8790-9959], Tavtigian, Sean [0000-0002-7543-8221], Thorogood, Adrian [0000-0001-5078-8164], Chanock, Stephen [0000-0002-2324-3393], Rätsch, Gunnar [0000-0001-5486-8532], Spurdle, Amanda B [0000-0003-1337-7897], and Apollo - University of Cambridge Repository

Subjects
0301 basic medicine, Male, Cancer Research, Research Facilities, endocrine system diseases, Epidemiology, Genes, BRCA2, Genes, BRCA1, Social Sciences, Penetrance, QH426-470, Patient advocacy, Database and Informatics Methods, 0302 clinical medicine, Resource (project management), Sociology, Gene Frequency, Consortia, Risk Factors, Databases, Genetic, Medicine and Health Sciences, Aetiology, skin and connective tissue diseases, Genetics (clinical), Cancer, Ovarian Neoplasms, education.field_of_study, Cancer Risk Factors, Genomics, Genomic Databases, 3. Good health, Viewpoints, Phenotype, Oncology, 030220 oncology & carcinogenesis, Female, Research Laboratories, Population, Genetic Causes of Cancer, MEDLINE, Information Dissemination, Breast Neoplasms, Patient Advocacy, Biology, Research and Analysis Methods, Human Genomics, 03 medical and health sciences, Databases, Genetic, Breast Cancer, Genetics, Humans, Genetic Predisposition to Disease, education, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Alleles, Human Genome, Biology and Life Sciences, Computational Biology, Genetic Variation, Genome Analysis, Genomic Libraries, BRCA1, Data science, BRCA2, Data sharing, Health Care, 030104 developmental biology, Biological Databases, Good Health and Well Being, Genes, Genetic Loci, Medical Risk Factors, BRCA Challenge Authors, Mutation, Leiden Open Variation Database, 2.6 Resources and infrastructure (aetiology), Government Laboratories, Developmental Biology
Abstract

The BRCA Challenge is a long-term data-sharing project initiated within the Global Alliance for Genomics and Health (GA4GH) to aggregate BRCA1 and BRCA2 data to support highly collaborative research activities. Its goal is to generate an informed and current understanding of the impact of genetic variation on cancer risk across the iconic cancer predisposition genes, BRCA1 and BRCA2. Initially, reported variants in BRCA1 and BRCA2 available from public databases were integrated into a single, newly created site, www.brcaexchange.org. The purpose of the BRCA Exchange is to provide the community with a reliable and easily accessible record of variants interpreted for a high-penetrance phenotype. More than 20,000 variants have been aggregated, three times the number found in the next-largest public database at the project’s outset, of which approximately 7,250 have expert classifications. The data set is based on shared information from existing clinical databases—Breast Cancer Information Core (BIC), ClinVar, and the Leiden Open Variation Database (LOVD)—as well as population databases, all linked to a single point of access. The BRCA Challenge has brought together the existing international Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium expert panel, along with expert clinicians, diagnosticians, researchers, and database providers, all with a common goal of advancing our understanding of BRCA1 and BRCA2 variation. Ongoing work includes direct contact with national centers with access to BRCA1 and BRCA2 diagnostic data to encourage data sharing, development of methods suitable for extraction of genetic variation at the level of individual laboratory reports, and engagement with participant communities to enable a more comprehensive understanding of the clinical significance of genetic variation in BRCA1 and BRCA2., Author summary The goal of this study and paper has been to develop an international resource to generate an informed and current understanding of the impact of genetic variation on cancer risk across the cancer predisposition genes, BRCA1 and BRCA2. Reported variants in BRCA1 and BRCA2 available from public databases were integrated into a single, newly created site, www.brcaexchange.org, to provide a reliable and easily accessible record of variants interpreted for a high-penetrance phenotype.

Published
2018

8. Enabling global clinical collaborations on identifiable patient data: The Minerva Initiative

Author

Nellåker, C., Alkuraya, F.S., Baynam, G., Bernier, R.A., Bernier, F.P.J., Boulanger, V., Brudno, M., Brunner, H.G., Clayton-Smith, J., Cogné, B., Dawkins, H.J.S., deVries, B.B.A., Douzgou, S., Dudding-Byth, T., Eichler, E.E., Ferlaino, M., Fieggen, K., Firth, H.V., FitzPatrick, D.R., Gration, D., Groza, T., Haendel, M., Hallowell, N., Hamosh, A., Hehir-Kwa, J., Hitz, M-P, Hughes, M., Kini, U., Kleefstra, T., Kooy, R.F., Krawitz, P., Küry, S., Lees, M., Lyon, G.J., Lyonnet, S., Marcadier, J.L., Meyn, S., Moslerová, V., Politei, J.M., Poulton, C.C., Raymond, F.L., Reijnders, M.R.F., Robinson, P.N., Romano, C., Rose, C.M., Sainsbury, D.C.G., Schofield, L., Sutton, V.R., Turnovec, M., Van Dijck, A., Van Esch, H., Wilkie, A.O.M., Nellåker, C., Alkuraya, F.S., Baynam, G., Bernier, R.A., Bernier, F.P.J., Boulanger, V., Brudno, M., Brunner, H.G., Clayton-Smith, J., Cogné, B., Dawkins, H.J.S., deVries, B.B.A., Douzgou, S., Dudding-Byth, T., Eichler, E.E., Ferlaino, M., Fieggen, K., Firth, H.V., FitzPatrick, D.R., Gration, D., Groza, T., Haendel, M., Hallowell, N., Hamosh, A., Hehir-Kwa, J., Hitz, M-P, Hughes, M., Kini, U., Kleefstra, T., Kooy, R.F., Krawitz, P., Küry, S., Lees, M., Lyon, G.J., Lyonnet, S., Marcadier, J.L., Meyn, S., Moslerová, V., Politei, J.M., Poulton, C.C., Raymond, F.L., Reijnders, M.R.F., Robinson, P.N., Romano, C., Rose, C.M., Sainsbury, D.C.G., Schofield, L., Sutton, V.R., Turnovec, M., Van Dijck, A., Van Esch, H., and Wilkie, A.O.M.

Abstract

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.

Published
2019

9. Spectrum of neurodevelopmental disease associated with the GNAO1 guanosine triphosphate-binding region

Author

Kelly, M, Park, M, Mihalek, I, Rochtus, A, Gramm, M, Perez-Palma, E, Axeen, ET, Hung, CY, Olson, H, Swanson, L, Anselm, I, Briere, LC, High, FA, Sweetser, DA, Kayani, S, Snyder, M, Calvert, S, Scheffer, IE, Yang, E, Waugh, JL, Lal, D, Bodamer, O, Poduri, A, Adams, DR, Aday, A, Alejandro, ME, Allard, P, Ashley, EA, Azamian, MS, Bacino, CA, Baker, E, Balasubramanyam, A, Barseghyan, H, Batzli, GF, Beggs, AH, Behnam, B, Bellen, HJ, Bernstein, JA, Bican, A, Bick, DP, Birch, CL, Bonner, D, Boone, BE, Bostwick, BL, Brokamp, E, Brown, DM, Brush, M, Burke, EA, Burrage, LC, Butte, MJ, Chen, S, Clark, GD, Coakley, TR, Cogan, JD, Colley, HA, Cooper, CM, Cope, H, Craigen, WJ, D'Souza, P, Davids, M, Davidson, JM, Dayal, JG, Dell'Angelica, EC, Dhar, SU, Dipple, KM, Donnell-Fink, LA, Dorrani, N, Dorset, DC, Douine, ED, Draper, DD, Dries, AM, Eckstein, DJ, Emrick, LT, Eng, CM, Enns, G-GM, Eskin, A, Esteves, C, Estwick, T, Fairbrother, L, Fernandez, L, Ferreira, C, Fieg, EL, Fisher, PG, Fogel, BL, Friedman, ND, Gahl, WA, Glanton, E, Godfrey, RA, Goldman, AM, Goldstein, DB, Gould, SE, Gourdine, J-PF, Groden, CA, Gropman, AL, Haendel, M, Hamid, R, Hanchard, NA, High, F, Holm, IA, Horn, J, Howerton, EM, Huang, Y, Jamal, F, Jiang, Y-H, Johnston, JM, Jones, AL, Karaviti, L, Koeller, DM, Kohane, IS, Kohler, JN, Konick, S, Koziura, M, Krasnewich, DM, Krier, JB, Kyle, JE, Lalani, SR, Lau, CC, Lazar, J, LeBlanc, K, Lee, BH, Lee, H, Levy, SE, Lewis, RA, Lincoln, SA, Loo, SK, Loscalzo, J, Maas, RL, Macnamara, EF, MacRae, CA, Maduro, VV, Majch-erska, MM, Malicdan, MC, Mamounas, LA, Manolio, TA, Markello, TC, Marom, R, Martin, MG, Martinez-Agosto, JA, Mar-waha, S, May, T, McConkie-Rosell, A, McCormack, CE, McCray, AF, Merker, JD, Metz, TO, Might, M, Moretti, PM, Morimoto, M, Mulvihill, JJ, Murdock, DR, Murphy, JL, Muzny, DM, Nehrebecky, ME, Nelson, SF, Newberry, JS, Newman, JH, Nicholas, SK, Novacic, D, Orange, JS, Orengo, JP, Pallais, JC, Palmer, CGS, Papp, JC, Parker, NH, Pena, LDM, Phillips, JA, Posey, JE, Postlethwait, JH, Potocki, L, Pusey, BN, Reuter, CM, Rives, L, Robertson, AK, Rodan, LH, Rosenfeld, JA, Sampson, JB, Samson, SL, Schoch, K, Scott, DA, Shakachite, L, Sharma, P, Shashi, V, Signer, R, Silverman, EK, Sinsheimer, JS, Smith, KS, Spillmann, RC, Stoler, JM, Stong, N, Sullivan, JA, Tan, QK-G, Tifft, CJ, Toro, C, Tran, AA, Urv, TK, Vilain, E, Vogel, TP, Waggott, DM, Wahl, CE, Walker, M, Walley, NM, Walsh, CA, Wan, J, Wangler, MF, Ward, PA, Waters, KM, Webb-Robertson, B-JM, Westerfield, M, Wheeler, MT, Wise, AL, Wolfe, LA, Worthey, EA, Yamamoto, S, Yang, Y, Yoon, AJ, Yu, G, Zastrow, DB, Zhao, C, Zheng, A, Kelly, M, Park, M, Mihalek, I, Rochtus, A, Gramm, M, Perez-Palma, E, Axeen, ET, Hung, CY, Olson, H, Swanson, L, Anselm, I, Briere, LC, High, FA, Sweetser, DA, Kayani, S, Snyder, M, Calvert, S, Scheffer, IE, Yang, E, Waugh, JL, Lal, D, Bodamer, O, Poduri, A, Adams, DR, Aday, A, Alejandro, ME, Allard, P, Ashley, EA, Azamian, MS, Bacino, CA, Baker, E, Balasubramanyam, A, Barseghyan, H, Batzli, GF, Beggs, AH, Behnam, B, Bellen, HJ, Bernstein, JA, Bican, A, Bick, DP, Birch, CL, Bonner, D, Boone, BE, Bostwick, BL, Brokamp, E, Brown, DM, Brush, M, Burke, EA, Burrage, LC, Butte, MJ, Chen, S, Clark, GD, Coakley, TR, Cogan, JD, Colley, HA, Cooper, CM, Cope, H, Craigen, WJ, D'Souza, P, Davids, M, Davidson, JM, Dayal, JG, Dell'Angelica, EC, Dhar, SU, Dipple, KM, Donnell-Fink, LA, Dorrani, N, Dorset, DC, Douine, ED, Draper, DD, Dries, AM, Eckstein, DJ, Emrick, LT, Eng, CM, Enns, G-GM, Eskin, A, Esteves, C, Estwick, T, Fairbrother, L, Fernandez, L, Ferreira, C, Fieg, EL, Fisher, PG, Fogel, BL, Friedman, ND, Gahl, WA, Glanton, E, Godfrey, RA, Goldman, AM, Goldstein, DB, Gould, SE, Gourdine, J-PF, Groden, CA, Gropman, AL, Haendel, M, Hamid, R, Hanchard, NA, High, F, Holm, IA, Horn, J, Howerton, EM, Huang, Y, Jamal, F, Jiang, Y-H, Johnston, JM, Jones, AL, Karaviti, L, Koeller, DM, Kohane, IS, Kohler, JN, Konick, S, Koziura, M, Krasnewich, DM, Krier, JB, Kyle, JE, Lalani, SR, Lau, CC, Lazar, J, LeBlanc, K, Lee, BH, Lee, H, Levy, SE, Lewis, RA, Lincoln, SA, Loo, SK, Loscalzo, J, Maas, RL, Macnamara, EF, MacRae, CA, Maduro, VV, Majch-erska, MM, Malicdan, MC, Mamounas, LA, Manolio, TA, Markello, TC, Marom, R, Martin, MG, Martinez-Agosto, JA, Mar-waha, S, May, T, McConkie-Rosell, A, McCormack, CE, McCray, AF, Merker, JD, Metz, TO, Might, M, Moretti, PM, Morimoto, M, Mulvihill, JJ, Murdock, DR, Murphy, JL, Muzny, DM, Nehrebecky, ME, Nelson, SF, Newberry, JS, Newman, JH, Nicholas, SK, Novacic, D, Orange, JS, Orengo, JP, Pallais, JC, Palmer, CGS, Papp, JC, Parker, NH, Pena, LDM, Phillips, JA, Posey, JE, Postlethwait, JH, Potocki, L, Pusey, BN, Reuter, CM, Rives, L, Robertson, AK, Rodan, LH, Rosenfeld, JA, Sampson, JB, Samson, SL, Schoch, K, Scott, DA, Shakachite, L, Sharma, P, Shashi, V, Signer, R, Silverman, EK, Sinsheimer, JS, Smith, KS, Spillmann, RC, Stoler, JM, Stong, N, Sullivan, JA, Tan, QK-G, Tifft, CJ, Toro, C, Tran, AA, Urv, TK, Vilain, E, Vogel, TP, Waggott, DM, Wahl, CE, Walker, M, Walley, NM, Walsh, CA, Wan, J, Wangler, MF, Ward, PA, Waters, KM, Webb-Robertson, B-JM, Westerfield, M, Wheeler, MT, Wise, AL, Wolfe, LA, Worthey, EA, Yamamoto, S, Yang, Y, Yoon, AJ, Yu, G, Zastrow, DB, Zhao, C, and Zheng, A

Abstract

OBJECTIVE: To characterize the phenotypic spectrum associated with GNAO1 variants and establish genotype-protein structure-phenotype relationships. METHODS: We evaluated the phenotypes of 14 patients with GNAO1 variants, analyzed their variants for potential pathogenicity, and mapped them, along with those in the literature, on a three-dimensional structural protein model. RESULTS: The 14 patients in our cohort, including one sibling pair, had 13 distinct, heterozygous GNAO1 variants classified as pathogenic or likely pathogenic. We attributed the same variant in two siblings to parental mosaicism. Patients initially presented with seizures beginning in the first 3 months of life (8/14), developmental delay (4/14), hypotonia (1/14), or movement disorder (1/14). All patients had hypotonia and developmental delay ranging from mild to severe. Nine had epilepsy, and nine had movement disorders, including dystonia, ataxia, chorea, and dyskinesia. The 13 GNAO1 variants in our patients are predicted to result in amino acid substitutions or deletions in the GNAO1 guanosine triphosphate (GTP)-binding region, analogous to those in previous publications. Patients with variants affecting amino acids 207-221 had only movement disorder and hypotonia. Patients with variants affecting the C-terminal region had the mildest phenotypes. SIGNIFICANCE: GNAO1 encephalopathy most frequently presents with seizures beginning in the first 3 months of life. Concurrent movement disorders are also a prominent feature in the spectrum of GNAO1 encephalopathy. All variants affected the GTP-binding domain of GNAO1, highlighting the importance of this region for G-protein signaling and neurodevelopment.

Published
2019

11. 10 simple rules for making data web friendly

Author

Juty, N, McMurry, J, BioMedBridges Consortium, Haendel, M, Goble, C, and Parkinson, H

Subjects
identifiers, standards, interoperability
Abstract

As the quantity and complexity of Life Science data continues to grow, so too does its availability through the web. This can cause potential integration issues where individual record or dataset identification was not conceived for use in a global data landscape. This can manifest in issues such as identifier or namespace collisions, resulting in erroneous integration of data, or simply as “link rot” and “content drift”. As a consequence, there is often a need to provide additional and potentially costly procedures for the source-dependent processing of data, or else the provision of alternative mapping solutions. BioMedBridges, in association with an international community of partners, have begun to determine the common technical bridges required to allow data integration across the biological domain. Based on our experience we describe ten simple rules for best practice in the provision and reuse of identifiers for web-based Life Science data. We further elicit feedback from the community, through the use of an interactive exercise, to determine both their perceived ranking of these rules, and their actual experiences when encountering web-based data.

Published
2015
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12. The Human Phenotype Ontology in 2017

Author

Kohler, S., Vasilevsky, N.A., Engelstad, M., Foster, E., McMurry, J., Ayme, S., Baynam, G., Bello, S.M., Boerkoel, C.F., Boycott, K.M., Brudno, M., Buske, O.J., Chinnery, P.F., Cipriani, V., Connell, L.E., Dawkins, H.J., DeMare, L.E., Devereau, A.D., Vries, B.B. de, Firth, H.V., Freson, K., Greene, D., Hamosh, A., Helbig, I., Hum, C., Jahn, J.A., James, R., Krause, R., Laulederkind, S.J.F., Lochmuller, H., Lyon, G.J., Ogishima, S., Olry, A., Ouwehand, W.H., Pontikos, N., Rath, A., Schaefer, F., Scott, R.H., Segal, M., Sergouniotis, P.I., Sever, R., Smith, C.L., Straub, V., Thompson, R., Turner, C., Turro, E., Veltman, M.W., Vulliamy, T., Yu, J., Ziegenweidt, J. von, Zankl, A., Zuchner, S., Zemojtel, T., Jacobsen, J.O., Groza, T., Smedley, D., Mungall, C.J., Haendel, M., Robinson, P.N., Kohler, S., Vasilevsky, N.A., Engelstad, M., Foster, E., McMurry, J., Ayme, S., Baynam, G., Bello, S.M., Boerkoel, C.F., Boycott, K.M., Brudno, M., Buske, O.J., Chinnery, P.F., Cipriani, V., Connell, L.E., Dawkins, H.J., DeMare, L.E., Devereau, A.D., Vries, B.B. de, Firth, H.V., Freson, K., Greene, D., Hamosh, A., Helbig, I., Hum, C., Jahn, J.A., James, R., Krause, R., Laulederkind, S.J.F., Lochmuller, H., Lyon, G.J., Ogishima, S., Olry, A., Ouwehand, W.H., Pontikos, N., Rath, A., Schaefer, F., Scott, R.H., Segal, M., Sergouniotis, P.I., Sever, R., Smith, C.L., Straub, V., Thompson, R., Turner, C., Turro, E., Veltman, M.W., Vulliamy, T., Yu, J., Ziegenweidt, J. von, Zankl, A., Zuchner, S., Zemojtel, T., Jacobsen, J.O., Groza, T., Smedley, D., Mungall, C.J., Haendel, M., and Robinson, P.N.

Abstract

Contains fulltext : 169849.pdf (publisher's version ) (Open Access), Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

Published
2017

13. The human phenotype ontology in 2017

Author

Köhler, S, Vasilevsky, NA, Engelstad, M, Foster, E, McMurry, J, Aymé, S, Baynam, G, Bello, SM, Boerkoel, CF, Boycott, KM, Brudno, M, Buske, OJ, Chinnery, PF, Cipriani, V, Connell, LE, Dawkins, HJS, DeMare, LE, Devereau, AD, De Vries, BBA, Firth, HV, Freson, K, Greene, D, Hamosh, A, Helbig, I, Hum, C, Jähn, JA, James, R, Krause, R, Laulederkind, SJF, Lochmüller, H, Lyon, GJ, Ogishima, S, Olry, A, Ouwehand, WH, Pontikos, N, Rath, A, Schaefer, F, Scott, RH, Segal, M, Sergouniotis, PI, Sever, R, Smith, CL, Straub, V, Thompson, R, Turner, C, Turro, E, Veltman, MWM, Vulliamy, T, Yu, J, Von Ziegenweidt, J, Zankl, A, Züchner, S, Zemojtel, T, Jacobsen, JOB, Groza, T, Smedley, D, Mungall, CJ, Haendel, M, Robinson, PN, Köhler, S, Vasilevsky, NA, Engelstad, M, Foster, E, McMurry, J, Aymé, S, Baynam, G, Bello, SM, Boerkoel, CF, Boycott, KM, Brudno, M, Buske, OJ, Chinnery, PF, Cipriani, V, Connell, LE, Dawkins, HJS, DeMare, LE, Devereau, AD, De Vries, BBA, Firth, HV, Freson, K, Greene, D, Hamosh, A, Helbig, I, Hum, C, Jähn, JA, James, R, Krause, R, Laulederkind, SJF, Lochmüller, H, Lyon, GJ, Ogishima, S, Olry, A, Ouwehand, WH, Pontikos, N, Rath, A, Schaefer, F, Scott, RH, Segal, M, Sergouniotis, PI, Sever, R, Smith, CL, Straub, V, Thompson, R, Turner, C, Turro, E, Veltman, MWM, Vulliamy, T, Yu, J, Von Ziegenweidt, J, Zankl, A, Züchner, S, Zemojtel, T, Jacobsen, JOB, Groza, T, Smedley, D, Mungall, CJ, Haendel, M, and Robinson, PN

Abstract

© The Author(s) 2016. Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human PhenotypeOntology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

Published
2017

14. Improved diagnosis and care for rare diseases through implementation of precision public health framework

Author

Baynam, G., Bowman, F., Lister, K., Walker, C.E., Pachter, N., Goldblatt, J., Boycott, K.M., Gahl, W.A., Kosaki, K., Adachi, T., Ishii, K., Mahede, T., McKenzie, F., Townshend, S., Slee, J., Kiraly-Borri, C., Vasudevan, A., Hawkins, A., Broley, S., Schofield, L., Verhoef, H., Groza, T., Zankl, A., Robinson, P.N., Haendel, M., Brudno, M., Mattick, J.S., Dinger, M.E., Roscioli, T., Cowley, M.J., Olry, A., Hanauer, M., Alkuraya, F.S., Taruscio, D., Posada de la Paz, M., Lochmüller, H., Bushby, K., Thompson, R., Hedley, V., Lasko, P., Mina, K., Beilby, J., Tifft, C., Davis, M., Laing, N.G., Julkowska, D., Le Cam, Y., Terry, S.F., Kaufmann, P., Eerola, I., Norstedt, I., Rath, A., Suematsu, M., Groft, S.C., Austin, C.P., Draghia-Akli, R., Weeramanthri, T.S., Molster, C., Dawkins, H.J.S., Baynam, G., Bowman, F., Lister, K., Walker, C.E., Pachter, N., Goldblatt, J., Boycott, K.M., Gahl, W.A., Kosaki, K., Adachi, T., Ishii, K., Mahede, T., McKenzie, F., Townshend, S., Slee, J., Kiraly-Borri, C., Vasudevan, A., Hawkins, A., Broley, S., Schofield, L., Verhoef, H., Groza, T., Zankl, A., Robinson, P.N., Haendel, M., Brudno, M., Mattick, J.S., Dinger, M.E., Roscioli, T., Cowley, M.J., Olry, A., Hanauer, M., Alkuraya, F.S., Taruscio, D., Posada de la Paz, M., Lochmüller, H., Bushby, K., Thompson, R., Hedley, V., Lasko, P., Mina, K., Beilby, J., Tifft, C., Davis, M., Laing, N.G., Julkowska, D., Le Cam, Y., Terry, S.F., Kaufmann, P., Eerola, I., Norstedt, I., Rath, A., Suematsu, M., Groft, S.C., Austin, C.P., Draghia-Akli, R., Weeramanthri, T.S., Molster, C., and Dawkins, H.J.S.

Abstract

Public health relies on technologies to produce and analyse data, as well as effectively develop and implement policies and practices. An example is the public health practice of epidemiology, which relies on computational technology to monitor the health status of populations, identify disadvantaged or at risk population groups and thereby inform health policy and priority setting. Critical to achieving health improvements for the underserved population of people living with rare diseases is early diagnosis and best care. In the rare diseases field, the vast majority of diseases are caused by destructive but previously difficult to identify protein-coding gene mutations. The reduction in cost of genetic testing and advances in the clinical use of genome sequencing, data science and imaging are converging to provide more precise understandings of the ‘person-time-place’ triad. That is: who is affected (people); when the disease is occurring (time); and where the disease is occurring (place). Consequently we are witnessing a paradigm shift in public health policy and practice towards ‘precision public health’. Patient and stakeholder engagement has informed the need for a national public health policy framework for rare diseases. The engagement approach in different countries has produced highly comparable outcomes and objectives. Knowledge and experience sharing across the international rare diseases networks and partnerships has informed the development of the Western Australian Rare Diseases Strategic Framework 2015–2018 (RD Framework) and Australian government health briefings on the need for a National plan. The RD Framework is guiding the translation of genomic and other technologies into the Western Australian health system, leading to greater precision in diagnostic pathways and care, and is an example of how a precision public health framework can improve health outcomes for the rare diseases population. Five vignettes are used to illustrate how policy decis

Published
2017

15. Improved diagnosis and care for rare diseases through implementation of precision public health framework

Author

Baynam, Gareth, Bowman, F., Lister, K., Walker, C., Pachter, N., Goldblatt, J., Boycott, K., Gahl, W., Kosaki, K., Adachi, T., Ishii, K., Mahede, T., McKenzie, Fiona, Townshend, S., Slee, J., Kiraly-Borri, C., Vasudevan, A., Hawkins, A., Broley, S., Schofield, L., Verhoef, H., Groza, T., Zankl, A., Robinson, P., Haendel, M., Brudno, M., Mattick, J., Dinger, M., Roscioli, T., Cowley, M., Olry, A., Hanauer, M., Alkuraya, F., Taruscio, D., Posada De La Paz, M., Lochmüller, H., Bushby, K., Thompson, R., Hedley, V., Lasko, P., Mina, K., Beilby, J., Tifft, C., Davis, M., Laing, N., Julkowska, D., Le Cam, Y., Terry, S., Kaufmann, P., Eerola, I., Norstedt, I., Rath, A., Suematsu, M., Groft, S., Austin, C., Draghia-Akli, R., Weeramanthri, Tarun, Molster, C., Dawkins, Hugh, Baynam, Gareth, Bowman, F., Lister, K., Walker, C., Pachter, N., Goldblatt, J., Boycott, K., Gahl, W., Kosaki, K., Adachi, T., Ishii, K., Mahede, T., McKenzie, Fiona, Townshend, S., Slee, J., Kiraly-Borri, C., Vasudevan, A., Hawkins, A., Broley, S., Schofield, L., Verhoef, H., Groza, T., Zankl, A., Robinson, P., Haendel, M., Brudno, M., Mattick, J., Dinger, M., Roscioli, T., Cowley, M., Olry, A., Hanauer, M., Alkuraya, F., Taruscio, D., Posada De La Paz, M., Lochmüller, H., Bushby, K., Thompson, R., Hedley, V., Lasko, P., Mina, K., Beilby, J., Tifft, C., Davis, M., Laing, N., Julkowska, D., Le Cam, Y., Terry, S., Kaufmann, P., Eerola, I., Norstedt, I., Rath, A., Suematsu, M., Groft, S., Austin, C., Draghia-Akli, R., Weeramanthri, Tarun, Molster, C., and Dawkins, Hugh

Abstract

© Springer International Publishing AG 2017. Public health relies on technologies to produce and analyse data, as well as effectively develop and implement policies and practices. An example is the public health practice of epidemiology, which relies on computational technology to monitor the health status of populations, identify disadvantaged or at risk population groups and thereby inform health policy and priority setting. Critical to achieving health improvements for the underserved population of people living with rare diseases is early diagnosis and best care. In the rare diseases field, the vast majority of diseases are caused by destructive but previously difficult to identify protein-coding gene mutations. The reduction in cost of genetic testing and advances in the clinical use of genome sequencing, data science and imaging are converging to provide more precise understandings of the ‘person-time-place’ triad. That is: who is affected (people); when the disease is occurring (time); and where the disease is occurring (place). Consequently we are witnessing a paradigm shift in public health policy and practice towards ‘precision public health’. Patient and stakeholder engagement has informed the need for a national public health policy framework for rare diseases. The engagement approach in different countries has produced highly comparable outcomes and objectives. Knowledge and experience sharing across the international rare diseases networks and partnerships has informed the development of the Western Australian Rare Diseases Strategic Framework 2015?2018 (RD Framework) and Australian government health briefings on the need for a National plan. The RD Framework is guiding the translation of genomic and other technologies into the Western Australian health system, leading to greater precision in diagnostic pathways and care, and is an example of how a precision public health framework can improve health outcomes for the rare diseases population. Five vign

Published
2017

16. Deletions of chromosomal regulatory boundaries are associated with congenital disease

Author

Ibn-Salem, J., Köhler, S., Love, M., Chung, H., Huang, N., Hurles, M., Haendel, M., Washington, N., Smedley, D., Mungall, C., Lewis, S., Ott, C., Bauer, S., Schofield, P., Mundlos, S., Spielmann, M., and Robinson, P.

Subjects
DNA Copy Number Variations, Models, Genetic, Genome, Human, Research, Gene Dosage, Genetic Diseases, Inborn, Molecular Sequence Annotation, Genomics, Haploinsufficiency, Gene Ontology, Phenotype, Databases, Genetic, Chromosomes, Human, Humans, Sequence Deletion
Abstract

Background Recent data from genome-wide chromosome conformation capture analysis indicate that the human genome is divided into conserved megabase-sized self-interacting regions called topological domains. These topological domains form the regulatory backbone of the genome and are separated by regulatory boundary elements or barriers. Copy-number variations can potentially alter the topological domain architecture by deleting or duplicating the barriers and thereby allowing enhancers from neighboring domains to ectopically activate genes causing misexpression and disease, a mutational mechanism that has recently been termed enhancer adoption. Results We use the Human Phenotype Ontology database to relate the phenotypes of 922 deletion cases recorded in the DECIPHER database to monogenic diseases associated with genes in or adjacent to the deletions. We identify combinations of tissue-specific enhancers and genes adjacent to the deletion and associated with phenotypes in the corresponding tissue, whereby the phenotype matched that observed in the deletion. We compare this computationally with a gene-dosage pathomechanism that attempts to explain the deletion phenotype based on haploinsufficiency of genes located within the deletions. Up to 11.8% of the deletions could be best explained by enhancer adoption or a combination of enhancer adoption and gene-dosage effects. Conclusions Our results suggest that enhancer adoption caused by deletions of regulatory boundaries may contribute to a substantial minority of copy-number variation phenotypes and should thus be taken into account in their medical interpretation. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0423-1) contains supplementary material, which is available to authorized users.

Published
2014

17. Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome

Author

Zemojtel, T, Kahler, S, Mackenroth, L, Jager, M, Hecht, J, Krawitz, P, Graul-Neumann, L, Doelken, S, Ehmke, N, Spielmann, M, Christine, N, Schweiger, M, Krager, U, Frommer, G, Fischer, B, Kornak, U, Ardeshirdavani, Amin, Moreau, Yves, Lewis, S, Haendel, M, Smedley, D, Horn, D, Mundlos, S, and NRobinson, P

Subjects
SISTA
Abstract

Less than half of patients with suspected genetic disease receive a molecular diagnosis. We have therefore integrated next-generation sequencing (NGS), bioinformatics, and clinical data into an effective diagnostic workflow. We used variants in the 2741 established Mendelian disease genes [the disease-associated genome (DAG)] to develop a targeted enrichment DAG panel (7.1 Mb), which achieves a coverage of 20-fold or better for 98% of bases. Furthermore, we established a computational method [Phenotypic Interpretation of eXomes (PhenIX)] that evaluated and ranked variants based on pathogenicity and semantic similarity of patients' phenotype described by Human Phenotype Ontology (HPO) terms to those of 3991 Mendelian diseases. In computer simulations, ranking genes based on the variant score put the true gene in first place less than 5% of the time; PhenIX placed the correct gene in first place more than 86% of the time. In a retrospective test of PhenIX on 52 patients with previously identified mutations and known diagnoses, the correct gene achieved a mean rank of 2.1. In a prospective study on 40 individuals without a diagnosis, PhenIX analysis enabled a diagnosis in 11 cases (28%, at a mean rank of 2.4). Thus, the NGS of the DAG followed by phenotype-driven bioinformatic analysis allows quick and effective differential diagnostics in medical genetics. ispartof: Science Translational Medicine vol:6 issue:252 pages:123-125 ispartof: location:United States status: published

Published
2014

18. The health care and life sciences community profile for dataset descriptions

Author

Dumontier, M. (Michel), Gray, A.J.G. (Alasdair J.G.), Marshall, M.S. (M. Scott), Alexiev, V. (Vladimir), Ansell, P. (Peter), Bader, G. (Gary), Baran, J. (Joachim), Bolleman, J.T. (Jerven T.), Callahan, A. (Alison), Cruz-Toledo, J. (José), Gaudet, P. (Pascale), Gombocz, E.A. (Erich A.), Gonzalez-Beltran, A.N. (Alejandra N.), Groth, P. (Paul), Haendel, M. (Melissa), Ito, M. (Maori), Jupp, S. (Simon), Juty, N. (Nick), Katayama, T. (Toshiaki), Kobayashi, N. (Norio), Krishnaswami, K. (Kalpana), Laibe, C. (Camille), Le Novère, N. (Nicolas), Lin, S. (Simon), Malone, J. (James), Miller, M. (Michael), Mungall, C.J. (Christopher J.), Rietveld, L. (Laurens), Wimalaratne, S.M. (Sarala M.), Yamaguchi, A. (Atsuko), Dumontier, M. (Michel), Gray, A.J.G. (Alasdair J.G.), Marshall, M.S. (M. Scott), Alexiev, V. (Vladimir), Ansell, P. (Peter), Bader, G. (Gary), Baran, J. (Joachim), Bolleman, J.T. (Jerven T.), Callahan, A. (Alison), Cruz-Toledo, J. (José), Gaudet, P. (Pascale), Gombocz, E.A. (Erich A.), Gonzalez-Beltran, A.N. (Alejandra N.), Groth, P. (Paul), Haendel, M. (Melissa), Ito, M. (Maori), Jupp, S. (Simon), Juty, N. (Nick), Katayama, T. (Toshiaki), Kobayashi, N. (Norio), Krishnaswami, K. (Kalpana), Laibe, C. (Camille), Le Novère, N. (Nicolas), Lin, S. (Simon), Malone, J. (James), Miller, M. (Michael), Mungall, C.J. (Christopher J.), Rietveld, L. (Laurens), Wimalaratne, S.M. (Sarala M.), and Yamaguchi, A. (Atsuko)

Abstract

Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.

Published
2016
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19. An overview of the BioCreative 2012 Workshop Track III: interactive text mining task

Author

Chatr-aryamontri, A., Li, Y., Roberts, P., Jimenez, S., Wilbur, W. J., Van Slyke, C. E., Licata, L., Park, J., Haendel, M., Mabee, P., Drabkin, H., Matthews, L., Balhoff, J. P., Carterette, B., Arighi, C. N., Cohen, K. B., Li, D., Cui, H., Becker, K., Fey, P., Chi-Yang Wu, J., Van Auken, K., Bello, S., Gillespie, M., Schaeffer, M. L., Chan, J., Dahdul, W., Muller, H.-M., Cooper, L., Harris, B., Krallinger, M., and Dodson, R.

Abstract

In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (∼1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators’ overall experience of a system, regardless of the system’s high score on design, learnability and usability. In addition, strategies to refine the annotation guidelines and systems documentation, to adapt the tools to the needs and query types the end user might have and to evaluate performance in terms of efficiency, user interface, result export and traditional evaluation metrics have been analyzed during this task. This analysis will help to plan for a more intense study in BioCreative IV.

Published
2013
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20. Achieving human and machine accessibility of cited data in scholarly publications

Author

Starr, J. (Joan), Castro, E. (Eleni), Crosas, M. (Mercè), Dumontier, M. (Michel), Downs, R. R. (Robert), Duerr, R. (Ruth), Haak, L. L. (Laurel), Haendel, M. (Melissa), Herman, I. (Ivan), Hodson, S. (Simon), Hourclé, J. (Joe), Kratz, J.E. (John Ernest), Lin, J. (Jennifer), Nielsen, L.H. (Lars Holm), Nurnberger, A. (Amy), Proell, S. (Stefan), Rauber, A. (Andreas), Sacchi, S. (Simone), Smith, A. (Arthur), Taylor, M. (Mike), Clark, T. (Tim), Starr, J. (Joan), Castro, E. (Eleni), Crosas, M. (Mercè), Dumontier, M. (Michel), Downs, R. R. (Robert), Duerr, R. (Ruth), Haak, L. L. (Laurel), Haendel, M. (Melissa), Herman, I. (Ivan), Hodson, S. (Simon), Hourclé, J. (Joe), Kratz, J.E. (John Ernest), Lin, J. (Jennifer), Nielsen, L.H. (Lars Holm), Nurnberger, A. (Amy), Proell, S. (Stefan), Rauber, A. (Andreas), Sacchi, S. (Simone), Smith, A. (Arthur), Taylor, M. (Mike), and Clark, T. (Tim)

Abstract

Reproducibility and reusability of research results is an important concern in scientific communication and science policy. A foundational element of reproducibility and reusability is the open and persistently available presentation of research data. However, many common approaches for primary data publication in use today do not achieve sufficient long-term robustness, openness, accessibility or uniformity. Nor do they permit comprehensive exploitation by modern Web technologies. This has led to several authoritative studies recommending uniform direct citation of data archived in persistent repositories. Data are to be considered as first-class scholarly objects, and treated similarly in many ways to cited and archived scientific and scholarly literature. Here we briefly review the most current and widely agreed set of principle-based recommendations for scholarly data citation, the Joint Declaration of Data Citation Principles (JDDCP). We then present a framework for operationalizing the JDDCP; and a set of initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data. The main target audience for the common implementation guidelines in this article consists of publishers, scholarly organizations, and persistent data repositories, including technical staff members in these organizations. But ordinary researchers can also benefit from these recommendations. The guidance provided here is intended to help achieve widespread, uniform human and machine accessibility of deposited data, in support of significantly improved verification, validation, reproducibility and re-use of scholarly/scientific data.

Published
2015

21. Eagle-i: Making Invisible Resources, Visible

Author

Haendel M, Wilson M, Torniai C, Erik Segerdell, Shaffer C, Frost R, Bourges D, Brownstein J, and McInnerney K

Subjects
Poster Session Abstracts
Abstract

RP-134

Published
2010

22. NLP and Phenotypes: using Ontologies to link Human Diseases to Animal Models

Author

Washington, N., Gibson, M., Mungall, C.J., Ashburner, Michael, Gkoutos, G., Westerfield, M., Haendel, M., and Lewis, S. E.

Subjects
Phenotypes, annotation, ontologies
Abstract

The path to disease gene discovery in humans is often a lengthy one, but can be significantly shortened if links between human and model organism phenotypes are readily available. Collecting and storing these descriptions in a common resource, recorded with ontologies, as well as developing the tools for annotation, access, and analysis are among the goals of the National Center for Biomedical Ontology. The use of well-structured, expert-reviewed ontologies during curation allows biological data to be understandable by both humans and computers, and thereby increases the capacity for meaningful analysis. We have developed the EQ annotation model, which uses ontology terms to label and link together entities, such as anatomical structures, with the qualities describing them. Phenotypes are represented in our model using any combination of entity (such as anatomy) ontologies in combination with an ontology of qualities (PATO). Together with the model organism databases Zfin and FlyBase, we are evaluating this model, using the Phenote Annotation Tool to capture the mutant phenotypes of 200 genes known to cause human disease (from OMIM records) that have corresponding fly and zebrafish mutant phenotypes. The phenotypic data modeled in this way is available from the NCBO Open Biomedical Database (OBD), which has the same underlying annotation data model, and can currently be accessed via a computational (REST) interface for utilization by other external application or databases. This work is funded by the NIH.

Published
2008
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23. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data

Author

Kohler, S., Doelken, S.C., Mungall, C.J., Bauer, S., Firth, H.V., Bailleul-Forestier, I., Black, G.C.M., Brown, D.L., Brudno, M., Campbell, J., FitzPatrick, D.R., Eppig, J.T., Jackson, A.P., Freson, K., Girdea, M., Helbig, I., Hurst, J.A., Jahn, J., Jackson, L.G., Kelly, A.M., Ledbetter, D.H., Mansour, S., Martin, C.L., Moss, C., Mumford, A., Ouwehand, W.H., Park, S.M., Riggs, E.R., Scott, R.H., Sisodiya, S., Vooren, S. van der, Wapner, R.J., Wilkie, A.O., Wright, C.F., Silfhout, A.T. van, Leeuw, N. de, Vries, B. de, Washingthon, N.L., Smith, C.L., Westerfield, M., Schofield, P., Ruef, B.J., Gkoutos, G.V., Haendel, M., Smedley, D., Lewis, S.E., Robinson, P.N., Kohler, S., Doelken, S.C., Mungall, C.J., Bauer, S., Firth, H.V., Bailleul-Forestier, I., Black, G.C.M., Brown, D.L., Brudno, M., Campbell, J., FitzPatrick, D.R., Eppig, J.T., Jackson, A.P., Freson, K., Girdea, M., Helbig, I., Hurst, J.A., Jahn, J., Jackson, L.G., Kelly, A.M., Ledbetter, D.H., Mansour, S., Martin, C.L., Moss, C., Mumford, A., Ouwehand, W.H., Park, S.M., Riggs, E.R., Scott, R.H., Sisodiya, S., Vooren, S. van der, Wapner, R.J., Wilkie, A.O., Wright, C.F., Silfhout, A.T. van, Leeuw, N. de, Vries, B. de, Washingthon, N.L., Smith, C.L., Westerfield, M., Schofield, P., Ruef, B.J., Gkoutos, G.V., Haendel, M., Smedley, D., Lewis, S.E., and Robinson, P.N.

Abstract

Contains fulltext : 136954.pdf (publisher's version ) (Open Access), The Human Phenotype Ontology (HPO) project, available at http://www.human-phenotype-ontology.org, provides a structured, comprehensive and well-defined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation. Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online.

Published
2014

24. An overview of the BioCreative 2012 Workshop Track III: interactive text mining task

Author

Arighi, C. N., primary, Carterette, B., additional, Cohen, K. B., additional, Krallinger, M., additional, Wilbur, W. J., additional, Fey, P., additional, Dodson, R., additional, Cooper, L., additional, Van Slyke, C. E., additional, Dahdul, W., additional, Mabee, P., additional, Li, D., additional, Harris, B., additional, Gillespie, M., additional, Jimenez, S., additional, Roberts, P., additional, Matthews, L., additional, Becker, K., additional, Drabkin, H., additional, Bello, S., additional, Licata, L., additional, Chatr-aryamontri, A., additional, Schaeffer, M. L., additional, Park, J., additional, Haendel, M., additional, Van Auken, K., additional, Li, Y., additional, Chan, J., additional, Muller, H.-M., additional, Cui, H., additional, Balhoff, J. P., additional, Chi-Yang Wu, J., additional, Lu, Z., additional, Wei, C.-H., additional, Tudor, C. O., additional, Raja, K., additional, Subramani, S., additional, Natarajan, J., additional, Cejuela, J. M., additional, Dubey, P., additional, and Wu, C., additional

Published
2013
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30. The Zebrafish Information Network: the zebrafish model organism database provides expanded support for genotypes and phenotypes

Author

Sprague, J., primary, Bayraktaroglu, L., additional, Bradford, Y., additional, Conlin, T., additional, Dunn, N., additional, Fashena, D., additional, Frazer, K., additional, Haendel, M., additional, Howe, D. G., additional, Knight, J., additional, Mani, P., additional, Moxon, S. A.T., additional, Pich, C., additional, Ramachandran, S., additional, Schaper, K., additional, Segerdell, E., additional, Shao, X., additional, Singer, A., additional, Song, P., additional, Sprunger, B., additional, Van Slyke, C. E., additional, and Westerfield, M., additional

Published
2007
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34. Noninvasive Detection of Anterior Wall Asynergies by Cardiokymography Compared to Electrocardiography

Author

Sund M, P. Mathes, Haendel M, Koenig W, and J. Gehring

Subjects
Male, medicine.medical_specialty, Asynergy, Heart Ventricles, Combined use, Anterior wall, Coronary Disease, Electrokymography, Single test, Electrocardiography, Internal medicine, medicine, Humans, Pharmacology (medical), cardiovascular diseases, Cardiokymography, High prevalence, medicine.diagnostic_test, business.industry, Middle Aged, Myocardial Contraction, Angiography, Cardiology, Cineangiography, Female, Cardiology and Cardiovascular Medicine, business
Abstract

In order to determine the value of cardiokymography in detecting left ventricular (LV) anterior wall asynergies, 80 consecutive patients had a cardiokymogram (CKG) and an electrocardiogram (ECG) on the day prior to coronary angiography. Technically adequate CKGs were obtained in 72 patients (67 men and 5 women, mean age 53 +/- 6.5 years). For validation of regional contraction abnormalities, quantitative LV angiography was used. Stepwise linear discriminant analysis was applied to investigate the diagnostic power of CKG. Sensitivity of the CKG for LV anterior wall asynergy was 67.9% (ECG: 39.6%) and specificity was 68.4% (ECG: 94.7%) on the basis of 1 SD of the mean values of the radial axis shortening of a control group. For 2 SD, the sensitivity was 65.6% (ECG: 56.3%) and the specificity 47.5% (ECG: 90%). By combined testing, the specificity increased to 98.3%, whereas the sensitivity dropped to 26.9%. The improvement of the post-test likelihood for a positive ECG by a positive CKG is especially pronounced in the intermediate prevalence range, whereas for a negative ECG the post-test likelihood can be further decreased by a negative CKG in the intermediate and high prevalence range. The ECG as a single test seems to be the more appropriate noninvasive method for detecting LV anterior wall asynergies; however, the combined use of both ECG and CKG may considerably improve the diagnostic accuracy.

Published
1988
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35. Mrj encodes a DnaJ-related co-chaperone that is essential for murine placental development.

Author

Hunter, P J, Swanson, B J, Haendel, M A, Lyons, G E, and Cross, J C

Abstract

We have identified a novel gene in a gene trap screen that encodes a protein related to the DnaJ co-chaperone in E. coli. The gene, named Mrj (mammalian relative of DnaJ) was expressed throughout development in both the embryo and placenta. Within the placenta, expression was particularly high in trophoblast giant cells but moderate levels were also observed in trophoblast cells of the chorion at embryonic day 8.5, and later in the labyrinth which arises from the attachment of the chorion to the allantois (a process called chorioallantoic fusion). Insertion of the ROSAbetageo gene trap vector into the Mrj gene created a null allele. Homozygous Mrj mutants died at mid-gestation due to a failure of chorioallantoic fusion at embryonic day 8.5, which precluded formation of the mature placenta. At embryonic day 8.5, the chorion in mutants was morphologically normal and expressed the cell adhesion molecule beta4 integrin that is known to be required for chorioallantoic fusion. However, expression of the chorionic trophoblast-specific transcription factor genes Err2 and Gcm1 was significantly reduced. The mutants showed no abnormal phenotypes in other trophoblast cell types or in the embryo proper. This study indicates a previously unsuspected role for chaperone proteins in placental development and represents the first genetic analysis of DnaJ-related protein function in higher eukaryotes. Based on a survey of EST databases representing different mouse tissues and embryonic stages, there are 40 or more DnaJ-related genes in mammals. In addition to Mrj, at least two of these genes are also expressed in the developing mouse placenta. The specificity of the developmental defect in Mrj mutants suggests that each of these genes may have unique tissue and cellular activities.

Published
1999

42. Effect of nirmatrelvir/ritonavir (Paxlovid) on hospitalization among adults with COVID-19: An electronic health record-based target trial emulation from N3C.

Author

Bhatia A, Preiss AJ, Xiao X, Brannock MD, Alexander GC, Chew RF, Davis H, Fitzgerald M, Hill E, Kelly EP, Mehta HB, Madlock-Brown C, Wilkins KJ, Chute CG, Haendel M, Moffitt R, and Pfaff ER

Abstract

Background: Nirmatrelvir with ritonavir (Paxlovid) is indicated for patients with Coronavirus Disease 2019 (COVID-19) who are at risk for progression to severe disease due to the presence of one or more risk factors. Millions of treatment courses have been prescribed in the United States alone. Paxlovid was highly effective at preventing hospitalization and death in clinical trials. Several studies have found a protective association in real-world data, but they variously used less recent study periods, correlational methods, and small, local cohorts. Their estimates also varied widely. The real-world effectiveness of Paxlovid remains uncertain, and it is unknown whether its effect is homogeneous across demographic strata. This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing severe COVID-19 outcomes., Methods and Findings: This target trial emulation used a cohort of 703,647 patients with COVID-19 seen at 34 clinical sites across the United States between April 1, 2022 and August 28, 2023. Treatment was defined as receipt of a Paxlovid prescription within 5 days of the patient's COVID-19 index date (positive test or diagnosis). To emulate randomization, we used the clone-censor-weight technique with inverse probability of censoring weights to balance a set of covariates including sex, age, race and ethnicity, comorbidities, community well-being index (CWBI), prior healthcare utilization, month of COVID-19 index, and site of care provision. The primary outcome was hospitalization; death was a secondary outcome. We estimated that Paxlovid reduced the risk of hospitalization by 39% (95% confidence interval (CI) [36%, 41%]; p < 0.001), with an absolute risk reduction of 0.9 percentage points (95% CI [0.9, 1.0]; p < 0.001), and reduced the risk of death by 61% (95% CI [55%, 67%]; p < 0.001), with an absolute risk reduction of 0.2 percentage points (95% CI [0.1, 0.2]; p < 0.001). We also conducted stratified analyses by vaccination status and age group. Absolute risk reduction for hospitalization was similar among patients that were vaccinated and unvaccinate, but was much greater among patients aged 65+ years than among younger patients. We observed disparities in Paxlovid treatment, with lower rates among black and Hispanic or Latino patients, and within socially vulnerable communities. This study's main limitation is that it estimates causal effects using observational data and could be biased by unmeasured confounding., Conclusions: In this study of Paxlovid's real-world effectiveness, we observed that Paxlovid is effective at preventing hospitalization and death, including among vaccinated patients, and particularly among older patients. This remains true in the era of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron subvariants. However, disparities in Paxlovid treatment rates imply that the benefit of Paxlovid's effectiveness is not equitably distributed., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2025 Bhatia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Published
2025
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43. AI-readiness for Biomedical Data: Bridge2AI Recommendations.

Author

Clark T, Caufield H, Parker JA, Al Manir S, Amorim E, Eddy J, Gim N, Gow B, Goar W, Haendel M, Hansen JN, Harris N, Hermjakob H, Joachimiak M, Jordan G, Lee IH, McWeeney SK, Nebeker C, Nikolov M, Shaffer J, Sheffield N, Sheynkman G, Stevenson J, Chen JY, Mungall C, Wagner A, Kong SW, Ghosh SS, Patel B, Williams A, and Munoz-Torres MC

Abstract

Biomedical research and clinical practice are in the midst of a transition toward significantly increased use of artificial intelligence (AI) and machine learning (ML) methods. These advances promise to enable qualitatively deeper insight into complex challenges formerly beyond the reach of analytic methods and human intuition while placing increased demands on ethical and explainable artificial intelligence (XAI), given the opaque nature of many deep learning methods. The U.S. National Institutes of Health (NIH) has initiated a significant research and development program, Bridge2AI, aimed at producing new "flagship" datasets designed to support AI/ML analysis of complex biomedical challenges, elucidate best practices, develop tools and standards in AI/ML data science, and disseminate these datasets, tools, and methods broadly to the biomedical community. An essential set of concepts to be developed and disseminated in this program along with the data and tools produced are criteria for AI-readiness of data, including critical considerations for XAI and ethical, legal, and social implications (ELSI) of AI technologies. NIH Bridge to Artificial Intelligence (Bridge2AI) Standards Working Group members prepared this article to present methods for assessing the AI-readiness of biomedical data and the data standards perspectives and criteria we have developed throughout this program. While the field is rapidly evolving, these criteria are foundational for scientific rigor and the ethical design and application of biomedical AI methods.

Published
2024
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44. Critical Data for Critical Care: A Primer on Leveraging Electronic Health Record Data for Research From Society of Critical Care Medicine's Panel on Data Sharing and Harmonization.

Author

Heavner SF, Kumar VK, Anderson W, Al-Hakim T, Dasher P, Armaignac DL, Clermont G, Cobb JP, Manion S, Remy KE, Reuter-Rice K, and Haendel M

Subjects
Humans, Societies, Medical, Electronic Health Records standards, Critical Care standards, Information Dissemination ethics, Information Dissemination methods
Abstract

A growing body of critical care research draws on real-world data from electronic health records (EHRs). The bedside clinician has myriad data sources to aid in clinical decision-making, but the lack of data sharing and harmonization standards leaves much of this data out of reach for multi-institution critical care research. The Society of Critical Care Medicine (SCCM) Discovery Data Science Campaign convened a panel of critical care and data science experts to explore and document unique advantages and opportunities for leveraging EHR data in critical care research. This article reviews and illustrates six organizing topics (data domains and common data elements; data harmonization; data quality; data interoperability and digital infrastructure; data access, sharing, and governance; and ethics and equity) as a data science primer for critical care researchers, laying a foundation for future publications from the SCCM Discovery Data Harmonization and Sharing Guiding Principles Panel., Competing Interests: Drs. Heavner, Kumar, and Anderson are co-investigators in the study Aggregating and Analyzing COVID-19 Treatments from Electronic Health Records and Registries Globally (CURE ID), funded by U.S. Food and Drug Administration (FDA) and Assistant Secretary for Planning and Evaluation (ASPE). Ms. Dasher’s position was funded under CURE ID, funded by FDA and ASPE. Dr. Clermont is the Cheif Medical Officer and equity holder at NOMA AI. Dr. Cobb has an equity interest in Bauhealth, Aikido laboratories, and GibLib. Dr. Remy is a member of Immune Functional Diagnostics, LLC, which is unrelated to this work. Dr. Haendel is a founder of Alamya Health and a B-corp focused on genomic diagnostics. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Published
2024
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46. Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools.

Author

Reese JT, Chimirri L, Bridges Y, Danis D, Caufield JH, Wissink K, McMurry JA, Graefe AS, Casiraghi E, Valentini G, Jacobsen JO, Haendel M, Smedley D, Mungall CJ, and Robinson PN

Abstract

Large language models (LLMs) show promise in supporting differential diagnosis, but their performance is challenging to evaluate due to the unstructured nature of their responses. To assess the current capabilities of LLMs to diagnose genetic diseases, we benchmarked these models on 5,213 case reports using the Phenopacket Schema, the Human Phenotype Ontology and Mondo disease ontology. Prompts generated from each phenopacket were sent to three generative pretrained transformer (GPT) models. The same phenopackets were used as input to a widely used diagnostic tool, Exomiser, in phenotype-only mode. The best LLM ranked the correct diagnosis first in 23.6% of cases, whereas Exomiser did so in 35.5% of cases. While the performance of LLMs for supporting differential diagnosis has been improving, it has not reached the level of commonly used traditional bioinformatics tools. Future research is needed to determine the best approach to incorporate LLMs into diagnostic pipelines.

Published
2024
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47. The Recent Increase in Invasive Bacterial Infections: A Report From the National COVID Cohort Collaborative.

Author

Martin B, DeWitt PE, Russell S, Haendel M, Sanchez-Pinto N, Albers DJ, Jhaveri RR, Moffitt R, and Bennett TD

Abstract

Background: When coronavirus disease 2019 (COVID-19) mitigation efforts waned, viral respiratory infections (VRIs) surged, potentially increasing the risk of postviral invasive bacterial infections (IBIs). We sought to evaluate the change in epidemiology and relationships between specific VRIs and IBIs [complicated pneumonia, complicated sinusitis and invasive group A streptococcus (iGAS)] over time using the National COVID Cohort Collaborative (N3C) dataset., Methods: We performed a secondary analysis of all prospectively collected pediatric (<19 years old) and adult encounters at 58 N3C institutions, stratified by era: pre-pandemic (January 1, 2018, to February 28, 2020) versus pandemic (March 1, 2020, to June 1, 2023). We compared the characteristics and outcomes of patients with prespecified VRIs and IBIs, including correlation between VRI cases and subsequent IBI cases., Results: We identified 965,777 pediatric and 9,336,737 adult hospitalizations. Compared with pre-pandemic, pandemic-era children demonstrated higher mean monthly cases of adenovirus (121 vs. 79.1), iGAS (5.8 vs. 3.3), complicated pneumonia (282 vs. 178) and complicated sinusitis (29.8 vs. 16.3), P < 0.005 for all. Among pandemic-era children, peak correlation between RSV cases and subsequent complicated sinusitis cases occurred with a 60-day lag (correlation coefficient 0.56, 95% confidence interval: 0.52-0.59, P < 0.001) while peak correlation between influenza and complicated sinusitis occurred with a 33-day lag (0.55, 0.51-0.58, P < 0.001). Correlation among other VRI-IBI pairs was modest during the pandemic and often lower than during the pre-pandemic era., Conclusions: Since COVID-19 emerged, mean monthly cases of iGAS, complicated pneumonia, and complicated sinusitis have been higher. Pandemic-era RSV and influenza cases were correlated with subsequent cases of complicated sinusitis in children. However, many other VRI-IBI correlations decreased during the pandemic., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Published
2024
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48. Implementation of a dyadic nomenclature for monogenic diseases.

Author

Thaxton C, Biesecker LG, DiStefano M, Haendel M, Hamosh A, Owens E, Plon SE, Rehm HL, and Berg JS

Subjects
Humans, Databases, Genetic, Phenotype, Terminology as Topic, Genetic Diseases, Inborn genetics
Abstract

A core task when establishing the strength of evidence for a gene's role in a monogenic disorder is determining the appropriate disease entity to curate. Establishing this concept determines which evidence can be applied and quantified toward the final gene-disease validity, variant pathogenicity, or actionability classification. Genes with implications in more than one phenotype can necessitate a process of lumping and splitting, disease reorganization, and updates to disease nomenclature. Reappraisal of the names that are used as labels for disease entities is therefore a necessary and perpetual process. The Clinical Genome Resource (ClinGen), in collaboration with representatives from Monarch Disease Ontology (Mondo) and Online Inheritance in Man (OMIM), formed the Disease Naming Advisory Committee (DNAC) to develop guidance for groups faced with the need to establish the "curated disease entity" for gene-phenotype validity and variant pathogenicity and to update disease names for clinical use when necessary. The objective of this group was to harmonize guidance for disease naming across these nosologic entities and among ClinGen curation groups in collaboration with other disease-related professional groups. Here, we present the initial guidance developed by the DNAC with representative examples provided by the ClinGen expert panels and working groups that warranted nomenclature updates. We also discuss the broader implications of these efforts and their benefits for harmonization of gene-disease validity curation. Overall, this work sheds light on current inconsistencies and/or discrepancies and is designed to engage the broader community on how ClinGen defines monogenic disorders using a consistent approach for disease naming., Competing Interests: Declaration of interests M.H. is a co-founder of Alamya Health, a genomic diagnostics company. L.G.B. is a member of the Illumina Medical ethics committee and receives royalties from Wolters-Kluwer and research funding from Merck Inc., (Copyright © 2024 American Society of Human Genetics. All rights reserved.)

Published
2024
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49. Effect of Paxlovid Treatment During Acute COVID-19 on Long COVID Onset: An EHR-Based Target Trial Emulation from the N3C and RECOVER Consortia.

Author

Preiss A, Bhatia A, Aragon LV, Baratta JM, Baskaran M, Blancero F, Brannock MD, Chew RF, Diaz I, Fitzgerald M, Kelly EP, Zhou AG, Carton TW, Chute CG, Haendel M, Moffitt R, and Pfaff E

Abstract

Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. In this study, we examined whether treatment with Paxlovid in the acute phase of COVID-19 helps prevent the onset of PASC. We used electronic health records from the National Covid Cohort Collaborative (N3C) to define a cohort of 426,352 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19. We used the target trial emulation (TTE) framework to estimate the effect of Paxlovid treatment on PASC incidence. We estimated overall PASC incidence using a computable phenotype. We also measured the onset of novel cognitive, fatigue, and respiratory symptoms in the post-acute period. Paxlovid treatment did not have a significant effect on overall PASC incidence (relative risk [RR] = 0.98, 95% confidence interval [CI] 0.95-1.01). However, it had a protective effect on cognitive (RR = 0.90, 95% CI 0.84-0.96) and fatigue (RR = 0.95, 95% CI 0.91-0.98) symptom clusters, which suggests that the etiology of these symptoms may be more closely related to viral load than that of respiratory symptoms.

Published
2024
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50. Insights from an N3C RECOVER EHR-based cohort study characterizing SARS-CoV-2 reinfections and Long COVID.

Author

Hadley E, Yoo YJ, Patel S, Zhou A, Laraway B, Wong R, Preiss A, Chew R, Davis H, Brannock MD, Chute CG, Pfaff ER, Loomba J, Haendel M, Hill E, and Moffitt R

Abstract

Background: Although the COVID-19 pandemic has persisted for over 3 years, reinfections with SARS-CoV-2 are not well understood. We aim to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection., Methods: We use an electronic health record study cohort of over 3 million patients from the National COVID Cohort Collaborative as part of the NIH Researching COVID to Enhance Recovery Initiative. We calculate summary statistics, effect sizes, and Kaplan-Meier curves to better understand COVID-19 reinfections., Results: Here we validate previous findings of reinfection incidence (6.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present findings that the proportion of Long COVID diagnoses is higher following initial infection than reinfection for infections in the same epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between initial infection and reinfection (chi-squared value: 25,697, p-value: <0.0001) with a medium effect size (Cramer's V: 0.20, DoF = 3). Individuals who experienced severe initial and first reinfection were older in age and at a higher mortality risk than those who had mild initial infection and reinfection., Conclusions: In a large patient cohort, we find that the severity of reinfection appears to be associated with the severity of initial infection and that Long COVID diagnoses appear to occur more often following initial infection than reinfection in the same epoch. Future research may build on these findings to better understand COVID-19 reinfections., (© 2024. The Author(s).)

Published
2024
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